From 86e4715cbea2cc0940954bf646d3957454d4e5ed Mon Sep 17 00:00:00 2001 From: hubert0527 Date: Fri, 28 Jan 2022 23:33:36 -0800 Subject: [PATCH] code release --- LICENSE-NVIDIA | 101 ++ LICENSE-StyleGAN2-PyTorch | 26 + README.md | 304 ++++- assets/OpenSans-Bold.ttf | Bin 0 -> 104120 bytes assets/favicon.ico | Bin 0 -> 1271 bytes assets/fig_000000.png | Bin 0 -> 385705 bytes assets/fig_000001.png | Bin 0 -> 424002 bytes assets/fig_000002.png | Bin 0 -> 432999 bytes assets/fig_000003.png | Bin 0 -> 522364 bytes ...hmark_infinite_gen_1024x1024_baseline.yaml | 10 + ...enchmark_infinite_gen_1024x1024_naive.yaml | 12 + ...k_infinite_gen_1024x1024_parabatch128.yaml | 12 + ...rk_infinite_gen_1024x1024_parabatch16.yaml | 12 + ...ark_infinite_gen_1024x1024_parabatch2.yaml | 12 + ...rk_infinite_gen_1024x1024_parabatch32.yaml | 12 + ...ark_infinite_gen_1024x1024_parabatch4.yaml | 12 + ...rk_infinite_gen_1024x1024_parabatch64.yaml | 12 + ...ark_infinite_gen_1024x1024_parabatch8.yaml | 12 + 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| 10 + ...enchmark_infinite_gen_8192x8192_naive.yaml | 12 + ...k_infinite_gen_8192x8192_parabatch128.yaml | 12 + ...rk_infinite_gen_8192x8192_parabatch16.yaml | 12 + ...ark_infinite_gen_8192x8192_parabatch2.yaml | 12 + ...rk_infinite_gen_8192x8192_parabatch32.yaml | 12 + ...ark_infinite_gen_8192x8192_parabatch4.yaml | 12 + ...rk_infinite_gen_8192x8192_parabatch64.yaml | 12 + ...ark_infinite_gen_8192x8192_parabatch8.yaml | 12 + configs/dataset/alis-lhq.yaml | 8 + configs/dataset/coco.yaml | 8 + configs/dataset/flickr-landscape-large.yaml | 8 + .../dataset/flickr-landscape-large197.yaml | 8 + configs/dataset/flickr-landscape-small.yaml | 8 + ...used_gen_512x4096_4styles_interactive.yaml | 21 + .../infinite_gen_1024x1024_interactive.yaml | 12 + configs/model/InfinityGAN-HR.yaml | 75 ++ configs/model/InfinityGAN-IOF.yaml | 75 ++ configs/model/InfinityGAN-IOP.yaml | 75 ++ configs/model/InfinityGAN-UR.yaml | 75 ++ configs/model/InfinityGAN.yaml | 76 ++ configs/model/InfinityGAN_maps.yaml | 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42 + libs/calc_inception.py | 116 ++ libs/fid.py | 238 ++++ libs/inception.py | 311 +++++ libs/inception_score.py | 71 + libs/lpips/__init__.py | 40 + libs/lpips/base_model.py | 58 + libs/lpips/dist_model.py | 284 ++++ libs/lpips/networks_basic.py | 186 +++ libs/lpips/pretrained_networks.py | 181 +++ libs/lpips/utils.py | 124 ++ libs/lpips/weights/v0.0/alex.pth | Bin 0 -> 5455 bytes libs/lpips/weights/v0.0/squeeze.pth | Bin 0 -> 10057 bytes libs/lpips/weights/v0.0/vgg.pth | Bin 0 -> 6735 bytes libs/lpips/weights/v0.1/alex.pth | Bin 0 -> 6009 bytes libs/lpips/weights/v0.1/squeeze.pth | Bin 0 -> 10811 bytes libs/lpips/weights/v0.1/vgg.pth | Bin 0 -> 7289 bytes meta_data/environment.yml | 160 +++ models/custom_ops/__init__.py | 2 + models/custom_ops/fused_act.py | 101 ++ models/custom_ops/fused_bias_act.cpp | 21 + models/custom_ops/fused_bias_act_kernel.cu | 99 ++ models/custom_ops/upfirdn2d.cpp | 23 + models/custom_ops/upfirdn2d.py | 205 +++ models/custom_ops/upfirdn2d_kernel.cu | 369 ++++++ models/diff_augment_dual.py | 94 ++ models/infinitygan_generator.py | 1165 +++++++++++++++++ models/losses.py | 115 ++ models/ops.py | 930 +++++++++++++ models/stylegan2discriminator.py | 231 ++++ prepare_data.py | 277 ++++ quant_eval_utils.py | 235 ++++ scripts/eval/eval_fid_job_s3.sh | 9 + scripts/eval/eval_outpaint_imgdir.sh | 15 + scripts/eval/eval_scaleinv_fids.sh | 23 + scripts/eval/eval_scaleinv_fids_v4.sh | 9 + scripts/eval/eval_scaleinv_fids_v5.sh | 9 + scripts/eval/eval_spatial_fids.sh | 14 + scripts/eval/run_benchmark.py | 81 ++ .../script_generator/gen_inbetween_permute.py | 93 ++ test.py | 335 +++++ test_distr.py | 228 ++++ test_managers/base_test_manager.py | 282 ++++ test_managers/direct_generation.py | 157 +++ test_managers/fused_generation.py | 438 +++++++ .../fused_seq_connecting_generation.py | 442 +++++++ test_managers/global_config.py | 1 + test_managers/infinite_generation.py | 298 +++++ test_managers/interactive_sampler.py | 487 +++++++ test_managers/inversion.py | 1062 +++++++++++++++ .../structure_texture_trait_maker.py | 385 ++++++ test_managers/testing_vars_wrapper.py | 314 +++++ train.py | 856 ++++++++++++ utils.py | 353 +++++ 135 files changed, 15066 insertions(+), 8 deletions(-) create mode 100644 LICENSE-NVIDIA create mode 100644 LICENSE-StyleGAN2-PyTorch create mode 100644 assets/OpenSans-Bold.ttf create mode 100644 assets/favicon.ico create mode 100644 assets/fig_000000.png create mode 100644 assets/fig_000001.png create mode 100644 assets/fig_000002.png create mode 100644 assets/fig_000003.png create mode 100644 benchmark_configs/benchmark_infinite_gen_1024x1024_baseline.yaml create mode 100644 benchmark_configs/benchmark_infinite_gen_1024x1024_naive.yaml create mode 100644 benchmark_configs/benchmark_infinite_gen_1024x1024_parabatch128.yaml create mode 100644 benchmark_configs/benchmark_infinite_gen_1024x1024_parabatch16.yaml create mode 100644 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create mode 100644 libs/fid.py create mode 100644 libs/inception.py create mode 100644 libs/inception_score.py create mode 100644 libs/lpips/__init__.py create mode 100644 libs/lpips/base_model.py create mode 100644 libs/lpips/dist_model.py create mode 100644 libs/lpips/networks_basic.py create mode 100644 libs/lpips/pretrained_networks.py create mode 100644 libs/lpips/utils.py create mode 100644 libs/lpips/weights/v0.0/alex.pth create mode 100644 libs/lpips/weights/v0.0/squeeze.pth create mode 100644 libs/lpips/weights/v0.0/vgg.pth create mode 100644 libs/lpips/weights/v0.1/alex.pth create mode 100644 libs/lpips/weights/v0.1/squeeze.pth create mode 100644 libs/lpips/weights/v0.1/vgg.pth create mode 100644 meta_data/environment.yml create mode 100644 models/custom_ops/__init__.py create mode 100644 models/custom_ops/fused_act.py create mode 100644 models/custom_ops/fused_bias_act.cpp create mode 100644 models/custom_ops/fused_bias_act_kernel.cu create mode 100644 models/custom_ops/upfirdn2d.cpp create mode 100644 models/custom_ops/upfirdn2d.py create mode 100644 models/custom_ops/upfirdn2d_kernel.cu create mode 100644 models/diff_augment_dual.py create mode 100644 models/infinitygan_generator.py create mode 100644 models/losses.py create mode 100644 models/ops.py create mode 100644 models/stylegan2discriminator.py create mode 100644 prepare_data.py create mode 100644 quant_eval_utils.py create mode 100644 scripts/eval/eval_fid_job_s3.sh create mode 100644 scripts/eval/eval_outpaint_imgdir.sh create mode 100644 scripts/eval/eval_scaleinv_fids.sh create mode 100644 scripts/eval/eval_scaleinv_fids_v4.sh create mode 100644 scripts/eval/eval_scaleinv_fids_v5.sh create mode 100644 scripts/eval/eval_spatial_fids.sh create mode 100644 scripts/eval/run_benchmark.py create mode 100644 scripts/script_generator/gen_inbetween_permute.py create mode 100644 test.py create mode 100644 test_distr.py create mode 100644 test_managers/base_test_manager.py create mode 100644 test_managers/direct_generation.py create mode 100644 test_managers/fused_generation.py create mode 100644 test_managers/fused_seq_connecting_generation.py create mode 100644 test_managers/global_config.py create mode 100644 test_managers/infinite_generation.py create mode 100644 test_managers/interactive_sampler.py create mode 100644 test_managers/inversion.py create mode 100644 test_managers/structure_texture_trait_maker.py create mode 100644 test_managers/testing_vars_wrapper.py create mode 100755 train.py create mode 100644 utils.py diff --git a/LICENSE-NVIDIA b/LICENSE-NVIDIA new file mode 100644 index 0000000..487096a --- /dev/null +++ b/LICENSE-NVIDIA @@ -0,0 +1,101 @@ +Copyright (c) 2019, NVIDIA Corporation. All rights reserved. + + +Nvidia Source Code License-NC + +======================================================================= + +1. Definitions + +"Licensor" means any person or entity that distributes its Work. + +"Software" means the original work of authorship made available under +this License. + +"Work" means the Software and any additions to or derivative works of +the Software that are made available under this License. + +"Nvidia Processors" means any central processing unit (CPU), graphics +processing unit (GPU), field-programmable gate array (FPGA), +application-specific integrated circuit (ASIC) or any combination +thereof designed, made, sold, or provided by Nvidia or its affiliates. + +The terms "reproduce," "reproduction," "derivative works," and +"distribution" have the meaning as provided under U.S. copyright law; +provided, however, that for the purposes of this License, derivative +works shall not include works that remain separable from, or merely +link (or bind by name) to the interfaces of, the Work. + +Works, including the Software, are "made available" under this License +by including in or with the Work either (a) a copyright notice +referencing the applicability of this License to the Work, or (b) a +copy of this License. + +2. License Grants + + 2.1 Copyright Grant. Subject to the terms and conditions of this + License, each Licensor grants to you a perpetual, worldwide, + non-exclusive, royalty-free, copyright license to reproduce, + prepare derivative works of, publicly display, publicly perform, + sublicense and distribute its Work and any resulting derivative + works in any form. + +3. Limitations + + 3.1 Redistribution. You may reproduce or distribute the Work only + if (a) you do so under this License, (b) you include a complete + copy of this License with your distribution, and (c) you retain + without modification any copyright, patent, trademark, or + attribution notices that are present in the Work. + + 3.2 Derivative Works. 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If you bring or threaten to bring a patent claim + against any Licensor (including any claim, cross-claim or + counterclaim in a lawsuit) to enforce any patents that you allege + are infringed by any Work, then your rights under this License from + such Licensor (including the grants in Sections 2.1 and 2.2) will + terminate immediately. + + 3.5 Trademarks. This License does not grant any rights to use any + Licensor's or its affiliates' names, logos, or trademarks, except + as necessary to reproduce the notices described in this License. + + 3.6 Termination. If you violate any term of this License, then your + rights under this License (including the grants in Sections 2.1 and + 2.2) will terminate immediately. + +4. Disclaimer of Warranty. + +THE WORK IS PROVIDED "AS IS" WITHOUT WARRANTIES OR CONDITIONS OF ANY +KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WARRANTIES OR CONDITIONS OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE OR +NON-INFRINGEMENT. YOU BEAR THE RISK OF UNDERTAKING ANY ACTIVITIES UNDER +THIS LICENSE. + +5. Limitation of Liability. + +EXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL +THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE +SHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT, +INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF +OR RELATED TO THIS LICENSE, THE USE OR INABILITY TO USE THE WORK +(INCLUDING BUT NOT LIMITED TO LOSS OF GOODWILL, BUSINESS INTERRUPTION, +LOST PROFITS OR DATA, COMPUTER FAILURE OR MALFUNCTION, OR ANY OTHER +COMMERCIAL DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN ADVISED OF +THE POSSIBILITY OF SUCH DAMAGES. + +======================================================================= \ No newline at end of file diff --git a/LICENSE-StyleGAN2-PyTorch b/LICENSE-StyleGAN2-PyTorch new file mode 100644 index 0000000..0fbbdd8 --- /dev/null +++ b/LICENSE-StyleGAN2-PyTorch @@ -0,0 +1,26 @@ +# # Chieh Hubert Lin commented on 2021/04/12 +# This is the license for the StyleGAN2 part codes. +# Despite with a substantial amount of changes, we acknowledge the efforts +# of the author(s) of "rosinality/stylegan2-pytorch" project. + +MIT License + +Copyright (c) 2019 Kim Seonghyeon + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/README.md b/README.md index c6d37fe..1f2f22c 100644 --- a/README.md +++ b/README.md @@ -1,33 +1,321 @@ # InfinityGAN: Towards Infinite-Resolution Image Synthesis +> *"To infinity and beyond!"* + **Chieh Hubert Lin1, Hsin-Ying Lee2, Yen-Chi Cheng3, Sergey Tulyakov2, Ming-Hsuan Yang1,4**
1UC Merced, 2Snap Research, 3CMU, 4Google Research -*(\* This is not an official product of Snap or Google)* +
+ Abstract (click to view) + We present a novel framework, InfinityGAN, for arbitrary-sized image generation. The task is associated with several key challenges. First, scaling existing models to an arbitrarily large image size is resource-constrained, in terms of both computation and availability of large-field-of-view training data. InfinityGAN trains and infers in a seamless patch-by-patch manner with low computational resources. Second, large images should be locally and globally consistent, avoid repetitive patterns, and look realistic. To address these, InfinityGAN disentangles global appearances, local structures, and textures. With this formulation, we can generate images with spatial size and level of details not attainable before. Experimental evaluation validates that InfinityGAN generates images with superior realism compared to baselines and features parallelizable inference. Finally, we show several applications unlocked by our approach, such as spatial style fusion, multi-modal outpainting, and image inbetweening. All applications can be operated with arbitrary input and output sizes. +
**[[Project Page]](https://bit.ly/InfinityGANProject) [[Paper]](https://bit.ly/InfinityGANPaper) [[Supplementary]](http://bit.ly/InfinityGANSupp)** ![Teaser](./assets/teaser.png) +
+ + +
+(*These samples are downsampled, please access the raw images via [Google Drive](https://drive.google.com/drive/folders/1Ej3dgWVagitJR7FYtrlEWu3DSC2cOvJi?usp=sharing)) + --- ## How To Use +- [A. Configure Environment](#a-configure-environment) +- [B. Prepare Data](#b-prepare-data) +- [C. Train Model](#c-train-model) +- [D. Test Model](#d-test-model) +- [E. Interactive Generation](#e-interactive-generation) +- [F. Evaluation](#f-evaluation) +- [G. Pretrained Models And Additional Materials](#g-pretrained-models-and-additional-materials) + +--- + +### A. Configure Environment +Our repository works on Ubuntu. (One of our machine setups: `Ubuntu + Python 3.8.5 + cudatoolkit 10.2`) + +**Setup**: +1. Create conda environement with `conda env create --name pt16 --file meta_data/environment.yml`. We only tested our pipeline on PyTorch 1.6. Please avoid using PyTorch 1.7 and 1.8 as we observe an awkward degradation in performance. +2. (Alternative) Directly install with `conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2 -c pytorch`, `conda install python-lmdb tqdm matplotlib imageio scikit-image scikit-learn scipy=1.5` and `pip install tensorboardx==2.1 pyyaml==5.4.1 easydict`. +3. Designate a directory where you are going to place all your lmdb dataset in `env_config.py`. + +P.S. Theoretically this repository should be workable on Windows if you manage to run [StyleGAN2](https://github.com/NVlabs/stylegan2), which requires extra efforts in dealing with cuda codes building with Visual Studio. + +--- + +### B. Prepare Data -To be released. +**Notes:** +We originally use "Flickr-Landscape (small)" in the V1 paper on Arxiv. We then update the results of all models to "Flickr-Landscape (large)" in the later versions of the paper. We only use the training split for all training and FID evaluation. Nevertheless, we still provide a validation set in "Flickr-Landscape (large)". Notice that the Flickr-Landscape dataset contains images at different sizes without aligning them in the lmdb, so you may add customized training augmentations if desired. + +- Use prepared lmdb: + +| Dataset | Used in latest paper | # images | Minimum image size | All images same shape? | Size | Has holdout set? | Link | +| :--- | :---: | ---: | ---: | :---: | ---: | :---: | :--- | +| Flickr-Landscape (small) | X | 50,000 | 1024 | X | 89G | X | [(Google Drive)](https://drive.google.com/drive/folders/1gwO8i_2ZsF84ORbWNtcixC-_BXbdL1Zh?usp=sharing) | +| Flickr-Landscape (large) | V | 400,000 | 1024 | X | 786G | V | [(Google Drive)](https://drive.google.com/drive/folders/1tuDyjGWy_GvioyJIDR8H7j0_BytKwWhF?usp=sharing) | +| Flickr-Scenery | V | 54,710 | 256 | V | 3.5G | V | [(Will release via In&Out)](https://github.com/yccyenchicheng/InOut) | +| Places2-Scenery-Subset | V | 56,431 | 256 | V | 3.2G | V | [(Will release via In&Out)](https://github.com/yccyenchicheng/InOut) | + +- **Construct your custom dataset** + 1. Prepare a config similar to `configs/dataset/flickr-landscape-small.yaml`. + 2. Run (we only use training set) + ``` + python prepare_data.py ./configs/dataset/flickr-landscape-small.yaml --train_only + ``` + 3. The lmdb will be constructed at the `LMDB_ROOTS` you specified in `env_config.py`. + 4. Remember to modify the `data_params.dataset` flag in your training config when you train the model. + +--- + +### C. Train Model +Our pipeline requires specifying `CUDA_VISIBLE_DEVICES`, and automatically switch to dataparallel if two or more GPUs are specified. + +- InfinityGAN:
+ ``` + CUDA_VISIBLE_DEVICES="0" python train.py ./configs/model/InfinityGAN.yaml + ``` +- StyleGAN2 + NCI:
+ ``` + CUDA_VISIBLE_DEVICES="0" python train.py ./configs/model/StyleGAN2_NCI.yaml + ``` +- StyleGAN2 + NCI + FCG
+ ``` + CUDA_VISIBLE_DEVICES="0" python train.py ./configs/model/StyleGAN2_NCI_FCG.yaml + ``` + +**Misc flags of `train.py`:** +- `--debug`: With this flag, the training pipeline will run one iteration training, execute all logging and evaluation for one iteration, then quit without writing any thing to your logs. Sometimes you may just want to test your environment or config without writing any thing. +- `--archive-mode`: Our pipeline automatically backups your codes at `./logs//codes/`. You may run the model training within that folder by using this flag. + +--- + +### D. Test Model +Our pipeline requires specifying `CUDA_VISIBLE_DEVICES`, and automatically switch to dataparallel if two or more GPUs are specified. + +Suppose with a model trained with a config `./configs/model/.yaml`, you want to generate images at `HxW` resolution. the testing configs are written as follow: + +- **Naive Generation**
+ Directly synthesize the whole image. `O(H*W)` memory allocation.
+ ``` + CUDA_VISIBLE_DEVICES="0,1" python test.py \ + --model-config=./configs/model/.yaml \ + --test-config=./configs/test/direct_gen_HxW.yaml + ``` +- **Infinite Generation**
+ Sequentially generate patches. `O(1)` memory allocation. + ``` + CUDA_VISIBLE_DEVICES="0,1" python test.py \ + --model-config=./configs/model/.yaml \ + --test-config=./configs/test/infinite_gen_HxW.yaml + ``` +- **Spatial Fusion Generation**
+ Spatially fuses multiple styles. Follows the "infinite generation" design. + ``` + CUDA_VISIBLE_DEVICES="0,1" python test.py \ + --model-config=./configs/model/.yaml \ + --test-config=./configs/test/fused_gen_HxW.yaml + +- **Inversion**
+ Please rememeber to update `override_dataset_data_size` and `override_dataset_full_size` if the inversion real image resolution is different from the training resolution. + ``` + CUDA_VISIBLE_DEVICES="0" python test.py \ + --model-config="./configs/model/.yaml" \ + --test-config="./test_configs/inversion_.yaml" + ``` +- **Outpainting** + Invert the latent variables, and outpaint the image. + ``` + # Run inversion first + CUDA_VISIBLE_DEVICES="0" python test.py \ + --model-config="./configs/model/.yaml" \ + --test-config="./test_configs/inversion_256x256_L2R.yaml" + + # Then outpaint + CUDA_VISIBLE_DEVICES="0" python test.py \ + --model-config="./configs/model/.yaml" \ + --test-config="./test_configs/outpaint_with_fused_gen_256x256.yaml" \ + --inv-records="./logs//test/outpaint_with_fused_gen_256x256/stats/.pkl" \ + --inv-placements=0.5,0.25 + ``` + +- **Inbetweening** + Invert the latent variables, and outpaint the image. + ``` + # Run inversion first + CUDA_VISIBLE_DEVICES="0" python test.py \ + --model-config="./configs/model/.yaml" \ + --test-config="./test_configs/inversion_IOF246_256x1280L_256x128.yaml" + CUDA_VISIBLE_DEVICES="0" python test.py \ + --model-config="./configs/model/.yaml" \ + --test-config="./test_configs/inversion_IOF246_256x1280R_256x128.yaml" + + # Then outpaint (the `inv-records` and `inv-placements` are ordered lists separated with `:`) + CUDA_VISIBLE_DEVICES="0" python test.py \ + --model-config="./configs/model/.yaml" \ + --test-config="./test_configs/inbetween_with_fused_gen_256x1280.yaml" \ + --inv-records="./logs//test/inversion_IOF246_256x1280L_256x128/stats/.pkl:./logs//test/inversion_IOF246_256x1280R_256x128/stats/.pkl" \ + --inv-placements=0.5,0.05:0.5,0.95 + ``` + P.S. As (i) the inversion area of the real image, (ii) the inversion area of the generated image, and (iii) the position of the inverted latents while outpainting can be different (as well as some further technical difficulties). Unfortunately, you need to invert the latent variables each time you change either the inversion area size, the position of the inversion area, or the outpainting target resolution. + +#### Additional yaml configs for `test.py`: +- `lowres_height`: High-resolution images are hard to download from remote, we additionally save a low-resolution version of the images by aspect-ratio downsampling the images to the specified height. +- `interactive`: See below. +- `parallel_batch_size`: The "parallel batching" application mentioned in the paper.
+ - Supports `test_manager.infinte_generation` and `test_manager.fused_generation`.
+ - The implementation is at: `test_manager.base_test_manager.py:maybe_parallel_inference()`.
+ - Despite `batch_size` can be simultaneously supported, we make them mutually exclusive as the mixing use of these two batching strategies is not meaningful. + +#### Additional args for `test.py`: + +- `--speed-benchmark`: Collects GPU execution time (includes dataparallel scatter and collection time). Ignores the first-ten iterations. +- `--calc-flops`: Get the total FLOPs used in synthesizing a full image. + +--- + +### E. Interactive Generation + +Set `interactive: True` in the config, or equivalently use `--interactive` in the command to `test.py`. + +The interactive generation is supported for the following `test_manager` classes: +- (for infinite generation)
+ `test_manager.infinte_generation` +- (for spatial fusion generation, outpainting, and inbetweening)
+ `test_manager.fused_generation` + +How to use: +- **Selection**: Left-click on the image two times to create a red selection bounding box that designates an area to re-sample. Right-click on the image two times to create a blue selection bounding box that designates an area to extract channel-wise statistics for the re-sampling mean and standard deviation (default is zero-mean and unit-variance if no blue boxes are selected). +- **Sampling**: Select the variables to resample. Only the spatially shaped latent variables (e.g., local latent and noises) can be regionally sampled with selection area. +- **Undo/redo**: Supports upto 100 steps of undo/redo. You may increase the value if you want and have a sufficient amout of CPU memory. + +**Note** If you find the image is too large (such as 4096x4096 does not fit into your monitor at all), you can increase the `self.fov_rescale` to 2 or 4, which downsamples the image before displaying in the canvas (but you are still interacting with the image at its original image). + +P.S. To quit the program, you need to close the interface window and kill (ctrl-c) the program in the terminal. + +--- + +### F. Evaluation + +#### Evaluate ScaleInv FID + +To test the model with x2 ScaleInv FID: +``` +CUDA_VISIBLE_DEVICES="0,1" python eval_fids.py \ + ./configs/model/.yaml \ + --type=scaleinv \ + --scale=2 \ + --batch-size=2 +``` + +**Other arguments** +- `--ckpt`: By default, we test the checkpoint at `./logs//ckpt/best_fid.pth.tar`. You may override the path with this argument if you want to test other checkpoints. +- `--img-folder`: You may use this in case you want to test with a folder with images. +- `--type`: We also implemented another FID schema `spatial`, which partitions the image into 16(=4x4) patches, extract Inception features for each patch, and concatenate them into a plain vector. This is much slower and consumes massive CPU memory. And the trend (FID v.s. scale) is similar to ScaleInv FID. +- `--seq-inference`: For InfinityGAN, due to the additional structure synthesizer, the model can OOM at higher resolution if generating the image at one-shot. You may use this flag to enable sequential inference (i.e., uses `test_managers.infinite_generation.py`). But this will slow down the inference due to some internal redundant computations. + +#### Evaluate Outpainting +(This script is based on the codes from [In&Out](https://github.com/yccyenchicheng/InOut)) + +Please run the inversion first. It will store results (images and inverted variables) at `./logs//test//`, then you can evaluate with the following command: +``` +CUDA_VISIBLE_DEVICES="0" python eval_outpaint_imgdir.py \ + --batch=48 \ + --size=256 \ + --real-dir=./logs//test//imgs/real_gt/ \ + --fake-dir=./logs//test//imgs/inv_cmp/ +``` +Note that this script only supports single GPU. + +--- + +### G. Pretrained Models and Additional Materials + +#### Pretrained Models (Test Only) + +You should structure the `./logs/` folder like this: +``` +logs/ --+--- exp_name_A/ --- ckpt/ --- best_fid.pth.tar + | + +--- exp_name_B/ --- ckpt/ --- best_fid.pth.tar + | + +--- exp_name_C/ --- ckpt/ --- best_fid.pth.tar + | + (...) +``` + +You should be able to find corresponding config for each of the released model under `./configs/model/`. You can run testing the model with: +``` +CUDA_VISIBLE_DEVICES="0,1" python test.py \ + --model-config=./config/model/.yaml \ + --test-config=./configs/test/infinite_gen_1024x1024.yaml +``` + +The test script will auto-detect the checkpoint at `./logs//ckpt/best_fid.pth.tar` + +| Name | Dataset | Used in paper | Training full image size | Training patch size | Trained w/ #GPUs | Link | +| :-- | :--- | :---: | ---: | ---: | ---: | :--- | +| InfinityGAN | Flickr-Landscape (large) | V | 197 | 101 | 1x TitanX | [(Google Drive)](https://drive.google.com/drive/folders/1RPWgCeO_yAeQCdDbH_owYIGWOt0SHL6M?usp=sharing) | +| InfinityGAN-HR | Flickr-Landscape (large) | X | 389 | 197 | 4x V100 | [(Google Drive)](https://drive.google.com/drive/folders/1VR6J-G9fDDPu-hP13tTcES6VN6ZKbv35?usp=sharing) | +| InfinityGAN-UR | Flickr-Landscape (large) | X | 1024 | 773 | 4x V100 | [(Google Drive)](https://drive.google.com/drive/folders/1f39Qvc6x6umW9HJ2a-SZg02465gEkwYr?usp=sharing) | +| InfinityGAN-IOF | InOut-Flickr-Scenery | V | 197 | 101 | 1x TitanX | [(Google Drive)](https://drive.google.com/drive/folders/1F43zYnLSlqaWIcv6s_Ru0yH7EacVICMl?usp=sharing) | +| InfinityGAN-IOP | InOut-Places2-Scenery-subset | V | 197 | 101 | 1x TitanX | [(Google Drive)](https://drive.google.com/drive/folders/11JFpgVCMhV7RN8BqqXs8KNBtE2UX0YBi?usp=sharing) | + + +#### Inversion results + +Inverting a large set of samples requires a large amount of computation. In order to save your time and our earth (just a bit), we release the inversion results [here](https://drive.google.com/drive/folders/1ugXkqYPPKvLMVNWfFqvJUVMHNpskzL7S?usp=sharing). + +The tar file (decompress with `tar zxf .tar`) contains following folders: +``` +---+---inv_cmp/ : Compare (left-half) real and (right-half) reconstruction via inversion. + | + +---inv_comp_cropped/ : Composed (left-half) real and (right-half) outpainting via inversion. + | + +---inv_raw/ : The whole inverted image. + | + +---real_gt/ : The real data. +``` + +Note: You may notice that there is a *cropped* in the folder name. InfinityGAN actually inverts images slightly larger than the conditional image, then crop those area away in the end. + +### Known Issues + +1. **The performance on PyTorch 1.4/1.7 and PyTorch 1.6 are different.** The root cause is unknown and still a misc event to us, so please use PyTorch 1.6 if possible. +2. **Please do not use dataparallel on two different types of GPUs** (e.g., data parallel with GTX1080 + GTX2080), one of the GPUs may generate gray or blank images. +3. **OOM while training with a single GPU.** PyTorch can sometimes raise OOM due to [unfortunate memory allocations](https://discuss.pytorch.org/t/unable-to-allocate-cuda-memory-when-there-is-enough-of-cached-memory/33296). Here are some tweaks that sometimes resolves the problem (if you indeed have only one GPU): + - [Reminder] We use "TITAN X (Pascal)" with 12196 MB GPU memery in the single-GPU setup in our paper. We are not certain about the results on other GPUs with less memory. + - Set `torch.backends.cudnn.benchmark` to `False` in `train.py`. Despite it was designed not to produce OOM, but somehow it sometimes unfortunately makes it. + - Set `calc_fid` and `calc_fid_ext2` to `False`. The evaluation allocates additional memory and uses different memory allocation pattern, which can mess up the PyTorch memory allocation schedule. You may directly use the last iteration, as all models converge well. + - Set `ext_mult_list` to `[2,]` instead of `[2, 4]`, which stops logging images generated at 4x testing resolution during training. + - Reduce training `batch_size` smaller. However, it may influence the model performance. + +--- + +## License + +This repository borrows codes from different sources, please follow the user licenses from each of the source while using this project. + +Notice that the code release aims to support the open-source culture in computer vision research community, facilitating the research efficiency and keeping the community open to new comers. The first author ***strongly discourage*** research groups that do not match such an open-source culture to use or read any piece of codes in this repository. Please keep the close-door culture bidirectional. --- ## Acknowledgement -The implementation is partly based on the [StyleGAN2-Pytorch](https://github.com/rosinality/stylegan2-pytorch) project. + +The implementation heavily borrows from [StyleGAN2-Pytorch](https://github.com/rosinality/stylegan2-pytorch), [pytorch-fid](https://github.com/mseitzer/pytorch-fid) and [PerceptualSimilarity](https://github.com/richzhang/PerceptualSimilarity). --- ## Citation ``` -@article{lin2021infinity, - title={InfinityGAN: Towards Infinite-Resolution Image Synthesis}, - author={Lin, Chieh Hubert and Cheng, Yen-Chi and Lee, Hsin-Ying and Tulyakov, Sergey and Yang, Ming-Hsuan}, - journal={arXiv preprint arXiv:2104.03963}, - year={2021} +@inproceedings{ + lin2021infinity, + title={Infinity{GAN}: Towards Infinite-Pixel Image Synthesis}, + author={Lin, Chieh Hubert and Cheng, Yen-Chi and Lee, Hsin-Ying and Tulyakov, Sergey and Yang, Ming-Hsuan}, + booktitle={International Conference on Learning Representations}, + year={2022}, + url={https://openreview.net/forum?id=ufGMqIM0a4b}, } ``` diff --git a/assets/OpenSans-Bold.ttf b/assets/OpenSans-Bold.ttf new file mode 100644 index 0000000000000000000000000000000000000000..efdd5e84a0397ecada7b9cfde51db87db08766bd GIT binary patch literal 104120 zcmb5X2Vj&{xJ(#LzMz1`J6cAs~amB29V`0TCl0BBCN7ii)g@ z2#74Jh%BOu1r!h)i(D_)i>$heiUrG}B1zu-f6sYmlE9YxeS{>FnKS1+=Q&R=X9Ok) zf(t)n!8vfiHI^dht^(>b8{Yr3=kxdD?>7ZOJU?Vu zQSp|UPXq-vVJKc7H(}-tbDsIj&rSGyw;)J&Pgrnsrl&CZL4nP2;`N=A=iE5+FY~s} z7ubzg34-C18*i95M=%O0nC~{cZoP5FqRDAX-nt+NELC8G-2_b&*^am0;|Exh7mJwxMhw~B4!Az z`V%~nIqQa*lSYnjECf6U0N+byT|c_MVKh4{ow)R2!-$dfL#~g}8Y`sjQvW!tDN&1T z5G2W{iUtXjN8T-VF--eTarbUa!p!()oAM9mi!bAcvajU_(MjJlPa*cKDSL4)8A4-p zfH5`0CwomMv&@7fIjAVA+hMoJW{=0~Pfs(*Y9J5{dR4QW88XTiODeNx2o6V*&uzz- zIpC30vl{?ZMBL@&MMc;gz@Y#9mzxC$eeQqt6ObB0K0xXT(|1Xz4BwuJ6rpdQ5-AHy zUluV@J5}>h?V;*ZRp*(nsF{UoPS+f%JyrWdOWGSnZ%EZ&y{AoM8|eGJuTHRswW;)d 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zzun$>{d{R;?J4-*Q?dbu#x9E#04Rovk=%s{3&CnFb8n+w>aF+#h00000NkvXXu0mjf5;CU? literal 0 HcmV?d00001 diff --git a/benchmark_configs/benchmark_infinite_gen_1024x1024_baseline.yaml b/benchmark_configs/benchmark_infinite_gen_1024x1024_baseline.yaml new file mode 100644 index 0000000..75f28c4 --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_1024x1024_baseline.yaml @@ -0,0 +1,10 @@ +task_manager: "test_managers.baseline_generation.BaselineGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 1024 +width: 1024 +batch_size: 1 +num_gen: 20 +lowres_height: 128 diff --git a/benchmark_configs/benchmark_infinite_gen_1024x1024_naive.yaml b/benchmark_configs/benchmark_infinite_gen_1024x1024_naive.yaml new file mode 100644 index 0000000..914a80a --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_1024x1024_naive.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 1024 +width: 1024 +batch_size: 1 +parallel_batch_size: 1 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_1024x1024_parabatch128.yaml b/benchmark_configs/benchmark_infinite_gen_1024x1024_parabatch128.yaml new file mode 100644 index 0000000..abbdc2d --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_1024x1024_parabatch128.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 1024 +width: 1024 +batch_size: 1 +parallel_batch_size: 128 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_1024x1024_parabatch16.yaml b/benchmark_configs/benchmark_infinite_gen_1024x1024_parabatch16.yaml new file mode 100644 index 0000000..14a464d --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_1024x1024_parabatch16.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 1024 +width: 1024 +batch_size: 1 +parallel_batch_size: 16 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_1024x1024_parabatch2.yaml b/benchmark_configs/benchmark_infinite_gen_1024x1024_parabatch2.yaml new file mode 100644 index 0000000..dc652a2 --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_1024x1024_parabatch2.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 1024 +width: 1024 +batch_size: 1 +parallel_batch_size: 2 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_1024x1024_parabatch32.yaml b/benchmark_configs/benchmark_infinite_gen_1024x1024_parabatch32.yaml new file mode 100644 index 0000000..f5bbec0 --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_1024x1024_parabatch32.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 1024 +width: 1024 +batch_size: 1 +parallel_batch_size: 32 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_1024x1024_parabatch4.yaml b/benchmark_configs/benchmark_infinite_gen_1024x1024_parabatch4.yaml new file mode 100644 index 0000000..810e320 --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_1024x1024_parabatch4.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 1024 +width: 1024 +batch_size: 1 +parallel_batch_size: 4 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_1024x1024_parabatch64.yaml b/benchmark_configs/benchmark_infinite_gen_1024x1024_parabatch64.yaml new file mode 100644 index 0000000..1428bcf --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_1024x1024_parabatch64.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 1024 +width: 1024 +batch_size: 1 +parallel_batch_size: 64 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_1024x1024_parabatch8.yaml b/benchmark_configs/benchmark_infinite_gen_1024x1024_parabatch8.yaml new file mode 100644 index 0000000..cb1de87 --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_1024x1024_parabatch8.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 1024 +width: 1024 +batch_size: 1 +parallel_batch_size: 8 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_2048x2048_baseline.yaml b/benchmark_configs/benchmark_infinite_gen_2048x2048_baseline.yaml new file mode 100644 index 0000000..1787d2f --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_2048x2048_baseline.yaml @@ -0,0 +1,10 @@ +task_manager: "test_managers.baseline_generation.BaselineGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 2048 +width: 2048 +batch_size: 1 +num_gen: 20 +lowres_height: 128 diff --git a/benchmark_configs/benchmark_infinite_gen_2048x2048_naive.yaml b/benchmark_configs/benchmark_infinite_gen_2048x2048_naive.yaml new file mode 100644 index 0000000..15d8b29 --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_2048x2048_naive.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 2048 +width: 2048 +batch_size: 1 +parallel_batch_size: 1 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_2048x2048_parabatch128.yaml b/benchmark_configs/benchmark_infinite_gen_2048x2048_parabatch128.yaml new file mode 100644 index 0000000..383065e --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_2048x2048_parabatch128.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 2048 +width: 2048 +batch_size: 1 +parallel_batch_size: 128 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_2048x2048_parabatch16.yaml b/benchmark_configs/benchmark_infinite_gen_2048x2048_parabatch16.yaml new file mode 100644 index 0000000..5c54a64 --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_2048x2048_parabatch16.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 2048 +width: 2048 +batch_size: 1 +parallel_batch_size: 16 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_2048x2048_parabatch2.yaml b/benchmark_configs/benchmark_infinite_gen_2048x2048_parabatch2.yaml new file mode 100644 index 0000000..d0680dd --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_2048x2048_parabatch2.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 2048 +width: 2048 +batch_size: 1 +parallel_batch_size: 2 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_2048x2048_parabatch32.yaml b/benchmark_configs/benchmark_infinite_gen_2048x2048_parabatch32.yaml new file mode 100644 index 0000000..eaa089f --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_2048x2048_parabatch32.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 2048 +width: 2048 +batch_size: 1 +parallel_batch_size: 32 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_2048x2048_parabatch4.yaml b/benchmark_configs/benchmark_infinite_gen_2048x2048_parabatch4.yaml new file mode 100644 index 0000000..505f519 --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_2048x2048_parabatch4.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 2048 +width: 2048 +batch_size: 1 +parallel_batch_size: 4 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_2048x2048_parabatch64.yaml b/benchmark_configs/benchmark_infinite_gen_2048x2048_parabatch64.yaml new file mode 100644 index 0000000..2c01936 --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_2048x2048_parabatch64.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 2048 +width: 2048 +batch_size: 1 +parallel_batch_size: 64 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_2048x2048_parabatch8.yaml b/benchmark_configs/benchmark_infinite_gen_2048x2048_parabatch8.yaml new file mode 100644 index 0000000..9edad8f --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_2048x2048_parabatch8.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 2048 +width: 2048 +batch_size: 1 +parallel_batch_size: 8 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_4096x4096_baseline.yaml b/benchmark_configs/benchmark_infinite_gen_4096x4096_baseline.yaml new file mode 100644 index 0000000..ee70119 --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_4096x4096_baseline.yaml @@ -0,0 +1,10 @@ +task_manager: "test_managers.baseline_generation.BaselineGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 4096 +width: 4096 +batch_size: 1 +num_gen: 20 +lowres_height: 128 diff --git a/benchmark_configs/benchmark_infinite_gen_4096x4096_naive.yaml b/benchmark_configs/benchmark_infinite_gen_4096x4096_naive.yaml new file mode 100644 index 0000000..c31ca08 --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_4096x4096_naive.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 4096 +width: 4096 +batch_size: 1 +parallel_batch_size: 1 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_4096x4096_parabatch128.yaml b/benchmark_configs/benchmark_infinite_gen_4096x4096_parabatch128.yaml new file mode 100644 index 0000000..f2caf04 --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_4096x4096_parabatch128.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 4096 +width: 4096 +batch_size: 1 +parallel_batch_size: 128 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_4096x4096_parabatch16.yaml b/benchmark_configs/benchmark_infinite_gen_4096x4096_parabatch16.yaml new file mode 100644 index 0000000..c0758dc --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_4096x4096_parabatch16.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 4096 +width: 4096 +batch_size: 1 +parallel_batch_size: 16 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_4096x4096_parabatch2.yaml b/benchmark_configs/benchmark_infinite_gen_4096x4096_parabatch2.yaml new file mode 100644 index 0000000..7c0864c --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_4096x4096_parabatch2.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 4096 +width: 4096 +batch_size: 1 +parallel_batch_size: 2 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_4096x4096_parabatch32.yaml b/benchmark_configs/benchmark_infinite_gen_4096x4096_parabatch32.yaml new file mode 100644 index 0000000..d5374f6 --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_4096x4096_parabatch32.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 4096 +width: 4096 +batch_size: 1 +parallel_batch_size: 32 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_4096x4096_parabatch4.yaml b/benchmark_configs/benchmark_infinite_gen_4096x4096_parabatch4.yaml new file mode 100644 index 0000000..9acf88f --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_4096x4096_parabatch4.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 4096 +width: 4096 +batch_size: 1 +parallel_batch_size: 4 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_4096x4096_parabatch64.yaml b/benchmark_configs/benchmark_infinite_gen_4096x4096_parabatch64.yaml new file mode 100644 index 0000000..b7f398a --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_4096x4096_parabatch64.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 4096 +width: 4096 +batch_size: 1 +parallel_batch_size: 64 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_4096x4096_parabatch8.yaml b/benchmark_configs/benchmark_infinite_gen_4096x4096_parabatch8.yaml new file mode 100644 index 0000000..542caf6 --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_4096x4096_parabatch8.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 4096 +width: 4096 +batch_size: 1 +parallel_batch_size: 8 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_8192x8192_baseline.yaml b/benchmark_configs/benchmark_infinite_gen_8192x8192_baseline.yaml new file mode 100644 index 0000000..1ad6329 --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_8192x8192_baseline.yaml @@ -0,0 +1,10 @@ +task_manager: "test_managers.baseline_generation.BaselineGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 8192 +width: 8192 +batch_size: 1 +num_gen: 20 +lowres_height: 128 diff --git a/benchmark_configs/benchmark_infinite_gen_8192x8192_naive.yaml b/benchmark_configs/benchmark_infinite_gen_8192x8192_naive.yaml new file mode 100644 index 0000000..9ba27bb --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_8192x8192_naive.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 8192 +width: 8192 +batch_size: 1 +parallel_batch_size: 1 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_8192x8192_parabatch128.yaml b/benchmark_configs/benchmark_infinite_gen_8192x8192_parabatch128.yaml new file mode 100644 index 0000000..43cb2e1 --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_8192x8192_parabatch128.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 8192 +width: 8192 +batch_size: 1 +parallel_batch_size: 128 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_8192x8192_parabatch16.yaml b/benchmark_configs/benchmark_infinite_gen_8192x8192_parabatch16.yaml new file mode 100644 index 0000000..4112576 --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_8192x8192_parabatch16.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 8192 +width: 8192 +batch_size: 1 +parallel_batch_size: 16 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_8192x8192_parabatch2.yaml b/benchmark_configs/benchmark_infinite_gen_8192x8192_parabatch2.yaml new file mode 100644 index 0000000..a81edfb --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_8192x8192_parabatch2.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 8192 +width: 8192 +batch_size: 1 +parallel_batch_size: 2 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_8192x8192_parabatch32.yaml b/benchmark_configs/benchmark_infinite_gen_8192x8192_parabatch32.yaml new file mode 100644 index 0000000..e74acce --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_8192x8192_parabatch32.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 8192 +width: 8192 +batch_size: 1 +parallel_batch_size: 32 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_8192x8192_parabatch4.yaml b/benchmark_configs/benchmark_infinite_gen_8192x8192_parabatch4.yaml new file mode 100644 index 0000000..b653f1f --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_8192x8192_parabatch4.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 8192 +width: 8192 +batch_size: 1 +parallel_batch_size: 4 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_8192x8192_parabatch64.yaml b/benchmark_configs/benchmark_infinite_gen_8192x8192_parabatch64.yaml new file mode 100644 index 0000000..31b9cb2 --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_8192x8192_parabatch64.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 8192 +width: 8192 +batch_size: 1 +parallel_batch_size: 64 +num_gen: 20 +lowres_height: 128 + diff --git a/benchmark_configs/benchmark_infinite_gen_8192x8192_parabatch8.yaml b/benchmark_configs/benchmark_infinite_gen_8192x8192_parabatch8.yaml new file mode 100644 index 0000000..2680fe7 --- /dev/null +++ b/benchmark_configs/benchmark_infinite_gen_8192x8192_parabatch8.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 8192 +width: 8192 +batch_size: 1 +parallel_batch_size: 8 +num_gen: 20 +lowres_height: 128 + diff --git a/configs/dataset/alis-lhq.yaml b/configs/dataset/alis-lhq.yaml new file mode 100644 index 0000000..ef914fe --- /dev/null +++ b/configs/dataset/alis-lhq.yaml @@ -0,0 +1,8 @@ +data_params: + dataset: "ALIS-LHQ" + raw_data_root: "/tmp2/hubert/data/img/ALIS-LHQ/images/" + num_train: 90000 + num_valid: 0 + +train_params: + data_size: 1024 diff --git a/configs/dataset/coco.yaml b/configs/dataset/coco.yaml new file mode 100644 index 0000000..6be1c23 --- /dev/null +++ b/configs/dataset/coco.yaml @@ -0,0 +1,8 @@ +data_params: + dataset: "flickr-landscape-small" + raw_data_root: "/home/OuO/infinityGAN/data/flickr-landcape-raw" + num_train: 50000 + num_valid: 50000 + +train_params: + data_size: 1024 diff --git a/configs/dataset/flickr-landscape-large.yaml b/configs/dataset/flickr-landscape-large.yaml new file mode 100644 index 0000000..15ab50c --- /dev/null +++ b/configs/dataset/flickr-landscape-large.yaml @@ -0,0 +1,8 @@ +data_params: + dataset: "flickr-landscape-large" + raw_data_root: "/tmp3/hubert/data/flickr-landscape/" + num_train: 400000 + num_valid: 50000 + +train_params: + data_size: 1024 diff --git a/configs/dataset/flickr-landscape-large197.yaml b/configs/dataset/flickr-landscape-large197.yaml new file mode 100644 index 0000000..2db11b7 --- /dev/null +++ b/configs/dataset/flickr-landscape-large197.yaml @@ -0,0 +1,8 @@ +data_params: + dataset: "flickr-landscape-large197" + raw_data_root: "/tmp3/hubert/data/flickr-landscape/" + num_train: 400000 + num_valid: 50000 + +train_params: + data_size: 197 diff --git a/configs/dataset/flickr-landscape-small.yaml b/configs/dataset/flickr-landscape-small.yaml new file mode 100644 index 0000000..6be1c23 --- /dev/null +++ b/configs/dataset/flickr-landscape-small.yaml @@ -0,0 +1,8 @@ +data_params: + dataset: "flickr-landscape-small" + raw_data_root: "/home/OuO/infinityGAN/data/flickr-landcape-raw" + num_train: 50000 + num_valid: 50000 + +train_params: + data_size: 1024 diff --git a/configs/interactive/fused_gen_512x4096_4styles_interactive.yaml b/configs/interactive/fused_gen_512x4096_4styles_interactive.yaml new file mode 100644 index 0000000..f1127bf --- /dev/null +++ b/configs/interactive/fused_gen_512x4096_4styles_interactive.yaml @@ -0,0 +1,21 @@ +task_manager: "test_managers.fused_generation.FusedGenerationManager" +interactive: True +seed: null + +# task specific configs +height: 512 +width: 4096 +batch_size: 1 +parallel_batch_size: 16 +num_gen: 8 +lowres_height: 128 + +blur_kernel: 255 +blur_type: "mean" # "mean" or "gaussian" + +style_centers: + - [0.00, 0.00] + - [0.33, 0.33] + - [0.66, 0.66] + - [1.00, 1.00] + diff --git a/configs/interactive/infinite_gen_1024x1024_interactive.yaml b/configs/interactive/infinite_gen_1024x1024_interactive.yaml new file mode 100644 index 0000000..ff582c3 --- /dev/null +++ b/configs/interactive/infinite_gen_1024x1024_interactive.yaml @@ -0,0 +1,12 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: True +seed: null + +# task specific configs +height: 1024 +width: 1024 +batch_size: 1 +parallel_batch_size: 8 +num_gen: 64 +lowres_height: 128 + diff --git a/configs/model/InfinityGAN-HR.yaml b/configs/model/InfinityGAN-HR.yaml new file mode 100644 index 0000000..08c924f --- /dev/null +++ b/configs/model/InfinityGAN-HR.yaml @@ -0,0 +1,75 @@ +data_params: + dataset: "flickr-landscape-large" + num_train: 400000 + num_valid: 50000 + +train_params: + + styleGAN2_baseline: False + + data_size: 1024 + full_size: 773 + patch_size: 389 + training_modality: "patch" + + batch_size: 16 + iter: 1600000 + + r1: 10 + path_regularize: 2 + path_batch_shrink: 2 + d_reg_every: 16 + g_reg_every: 4 + mixing: 0.9 + lr: 0.002 + + # Arch + g_arch: "models.infinitygan_generator.InfinityGanGenerator" + d_arch: "models.stylegan2discriminator.StyleGan2Discriminator" + global_latent_dim: 512 + local_latent_dim: 256 + n_mlp: 8 + channel_multiplier: 2 + + # SS + use_ss: True + ss_n_layers: 4 + ss_unfold_radius: 3 + ss_coord_all_layers: True + ss_disable_noise: True + + # TS + ts_input_size: 11 + ts_no_zero_pad: True + + # Diversity Loss (Mode-Seeking) + diversity_z_w: 1 + diversity_angular: True + diversity_dual: True + + # Coord configs + coord_continuous: True + coord_vert_sample_size: 10 # V in paper + coord_hori_occupy_ratio: 0.25 + coord_vert_cut_pt: 3 # equivalent to 0.995 after tanh + coord_num_dir: 3 # x, a, b + + coord_use_ac: True + coord_ac_w: 1 + coord_use_pd: False + coord_pd_w: 0 + coord_ac_vert_only: True # Only apply AC loss on vertical direction + +log_params: + n_save_sample: 64 + log_tick: 100 + img_tick: 3000 + eval_tick: 15000 + save_tick: 3000 + fid_ext2_tick: 45000 + +test_params: + calc_fid: True + calc_fid_ext2: True + n_fid_sample: 50000 + diff --git a/configs/model/InfinityGAN-IOF.yaml b/configs/model/InfinityGAN-IOF.yaml new file mode 100644 index 0000000..2beafdf --- /dev/null +++ b/configs/model/InfinityGAN-IOF.yaml @@ -0,0 +1,75 @@ +data_params: + dataset: "InOut-flickr" + num_train: 34710 + num_valid: -1 + +train_params: + + styleGAN2_baseline: False + + data_size: 256 + full_size: 197 + patch_size: 101 + training_modality: "patch" + + batch_size: 16 + iter: 800000 + + r1: 10 + path_regularize: 2 + path_batch_shrink: 2 + d_reg_every: 16 + g_reg_every: 4 + mixing: 0.9 + lr: 0.002 + + # Arch + g_arch: "models.infinitygan_generator.InfinityGanGenerator" + d_arch: "models.stylegan2discriminator.StyleGan2Discriminator" + global_latent_dim: 512 + local_latent_dim: 256 + n_mlp: 8 + channel_multiplier: 2 + + # SS + use_ss: True + ss_n_layers: 4 + ss_unfold_radius: 3 + ss_coord_all_layers: True + ss_disable_noise: True + + # TS + ts_input_size: 11 + ts_no_zero_pad: True + + # Diversity Loss (Mode-Seeking) + diversity_z_w: 1 + diversity_angular: True + diversity_dual: True + + # Coord configs + coord_continuous: True + coord_vert_sample_size: 10 # V in paper + coord_hori_occupy_ratio: 0.25 + coord_vert_cut_pt: 3 # equivalent to 0.995 after tanh + coord_num_dir: 3 # x, a, b + + coord_use_ac: True + coord_ac_w: 1 + coord_use_pd: False + coord_pd_w: 0 + coord_ac_vert_only: True # Only apply AC loss on vertical direction + +log_params: + n_save_sample: 64 + log_tick: 100 + img_tick: 3000 + eval_tick: 15000 + save_tick: 3000 + fid_ext2_tick: 45000 + +test_params: + calc_fid: True + calc_fid_ext2: True + n_fid_sample: 50000 + diff --git a/configs/model/InfinityGAN-IOP.yaml b/configs/model/InfinityGAN-IOP.yaml new file mode 100644 index 0000000..d734032 --- /dev/null +++ b/configs/model/InfinityGAN-IOP.yaml @@ -0,0 +1,75 @@ +data_params: + dataset: "InOut-place365" + num_train: 56431 + num_valid: -1 + +train_params: + + styleGAN2_baseline: False + + data_size: 256 + full_size: 197 + patch_size: 101 + training_modality: "patch" + + batch_size: 16 + iter: 800000 + + r1: 10 + path_regularize: 2 + path_batch_shrink: 2 + d_reg_every: 16 + g_reg_every: 4 + mixing: 0.9 + lr: 0.002 + + # Arch + g_arch: "models.infinitygan_generator.InfinityGanGenerator" + d_arch: "models.stylegan2discriminator.StyleGan2Discriminator" + global_latent_dim: 512 + local_latent_dim: 256 + n_mlp: 8 + channel_multiplier: 2 + + # SS + use_ss: True + ss_n_layers: 4 + ss_unfold_radius: 3 + ss_coord_all_layers: True + ss_disable_noise: True + + # TS + ts_input_size: 11 + ts_no_zero_pad: True + + # Diversity Loss (Mode-Seeking) + diversity_z_w: 1 + diversity_angular: True + diversity_dual: True + + # Coord configs + coord_continuous: True + coord_vert_sample_size: 10 # V in paper + coord_hori_occupy_ratio: 0.25 + coord_vert_cut_pt: 3 # equivalent to 0.995 after tanh + coord_num_dir: 3 # x, a, b + + coord_use_ac: True + coord_ac_w: 1 + coord_use_pd: False + coord_pd_w: 0 + coord_ac_vert_only: True # Only apply AC loss on vertical direction + +log_params: + n_save_sample: 64 + log_tick: 100 + img_tick: 3000 + eval_tick: 15000 + save_tick: 3000 + fid_ext2_tick: 45000 + +test_params: + calc_fid: True + calc_fid_ext2: True + n_fid_sample: 50000 + diff --git a/configs/model/InfinityGAN-UR.yaml b/configs/model/InfinityGAN-UR.yaml new file mode 100644 index 0000000..ba374ef --- /dev/null +++ b/configs/model/InfinityGAN-UR.yaml @@ -0,0 +1,75 @@ +data_params: + dataset: "flickr-landscape-large" + num_train: 400000 + num_valid: 50000 + +train_params: + + styleGAN2_baseline: False + + data_size: 1024 + full_size: 1024 + patch_size: 773 + training_modality: "patch" + + batch_size: 16 + iter: 1600000 + + r1: 10 + path_regularize: 2 + path_batch_shrink: 2 + d_reg_every: 16 + g_reg_every: 4 + mixing: 0.9 + lr: 0.002 + + # Arch + g_arch: "models.infinitygan_generator.InfinityGanGenerator" + d_arch: "models.stylegan2discriminator.StyleGan2Discriminator" + global_latent_dim: 512 + local_latent_dim: 256 + n_mlp: 8 + channel_multiplier: 2 + + # SS + use_ss: True + ss_n_layers: 4 + ss_unfold_radius: 3 + ss_coord_all_layers: True + ss_disable_noise: True + + # TS + ts_input_size: 11 + ts_no_zero_pad: True + + # Diversity Loss (Mode-Seeking) + diversity_z_w: 1 + diversity_angular: True + diversity_dual: True + + # Coord configs + coord_continuous: True + coord_vert_sample_size: 10 # V in paper + coord_hori_occupy_ratio: 0.25 + coord_vert_cut_pt: 3 # equivalent to 0.995 after tanh + coord_num_dir: 3 # x, a, b + + coord_use_ac: True + coord_ac_w: 1 + coord_use_pd: False + coord_pd_w: 0 + coord_ac_vert_only: True # Only apply AC loss on vertical direction + +log_params: + n_save_sample: 64 + log_tick: 100 + img_tick: 3000 + eval_tick: 15000 + save_tick: 3000 + fid_ext2_tick: 45000 + +test_params: + calc_fid: True + calc_fid_ext2: True + n_fid_sample: 50000 + diff --git a/configs/model/InfinityGAN.yaml b/configs/model/InfinityGAN.yaml new file mode 100644 index 0000000..5df8cc5 --- /dev/null +++ b/configs/model/InfinityGAN.yaml @@ -0,0 +1,76 @@ +data_params: + dataset: "flickr-landscape-large" + num_train: 400000 + num_valid: 50000 + +train_params: + + styleGAN2_baseline: False + + data_size: 1024 + extra_pre_resize: 128 + full_size: 197 + patch_size: 101 + training_modality: "patch" + + batch_size: 16 + iter: 800000 + + r1: 10 + path_regularize: 2 + path_batch_shrink: 2 + d_reg_every: 16 + g_reg_every: 4 + mixing: 0.9 + lr: 0.002 + + # Arch + g_arch: "models.infinitygan_generator.InfinityGanGenerator" + d_arch: "models.stylegan2discriminator.StyleGan2Discriminator" + global_latent_dim: 512 + local_latent_dim: 256 + n_mlp: 8 + channel_multiplier: 2 + + # SS + use_ss: True + ss_n_layers: 4 + ss_unfold_radius: 3 + ss_coord_all_layers: True + ss_disable_noise: True + + # TS + ts_input_size: 11 + ts_no_zero_pad: True + + # Diversity Loss (Mode-Seeking) + diversity_z_w: 1 + diversity_angular: True + diversity_dual: True + + # Coord configs + coord_continuous: True + coord_vert_sample_size: 10 # V in paper + coord_hori_occupy_ratio: 0.25 + coord_vert_cut_pt: 3 # equivalent to 0.995 after tanh + coord_num_dir: 3 # x, a, b + + coord_use_ac: True + coord_ac_w: 1 + coord_use_pd: False + coord_pd_w: 0 + coord_ac_vert_only: True # Only apply AC loss on vertical direction + +log_params: + n_save_sample: 64 + log_tick: 100 + img_tick: 3000 + eval_tick: 15000 + save_tick: 3000 + fid_ext2_tick: 45000 + +test_params: + calc_fid: True + calc_fid_ext2: True + n_fid_sample: 50000 + diff --git a/configs/model/InfinityGAN_maps.yaml b/configs/model/InfinityGAN_maps.yaml new file mode 100644 index 0000000..90cebef --- /dev/null +++ b/configs/model/InfinityGAN_maps.yaml @@ -0,0 +1,75 @@ +data_params: + dataset: "maps" + num_train: 2196 + num_valid: 0 + +train_params: + + styleGAN2_baseline: False + + data_size: 256 + full_size: 197 + patch_size: 101 + training_modality: "patch" + + batch_size: 16 + iter: 800000 + + r1: 10 + path_regularize: 2 + path_batch_shrink: 2 + d_reg_every: 16 + g_reg_every: 4 + mixing: 0.9 + lr: 0.002 + + # Arch + g_arch: "models.infinitygan_generator.InfinityGanGenerator" + d_arch: "models.stylegan2discriminator.StyleGan2Discriminator" + global_latent_dim: 512 + local_latent_dim: 256 + n_mlp: 8 + channel_multiplier: 2 + + # SS + use_ss: True + ss_n_layers: 4 + ss_unfold_radius: 3 + ss_coord_all_layers: True + ss_disable_noise: True + + # TS + ts_input_size: 11 + ts_no_zero_pad: True + + # Diversity Loss (Mode-Seeking) + diversity_z_w: 1 + diversity_angular: True + diversity_dual: True + + # Coord configs + coord_continuous: True + coord_vert_sample_size: 10 # V in paper + coord_hori_occupy_ratio: 0.25 + coord_vert_cut_pt: 3 # equivalent to 0.995 after tanh + coord_num_dir: 4 # xa, xb, ya, yb + + coord_use_ac: False + coord_ac_w: 0 + coord_use_pd: False + coord_pd_w: 0 + coord_ac_vert_only: True # Only apply AC loss on vertical direction + +log_params: + n_save_sample: 64 + log_tick: 100 + img_tick: 3000 + eval_tick: 15000 + save_tick: 3000 + fid_ext2_tick: 45000 + +test_params: + calc_fid: True + calc_fid_ext2: True + n_fid_sample: 2196 + diff --git a/configs/model/StyleGAN2_NCI.yaml b/configs/model/StyleGAN2_NCI.yaml new file mode 100644 index 0000000..cbea3e8 --- /dev/null +++ b/configs/model/StyleGAN2_NCI.yaml @@ -0,0 +1,56 @@ +data_params: + dataset: "flickr-landscape-large" + num_train: 400000 + num_valid: 50000 + +train_params: + + styleGAN2_baseline: True + + data_size: 1024 + full_size: 128 + patch_size: 128 + training_modality: "patch" + + batch_size: 16 + iter: 800000 + + r1: 10 + path_regularize: 2 + path_batch_shrink: 2 + d_reg_every: 16 + g_reg_every: 4 + mixing: 0.9 + lr: 0.002 + + # Arch + g_arch: "models.infinitygan_generator.InfinityGanGenerator" + d_arch: "models.stylegan2discriminator.StyleGan2Discriminator" + global_latent_dim: 512 + local_latent_dim: 512 # StyleGAN constant-input dim + n_mlp: 8 + channel_multiplier: 2 + + # SS + use_ss: False + ss_n_layers: 0 + ss_unfold_radius: 0 + + # TS + ts_input_size: 4 + ts_no_zero_pad: False + +log_params: + n_save_sample: 64 + log_tick: 100 + img_tick: 3000 + eval_tick: 15000 + save_tick: 3000 + fid_ext2_tick: 45000 + +test_params: + calc_fid: True + calc_fid_ext2: False # Training with full image has no fid_ext2 + n_fid_sample: 50000 + + diff --git a/configs/model/StyleGAN2_NCI_FCG.yaml b/configs/model/StyleGAN2_NCI_FCG.yaml new file mode 100644 index 0000000..45a8c6b --- /dev/null +++ b/configs/model/StyleGAN2_NCI_FCG.yaml @@ -0,0 +1,56 @@ +data_params: + dataset: "flickr-landscape-large" + num_train: 400000 + num_valid: 50000 + +train_params: + + styleGAN2_baseline: True + + data_size: 1024 + full_size: 197 + patch_size: 101 + training_modality: "patch" + + batch_size: 16 + iter: 800000 + + r1: 10 + path_regularize: 2 + path_batch_shrink: 2 + d_reg_every: 16 + g_reg_every: 4 + mixing: 0.9 + lr: 0.002 + + # Arch + g_arch: "models.infinitygan_generator.InfinityGanGenerator" + d_arch: "models.stylegan2discriminator.StyleGan2Discriminator" + global_latent_dim: 512 + local_latent_dim: 256 # InfinityGAN setup + n_mlp: 8 + channel_multiplier: 2 + + # SS + use_ss: False + ss_n_layers: 0 + ss_unfold_radius: 0 + + # TS + ts_input_size: 11 + ts_no_zero_pad: True + +log_params: + n_save_sample: 64 + log_tick: 100 + img_tick: 3000 + eval_tick: 15000 + save_tick: 3000 + fid_ext2_tick: 45000 + +test_params: + calc_fid: True + calc_fid_ext2: False # Training with full image has no fid_ext2 + n_fid_sample: 50000 + + diff --git a/configs/test/direct_gen_1024x1024.yaml b/configs/test/direct_gen_1024x1024.yaml new file mode 100644 index 0000000..1876ef4 --- /dev/null +++ b/configs/test/direct_gen_1024x1024.yaml @@ -0,0 +1,11 @@ +task_manager: "test_managers.direct_generation.DirectGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 1024 +width: 1024 +batch_size: 1 +num_gen: 20 +lowres_height: 128 + diff --git a/configs/test/direct_gen_128x128.yaml b/configs/test/direct_gen_128x128.yaml new file mode 100644 index 0000000..2d6dfaf --- /dev/null +++ b/configs/test/direct_gen_128x128.yaml @@ -0,0 +1,11 @@ +task_manager: "test_managers.direct_generation.DirectGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 128 +width: 128 +batch_size: 1 +num_gen: 64 +lowres_height: 64 + diff --git a/configs/test/fused_gen_101x389.yaml b/configs/test/fused_gen_101x389.yaml new file mode 100644 index 0000000..0cb58c3 --- /dev/null +++ b/configs/test/fused_gen_101x389.yaml @@ -0,0 +1,24 @@ +task_manager: "test_managers.fused_generation.FusedGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 197 +width: 197 +batch_size: 8 +num_gen: 8 +lowres_height: 128 + +blur_kernel: 65 +blur_type: "mean" # "mean" or "gaussian" + +style_centers: + # # Looking like this + # X X X X X X X X X + # X X[O]X X X X X X + # X X X X X X X X X + # X X X X X X[O]X X + # X X X X X X X X X + - [0.25, 0.25] + - [0.75, 0.75] + diff --git a/configs/test/fused_gen_512x4096_4styles.yaml b/configs/test/fused_gen_512x4096_4styles.yaml new file mode 100644 index 0000000..57e2a69 --- /dev/null +++ b/configs/test/fused_gen_512x4096_4styles.yaml @@ -0,0 +1,20 @@ +task_manager: "test_managers.fused_generation.FusedGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 512 +width: 4096 +batch_size: 8 +num_gen: 8 +lowres_height: 128 + +blur_kernel: 255 +blur_type: "mean" # "mean" or "gaussian" + +style_centers: + - [0.00, 0.00] + - [0.33, 0.33] + - [0.66, 0.66] + - [1.00, 1.00] + diff --git a/configs/test/fused_gen_alis_connect.yaml b/configs/test/fused_gen_alis_connect.yaml new file mode 100644 index 0000000..3a8d6f9 --- /dev/null +++ b/configs/test/fused_gen_alis_connect.yaml @@ -0,0 +1,43 @@ +task_manager: "test_managers.fused_seq_connecting_generation.FusedSeqConnectingGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 197 +anchor_area_width: 197 +anchor_gap: 2 # Make fusion area larger than a single anchor (should accept negative values, but not tested, it also requires more style centers in the anchor_stack) +fusion_map_pad: [0, 416] # 197 + 219, see the illustration below +batch_size: 1 # Batch size >1 has not implemented. No motivation to do so as well, it won't speed up too much. +num_gen: 32 + +blur_kernel: 255 +blur_type: "mean" # "mean" or "gaussian" + +# In connecting anchor setup, this is applied between each pair of anchors. +# +# Note that we fix the vertical position at the center here for implementation simplicity. +# Otherwise, we need to additionally deal with the vertical position change while horizontally and iteratively connecting the new anchors. +# +# Illustration: +# | anchor 1 | fusion | anchor 2 | fusion | anchor 3 | fusion | anchor 4 | +# | 197 | 219 | 197 | 219 | 197 | 219 | 197 | +# | generation area | +# | 197*2+219 = 613 | +# | fusion map pad | | fusion map pad | +# | 197 + 219 = 416 | | 197 + 219 = 416 | +style_centers: + - [0.5, -0.517129] # A: - (197//2 + 219) / 613 + - [0.5, 0.159869] # B: (197//2) / 613 + - [0.5, 0.840131] # C: 1 - + - [0.5, 1.517129] # D: 1 + ((197//2 + 219) / 613) + + +# "patches": +# Sequentially (anchor=>fusion=>anchor=>fusion) save the image patches for infinite-fid calculation. +# "patches-centercrop": +# Similar to the one above, but center crop the fusion patch (which is usually larger than the anchor patch). +# "anchors": +# Save each pair of inbetweened anchors as a whole. +# "all-in-one": +# Save a super-long that contains all patches +save_type: "all-in-one" \ No newline at end of file diff --git a/configs/test/fused_gen_alis_connect389.yaml b/configs/test/fused_gen_alis_connect389.yaml new file mode 100644 index 0000000..84a027c --- /dev/null +++ b/configs/test/fused_gen_alis_connect389.yaml @@ -0,0 +1,43 @@ +task_manager: "test_managers.fused_seq_connecting_generation.FusedSeqConnectingGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 389 +anchor_area_width: 389 +anchor_gap: 2 # Make fusion area larger than a single anchor (should accept negative values, but not tested, it also requires more style centers in the anchor_stack) +fusion_map_pad: [0, 832] # 197 + 219, see the illustration below +batch_size: 1 # Batch size >1 has not implemented. No motivation to do so as well, it won't speed up too much. +num_gen: 32 + +blur_kernel: 255 +blur_type: "mean" # "mean" or "gaussian" + +# In connecting anchor setup, this is applied between each pair of anchors. +# +# Note that we fix the vertical position at the center here for implementation simplicity. +# Otherwise, we need to additionally deal with the vertical position change while horizontally and iteratively connecting the new anchors. +# +# Illustration: +# | anchor 1 | fusion | anchor 2 | fusion | anchor 3 | fusion | anchor 4 | +# | 389 | 443 | 389 | 443 | 389 | 443 | 389 | +# | generation area | +# | 389*2+443 = 1221 | +# | fusion map pad | | fusion map pad | +# | 389 + 443 = 832 | | 389 + 443 = 832 | +style_centers: + - [0.5, -0.521704] # A: - (389//2 + 443) / 1221 + - [0.5, 0.158886] # B: (389//2) / 1221 + - [0.5, 0.841114] # C: 1 - + - [0.5, 1.521704] # D: 1 + ((389//2 + 443) / 1221) + + +# "patches": +# Sequentially (anchor=>fusion=>anchor=>fusion) save the image patches for infinite-fid calculation. +# "patches-centercrop": +# Similar to the one above, but center crop the fusion patch (which is usually larger than the anchor patch). +# "anchors": +# Save each pair of inbetweened anchors as a whole. +# "all-in-one": +# Save a super-long that contains all patches +save_type: "all-in-one" diff --git a/configs/test/inbetween_with_fused_gen_256x1280.yaml b/configs/test/inbetween_with_fused_gen_256x1280.yaml new file mode 100644 index 0000000..33bcf28 --- /dev/null +++ b/configs/test/inbetween_with_fused_gen_256x1280.yaml @@ -0,0 +1,20 @@ +# Acutually, the same config LOL + +task_manager: "test_managers.fused_generation.FusedGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 256 +width: 1280 +batch_size: 1 +parallel_batch_size: 16 +num_gen: 1 +lowres_height: 128 + +blur_kernel: 127 +blur_type: "mean" # "mean" or "gaussian" + +style_centers: + - [0.5, 0.05] + - [0.5, 0.95] \ No newline at end of file diff --git a/configs/test/infinite_gen_1024x1024.yaml b/configs/test/infinite_gen_1024x1024.yaml new file mode 100644 index 0000000..f6fd36e --- /dev/null +++ b/configs/test/infinite_gen_1024x1024.yaml @@ -0,0 +1,11 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 1024 +width: 1024 +batch_size: 16 +num_gen: 128 +lowres_height: 128 + diff --git a/configs/test/infinite_gen_197x197.yaml b/configs/test/infinite_gen_197x197.yaml new file mode 100644 index 0000000..7888a12 --- /dev/null +++ b/configs/test/infinite_gen_197x197.yaml @@ -0,0 +1,11 @@ +task_manager: "test_managers.infinite_generation.InfiniteGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 197 +width: 197 +batch_size: 8 +num_gen: 64 +lowres_height: 128 + diff --git a/configs/test/inversion_256x256_L2R.yaml b/configs/test/inversion_256x256_L2R.yaml new file mode 100644 index 0000000..76b8d6e --- /dev/null +++ b/configs/test/inversion_256x256_L2R.yaml @@ -0,0 +1,57 @@ +task_manager: "test_managers.inversion.InversionManager" +interactive: False +seed: 9487 +# override_dataset_name: # Change dataset +override_dataset_data_size: 256 +override_dataset_full_size: 256 + +# task specific configs +data_split: "test" +inv_mode: "l2r" # choices: ["l2r", "r2l", "u2b", "b2u", "center"] +height: 256 # The inversion area, encourage but do not need to equal to target image +width: 256 +num_gen: 1 +batch_size: 1 +init_index: 0 # starting point of the data sampler, round back to 0-th sample after reached end +lowres_height: 128 + +losses_weights: + pixel_dist: 10 + lpips_dist: 10 + noise_reg: 1000 + ss_global_prior_loss: 10 #100000 + ss_local_prior_loss: 10 #100000 + ss_global_std_loss: 0 + ss_local_std_loss: 0 + ts_prior_loss: 0.01 + ts_mean_loss: 0 #100 + weight_reg: 0 # 0 means disabled, not encouraged to play with it too early + +# Inversion algo hyperparams +n_steps: 3000 +lr: 0.1 +diff_aug: True +noise_renorm: True +local_latent_renorm: False +learned_coords: False + +rand_perturb_ramp: 0.75 +rand_styles_perturb: 0.05 + +rand_ss_global_perturb_ramp: 0.75 +rand_ss_global_perturb: 0.2 + +rand_ss_local_perturb_ramp: 0.75 +rand_ss_local_perturb: 0.2 + + +future_resolution: [256, 256] +future_placement: [0.5, 0.5] # center + +# logging +eval: False +tqdm: True +write_tb_logs: False +log_n_samples: 64 +log_value_steps: 100 +log_img_steps: 100 \ No newline at end of file diff --git a/configs/test/inversion_IOF246_256x1280L_256x128.yaml b/configs/test/inversion_IOF246_256x1280L_256x128.yaml new file mode 100644 index 0000000..59fc0a5 --- /dev/null +++ b/configs/test/inversion_IOF246_256x1280L_256x128.yaml @@ -0,0 +1,67 @@ +task_manager: "test_managers.inversion.InversionManager" +interactive: False +seed: 9487 +# override_dataset_name: # Change dataset +override_dataset_data_size: 256 +override_dataset_full_size: 256 + +# task specific configs +data_split: "test" +height: 256 # The inversion area, encourage but do not need to equal to target image +width: 128 +inv_region: [0, 128, 256, 256] # (xst, yst, xed, yed) +eval_region: [0, 128, 256, 256] # (xst, yst, xed, yed) +center_drift: [0, 0] +num_gen: 1 +batch_size: 1 +init_index: 0 # starting point of the data sampler, round back to 0-th sample after reached end +lowres_height: 128 + +losses_weights: + pixel_dist: 10 + lpips_dist: 10 + noise_reg: 1000 + ss_global_prior_loss: 10 #100000 + ss_local_prior_loss: 10 #100000 + ss_global_std_loss: 1 + ss_local_std_loss: 1 + ts_prior_loss: 0.01 + ts_mean_loss: 0 #100 + weight_reg: 0 # 0 means disabled, not encouraged to play with it too early + +# Inversion algo hyperparams +n_steps: 3000 +lr: 0.1 +diff_aug: True +noise_renorm: True +local_latent_renorm: False +learned_coords: False + +rand_perturb_ramp: 0.75 +rand_styles_perturb: 0.05 + +rand_ss_global_perturb_ramp: 0.75 +rand_ss_global_perturb: 0.2 + +rand_ss_local_perturb_ramp: 0.75 +rand_ss_local_perturb: 0.2 + + +# Placement information for later generation +# +# # Formula of `future_placement`: +# future_placement[0] = 0.5, # vertical position is usually at center +# if right-most: +# future_placement[1] = ( - //2) / +# elif left-most: +# future_placement[1] = //2 / +future_resolution: [256, 1280] +future_placement: [0.5, 0.05] # (128//2) / 1280 + +# logging +eval: False +tqdm: True +write_tb_logs: True +log_n_samples: 64 +log_value_steps: 100 +log_img_steps: 100 \ No newline at end of file diff --git a/configs/test/inversion_IOF246_256x1280R_256x128.yaml b/configs/test/inversion_IOF246_256x1280R_256x128.yaml new file mode 100644 index 0000000..76678a4 --- /dev/null +++ b/configs/test/inversion_IOF246_256x1280R_256x128.yaml @@ -0,0 +1,67 @@ +task_manager: "test_managers.inversion.InversionManager" +interactive: False +seed: 9487 +# override_dataset_name: # Change dataset +override_dataset_data_size: 256 +override_dataset_full_size: 256 + +# task specific configs +data_split: "test" +height: 256 # The inversion area, encourage but do not need to equal to target image +width: 128 +inv_region: [0, 128, 256, 256] # (xst, yst, xed, yed) +eval_region: [0, 128, 256, 256] # (xst, yst, xed, yed) +center_drift: [0, 0] +num_gen: 1 +batch_size: 1 +init_index: 0 # starting point of the data sampler, round back to 0-th sample after reached end +lowres_height: 128 + +losses_weights: + pixel_dist: 10 + lpips_dist: 10 + noise_reg: 1000 + ss_global_prior_loss: 10 #100000 + ss_local_prior_loss: 10 #100000 + ss_global_std_loss: 1 + ss_local_std_loss: 1 + ts_prior_loss: 0.01 + ts_mean_loss: 0 #100 + weight_reg: 0 # 0 means disabled, not encouraged to play with it too early + +# Inversion algo hyperparams +n_steps: 3000 +lr: 0.1 +diff_aug: True +noise_renorm: True +local_latent_renorm: False +learned_coords: False + +rand_perturb_ramp: 0.75 +rand_styles_perturb: 0.05 + +rand_ss_global_perturb_ramp: 0.75 +rand_ss_global_perturb: 0.2 + +rand_ss_local_perturb_ramp: 0.75 +rand_ss_local_perturb: 0.2 + + +# Placement information for later generation +# +# # Formula of `future_placement`: +# future_placement[0] = 0.5, # vertical position is usually at center +# if right-most: +# future_placement[1] = ( - //2) / +# elif left-most: +# future_placement[1] = //2 / +future_resolution: [256, 1280] +future_placement: [0.5, 0.95] # (1280 - 128//2) / 1280 + +# logging +eval: False +tqdm: True +write_tb_logs: True +log_n_samples: 64 +log_value_steps: 100 +log_img_steps: 100 \ No newline at end of file diff --git a/configs/test/inversion_IOF246_256x2080L_256x128.yaml b/configs/test/inversion_IOF246_256x2080L_256x128.yaml new file mode 100644 index 0000000..9817ffc --- /dev/null +++ b/configs/test/inversion_IOF246_256x2080L_256x128.yaml @@ -0,0 +1,67 @@ +task_manager: "test_managers.inversion.InversionManager" +interactive: False +seed: 9487 +# override_dataset_name: # Change dataset +override_dataset_data_size: 256 +override_dataset_full_size: 256 + +# task specific configs +data_split: "test" +height: 256 # The inversion area, encourage but do not need to equal to target image +width: 128 +inv_region: [0, 128, 256, 256] # (xst, yst, xed, yed) +eval_region: [0, 128, 256, 256] # (xst, yst, xed, yed) +center_drift: [0, 0] +num_gen: 1 +batch_size: 1 +init_index: 0 # starting point of the data sampler, round back to 0-th sample after reached end +lowres_height: 128 + +losses_weights: + pixel_dist: 10 + lpips_dist: 10 + noise_reg: 1000 + ss_global_prior_loss: 10 #100000 + ss_local_prior_loss: 10 #100000 + ss_global_std_loss: 1 + ss_local_std_loss: 1 + ts_prior_loss: 0.01 + ts_mean_loss: 0 #100 + weight_reg: 0 # 0 means disabled, not encouraged to play with it too early + +# Inversion algo hyperparams +n_steps: 3000 +lr: 0.1 +diff_aug: True +noise_renorm: True +local_latent_renorm: False +learned_coords: False + +rand_perturb_ramp: 0.75 +rand_styles_perturb: 0.05 + +rand_ss_global_perturb_ramp: 0.75 +rand_ss_global_perturb: 0.2 + +rand_ss_local_perturb_ramp: 0.75 +rand_ss_local_perturb: 0.2 + + +# Placement information for later generation +# +# # Formula of `future_placement`: +# future_placement[0] = 0.5, # vertical position is usually at center +# if right-most: +# future_placement[1] = ( - //2) / +# elif left-most: +# future_placement[1] = //2 / +future_resolution: [256, 2080] +future_placement: [0.5, 0.030769] # (128//2) / 2080 + +# logging +eval: False +tqdm: True +write_tb_logs: True +log_n_samples: 64 +log_value_steps: 100 +log_img_steps: 100 \ No newline at end of file diff --git a/configs/test/inversion_IOF246_256x2080R_256x128.yaml b/configs/test/inversion_IOF246_256x2080R_256x128.yaml new file mode 100644 index 0000000..bd09f9d --- /dev/null +++ b/configs/test/inversion_IOF246_256x2080R_256x128.yaml @@ -0,0 +1,68 @@ +task_manager: "test_managers.inversion.InversionManager" +interactive: False +seed: 9487 +# override_dataset_name: # Change dataset +override_dataset_data_size: 256 +override_dataset_full_size: 256 + +# task specific configs +data_split: "test" +height: 256 # The inversion area, encourage but do not need to equal to target image +width: 128 +inv_region: [0, 128, 256, 256] # (xst, yst, xed, yed) +eval_region: [0, 128, 256, 256] # (xst, yst, xed, yed) +center_drift: [0, 0] +num_gen: 1 +batch_size: 1 +init_index: 0 # starting point of the data sampler, round back to 0-th sample after reached end +lowres_height: 128 + +losses_weights: + pixel_dist: 10 + lpips_dist: 10 + noise_reg: 1000 + ss_global_prior_loss: 10 #100000 + ss_local_prior_loss: 10 #100000 + ss_global_std_loss: 1 + ss_local_std_loss: 1 + ts_prior_loss: 0.01 + ts_mean_loss: 0 #100 + # ts_reg: 100 # Not Implemented Yet, for vwspace, need to regularize the ts-style magnitude + weight_reg: 0 # 0 means disabled, not encouraged to play with it too early + +# Inversion algo hyperparams +n_steps: 3000 +lr: 0.1 +diff_aug: True +noise_renorm: True +local_latent_renorm: False +learned_coords: False + +rand_perturb_ramp: 0.75 +rand_styles_perturb: 0.05 + +rand_ss_global_perturb_ramp: 0.75 +rand_ss_global_perturb: 0.2 + +rand_ss_local_perturb_ramp: 0.75 +rand_ss_local_perturb: 0.2 + + +# Placement information for later generation +# +# # Formula of `future_placement`: +# future_placement[0] = 0.5, # vertical position is usually at center +# if right-most: +# future_placement[1] = ( - //2) / +# elif left-most: +# future_placement[1] = //2 / +future_resolution: [256, 2080] +future_placement: [0.5, 0.96923] # (2080-128//2) / 2080 + +# logging +eval: False +tqdm: True +write_tb_logs: True +log_n_samples: 64 +log_value_steps: 100 +log_img_steps: 100 \ No newline at end of file diff --git a/configs/test/outpaint_with_fused_gen_256x256.yaml b/configs/test/outpaint_with_fused_gen_256x256.yaml new file mode 100644 index 0000000..b7853b5 --- /dev/null +++ b/configs/test/outpaint_with_fused_gen_256x256.yaml @@ -0,0 +1,24 @@ +task_manager: "test_managers.fused_generation.FusedGenerationManager" +interactive: False +seed: 9487 + +# task specific configs +height: 101 +width: 197 +batch_size: 8 +num_gen: 8 +lowres_height: 128 + +blur_kernel: 65 +blur_type: "mean" # "mean" or "gaussian" + +style_centers: + # # Looking like this + # X X X X X X X X X + # X X[O]X X X X X X + # X X X X X X X X X + # X X X X X X[O]X X + # X X X X X X X X X + - [0.25, 0.25] + - [0.75, 0.75] + diff --git a/coord_handler.py b/coord_handler.py new file mode 100644 index 0000000..25a510c --- /dev/null +++ b/coord_handler.py @@ -0,0 +1,382 @@ +import numpy as np +import torch +import torch.nn as nn +import math + + +# def invert_ycoord_to_idx(y_cos, y_sin, period): +# ratio_cos = math.acos(y_cos) / np.pi +# ratio_sin = math.asin(y_sin) / np.pi +# if ratio_cos > 0 and ratio_sin > 0: + +class CoordHandler(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.coord_num_dir = self.config.train_params.coord_num_dir + + ss_unfold_size = self.config.train_params.ss_n_layers * self.config.train_params.ss_unfold_radius + self.ts_spatial_size = config.train_params.ts_input_size + self.ss_spatial_size = config.train_params.ts_input_size + 2 * ss_unfold_size + + self.const_grid_size_x = \ + self.ss_spatial_size + self.config.train_params.coord_vert_sample_size + self.const_grid_size_y = \ + int(round(self.ss_spatial_size / self.config.train_params.coord_hori_occupy_ratio)) + if self.coord_num_dir == 4: + self.const_grid_size_x = self.const_grid_size_y + + self.const_grid = self._creat_coord_grid( + height=self.const_grid_size_x, + width=self.const_grid_size_y, + ).contiguous().cuda() + + # Uniform perturbation that creates continuous coordinates + if config.train_params.coord_continuous: + if self.coord_num_dir == 1: + self.coord_perturb_range = ( + abs(self.const_grid[0, 0, 0].item() - self.const_grid[0, 1, 0].item()) / 2, + ) + elif self.coord_num_dir == 2: + self.coord_perturb_range = ( + abs(self.const_grid[0, 0, 0].item() - self.const_grid[0, 0, 1].item()) / 2, + abs(self.const_grid[1, 0, 0].item() - self.const_grid[1, 0, 1].item()) / 2, + ) + elif self.coord_num_dir == 4: + self.coord_perturb_range = ( + abs(self.const_grid[0, 0, 0].item() - self.const_grid[0, 1, 0].item()) / 2, + abs(self.const_grid[1, 0, 0].item() - self.const_grid[1, 1, 0].item()) / 2, + abs(self.const_grid[2, 0, 0].item() - self.const_grid[2, 0, 1].item()) / 2, + abs(self.const_grid[3, 0, 0].item() - self.const_grid[3, 0, 1].item()) / 2, + ) + elif self.coord_num_dir in {3, 21}: + self.coord_perturb_range = ( + abs(self.const_grid[0, 0, 0].item() - self.const_grid[0, 1, 0].item()) / 2, + abs(self.const_grid[1, 0, 0].item() - self.const_grid[1, 0, 1].item()) / 2, + abs(self.const_grid[2, 0, 0].item() - self.const_grid[2, 0, 1].item()) / 2, + ) + else: + raise ValueError() + for v in self.coord_perturb_range: + assert v > 0, " [Sanity check failed] perturb should always >0 while enabled, but got {}".format(self.coord_perturb_range) + else: + print(" [!] Discrete coords should be depricated!") + self.coord_perturb_range = (0,) * self.coord_num_dir + + def sample_xy_st_index(self, batch_size): + if self.config.train_params.coord_num_dir == 4: + x_rnd_range = self.const_grid_size_x + else: + x_rnd_range = self.config.train_params.coord_vert_sample_size #self.const_grid_size_x - x_size + + if x_rnd_range == 0: + x_st = np.zeros(batch_size).astype(np.uint8) + else: + x_st = np.random.randint(0, x_rnd_range, batch_size) + y_st = np.random.randint(0, self.const_grid_size_y, batch_size) + return x_st, y_st + + def add_rnd_perturb(self, mesh_indices): + # mesh_indices: [B, C, H, W] + B, C, H, W = mesh_indices.shape + for dim,perturb_range in enumerate(self.coord_perturb_range): + perturb_v = (torch.rand(B) * perturb_range * 2) - perturb_range + perturb_v = perturb_v.unsqueeze(-1).unsqueeze(-1) + perturb_v = perturb_v.to(mesh_indices.device) + mesh_indices[:, dim, :, :] += perturb_v + return mesh_indices + + def _creat_coord_grid(self, height, width, coord_init=None, + auto_calibrate_pano_coord=False): + + if coord_init is None: + coord_init = (0, 0) # Workaround + + x = torch.arange(height).type(torch.float32) + coord_init[0] + y = torch.arange(width).type(torch.float32) + coord_init[1] + + # To [0, 1], but may exceed this range at testing + x = x / (self.ts_spatial_size+self.config.train_params.coord_vert_sample_size-1) + if not auto_calibrate_pano_coord: + y = y / (self.const_grid_size_y-1) + else: + # Disregard the coordinate frequency on the horizontal axis, enforced to [-pi, +pi] + # at the start and the end of the y-sequence, and ss_unfold_size is auto extrapolated. + y = y / width + + # Re-center the x coords + exceeding_part = (x[-1] - 1) + x = x - exceeding_part / 2 + + x = x * 2 - 1 # [-1, 1] + y = y * 2 - 1 # [-1, 1] + + # X-axis: + # 1. to [-N, N], which N is `coord_y_cut_pt` + x = x * self.config.train_params.coord_vert_cut_pt #* x_extra_scale + x_tiled = x.view(-1, 1).repeat(1, width) + + if self.coord_num_dir == 1: + meshed = x_tiled.unsqueeze_(0) # [1, H, W] + elif self.config.train_params.coord_num_dir == 2: + y_tiled = y.view(1, -1).repeat(height, 1) + meshed = torch.cat([ + y_tiled.unsqueeze(0), # apply cos later + y_tiled.unsqueeze(0), # apply sin later + ], 0) # [2, H, W] + elif self.config.train_params.coord_num_dir == 3: + y_tiled = y.view(1, -1).repeat(height, 1) + meshed = torch.cat([ + x_tiled.unsqueeze_(0), + y_tiled.unsqueeze(0), # apply cos later + y_tiled.unsqueeze(0), # apply sin later + ], 0) # [3, H, W] + elif self.config.train_params.coord_num_dir == 4: + x_tiled = y.view(-1, 1).repeat(1, width) # Reuse y + y_tiled = y.view(1, -1).repeat(width, 1) # make into shape (w, w) + meshed = torch.cat([ + x_tiled.unsqueeze(0), # apply cos later + x_tiled.unsqueeze(0), # apply sin later + y_tiled.unsqueeze(0), # apply cos later + y_tiled.unsqueeze(0), # apply sin later + ], 0) # [4, H, W] + elif self.config.train_params.coord_num_dir == 21: + y_tiled = y.view(1, -1).repeat(height, 1) + meshed = torch.cat( + [x_tiled.unsqueeze_(0)] + + [y_tiled.unsqueeze(0) for _ in range(20)], # apply cos/sin later + 0) # [3, H, W] + else: + raise NotImplementedError("Unkown coord dimension {}".format( + self.config.train_params.coord_num_dir)) + return meshed + + def invert_coord_to_idx(self, coord, is_x_dir=False, is_y_dir=False): + assert False, "Not well-tested, use with care!" + assert is_x_dir or is_y_dir + if is_x_dir: + back_proj = math.atanh(coord.clamp(-1, 1)) + denum = (self.ts_spatial_size-1) + elif is_y_dir: + assert False, "This functions is malfunctioned, it only considers one quadrant." + try: + back_proj = math.asin(coord) / math.pi + denum = (self.ts_spatial_size-1) + except Exception as e: + print("Coord:", coord) + raise e + + back_proj = (back_proj + 1) / 2 # to [0, 1] + back_proj = back_proj * denum + return round(back_proj) + + def convert_idx_to_input_coords(self, mesh): + assert mesh.ndim == 4, "Did not consider other cases, got mesh shape {}".format(mesh.shape) + if self.config.train_params.coord_num_dir == 1: + mesh[:, 0, :] = torch.tanh(mesh[:, 0, :]) + elif self.config.train_params.coord_num_dir == 2: + mesh[:, 0, :] = torch.cos(mesh[:, 0, :] * np.pi) + mesh[:, 1, :] = torch.sin(mesh[:, 1, :] * np.pi) + elif self.config.train_params.coord_num_dir == 3: + mesh[:, 0, :] = torch.tanh(mesh[:, 0, :]) + mesh[:, 1, :] = torch.cos(mesh[:, 1, :] * np.pi) + mesh[:, 2, :] = torch.sin(mesh[:, 2, :] * np.pi) + elif self.config.train_params.coord_num_dir == 4: + mesh[:, 0, :] = torch.cos(mesh[:, 0, :] * np.pi) + mesh[:, 1, :] = torch.sin(mesh[:, 1, :] * np.pi) + mesh[:, 2, :] = torch.cos(mesh[:, 2, :] * np.pi) + mesh[:, 3, :] = torch.cos(mesh[:, 3, :] * np.pi) + elif self.config.train_params.coord_num_dir == 21: + mesh[:, 0, :] = torch.tanh(mesh[:, 0, :]) + for i in range(10): + mesh[:, i*2+1, :] = torch.cos(mesh[:, i*2+1, :] * np.pi * 2**i) + mesh[:, i*2+2, :] = torch.sin(mesh[:, i*2+2, :] * np.pi * 2**i) + return mesh + + def _safe_select(self, meta_grid, x_st, y_st, x_size, y_size): + grids = [] + for xx, yy in zip(x_st, y_st): + if self.coord_num_dir == 4: + # Need wrapping + if yy > self.const_grid_size_y - y_size: + if xx > self.const_grid_size_x - x_size: + remainings_x = xx + x_size - self.const_grid_size_x + remainings_y = yy + y_size - self.const_grid_size_y + grid = torch.cat([ + torch.cat([ + meta_grid[:, xx:, yy:], + meta_grid[:, :remainings_x, yy:], + ], 1), + torch.cat([ + meta_grid[:, xx:, :remainings_y], + meta_grid[:, :remainings_x, :remainings_y], + ], 1) + ], 2) + else: + remainings = yy + y_size - self.const_grid_size_y + grid = torch.cat([ + meta_grid[:, xx:xx+x_size, yy:], + meta_grid[:, xx:xx+x_size, :remainings], + ], 2) + else: + if xx > self.const_grid_size_x - x_size: + remainings = xx + x_size - self.const_grid_size_x + grid = torch.cat([ + meta_grid[:, xx:, yy:yy+y_size], + meta_grid[:, :remainings, yy:yy+y_size], + ], 1) + else: + grid = meta_grid[:, xx:xx+x_size, yy:yy+y_size] + else: + # Need wrapping + if yy > self.const_grid_size_y - y_size: + remainings = yy + y_size - self.const_grid_size_y + grid = torch.cat([ + meta_grid[:, xx:xx+x_size, yy:], + meta_grid[:, xx:xx+x_size, :remainings], + ], 2) + # Naive selection + else: + grid = meta_grid[:, xx:xx+x_size, yy:yy+y_size] + + grids.append(grid) + + grids = torch.stack(grids).contiguous().clone().detach() + return grids + + def create_coord_sequence(self, abs_disp): + x_max = np.abs(abs_disp[:, 0]).max() + y_max = np.abs(abs_disp[:, 1]).max() + meta_grid = self._creat_coord_grid( + height=x_max*2+1, + width=y_max*2+1) + center = (x_max, y_max) # center of a 2x larger grid + ret = [] + for (x,y) in abs_disp: + coord = meta_grid[:, center[0]+x, center[1]+y] + ret.append(coord) + return torch.stack(ret) + + def sample_coord_grid(self, spatial_latent, coord_init=None, is_training=True, is_fid_eval=False, + override_coords=None, return_ac_coords=False, + auto_calibrate_pano_coord=False, + specific_shape=None, device=None, batch_size=None): + if specific_shape is None: + # Note: + # spatial_latent shape can be slightly different from different configs, use runtime size is better + bs, _, x_size, y_size = spatial_latent.shape # (B, C, H, W) + device = spatial_latent.device + else: + assert device is not None, "Device must be specified." + assert batch_size is not None, "Batch size must be specified." + if isinstance(specific_shape, tuple) or isinstance(specific_shape, list): + x_size, y_size = specific_shape + assert len(specific_shape) == 2, "Got {}".format(specific_shape) + else: + x_size = specific_shape + y_size = specific_shape + bs = batch_size + + needs_extrap = (x_size > self.ss_spatial_size) or (y_size > self.ss_spatial_size) + fid_use_training = is_fid_eval and (not needs_extrap) + + ac_coords = None + if is_training or fid_use_training: + assert coord_init is None, "Not considered" + assert auto_calibrate_pano_coord is False, \ + "This argument is specifically designed for panorama generation at testing" + if override_coords is None: + if needs_extrap: + # Select random disp first, then extrapolate coordinates base on the disp + x_disp, y_disp = self.sample_xy_st_index(bs) + # In some special cases (very few) that we need to sample coords larger than + # training coords grid, we have to create new at runtime... + grid_indices = torch.stack([self._creat_coord_grid( + height=x_size, width=y_size, coord_init=(xx, yy)) + for xx, yy in zip(x_disp, y_disp)]) + grid_indices = self.add_rnd_perturb(grid_indices) + coords = self.convert_idx_to_input_coords(grid_indices) + x_st, y_st = x_disp, y_disp + else: + x_st, y_st = self.sample_xy_st_index(bs) + grid_indices = self._safe_select(self.const_grid, x_st, y_st, x_size, y_size) + grid_indices = self.add_rnd_perturb(grid_indices) + coords = self.convert_idx_to_input_coords(grid_indices) + coords = coords.to(device) + + if return_ac_coords: + # x_denom = self.const_grid_size_x - x_size - 1 + x_denom = self.config.train_params.coord_vert_sample_size - 1 + norm_x_st = (x_st / x_denom) * 2 - 1 # [-1, 1] + norm_y_st = (y_st / (self.const_grid_size_y-1)) * 2 - 1 # [-1, 1] + ac_coords_x = norm_x_st # np.tanh(norm_x_st) # meaningless to do this projection + if self.config.train_params.coord_num_dir == 1: + ac_coords = torch.from_numpy(ac_coords_x).unsqueeze(1).float().to(device) + elif self.config.train_params.coord_num_dir == 2: + ac_coords_a = np.cos(norm_y_st * np.pi) + ac_coords_b = np.sin(norm_y_st * np.pi) + ac_coords = np.stack([ac_coords_a, ac_coords_b], 1) + ac_coords = torch.from_numpy(ac_coords).float().to(device) + elif self.config.train_params.coord_num_dir == 4: + norm_x_st = (x_st / (self.const_grid_size_y-1)) * 2 - 1 # [-1, 1] + ac_coords = np.stack([ + np.cos(norm_x_st * np.pi), + np.sin(norm_x_st * np.pi), + np.cos(norm_y_st * np.pi), + np.sin(norm_y_st * np.pi), + ], 1) + ac_coords = torch.from_numpy(ac_coords).float().to(device) + elif self.config.train_params.coord_num_dir in {3, 21}: + ac_coords_a = np.cos(norm_y_st * np.pi) + ac_coords_b = np.sin(norm_y_st * np.pi) + ac_coords = np.stack([ac_coords_x, ac_coords_a, ac_coords_b], 1) + ac_coords = torch.from_numpy(ac_coords).float().to(device) + else: + raise ValueError("Unknown coord_num_dir {}".format(self.config.train_params.coord_num_dir)) + if self.training: + assert ac_coords.min()>-1.1, "Got unexpected ac_coords min {} < -1".format(ac_coords.min()) + assert ac_coords.max()<1.1, "Got unexpected ac_coords max {} > 1".format(ac_coords.max()) + else: + # Scale invariant loss requires this "feature" ... + # raise ValueError("Unexpected sending override_coords during training!") + coords = override_coords + ac_coords = None + else: + # # Testing + # Assumes the center of z_spatial and coord grid are aligned, then accordingly extrapolate the coord + # Note: supports extrapolate on x-axis. + if override_coords is None: + grid_indices = self._creat_coord_grid( + height=x_size, width=y_size, coord_init=coord_init, + auto_calibrate_pano_coord=auto_calibrate_pano_coord) + grid_indices = grid_indices.unsqueeze(0).repeat(bs, 1, 1, 1) + # grid_indices = self.add_rnd_perturb(grid_indices) # Probably don't want this, which may break consistency at testing + coords = self.convert_idx_to_input_coords(grid_indices) + coords = coords.to(device) + else: + coords = override_coords + ac_coords = None + + if return_ac_coords: + return coords, ac_coords + else: + return coords + + def update_coords_by_mean(self, coords, new_mean): + # Coords may not be a valid one after inversion update, + # here we calibrates a new coords that matches the mean of the dirty region. + + for i in range(coords.shape[0]): + new_init = ( + self.invert_coord_to_idx(new_mean[i, 0], is_x_dir=True), + self.invert_coord_to_idx(new_mean[i, 1], is_y_dir=True), + ) + new_coords = self.sample_coord_grid( + coords[i:i+1], + coord_init=new_init, + is_training=False) + coords[i:i+1].data = new_coords[i:i+1].data + + + + diff --git a/dataset.py b/dataset.py new file mode 100644 index 0000000..1471a74 --- /dev/null +++ b/dataset.py @@ -0,0 +1,464 @@ +import os +import lmdb +import yaml +import math +import socket +import argparse +import numpy as np +from io import BytesIO +from PIL import Image +from glob import glob +from tqdm import tqdm +from easydict import EasyDict +from random import randrange + +import torch +from torch.utils import data +from torch.utils.data import Dataset +from torchvision import transforms + +from env_config import LMDB_ROOTS + +from PIL import PngImagePlugin +PngImagePlugin.MAX_TEXT_CHUNK = 10 * (1024**2) + + +def safe_randrange(low, high): + if low==high: + return low + else: + return randrange(low, high) + + +class DictTensor(dict): + + def to(self, device): + new_self = DictTensor() + for k,v in self.items(): + if isinstance(v, torch.Tensor): + new_self[k] = v.to(device) + else: + new_self[k] = v + return new_self + + def cpu(self): + new_self = DictTensor() + for k,v in self.items(): + if isinstance(v, torch.Tensor): + new_self[k] = v.cpu() + else: + new_self[k] = v + return new_self + + def detach(self): + new_self = DictTensor() + for k,v in self.items(): + if isinstance(v, torch.Tensor): + new_self[k] = v.detach() + else: + new_self[k] = v + return new_self + + def get_device(self): + return list(self.values())[0].device + + def __setattr__(self, attr, value): + if attr == "requires_grad": + for v in self.values(): + # Note: Tensor with non-float type cannot requires grad + if isinstance(v, torch.Tensor) and v.dtype not in {torch.int32, torch.int64}: + v.requires_grad = value + #for v in self.attrs.values(): + # v.requires_grad = value + else: + super().__setattr__(attr, value) + + +class MaybeCenterCrop(): + def __init__(self, crop_size): + self.crop_size = crop_size + + def __call__(self, img): + if self.crop_size is None: + return img + else: + pad_h = (img.height - self.crop_size) // 2 + pad_w = (img.width - self.crop_size) // 2 + img = img.crop((pad_w, pad_h, pad_w+self.crop_size, pad_h+self.crop_size)) + return img + + +class MaybeResize(): + def __init__(self, full_size): + self.full_size = full_size + + def __call__(self, img): + if img.width == self.full_size and img.height==self.full_size: + return img + else: + if img.height != img.width: + if img.height > img.width: + pad_h = (img.height - img.width) // 2 + pad_w = 0 + size = img.width + else: + pad_h = 0 + pad_w = (img.width - img.height) // 2 + size = img.height + img = img.crop((pad_w, pad_h, pad_w+size, pad_h+size)) + assert img.height == img.width + return img.resize([self.full_size, self.full_size], resample=Image.LANCZOS) + + +class CropPatch(): + def __init__(self, input_size, patch_size, config, + random_crop=False, center_crop=False, disable_ac=False): + assert (random_crop or center_crop) and not (random_crop and center_crop) + self.input_size = input_size + self.patch_size = patch_size + self.config = config + + self.random_crop = random_crop + self.center_crop = center_crop + + if disable_ac: + self.return_ac_coords = False + elif self.input_size != self.patch_size: + self.return_ac_coords = True + else: + self.return_ac_coords = False # always 1, meaningless, will randomly sample outside + + self.skip_cropping = (self.input_size == self.patch_size) + + def ac_coords_from_ratio(self, coord_ratio, proj): + coord_ratio = coord_ratio * 2 - 1 # [-1, 1] + if proj == "raw": + return coord_ratio + # elif proj == "tanh": + # return math.tanh(coord_ratio) + elif proj == "sin": + return math.sin(coord_ratio * math.pi) + elif proj == "cos": + return math.cos(coord_ratio * math.pi) + else: + raise ValueError("Unknown proj {}".format(proj)) + + def __call__(self, img): + + assert img.size == (self.input_size, self.input_size) + if self.skip_cropping: + return img, None + + if self.random_crop: + xst = safe_randrange(0, self.input_size - self.patch_size) + yst = safe_randrange(0, self.input_size - self.patch_size) + if self.return_ac_coords: + if self.config.train_params.coord_num_dir == 1: + ac_coords = torch.FloatTensor([ + self.ac_coords_from_ratio(xst / (self.input_size - self.patch_size - 1), "raw"), + ]) + elif self.config.train_params.coord_num_dir == 2: + ac_coords = torch.FloatTensor([ + self.ac_coords_from_ratio(yst / (self.input_size - self.patch_size - 1), "sin"), + self.ac_coords_from_ratio(yst / (self.input_size - self.patch_size - 1), "cos"), + ]) + elif self.config.train_params.coord_num_dir == 4: + ac_coords = torch.FloatTensor([ + self.ac_coords_from_ratio(xst / (self.input_size - self.patch_size - 1), "sin"), + self.ac_coords_from_ratio(xst / (self.input_size - self.patch_size - 1), "cos"), + self.ac_coords_from_ratio(yst / (self.input_size - self.patch_size - 1), "sin"), + self.ac_coords_from_ratio(yst / (self.input_size - self.patch_size - 1), "cos"), + ]) + elif self.config.train_params.coord_num_dir in {3, 21}: + ac_coords = torch.FloatTensor([ + self.ac_coords_from_ratio(xst / (self.input_size - self.patch_size - 1), "raw"), + self.ac_coords_from_ratio(yst / (self.input_size - self.patch_size - 1), "sin"), + self.ac_coords_from_ratio(yst / (self.input_size - self.patch_size - 1), "cos"), + ]) + else: + raise ValueError("Unknown coord_num_dir {}".format(self.coord_num_dir)) + else: # center crop + x_size, y_size = img.width, img.height + if x_size == self.patch_size: + xst = 0 + else: + xst = (x_size - self.patch_size) // 2 + if y_size == self.patch_size: + yst = 0 + else: + yst = (y_size - self.patch_size) // 2 + if self.return_ac_coords: + if self.config.train_params.coord_num_dir == 1: + ac_coords = torch.FloatTensor([ + self.ac_coords_from_ratio(xst / (x_size - self.patch_size), "raw"), + ]) + elif self.config.train_params.coord_num_dir == 2: + ac_coords = torch.FloatTensor([ + self.ac_coords_from_ratio(yst / (y_size - self.patch_size), "sin"), + self.ac_coords_from_ratio(yst / (y_size - self.patch_size), "cos"), + ]) + elif self.config.train_params.coord_num_dir == 4: + ac_coords = torch.FloatTensor([ + self.ac_coords_from_ratio(xst / (x_size - self.patch_size), "sin"), + self.ac_coords_from_ratio(xst / (x_size - self.patch_size), "cos"), + self.ac_coords_from_ratio(yst / (y_size - self.patch_size), "sin"), + self.ac_coords_from_ratio(yst / (y_size - self.patch_size), "cos"), + ]) + elif self.config.train_params.coord_num_dir in {3, 21}: + ac_coords = torch.FloatTensor([ + self.ac_coords_from_ratio(xst / (x_size - self.patch_size), "raw"), + self.ac_coords_from_ratio(yst / (y_size - self.patch_size), "sin"), + self.ac_coords_from_ratio(yst / (y_size - self.patch_size), "cos"), + ]) + else: + raise ValueError("Unknown coord_num_dir {}".format(self.coord_num_dir)) + + cropped = img.crop((yst, xst, yst + self.patch_size, xst + self.patch_size)) + + if self.return_ac_coords: + return cropped, ac_coords + else: + return cropped, None + + +class MultiResolutionDataset(Dataset): + def __init__(self, split=None, img_dir=None, config=None, is_training=True, + disable_extra_cropping=False, simple_return_full=False, override_full_size=None): + + assert (split is not None) or (img_dir is not None), "Either specify split or img_dir." + assert (split is None) or (img_dir is None), "Can't specify both split and img_dir at the same time." + + self.split = split + self.img_dir = img_dir + self.config = config + self.resolution = config.train_params.data_size + self.simple_return_full = simple_return_full + + + if self.split is not None: + + if "InOut" in self.config.data_params.dataset: + self.n_zfill = 5 + else: + self.n_zfill = 8 + + hostname = socket.gethostname() + cur_lmdb_root = None + if hostname in LMDB_ROOTS: + cur_lmdb_root = LMDB_ROOTS[hostname] + print(" [*] Found lmdb root on local hard drive: {}".format(cur_lmdb_root)) + else: + for entry in LMDB_ROOTS["unspecified"]: + if os.path.exists(entry): + print(" [*] Found unspecified lmdb root at {}".format(entry)) + cur_lmdb_root = entry + + if cur_lmdb_root is None: + print(" [!] Couldn't find lmdb root on local hard drive, use specification in config file...") + cur_lmdb_root = config.data_params.lmdb_root + + self.path = os.path.join(cur_lmdb_root, config.data_params.dataset, split) + if os.path.exists(self.path): + self.env = lmdb.open( + self.path, + max_readers=32, + readonly=True, + lock=False, + readahead=False, + meminit=False, + ) + else: + raise IOError("Cannot find dataset split {} at {}".format(split, self.path)) + + if not self.env: + raise IOError('Cannot open lmdb dataset', self.path) + + with self.env.begin(write=False) as txn: + self.length = int(txn.get('length'.encode('utf-8')).decode('utf-8')) + print(" [*] Loaded data with length {}".format(self.length)) + + + if self.img_dir is not None: + self.img_paths = sorted(glob(os.path.join(self.img_dir, "*"))) + self.length = len(self.img_paths) + + + if hasattr(config.train_params, "extra_center_crop") and config.train_params.extra_center_crop: + if disable_extra_cropping: + extra_center_crop_res = None + else: + extra_center_crop_res = config.train_params.extra_center_crop + else: + extra_center_crop_res = None + + if hasattr(config.train_params, "extra_pre_resize"): + pre_resize_op = [MaybeResize(config.train_params.extra_pre_resize)] + else: + pre_resize_op = [] + + if override_full_size is None: + raw_resize_size = config.train_params.full_size + else: + raw_resize_size = override_full_size + + if is_training: + self.transform = transforms.Compose( + pre_resize_op + [ + MaybeResize(raw_resize_size), + MaybeCenterCrop(extra_center_crop_res), # Center crop for fare comparison + transforms.RandomHorizontalFlip(), + ] + ) + else: + self.transform = transforms.Compose( + pre_resize_op + [ + MaybeResize(raw_resize_size), + MaybeCenterCrop(extra_center_crop_res), + # transforms.RandomHorizontalFlip(), + ] + ) + + self.finalize = transforms.Compose( + [ + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True), + ] + ) + + if hasattr(config.train_params, "extra_center_crop"): + if disable_extra_cropping: + crop_input_size = config.train_params.full_size + else: + crop_input_size = config.train_params.extra_center_crop + else: + crop_input_size = config.train_params.full_size + + is_styleGAN2_baseline = hasattr(self.config.train_params, "styleGAN2_baseline") and self.config.train_params.styleGAN2_baseline + is_singan = hasattr(self.config.train_params, "singan") and self.config.train_params.singan + if is_training: + assert split=="train", "Unlikely training with testing set or validation set!" + self.crop_fn = CropPatch( + input_size=crop_input_size, + patch_size=config.train_params.patch_size, + config=config, + random_crop=True, + disable_ac=is_styleGAN2_baseline or is_singan) + else: + self.crop_fn = CropPatch( + input_size=crop_input_size, + patch_size=config.train_params.patch_size, + config=config, + center_crop=True, + disable_ac=is_styleGAN2_baseline or is_singan) + + if (not is_training) and hasattr(config.test_params, "calc_fid_ext2") and (config.test_params.calc_fid_ext2): + self.test_full = True + else: + self.test_full = False + + def __len__(self): + return self.length + + def __getitem__(self, index): + + if self.img_dir is not None: + full_img = Image.open(self.img_paths[index]) + else: + try: + with self.env.begin(write=False) as txn: + key = f'{self.resolution}-{str(index).zfill(self.n_zfill)}'.encode('utf-8') + img_bytes = txn.get(key) + + buffer = BytesIO(img_bytes) + if buffer is None: + raise ValueError(" [!] Meet empty image while loading with key {}".format(key)) + full_img = Image.open(buffer) + except Exception as e: + print(" [!] Error at idx {}".format(index)) + raise e + full_img = self.transform(full_img) + + ac_coords = None + if self.simple_return_full: + data_pack = dict(full=full_img) + elif self.config.train_params.training_modality == "full": + data_pack = dict(full=full_img) + elif self.config.train_params.training_modality == "patch": + patch, ac_coords = self.crop_fn(full_img) + data_pack = dict(patch=patch, full=full_img) + else: + raise NotImplementedError() + + if self.test_full: + data_pack["full"] = full_img + + if ac_coords is not None: + data_pack["ac_coords"] = ac_coords + + not_img_keys = {"ac_coords"} + data_pack = { + k: self.finalize(v) if k not in not_img_keys else v + for k,v in data_pack.items()} + + return data_pack + + +def data_sampler(dataset, shuffle): + if shuffle: + return data.RandomSampler(dataset) + else: + return data.SequentialSampler(dataset) + + + +if __name__ == "__main__": + device = "cuda" + parser = argparse.ArgumentParser() + parser.add_argument("config", type=str) + args = parser.parse_args() + + with open(args.config) as f: + config = yaml.load(f, Loader=yaml.FullLoader) + config = EasyDict(config) + config.var = EasyDict() + config.var.exp_name = os.path.basename(args.config).split(".")[0] + print(" [*] Config {} loaded!".format(args.config)) + + train_set = MultiResolutionDataset( + split="train", + config=config, + is_training=True) + valid_set = MultiResolutionDataset( + split="valid", + config=config, + is_training=False) + + loaders = { + "train": iter(data.DataLoader( + train_set, + batch_size=config.train_params.batch_size, + sampler=data_sampler(train_set, shuffle=False), + drop_last=False, + num_workers=16, + )), + "valid": iter(data.DataLoader( + valid_set, + batch_size=config.train_params.batch_size, + sampler=data_sampler(valid_set, shuffle=False), + drop_last=False, + num_workers=16 + )), + } + #import pdb; pdb.set_trace() + + for i in tqdm(range(len(loaders["train"]))): + try: + next(loaders["train"]) + except: + pass + + for i in tqdm(range(len(loaders["valid"]))): + try: + next(loaders["valid"]) + except: + pass diff --git a/env_config.py b/env_config.py new file mode 100644 index 0000000..5fbea78 --- /dev/null +++ b/env_config.py @@ -0,0 +1,13 @@ +LMDB_ROOTS = { + # Definition: + # str(socket.gethostname()): "" + "my-server": "/data/hubert/data/infinityGAN-lmdb/", + "my-PC" : "/mnt/merced-pc/infinityGAN/data/infinityGAN-lmdb/", + # "OuO" : "/mnt/hubert/data/infinityGAN-lmdb/", + "unspecified": [ + # list paths here is don't wanna specify the server name, but have risks in popping unhandled errors. + + ], +} + +REMOTE_CKPT_URL = "http://vllab1.ucmerced.edu/~hubert/shared_files/" diff --git a/eval_fids.py b/eval_fids.py new file mode 100644 index 0000000..bce47d5 --- /dev/null +++ b/eval_fids.py @@ -0,0 +1,291 @@ +import os +import time +import yaml +import argparse +import traceback +import numpy as np +from tqdm import tqdm +from glob import glob +from easydict import EasyDict + +import torch +from torch import nn + +torch.backends.cudnn.benchmark = True +#torch.autograd.set_detect_anomaly(True) + +from dataset import MultiResolutionDataset +from libs.fid import eval_fid +from libs.inception_score import inception_score +from libs.calc_inception import load_patched_inception_v3 +from latent_sampler import LatentSampler +from utils import * +from quant_eval_utils import QuantEvalSampleGenerator, QuantEvalDataLoader + +SET_TEST_ID = True # Making testing-time randomized noise inputs from StyleGAN2 fixed +TB_PARTITION_STEPS = 100000 # Partition event file for efficient rsync + + +def eval(args, latent_sampler, g_ema, inception, device, config): + + if g_ema is not None: + g_ema.eval() + + """ + Cast FID calculation spec + """ + if hasattr(config.train_params, "extra_pre_resize"): + real_data_res = config.train_params.extra_pre_resize + else: # StyleGAN2 baseline + assert config.train_params.styleGAN2_baseline + real_data_res = config.train_params.full_size + assert real_data_res in {128, 256}, "In this paper, we only benchmark in size {128, 256}. Got {}.".format(real_data_res) + + eval_gen_res = real_data_res * args.scale + + # InfinityGAN is trained with larger image, so the same resolution equivalents to smaller FoV. + # Here, we ensures the FoV is the same as the StyleGAN2 baseline + fov_scale = config.train_params.full_size / real_data_res + raw_gen_res = int(np.ceil(eval_gen_res * fov_scale)) + + if args.seq_inference: + assert (not hasattr(config.train_params, "styleGAN2_baseline")) or (not config.train_params.styleGAN2_baseline) + assert args.scale > 1, "Set sequential inference with scale==1 is meaningless" + use_seq_inf = True + else: + use_seq_inf = False + + """ + Create dataloader and generator + """ + if args.img_folder is not None: + postprocessing_params = [ + ["assert", eval_gen_res], + ["resize", real_data_res], + ] + else: + postprocessing_params = [ + ["scale", 1 / fov_scale], + ["crop", eval_gen_res], + ["resize", real_data_res], + ] + fake_generator = \ + QuantEvalSampleGenerator( + g_ema, + latent_sampler, + img_folder=args.img_folder, # if applicable + output_size=raw_gen_res, + use_seq_inf=use_seq_inf, + postprocessing_params=postprocessing_params, + fid_type=args.type, + device=device, + config=config, + use_pil_resize=args.use_pil_resize) + + + stats_key = "benchmark-{}-{}-RealRes{}".format( + args.type, config.data_params.dataset, real_data_res) + # FID statistics can be different for different PyTorch version, not sure about cuda + stats_key += f"_PT{torch.__version__}_cu{torch.version.cuda}" + fid_cache_path = os.path.join(".fid-cache/", stats_key+".pkl") + if os.path.exists(fid_cache_path): + if args.clear_fid_cache: + os.remove(fid_cache_path) + use_cache = False + else: + use_cache = True + else: + use_cache = False + + if not use_cache: + dataset = MultiResolutionDataset( + split="train", + config=config, + is_training=False, + # return "full" of real full images and crop on-the-fly + disable_extra_cropping=True, + simple_return_full=True, + override_full_size=real_data_res) + real_dataloader = QuantEvalDataLoader(dataset, real_data_res, device, config) + else: + real_dataloader = None + + """ + Eval + """ + st = time.time() + if args.metric == "is": + assert args.scale == 1, "We didn't implement scaleinv IS." + n_batch = int(np.ceil(config.test_params.n_fid_sample / config.train_params.batch_size)) + all_imgs = [] + for img_batch in tqdm(fake_generator(n_batch), total=n_batch): + img_batch = ((img_batch + 1) / 2).cpu() # [-1, 1] => [0, 1] + all_imgs.append(img_batch) + all_imgs = torch.cat(all_imgs, 0) + is_mean, is_std = inception_score(all_imgs, device="cuda", batch_size=config.train_params.batch_size, resize=False, splits=10) + print(" [*] IS time spend {}".format(args.type, time.time()-st)) + print(" [*] IS at eval_gen_res {} is {}+-{} (ckpt patch FID = {})".format( + eval_gen_res, is_mean, is_std, config.var.best_fid)) + elif args.metric == "fid": + if args.type == "spatial": + fid = eval_fid( + real_dataloader, fake_generator, inception, stats_key, None, device, config, + spatial_partition_cat=True, assert_eval_shape=real_data_res) + elif args.type in {"scaleinv", "alis"}: + fid = eval_fid( + real_dataloader, fake_generator, inception, stats_key, None, device, config, + spatial_partition_cat=False, assert_eval_shape=real_data_res) + else: + raise NotImplementedError("Unknown FID variant {}".format(args.type)) + print(" [*] {} FID time spend {}".format(args.type, time.time()-st)) + print(" [*] FID (type {}) at eval_gen_res {} is {} (ckpt patch FID = {})".format( + args.type, eval_gen_res, fid, config.var.best_fid)) + + """ + Setup Logging + """ + if args.metric == "is": + log_root = os.path.join("logs-quant", "IS") + filename = f"EvalGenRes{eval_gen_res}-Exp-{config.var.exp_name}.txt" + score = "{:.6f}+-{:.6f}\n".format(is_mean, is_std) + else: + log_root = os.path.join("logs-quant", "FID-"+args.type) + filename = f"Scale{args.scale}-EvalGenRes{eval_gen_res}-Exp-{config.var.exp_name}.txt" + score = "{:.6f}\n".format(fid) + + if not os.path.exists(log_root): + os.makedirs(log_root) + with open(os.path.join(log_root, filename), "a") as lf: + lf.write(score) + + +if __name__ == "__main__": + + parser = argparse.ArgumentParser() + parser.add_argument("config", type=str) + parser.add_argument("--batch-size", type=int, default=None) + parser.add_argument("--scale", type=int, default=1) + parser.add_argument("--type", type=str, choices=["default", "scaleinv", "spatial", "alis"], default="default") + parser.add_argument("--seq-inference", action="store_true") + parser.add_argument("--alis-config", type=str, default=None) + + parser.add_argument("--metric", type=str, choices=["fid", "is"], default="fid") + + # Other evaluation methods other than /ckpt/best_fid.pth.tar + parser.add_argument("--ckpt", type=str, default=None) + parser.add_argument("--img-folder", type=str, default=None) + parser.add_argument("--use-pil-resize", action="store_true") + + parser.add_argument("--verbose", action="store_true", default=False) + parser.add_argument("--archive-mode", action="store_true", default=False) + parser.add_argument("--clear-fid-cache", action="store_true", default=False) + args = parser.parse_args() + + if args.verbose: + def annoy_print(x): + torch.cuda.synchronize() + print(x, end="") + else: + annoy_print = dummy_func + + with open(args.config, "r") as f: + config = yaml.load(f, Loader=yaml.FullLoader) + config = EasyDict(config) + config.var = EasyDict() + config.var.exp_name = os.path.basename(args.config).split(".yaml")[0] + print(" [*] Config {} loaded!".format(args.config)) + + if args.type == "alis": + assert args.alis_config is not None, "Evaluate alis fid must specify a config!" + with open(args.alis_config, "r") as f: + config.task = EasyDict(yaml.load(f, Loader=yaml.FullLoader)) + config.task.config_name = os.path.basename(args.alis_config).split(".yaml")[0] + config.task.config_path = args.alis_config + config.train_params.batch_size = config.task.batch_size # Usually 1, never tested other cases. + config.task.save_type = "patches-centercrop" # FID requires shape-aligned patches + print(" [*] ALIS eval config {} loaded!".format(args.alis_config)) + + if args.archive_mode: + config.var.log_dir = "../../" # We are running in ./logs//codes/ + else: + config.var.log_dir = "./logs/" + + # Remove previous error file (will make confusion on log synchronizing) + error_f = os.path.join(config.var.log_dir, config.var.exp_name, "error-log.txt") + if os.path.exists(error_f): + os.remove(error_f) + + if "CUDA_VISIBLE_DEVICES" in os.environ: + n_gpu = len(os.environ["CUDA_VISIBLE_DEVICES"].split(",")) + config.var.dataparallel = n_gpu > 1 + config.var.n_gpu = n_gpu + else: + raise ValueError(" [!] Please specify CUDA_VISIBLE_DEVICES!") + + if args.batch_size is not None: + config.train_params.batch_size = args.batch_size + + try: + if args.img_folder: + g_ema = None + latent_sampler = None + config.var.best_fid = -1 + else: + """ + Build G & D + """ + g_ema = import_func(config.train_params.g_arch)(config=config) + latent_sampler = LatentSampler(g_ema, config) + + """ + Multi-GPU + """ + if config.var.dataparallel: + device = "cpu" # torch will auto do the GPU partitioning in backend + g_ema = nn.DataParallel(g_ema).cuda() + else: + device = "cuda" + g_ema = g_ema.to(device) + + """ + Load checkpoint + """ + if args.ckpt is None: + ckpt_dir = os.path.join(config.var.log_dir, config.var.exp_name, "ckpt") + best_ckpt = os.path.join(ckpt_dir, "best_fid.pth.tar") + + assert os.path.exists(best_ckpt), "Cannot find checkpoint at {}!".format(best_ckpt) + print(" [*] Found ckpt, load model from:", best_ckpt) + ckpt = torch.load(best_ckpt, map_location=lambda storage, loc: storage) + else: + ckpt = torch.load(args.ckpt, map_location=lambda storage, loc: storage) + safe_load_state_dict(g_ema, ckpt["g_ema"]) #, strict=False) + print(" [*] Loaded ckpt at {} iter with FID {:.4f}".format(ckpt["iter"], ckpt["best_fid"])) + config.var.best_fid = ckpt["best_fid"] + + + """ + FID setup + """ + inception = load_patched_inception_v3() + inception.eval() + """ + Multi-GPU + """ + if config.var.dataparallel: + device = "cpu" # torch will auto do the GPU partitioning in backend + inception = nn.DataParallel(inception).cuda() + else: + device = "cuda" + inception = inception.to(device) + + eval(args, latent_sampler, g_ema, inception, device, config) + except Exception as e: + if e is not KeyboardInterrupt: + error_dirs = sorted(glob("./burst-errors-*")) + error_f = os.path.join(config.var.log_dir, config.var.exp_name, "error-log.txt") + with open(error_f, "w") as f: + f.write(str(e) + "\n") + f.write(" *** stack trace *** \n") + f.write(traceback.format_exc()) + raise e diff --git a/eval_outpaint_imgdir.py b/eval_outpaint_imgdir.py new file mode 100755 index 0000000..b370e6d --- /dev/null +++ b/eval_outpaint_imgdir.py @@ -0,0 +1,259 @@ +import os +import sys +import pdb +import pickle +import argparse +from glob import glob + +import numpy as np +from scipy import linalg +from scipy.stats import entropy +from tqdm import tqdm +from PIL import Image + +import torch +import torch.nn.functional as F +torch.backends.cudnn.benchmark = True +from torch import nn +from torch.utils import data +from torchvision import transforms + +from libs.calc_inception import load_patched_inception_v3 +from torchvision.models.inception import inception_v3 + + +@torch.no_grad() +def extract_fid_feature_from_dir(imgs, inception, batch_size, device): + nsample = len(imgs) + n_batch = nsample // batch_size + resid = nsample - (n_batch * batch_size) + batch_sizes = [batch_size] * n_batch + [resid] + features = [] + + offset = 0 + for batch in tqdm(batch_sizes): + if batch == 0: + continue + batch_img = imgs[offset : offset+batch] + if type(batch_img[0]) == str: + batch_img_tensor = [] + for img in batch_img: + # read them + img = Image.open(imgfile).convert("RGB") + img = transform(img) + batch_img_tensor.append(img) + batch_img = torch.stack(batch_img_tensor, dim=0) + + batch_img = batch_img.to(device) + offset += batch + + # feat = inception(img)[0].view(img.shape[0], -1) + feat = inception(batch_img.clamp(-1, 1))[0].view(batch_img.shape[0], -1) + features.append(feat.to('cpu')) + + features = torch.cat(features, 0) + return features + + +def calc_fid(sample_mean, sample_cov, real_mean, real_cov, eps=1e-6): + cov_sqrt, _ = linalg.sqrtm(sample_cov @ real_cov, disp=False) + + if not np.isfinite(cov_sqrt).all(): + print('product of cov matrices is singular') + offset = np.eye(sample_cov.shape[0]) * eps + cov_sqrt = linalg.sqrtm((sample_cov + offset) @ (real_cov + offset)) + + if np.iscomplexobj(cov_sqrt): + if not np.allclose(np.diagonal(cov_sqrt).imag, 0, atol=1e-3): + m = np.max(np.abs(cov_sqrt.imag)) + raise ValueError(f'Imaginary component {m}') + cov_sqrt = cov_sqrt.real + + mean_diff = sample_mean - real_mean + mean_norm = mean_diff @ mean_diff + trace = np.trace(sample_cov) + np.trace(real_cov) - 2 * np.trace(cov_sqrt) + + fid = mean_norm + trace + return fid + + +def calc_dir_fid(args, imgs1, imgs2, inception, batch_size, device): + + features1 = extract_fid_feature_from_dir(imgs1, inception, batch_size, device) + features2 = extract_fid_feature_from_dir(imgs2, inception, batch_size, device) + + features1 = features1.numpy() + features2 = features2.numpy() + + mu1, cov1 = np.mean(features1, 0), np.cov(features1, rowvar=False) + mu2, cov2 = np.mean(features2, 0), np.cov(features2, rowvar=False) + + fid = calc_fid(mu1, cov1, mu2, cov2, eps=1e-6) + return fid + + +def inception_score(imgs, device, batch_size=32, resize=False, splits=1): + """Computes the inception score of the generated images imgs + imgs -- Torch dataset of (3xHxW) numpy images normalized in the range [-1, 1] + device -- whether or not to run on GPU + batch_size -- batch size for feeding into Inception v3 + splits -- number of splits + """ + + # Load inception model + inception_model = inception_v3(pretrained=True, transform_input=False).to(device) + inception_model.eval() + upsample = nn.Upsample(size=(299, 299), mode='bilinear').to(device) + + def get_pred(x): + if resize: + x = upsample(x) + x = inception_model(x) + # return F.softmax(x).data.cpu().numpy() + return F.softmax(x, dim=-1).data.cpu().numpy() + + n_sample = len(imgs) + n_batch = n_sample // batch_size + resid = n_sample - (n_batch * batch_size) + batch_sizes = [batch_size] * n_batch + [resid] + + offset = 0 + preds = np.zeros((n_sample, 1000)) + for batch in tqdm(batch_sizes): + if batch == 0: + continue + batch_img = imgs[offset : offset+batch] + if type(batch_img[0]) == str: + + batch_img_tensor = [] + for img in batch_img: + # read them + img = Image.open(imgfile).convert("RGB") + img = transform(img) + batch_img_tensor.append(img) + + batch_img = torch.stack(batch_img_tensor, dim=0) + + # import pdb; pdb.set_trace() + + batch_img = batch_img.to(device) + + pred = get_pred(batch_img) + preds[offset : offset+batch] = pred + + offset += batch + + # Now compute the mean kl-div + split_scores = [] + + for k in range(splits): + part = preds[k * (n_sample // splits): (k+1) * (n_sample // splits), :] + py = np.mean(part, axis=0) + scores = [] + for i in range(part.shape[0]): + pyx = part[i, :] + scores.append(entropy(pyx, py)) + split_scores.append(np.exp(np.mean(scores))) + + return np.mean(split_scores), np.std(split_scores) + + + +if __name__ == '__main__': + device = 'cuda' + + parser = argparse.ArgumentParser() + parser.add_argument('--batch', type=int, default=48) + parser.add_argument('--size', type=int, default=256) + parser.add_argument('--real-dir', type=str) + parser.add_argument('--fake-dir', type=str) + args = parser.parse_args() + + transform = transforms.Compose( + [ + transforms.Resize(args.size), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True), + ] + ) + + ## inception + inception = load_patched_inception_v3().to(device) + inception.eval() + + if "~" in args.real_dir: + args.real_dir = os.path.expanduser(args.real_dir) + if "~" in args.fake_dir: + args.fake_dir = os.path.expanduser(args.fake_dir) + + """ + Load data + """ + real_imgs = [] + real_img_paths = sorted(glob(os.path.join(args.real_dir, "*"))) + has_failure = False + assert len(real_img_paths) > 0 + for ix, imgfile in tqdm(enumerate(real_img_paths), total=len(real_img_paths), desc=f'=> loading imgs from {args.real_dir}'): + try: + img = Image.open(imgfile).convert("RGB") + except Exception as e: + print(ix, e) + has_failure = True + continue + img = transform(img) + real_imgs.append(img) + real_imgs = torch.stack(real_imgs, dim=0) + + real_h, real_w = real_imgs[0, 0].shape + + fake_imgs = [] + fake_img_paths = sorted(glob(os.path.join(args.fake_dir, "*"))) + assert len(fake_img_paths) > 0 + for ix, imgfile in tqdm(enumerate(fake_img_paths), total=len(fake_img_paths), desc=f'=> loading imgs from {args.fake_dir}'): + try: + img = Image.open(imgfile).convert("RGB") + except Exception as e: + print(ix, e) + has_failure = True + continue + img = transform(img) + + # Center crop, since InfinityGAN will generate slightly larger outpainting results + _, fake_h, fake_w = img.shape + pad_h = (fake_h - real_h) + pad_w = (fake_w - real_w) + img = img[:, pad_h:pad_h+real_h, pad_w:pad_w+real_w] + + fake_imgs.append(img) + fake_imgs = torch.stack(fake_imgs, dim=0) + + assert (not has_failure), "Receives error(s) while loading data, please check the messages above!" + + ## IS + # is_score_real_mean, is_score_real_std = \ + # inception_score(real_imgs, device, batch_size=args.batch, resize=True, splits=1) + is_score_fake_mean, is_score_fake_std = \ + inception_score(fake_imgs, device, batch_size=args.batch, resize=True, splits=1) + # print(f'=> {args.real_dir} IS: {is_score_real}') + print('=> IS: {:.6f} +- {:.6f}'.format(is_score_fake_mean, is_score_fake_std)) + + ## FID + fid = calc_dir_fid(args, real_imgs, fake_imgs, inception, args.batch, device) + print('=> FID: {:.6f}'.format(fid)) + + # Generate report + def fprint(f, s): + print(s) + f.write(s+"\n") + exp_toks = [tok for tok in args.fake_dir.split("/")] + exp_name = exp_toks[exp_toks.index("logs") + 1] + log_file = os.path.join("./logs-quant/outpaint/", exp_name+".txt") + if not os.path.exists(os.path.dirname(log_file)): + os.makedirs(os.path.dirname(log_file)) + with open(log_file, "a") as f: + fprint(f, "*" * 30) + fprint(f, " [*] EXP: {}".format(exp_name)) + fprint(f, " [*] PATH: {}".format(args.fake_dir)) + fprint(f, ' => IS: {:.6f} +- {:.6f}'.format(is_score_fake_mean, is_score_fake_std)) + fprint(f, ' => FID: {:.6f}'.format(fid)) + fprint(f, "*" * 30) diff --git a/latent_sampler.py b/latent_sampler.py new file mode 100644 index 0000000..2486c14 --- /dev/null +++ b/latent_sampler.py @@ -0,0 +1,61 @@ +import torch +import random + + +class LatentSampler(): + def __init__(self, generator, config): + self.config = config + self.generator = generator + + @torch.no_grad() + def sample_global_latent(self, batch_size, device, requires_grad=False, mixing=True): + global_latent_dim = self.config.train_params.global_latent_dim + is_mixing = random.random() < self.config.train_params.mixing if mixing else False + + latent_1 = torch.randn(batch_size, global_latent_dim, device=device) + latent_2 = torch.randn(batch_size, global_latent_dim, device=device) + latent = torch.stack([ + latent_1, + latent_2 if is_mixing else latent_1, + ], 1) # shape: (B, 2, D) # batch-first for dataparallel + + latent.requires_grad = requires_grad + return latent + + def sample_local_latent(self, batch_size, device, requires_grad=False, + spatial_size_enlarge=1, specific_shape=None, exclude_padding=False): + + local_latent_dim = self.config.train_params.local_latent_dim + + if specific_shape is not None: + spatial_shape = specific_shape + elif spatial_size_enlarge != 1: + if hasattr(self.config.train_params, "styleGAN2_baseline") and self.config.train_params.styleGAN2_baseline: + size = self.config.train_params.ts_input_size * spatial_size_enlarge + spatial_shape = (size, size) + else: + base = self.config.train_params.ts_input_size // 2 + size = (int(round(base * spatial_size_enlarge)) * 2) + 1 + spatial_shape = (size, size) + else: + size = self.config.train_params.ts_input_size + spatial_shape = (size, size) + + if self.config.train_params.use_ss and self.config.train_params.ss_unfold_radius > 0: + if self.config.train_params.ss_n_layers > 0: + ss_unfold_size = self.config.train_params.ss_n_layers * self.config.train_params.ss_unfold_radius + else: + ss_unfold_size = 0 + if exclude_padding: + spatial_shape_ext = spatial_shape + else: + spatial_shape_ext = [ + spatial_shape[0] + 2 * ss_unfold_size, + spatial_shape[1] + 2 * ss_unfold_size] + z_local = torch.randn(batch_size, local_latent_dim, spatial_shape_ext[0], spatial_shape_ext[1], device=device) + else: + z_local = torch.randn(batch_size, local_latent_dim, spatial_shape[0], spatial_shape[1], device=device) + + z_local.requires_grad = requires_grad + return z_local + diff --git a/libs/backup.py b/libs/backup.py new file mode 100644 index 0000000..3ee894b --- /dev/null +++ b/libs/backup.py @@ -0,0 +1,42 @@ +import os +import shutil +from glob import glob + +backup_file_types = { + "py", "cpp", "cu", +} + +backup_dirs = { + "models", + "custom_ops", + "libs", + "lpips", + "dev", + "test_managers", +} + +excluded_dirs = { + "configs", + "logs", + "data", + "lmdb", + "__pycache__", + ".build_cache", + "weights", +} + +def backup_files(cur_dir, backup_dir): + if not os.path.exists(backup_dir): + os.makedirs(backup_dir) + cur_level_files = [p for p in glob(os.path.join(cur_dir, "*")) if os.path.basename(p) not in excluded_dirs] + for path in cur_level_files: + if os.path.isdir(path): + dir_name = os.path.basename(path) + if dir_name in backup_dirs: + backup_files(path, os.path.join(backup_dir, dir_name)) + else: + file_extension = path.split(".")[-1] + if file_extension in backup_file_types: + copy_path = os.path.join(backup_dir, os.path.basename(path)) + shutil.copy2(path, copy_path) + diff --git a/libs/calc_inception.py b/libs/calc_inception.py new file mode 100644 index 0000000..a4483ea --- /dev/null +++ b/libs/calc_inception.py @@ -0,0 +1,116 @@ +import argparse +import pickle +import os + +import torch +from torch import nn +from torch.nn import functional as F +from torch.utils.data import DataLoader +from torchvision import transforms +from torchvision.models import inception_v3, Inception3 +import numpy as np +from tqdm import tqdm + +from libs.inception import InceptionV3 +from dataset import MultiResolutionDataset + + +class Inception3Feature(Inception3): + def forward(self, x): + if x.shape[2] != 299 or x.shape[3] != 299: + x = F.interpolate(x, size=(299, 299), mode='bilinear', align_corners=True) + + x = self.Conv2d_1a_3x3(x) # 299 x 299 x 3 + x = self.Conv2d_2a_3x3(x) # 149 x 149 x 32 + x = self.Conv2d_2b_3x3(x) # 147 x 147 x 32 + x = F.max_pool2d(x, kernel_size=3, stride=2) # 147 x 147 x 64 + + x = self.Conv2d_3b_1x1(x) # 73 x 73 x 64 + x = self.Conv2d_4a_3x3(x) # 73 x 73 x 80 + x = F.max_pool2d(x, kernel_size=3, stride=2) # 71 x 71 x 192 + + x = self.Mixed_5b(x) # 35 x 35 x 192 + x = self.Mixed_5c(x) # 35 x 35 x 256 + x = self.Mixed_5d(x) # 35 x 35 x 288 + + x = self.Mixed_6a(x) # 35 x 35 x 288 + x = self.Mixed_6b(x) # 17 x 17 x 768 + x = self.Mixed_6c(x) # 17 x 17 x 768 + x = self.Mixed_6d(x) # 17 x 17 x 768 + x = self.Mixed_6e(x) # 17 x 17 x 768 + + x = self.Mixed_7a(x) # 17 x 17 x 768 + x = self.Mixed_7b(x) # 8 x 8 x 1280 + x = self.Mixed_7c(x) # 8 x 8 x 2048 + + x = F.avg_pool2d(x, kernel_size=8) # 8 x 8 x 2048 + + return x.view(x.shape[0], x.shape[1]) # 1 x 1 x 2048 + + +def load_patched_inception_v3(): + # inception = inception_v3(pretrained=True) + # inception_feat = Inception3Feature() + # inception_feat.load_state_dict(inception.state_dict()) + inception_feat = InceptionV3([3], normalize_input=False) + + return inception_feat + + +@torch.no_grad() +def extract_features(loader, inception, device): + pbar = tqdm(loader) + + feature_list = [] + + for img in pbar: + img = img.to(device) + feature = inception(img)[0].view(img.shape[0], -1) + feature_list.append(feature.to('cpu')) + + features = torch.cat(feature_list, 0) + + return features + + +if __name__ == '__main__': + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + parser = argparse.ArgumentParser( + description='Calculate Inception v3 features for datasets' + ) + parser.add_argument('--size', type=int, default=256) + parser.add_argument('--batch', default=64, type=int, help='batch size') + parser.add_argument('--n_sample', type=int, default=50000) + parser.add_argument('--flip', action='store_true') + parser.add_argument('path', metavar='PATH', help='path to datset lmdb file') + + args = parser.parse_args() + + inception = load_patched_inception_v3() + inception = nn.DataParallel(inception).eval().to(device) + + transform = transforms.Compose( + [ + transforms.RandomHorizontalFlip(p=0.5 if args.flip else 0), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]), + ] + ) + + dset = MultiResolutionDataset(config.data_params.lmdb_path, transform=transform, resolution=config.train_params.full_size) + loader = DataLoader(dset, batch_size=config.train_params.batch_size, num_workers=4) + + features = extract_features(loader, inception, device).numpy() + + features = features[: params.test_params.n_fid_sample] + + print(f'extracted {features.shape[0]} features') + + mean = np.mean(features, 0) + cov = np.cov(features, rowvar=False) + + name = os.path.splitext(os.path.basename(config.data_params.lmdb_path))[0] + + with open(f'inception_{name}.pkl', 'wb') as f: + pickle.dump({'mean': mean, 'cov': cov, 'size': config.train_params.full_size, 'path': config.data_params.lmdb_path}, f) diff --git a/libs/fid.py b/libs/fid.py new file mode 100644 index 0000000..fb664a7 --- /dev/null +++ b/libs/fid.py @@ -0,0 +1,238 @@ +import os +import argparse +import pickle + +import torch +from torch import nn +from torch.utils.data import DataLoader +import numpy as np +import pickle as pkl +from scipy import linalg +from tqdm import tqdm + +from libs.calc_inception import load_patched_inception_v3 + + +def gan_img_to_classify_img(img): + img = (img + 1) / 2 + return torch.clamp(img, 0, 1) + +def spatial_partition_cat_func(img, inception, num_patches=16): + B, C, H, W = img.shape + patch_len = int(np.sqrt(num_patches)) + assert patch_len**2 == num_patches + + patch_size = img.shape[2] // patch_len + #assert img.shape[2] % patch_len == 0, "Got image shape {}, cannot be partitioned in {} patches".format(img.shape, patch_len) + assert img.shape[2] == img.shape[3] + + patches = [] + for i in range(patch_len): + for j in range(patch_len): + patch = img[:, :, i*patch_size:(i+1)*patch_size, j*patch_size:(j+1)*patch_size] + patches.append(patch) + patches = torch.cat(patches, 0) + patches_feat = inception(patches)[0].squeeze() + inception_ch = patches_feat.shape[-1] + patches_feat = patches_feat.view(B, num_patches, inception_ch).view(B, num_patches*inception_ch) + return patches_feat + + +@torch.no_grad() +def extract_feature_from_samples( + generation_fn, inception, batch_size, n_sample, device, + cocogan_patched_fid = False, create_graph_only=False, spatial_partition_cat=False, assert_eval_shape=None): + + if cocogan_patched_fid: + n_sample = n_sample // 16 + 1 # hard coded + + if create_graph_only: + n_batch = 1 + else: + if n_sample % batch_size == 0: + n_batch = n_sample // batch_size + else: + n_batch = n_sample // batch_size + 1 + features = [] + + for img in tqdm(generation_fn(n_batch), total=n_batch): + if isinstance(inception, nn.DataParallel): # Dataparallel takes CPU tensor + img = img.cpu() + elif next(inception.parameters()).is_cuda: + img = img.cuda() + else: + img = img.cpu() + img = gan_img_to_classify_img(img) + if img.shape[1] == 1: # grayscale + img = img.repeat(1, 3, 1, 1) + + if assert_eval_shape is not None: + assert (img.shape[2] == assert_eval_shape) and (img.shape[3] == assert_eval_shape), \ + "Set assert image shape {}, but got {}".format(assert_eval_shape, img.shape) + + if cocogan_patched_fid: + patches = [] + patch_size = img.shape[2] // 4 + for i in range(4): + for j in range(4): + xst = i*patch_size + yst = j*patch_size + xed = xst + patch_size + yed = yst + patch_size + patches.append(img[:, :, xst:xed, yst:yed]) + img = torch.cat(patches, 0) + + if spatial_partition_cat: + feat = spatial_partition_cat_func(img, inception, num_patches=16) + else: + feat = inception(img)[0].view(img.shape[0], -1) + features.append(feat.to('cpu')) + + features = torch.cat(features, 0) + + return features[:n_sample] + + +def calc_fid(sample_mean, sample_cov, real_mean, real_cov, eps=1e-6): + cov_sqrt, _ = linalg.sqrtm(sample_cov @ real_cov, disp=False) + + if not np.isfinite(cov_sqrt).all(): + print('product of cov matrices is singular') + offset = np.eye(sample_cov.shape[0]) * eps + cov_sqrt = linalg.sqrtm((sample_cov + offset) @ (real_cov + offset)) + + if np.iscomplexobj(cov_sqrt): + if not np.allclose(np.diagonal(cov_sqrt).imag, 0, atol=1e-3): + m = np.max(np.abs(cov_sqrt.imag)) + + raise ValueError(f'Imaginary component {m}') + + cov_sqrt = cov_sqrt.real + + mean_diff = sample_mean - real_mean + mean_norm = mean_diff @ mean_diff + + trace = np.trace(sample_cov) + np.trace(real_cov) - 2 * np.trace(cov_sqrt) + + fid = mean_norm + trace + + return fid + +@torch.no_grad() +def calc_stats_from_dataloader(dataloader, inception, config, n_samples, modality, + cocogan_patched_fid=False, spatial_partition_cat=False, assert_eval_shape=None): + features = [] + total = 0 + iterator = iter(dataloader) + batch_size = config.train_params.batch_size + n_batches = int(np.ceil(n_samples / batch_size)) + if cocogan_patched_fid: + n_batches = n_batches // 16 + 1 + for _ in tqdm(range(n_batches)): + real_data = next(iterator) + if modality is None: + img = real_data + else: + img = real_data[modality] + if img.min() > 0: + print(" [!] Whole batch has minimum intensity > 0, can be a bug if happens to all batches! (in `libs.fid.calc_stats_from_dataloader`)") + img = gan_img_to_classify_img(img) + if (not isinstance(inception, nn.DataParallel)): # Dataparallel takes CPU tensor + if next(inception.parameters()).is_cuda: + img = img.cuda() + if img.shape[1] == 1: # grayscale + img = img.repeat(1, 3, 1, 1) + + if assert_eval_shape is not None: + assert (img.shape[2] == assert_eval_shape) and (img.shape[3] == assert_eval_shape), \ + "Set assert image shape {}, but got {}".format(assert_eval_shape, img.shape) + + if cocogan_patched_fid: + patches = [] + patch_size = img.shape[2] // 4 + for i in range(4): + for j in range(4): + xst = i*patch_size + yst = j*patch_size + xed = xst + patch_size + yed = yst + patch_size + patches.append(img[:, :, xst:xed, yst:yed]) + img = torch.cat(patches, 0) + + if spatial_partition_cat: + feat = spatial_partition_cat_func(img, inception, num_patches=16) + else: + feat = inception(img)[0].view(img.shape[0], -1) + features.append(feat.to('cpu')) + total += img.shape[0] + if total > n_samples: + break + + features = torch.cat(features, 0).numpy()[:n_samples] + sample_mean = np.mean(features, 0) + sample_cov = np.cov(features, rowvar=False) + return sample_mean, sample_cov + + +def eval_fid(dataloader, generation_fn, inception, dataset_key, modality, device, config, + no_write_cache=False, create_graph_only=False, spatial_partition_cat=False, cocogan_patched_fid=False, + assert_eval_shape=None, external_cache_root=None): + if external_cache_root is None: + cache_root = ".fid-cache/" + else: + cache_root = external_cache_root + assert os.path.exists(cache_root), \ + "Specified a non-existing fid cache root at {}".format(external_cache_root) + + if not os.path.exists(cache_root): os.makedirs(cache_root) + cache_path = os.path.join(cache_root, "{}.pkl".format(dataset_key)) + real_stats = None + if os.path.exists(cache_path): + try: + real_stats = pkl.load(open(cache_path, "rb")) + except: + real_stats = None + if real_stats is not None: + real_mean = real_stats['mean'] + real_cov = real_stats['cov'] + elif create_graph_only: + real_mean, real_cov = None, None + else: + print(" [!] Failed to find FID stats of dataset_key={}, start generating cache...".format(dataset_key)) + real_mean, real_cov = calc_stats_from_dataloader( + dataloader, inception, config, config.test_params.n_fid_sample, modality=modality, + cocogan_patched_fid=cocogan_patched_fid, + spatial_partition_cat=spatial_partition_cat, assert_eval_shape=assert_eval_shape) + if (not no_write_cache): + pkl.dump({"mean": real_mean, "cov": real_cov}, open(cache_path, "wb"), protocol=4) + + if isinstance(generation_fn, DataLoader): # Eval with image folder instead of generator + sample_mean, sample_cov = calc_stats_from_dataloader( + generation_fn, inception, config, config.test_params.n_fid_sample, modality=modality, + cocogan_patched_fid=cocogan_patched_fid, + spatial_partition_cat=spatial_partition_cat, assert_eval_shape=assert_eval_shape) + if create_graph_only: + return 500 + else: + features = extract_feature_from_samples( + generation_fn, + inception, + config.train_params.batch_size, + config.test_params.n_fid_sample, + device, + cocogan_patched_fid=cocogan_patched_fid, + spatial_partition_cat=spatial_partition_cat, + create_graph_only=create_graph_only, + assert_eval_shape=assert_eval_shape, + ).numpy() + + if create_graph_only: + return 500 + + sample_mean = np.mean(features, 0) + sample_cov = np.cov(features, rowvar=False) + + fid = calc_fid(sample_mean, sample_cov, real_mean, real_cov) + + return fid + diff --git a/libs/inception.py b/libs/inception.py new file mode 100644 index 0000000..855b9e5 --- /dev/null +++ b/libs/inception.py @@ -0,0 +1,311 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torchvision import models + +try: + from torchvision.models.utils import load_state_dict_from_url +except ImportError: + from torch.utils.model_zoo import load_url as load_state_dict_from_url + +# Inception weights ported to Pytorch from +# http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz +FID_WEIGHTS_URL = 'https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth' + + +class InceptionV3(nn.Module): + """Pretrained InceptionV3 network returning feature maps""" + + # Index of default block of inception to return, + # corresponds to output of final average pooling + DEFAULT_BLOCK_INDEX = 3 + + # Maps feature dimensionality to their output blocks indices + BLOCK_INDEX_BY_DIM = { + 64: 0, # First max pooling features + 192: 1, # Second max pooling featurs + 768: 2, # Pre-aux classifier features + 2048: 3 # Final average pooling features + } + + def __init__(self, + output_blocks=[DEFAULT_BLOCK_INDEX], + resize_input=True, + normalize_input=True, + requires_grad=False, + use_fid_inception=True): + """Build pretrained InceptionV3 + + Parameters + ---------- + output_blocks : list of int + Indices of blocks to return features of. Possible values are: + - 0: corresponds to output of first max pooling + - 1: corresponds to output of second max pooling + - 2: corresponds to output which is fed to aux classifier + - 3: corresponds to output of final average pooling + resize_input : bool + If true, bilinearly resizes input to width and height 299 before + feeding input to model. As the network without fully connected + layers is fully convolutional, it should be able to handle inputs + of arbitrary size, so resizing might not be strictly needed + normalize_input : bool + If true, scales the input from range (0, 1) to the range the + pretrained Inception network expects, namely (-1, 1) + requires_grad : bool + If true, parameters of the model require gradients. Possibly useful + for finetuning the network + use_fid_inception : bool + If true, uses the pretrained Inception model used in Tensorflow's + FID implementation. If false, uses the pretrained Inception model + available in torchvision. The FID Inception model has different + weights and a slightly different structure from torchvision's + Inception model. If you want to compute FID scores, you are + strongly advised to set this parameter to true to get comparable + results. + """ + super(InceptionV3, self).__init__() + + self.resize_input = resize_input + self.normalize_input = normalize_input + self.output_blocks = sorted(output_blocks) + self.last_needed_block = max(output_blocks) + + assert self.last_needed_block <= 3, \ + 'Last possible output block index is 3' + + self.blocks = nn.ModuleList() + + if use_fid_inception: + inception = fid_inception_v3() + else: + inception = models.inception_v3(pretrained=True) + + # Block 0: input to maxpool1 + block0 = [ + inception.Conv2d_1a_3x3, + inception.Conv2d_2a_3x3, + inception.Conv2d_2b_3x3, + nn.MaxPool2d(kernel_size=3, stride=2) + ] + self.blocks.append(nn.Sequential(*block0)) + + # Block 1: maxpool1 to maxpool2 + if self.last_needed_block >= 1: + block1 = [ + inception.Conv2d_3b_1x1, + inception.Conv2d_4a_3x3, + nn.MaxPool2d(kernel_size=3, stride=2) + ] + self.blocks.append(nn.Sequential(*block1)) + + # Block 2: maxpool2 to aux classifier + if self.last_needed_block >= 2: + block2 = [ + inception.Mixed_5b, + inception.Mixed_5c, + inception.Mixed_5d, + inception.Mixed_6a, + inception.Mixed_6b, + inception.Mixed_6c, + inception.Mixed_6d, + inception.Mixed_6e, + ] + self.blocks.append(nn.Sequential(*block2)) + + # Block 3: aux classifier to final avgpool + if self.last_needed_block >= 3: + block3 = [ + inception.Mixed_7a, + inception.Mixed_7b, + inception.Mixed_7c, + nn.AdaptiveAvgPool2d(output_size=(1, 1)) + ] + self.blocks.append(nn.Sequential(*block3)) + + for param in self.parameters(): + param.requires_grad = requires_grad + + def forward(self, inp): + """Get Inception feature maps + + Parameters + ---------- + inp : torch.autograd.Variable + Input tensor of shape Bx3xHxW. Values are expected to be in + range (0, 1) + + Returns + ------- + List of torch.autograd.Variable, corresponding to the selected output + block, sorted ascending by index + """ + outp = [] + x = inp + + if self.resize_input: + x = F.interpolate(x, + size=(299, 299), + mode='bilinear', + align_corners=False) + + if self.normalize_input: + x = 2 * x - 1 # Scale from range (0, 1) to range (-1, 1) + + for idx, block in enumerate(self.blocks): + x = block(x) + if idx in self.output_blocks: + outp.append(x) + + if idx == self.last_needed_block: + break + + return outp + + +def fid_inception_v3(): + """Build pretrained Inception model for FID computation + + The Inception model for FID computation uses a different set of weights + and has a slightly different structure than torchvision's Inception. + + This method first constructs torchvision's Inception and then patches the + necessary parts that are different in the FID Inception model. + """ + inception = models.inception_v3(num_classes=1008, + aux_logits=False, + pretrained=False) + #init_weights=False) + inception.Mixed_5b = FIDInceptionA(192, pool_features=32) + inception.Mixed_5c = FIDInceptionA(256, pool_features=64) + inception.Mixed_5d = FIDInceptionA(288, pool_features=64) + inception.Mixed_6b = FIDInceptionC(768, channels_7x7=128) + inception.Mixed_6c = FIDInceptionC(768, channels_7x7=160) + inception.Mixed_6d = FIDInceptionC(768, channels_7x7=160) + inception.Mixed_6e = FIDInceptionC(768, channels_7x7=192) + inception.Mixed_7b = FIDInceptionE_1(1280) + inception.Mixed_7c = FIDInceptionE_2(2048) + + state_dict = load_state_dict_from_url(FID_WEIGHTS_URL, progress=True) + inception.load_state_dict(state_dict) + return inception + + +class FIDInceptionA(models.inception.InceptionA): + """InceptionA block patched for FID computation""" + def __init__(self, in_channels, pool_features): + super(FIDInceptionA, self).__init__(in_channels, pool_features) + + def forward(self, x): + branch1x1 = self.branch1x1(x) + + branch5x5 = self.branch5x5_1(x) + branch5x5 = self.branch5x5_2(branch5x5) + + branch3x3dbl = self.branch3x3dbl_1(x) + branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) + branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl) + + # Patch: Tensorflow's average pool does not use the padded zero's in + # its average calculation + branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, + count_include_pad=False) + branch_pool = self.branch_pool(branch_pool) + + outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool] + return torch.cat(outputs, 1) + + +class FIDInceptionC(models.inception.InceptionC): + """InceptionC block patched for FID computation""" + def __init__(self, in_channels, channels_7x7): + super(FIDInceptionC, self).__init__(in_channels, channels_7x7) + + def forward(self, x): + branch1x1 = self.branch1x1(x) + + branch7x7 = self.branch7x7_1(x) + branch7x7 = self.branch7x7_2(branch7x7) + branch7x7 = self.branch7x7_3(branch7x7) + + branch7x7dbl = self.branch7x7dbl_1(x) + branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl) + branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl) + branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl) + branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl) + + # Patch: Tensorflow's average pool does not use the padded zero's in + # its average calculation + branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, + count_include_pad=False) + branch_pool = self.branch_pool(branch_pool) + + outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool] + return torch.cat(outputs, 1) + + +class FIDInceptionE_1(models.inception.InceptionE): + """First InceptionE block patched for FID computation""" + def __init__(self, in_channels): + super(FIDInceptionE_1, self).__init__(in_channels) + + def forward(self, x): + branch1x1 = self.branch1x1(x) + + branch3x3 = self.branch3x3_1(x) + branch3x3 = [ + self.branch3x3_2a(branch3x3), + self.branch3x3_2b(branch3x3), + ] + branch3x3 = torch.cat(branch3x3, 1) + + branch3x3dbl = self.branch3x3dbl_1(x) + branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) + branch3x3dbl = [ + self.branch3x3dbl_3a(branch3x3dbl), + self.branch3x3dbl_3b(branch3x3dbl), + ] + branch3x3dbl = torch.cat(branch3x3dbl, 1) + + # Patch: Tensorflow's average pool does not use the padded zero's in + # its average calculation + branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, + count_include_pad=False) + branch_pool = self.branch_pool(branch_pool) + + outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool] + return torch.cat(outputs, 1) + + +class FIDInceptionE_2(models.inception.InceptionE): + """Second InceptionE block patched for FID computation""" + def __init__(self, in_channels): + super(FIDInceptionE_2, self).__init__(in_channels) + + def forward(self, x): + branch1x1 = self.branch1x1(x) + + branch3x3 = self.branch3x3_1(x) + branch3x3 = [ + self.branch3x3_2a(branch3x3), + self.branch3x3_2b(branch3x3), + ] + branch3x3 = torch.cat(branch3x3, 1) + + branch3x3dbl = self.branch3x3dbl_1(x) + branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) + branch3x3dbl = [ + self.branch3x3dbl_3a(branch3x3dbl), + self.branch3x3dbl_3b(branch3x3dbl), + ] + branch3x3dbl = torch.cat(branch3x3dbl, 1) + + # Patch: The FID Inception model uses max pooling instead of average + # pooling. This is likely an error in this specific Inception + # implementation, as other Inception models use average pooling here + # (which matches the description in the paper). + branch_pool = F.max_pool2d(x, kernel_size=3, stride=1, padding=1) + branch_pool = self.branch_pool(branch_pool) + + outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool] + return torch.cat(outputs, 1) diff --git a/libs/inception_score.py b/libs/inception_score.py new file mode 100644 index 0000000..8c306f3 --- /dev/null +++ b/libs/inception_score.py @@ -0,0 +1,71 @@ +import numpy as np +from PIL import Image +from tqdm import tqdm +from scipy.stats import entropy + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.utils import data +from torchvision import transforms +from torchvision.models.inception import inception_v3 + + +def inception_score(imgs, device, batch_size=32, resize=False, splits=1, transform=None): + """Computes the inception score of the generated images imgs + imgs -- Torch dataset of (3xHxW) numpy images normalized in the range [-1, 1] + device -- whether or not to run on GPU + batch_size -- batch size for feeding into Inception v3 + splits -- number of splits + """ + + # Load inception model + inception_model = inception_v3(pretrained=True, transform_input=False).to(device) + inception_model.eval() + upsample = nn.Upsample(size=(299, 299), mode='bilinear').to(device) + + def get_pred(x): + if resize: + x = upsample(x) + x = inception_model(x) + # return F.softmax(x).data.cpu().numpy() + return F.softmax(x, dim=-1).data.cpu().numpy() + + n_sample = len(imgs) + n_batch = n_sample // batch_size + resid = n_sample - (n_batch * batch_size) + batch_sizes = [batch_size] * n_batch + [resid] + + offset = 0 + preds = np.zeros((n_sample, 1000)) + for batch in tqdm(batch_sizes): + if batch == 0: + continue + batch_img = imgs[offset : offset+batch] + if type(batch_img[0]) == str: + + batch_img_tensor = [] + for img in batch_img: + # read them + img = Image.open(img).convert("RGB") + img = transform(img) + batch_img_tensor.append(img) + batch_img = torch.stack(batch_img_tensor, dim=0) + + batch_img = batch_img.to(device) + pred = get_pred(batch_img) + preds[offset : offset+batch] = pred + offset += batch + + # Now compute the mean kl-div + split_scores = [] + for k in range(splits): + part = preds[k * (n_sample // splits): (k+1) * (n_sample // splits), :] + py = np.mean(part, axis=0) + scores = [] + for i in range(part.shape[0]): + pyx = part[i, :] + scores.append(entropy(pyx, py)) + split_scores.append(np.exp(np.mean(scores))) + + return np.mean(split_scores), np.std(split_scores) \ No newline at end of file diff --git a/libs/lpips/__init__.py b/libs/lpips/__init__.py new file mode 100644 index 0000000..f29074b --- /dev/null +++ b/libs/lpips/__init__.py @@ -0,0 +1,40 @@ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +from skimage.measure import compare_ssim +import torch +from torch.autograd import Variable + +from . import dist_model + +class PerceptualLoss(torch.nn.Module): + def __init__(self, model='net-lin', net='alex', colorspace='rgb', spatial=False, use_gpu=True, gpu_ids=[0]): # VGG using our perceptually-learned weights (LPIPS metric) + # def __init__(self, model='net', net='vgg', use_gpu=True): # "default" way of using VGG as a perceptual loss + super(PerceptualLoss, self).__init__() + print('Setting up Perceptual loss...') + self.use_gpu = use_gpu + self.spatial = spatial + self.gpu_ids = gpu_ids + self.model = dist_model.DistModel() + self.model.initialize(model=model, net=net, use_gpu=use_gpu, colorspace=colorspace, spatial=self.spatial, gpu_ids=gpu_ids) + print('...[%s] initialized'%self.model.name()) + print('...Done') + + def forward(self, pred, target, normalize=False): + """ + Pred and target are Variables. + If normalize is True, assumes the images are between [0,1] and then scales them between [-1,+1] + If normalize is False, assumes the images are already between [-1,+1] + + Inputs pred and target are Nx3xHxW + Output pytorch Variable N long + """ + + if normalize: + target = 2 * target - 1 + pred = 2 * pred - 1 + + return self.model.forward(target, pred) diff --git a/libs/lpips/base_model.py b/libs/lpips/base_model.py new file mode 100644 index 0000000..cadb09f --- /dev/null +++ b/libs/lpips/base_model.py @@ -0,0 +1,58 @@ +import os +import numpy as np +import torch +from torch.autograd import Variable +from pdb import set_trace as st +#from IPython import embed + +class BaseModel(): + def __init__(self): + pass; + + def name(self): + return 'BaseModel' + + def initialize(self, use_gpu=True, gpu_ids=[0]): + self.use_gpu = use_gpu + self.gpu_ids = gpu_ids + + def forward(self): + pass + + def get_image_paths(self): + pass + + def optimize_parameters(self): + pass + + def get_current_visuals(self): + return self.input + + def get_current_errors(self): + return {} + + def save(self, label): + pass + + # helper saving function that can be used by subclasses + def save_network(self, network, path, network_label, epoch_label): + save_filename = '%s_net_%s.pth' % (epoch_label, network_label) + save_path = os.path.join(path, save_filename) + torch.save(network.state_dict(), save_path) + + # helper loading function that can be used by subclasses + def load_network(self, network, network_label, epoch_label): + save_filename = '%s_net_%s.pth' % (epoch_label, network_label) + save_path = os.path.join(self.save_dir, save_filename) + print('Loading network from %s'%save_path) + network.load_state_dict(torch.load(save_path)) + + def update_learning_rate(): + pass + + def get_image_paths(self): + return self.image_paths + + def save_done(self, flag=False): + np.save(os.path.join(self.save_dir, 'done_flag'),flag) + np.savetxt(os.path.join(self.save_dir, 'done_flag'),[flag,],fmt='%i') diff --git a/libs/lpips/dist_model.py b/libs/lpips/dist_model.py new file mode 100644 index 0000000..a29f91c --- /dev/null +++ b/libs/lpips/dist_model.py @@ -0,0 +1,284 @@ + +from __future__ import absolute_import + +import sys +import numpy as np +import torch +from torch import nn +import os +from collections import OrderedDict +from torch.autograd import Variable +import itertools +from .base_model import BaseModel +from scipy.ndimage import zoom +import fractions +import functools +import skimage.transform +from tqdm import tqdm + +#from IPython import embed + +from . import networks_basic as networks +from . import utils + +class DistModel(BaseModel): + def name(self): + return self.model_name + + def initialize(self, model='net-lin', net='alex', colorspace='Lab', pnet_rand=False, pnet_tune=False, model_path=None, + use_gpu=True, printNet=False, spatial=False, + is_train=False, lr=.0001, beta1=0.5, version='0.1', gpu_ids=[0]): + ''' + INPUTS + model - ['net-lin'] for linearly calibrated network + ['net'] for off-the-shelf network + ['L2'] for L2 distance in Lab colorspace + ['SSIM'] for ssim in RGB colorspace + net - ['squeeze','alex','vgg'] + model_path - if None, will look in weights/[NET_NAME].pth + colorspace - ['Lab','RGB'] colorspace to use for L2 and SSIM + use_gpu - bool - whether or not to use a GPU + printNet - bool - whether or not to print network architecture out + spatial - bool - whether to output an array containing varying distances across spatial dimensions + spatial_shape - if given, output spatial shape. if None then spatial shape is determined automatically via spatial_factor (see below). + spatial_factor - if given, specifies upsampling factor relative to the largest spatial extent of a convolutional layer. if None then resized to size of input images. + spatial_order - spline order of filter for upsampling in spatial mode, by default 1 (bilinear). + is_train - bool - [True] for training mode + lr - float - initial learning rate + beta1 - float - initial momentum term for adam + version - 0.1 for latest, 0.0 was original (with a bug) + gpu_ids - int array - [0] by default, gpus to use + ''' + BaseModel.initialize(self, use_gpu=use_gpu, gpu_ids=gpu_ids) + + self.model = model + self.net = net + self.is_train = is_train + self.spatial = spatial + self.gpu_ids = gpu_ids + self.model_name = '%s [%s]'%(model,net) + + if(self.model == 'net-lin'): # pretrained net + linear layer + self.net = networks.PNetLin(pnet_rand=pnet_rand, pnet_tune=pnet_tune, pnet_type=net, + use_dropout=True, spatial=spatial, version=version, lpips=True) + kw = {} + if not use_gpu: + kw['map_location'] = 'cpu' + if(model_path is None): + import inspect + model_path = os.path.abspath(os.path.join(inspect.getfile(self.initialize), '..', 'weights/v%s/%s.pth'%(version,net))) + + if(not is_train): + print('Loading model from: %s'%model_path) + self.net.load_state_dict(torch.load(model_path, **kw), strict=False) + + elif(self.model=='net'): # pretrained network + self.net = networks.PNetLin(pnet_rand=pnet_rand, pnet_type=net, lpips=False) + elif(self.model in ['L2','l2']): + self.net = networks.L2(use_gpu=use_gpu,colorspace=colorspace) # not really a network, only for testing + self.model_name = 'L2' + elif(self.model in ['DSSIM','dssim','SSIM','ssim']): + self.net = networks.DSSIM(use_gpu=use_gpu,colorspace=colorspace) + self.model_name = 'SSIM' + else: + raise ValueError("Model [%s] not recognized." % self.model) + + self.parameters = list(self.net.parameters()) + + if self.is_train: # training mode + # extra network on top to go from distances (d0,d1) => predicted human judgment (h*) + self.rankLoss = networks.BCERankingLoss() + self.parameters += list(self.rankLoss.net.parameters()) + self.lr = lr + self.old_lr = lr + self.optimizer_net = torch.optim.Adam(self.parameters, lr=lr, betas=(beta1, 0.999)) + else: # test mode + self.net.eval() + + if(use_gpu): + self.net.to(gpu_ids[0]) + self.net = torch.nn.DataParallel(self.net, device_ids=gpu_ids) + if(self.is_train): + self.rankLoss = self.rankLoss.to(device=gpu_ids[0]) # just put this on GPU0 + + if(printNet): + print('---------- Networks initialized -------------') + networks.print_network(self.net) + print('-----------------------------------------------') + + def forward(self, in0, in1, retPerLayer=False): + ''' Function computes the distance between image patches in0 and in1 + INPUTS + in0, in1 - torch.Tensor object of shape Nx3xXxY - image patch scaled to [-1,1] + OUTPUT + computed distances between in0 and in1 + ''' + + return self.net.forward(in0, in1, retPerLayer=retPerLayer) + + # ***** TRAINING FUNCTIONS ***** + def optimize_parameters(self): + self.forward_train() + self.optimizer_net.zero_grad() + self.backward_train() + self.optimizer_net.step() + self.clamp_weights() + + def clamp_weights(self): + for module in self.net.modules(): + if(hasattr(module, 'weight') and module.kernel_size==(1,1)): + module.weight.data = torch.clamp(module.weight.data,min=0) + + def set_input(self, data): + self.input_ref = data['ref'] + self.input_p0 = data['p0'] + self.input_p1 = data['p1'] + self.input_judge = data['judge'] + + if(self.use_gpu): + self.input_ref = self.input_ref.to(device=self.gpu_ids[0]) + self.input_p0 = self.input_p0.to(device=self.gpu_ids[0]) + self.input_p1 = self.input_p1.to(device=self.gpu_ids[0]) + self.input_judge = self.input_judge.to(device=self.gpu_ids[0]) + + self.var_ref = Variable(self.input_ref,requires_grad=True) + self.var_p0 = Variable(self.input_p0,requires_grad=True) + self.var_p1 = Variable(self.input_p1,requires_grad=True) + + def forward_train(self): # run forward pass + # print(self.net.module.scaling_layer.shift) + # print(torch.norm(self.net.module.net.slice1[0].weight).item(), torch.norm(self.net.module.lin0.model[1].weight).item()) + + self.d0 = self.forward(self.var_ref, self.var_p0) + self.d1 = self.forward(self.var_ref, self.var_p1) + self.acc_r = self.compute_accuracy(self.d0,self.d1,self.input_judge) + + self.var_judge = Variable(1.*self.input_judge).view(self.d0.size()) + + self.loss_total = self.rankLoss.forward(self.d0, self.d1, self.var_judge*2.-1.) + + return self.loss_total + + def backward_train(self): + torch.mean(self.loss_total).backward() + + def compute_accuracy(self,d0,d1,judge): + ''' d0, d1 are Variables, judge is a Tensor ''' + d1_lt_d0 = (d1 %f' % (type,self.old_lr, lr)) + self.old_lr = lr + +def score_2afc_dataset(data_loader, func, name=''): + ''' Function computes Two Alternative Forced Choice (2AFC) score using + distance function 'func' in dataset 'data_loader' + INPUTS + data_loader - CustomDatasetDataLoader object - contains a TwoAFCDataset inside + func - callable distance function - calling d=func(in0,in1) should take 2 + pytorch tensors with shape Nx3xXxY, and return numpy array of length N + OUTPUTS + [0] - 2AFC score in [0,1], fraction of time func agrees with human evaluators + [1] - dictionary with following elements + d0s,d1s - N arrays containing distances between reference patch to perturbed patches + gts - N array in [0,1], preferred patch selected by human evaluators + (closer to "0" for left patch p0, "1" for right patch p1, + "0.6" means 60pct people preferred right patch, 40pct preferred left) + scores - N array in [0,1], corresponding to what percentage function agreed with humans + CONSTS + N - number of test triplets in data_loader + ''' + + d0s = [] + d1s = [] + gts = [] + + for data in tqdm(data_loader.load_data(), desc=name): + d0s+=func(data['ref'],data['p0']).data.cpu().numpy().flatten().tolist() + d1s+=func(data['ref'],data['p1']).data.cpu().numpy().flatten().tolist() + gts+=data['judge'].cpu().numpy().flatten().tolist() + + d0s = np.array(d0s) + d1s = np.array(d1s) + gts = np.array(gts) + scores = (d0s= t) == 0: + p = 0 + else: + p = np.max(prec[rec >= t]) + ap = ap + p / 11. + else: + # correct AP calculation + # first append sentinel values at the end + mrec = np.concatenate(([0.], rec, [1.])) + mpre = np.concatenate(([0.], prec, [0.])) + + # compute the precision envelope + for i in range(mpre.size - 1, 0, -1): + mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) + + # to calculate area under PR curve, look for points + # where X axis (recall) changes value + i = np.where(mrec[1:] != mrec[:-1])[0] + + # and sum (\Delta recall) * prec + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) + return ap + +def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255./2.): +# def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=1.): + image_numpy = image_tensor[0].cpu().float().numpy() + image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor + return image_numpy.astype(imtype) + +def im2tensor(image, imtype=np.uint8, cent=1., factor=255./2.): +# def im2tensor(image, imtype=np.uint8, cent=1., factor=1.): + return torch.Tensor((image / factor - cent) + [:, :, :, np.newaxis].transpose((3, 2, 0, 1))) diff --git a/libs/lpips/weights/v0.0/alex.pth b/libs/lpips/weights/v0.0/alex.pth new file mode 100644 index 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+channels: + - pytorch + - defaults +dependencies: + - _libgcc_mutex=0.1=main + - astroid=2.4.2=py38_0 + - blas=1.0=mkl + - blosc=1.20.1=hd408876_0 + - brotli=1.0.9=he6710b0_2 + - brunsli=0.1=h2531618_0 + - bzip2=1.0.8=h7b6447c_0 + - ca-certificates=2021.1.19=h06a4308_0 + - certifi=2020.12.5=py38h06a4308_0 + - charls=2.1.0=he6710b0_2 + - cloudpickle=1.6.0=py_0 + - cudatoolkit=10.1.243=h6bb024c_0 + - cycler=0.10.0=py38_0 + - cytoolz=0.11.0=py38h7b6447c_0 + - dask-core=2021.1.1=pyhd3eb1b0_0 + - dbus=1.13.18=hb2f20db_0 + - decorator=4.4.2=pyhd3eb1b0_0 + - expat=2.2.10=he6710b0_2 + - fontconfig=2.13.1=h6c09931_0 + - freetype=2.10.4=h5ab3b9f_0 + - giflib=5.1.4=h14c3975_1 + - glib=2.63.1=h5a9c865_0 + - gst-plugins-base=1.14.0=hbbd80ab_1 + - gstreamer=1.14.0=hb453b48_1 + - icu=58.2=he6710b0_3 + - imagecodecs=2021.1.11=py38h581e88b_1 + - imageio=2.9.0=py_0 + - intel-openmp=2020.2=254 + - isort=5.7.0=pyhd3eb1b0_0 + - joblib=1.0.0=pyhd3eb1b0_0 + - jpeg=9b=h024ee3a_2 + - jxrlib=1.1=h7b6447c_2 + - kiwisolver=1.3.1=py38h2531618_0 + - lazy-object-proxy=1.4.3=py38h27cfd23_2 + - lcms2=2.11=h396b838_0 + - lerc=2.2.1=h2531618_0 + - libaec=1.0.4=he6710b0_1 + - libdeflate=1.7=h27cfd23_5 + - libedit=3.1.20191231=h14c3975_1 + - libffi=3.2.1=hf484d3e_1007 + - libgcc-ng=9.1.0=hdf63c60_0 + - libgfortran-ng=7.3.0=hdf63c60_0 + - libpng=1.6.37=hbc83047_0 + - libstdcxx-ng=9.1.0=hdf63c60_0 + - libtiff=4.1.0=h2733197_1 + - libuuid=1.0.3=h1bed415_2 + - libwebp=1.0.1=h8e7db2f_0 + - libxcb=1.14=h7b6447c_0 + - libxml2=2.9.10=hb55368b_3 + - libzopfli=1.0.3=he6710b0_0 + - lz4-c=1.9.3=h2531618_0 + - matplotlib=3.3.2=h06a4308_0 + - matplotlib-base=3.3.2=py38h817c723_0 + - mccabe=0.6.1=py38_1 + - mkl=2020.2=256 + - mkl-service=2.3.0=py38he904b0f_0 + - mkl_fft=1.2.0=py38h23d657b_0 + - mkl_random=1.1.1=py38h0573a6f_0 + - mypy=0.800=pyhd3eb1b0_0 + - mypy_extensions=0.4.3=py38_0 + - ncurses=6.2=he6710b0_1 + - networkx=2.5=py_0 + - ninja=1.10.2=py38hff7bd54_0 + - numpy=1.19.2=py38h54aff64_0 + - numpy-base=1.19.2=py38hfa32c7d_0 + - olefile=0.46=py_0 + - openjpeg=2.3.0=h05c96fa_1 + - openssl=1.1.1i=h27cfd23_0 + - pandas=1.2.1=py38ha9443f7_0 + - pcre=8.44=he6710b0_0 + - pexpect=4.8.0=pyhd3eb1b0_3 + - pillow=7.2.0=py38hb39fc2d_0 + - pip=20.3.3=py38h06a4308_0 + - psutil=5.8.0=py38h27cfd23_1 + - ptyprocess=0.7.0=pyhd3eb1b0_2 + - pylint=2.6.0=py38_0 + - pyparsing=2.4.7=pyhd3eb1b0_0 + - pyqt=5.9.2=py38h05f1152_4 + - python=3.8.0=h0371630_2 + - python-dateutil=2.8.1=pyhd3eb1b0_0 + - python-lmdb=0.98=py38he6710b0_0 + - pytorch=1.6.0=py3.8_cuda10.1.243_cudnn7.6.3_0 + - pytz=2021.1=pyhd3eb1b0_0 + - pywavelets=1.1.1=py38h7b6447c_2 + - qt=5.9.7=h5867ecd_1 + - readline=7.0=h7b6447c_5 + - scikit-image=0.17.2=py38hdf5156a_0 + - scikit-learn=0.23.2=py38h0573a6f_0 + - scipy=1.5.0=py38h0b6359f_0 + - setuptools=52.0.0=py38h06a4308_0 + - sip=4.19.13=py38he6710b0_0 + - six=1.15.0=py38h06a4308_0 + - snappy=1.1.8=he6710b0_0 + - sqlite=3.33.0=h62c20be_0 + - threadpoolctl=2.1.0=pyh5ca1d4c_0 + - tifffile=2021.1.14=pyhd3eb1b0_1 + - tk=8.6.10=hbc83047_0 + - toml=0.10.1=py_0 + - toolz=0.11.1=pyhd3eb1b0_0 + - torchvision=0.7.0=py38_cu101 + - tornado=6.1=py38h27cfd23_0 + - tqdm=4.56.0=pyhd3eb1b0_0 + - typed-ast=1.4.2=py38h27cfd23_1 + - typing_extensions=3.7.4.3=pyha847dfd_0 + - wheel=0.36.2=pyhd3eb1b0_0 + - wrapt=1.11.2=py38h7b6447c_0 + - xz=5.2.5=h7b6447c_0 + - yaml=0.2.5=h7b6447c_0 + - zfp=0.5.5=h2531618_4 + - zlib=1.2.11=h7b6447c_3 + - zstd=1.4.5=h9ceee32_0 + - pip: + - absl-py==0.11.0 + - ase==3.21.1 + - bcrypt==3.2.0 + - cachetools==4.2.1 + - cffi==1.14.4 + - chardet==4.0.0 + - cryptography==3.3.1 + - easydict==1.9 + - future==0.18.2 + - google-auth==1.25.0 + - google-auth-oauthlib==0.4.2 + - googledrivedownloader==0.4 + - grpcio==1.35.0 + - h5py==3.1.0 + - idna==2.10 + - isodate==0.6.0 + - jinja2==2.11.3 + - llvmlite==0.35.0 + - markdown==3.3.3 + - markupsafe==1.1.1 + - numba==0.52.0 + - oauthlib==3.1.0 + - paramiko==2.7.2 + - protobuf==3.14.0 + - pyasn1==0.4.8 + - pyasn1-modules==0.2.8 + - pycparser==2.20 + - pynacl==1.4.0 + - python-louvain==0.15 + - pyyaml==5.4.1 + - rdflib==5.0.0 + - requests==2.25.1 + - requests-oauthlib==1.3.0 + - rsa==4.7 + - tensorboard==2.4.1 + - tensorboard-plugin-wit==1.8.0 + - tensorboardx==2.1 + - torch-cluster==1.5.8 + - torch-scatter==2.0.5 + - torch-sparse==0.6.8 + - torch-spline-conv==1.2.0 + - urllib3==1.26.3 + - werkzeug==1.0.1 diff --git a/models/custom_ops/__init__.py b/models/custom_ops/__init__.py new file mode 100644 index 0000000..d0918d9 --- /dev/null +++ b/models/custom_ops/__init__.py @@ -0,0 +1,2 @@ +from .fused_act import FusedLeakyReLU, fused_leaky_relu +from .upfirdn2d import upfirdn2d diff --git a/models/custom_ops/fused_act.py b/models/custom_ops/fused_act.py new file mode 100644 index 0000000..4b70736 --- /dev/null +++ b/models/custom_ops/fused_act.py @@ -0,0 +1,101 @@ +import os +import socket + +import torch +from torch import nn +from torch.nn import functional as F +from torch.autograd import Function +from torch.utils.cpp_extension import load + + +module_path = os.path.dirname(__file__) +gpu_name = "".join(torch.cuda.get_device_name().split(" ")) +build_dir = os.path.join(module_path, ".build_cache_{}_PT{}_cu{}_gpu{}".format(socket.gethostname(), torch.__version__, torch.version.cuda, gpu_name)) +if not os.path.exists(build_dir): os.makedirs(build_dir) +fused = load( + "fused", + sources=[ + os.path.join(module_path, "fused_bias_act.cpp"), + os.path.join(module_path, "fused_bias_act_kernel.cu"), + ], + build_directory=build_dir, +) + +class FusedLeakyReLUFunctionBackward(Function): + @staticmethod + def forward(ctx, grad_output, out, negative_slope, scale): + ctx.save_for_backward(out) + ctx.negative_slope = negative_slope + ctx.scale = scale + + empty = grad_output.new_empty(0) + + grad_input = fused.fused_bias_act( + grad_output, empty, out, 3, 1, negative_slope, scale + ) + + dim = [0] + + if grad_input.ndim > 2: + dim += list(range(2, grad_input.ndim)) + + grad_bias = grad_input.sum(dim).detach() + + return grad_input, grad_bias + + @staticmethod + def backward(ctx, gradgrad_input, gradgrad_bias): + out, = ctx.saved_tensors + gradgrad_out = fused.fused_bias_act( + gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale + ) + + return gradgrad_out, None, None, None + + +class FusedLeakyReLUFunction(Function): + @staticmethod + def forward(ctx, input, bias, negative_slope, scale): + empty = input.new_empty(0) + out = fused.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale) + ctx.save_for_backward(out) + ctx.negative_slope = negative_slope + ctx.scale = scale + + return out + + @staticmethod + def backward(ctx, grad_output): + out, = ctx.saved_tensors + + grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply( + grad_output, out, ctx.negative_slope, ctx.scale + ) + + return grad_input, grad_bias, None, None + + +class FusedLeakyReLU(nn.Module): + def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5): + super().__init__() + + self.bias = nn.Parameter(torch.zeros(channel)) + self.negative_slope = negative_slope + self.scale = scale + + def forward(self, input): + return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale) + + +def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5): + if input.device.type == "cpu": + rest_dim = [1] * (input.ndim - bias.ndim - 1) + return ( + F.leaky_relu( + input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=0.2 + ) + * scale + ) + + else: + return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale) diff --git a/models/custom_ops/fused_bias_act.cpp b/models/custom_ops/fused_bias_act.cpp new file mode 100644 index 0000000..02be898 --- /dev/null +++ b/models/custom_ops/fused_bias_act.cpp @@ -0,0 +1,21 @@ +#include + + +torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer, + int act, int grad, float alpha, float scale); + +#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") +#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) + +torch::Tensor fused_bias_act(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer, + int act, int grad, float alpha, float scale) { + CHECK_CUDA(input); + CHECK_CUDA(bias); + + return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)"); +} \ No newline at end of file diff --git a/models/custom_ops/fused_bias_act_kernel.cu b/models/custom_ops/fused_bias_act_kernel.cu new file mode 100644 index 0000000..c9fa56f --- /dev/null +++ b/models/custom_ops/fused_bias_act_kernel.cu @@ -0,0 +1,99 @@ +// Copyright (c) 2019, NVIDIA Corporation. All rights reserved. +// +// This work is made available under the Nvidia Source Code License-NC. +// To view a copy of this license, visit +// https://nvlabs.github.io/stylegan2/license.html + +#include + +#include +#include +#include +#include + +#include +#include + + +template +static __global__ void fused_bias_act_kernel(scalar_t* out, const scalar_t* p_x, const scalar_t* p_b, const scalar_t* p_ref, + int act, int grad, scalar_t alpha, scalar_t scale, int loop_x, int size_x, int step_b, int size_b, int use_bias, int use_ref) { + int xi = blockIdx.x * loop_x * blockDim.x + threadIdx.x; + + scalar_t zero = 0.0; + + for (int loop_idx = 0; loop_idx < loop_x && xi < size_x; loop_idx++, xi += blockDim.x) { + scalar_t x = p_x[xi]; + + if (use_bias) { + x += p_b[(xi / step_b) % size_b]; + } + + scalar_t ref = use_ref ? p_ref[xi] : zero; + + scalar_t y; + + switch (act * 10 + grad) { + default: + case 10: y = x; break; + case 11: y = x; break; + case 12: y = 0.0; break; + + case 30: y = (x > 0.0) ? x : x * alpha; break; + case 31: y = (ref > 0.0) ? x : x * alpha; break; + case 32: y = 0.0; break; + } + + out[xi] = y * scale; + } +} + + +torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer, + int act, int grad, float alpha, float scale) { + int curDevice = -1; + cudaGetDevice(&curDevice); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice); + + auto x = input.contiguous(); + auto b = bias.contiguous(); + auto ref = refer.contiguous(); + + int use_bias = b.numel() ? 1 : 0; + int use_ref = ref.numel() ? 1 : 0; + + int size_x = x.numel(); + int size_b = b.numel(); + int step_b = 1; + + for (int i = 1 + 1; i < x.dim(); i++) { + step_b *= x.size(i); + } + + int loop_x = 4; + int block_size = 4 * 32; + int grid_size = (size_x - 1) / (loop_x * block_size) + 1; + + auto y = torch::empty_like(x); + + AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "fused_bias_act_kernel", [&] { + fused_bias_act_kernel<<>>( + y.data_ptr(), + x.data_ptr(), + b.data_ptr(), + ref.data_ptr(), + act, + grad, + alpha, + scale, + loop_x, + size_x, + step_b, + size_b, + use_bias, + use_ref + ); + }); + + return y; +} \ No newline at end of file diff --git a/models/custom_ops/upfirdn2d.cpp b/models/custom_ops/upfirdn2d.cpp new file mode 100644 index 0000000..d2e633d --- /dev/null +++ b/models/custom_ops/upfirdn2d.cpp @@ -0,0 +1,23 @@ +#include + + +torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel, + int up_x, int up_y, int down_x, int down_y, + int pad_x0, int pad_x1, int pad_y0, int pad_y1); + +#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") +#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) + +torch::Tensor upfirdn2d(const torch::Tensor& input, const torch::Tensor& kernel, + int up_x, int up_y, int down_x, int down_y, + int pad_x0, int pad_x1, int pad_y0, int pad_y1) { + CHECK_CUDA(input); + CHECK_CUDA(kernel); + + return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)"); +} \ No newline at end of file diff --git a/models/custom_ops/upfirdn2d.py b/models/custom_ops/upfirdn2d.py new file mode 100644 index 0000000..5825fdc --- /dev/null +++ b/models/custom_ops/upfirdn2d.py @@ -0,0 +1,205 @@ +import os +import socket + +import torch +from torch.nn import functional as F +from torch.autograd import Function +from torch.utils.cpp_extension import load + + +module_path = os.path.dirname(__file__) +gpu_name = "".join(torch.cuda.get_device_name().split(" ")) +build_dir = os.path.join(module_path, ".build_cache_{}_PT{}_cu{}_gpu{}".format(socket.gethostname(), torch.__version__, torch.version.cuda, gpu_name)) +if not os.path.exists(build_dir): os.makedirs(build_dir) +upfirdn2d_op = load( + "upfirdn2d", + sources=[ + os.path.join(module_path, "upfirdn2d.cpp"), + os.path.join(module_path, "upfirdn2d_kernel.cu"), + ], + build_directory=build_dir, +) + + +class UpFirDn2dBackward(Function): + @staticmethod + def forward( + ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size + ): + + up_x, up_y = up + down_x, down_y = down + g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad + + grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1) + + grad_input = upfirdn2d_op.upfirdn2d( + grad_output, + grad_kernel, + down_x, + down_y, + up_x, + up_y, + g_pad_x0, + g_pad_x1, + g_pad_y0, + g_pad_y1, + ) + grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3]) + + ctx.save_for_backward(kernel) + + pad_x0, pad_x1, pad_y0, pad_y1 = pad + + ctx.up_x = up_x + ctx.up_y = up_y + ctx.down_x = down_x + ctx.down_y = down_y + ctx.pad_x0 = pad_x0 + ctx.pad_x1 = pad_x1 + ctx.pad_y0 = pad_y0 + ctx.pad_y1 = pad_y1 + ctx.in_size = in_size + ctx.out_size = out_size + + return grad_input + + @staticmethod + def backward(ctx, gradgrad_input): + kernel, = ctx.saved_tensors + + gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1) + + gradgrad_out = upfirdn2d_op.upfirdn2d( + gradgrad_input, + kernel, + ctx.up_x, + ctx.up_y, + ctx.down_x, + ctx.down_y, + ctx.pad_x0, + ctx.pad_x1, + ctx.pad_y0, + ctx.pad_y1, + ) + # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], ctx.out_size[1], ctx.in_size[3]) + gradgrad_out = gradgrad_out.view( + ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1] + ) + + return gradgrad_out, None, None, None, None, None, None, None, None + + +class UpFirDn2d(Function): + @staticmethod + def forward(ctx, input, kernel, up, down, pad): + up_x, up_y = up + down_x, down_y = down + pad_x0, pad_x1, pad_y0, pad_y1 = pad + + kernel_h, kernel_w = kernel.shape + batch, channel, in_h, in_w = input.shape + ctx.in_size = input.shape + + input = input.reshape(-1, in_h, in_w, 1) + + ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1])) + + out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 + out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 + ctx.out_size = (out_h, out_w) + + ctx.up = (up_x, up_y) + ctx.down = (down_x, down_y) + ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1) + + g_pad_x0 = kernel_w - pad_x0 - 1 + g_pad_y0 = kernel_h - pad_y0 - 1 + g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1 + g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1 + + ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1) + + out = upfirdn2d_op.upfirdn2d( + input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 + ) + # out = out.view(major, out_h, out_w, minor) + out = out.view(-1, channel, out_h, out_w) + + return out + + @staticmethod + def backward(ctx, grad_output): + kernel, grad_kernel = ctx.saved_tensors + + grad_input = UpFirDn2dBackward.apply( + grad_output, + kernel, + grad_kernel, + ctx.up, + ctx.down, + ctx.pad, + ctx.g_pad, + ctx.in_size, + ctx.out_size, + ) + + return grad_input, None, None, None, None + + +def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): + if input.device.type == "cpu": + out = upfirdn2d_native( + input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1] + ) + + else: + out = UpFirDn2d.apply( + input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1]) + ) + + return out + + +def upfirdn2d_native( + input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 +): + _, channel, in_h, in_w = input.shape + input = input.reshape(-1, in_h, in_w, 1) + + _, in_h, in_w, minor = input.shape + kernel_h, kernel_w = kernel.shape + + out = input.view(-1, in_h, 1, in_w, 1, minor) + out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1]) + out = out.view(-1, in_h * up_y, in_w * up_x, minor) + + out = F.pad( + out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)] + ) + out = out[ + :, + max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0), + max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0), + :, + ] + + out = out.permute(0, 3, 1, 2) + out = out.reshape( + [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1] + ) + w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) + out = F.conv2d(out, w) + out = out.reshape( + -1, + minor, + in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, + in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, + ) + out = out.permute(0, 2, 3, 1) + out = out[:, ::down_y, ::down_x, :] + + out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 + out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 + + return out.view(-1, channel, out_h, out_w) diff --git a/models/custom_ops/upfirdn2d_kernel.cu b/models/custom_ops/upfirdn2d_kernel.cu new file mode 100644 index 0000000..a88bc77 --- /dev/null +++ b/models/custom_ops/upfirdn2d_kernel.cu @@ -0,0 +1,369 @@ +// Copyright (c) 2019, NVIDIA Corporation. All rights reserved. +// +// This work is made available under the Nvidia Source Code License-NC. +// To view a copy of this license, visit +// https://nvlabs.github.io/stylegan2/license.html + +#include + +#include +#include +#include +#include + +#include +#include + +static __host__ __device__ __forceinline__ int floor_div(int a, int b) { + int c = a / b; + + if (c * b > a) { + c--; + } + + return c; +} + +struct UpFirDn2DKernelParams { + int up_x; + int up_y; + int down_x; + int down_y; + int pad_x0; + int pad_x1; + int pad_y0; + int pad_y1; + + int major_dim; + int in_h; + int in_w; + int minor_dim; + int kernel_h; + int kernel_w; + int out_h; + int out_w; + int loop_major; + int loop_x; +}; + +template +__global__ void upfirdn2d_kernel_large(scalar_t *out, const scalar_t *input, + const scalar_t *kernel, + const UpFirDn2DKernelParams p) { + int minor_idx = blockIdx.x * blockDim.x + threadIdx.x; + int out_y = minor_idx / p.minor_dim; + minor_idx -= out_y * p.minor_dim; + int out_x_base = blockIdx.y * p.loop_x * blockDim.y + threadIdx.y; + int major_idx_base = blockIdx.z * p.loop_major; + + if (out_x_base >= p.out_w || out_y >= p.out_h || + major_idx_base >= p.major_dim) { + return; + } + + int mid_y = out_y * p.down_y + p.up_y - 1 - p.pad_y0; + int in_y = min(max(floor_div(mid_y, p.up_y), 0), p.in_h); + int h = min(max(floor_div(mid_y + p.kernel_h, p.up_y), 0), p.in_h) - in_y; + int kernel_y = mid_y + p.kernel_h - (in_y + 1) * p.up_y; + + for (int loop_major = 0, major_idx = major_idx_base; + loop_major < p.loop_major && major_idx < p.major_dim; + loop_major++, major_idx++) { + for (int loop_x = 0, out_x = out_x_base; + loop_x < p.loop_x && out_x < p.out_w; loop_x++, out_x += blockDim.y) { + int mid_x = out_x * p.down_x + p.up_x - 1 - p.pad_x0; + int in_x = min(max(floor_div(mid_x, p.up_x), 0), p.in_w); + int w = min(max(floor_div(mid_x + p.kernel_w, p.up_x), 0), p.in_w) - in_x; + int kernel_x = mid_x + p.kernel_w - (in_x + 1) * p.up_x; + + const scalar_t *x_p = + &input[((major_idx * p.in_h + in_y) * p.in_w + in_x) * p.minor_dim + + minor_idx]; + const scalar_t *k_p = &kernel[kernel_y * p.kernel_w + kernel_x]; + int x_px = p.minor_dim; + int k_px = -p.up_x; + int x_py = p.in_w * p.minor_dim; + int k_py = -p.up_y * p.kernel_w; + + scalar_t v = 0.0f; + + for (int y = 0; y < h; y++) { + for (int x = 0; x < w; x++) { + v += static_cast(*x_p) * static_cast(*k_p); + x_p += x_px; + k_p += k_px; + } + + x_p += x_py - w * x_px; + k_p += k_py - w * k_px; + } + + out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim + + minor_idx] = v; + } + } +} + +template +__global__ void upfirdn2d_kernel(scalar_t *out, const scalar_t *input, + const scalar_t *kernel, + const UpFirDn2DKernelParams p) { + const int tile_in_h = ((tile_out_h - 1) * down_y + kernel_h - 1) / up_y + 1; + const int tile_in_w = ((tile_out_w - 1) * down_x + kernel_w - 1) / up_x + 1; + + __shared__ volatile float sk[kernel_h][kernel_w]; + __shared__ volatile float sx[tile_in_h][tile_in_w]; + + int minor_idx = blockIdx.x; + int tile_out_y = minor_idx / p.minor_dim; + minor_idx -= tile_out_y * p.minor_dim; + tile_out_y *= tile_out_h; + int tile_out_x_base = blockIdx.y * p.loop_x * tile_out_w; + int major_idx_base = blockIdx.z * p.loop_major; + + if (tile_out_x_base >= p.out_w | tile_out_y >= p.out_h | + major_idx_base >= p.major_dim) { + return; + } + + for (int tap_idx = threadIdx.x; tap_idx < kernel_h * kernel_w; + tap_idx += blockDim.x) { + int ky = tap_idx / kernel_w; + int kx = tap_idx - ky * kernel_w; + scalar_t v = 0.0; + + if (kx < p.kernel_w & ky < p.kernel_h) { + v = kernel[(p.kernel_h - 1 - ky) * p.kernel_w + (p.kernel_w - 1 - kx)]; + } + + sk[ky][kx] = v; + } + + for (int loop_major = 0, major_idx = major_idx_base; + loop_major < p.loop_major & major_idx < p.major_dim; + loop_major++, major_idx++) { + for (int loop_x = 0, tile_out_x = tile_out_x_base; + loop_x < p.loop_x & tile_out_x < p.out_w; + loop_x++, tile_out_x += tile_out_w) { + int tile_mid_x = tile_out_x * down_x + up_x - 1 - p.pad_x0; + int tile_mid_y = tile_out_y * down_y + up_y - 1 - p.pad_y0; + int tile_in_x = floor_div(tile_mid_x, up_x); + int tile_in_y = floor_div(tile_mid_y, up_y); + + __syncthreads(); + + for (int in_idx = threadIdx.x; in_idx < tile_in_h * tile_in_w; + in_idx += blockDim.x) { + int rel_in_y = in_idx / tile_in_w; + int rel_in_x = in_idx - rel_in_y * tile_in_w; + int in_x = rel_in_x + tile_in_x; + int in_y = rel_in_y + tile_in_y; + + scalar_t v = 0.0; + + if (in_x >= 0 & in_y >= 0 & in_x < p.in_w & in_y < p.in_h) { + v = input[((major_idx * p.in_h + in_y) * p.in_w + in_x) * + p.minor_dim + + minor_idx]; + } + + sx[rel_in_y][rel_in_x] = v; + } + + __syncthreads(); + for (int out_idx = threadIdx.x; out_idx < tile_out_h * tile_out_w; + out_idx += blockDim.x) { + int rel_out_y = out_idx / tile_out_w; + int rel_out_x = out_idx - rel_out_y * tile_out_w; + int out_x = rel_out_x + tile_out_x; + int out_y = rel_out_y + tile_out_y; + + int mid_x = tile_mid_x + rel_out_x * down_x; + int mid_y = tile_mid_y + rel_out_y * down_y; + int in_x = floor_div(mid_x, up_x); + int in_y = floor_div(mid_y, up_y); + int rel_in_x = in_x - tile_in_x; + int rel_in_y = in_y - tile_in_y; + int kernel_x = (in_x + 1) * up_x - mid_x - 1; + int kernel_y = (in_y + 1) * up_y - mid_y - 1; + + scalar_t v = 0.0; + +#pragma unroll + for (int y = 0; y < kernel_h / up_y; y++) +#pragma unroll + for (int x = 0; x < kernel_w / up_x; x++) + v += sx[rel_in_y + y][rel_in_x + x] * + sk[kernel_y + y * up_y][kernel_x + x * up_x]; + + if (out_x < p.out_w & out_y < p.out_h) { + out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim + + minor_idx] = v; + } + } + } + } +} + +torch::Tensor upfirdn2d_op(const torch::Tensor &input, + const torch::Tensor &kernel, int up_x, int up_y, + int down_x, int down_y, int pad_x0, int pad_x1, + int pad_y0, int pad_y1) { + int curDevice = -1; + cudaGetDevice(&curDevice); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice); + + UpFirDn2DKernelParams p; + + auto x = input.contiguous(); + auto k = kernel.contiguous(); + + p.major_dim = x.size(0); + p.in_h = x.size(1); + p.in_w = x.size(2); + p.minor_dim = x.size(3); + p.kernel_h = k.size(0); + p.kernel_w = k.size(1); + p.up_x = up_x; + p.up_y = up_y; + p.down_x = down_x; + p.down_y = down_y; + p.pad_x0 = pad_x0; + p.pad_x1 = pad_x1; + p.pad_y0 = pad_y0; + p.pad_y1 = pad_y1; + + p.out_h = (p.in_h * p.up_y + p.pad_y0 + p.pad_y1 - p.kernel_h + p.down_y) / + p.down_y; + p.out_w = (p.in_w * p.up_x + p.pad_x0 + p.pad_x1 - p.kernel_w + p.down_x) / + p.down_x; + + auto out = + at::empty({p.major_dim, p.out_h, p.out_w, p.minor_dim}, x.options()); + + int mode = -1; + + int tile_out_h = -1; + int tile_out_w = -1; + + if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 && + p.kernel_h <= 4 && p.kernel_w <= 4) { + mode = 1; + tile_out_h = 16; + tile_out_w = 64; + } + + if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 && + p.kernel_h <= 3 && p.kernel_w <= 3) { + mode = 2; + tile_out_h = 16; + tile_out_w = 64; + } + + if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 && + p.kernel_h <= 4 && p.kernel_w <= 4) { + mode = 3; + tile_out_h = 16; + tile_out_w = 64; + } + + if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 && + p.kernel_h <= 2 && p.kernel_w <= 2) { + mode = 4; + tile_out_h = 16; + tile_out_w = 64; + } + + if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 && + p.kernel_h <= 4 && p.kernel_w <= 4) { + mode = 5; + tile_out_h = 8; + tile_out_w = 32; + } + + if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 && + p.kernel_h <= 2 && p.kernel_w <= 2) { + mode = 6; + tile_out_h = 8; + tile_out_w = 32; + } + + dim3 block_size; + dim3 grid_size; + + if (tile_out_h > 0 && tile_out_w > 0) { + p.loop_major = (p.major_dim - 1) / 16384 + 1; + p.loop_x = 1; + block_size = dim3(32 * 8, 1, 1); + grid_size = dim3(((p.out_h - 1) / tile_out_h + 1) * p.minor_dim, + (p.out_w - 1) / (p.loop_x * tile_out_w) + 1, + (p.major_dim - 1) / p.loop_major + 1); + } else { + p.loop_major = (p.major_dim - 1) / 16384 + 1; + p.loop_x = 4; + block_size = dim3(4, 32, 1); + grid_size = dim3((p.out_h * p.minor_dim - 1) / block_size.x + 1, + (p.out_w - 1) / (p.loop_x * block_size.y) + 1, + (p.major_dim - 1) / p.loop_major + 1); + } + + AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] { + switch (mode) { + case 1: + upfirdn2d_kernel + <<>>(out.data_ptr(), + x.data_ptr(), + k.data_ptr(), p); + + break; + + case 2: + upfirdn2d_kernel + <<>>(out.data_ptr(), + x.data_ptr(), + k.data_ptr(), p); + + break; + + case 3: + upfirdn2d_kernel + <<>>(out.data_ptr(), + x.data_ptr(), + k.data_ptr(), p); + + break; + + case 4: + upfirdn2d_kernel + <<>>(out.data_ptr(), + x.data_ptr(), + k.data_ptr(), p); + + break; + + case 5: + upfirdn2d_kernel + <<>>(out.data_ptr(), + x.data_ptr(), + k.data_ptr(), p); + + break; + + case 6: + upfirdn2d_kernel + <<>>(out.data_ptr(), + x.data_ptr(), + k.data_ptr(), p); + + break; + + default: + upfirdn2d_kernel_large<<>>( + out.data_ptr(), x.data_ptr(), + k.data_ptr(), p); + } + }); + + return out; +} \ No newline at end of file diff --git a/models/diff_augment_dual.py b/models/diff_augment_dual.py new file mode 100644 index 0000000..b833fef --- /dev/null +++ b/models/diff_augment_dual.py @@ -0,0 +1,94 @@ +# Differentiable Augmentation for Data-Efficient GAN Training +# Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han +# https://arxiv.org/pdf/2006.10738 + +import torch +import torch.nn.functional as F + + +def DiffAugmentDual(a, b, policy='', channels_first=True): + assert a.shape == b.shape, "Got {} != {}".format(a.shape, b.shape) + if policy: + if not channels_first: + a = a.permute(0, 3, 1, 2) + b = b.permute(0, 3, 1, 2) + for p in policy.split(','): + for f in AUGMENT_FNS[p]: + a, b = f(a, b) + if not channels_first: + a = a.permute(0, 2, 3, 1) + b = b.permute(0, 2, 3, 1) + a = a.contiguous() + b = b.contiguous() + return a, b + + +def rand_brightness(a, b): + aug = (torch.rand(a.size(0), 1, 1, 1, dtype=a.dtype, device=a.device) - 0.5) + a = a + aug + b = b + aug + return a, b + + +def rand_saturation(a, b): + a_mean = a.mean(dim=1, keepdim=True) + b_mean = b.mean(dim=1, keepdim=True) + aug = (torch.rand(a.size(0), 1, 1, 1, dtype=a.dtype, device=a.device) * 2) + a = (a - a_mean) * aug + a_mean + b = (b - b_mean) * aug + b_mean + return a, b + + +def rand_contrast(a, b): + a_mean = a.mean(dim=[1, 2, 3], keepdim=True) + b_mean = b.mean(dim=[1, 2, 3], keepdim=True) + aug = (torch.rand(a.size(0), 1, 1, 1, dtype=a.dtype, device=a.device) + 0.5) + a = (a - a_mean) * aug + a_mean + b = (b - b_mean) * aug + b_mean + return a, b + + +def rand_translation(a, b, ratio=0.125): + shift_x, shift_y = int(a.size(2) * ratio + 0.5), int(a.size(3) * ratio + 0.5) + translation_x = torch.randint(-shift_x, shift_x + 1, size=[a.size(0), 1, 1], device=a.device) + translation_y = torch.randint(-shift_y, shift_y + 1, size=[a.size(0), 1, 1], device=a.device) + grid_batch, grid_x, grid_y = torch.meshgrid( + torch.arange(a.size(0), dtype=torch.long, device=a.device), + torch.arange(a.size(2), dtype=torch.long, device=a.device), + torch.arange(a.size(3), dtype=torch.long, device=a.device), + ) + grid_x = torch.clamp(grid_x + translation_x + 1, 0, a.size(2) + 1) + grid_y = torch.clamp(grid_y + translation_y + 1, 0, a.size(3) + 1) + + a_pad = F.pad(a, [1, 1, 1, 1, 0, 0, 0, 0]) + b_pad = F.pad(b, [1, 1, 1, 1, 0, 0, 0, 0]) + a = a_pad.permute(0, 2, 3, 1).contiguous()[grid_batch, grid_x, grid_y].permute(0, 3, 1, 2) + b = b_pad.permute(0, 2, 3, 1).contiguous()[grid_batch, grid_x, grid_y].permute(0, 3, 1, 2) + # print('translation!!!') + return a, b + + +def rand_cutout(a, b, ratio=0.5): + cutout_size = int(a.size(2) * ratio + 0.5), int(a.size(3) * ratio + 0.5) + offset_x = torch.randint(0, a.size(2) + (1 - cutout_size[0] % 2), size=[a.size(0), 1, 1], device=a.device) + offset_y = torch.randint(0, a.size(3) + (1 - cutout_size[1] % 2), size=[a.size(0), 1, 1], device=a.device) + grid_batch, grid_x, grid_y = torch.meshgrid( + torch.arange(a.size(0), dtype=torch.long, device=a.device), + torch.arange(cutout_size[0], dtype=torch.long, device=a.device), + torch.arange(cutout_size[1], dtype=torch.long, device=a.device), + ) + grid_x = torch.clamp(grid_x + offset_x - cutout_size[0] // 2, min=0, max=a.size(2) - 1) + grid_y = torch.clamp(grid_y + offset_y - cutout_size[1] // 2, min=0, max=a.size(3) - 1) + mask = torch.ones(a.size(0), a.size(2), a.size(3), dtype=a.dtype, device=a.device) + mask[grid_batch, grid_x, grid_y] = 0 + a = a * mask.unsqueeze(1) + b = b * mask.unsqueeze(1) + # print('cutout!!!') + return a, b + + +AUGMENT_FNS = { + 'color': [rand_brightness, rand_saturation, rand_contrast], + 'translation': [rand_translation], + 'cutout': [rand_cutout], +} \ No newline at end of file diff --git a/models/infinitygan_generator.py b/models/infinitygan_generator.py new file mode 100644 index 0000000..68c6c3a --- /dev/null +++ b/models/infinitygan_generator.py @@ -0,0 +1,1165 @@ +import os +import random +import numpy as np +import pickle as pkl + +import torch +import torch.nn as nn +from torch.nn import functional as F +from torch.nn.utils import spectral_norm +from torch.autograd import Function, Variable + +from coord_handler import CoordHandler +from models.ops import * +from models.losses import calc_path_lengths +from dataset import DictTensor + + + +def sequential_get_flops(sequential_module, inputs): + flops = 0 + h = inputs + for layer in sequential_module: + flops += layer.get_flops(h) + h = sequential_module(h) + return h, flops + +def create_fusion_styles(cur_fusion_map, styles, config): + """ + (N: number of styles) + cur_fusion_map: shape (B, N, H, W) + styles : [shape (B, C)] * N + """ + device = styles[0].device + bs, _, cur_height, cur_width = cur_fusion_map.shape + fused_style = torch.zeros( + bs, + config.train_params.global_latent_dim, + cur_height, + cur_width) + fused_style = fused_style.to(device) + + for i in range(len(config.task.style_centers)): + fused_style += cur_fusion_map[:, i:i+1].to(device) * styles[i].unsqueeze(2).unsqueeze(3) + return fused_style + + +def setup_paired_inputs(inputs): + # Make a batch of samples, (e.g., [A, B, C, D]) into paired form (e.g., [A, A, C, C]) + # + # [Warning] I'm not sure if this resulting in inplace replacement. As a result, this + # function should only be used in input nodes. Do not apply it on intermediate data such + # as z_spatial. + inputs_clone = inputs.detach().clone() + + batch_size = inputs_clone.shape[0] + if batch_size%2 == 0: # Easy case + inputs_clone[1::2] = inputs_clone[0::2] + else: + inputs_clone[1::2] = inputs_clone[0:-1:2] + return inputs_clone + + +def flatten(v): + sp_dim = np.prod(v.shape[1:]) + return v.reshape(v.shape[0], sp_dim) if v.ndim > 2 else v + + +def angular_similarity(a, b): + a, b = flatten(a), flatten(b) + # Some how detaching this in high-res setup will make model explode + denom = (a.norm(2, dim=1) * b.norm(2, dim=1)) #.detach() + cosine_sim = (a * b).sum(1) / denom + return (1 - torch.acos(cosine_sim) / np.pi) + + +class ConditionalBlock(nn.Module): + def __init__(self, idx, config): + super().__init__() + self.config = config + self.ss_disable_noise = config.train_params.ss_disable_noise + + local_dim = config.train_params.local_latent_dim + global_dim = config.train_params.global_latent_dim + + in_channel = local_dim + if config.train_params.ss_coord_all_layers: + in_channel += self.config.train_params.coord_num_dir + elif idx==0: + in_channel += self.config.train_params.coord_num_dir + + kernel_size = config.train_params.ss_unfold_radius * 2 + 1 + self.conv = StyledConv( + in_channel=in_channel, + out_channel=local_dim, + kernel_size=kernel_size, + style_dim=global_dim, + no_zero_pad=True, + disable_noise=self.ss_disable_noise, + config=config, + side="ss") + + def calibrate_spatial_shape(self, feature, direction, padding_mode="replicate", verbose=False, pin_loc=None): + return self.conv.calibrate_spatial_shape(feature, direction, padding_mode=padding_mode, verbose=verbose, pin_loc=pin_loc) + + def forward(self, x, cond, coords, noise=None, test_ids=None, calc_flops=False): + if self.config.train_params.ss_coord_all_layers: + x = torch.cat([x, coords], 1) + else: + pass # Already assigned coord at the beginning + return self.conv(x, cond, noise=noise, coords=coords, test_ids=test_ids, calc_flops=calc_flops) + + +class ImplicitFunction(nn.Module): + def __init__(self, config): + super().__init__() + num_res_block = config.train_params.ss_n_layers + + convs = [ + ConditionalBlock(idx=i, config=config) + for i in range(num_res_block) + ] + + self.conv_stack = nn.Sequential(*convs) + + if hasattr(config.train_params, "ss_mapping") and config.train_params.ss_mapping: + n_mlp = 8 + global_latent_dim = config.train_params.global_latent_dim + lr_mlp = 0.01 + layers = [PixelNorm()] + for i in range(n_mlp): + layers.append( + EqualLinear( + global_latent_dim, global_latent_dim, lr_mul=lr_mlp, activation='fused_lrelu' + ) + ) + self.global_mapping = nn.Sequential(*layers) + else: + self.global_mapping = None + + def _select_center(self, src, ref): + pad_h = (src.shape[2] - ref.shape[2]) // 2 + pad_w = (src.shape[3] - ref.shape[3]) // 2 + if pad_h == 0 and pad_w == 0: + return src + fh, fw = ref.shape[2], ref.shape[3] + return src[:, :, pad_h:pad_h+fh, pad_w:pad_w+fw] + + def calibrate_spatial_shape(self, feature, direction, padding_mode="replicate", verbose=False, pin_loc=None): + # We only calibrates for the convs in the main stream, + # the to_rgb stream can directly use the center-cropped region. + ret_latents = [] + ret_pin_locs = [] + + if direction == "forward": + ops = self.conv_stack + elif direction == "backward": + ops = self.conv_stack[::-1] + else: + raise NotImplementedError() + + for conv in ops: + feature, pin_loc = \ + conv.calibrate_spatial_shape(feature, direction, padding_mode=padding_mode, verbose=verbose, pin_loc=pin_loc) + ret_latents.append(feature) + ret_pin_locs.append(pin_loc) + + return ret_latents, ret_pin_locs + + def forward(self, global_latent, local_latent, coords, noises=None, test_ids=None, calc_flops=False): + + if self.global_mapping is not None: + global_latent = self.global_mapping(global_latent) + + h = local_latent + cond = global_latent + flops = 0 + for i,conv in enumerate(self.conv_stack): + coords = self._select_center(src=coords, ref=h) + if isinstance(cond, list): + cur_cond = cond[i] + else: + cur_cond = cond + if noises is not None: + if isinstance(cond, list): + cur_noise = noises[i] + else: + cur_noise = noises + else: + cur_noise = None + + h, cur_flops = conv(h, cur_cond, coords, noise=cur_noise, test_ids=test_ids, calc_flops=calc_flops) + flops += cur_flops + + return h, flops + + +class StructureSynthesizer(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.coord_handler = CoordHandler(config) + self.implicit_model = ImplicitFunction(config) + + if hasattr(self.config.train_params, "diversity_z_w") and self.config.train_params.diversity_z_w!=0: + self.use_div_z_loss = True + else: + self.use_div_z_loss = False + + if hasattr(self.config.train_params, "diversity_angular") and self.config.train_params.diversity_angular != 0: + self.use_angular_div = True + else: + self.use_angular_div = False + + + def calc_out_spatial_size(self, in_spatial_size, return_list=False): + if return_list: + assert False, "Unused old codes, use with care!" + # ret = [] + # single_ss_unfold_size = self.config.train_params.ss_unfold_radius + # cur_size = in_spatial_size + # for i in range(self.config.train_params.ss_n_layers): + # next_size = cur_size - single_ss_unfold_size * 2 + # if i == (self.config.train_params.ss_n_layers-1): + # next_size -= 2 + # ret.append(next_size) + # cur_size = next_size + # return ret + else: + ss_unfold_size = self.config.train_params.ss_n_layers * self.config.train_params.ss_unfold_radius + return in_spatial_size - ss_unfold_size * 2 + + def calibrate_spatial_shape(self, feature, direction, padding_mode="replicate", verbose=False, pin_loc=None): + return self.implicit_model.calibrate_spatial_shape(feature, direction, padding_mode=padding_mode, verbose=verbose, pin_loc=pin_loc) + + def _diversity_latent_dist(self, global_latent, local_latent): + if self.use_angular_div: + if local_latent.shape[0]%2 == 0: + z_dist = angular_similarity(local_latent[0::2], local_latent[1::2]).mean() + else: + z_dist = angular_similarity(local_latent[0:-1:2], local_latent[1::2]).mean() + else: + if local_latent.shape[0]%2 == 0: + z_dist = (local_latent[0::2] - local_latent[1::2]).abs().mean() + else: + z_dist = (local_latent[0:-1:2] - local_latent[1::2]).abs().mean() + return z_dist + + def _diversity_image_dist(self, syn_feat): + if self.use_angular_div: + if syn_feat.shape[0]%2 == 0: + x_dist = angular_similarity(syn_feat[0::2], syn_feat[1::2]).mean() + else: + x_dist = angular_similarity(syn_feat[0:-1:2], syn_feat[1::2]).mean() + else: + if syn_feat.shape[0]%2 == 0: + x_dist = (syn_feat[0::2] - syn_feat[1::2]).abs().mean() + else: + x_dist = (syn_feat[0:-1:2] - syn_feat[1::2]).abs().mean() + return x_dist + + def diversity_z_loss(self, global_latent, local_latent, syn_feat, eps=1e-5): + z_dist = self._diversity_latent_dist(global_latent, local_latent) + x_dist = self._diversity_image_dist(syn_feat) + div_loss = 1 / (x_dist/z_dist + eps) + return div_loss + + def get_coords(self, local_latent, override_coords=None, is_fid_eval=False, disable_dual_latents=False): + coords, ac_coords = self.coord_handler.sample_coord_grid( + local_latent, + is_training=self.training, + is_fid_eval=is_fid_eval, + override_coords=override_coords, + return_ac_coords=True) + + if self.use_div_z_loss: + if disable_dual_latents: + pass + else: + assert self.training + coords = setup_paired_inputs(coords) + + return coords, ac_coords + + def forward( + self, + + # Possible inputs + global_latent, + local_latent, + override_coords=None, + noises=None, + style_fusion_map=None, + test_ids=None, + + # Control signals + disable_dual_latents=False, + is_fid_eval=False, + calc_flops=False): + + flops = 0 + + coords, ac_coords = self.get_coords( + local_latent, override_coords=override_coords, + is_fid_eval=is_fid_eval, disable_dual_latents=disable_dual_latents) + + if style_fusion_map is not None: + # global_latent: [Tensor (BxC)] * N => Tensor (BxCxHxW) + # Note: The input global_latent is already a single (non-style-fused) latent here + global_latent = create_fusion_styles(style_fusion_map, global_latent, self.config) + + """ + GNN Prop + """ + if self.config.train_params.ss_coord_all_layers: + pass # Assign coordinates within each conv + else: + local_latent = torch.cat([ + local_latent, + coords + ], 1) + + output, cur_flops = self.implicit_model( + global_latent, local_latent, coords=coords, noises=noises, test_ids=test_ids, calc_flops=calc_flops) + flops += cur_flops + + return output, coords, ac_coords, flops + +class TextureSynthesizer(nn.Module): + def __init__(self, config): + super().__init__() + + is_styleGAN2_baseline = (hasattr(config.train_params, "styleGAN2_baseline") and config.train_params.styleGAN2_baseline) + + self.config = config + if config.train_params.training_modality == "patch": + self.size = config.train_params.patch_size + else: + raise NotImplementedError() + self.global_latent_dim = config.train_params.global_latent_dim + self.local_latent_dim = config.train_params.local_latent_dim + n_mlp = config.train_params.n_mlp + channel_multiplier = config.train_params.channel_multiplier + + if config.train_params.ts_no_zero_pad: + blur_kernel = [1, 2, 1] + else: + assert hasattr(self.config.train_params, "styleGAN2_baseline") and self.config.train_params.styleGAN2_baseline + blur_kernel = [1, 3, 3, 1] + + lr_mlp = 0.01 + layers = [PixelNorm()] + for i in range(n_mlp): + layers.append( + EqualLinear( + self.global_latent_dim, self.global_latent_dim, lr_mul=lr_mlp, activation='fused_lrelu' + ) + ) + self.mapping = nn.Sequential(*layers) + + # self.channels = { + # 4: self.local_latent_dim, + # 8: 512, + # 16: 512, + # 32: 512, + # 64: 256 * channel_multiplier, + # 128: 128 * channel_multiplier, + # 256: 64 * channel_multiplier, + # 512: 32 * channel_multiplier, + # 1024: 16 * channel_multiplier, + # } + + if config.train_params.training_modality == "patch": + g_output_res = config.train_params.patch_size + elif config.train_params.training_modality == "full": + g_output_res = config.train_params.full_size + else: + raise NotImplementedError() + + if g_output_res == 101 and config.train_params.ts_input_size == 11: + self.convs_specs = [ + dict(out_ch=512, upsample=True), # 11 -> 19 + # skip-node 0 + dict(out_ch=512, upsample=False), # 19 -> 17 + # skip-node 1 + dict(out_ch=512, upsample=True), # 17 -> 31 + # skip-node 2 + dict(out_ch=512, upsample=False), # 31 -> 29 + # skip-node 3 + dict(out_ch=512, upsample=True), # 29 -> 55 + # skip-node 4 + dict(out_ch=512, upsample=False), # 55 -> 53 + # skip-node 5 + dict(out_ch=256*channel_multiplier, upsample=True), # 53 -> 103 + # skip-node 6 + dict(out_ch=256*channel_multiplier, upsample=False), # 103 -> 101 + # skip-node 7 + ] + self.to_rgbs_specs = [ + dict(src=1, tgt=3, upsample=True), + dict(src=3, tgt=5, upsample=True), + dict(src=5, tgt=7, upsample=True), + dict(src=7, tgt=8, upsample=True), + ] + elif g_output_res == 197 and config.train_params.ts_input_size == 11: + self.convs_specs = [ + dict(out_ch=512, upsample=True), # 11 -> 19 + # skip-node 0 + dict(out_ch=512, upsample=False), # 19 -> 17 + # skip-node 1 + dict(out_ch=512, upsample=True), # 17 -> 31 + # skip-node 2 + dict(out_ch=512, upsample=False), # 31 -> 29 + # skip-node 3 + dict(out_ch=512, upsample=True), # 29 -> 55 + # skip-node 4 + dict(out_ch=512, upsample=False), # 55 -> 53 + # skip-node 5 + dict(out_ch=256*channel_multiplier, upsample=True), # 53 -> 103 + # skip-node 6 + dict(out_ch=256*channel_multiplier, upsample=False), # 103 -> 101 + # skip-node 7 + dict(out_ch=128*channel_multiplier, upsample=True), # 101 -> 199 + # skip-node 8 + dict(out_ch=128*channel_multiplier, upsample=False), # 199 -> 197 + # skip-node 9 + ] + self.to_rgbs_specs = [ + dict(src=1, tgt=3, upsample=True), + dict(src=3, tgt=5, upsample=True), + dict(src=5, tgt=7, upsample=True), + dict(src=7, tgt=9, upsample=True), + dict(src=9, tgt=10, upsample=True), + ] + elif g_output_res == 389 and config.train_params.ts_input_size == 11: + self.convs_specs = [ + dict(out_ch=512, upsample=True), # 11 -> 19 + # skip-node 0 + dict(out_ch=512, upsample=False), # 19 -> 17 + # skip-node 1 + dict(out_ch=512, upsample=True), # 17 -> 31 + # skip-node 2 + dict(out_ch=512, upsample=False), # 31 -> 29 + # skip-node 3 + dict(out_ch=512, upsample=True), # 29 -> 55 + # skip-node 4 + dict(out_ch=512, upsample=False), # 55 -> 53 + # skip-node 5 + dict(out_ch=256*channel_multiplier, upsample=True), # 53 -> 103 + # skip-node 6 + dict(out_ch=256*channel_multiplier, upsample=False), # 103 -> 101 + # skip-node 7 + dict(out_ch=128*channel_multiplier, upsample=True), # 101 -> 199 + # skip-node 8 + dict(out_ch=128*channel_multiplier, upsample=False), # 199 -> 197 + # skip-node 9 + dict(out_ch=64*channel_multiplier, upsample=True), # 197 -> 391 + # skip-node 10 + dict(out_ch=64*channel_multiplier, upsample=False), # 391 -> 389 + # skip-node 11 + ] + self.to_rgbs_specs = [ + dict(src=1, tgt=3, upsample=True), + dict(src=3, tgt=5, upsample=True), + dict(src=5, tgt=7, upsample=True), + dict(src=7, tgt=9, upsample=True), + dict(src=9, tgt=11, upsample=True), + dict(src=11, tgt=12, upsample=True), + ] + elif g_output_res == 773 and config.train_params.ts_input_size == 11: + self.convs_specs = [ + dict(out_ch=512, upsample=True), # 11 -> 19 + # skip-node 0 + dict(out_ch=512, upsample=False), # 19 -> 17 + # skip-node 1 + dict(out_ch=512, upsample=True), # 17 -> 31 + # skip-node 2 + dict(out_ch=512, upsample=False), # 31 -> 29 + # skip-node 3 + dict(out_ch=512, upsample=True), # 29 -> 55 + # skip-node 4 + dict(out_ch=512, upsample=False), # 55 -> 53 + # skip-node 5 + dict(out_ch=256*channel_multiplier, upsample=True), # 53 -> 103 + # skip-node 6 + dict(out_ch=256*channel_multiplier, upsample=False), # 103 -> 101 + # skip-node 7 + dict(out_ch=128*channel_multiplier, upsample=True), # 101 -> 199 + # skip-node 8 + dict(out_ch=128*channel_multiplier, upsample=False), # 199 -> 197 + # skip-node 9 + dict(out_ch=64*channel_multiplier, upsample=True), # 197 -> 391 + # skip-node 10 + dict(out_ch=64*channel_multiplier, upsample=False), # 391 -> 389 + # skip-node 11 + dict(out_ch=32*channel_multiplier, upsample=True), # 389 -> 775 + # skip-node 12 + dict(out_ch=32*channel_multiplier, upsample=False), # 775 -> 773 + # skip-node 13 + ] + self.to_rgbs_specs = [ + dict(src=1, tgt=3, upsample=True), + dict(src=3, tgt=5, upsample=True), + dict(src=5, tgt=7, upsample=True), + dict(src=7, tgt=9, upsample=True), + dict(src=9, tgt=11, upsample=True), + dict(src=11, tgt=13, upsample=True), + dict(src=13, tgt=14, upsample=True), + ] + elif g_output_res == 1541 and config.train_params.ts_input_size == 11: + self.convs_specs = [ + dict(out_ch=512, upsample=True), # 11 -> 19 + # skip-node 0 + dict(out_ch=512, upsample=False), # 19 -> 17 + # skip-node 1 + dict(out_ch=512, upsample=True), # 17 -> 31 + # skip-node 2 + dict(out_ch=512, upsample=False), # 31 -> 29 + # skip-node 3 + dict(out_ch=512, upsample=True), # 29 -> 55 + # skip-node 4 + dict(out_ch=512, upsample=False), # 55 -> 53 + # skip-node 5 + dict(out_ch=256*channel_multiplier, upsample=True), # 53 -> 103 + # skip-node 6 + dict(out_ch=256*channel_multiplier, upsample=False), # 103 -> 101 + # skip-node 7 + dict(out_ch=128*channel_multiplier, upsample=True), # 101 -> 199 + # skip-node 8 + dict(out_ch=128*channel_multiplier, upsample=False), # 199 -> 197 + # skip-node 9 + dict(out_ch=64*channel_multiplier, upsample=True), # 197 -> 391 + # skip-node 10 + dict(out_ch=64*channel_multiplier, upsample=False), # 391 -> 389 + # skip-node 11 + dict(out_ch=32*channel_multiplier, upsample=True), # 389 -> 775 + # skip-node 12 + dict(out_ch=32*channel_multiplier, upsample=False), # 775 -> 773 + # skip-node 13 + dict(out_ch=16*channel_multiplier, upsample=True), # 773 -> 1543 + # skip-node 14 + dict(out_ch=16*channel_multiplier, upsample=False), # 1543 -> 1541 + # skip-node 15 + ] + self.to_rgbs_specs = [ + dict(src=1, tgt=3, upsample=True), + dict(src=3, tgt=5, upsample=True), + dict(src=5, tgt=7, upsample=True), + dict(src=7, tgt=9, upsample=True), + dict(src=9, tgt=11, upsample=True), + dict(src=11, tgt=13, upsample=True), + dict(src=13, tgt=15, upsample=True), + dict(src=15, tgt=16, upsample=True), + ] + elif g_output_res == 128 and config.train_params.ts_input_size == 4: # Basline + self.convs_specs = [ + dict(out_ch=512, upsample=True), # 4 -> 8 + # skip-node 0 + dict(out_ch=512, upsample=False), + # skip-node 1 + dict(out_ch=512, upsample=True), # 8 -> 16 + # skip-node 2 + dict(out_ch=512, upsample=False), + # skip-node 3 + dict(out_ch=512, upsample=True), # 16 -> 32 + # skip-node 4 + dict(out_ch=512, upsample=False), + # skip-node 5 + dict(out_ch=512, upsample=True), # 32 -> 64 + # skip-node 6 + dict(out_ch=512, upsample=False), + # skip-node 7 + dict(out_ch=256 * channel_multiplier, upsample=True), # 64 -> 128 + # skip-node 8 + dict(out_ch=256 * channel_multiplier, upsample=False), + # skip-node 9 + ] + self.to_rgbs_specs = [ + dict(src=1, tgt=3, upsample=True), + dict(src=3, tgt=5, upsample=True), + dict(src=5, tgt=7, upsample=True), + dict(src=7, tgt=9, upsample=True), + dict(src=9, tgt=10, upsample=True), + ] + elif g_output_res == 64 and config.train_params.ts_input_size == 4: # Basline + self.convs_specs = [ + dict(out_ch=512, upsample=True), # 4 -> 8 + # skip-node 0 + dict(out_ch=512, upsample=False), + # skip-node 1 + dict(out_ch=512, upsample=True), # 8 -> 16 + # skip-node 2 + dict(out_ch=512, upsample=False), + # skip-node 3 + dict(out_ch=512, upsample=True), # 16 -> 32 + # skip-node 4 + dict(out_ch=512, upsample=False), + # skip-node 5 + dict(out_ch=512, upsample=True), # 32 -> 64 + # skip-node 6 + dict(out_ch=512, upsample=False), + # skip-node 7 + ] + self.to_rgbs_specs = [ + dict(src=1, tgt=3, upsample=True), + dict(src=3, tgt=5, upsample=True), + dict(src=5, tgt=7, upsample=True), + dict(src=7, tgt=8, upsample=True), + ] + else: + raise NotImplementedError(" Not yet designed arch for G input size {} and output res {}".format( + config.train_params.ts_input_size, g_output_res)) + + self.const_z = ConstantInput(self.local_latent_dim) + + self.num_layers = len(self.convs_specs) + self.n_latent = self.num_layers + 1 # The last latent is for the additional to_rgb skip only + + self.convs = nn.ModuleList() + self.to_rgbs = nn.ModuleList() + self.noises = nn.Module() + + for layer_idx in range(self.num_layers): + res = (layer_idx + 5) // 2 + shape = [1, 1, 2 ** res, 2 ** res] + self.noises.register_buffer(f'noise_{layer_idx}', torch.randn(*shape)) + + in_ch = self.local_latent_dim + + for i,conv_spec in enumerate(self.convs_specs): + + self.convs.append( + StyledConv( + in_ch, + conv_spec["out_ch"], + 3, + self.global_latent_dim, + upsample=conv_spec["upsample"], + blur_kernel=blur_kernel, + no_zero_pad=config.train_params.ts_no_zero_pad, + config=config, + side="ts") + ) + in_ch = conv_spec["out_ch"] + + for to_rgb_spec in self.to_rgbs_specs: + src_conv_spec = self.convs_specs[to_rgb_spec["src"]] + in_ch = src_conv_spec["out_ch"] + self.to_rgbs.append( + ToRGB( + in_ch, + self.global_latent_dim, + upsample=to_rgb_spec["upsample"], + no_zero_pad=config.train_params.ts_no_zero_pad, + blur_kernel=blur_kernel, + config=config, + side="ts", + ) + ) + + def calc_in_spatial_size(self, out_spatial_size, return_list=False): + # print(" [*] Calculating input spatial size:") + in_spatial_sizes = [] + for i,conv in enumerate(self.convs[::-1]): + if conv.conv.upsample: + conv_type = "upsample" + # elif conv.conv.downsample: + # conv_type = "downsample" + else: + conv_type = "normal" + in_spatial_size = conv.calc_in_spatial_size(out_spatial_size) + # print("\t {}-th layer ({}), output {} => input {}".format(len(self.convs)-i, conv_type, out_spatial_size, in_spatial_size)) + out_spatial_size = in_spatial_size + in_spatial_sizes.append(in_spatial_size) + if return_list: + return in_spatial_sizes[::-1] # retain the z->ss->ts->img order + else: + return in_spatial_sizes[-1] + + def calc_out_spatial_size(self, in_spatial_size, return_list=False): + # print(" [*] Calculating output spatial size:") + out_spatial_sizes = [] + for i,conv in enumerate(self.convs): + if conv.conv.upsample: + conv_type = "upsample" + # elif conv.conv.downsample: + # conv_type = "downsample" + else: + conv_type = "normal" + out_spatial_size = conv.calc_out_spatial_size(in_spatial_size) + # print("\t {}-th layer ({}), input {} => output {}".format(i, conv_type, in_spatial_size, out_spatial_size)) + in_spatial_size = out_spatial_size + out_spatial_sizes.append(out_spatial_size) + + if return_list: + return out_spatial_sizes + else: + return out_spatial_sizes[-1] + + def calibrate_spatial_shape(self, feature, direction, padding_mode="replicate", verbose=False, pin_loc=None): + # We only calibrates for the convs in the main stream, + # the to_rgb stream can directly use the center-cropped region. + ret_latents = [] + ret_pin_locs = [] + if direction == "forward": + ops = self.convs + elif direction == "backward": + ops = self.convs[::-1] + else: + raise NotImplementedError() + for i,conv in enumerate(ops): + feature, pin_loc = \ + conv.calibrate_spatial_shape(feature, direction, padding_mode=padding_mode, verbose=verbose, pin_loc=pin_loc) + ret_latents.append(feature) + ret_pin_locs.append(pin_loc) + return ret_latents, ret_pin_locs + + def mean_latent(self, n_latent): + latent_in = torch.randn( + n_latent, self.global_latent_dim, device=self.const_z.input.device + ) + latent = self.mapping(latent_in).mean(0, keepdim=True) + return latent + + def get_style(self, input): + return self.mapping(input) + + def forward( + self, + *args, + + # Possible inputs + global_latent=None, + structure_latent=None, + styles=None, + wplus_styles=None, + noises=None, + style_fusion_map=None, + test_ids=None, + + # Additional outputs + return_latents=False, + return_path_length=False, + + # Control signals + inject_index=None, + calc_flops=False, + + **kwargs): + + assert len(args) == 0, "Do not allow non-keyword argument in generator!" + flops = 0 + + """ + Noise inputs (StyleGAN paper section 3.2) + """ + if noises is None: + noises = [None] * self.num_layers + + # Style is already given, we assume all mixing stuffs (if desired) are already handled + if (styles is None) and (wplus_styles is None): + + """ + Mapping + """ + if global_latent.ndim == 3: # style mixing multiple global latent + styles = [self.mapping(global_latent[:, i]) for i in range(global_latent.shape[1])] + if calc_flops: # disregard style mixing + _, flops_mapping = sequential_get_flops(self.mapping, global_latent) + else: + if calc_flops: + styles, flops_mapping = sequential_get_flops(self.mapping, global_latent) + styles = [styles] + flops += flops_mapping + else: + styles = [self.mapping(global_latent)] + + """ + Truncation + """ + # # No cheating plz OuO + # The latent space is what you are modeling. You should never trade recall by eliminating a part of it. + # For instance, like in the image classification task, can you refuse to classify testing samples with low confidence? + + """ + Style Mixing + """ + if global_latent.ndim == 3: # style mixing multiple global latent + if inject_index is None: + if self.training: + inject_index = random.randint(1, self.n_latent - 1) + else: + inject_index = self.n_latent + if inject_index == self.n_latent: + styles = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: + styles = torch.cat([ + styles[0].unsqueeze(1).repeat(1, inject_index, 1), + styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1), + ], 1) + else: + inject_index = self.n_latent + if styles[0].ndim < 3: + styles = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: + styles = styles[0] + + + # Spatial style fusion + if style_fusion_map is not None: + if wplus_styles is None: + assert calc_flops == False + convs_fused_styles = [ + create_fusion_styles(fm, styles, self.config) for fm in style_fusion_map] + torgbs_fused_styles = [] + for to_rgbs_spec in self.to_rgbs_specs: + cur_fusion_map = style_fusion_map[to_rgbs_spec["src"]] + fused_style = create_fusion_styles(cur_fusion_map, styles, self.config) + torgbs_fused_styles.append(fused_style) + else: + assert calc_flops == False + convs_fused_styles = [] + for i in range(len(style_fusion_map)): + cur_wplus_style_centers = [ + s[:, i] for s in wplus_styles] + cur_fusion_map = create_fusion_styles( + style_fusion_map[i], cur_wplus_style_centers, self.config) + convs_fused_styles.append(cur_fusion_map) + + torgbs_fused_styles = [] + for to_rgbs_spec in self.to_rgbs_specs: + src_idx = to_rgbs_spec["src"] + tgt_idx = to_rgbs_spec["tgt"] + cur_fusion_map = style_fusion_map[src_idx] + cur_wplus_style_centers = [ + s[:, tgt_idx] for s in wplus_styles] + fused_style = create_fusion_styles( + cur_fusion_map, cur_wplus_style_centers, self.config) + torgbs_fused_styles.append(fused_style) + + """ + Synthesis image from styles + """ + cur_to_rgb_idx = 0 + skip = None + h = structure_latent + upsample_between_skips = True # The first ToRGB does not take skip input, make it always valid + for i,(conv,noise) in enumerate(zip(self.convs, noises)): + + # Deal with different types of input + if style_fusion_map is not None: # Supposts spatially-shaped style at testing + cur_style = convs_fused_styles[i] + elif wplus_styles is not None: + cur_style = wplus_styles[:, i] + elif isinstance(styles, list): + cur_style = styles[i] + else: # Native + cur_style = styles[:, i] + + h, cur_flops = conv(h, cur_style, noise=noise, test_ids=test_ids, calc_flops=calc_flops) + flops += cur_flops + + if conv.upsample: + # Safety check, since my implementation trusts all the feature shapes are correctly justified, + # then automatically performs shape callibrations on shape mismatches. + upsample_between_skips = True + + + to_rgb_spec = self.to_rgbs_specs[cur_to_rgb_idx] + skip_src = to_rgb_spec["src"] + skip_tgt = to_rgb_spec["tgt"] + if i == skip_src: + assert upsample_between_skips, \ + "At least one upsampling conv between two consecutive skip" + \ + " layers (ToRGB) is required. Failed to meet the requirement" + \ + " before ToRGB ({} => {})".format(skip_src, skip_tgt) + to_rgb_op = self.to_rgbs[cur_to_rgb_idx] + + # Deal with different types of input + if style_fusion_map is not None: # Supposts spatially-shaped style at testing + cur_style = torgbs_fused_styles[cur_to_rgb_idx] + elif wplus_styles is not None: + cur_style = wplus_styles[:, skip_tgt] + elif isinstance(styles, list): + cur_style = styles[skip_tgt] + else: # Native + cur_style = styles[:, skip_tgt] + + skip, cur_flops = to_rgb_op(h, cur_style, skip=skip, calc_flops=calc_flops) + flops += cur_flops + + cur_to_rgb_idx += 1 + upsample_between_skips = False + + image = skip + + + """ + Output + """ + output = DictTensor(gen=image) + + if return_latents: + output["latents"] = styles + if return_path_length: + output["path_lengths"] = calc_path_lengths(image, [styles]) + if calc_flops: + output["flops_ts"] = flops + return output + + +class InfinityGanGenerator(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + if hasattr(config.train_params, "styleGAN2_baseline") and config.train_params.styleGAN2_baseline: + if hasattr(config.train_params, "force_use_ss") and config.train_params.force_use_ss: + self.structure_synthesizer = StructureSynthesizer(config) + else: + self.structure_synthesizer = None + elif config.train_params.use_ss: + self.structure_synthesizer = StructureSynthesizer(config) + else: + self.structure_synthesizer = None + self.texture_synthesizer = TextureSynthesizer(config) + + if hasattr(self.config.train_params, "diversity_z_w") and self.config.train_params.diversity_z_w != 0: + self.use_div_z = True + else: + self.use_div_z = False + + if hasattr(self.config.train_params, "diversity_angular") and self.config.train_params.diversity_angular: + self.use_angular_div = True + else: + self.use_angular_div = False + + def _diversity_dist(self, values): + if self.use_angular_div: + if values.shape[0] % 2 == 0: + x_dist = angular_similarity(values[0::2], values[1::2]).mean() + else: # Deal with cases that batch size is odd due to nn.Dataparallel + x_dist = angular_similarity(values[0:-1:2], values[1::2]).mean() + return x_dist # already in [0, 1] + else: + if values.shape[0] % 2 == 0: + x_dist = (values[0::2] - values[1::2]).abs().mean() + else: # Deal with cases that batch size is odd due to nn.Dataparallel + x_dist = (values[0:-1:2] - values[1::2]).abs().mean() + return x_dist + + def calc_in_spatial_size(self, out_spatial_size, include_ss=False, return_list=False): + assert include_ss == False, "Never used. GNN padding is always explicitly handled outside." + in_spatial_sizes_ts = self.texture_synthesizer.calc_in_spatial_size( + out_spatial_size, return_list=return_list) + + if include_ss: + out_spatial_size = in_spatial_sizes_ts[0] if return_list else in_spatial_sizes_ts + in_spatial_sizes_ss = self.structure_synthesizer.calc_in_spatial_size( + out_spatial_size, return_list=return_list) + if return_list: + return [int(np.ceil(v)) for v in in_spatial_sizes_ss + in_spatial_sizes_ts] + else: + return int(np.ceil(in_spatial_sizes_ss)) + else: + if return_list: + return [int(np.ceil(v)) for v in in_spatial_sizes_ts] + else: + return int(np.ceil(in_spatial_sizes_ts)) + + def calc_out_spatial_size(self, in_spatial_size, include_ss=False, return_list=False): + # assert include_ss == False, "Never used. GNN padding is always explicitly handled outside." + if include_ss: # GNN padding are usually already accounted elsewhere + out_spatial_sizes_ss = self.structure_synthesizer.calc_out_spatial_size( + in_spatial_size, return_list=return_list) + ts_in_spatial_size = out_spatial_sizes_ss[-1] if return_list else out_spatial_sizes_ss + out_spatial_sizes_ts = self.texture_synthesizer.calc_out_spatial_size( + ts_in_spatial_size, return_list=return_list) + else: + out_spatial_sizes_ss = [] + out_spatial_sizes_ts = self.texture_synthesizer.calc_out_spatial_size( + in_spatial_size, return_list=return_list) + + if return_list: + return [int(np.ceil(v)) for v in out_spatial_sizes_ss + out_spatial_sizes_ts] + else: + return int(np.ceil(out_spatial_sizes_ts)) + + def calibrate_spatial_shape(self, feature, direction, padding_mode="replicate", verbose=False, pin_loc=None): + if direction == "forward": + feature_list_ss, pin_loc_list_ss = \ + self.structure_synthesizer.calibrate_spatial_shape( + feature, + direction, + padding_mode=padding_mode, + verbose=verbose, + pin_loc=pin_loc) + feature_list_ts, pin_loc_list_ts = \ + self.texture_synthesizer.calibrate_spatial_shape( + feature_list_ss[-1], # order is `SS input => SS output` + direction, + padding_mode=padding_mode, + verbose=verbose, + pin_loc=pin_loc_list_ss[-1] if pin_loc is not None else None) + return feature_list_ss, feature_list_ts, pin_loc_list_ss, pin_loc_list_ts + elif direction == "backward": + feature_list_ts, pin_loc_list_ts = \ + self.texture_synthesizer.calibrate_spatial_shape( + feature, + direction, + padding_mode=padding_mode, + verbose=verbose, + pin_loc=pin_loc) + feature_list_ss, pin_loc_list_ss = \ + self.structure_synthesizer.calibrate_spatial_shape( + feature_list_ts[-1], # order is `TS output => TS input` + direction, + padding_mode=padding_mode, + verbose=verbose, + pin_loc=pin_loc_list_ts[-1] if pin_loc is not None else None) + return feature_list_ss[::-1], feature_list_ts[::-1], pin_loc_list_ss[::-1], pin_loc_list_ts[::-1] + else: + raise NotImplementedError() + + def get_style(self, global_latent): + return self.texture_synthesizer.get_style(global_latent) + + def forward( + self, + *args, + + # Possible inputs + global_latent=None, + local_latent=None, + structure_latent=None, + styles=None, + wplus_styles=None, + noises=None, + ss_noises=None, + override_coords=None, + test_ids=None, + + # Additional outputs + return_latents=False, + return_path_length=False, + early_return_structure_latent=False, + + # Control signals + inject_index=None, + disable_dual_latents=False, + is_fid_eval=False, + calc_flops=False, + style_fusion_map_ss=None, + style_fusion_map_ts=None, + call_internal_method=None, + internal_method_kwargs={}, + **kwargs): + + if call_internal_method is not None: + return getattr(self, call_internal_method)(**internal_method_kwargs) + + assert ("coords" not in kwargs) and ("testing_coords" not in kwargs), \ + "Depricated argument, should use `override_coords` instead!" + + # Get device + if global_latent is not None: + if isinstance(global_latent, list): + device = global_latent[0].device + else: + device = global_latent.device + else: + if isinstance(styles, list): + device = styles[0].device + else: + device = styles.device + + """ + Setup dual global_latent for diversity_z_loss + """ + if self.use_div_z: + if disable_dual_latents: + pass + else: + assert self.training + global_latent = setup_paired_inputs(global_latent) + + """ + Structure Synthesizer + """ + if (local_latent is not None) and (self.structure_synthesizer is not None): + structure_latent, coords, ac_coords, flops_ss = self.structure_synthesizer( + global_latent=global_latent if isinstance(global_latent, list) else global_latent[:, 0], + local_latent=local_latent, + override_coords=override_coords, + noises=ss_noises, + test_ids=test_ids, + disable_dual_latents=disable_dual_latents, + is_fid_eval=is_fid_eval, + style_fusion_map=style_fusion_map_ss, + calc_flops=calc_flops) + else: + ac_coords = None + + # Cases without structure synthesizer, like baseline StyleGAN2 + if self.structure_synthesizer is None: + if local_latent is None: + assert structure_latent is not None # "Directly assigned structure_latent" + else: + structure_latent = local_latent + coords = None + flops_ss = 0 + else: + #assert override_coords is not None + coords = override_coords + flops_ss = 0 + + + if early_return_structure_latent: + return DictTensor( + structure_latent=structure_latent, + coords=coords) + + + """ + Texture Synthesizer + """ + output = self.texture_synthesizer( + global_latent=global_latent, + structure_latent=structure_latent, + styles=styles, + wplus_styles=wplus_styles, + noises=noises, + return_latents=return_latents, + return_path_length=return_path_length, + inject_index=inject_index, + test_ids=test_ids, + style_fusion_map=style_fusion_map_ts, + calc_flops=calc_flops) + + """ + Mode-seeking diversity loss + """ + if self.use_div_z and (not (local_latent is None)) and self.training: + output["diversity_z_loss"] = \ + self.structure_synthesizer.diversity_z_loss( + global_latent[:, 0], local_latent, structure_latent, eps=1e-5) + else: + output["diversity_z_loss"] = torch.tensor(0).float().to(device) + + """ + Manage Outputs + """ + output["structure_latent"] = structure_latent + if ac_coords is not None: + output["ac_coords"] = ac_coords + + if calc_flops: + assert isinstance(flops_ss, int) or isinstance(flops_ss, np.int64), \ + "Got SS flops result with type {}, should be int!".format(type(flops_ss)) + assert isinstance(output["flops_ts"], int) or isinstance(output["flops_ts"], np.int64), \ + "Got TS flops result with type {}, should be int!".format(type(output["flops_ts"])) + output["flops_ss"] = flops_ss + output["flops_ts"] = output["flops_ts"] + output["flops_all"] = output["flops_ss"] + output["flops_ts"] + + return output + diff --git a/models/losses.py b/models/losses.py new file mode 100644 index 0000000..66facb1 --- /dev/null +++ b/models/losses.py @@ -0,0 +1,115 @@ +import math + +import torch +from torch import nn, autograd +from torch.nn import functional as F + + + +def l1_loss(a, b, reduce_all=False): + if reduce_all: + return (a-b).abs().mean() + else: # return in batch shape + bs = a.shape[0] + return (a - b).abs().view(bs, -1).mean(1) + + +def l2_loss(a, b, reduce_all=False): + if reduce_all: + return 0.5 * ((a-b)**2).mean() + else: # return in batch shape + bs = a.shape[0] + return (0.5 * (a - b)**2).view(bs, -1).mean(1) + + +def d_logistic_loss(real_pred, fake_pred): + real_loss = F.softplus(-real_pred) + fake_loss = F.softplus(fake_pred) + return real_loss.mean() + fake_loss.mean() + + +def d_logistic_loss_fake(fake_pred): + fake_loss = F.softplus(fake_pred) + return fake_loss.mean() + + +def d_r1_loss(real_pred, real_img): + grad_real, = autograd.grad( + outputs=real_pred.sum(), inputs=real_img, create_graph=True + ) + grad_penalty = grad_real.pow(2).reshape(grad_real.shape[0], -1).sum(1).mean() + return grad_penalty + + +def g_nonsaturating_loss(fake_pred): + loss = F.softplus(-fake_pred).mean() + return loss + + +def grad_reduce(grad): + if grad.ndim == 2: + return torch.sqrt(grad.pow(2).mean(1)) + elif grad.ndim == 3: + return torch.sqrt(grad.pow(2).mean([1,2])) + elif grad.ndim == 4: + return torch.sqrt(grad.pow(2).mean([1,2,3])) + else: + raise NotImplementedError() + + +def calc_path_lengths(fake_img, latents): + noise = torch.randn_like(fake_img) / math.sqrt( + fake_img.shape[2] * fake_img.shape[3] + ) + grads = autograd.grad( + outputs=(fake_img * noise).sum(), inputs=latents, create_graph=True + ) + path_lengths = [grad_reduce(grad) for grad in grads] + return path_lengths + + +def g_path_regularize(path_lengths, mean_path_lengths, decay=0.01): + path_penalty = 0 + new_path_means = [] + for path_length,mean_path_length in zip(path_lengths, mean_path_lengths): + path_mean = mean_path_length.item() + decay * (path_length.mean() - mean_path_length) + path_penalty += (path_length - path_mean).pow(2).mean() + new_path_means.append(path_mean.detach()) + return path_penalty, new_path_means + + +def coord_ac_loss(pred, label, side, config): + if label.device != pred.device: + label = label.to(pred.device) # Workaround for cpu device of Dataparallel + + if hasattr(config.train_params, "coord_ac_vert_only") and config.train_params.coord_ac_vert_only: + return l1_loss(pred[:, 0], label[:, 0], reduce_all=True) + else: + if hasattr(config.train_params, "coord_ac_categorical") and config.train_params.coord_ac_categorical: + assert config.train_params.coord_ac_vert_only, "experimental setup" + label = ((label[:, 0] + 1) * 2 * config.train_params.coord_vert_sample_size).uint8() # [-1, 1] => class + return F.cross_entropy(pred, label) + else: + return l1_loss(pred, label, reduce_all=True) + + +def noise_regularize(noises): + loss = 0 + for noise in noises: + while True: + _, _, size_h, size_w = noise.shape + loss = ( + loss + + (noise * torch.roll(noise, shifts=1, dims=3)).mean().pow(2) + + (noise * torch.roll(noise, shifts=1, dims=2)).mean().pow(2) + ) + if min(size_h, size_w) <= 8: + break + if size_h % 2 != 0: + noise = noise[:, :, :-1, :] + if size_w % 2 != 0: + noise = noise[:, :, :, :-1] + noise = noise.reshape([-1, 1, size_h // 2, 2, size_w // 2, 2]) + noise = noise.mean([3, 5]) + return loss + diff --git a/models/ops.py b/models/ops.py new file mode 100644 index 0000000..a65b865 --- /dev/null +++ b/models/ops.py @@ -0,0 +1,930 @@ +import math +import numpy as np +from scipy import signal + +import torch +from torch import nn +from torch.nn import functional as F + +from models.custom_ops import FusedLeakyReLU, fused_leaky_relu, upfirdn2d + + + +class PixelNorm(nn.Module): + def __init__(self): + super().__init__() + + def get_flops(self, input): + return np.prod(input.shape[1:]) + + def forward(self, input): + return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8) + + +def make_kernel(k): + k = torch.tensor(k, dtype=torch.float32) + if k.ndim == 1: + k = k[None, :] * k[:, None] + k /= k.sum() + return k + + +class Upsample(nn.Module): + def __init__(self, kernel, factor=2, no_zero_pad=False): + super().__init__() + self.no_zero_pad = no_zero_pad + + self.factor = factor + kernel = make_kernel(kernel) * (factor ** 2) + self.register_buffer('kernel', kernel) + + if no_zero_pad: + self.pad = (0, 0) + else: + p = kernel.shape[0] - factor + pad0 = (p + 1) // 2 + factor - 1 + pad1 = p // 2 + self.pad = (pad0, pad1) + + if no_zero_pad: + self.upsample = nn.Upsample(scale_factor=2, mode="linear") + + def forward(self, input): + if self.no_zero_pad: + B, C, H, W = input.shape + out = F.conv_transpose2d(input.view(B*C, 1, H, W), self.kernel.unsqueeze(0).unsqueeze(1), stride=2) + _, _, nH, nW = out.shape + out = out.view(B, C, nH, nW)[:, :, 1:-1, 1:-1] + else: + out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad) + + return out + + +class Downsample(nn.Module): + def __init__(self, kernel, factor=2): + super().__init__() + + self.factor = factor + kernel = make_kernel(kernel) + self.register_buffer('kernel', kernel) + + p = kernel.shape[0] - factor + pad0 = (p + 1) // 2 + pad1 = p // 2 + self.pad = (pad0, pad1) + + def forward(self, input): + out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad) + return out + + +def create_gaussian_kernel(kernel_size, std=1): + gkern1d = signal.gaussian(kernel_size, std=std).reshape(kernel_size, 1) + gkern2d = np.outer(gkern1d, gkern1d) + return gkern2d / gkern2d.sum() + + +class Blur(nn.Module): + def __init__(self, kernel, pad, upsample_factor=1, padding_mode="zero", prior="gaussian"): + super().__init__() + + if isinstance(kernel, int): + if prior.lower() == "gaussian".lower(): + kernel = create_gaussian_kernel(kernel_size=kernel) + elif prior.lower() == "mean".lower(): + kernel = torch.ones(kernel, kernel, dtype=torch.float32) + else: + raise NotImplementedError("Unknown prior {}".format(prior)) + + kernel = make_kernel(kernel) + if upsample_factor > 1: + kernel = kernel * (upsample_factor ** 2) + self.register_buffer('kernel', kernel) + + if padding_mode == "replicate": + self.zero_pad = (0, 0) + self.replicate_pad = pad if isinstance(pad, tuple) else (pad, pad, pad, pad) + self.use_replicate_pad = True + elif padding_mode == "zero": + self.zero_pad = pad if isinstance(pad, tuple) else (pad, pad) + self.replicate_pad = 0 + self.use_replicate_pad = False + else: + raise NotImplementedError("Unknown padding_mode {}".format(padding_mode)) + self.upsample_factor = upsample_factor + + def get_flops(self, shape): + _, C, H, W = shape + ks = self.kernel.shape[0] + _, _, h_num_iters, w_num_iters = self.get_output_shape(shape) + return h_num_iters * w_num_iters * C * (ks**2) + + def get_output_shape(self, shape): + B, C, H, W = shape + ks = self.kernel.shape[0] + if self.use_replicate_pad: + H += self.replicate_pad[2] + self.replicate_pad[3] + W += self.replicate_pad[0] + self.replicate_pad[1] + else: + H += self.zero_pad[0] + W += self.zero_pad[1] + out_h = (H - ks//2*2) + out_w = (W - ks//2*2) + return B, C, out_h, out_w + + def forward(self, input): + if self.use_replicate_pad: + input = F.pad(input, self.replicate_pad, mode="replicate") + out = upfirdn2d(input, self.kernel, pad=self.zero_pad) + return out + + +class EqualConv2d(nn.Module): + def __init__(self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True): + super().__init__() + + self.weight = nn.Parameter( + torch.randn(out_channel, in_channel, kernel_size, kernel_size) + ) + self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2) + + self.stride = stride + self.padding_type = padding + self.extra_padding_layer = None + if type(padding) is str: + if padding == "reflect": + # Pytorch reflection padding is broken in some early versions. + self.extra_padding_layer = nn.ReflectionPad2d(kernel_size//2) + self.zero_pad_size = 0 + elif padding == "zero": + self.zero_pad_size = kernel_size//2 + else: + raise NotImplementedError("Unknown padding type {}".format(padding)) + else: + self.zero_pad_size = padding + + if bias: + self.bias = nn.Parameter(torch.zeros(out_channel)) + else: + self.bias = None + + def forward(self, input): + if self.extra_padding_layer is not None: + input = self.extra_padding_layer(input) + out = F.conv2d( + input, + self.weight * self.scale, + bias=self.bias, + stride=self.stride, + padding=self.zero_pad_size) + + return out + + def __repr__(self): + return ( + f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},' + f' {self.weight.shape[2]}, stride={self.stride}, padding_type={self.padding_type})') + + +class EqualLinear(nn.Module): + def __init__(self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None): + super().__init__() + + self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul)) + if bias: + self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init)) + else: + self.bias = None + + self.activation = activation + self.scale = (1 / math.sqrt(in_dim)) * lr_mul + self.lr_mul = lr_mul + + def get_flops(self, input): + flops = 0 + if self.activation: + flops += self.bias.shape[0] + self.bias.shape[0] # activation + flops += np.prod(self.weight.shape) + np.prod(self.weight.shape) # linear weight, includes self.scale + flops += self.bias.shape[0] + self.bias.shape[0] # linear bias, includes self.lr_mul + return flops + + def forward(self, input): + if self.activation: + out = F.linear(input, self.weight * self.scale) + out = fused_leaky_relu(out, self.bias * self.lr_mul) + else: + out = F.linear(input, self.weight * self.scale, bias=self.bias * self.lr_mul) + + return out + + def __repr__(self): + return f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})' + + +class ScaledLeakyReLU(nn.Module): + def __init__(self, negative_slope=0.2): + super().__init__() + self.negative_slope = negative_slope + + def forward(self, input): + out = F.leaky_relu(input, negative_slope=self.negative_slope) + return out * math.sqrt(2) + + +class ModulatedConv2d(nn.Module): + def __init__( + self, + in_channel, + out_channel, + kernel_size, + style_dim, + demodulate=True, + upsample=False, + downsample=False, + blur_kernel=[1, 2, 1], + no_zero_pad=False, + config=None, + side=None, + ): + super().__init__() + + self.eps = 1e-8 + self.kernel_size = kernel_size + self.in_channel = in_channel + self.out_channel = out_channel + self.style_dim = style_dim + self.upsample = upsample + self.downsample = downsample + self.no_zero_pad = no_zero_pad + self.config = config + self.side = side + + fan_in = in_channel * kernel_size ** 2 + self.scale = 1 / math.sqrt(fan_in) + + if upsample: + factor = 2 + assert kernel_size == 3, \ + "I'm only sure about the implementation of 3x3 kernels, " + \ + "the sizes may (VERY LIKELY) have size handling bugs." + if len(blur_kernel) % 2 == 1: # Ours + pad0 = pad1 = len(blur_kernel) // 2 + else: # Original StyleGAN2 + # [Hubert] Logic from: https://github.com/rosinality/stylegan2-pytorch/blob/3dee637b8937bf3830991c066ed8d9cc58afd661/model.py#L191 + # I can't really decode the logic, but seems generally right for even-sized blur_kernel. + p = (len(blur_kernel) - factor) - (kernel_size - 1) + pad0 = (p + 1) // 2 + factor - 1 + pad1 = p // 2 + 1 + if no_zero_pad: + self.dirty_rm_size = (pad0, pad1) # The dirty pixels going to be cut-off from features during runtime + self.blur = Blur(blur_kernel, pad=(0, 0), upsample_factor=factor) + else: + self.dirty_rm_size = (0, 0) + self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor) + elif downsample: + raise NotImplementedError("Never used.") + else: + if no_zero_pad: + self.padding = 0 # Directly use input and cut off + self.dirty_rm_size = (kernel_size//2, kernel_size//2) + else: + self.padding = kernel_size // 2 + self.dirty_rm_size = (0, 0) + + self.weight = nn.Parameter( + torch.randn(1, out_channel, in_channel, kernel_size, kernel_size) + ) + + self.demodulate = demodulate + if style_dim > 0: + self.modulation = EqualLinear(style_dim, in_channel, bias_init=1) + else: + self.modulation = None + + + def __repr__(self): + return ( + f'{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, ' + f'upsample={self.upsample}, downsample={self.downsample})' + ) + + + def calc_in_spatial_size(self, out_spatial_size, verbose=False): + """ + Calculate the "minimum" input spatial size that covers the given `out_spatial_size` after `forward()`. + + [Note] Here has some rounding involved (you see a `//` and `round_to_even` here). + As a result, if you `forward()` with the yielded `in_spatial_size`, + it is expected that the resulting output feature is equal-or-larger than `out_spatial_size`. + """ + if self.upsample: + round_to_even = lambda v: v if v % 2 == 0 else v + 1 + in_spatial_size = round_to_even(out_spatial_size + 1 + self.dirty_rm_size[0] + self.dirty_rm_size[1]) // 2 + if verbose: + print("\t\t {} = ({} + {} + {}) // 2".format(in_spatial_size, out_spatial_size, self.dirty_rm_size[0], self.dirty_rm_size[1])) + elif self.downsample: + assert False, "Never used, not sure if exists any problem here." + in_spatial_size = (out_spatial_size + self.dirty_rm_size[0] + self.dirty_rm_size[1]) * 2 + if verbose: + print("\t\t {} = ({} + {} + {}) * 2".format(in_spatial_size, out_spatial_size, self.dirty_rm_size[0], self.dirty_rm_size[1])) + else: + in_spatial_size = out_spatial_size + self.dirty_rm_size[0] + self.dirty_rm_size[1] + if verbose: + print("\t\t {} = {} + {} + {}".format(in_spatial_size, out_spatial_size, self.dirty_rm_size[0], self.dirty_rm_size[1])) + return in_spatial_size + + + def calc_out_spatial_size(self, in_spatial_size): + """ + Calculate the output spatial size when the module is `forward()`-ed with a feature with shape `(B, C, in_spatial_size, in_spatial_size)`. + """ + if self.upsample: + out_spatial_size = in_spatial_size * 2 - 1 - self.dirty_rm_size[0] - self.dirty_rm_size[1] + elif self.downsample: + assert False, "Never used, not sure if exists any problem here." + out_spatial_size = in_spatial_size // 2 - self.dirty_rm_size[0] - self.dirty_rm_size[1] + else: + out_spatial_size = in_spatial_size - self.dirty_rm_size[0] - self.dirty_rm_size[1] + return out_spatial_size + + + def calibrate_spatial_shape(self, feature, direction, padding_mode="replicate", verbose=False, pin_loc=None): + """ + With a given `direction` ("forward" or "backward"), generate the reversed `feature` + that preserves the correct spatial alignment by discounting the up/down-sampling and padding. + + `pin_loc`: tuple(, ) + In certain cases, with a designated pixel in the image space, we need to identify the (one) pixel in each layer that covers the pixel in the image space. + + [Note] + This function is not intended to use in formal generation (i.e., `forward()`), it is frequently used in + "spatial style fusion", "outpainting with inverted latent variables", and "interactive generation." + It is used to reverse-engineer the geometrical patterns designated in the pixel space. + More specifically, for "spatial style fusion", there exists a (blurry-)boundary between styles, + this function reverse and maintains the boundary of the styles at the approximately (not exact due to rounding) same position. + + [Note 2] + And yes, the `direction="forward"` is never used LoL + """ + _, _, h, w = feature.shape + prev_pin_loc = pin_loc # For debugging + + if direction == "forward": + if self.upsample: + new_h = h * 2 - 1 # align_corners=True already discounted the extra pixel created by conv_transpose + new_w = w * 2 - 1 + feature = F.interpolate( + feature, size=[new_h, new_w], mode="bilinear", align_corners=True) + feature = feature[:, :, 1:-1, 1:-1] # discounting blur + + if pin_loc is not None: + old_center = [h//2, w//2] + new_center = [new_h//2, new_w//2] + pin_loc_to_old_center = [ + pin_loc[0]-old_center[0], + pin_loc[1]-old_center[1]] + pin_loc_to_new_center = [ + pin_loc_to_old_center[0] * 2, + pin_loc_to_old_center[1] * 2] + pin_loc = [ + pin_loc_to_new_center[0] + new_center[0], + pin_loc_to_new_center[1] + new_center[1]] + elif self.downsample: + raise NotImplementedError("Never used.") + else: + if (self.padding == 0): + assert self.dirty_rm_size[0] != 0 and self.dirty_rm_size[1] != 0 + feature = feature[:, :, self.dirty_rm_size[0]:-self.dirty_rm_size[0], self.dirty_rm_size[1]:-self.dirty_rm_size[1]] + if pin_loc is not None: + pin_loc = [ + pin_loc[0] - self.dirty_rm_size[0], + pin_loc[1] - self.dirty_rm_size[1], + ] + if verbose: + if pin_loc is None: + print(" [*] Calibration: ({}, {}) => ({}, {})".format( + h, w, feature.shape[2], feature.shape[3])) + else: + print(" [*] Calibration: ({}, {}) => ({}, {}) ; Pin: ({}, {}) => ({}, {})".format( + h, w, feature.shape[2], feature.shape[3], + prev_pin_loc[0], prev_pin_loc[1], pin_loc[0], pin_loc[1])) + elif direction == "backward": + recovered_input_size = (self.calc_in_spatial_size(h), self.calc_in_spatial_size(w)) + if self.upsample: + # [Hubert] Recall the real `forward` procedure is: + # + # (Input) + # | O O O O O | + # => [a]: conv_transpose view + # | X O X O X O X O X O X | + # => [b]: after conv_tranpose + # | D O O O O O O O O O D | + # => [c]: Then blur + # | D B O O O O O O O B D | + # => [d]: Remove dirty pixels + # | O O O O O O O | + # (Output) + # + # Let's reverse this QuQ + + # [d] Add dirty padding back + if (self.dirty_rm_size != (0,0)): + feature = F.pad( + feature, + (self.dirty_rm_size[1], self.dirty_rm_size[1], self.dirty_rm_size[0], self.dirty_rm_size[0]), # (L, R, T, B) + mode=padding_mode) + # [c][b] No changes in spatial size, no need to deal with. (Note: The "D" pixel in [b][c] steps are ignored) + # [a] Interpolate to reverse the conv_transpose + feature = F.interpolate( + feature, + size=recovered_input_size, + mode="bilinear", + align_corners=True) + + if pin_loc is not None: + # [d] + pin_loc = [ + pin_loc[0] + self.dirty_rm_size[0], + pin_loc[1] + self.dirty_rm_size[1]] + # [c][b] skip + # [a] + old_center = [ # old center at [b] step + h+self.dirty_rm_size[0], w+self.dirty_rm_size[1]] + new_center = [ # new center at [a] step + old_center[0]//2, old_center[1]//2] + pin_loc_to_old_center = [ + pin_loc[0]-old_center[0], + pin_loc[1]-old_center[1]] + pin_loc_to_new_center = [ + pin_loc_to_old_center[0] // 2, + pin_loc_to_old_center[1] // 2] + pin_loc = [ + pin_loc_to_new_center[0] + new_center[0], + pin_loc_to_new_center[1] + new_center[1]] + elif self.downsample: + raise NotImplementedError("Never used.") + else: + # Add padding back due to the lost in Conv2d + if (self.padding == 0): + feature = F.pad( + feature, + (self.dirty_rm_size[1], self.dirty_rm_size[1], self.dirty_rm_size[0], self.dirty_rm_size[0]), # (L, R, T, B) + mode=padding_mode) + if pin_loc is not None: + pin_loc = [ + pin_loc[0] + self.dirty_rm_size[0], + pin_loc[1] + self.dirty_rm_size[1], + ] + if verbose: + if pin_loc is None: + print(" [*] Calibration: ({}, {}) => ({}, {})".format(h, w, feature.shape[2], feature.shape[3])) + else: + print(" [*] Calibration: ({}, {}) => ({}, {}) ; Pin: ({}, {}) => ({}, {})".format( + h, w, feature.shape[2], feature.shape[3], + prev_pin_loc[0], prev_pin_loc[1], pin_loc[0], pin_loc[1])) + else: + raise NotImplementedError("Unknown direction {} (valid: 'forward' or 'backward')".format(direction)) + + return feature, pin_loc + + def _auto_shape_align(self, source=None, target=None): + assert source.shape[2] >= target.shape[2], \ + "Got shape, source: {}, target: {}".format(source.shape, target.shape) + assert source.shape[3] >= target.shape[3], \ + "Got shape, source: {}, target: {}".format(source.shape, target.shape) + assert (source.shape[2] - target.shape[2]) % 2 == 0 + assert (source.shape[3] - target.shape[3]) % 2 == 0 + pad_h = (source.shape[2] - target.shape[2]) // 2 + pad_w = (source.shape[3] - target.shape[3]) // 2 + return source[:, :, pad_h:pad_h+target.shape[2], pad_w:pad_w+target.shape[3]] + + def get_flops(self, input, style): + flops = 0 # per sample, so do not consider batch dim + _, in_ch, H, W = input.shape + _, style_ch = style.shape + out_ch = self.out_channel + w_num_params = np.prod(self.weight.shape) + + # style = self.modulation(style).view(batch, 1, in_channel, 1, 1) + if self.modulation is not None: + flops += self.modulation.get_flops(None) + # # (1, ) * (1, out_ch, in_ch, k, k) * (B, 1, in_ch, 1, 1) + # # => (B, out_ch, in_ch, k, k) + # weight = self.scale * self.weight * style + flops += w_num_params + w_num_params * style_ch + + if self.demodulate: + # demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8) + flops += w_num_params + # weight = weight * demod.view(batch, self.out_channel, 1, 1, 1) + flops += w_num_params * in_ch + + # weight = weight.view( + # batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size + # ) + + if self.upsample: + # input = input.view(1, batch * in_channel, height, width) + # weight = weight.view( + # batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size + # ) + # weight = weight.transpose(1, 2).reshape( + # batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size + # ) + # out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch) + h_num_iters = (H * 2 + 1) + w_num_iters = (W * 2 + 1) + flops += w_num_params * h_num_iters * w_num_iters + # if self.no_zero_pad: + # out = out[:, :, 1:-1, 1:-1] # Clipping head and tail, which involves zero padding + # _, _, height, width = out.shape + # out = out.view(batch, self.out_channel, height, width) + # out = self.blur(out) + out_h = H * 2 + 1 + out_w = W * 2 + 1 + if self.no_zero_pad: + out_h -= 2 + out_w -= 2 + flops += self.blur.get_flops(shape=(-1, out_ch, out_h, out_w)) + + elif self.downsample: + # input = self.blur(input) + flops += self.blur.get_flops(shape=(-1, in_ch, H, W)) + # _, _, height, width = input.shape + # input = input.view(1, batch * in_channel, height, width) + # out = F.conv2d(input, weight, padding=self.padding, stride=2, groups=batch) + _, _, cur_h, cur_w = self.blur.get_output_shape(shape=(-1, in_ch, H, W)) + cur_h += self.padding * 2 + cur_w += self.padding * 2 + h_num_iters = (cur_h - self.kernel_size//2*2) // 2 + 1 + w_num_iters = (cur_w - self.kernel_size//2*2) // 2 + 1 + flops += w_num_params * (h_num_iters*w_num_iters) + # _, _, height, width = out.shape + # out = out.view(batch, self.out_channel, height, width) + else: + # input = input.view(1, batch * in_channel, height, width) + # out = F.conv2d(input, weight, padding=self.padding, groups=batch) + H += self.padding * 2 + W += self.padding * 2 + h_num_iters = (H - self.kernel_size//2*2) + w_num_iters = (W - self.kernel_size//2*2) + flops += w_num_params * (h_num_iters*w_num_iters) + # _, _, height, width = out.shape + # out = out.view(batch, self.out_channel, height, width) + + # print(input.shape, self.weight.shape, w_num_params * (h_num_iters*w_num_iters)) + return flops + + + def forward(self, input, style, coords=None, calc_flops=False): + batch, in_channel, height, width = input.shape + + if calc_flops: + flops = self.get_flops(input, style) + else: + flops = 0 + + # Special case for spatially-shaped style + # Here, we early justify whether the whole feature uses the same style. + # If that's the case, we simply use the same style, otherwise, it will use another slower logic. + if (style is not None) and (style.ndim == 4): + mean_style = style.mean([2,3], keepdim=True) + is_mono_style = ((style - mean_style) < 1e-8).all() + if is_mono_style: + style = mean_style.squeeze() + + # Regular forward + if style.ndim == 2: + style = self.modulation(style).view(batch, 1, in_channel, 1, 1) + # (1, ) * (1, out_ch, in_ch, k, k) * (B, 1, in_ch, 1, 1) + # => (B, out_ch, in_ch, k, k) + weight = self.scale * self.weight * style + + if self.demodulate: + demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8) + weight = weight * demod.view(batch, self.out_channel, 1, 1, 1) + + weight = weight.view( + batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size) + + if self.upsample: + input = input.view(1, batch * in_channel, height, width) + weight = weight.view( + batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size) + weight = weight.transpose(1, 2).reshape( + batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size) + out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch) + if self.no_zero_pad: + out = out[:, :, 1:-1, 1:-1] # Clipping head and tail, which involves zero padding + _, _, height, width = out.shape + out = out.view(batch, self.out_channel, height, width) + out = self.blur(out) + + elif self.downsample: + input = self.blur(input) + _, _, height, width = input.shape + input = input.view(1, batch * in_channel, height, width) + out = F.conv2d(input, weight, padding=self.padding, stride=2, groups=batch) + _, _, height, width = out.shape + out = out.view(batch, self.out_channel, height, width) + + else: + input = input.view(1, batch * in_channel, height, width) + out = F.conv2d(input, weight, padding=self.padding, groups=batch) + _, _, height, width = out.shape + out = out.view(batch, self.out_channel, height, width) + else: + assert (not self.training), \ + "Only accepts spatially-shaped global-latent for testing-time manipulation!" + assert (style.ndim == 4), \ + "Only considered BxCxHxW case, but got shape {}".format(style.shape) + + # For simplicity (and laziness), we sometimes feed spatial latents + # that are larger than the input, center-crop for such kind of cases. + style = self._auto_shape_align(source=style, target=input) + + # [Note] + # Original (lossy expression): input * (style * weight) + # What we equivalently do here (still lossy): (input * style) * weight + sb, sc, sh, sw = style.shape + flat_style = style.permute(0, 2, 3, 1).reshape(-1, sc) # (BxHxW, C) + style_mod = self.modulation(flat_style) # (BxHxW, C) + style_mod = style_mod.view(sb, sh, sw, self.in_channel).permute(0, 3, 1, 2) # (B, C, H, W) + + input_st = (style_mod * input) # (B, C, H, W) + weight = self.scale * self.weight + + if self.demodulate: + # [Hubert] + # This will be an estimation if spatilly fused styles are different. + # In practice, the interpolation of styles do not (numerically) change drastically, so the approximation here is invisible. + + """ + # This is the implementation we shown in the paper Appendix, the for-loop is slow. + # But this version surely allocates a constant amount of memory. + for i in range(sh): + for j in range(sw): + style_expand_s = style_mod[:, :, i, j].view(sb, 1, self.in_channel, 1, 1) # shape: (B, 1, in_ch, 1, 1) + simulated_weight_s = weight * style_expand_s # shape: (B, out_ch, in_ch, k, k) + demod_s[:, :, i, j] = torch.rsqrt(simulated_weight_s.pow(2).sum([2, 3, 4]) + 1e-8) # shape: (B, out_ch) + """ + + """ + Logically equivalent version, omits one for-loop by batching one spatial dimension. + """ + demod = torch.zeros(sb, self.out_channel, sh, sw).to(style.device) + for i in range(sh): + style_expand = style_mod[:, :, i, :].view(sb, 1, self.in_channel, sw).pow(2) # shape: (B, 1, in_ch, W) + weight_expand = weight.pow(2).sum([3, 4]).unsqueeze(-1) # shape: (B, out_ch, in_ch, 1) + simulated_weight = weight_expand * style_expand # shape: (B, out_ch, in_ch, W) + demod[:, :, i, :] = torch.rsqrt(simulated_weight.sum(2) + 1e-8) # shape: (B, out_ch, W) + + """ + # An even faster version that batches both height and width dimension, but allocates too much memory that is impractical in reality. + # For instance, it allocates 40GB memory with shape (8, 512, 128, 3, 3, 31, 31). + style_expand = style_mod.view(sb, 1, self.in_channel, 1, 1, sh, sw) # (B, 1 in_ch, 1, 1, H, W) + weight_expand = weight.unsqueeze(5).unsqueeze(6) # (B, out_ch, in_ch, k, k, 1, 1) + simulated_weight = weight_expand * style_expand # shape: (B, out_ch, in_ch, k, k, H, W) + demod = torch.rsqrt(simulated_weight.pow(2).sum([2, 3, 4]) + 1e-8) # shape: (B, out_ch, H, W) + """ + + """ + # Just FYI. If you use the mean style over the patch, it creates blocky artifacts + mean_style = style_mod.mean([2,3]).view(sb, 1, self.in_channel, 1, 1) + simulated_weight_ = weight * mean_style # shape: (B, out_ch, in_ch, k, k) + demod_ = torch.rsqrt(simulated_weight_.pow(2).sum([2, 3, 4]) + 1e-8) + demod_ = demod_.unsqueeze(2).unsqueeze(3) + """ + + weight = weight.view(self.out_channel, in_channel, self.kernel_size, self.kernel_size) + + if self.upsample: + weight = weight.transpose(0, 1).contiguous() + out = F.conv_transpose2d(input_st, weight, padding=0, stride=2, groups=1) + out = out[:, :, 1:-1, 1:-1] # Clipping head and tail, which involves zero padding + _, _, height, width = out.shape + if self.demodulate: + demod = F.interpolate( + demod, + size=(height, width), + mode="bilinear", + align_corners=True) + out = out * demod + out = self.blur(out) + elif self.downsample: + input_st = self.blur(input_st) + out = F.conv2d(input_st, weight, padding=self.padding, stride=2, groups=1) + if self.demodulate: + raise NotImplementedError("Unused, not implemented!") + out = out * demod + else: + out = F.conv2d(input_st, weight, padding=self.padding, groups=1) + if self.demodulate and (self.padding == 0): + demod = demod[:, :, self.dirty_rm_size[0]:-self.dirty_rm_size[0], self.dirty_rm_size[1]:-self.dirty_rm_size[1]] + out = out * demod + + out = out.contiguous() # Don't know where causes discontiguity. + + return out, flops + + +class NoiseInjection(nn.Module): + def __init__(self): + super().__init__() + self.weight = nn.Parameter(torch.zeros(1)) + self.testing_noise = {} + + def forward(self, image, noise=None, test_ids=None, calc_flops=False): + + # Testing with fixed noise + if (not self.training) and (test_ids is not None): + assert noise is None, "`test_ids` and `noise` are mutually exclusive!" + + batch, _, height, width = image.shape + assert len(test_ids) == batch + + batch_cur_testing_noise = [] + for test_id in test_ids: + test_id = test_id.item() if isinstance(test_id, torch.Tensor) else test_id + if test_id not in self.testing_noise: + cur_testing_noise = image.new_empty(1, height, width).normal_() + self.testing_noise[test_id] = cur_testing_noise.cpu().detach() + cache_h, cache_w = height, width + else: + cur_testing_noise = self.testing_noise[test_id].detach().to(image.device) + _, cache_h, cache_w = cur_testing_noise.shape + + if (cache_h < height) or (cache_w < width): + new_testing_noise = image.new_empty(1, height, width).normal_() + # Replace center with old values, always assume the old noises are at the center + pad_h = (height - cache_h) // 2 + pad_w = (width - cache_w) // 2 + new_testing_noise[:, pad_h:pad_h+cache_h, pad_w:pad_w+cache_w] = cur_testing_noise + self.testing_noise[test_id] = new_testing_noise + cur_testing_noise = new_testing_noise + cache_h, cache_w = height, width + + + # At this point, testing noise is always spatially larger or equal to current input feature + pad_h = (cache_h - height) // 2 + pad_w = (cache_w - width) // 2 + batch_cur_testing_noise.append(cur_testing_noise[:, pad_h:pad_h+height, pad_w:pad_w+width]) + noise = torch.stack(batch_cur_testing_noise) + + elif noise is None: + batch, _, height, width = image.shape + noise = image.new_empty(batch, 1, height, width).normal_() + + if calc_flops: + flops = np.prod(image.shape[1:]) * 2 + else: + flops = 0 + + output = image + self.weight * noise + return output, flops + + +class ConstantInput(nn.Module): + def __init__(self, channel, size=4): + super().__init__() + + self.input = nn.Parameter(torch.randn(1, channel, size, size)) + + def forward(self, batch_size): + return self.input.repeat(batch_size, 1, 1, 1) + + +class StyledConv(nn.Module): + def __init__( + self, + in_channel, + out_channel, + kernel_size, + style_dim, + upsample=False, + blur_kernel=[1, 2, 1], + demodulate=True, + no_zero_pad=False, + disable_noise=False, + activation="LeakyReLU", + config=None, + side=None, + ): + super().__init__() + self.no_zero_pad = no_zero_pad + self.upsample = upsample + + self.conv = ModulatedConv2d( + in_channel, + out_channel, + kernel_size, + style_dim, + upsample=upsample, + blur_kernel=blur_kernel, + demodulate=demodulate, + no_zero_pad=no_zero_pad, + config=config, + side=side, + ) + + if disable_noise: + self.noise = None + else: + self.noise = NoiseInjection() + + if activation.lower() == "LeakyReLU".lower(): + self.activate = FusedLeakyReLU(out_channel) + else: + raise NotImplementedError("Unknown activation {}".format(activation)) + + def calc_in_spatial_size(self, out_spatial_size): + return self.conv.calc_in_spatial_size(out_spatial_size) + + def calc_out_spatial_size(self, in_spatial_size): + return self.conv.calc_out_spatial_size(in_spatial_size) + + def calibrate_spatial_shape(self, spatial_latent, direction, padding_mode="replicate", verbose=False, pin_loc=None): + return self.conv.calibrate_spatial_shape(spatial_latent, direction, padding_mode=padding_mode, verbose=verbose, pin_loc=pin_loc) + + def get_noise_nch(self): + return self.conv.out_channel + + def forward(self, input, style, noise=None, coords=None, test_ids=None, calc_flops=False): + flops = 0 + out, cur_flops = self.conv(input, style, coords=coords, calc_flops=calc_flops) + flops += cur_flops + if self.noise is not None: + out, cur_flops = self.noise(out, noise=noise, test_ids=test_ids, calc_flops=calc_flops) + flops += cur_flops + out = self.activate(out) + if calc_flops: + flops += np.prod(out.shape[1:]) + return out, flops + + +class ToRGB(nn.Module): + def __init__( + self, + in_channel, + style_dim, + upsample=True, + blur_kernel=[1, 2, 1], + no_zero_pad=False, + config=None, + side=None, + ): + super().__init__() + self.no_zero_pad = no_zero_pad + + if upsample: + self.upsample = Upsample(blur_kernel, no_zero_pad=no_zero_pad) + + self.conv = ModulatedConv2d( + in_channel=in_channel, + out_channel=3, + kernel_size=1, + style_dim=style_dim, + demodulate=False, + no_zero_pad=no_zero_pad, + config=config, + side=side) + self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) + + def align_spatial_size(self, source, target): + if source is None: return source + _, _, cH, cW = target.shape + _, _, sH, sW = source.shape + if (cH == sH) and (cW == sW): return source + # Conv has more (or even) layers, the size is always smaller than or equal to source + assert ((sH - cH) % 2 == 0) and ((sW - cW) % 2 == 0), \ + "Should always have equal padding on two sides, got target ({}x{}) and source ({}x{})".format(cH, cW, sH, sW) + h_st = (sH - cH) // 2 + w_st = (sW - cW) // 2 + return source[:, :, h_st:h_st+cH, w_st:w_st+cW] + + def forward(self, input, style, skip=None, coords=None, calc_flops=False): + flops = 0 + if style.ndim == 4: # Special case that style is spatially-shaped for style fusion generation + style = self.align_spatial_size(style, target=input) + + if coords is not None: + coords = self.align_spatial_size(coords, target=input) + out, cur_flops = self.conv(input, style, coords=coords, calc_flops=calc_flops) + flops += cur_flops + + out = out + self.bias + if calc_flops: + flops += np.prod(out.shape[1:]) + + if skip is not None: + skip = self.upsample(skip) + if self.no_zero_pad: + skip = self.align_spatial_size(skip, target=out) + + out = out + skip + if calc_flops: + flops += np.prod(out.shape[1:]) + + return out, flops + diff --git a/models/stylegan2discriminator.py b/models/stylegan2discriminator.py new file mode 100644 index 0000000..3b08dda --- /dev/null +++ b/models/stylegan2discriminator.py @@ -0,0 +1,231 @@ +import math +import torch +from torch import nn + +from models.ops import * +from dataset import DictTensor + + +class ConvLayer(nn.Sequential): + def __init__( + self, + in_channel, + out_channel, + kernel_size, + downsample=False, + blur_kernel=[1, 3, 3, 1], + bias=True, + activate=True, + ): + layers = [] + + if downsample: + factor = 2 + p = (len(blur_kernel) - factor) + (kernel_size - 1) + pad0 = (p + 1) // 2 + pad1 = p // 2 + + layers.append(Blur(blur_kernel, pad=(pad0, pad1))) + + stride = 2 + self.padding = 0 + + else: + stride = 1 + self.padding = kernel_size // 2 + + layers.append( + EqualConv2d( + in_channel, + out_channel, + kernel_size, + padding=self.padding, + stride=stride, + bias=bias and not activate, + ) + ) + + if activate: + if bias: + layers.append(FusedLeakyReLU(out_channel)) + + else: + layers.append(ScaledLeakyReLU(0.2)) + + super().__init__(*layers) + + +class ResBlock(nn.Module): + def __init__(self, in_channel, out_channel, kernel_size=3, + blur_kernel=[1, 3, 3, 1], downsample=True): + super().__init__() + + self.conv1 = ConvLayer(in_channel, in_channel, kernel_size) + self.conv2 = ConvLayer(in_channel, out_channel, kernel_size, downsample=downsample) + + self.skip = ConvLayer( + in_channel, out_channel, kernel_size=1, downsample=downsample, activate=False, bias=False + ) + + def forward(self, input): + out = self.conv1(input) + out = self.conv2(out) + + skip = self.skip(input) + out = (out + skip) / math.sqrt(2) + + return out + + +class StyleGan2Discriminator(nn.Module): + def __init__(self, config, no_adds_on=False): + super().__init__() + self.config = config + self.size = size = config.train_params.patch_size + channel_multiplier = config.train_params.channel_multiplier + blur_kernel = [1, 3, 3, 1] + + channels = { + 4: 512, + 8: 512, + 16: 512, + 32: 512, + 64: 256 * channel_multiplier, + 128: 128 * channel_multiplier, + 256: 64 * channel_multiplier, + 512: 32 * channel_multiplier, + 1024: 16 * channel_multiplier, + 2048: 8 * channel_multiplier, + } + linear_ch = 512 + + if hasattr(self.config.train_params, "d_extra_multiplier"): + linear_ch = round(linear_ch * self.config.train_params.d_extra_multiplier) + for k in channels: + channels[k] = round(channels[k] * self.config.train_params.d_extra_multiplier) + + log_size = int(round(math.log(size, 2))) + convs = [ConvLayer(3, channels[2 ** log_size], kernel_size=1)] + + in_channel = channels[2 ** log_size] + + cur_out_spatial_size = size + for i in range(log_size, 2, -1): + out_channel = channels[2 ** (i - 1)] + convs.append(ResBlock(in_channel, out_channel, 3, blur_kernel)) + in_channel = out_channel + cur_out_spatial_size //= 2 + self.last_feat_ch = in_channel + + if no_adds_on: + self.use_coord_ac = False + self.use_coord_pd = False + else: + self.use_coord_ac = hasattr(self.config.train_params, "coord_use_ac") and self.config.train_params.coord_use_ac + self.use_coord_pd = hasattr(self.config.train_params, "coord_use_pd") and self.config.train_params.coord_use_pd + + if self.use_coord_pd: + self.convs_head= nn.Sequential(*convs[:-1]) + self.convs_tail = convs[-1] + else: + self.convs = nn.Sequential(*convs) + + # self.stddev_group = 4 + # Will remain 4 if batch is divisable by 4 + self.stddev_group = self._smallest_divisor_larger_than(config.train_params.batch_size, start=4) + self.stddev_feat = 1 + + self.final_conv = ConvLayer(in_channel + 1, linear_ch, kernel_size=3) + self.final_linear = nn.Sequential( + EqualLinear(linear_ch * cur_out_spatial_size * cur_out_spatial_size, linear_ch, activation='fused_lrelu'), + EqualLinear(linear_ch, 1), + ) + + if self.use_coord_ac: + if hasattr(config.train_params, "coord_ac_categorical") and config.train_params.coord_ac_categorical: + assert config.train_params.coord_ac_vert_only, "experimental setup" + self.coord_linear = nn.Sequential( + EqualLinear( + linear_ch * cur_out_spatial_size * cur_out_spatial_size, + linear_ch, + activation='fused_lrelu'), + EqualLinear(linear_ch, config.train_params.coord_num_dir * config.train_params.coord_vert_sample_size), + ) + else: + self.coord_linear = nn.Sequential( + EqualLinear( + linear_ch * cur_out_spatial_size * cur_out_spatial_size, + linear_ch, + activation='fused_lrelu'), + EqualLinear(linear_ch, config.train_params.coord_num_dir), + ) + + if self.use_coord_pd: + if hasattr(config.train_params, "coord_pd_hori_only") and config.train_params.coord_pd_hori_only: + self.coord_proj_dim = config.train_params.coord_num_dir - 1 + else: + self.coord_proj_dim = config.train_params.coord_num_dir + self.coord_proj = nn.Sequential( + EqualLinear( + self.coord_proj_dim, + linear_ch, + activation='fused_lrelu'), + EqualLinear(linear_ch, linear_ch), + ) + + + def _smallest_divisor_larger_than(self, number, start): + for i in range(start, int(math.sqrt(number))): + if number % i == 0: + return i + return number # No other divisor other than self + + + def forward(self, input_data, **kwargs): + + if "feature_disc"in kwargs and kwargs["feature_disc"]: + return self.feat_discriminator(input_data) + + if isinstance(input_data, torch.Tensor): + img = input_data + elif "gen" in input_data: + img = input_data["gen"] + else: + modality = self.config.train_params.training_modality + img = input_data[modality] + + + if self.use_coord_pd: + h = self.convs_head(img) + last_feat = h + h = self.convs_tail(h) + else: + h = self.convs(img) + + + batch, channel, height, width = h.shape + group = min(batch, self.stddev_group) + stddev = h.view( + group, -1, self.stddev_feat, channel // self.stddev_feat, height, width + ) + stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8) + stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2) + stddev = stddev.repeat(group, 1, height, width) + h = torch.cat([h, stddev], 1) + + out = self.final_conv(h).view(batch, -1) + before_linear = out + out = self.final_linear(out) + + + ret = DictTensor(d_patch=out) + if self.use_coord_ac: + ret["ac_coords_pred"] = self.coord_linear(before_linear) + if self.use_coord_pd and self.training: + label = input_data["ac_coords"][:, -self.coord_proj_dim:] # shape (B, num_dir) + label_proj = self.coord_proj(label) # shape (B, C) + feat_proj = last_feat.sum([2, 3]) # shape (B, C) + proj_pred = (label_proj * feat_proj).sum(1, keepdim=True) # shape (B, 1) + ret["d_patch"] = ret["d_patch"] + proj_pred * self.config.train_params.coord_pd_w + + return ret diff --git a/prepare_data.py b/prepare_data.py new file mode 100644 index 0000000..dce2d36 --- /dev/null +++ b/prepare_data.py @@ -0,0 +1,277 @@ +# -*- coding=utf-8 -*- +import os +import yaml +import argparse +import socket +from io import BytesIO +import multiprocessing +from functools import partial +from easydict import EasyDict +from glob import glob + +import numpy as np +from PIL import Image +import lmdb +from tqdm import tqdm +from torchvision import datasets +from torchvision.transforms import functional as trans_fn + +from env_config import LMDB_ROOTS + + +Image.MAX_IMAGE_PIXELS = np.inf + +def resize_and_convert(img, size, resample, quality=100, return_img=False): + # # Already done in downloading script + img = trans_fn.center_crop(img, size) + img = trans_fn.resize(img, size, resample) + if return_img: + return img + else: + buffer = BytesIO() + #img.save(buffer, format='jpeg', quality=quality) + img.save(buffer, format='png') + val = buffer.getvalue() + #img.seek(0) + #val = img.read() + return val + + +def resize_multiple(img, sizes=(128, 256, 512, 1024), resample=Image.LANCZOS, quality=100): + imgs = [] + + for size in sizes: + imgs.append(resize_and_convert(img, size, resample, quality)) + + return imgs + + +global_semaphore = None +def resize_worker(img_file, size, resample, return_img=False): + i, file = img_file + global_semaphore.acquire() + try: + img = Image.open(file) + img = img.convert('RGB') + #out = resize_multiple(img, sizes=sizes, resample=resample) + out = resize_and_convert(img, size, resample, quality=100, return_img=return_img) + except Exception as e: + print(" Exception while processing {}; Message: {}".format(file, e)) + raise e + + return i, out + + +def worker_init(semaphore): + global global_semaphore + global_semaphore = semaphore + + +def prepare_img(output_dir, img_paths, n_worker, size=128, resample=Image.LANCZOS): + semaphore = multiprocessing.Semaphore(128) + resize_fn = partial(resize_worker, size=size, resample=resample, return_img=True) + sample_ids = np.arange(len(img_paths)) + total = 0 + # Start processing + files = [] + for sid,path in zip(sample_ids[total:], img_paths[total:]): + key = f'{size}-{str(sid).zfill(8)}' + output_path = os.path.join(output_dir, key+".png") + if not os.path.exists(output_path): + files.append((sid,path)) + with multiprocessing.Pool(n_worker, initializer=worker_init, initargs=(semaphore,)) as pool: + for i, img in tqdm(pool.imap_unordered(resize_fn, files), initial=total, total=len(img_paths)): + key = f'{size}-{str(i).zfill(8)}' + output_path = os.path.join(output_dir, key+".png") + try: + img.save(output_path) + except BaseException as e: + if os.path.exists(output_path): + os.remove(output_path) + raise e + semaphore.release() + + n_imgs = len(glob(os.path.join(output_dir, "*"))) + print(" [*] Total {} image saved to {}".format(n_imgs, output_dir)) + assert n_imgs == len(img_paths) + + +def prepare_lmdb(env_func, img_paths, n_worker, size=128, resample=Image.LANCZOS, specific_indices=None, scan=False, n_steps=None): + semaphore = multiprocessing.Semaphore(128) + resize_fn = partial(resize_worker, size=size, resample=resample) + sample_ids = np.arange(len(img_paths)) + + total = 0 + + with env_func() as env: + with env.begin(write=True) as txn: + + if txn.id() != 0: # DB already has entries! + """ + print(" [*] Filtering already processed entries...") + for cursor,(sid,path) in enumerate(tqdm(zip(sample_ids,img_paths), total=len(img_paths))): + key = f'{size}-{str(sid).zfill(8)}'.encode('utf-8') + if txn.get(key) is None: + print(" [*] Ignores {} existing entries, {} remaining!".format(total, len(img_paths)-total)) + break # end searching on the first failure, i.e., discard results afterward + else: + total += 1 + """ + length_record = txn.get("length".encode("utf-8")) + if specific_indices is not None: + total = 0 + print(" [*] Dataset fixing mode for specific indices = {}".format(specific_indices)) + elif length_record is None: # somehow corrupted + total = 0 + print(" [!] Length record is corrupted, reset to 0...") + else: + total = int(length_record.decode("utf-8")) + if total >= len(img_paths): + print(" [*] Found existing complete db, skip!") + if scan: + pass # Keep going for scanning + else: + return + else: + total = max(total-2*n_worker, 0) + print(" [*] Start from previous end point at {}!".format(total)) + + if n_steps is not None: + sample_ids = sample_ids[:total+n_steps] + img_paths = img_paths[:total+n_steps] + + if scan and total > 0: + print(" [*] Start scanning") + with env_func() as env: + with env.begin(write=False) as txn: + files = [] + for i in tqdm(range(total)): + key = f'{size}-{str(i).zfill(8)}'.encode('utf-8') + if txn.get(key) is None: + files.append(sample_ids[i], img_paths[i]) + print(" [!] Found corrupted key at {}".format(i)) + print(" [*] Found {} corrupted records, fixing...".format(len(files))) + elif specific_indices is None: + files = [(sid,path) for sid,path in zip(sample_ids[total:], img_paths[total:])] + else: + files = [(sample_ids[idx],img_paths[idx]) for idx in specific_indices] + # Start processing + with multiprocessing.Pool(n_worker, initializer=worker_init, initargs=(semaphore,)) as pool: + env = env_func() + try: + for i, img in tqdm(pool.imap(resize_fn, files), initial=total, total=len(img_paths)): + key = f'{size}-{str(i).zfill(8)}'.encode('utf-8') + with env.begin(write=True) as txn: + txn.put(key, img) + if specific_indices is None: + total += 1 + txn.put('length'.encode('utf-8'), str(total).encode('utf-8')) + del img + semaphore.release() + if i%100000: + import gc; gc.collect() + finally: + env.close() + +def parse_list(s): + return [int(v) for v in s.split(",")] + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument("config", type=str) + parser.add_argument('--n_worker', type=int, default=8) + parser.add_argument("--specific_indices", type=parse_list, default=None) + parser.add_argument("--img", action="store_true") + parser.add_argument("--train_only", action="store_true") + parser.add_argument("--scan", action="store_true") + parser.add_argument("--n_steps", type=int, default=None) + args = parser.parse_args() + + hostname = socket.gethostname() + cur_lmdb_root = None + if hostname in LMDB_ROOTS: + cur_lmdb_root = LMDB_ROOTS[hostname] + else: + for entry in LMDB_ROOTS["unspecified"]: + if os.path.exists(entry): + cur_lmdb_root = entry + if cur_lmdb_root is None: + cur_lmdb_root = config.data_params.lmdb_root + if not os.path.exists(cur_lmdb_root): + os.makedirs(cur_lmdb_root) + + with open(args.config) as f: + config = yaml.load(f, Loader=yaml.FullLoader) + config = EasyDict(config) + print(" [*] Config {} loaded!".format(args.config)) + + if not hasattr(config.data_params, "num_valid"): + config.data_params.num_valid = 0 + + img_paths = sorted(glob(os.path.join(config.data_params.raw_data_root, "*"))) + print(" [*] Make dataset `{}` at `{}`, resolution {}, num samples {} (will use {} train, {} valid)".format( + config.data_params.dataset, + os.path.join(config.data_params.raw_data_root, "*"), + config.train_params.data_size, + len(img_paths), + config.data_params.num_train, + config.data_params.num_valid)) + + if not args.scan: + assert len(img_paths) != 0, "Found no samples at {}".format(os.path.join(config.data_params.raw_data_root, "*")) + assert len(img_paths) >= config.data_params.num_train + config.data_params.num_valid, \ + "{} train and {} valid, sum up {} samples is more than dataset with {} samples".format(config.data_params.num_train, config.data_params.num_valid, config.data_params.num_train + config.data_params.num_valid, len(img_paths)) + + train_paths = img_paths[:config.data_params.num_train] + valid_paths = img_paths[config.data_params.num_train:config.data_params.num_train+config.data_params.num_valid] + + if args.img: + train_img_dir = os.path.join(cur_lmdb_root, config.data_params.dataset, "train-img") + valid_img_dir = os.path.join(cur_lmdb_root, config.data_params.dataset, "valid-img") + + if not os.path.exists(train_img_dir): os.makedirs(train_img_dir) + if not args.train_only: + if not os.path.exists(valid_img_dir): os.makedirs(valid_img_dir) + + print(" [*] Processing training set...") + prepare_img( + train_img_dir, train_paths, args.n_worker, + size=config.train_params.data_size, + resample=Image.LANCZOS) + + if not args.train_only: + print(" [*] Processing validation set...") + prepare_img( + valid_img_dir, valid_paths, args.n_worker, + size=config.train_params.data_size, + resample=Image.LANCZOS) + else: + train_lmdb_dir = os.path.join(cur_lmdb_root, config.data_params.dataset, "train") + valid_lmdb_dir = os.path.join(cur_lmdb_root, config.data_params.dataset, "valid") + + if not os.path.exists(train_lmdb_dir): os.makedirs(train_lmdb_dir) + if not args.train_only: + if not os.path.exists(valid_lmdb_dir): os.makedirs(valid_lmdb_dir) + + print(" [*] Processing training set...") + env_func = lambda: lmdb.open(train_lmdb_dir, map_size=1024 ** 4, readahead=False) + prepare_lmdb( + env_func, train_paths, args.n_worker, + size=config.train_params.data_size, + resample=Image.LANCZOS, + specific_indices=args.specific_indices, + scan=args.scan, + n_steps=args.n_steps) + + if not args.train_only: + print(" [*] Processing validation set...") + env_func = lambda: lmdb.open(valid_lmdb_dir, map_size=1024 ** 4, readahead=False) + prepare_lmdb( + env_func, valid_paths, args.n_worker, + size=config.train_params.data_size, + resample=Image.LANCZOS, + specific_indices=args.specific_indices, + scan=args.scan, + n_steps=args.n_steps) + diff --git a/quant_eval_utils.py b/quant_eval_utils.py new file mode 100644 index 0000000..1e3c9f3 --- /dev/null +++ b/quant_eval_utils.py @@ -0,0 +1,235 @@ +import os +import numpy as np +import torch +from torch.functional import align_tensors +import torch.nn.functional as F +from glob import glob +from easydict import EasyDict +from torch.utils import data +from torch.utils.data import Dataset, BatchSampler, SequentialSampler +from torchvision import transforms +import torchvision.transforms.functional as ttF +from PIL import Image + +class QuantEvalSampleGenerator(): + def __init__(self, g_ema, latent_sampler, output_size, use_seq_inf, postprocessing_params, device, config, fid_type, + img_folder=None, sample_fn=None, use_pil_resize=False): + self.g_ema = g_ema + self.latent_sampler = latent_sampler + self.output_size = output_size + self.use_seq_inf = use_seq_inf + self.postprocessing_params = postprocessing_params + self.device = device + self.config = config + self.fid_type = fid_type + self.use_pil_resize = use_pil_resize + + self.use_img_folder = (img_folder is not None) + self.use_sample_fn = (sample_fn is not None) + + assert isinstance(postprocessing_params, list), \ + "Postprocessing params should be passed in with an ordered list." + + # Procedures that use external sources + if img_folder is not None: + assert False, "Not tested." + dataset = ImageFolder(img_folder) + self.img_folder_loader = DataLoader( + dataset=dataset, + batch_sampler=BatchSampler( + SequentialSampler(dataset), batch_size=self.config.train_params.batch_size, drop_last=False), + num_workers=8) + elif sample_fn is not None: + self.sample_fn = sample_fn + # Procedures that requires generate images + elif hasattr(config.train_params, "styleGAN2_baseline") and config.train_params.styleGAN2_baseline: + mult = int(np.ceil(output_size / config.train_params.patch_size)) + self.latent_size = config.train_params.ts_input_size * mult + else: # InfinityGAN generation at testing time + if fid_type == "alis": + from test_managers.fused_seq_connecting_generation import FusedSeqConnectingGenerationManager + self.task_manager = FusedSeqConnectingGenerationManager(g_ema, device, None, config) + self.task_manager.task_specific_init() + elif self.use_seq_inf: + from test_managers.infinite_generation import InfiniteGenerationManager + config.task = EasyDict() + self.task_manager = InfiniteGenerationManager(g_ema, device, None, config) + self.task_manager.task_specific_init(output_size=(output_size, output_size)) + else: + self.task_manager = None + g_ema_module = g_ema.module if config.var.dataparallel else g_ema + self.latent_size = g_ema_module.calc_in_spatial_size(output_size) + + def run_postprocessing(self, images): + for name,param in self.postprocessing_params: + if name == "crop": + images = self._center_crop(images, param) + elif name == "scale": + assert param < 10, \ + "Scaling function get an unexpected large value {}".format(param) +\ + " are you accidentally feeding target resolution instead of a scale?" + images = self._scale(images, param) + elif name == "resize": + assert param > 1, \ + "Scaling function get an unexpected small value {}".format(param) +\ + " are you accidentally feeding scale instead of a target resolution?" + images = self._resize(images, param) + elif name == "assert": + self._assert(images, param) + elif name == "img_to_gan_img": + images = self._img_to_gan_img(images) + elif name == "np_to_tensor": + images = self._np_to_tensor(images) + else: + raise NotImplementedError( + "Unkown postprocessing method {} with param {}".format(name, param)) + # print(" Shape after {} ({}) is {}".format(name, param, images.shape)) + return images + + def _gan_tensor_to_pil_img(self, images): + images = (images + 1) / 2 + images = images.cpu() + return [ttF.to_pil_image(im) for im in images] + + def _pil_img_to_gan_tensor(self, images): + images = torch.stack([ttF.to_tensor(im) for im in images]) + return (images * 2) - 1 + + def _scale(self, images, scale): + #if self.use_pil_resize: + # h, w = images[0].height, images[0].width + # new_h = round(h * scale) + # new_w = round(w * scale) + # return [im.resize([new_w, new_h], resample=Image.BILINEAR) for im in images] + #else: + return F.interpolate(images, scale_factor=scale, mode="bilinear", align_corners=True) + + def _resize(self, images, target_size): + if self.use_pil_resize: + device = images.device + images = self._gan_tensor_to_pil_img(images) + images = [im.resize([target_size, target_size], resample=Image.BILINEAR) for im in images] + images = self._pil_img_to_gan_tensor(images) + return images.to(device) + else: + return F.interpolate(images, size=target_size, mode="bilinear", align_corners=True) + + def _assert(self, images, target_shape): + assert images.shape[-1] == target_shape, \ + "Assert shape to be {}, but get {}!".format(target_shape, images.shape[-1]) + assert images.shape[-2] == target_shape, \ + "Assert shape to be {}, but get {}!".format(target_shape, images.shape[-2]) + + def _img_to_gan_img(self, images): + assert (images.min() > 0) and (images.max() < 1), \ + "Got images not in [0, 1] range, with range ({}, {})".format(images.min(), images.max()) + return images * 2 - 1 + + def _np_to_tensor(self, images): + return torch.from_numpy(images).permute(0, 3, 1, 2).to(self.device) + + def _center_crop(self, images, size): + _, _, H, W = images.shape + if H <= size: + assert H == W, \ + "Images here should all be squared, but got {}".format(images.shape) + return images + pad_h = (H - size) // 2 + pad_w = (W - size) // 2 + return images[:, :, pad_h:pad_h+size, pad_w:pad_w+size] + + @torch.no_grad() + def __call__(self, n_batch): + + for _ in range(n_batch): + + if self.use_img_folder: + images = next(self.img_folder_loader) + images = images.to(self.device) + elif self.use_sample_fn: + images = self.sample_fn() + elif self.use_seq_inf or self.fid_type=="alis": + images = self.task_manager.run_next(save=False, write_gpu_time=False, disable_pbar=True) + else: + global_latent = self.latent_sampler.sample_global_latent( + self.config.train_params.batch_size, self.device) + local_latent = self.latent_sampler.sample_local_latent( + self.config.train_params.batch_size, self.device, + specific_shape=(self.latent_size, self.latent_size)) + + sample = self.g_ema( + global_latent=global_latent, + local_latent=local_latent, + disable_dual_latents=True) + images = sample["gen"] + images = self.run_postprocessing(images) + yield images.contiguous() + + +class QuantEvalDataLoader(): + def __init__(self, dataset, output_size, device, config): + self.dataset = dataset + self.output_size = output_size + self.device = device + + if hasattr(config, "task") and hasattr(config.task, "init_index"): + from utils import data_sampler # Avoid import error from COCO-GAN + sampler = data_sampler( + dataset, shuffle=False, init_index=config.task.init_index) + else: + sampler = data.SequentialSampler(dataset) + + self.dataloader_proto = data.DataLoader( + dataset, + batch_size=config.train_params.batch_size, + sampler=sampler, + drop_last=False, + num_workers=16, + ) + self.dataloader = None + + def _center_crop(self, images): + _, _, H, W = images.shape + if H == self.output_size and W == self.output_size: + return images + else: + assert self.output_size < H, "Got {} > {}".format(self.output_size, H) + assert self.output_size < W, "Got {} > {}".format(self.output_size, W) + pad_h = (H - self.output_size) // 2 + pad_w = (W - self.output_size) // 2 + return images[:, :, pad_h:pad_h+self.output_size, pad_w:pad_w+self.output_size] + + def __len__(self): + return len(self.dataloader) + + def __next__(self): + images = next(self.dataloader)["full"] + assert images.shape[2] == self.output_size and images.shape[3] == self.output_size, \ + "Got unexpected image shape {} instead of size {}".format(images.shape, self.output_size) + return images + # return self._center_crop(images["full"]) + + def __iter__(self): + self.dataloader = iter(self.dataloader_proto) + return self + + +class ImageFolder(Dataset): + # torchvision's requires labels... + def __init__(self, image_dir): + self.image_dir = image_dir + self.image_paths = glob(os.path.join(self.image_dir, "*")) + self.transforms = transforms.Compose( + [ + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True), + ] + ) + + def __getitem__(self, index): + image_path = self.image_paths[index] + img = Image.open(image_path) + return self.transforms(img) + + def __len__(self): + return len(self.image_paths) diff --git a/scripts/eval/eval_fid_job_s3.sh b/scripts/eval/eval_fid_job_s3.sh new file mode 100644 index 0000000..d4a249b --- /dev/null +++ b/scripts/eval/eval_fid_job_s3.sh @@ -0,0 +1,9 @@ +GPUs="2" +exp_name="v19N-ID348B-F128" +CUDA_VISIBLE_DEVICES="$GPUs" python eval_spatial_fids.py ./configs/$exp_name.yaml --bs=16 --scale=1 + +GPUs="2" +exp_name="v20N-ID354-RandY-DivAngZ-F197P101S11-L4C3SSC256-VCut3-VC10" +CUDA_VISIBLE_DEVICES="$GPUs" python eval_spatial_fids.py ./configs/$exp_name.yaml --bs=16 --scale=1 + + diff --git a/scripts/eval/eval_outpaint_imgdir.sh b/scripts/eval/eval_outpaint_imgdir.sh new file mode 100644 index 0000000..c6af3df --- /dev/null +++ b/scripts/eval/eval_outpaint_imgdir.sh @@ -0,0 +1,15 @@ +infinityGAN_root="~/infinityGAN/infinityGAN" + +exp_name="v20N-ID378IOF-RandY-DivAngZ-F197P101S11-L4C3SSC256-VCut3-VC10" +test_name="inversion-eval-IOF_128x128" +python eval_outpaint_imgdir.py \ + --fake-dir=${infinityGAN_root}/logs/${exp_name}/test/${test_name}/imgs/inv_raw/ \ + --real-dir=${infinityGAN_root}/logs/${exp_name}/test/${test_name}/imgs/real_gt + +exp_name="v20N-ID379IOP-RandY-DivAngZ-F197P101S11-L4C3SSC256-VCut3-VC10" +test_name="inversion-eval-IOP_128x128" +python eval_outpaint_imgdir.py \ + --fake-dir=${infinityGAN_root}/logs/${exp_name}/test/${test_name}/imgs/inv_raw/ \ + --real-dir=${infinityGAN_root}/logs/${exp_name}/test/${test_name}/imgs/real_gt + +echo " [Completed] Please find the results at ./logs-quant/outpaint/" \ No newline at end of file diff --git a/scripts/eval/eval_scaleinv_fids.sh b/scripts/eval/eval_scaleinv_fids.sh new file mode 100644 index 0000000..d071c3f --- /dev/null +++ b/scripts/eval/eval_scaleinv_fids.sh @@ -0,0 +1,23 @@ +GPUs="2,5" + +#exp_name="v19N-ID348B-F128" +#CUDA_VISIBLE_DEVICES="$GPUs" python eval_fids.py ./configs/$exp_name.yaml --type="scaleinv" --bs=16 --scale=1 +#CUDA_VISIBLE_DEVICES="$GPUs" python eval_fids.py ./configs/$exp_name.yaml --type="scaleinv" --bs=4 --scale=2 +#CUDA_VISIBLE_DEVICES="$GPUs" python eval_fids.py ./configs/$exp_name.yaml --type="scaleinv" --bs=4 --scale=4 +#CUDA_VISIBLE_DEVICES="$GPUs" python eval_fids.py ./configs/$exp_name.yaml --type="scaleinv" --bs=1 --scale=8 + +exp_name="v20N-ID354-RandY-DivAngZ-F197P101S11-L4C3SSC256-VCut3-VC10" +#CUDA_VISIBLE_DEVICES="$GPUs" python eval_fids.py ./configs/$exp_name.yaml --type="scaleinv" --bs=16 --scale=1 +#CUDA_VISIBLE_DEVICES="$GPUs" python eval_fids.py ./configs/$exp_name.yaml --type="scaleinv" --bs=16 --scale=2 +#CUDA_VISIBLE_DEVICES="$GPUs" python eval_fids.py ./configs/$exp_name.yaml --type="scaleinv" --bs=16 --scale=4 +#CUDA_VISIBLE_DEVICES="$GPUs" python eval_fids.py ./configs/$exp_name.yaml --type="scaleinv" --bs=48 --scale=8 +CUDA_VISIBLE_DEVICES="$GPUs" python eval_fids.py ./configs/$exp_name.yaml --type="scaleinv" --bs=128 --scale=16 + +#exp_name="v20N-ID389S-F197P101S11-StyleGAN2-TFC" +#CUDA_VISIBLE_DEVICES="$GPUs" python eval_fids.py ./configs/$exp_name.yaml --type="scaleinv" --bs=16 --scale=1 +#CUDA_VISIBLE_DEVICES="$GPUs" python eval_fids.py ./configs/$exp_name.yaml --type="scaleinv" --bs=16 --scale=2 +#CUDA_VISIBLE_DEVICES="$GPUs" python eval_fids.py ./configs/$exp_name.yaml --type="scaleinv" --bs=16 --scale=4 +#CUDA_VISIBLE_DEVICES="$GPUs" python eval_fids.py ./configs/$exp_name.yaml --type="scaleinv" --bs=64 --scale=8 +#CUDA_VISIBLE_DEVICES="$GPUs" python eval_fids.py ./configs/$exp_name.yaml --type="scaleinv" --bs=128 --scale=16 + + diff --git a/scripts/eval/eval_scaleinv_fids_v4.sh b/scripts/eval/eval_scaleinv_fids_v4.sh new file mode 100644 index 0000000..cfab4d5 --- /dev/null +++ b/scripts/eval/eval_scaleinv_fids_v4.sh @@ -0,0 +1,9 @@ +GPUs="0,1,2,3,4,5,6,7" +exp_name="v20N-ID389S-F197P101S11-StyleGAN2-TFC" + +echo "Downloading ckpt..." +mkdir -p logs/${exp_name}/ckpt/ +scp hylee@vllab5.ucmerced.edu:/home/hubert/infinityGAN/infinityGAN/logs/${exp_name}/ckpt/best_fid.pth.tar ./logs/${exp_name}/ckpt/best_fid.pth.tar + +CUDA_VISIBLE_DEVICES="$GPUs" python eval_fids.py ./configs/$exp_name.yaml --type="scaleinv" --bs=128 --scale=16 + diff --git a/scripts/eval/eval_scaleinv_fids_v5.sh b/scripts/eval/eval_scaleinv_fids_v5.sh new file mode 100644 index 0000000..6022e46 --- /dev/null +++ b/scripts/eval/eval_scaleinv_fids_v5.sh @@ -0,0 +1,9 @@ +GPUs="0,1,2,3,4,5,6,7" +exp_name="v20N-ID354-RandY-DivAngZ-F197P101S11-L4C3SSC256-VCut3-VC10" + +echo "Downloading ckpt..." +mkdir -p logs/${exp_name}/ckpt/ +scp hylee@vllab5.ucmerced.edu:/home/hubert/infinityGAN/infinityGAN/logs/${exp_name}/ckpt/best_fid.pth.tar ./logs/${exp_name}/ckpt/best_fid.pth.tar + +CUDA_VISIBLE_DEVICES="$GPUs" python eval_fids.py ./configs/$exp_name.yaml --type="scaleinv" --bs=128 --scale=16 + diff --git a/scripts/eval/eval_spatial_fids.sh b/scripts/eval/eval_spatial_fids.sh new file mode 100644 index 0000000..e05f51c --- /dev/null +++ b/scripts/eval/eval_spatial_fids.sh @@ -0,0 +1,14 @@ +GPUs="4" + +#exp_name="v19N-ID348B-F128" +#CUDA_VISIBLE_DEVICES="$GPUs" python eval_fids.py ./configs/$exp_name.yaml --type="spatial" --bs=16 --scale=1 +#CUDA_VISIBLE_DEVICES="$GPUs" python eval_fids.py ./configs/$exp_name.yaml --type="spatial" --bs=4 --scale=2 +#CUDA_VISIBLE_DEVICES="$GPUs" python eval_fids.py ./configs/$exp_name.yaml --type="spatial" --bs=4 --scale=4 +#CUDA_VISIBLE_DEVICES="$GPUs" python eval_fids.py ./configs/$exp_name.yaml --type="spatial" --bs=1 --scale=8 + +#exp_name="v20N-ID354-RandY-DivAngZ-F197P101S11-L4C3SSC256-VCut3-VC10" +#CUDA_VISIBLE_DEVICES="$GPUs" python eval_fids.py ./configs/$exp_name.yaml --type="spatial" --bs=16 --scale=1 +#CUDA_VISIBLE_DEVICES="$GPUs" python eval_fids.py ./configs/$exp_name.yaml --type="spatial" --bs=16 --scale=2 +#CUDA_VISIBLE_DEVICES="$GPUs" python eval_fids.py ./configs/$exp_name.yaml --type="spatial" --bs=16 --scale=4 +#CUDA_VISIBLE_DEVICES="$GPUs" python eval_fids.py ./configs/$exp_name.yaml --type="spatial" --bs=16 --scale=8 + diff --git a/scripts/eval/run_benchmark.py b/scripts/eval/run_benchmark.py new file mode 100644 index 0000000..d91decd --- /dev/null +++ b/scripts/eval/run_benchmark.py @@ -0,0 +1,81 @@ +import os +import shutil +import socket +import subprocess +from tqdm import tqdm +from glob import glob + +import urllib.request + +baseline_ckpt_rmt = "http://vllab1.ucmerced.edu/~hubert/shared_files/v19N-ID348B-F128.pth.tar" +baseline_ckpt_stash = "./depricated_logs/v19N-ID348B-F128/ckpt/best_fid.pth.tar" +baseline_ckpt_active = "./logs/v19N-ID348B-F128/ckpt/best_fid.pth.tar" + + +if socket.gethostname() == "OuO": # Local debugging + ours_exp = "v19N-ID326-CDropV2-RandY-DivAngZ-F197P101S11-L8C3-VCut3-VC10" +else: + ours_exp = "v20N-ID354-RandY-DivAngZ-F197P101S11-L4C3SSC256-VCut3-VC10" +baseline_exp = "v19N-ID348B-F128" + + +GPU_CFG = { + "baseline.yaml": ["0", baseline_exp], + "naive.yaml": ["0", ours_exp], + "parabatch2.yaml": ["0", ours_exp], + "parabatch4.yaml": ["0", ours_exp], + "parabatch8.yaml": ["0", ours_exp], + "parabatch16.yaml": ["0", ours_exp], + "parabatch32.yaml": ["0,1", ours_exp], + "parabatch64.yaml": ["0,1,2,3", ours_exp], + "parabatch128.yaml": ["0,1,2,3,4,5,6,7", ours_exp], +} + +if __name__ == "__main__": + + if not os.path.exists(baseline_ckpt_stash) and not os.path.exists(baseline_ckpt_active): + print(" [*] Downloading checkpoint, may take a while...") + os.makedirs(os.path.dirname(baseline_ckpt_stash)) + urllib.request.urlretrieve(baseline_ckpt_rmt, baseline_ckpt_stash) + + # Compose paths + ours_stash_dir = os.path.join("./depricated_logs/", ours_exp) + ours_active_dir = os.path.join("./logs/", ours_exp) + baseline_stash_dir = os.path.join("./depricated_logs/", baseline_exp) + baseline_active_dir = os.path.join("./logs/", baseline_exp) + + # Move stashed ckpt to active + if os.path.exists(ours_stash_dir): + shutil.move(ours_stash_dir, ours_active_dir) + else: + assert os.path.exists(ours_active_dir) + if os.path.exists(ours_stash_dir): + shutil.move(baseline_stash_dir, baseline_active_dir) + else: + assert os.path.exists(baseline_active_dir) + + all_configs = sorted(glob("./benchmark_configs/*"))[::-1] + for benchmark_config in all_configs: + + for k,v in GPU_CFG.items(): + if k in benchmark_config: + gpu_ids, selected_exp = v + break + model_config = os.path.join("./configs/", selected_exp+".yaml") + + job_env = os.environ.copy() + job_env["CUDA_VISIBLE_DEVICES"] = gpu_ids + + print(" [*] Running new exp: {}".format(benchmark_config)) + # So run subprocess.Popen, which creates legit process, which has access to GPUs + proc = subprocess.Popen([ + "python", "test.py", + "--model-config", model_config, + "--test-config", benchmark_config, + "--speed-benchmark", + ], env=job_env) + proc.communicate() + + # Move active back to stash + shutil.move(ours_active_dir, ours_stash_dir) + shutil.move(baseline_active_dir, baseline_stash_dir) diff --git a/scripts/script_generator/gen_inbetween_permute.py b/scripts/script_generator/gen_inbetween_permute.py new file mode 100644 index 0000000..b0a687f --- /dev/null +++ b/scripts/script_generator/gen_inbetween_permute.py @@ -0,0 +1,93 @@ +import os +import sys +import shutil +from glob import glob + +resolution = int(sys.argv[1]) +assert "CUDA_VISIBLE_DEVICES" in os.environ, "Must specify cuda devices at run time!" +gpu_ids = os.environ["CUDA_VISIBLE_DEVICES"].split(",") +n_gpus = len(gpu_ids) + +# Generate script for resuming previous progress +start_idx = 0 + +output_script_root = os.path.join("scripts", "quali-permute", "inbetween{}".format(resolution)) +if os.path.exists(output_script_root): + shutil.rmtree(output_script_root) +os.makedirs(output_script_root) + +test_cfg = "./test_configs/fused_gen_IOF246_pano_inbetween_256x{}.yaml".format(resolution) +model_cfg = "./configs/v20N-ID378IOF-RandY-DivAngZ-F197P101S11-L4C3SSC256-VCut3-VC10.yaml" +exp_name = os.path.basename(model_cfg) +if ".yaml" in exp_name: + exp_name = exp_name.split(".yaml")[0] + +assert os.path.exists(test_cfg), "Path not exists: "+test_cfg +assert os.path.exists(model_cfg), "Path not exists: "+model_cfg + +num_l = 32 +inv_cfg_l = "inversion_for_fusedgen_IOF246_256x{}L_256x128".format(resolution) +inv_placement_l = "{:.6f},{:.6f}".format(0.5, (128//2)/resolution) +stats_dir_l = os.path.join("./logs/", exp_name, "test", inv_cfg_l, "stats") +stats_files_l = sorted(glob(os.path.join(stats_dir_l, "*.pkl"))) + +num_r = 32 +inv_cfg_r = "inversion_for_fusedgen_IOF246_256x{}R_256x128".format(resolution) +inv_placement_r = "{:.6f},{:.6f}".format(0.5, (resolution-128//2)/resolution) +stats_dir_r = os.path.join("./logs/", exp_name, "test", inv_cfg_r, "stats") +stats_files_r = sorted(glob(os.path.join(stats_dir_r, "*.pkl"))) + +n_jobs = num_l * num_r - start_idx +n_jobs_per_gpu = n_jobs // n_gpus + 1 + + +# Generate script for filling in missing files +test_name = os.path.basename(test_cfg).split(".yaml")[0] +scan_root = os.path.join("./logs/", exp_name, "test", test_name) +fused_gen_IOF246_pano_inbetween_256x2150 +skip_ids = [] +if os.path.exists(scan_root): + ps = glob(os.path.join(scan_root, "0*.png")) + for p in ps: + with open(p, "r") as f: + for l in f: + l = l.split("--override-save-idx=")[1] + if l[-1] == "\n": + l = l[:-1] + skip_ids.append(int(l)) +skip_ids = set(skip_ids) + +counter = 0 +cur_gpu_id = 0 +for idx_l in range(num_l): + for idx_r in range(num_r): + + global_id = idx_l * num_r + idx_r + if global_id < start_idx: + continue + if global_id in skip_ids: + continue + + output_script_path = os.path.join(output_script_root, "{}th-gpu.sh".format(cur_gpu_id)) + with open(output_script_path, "a") as f: + + inv_rec_l = stats_files_l[idx_l] + inv_rec_r = stats_files_r[idx_r] + assert os.path.exists(inv_rec_l), "Path not exists: "+inv_rec_l + assert os.path.exists(inv_rec_r), "Path not exists: "+inv_rec_r + + cmd = " ".join([ + "CUDA_VISIBLE_DEVICES={}".format(gpu_ids[cur_gpu_id]), + "python test.py", + "--test-config=" + test_cfg, + "--model-config=" + model_cfg, + "--inv-records=" + inv_rec_l + ":" + inv_rec_r, + "--inv-placements={}:{}".format(inv_placement_l, inv_placement_r), + "--override-save-idx={}".format(global_id), + ]) + f.write(cmd + "\n") + + counter += 1 + if counter > n_jobs_per_gpu: + cur_gpu_id += 1 + cur_gpu_id = min(cur_gpu_id, n_gpus-1) diff --git a/test.py b/test.py new file mode 100644 index 0000000..a73f3ee --- /dev/null +++ b/test.py @@ -0,0 +1,335 @@ +import os +import yaml +import math +import shutil +import socket +import argparse +import numpy as np +from tqdm import tqdm +from glob import glob +from easydict import EasyDict + +import torch +from torch import nn + +from test_managers.interactive_sampler import InteractiveSampler +from utils import * + + +def inference(g_ema, device, config, args): + + assert (not hasattr(config.train_params, "imp_feat_unfold")) or \ + (hasattr(config.train_params, "imp_feat_unfold") and config.train_params.imp_feat_unfold == False), \ + "Not carefully investigated!" + + """ + Setup env + """ + exp_root = os.path.join(config.var.log_dir, config.var.exp_name) + if args.debug: + save_root = os.path.join( + exp_root, + "test", + "debug-{}".format(config.task.config_name)) + else: + save_root = os.path.join( + exp_root, + "test", + "{}".format(config.task.config_name)) + if not os.path.exists(save_root): os.makedirs(save_root) + shutil.copy2( + config.task.config_path, + os.path.join(save_root, os.path.basename(config.task.config_path))) + + """ + Start inference + """ + if config.task.num_gen == -1: # all, read number of images from dataset + assert hasattr(config.task, "dataset_size"), \ + "Generate all should only be used for tasks with a real image dataset." + config.task.num_gen = config.task.dataset_size + iter_ = math.ceil(config.task.num_gen / config.task.batch_size) + pbar = tqdm( + range(iter_), + initial=0, + total=iter_, + dynamic_ncols=True, + smoothing=0.01) + + """ + Calculate shape and coordinates for patches + """ + task_manager = import_func(config.task.task_manager)(g_ema, device, save_root, config) + task_manager.task_specific_init() + + if args.inter_ckpt: + if os.path.isfile(args.inter_ckpt): + print(" [!] A single inter ckpt is loaded for all samples!") + testing_vars_load_from = args.inter_ckpt + else: + testing_vars_load_from = sorted(glob(os.path.join(args.inter_ckpt, "*.pkl"))) + + print(" [*] Setup complete, start testing!") + for iter_ in pbar: + + if config.task.interactive or args.interactive: + testing_vars = task_manager.create_vars( + inv_records=args.inv_records, inv_placements=args.inv_placements) + if args.inter_ckpt: + if isinstance(testing_vars_load_from, list): + assert config.task.batch_size == 1, "Does not consider batch_size > 1 case!" + if task_manager.cur_global_id < len(testing_vars_load_from): + testing_vars.load(testing_vars_load_from[task_manager.cur_global_id]) + else: + print(" [!] Run out of previous ckpt! Start from random!") + else: + testing_vars.load(testing_vars_load_from) + InteractiveSampler(task_manager, testing_vars, config) + else: + if args.speed_benchmark: + assert (not hasattr(args, "inv_record")) or args.inv_record is None, "No fancy stuffs in benchmark." + if iter_ < 10: # Do not use the stats from first ten, still unstable + task_manager.run_next( + save=False, write_gpu_time=False) + else: + task_manager.run_next( + save=False, write_gpu_time=True) + elif args.calc_flops: + task_manager.run_next(save=False, write_gpu_time=False, calc_flops=True) + if iter_ == 1: exit() + else: + task_manager.run_next( + save=True, write_gpu_time=False, + inv_records=args.inv_records, inv_placements=args.inv_placements) + if args.debug: + task_manager.exit() + exit() + + task_manager.exit() + + if args.speed_benchmark: + if not hasattr(config.task, "parallel_batch_size"): + config.task.parallel_batch_size = -1 + exec_mean, exec_std = task_manager.get_exec_time_stats() + print(" [*] Benchmark results over {} samples: {:.4f} +- {:.4f} (sec/image)".format( + (config.task.num_gen - 1), exec_mean, exec_std)) + benchmark_record_path = "./logs-quant/benchmark_results/benchmark-{}.txt".format(socket.gethostname()) + if not os.path.exists(os.path.dirname(benchmark_record_path)): + os.makedirs(os.path.dirname(benchmark_record_path)) + with open(benchmark_record_path, "a") as f: + f.write("[-] EXP: Res {}x{} ; Parabatch {} ; {} GPUs\n".format( + config.task.height, config.task.width, config.task.parallel_batch_size, config.var.n_gpu)) + f.write("{:.6f} +- {:.6f}\n".format(exec_mean, exec_std)) + f.write("\n") + + +if __name__ == "__main__": + + try: + + parser = argparse.ArgumentParser() + parser.add_argument("--model-config", type=str) + parser.add_argument("--test-config", type=str) + + parser.add_argument("--exp-suffix", type=str, default=None) + parser.add_argument("--ckpt", type=str, default=None) + parser.add_argument("--seed", type=int, default=None) + parser.add_argument("--interactive", action="store_true") + parser.add_argument("--override-save-idx", type=int, default=None) + + parser.add_argument("--speed-benchmark", action="store_true") + parser.add_argument("--calc-flops", action="store_true") + + # Flag for inversion distributed testing + parser.add_argument("--inv-start-idx", type=int, default=None) + parser.add_argument("--try-restrict-memory", type=float, default=1.) + + # Load from inversion + parser.add_argument("--inv-records", type=str, default=None) + parser.add_argument("--inv-placements", type=str, default=None) + + # Interactive recover from ckpt + parser.add_argument("--inter-ckpt", type=str, default=None) + + parser.add_argument("--debug", action="store_true") + parser.add_argument("--verbose", action="store_true") + parser.add_argument("--archive-mode", action="store_true") + parser.add_argument("--clear-fid-cache", action="store_true") + args = parser.parse_args() + + if hasattr(torch.cuda.memory, "set_per_process_memory_fraction"): + torch.cuda.memory.set_per_process_memory_fraction(args.try_restrict_memory) + print(" [*] Set memory limit to {}%!".format(args.try_restrict_memory*100)) + + """ + Parse Inv args + """ + def parse_tuple(v): + return tuple([float(vv) for vv in v.split(",")]) + if args.inv_records is not None: + args.inv_records = [ + param for param in args.inv_records.split(":")] + if args.inv_placements is None: + args.inv_placements = (0.5, 0.5) + else: + args.inv_placements = [ + parse_tuple(param) for param in args.inv_placements.split(":")] + + """ + Normal init + """ + if args.verbose: + def annoy_print(x): + torch.cuda.synchronize() + print(x, end="") + else: + annoy_print = dummy_func + + with open(args.model_config, "r") as f: + config = yaml.load(f, Loader=yaml.FullLoader) + config = EasyDict(config) + config.var = EasyDict() + config.var.exp_name = os.path.basename(args.model_config).split(".yaml")[0] + print(" [*] Config {} loaded!".format(args.model_config)) + + with open(args.test_config, "r") as f: + config.task = EasyDict(yaml.load(f, Loader=yaml.FullLoader)) + config.task.config_name = os.path.basename(args.test_config).split(".yaml")[0] + config.task.config_path = args.test_config + config.task.exp_suffix = args.exp_suffix + + if args.inv_start_idx is not None: + config.task.init_index = args.inv_start_idx + assert config.task.batch_size == 1 + assert config.task.num_gen == 1 + elif args.override_save_idx is not None: + config.task.init_index = args.override_save_idx + assert config.task.batch_size == 1 + assert config.task.num_gen == 1 + else: + config.task.init_index = 0 + + if hasattr(config.task, "override_dataset_name"): + print(" [!] Override dataset name to {} with specification in test-config!".format( + config.task.override_dataset_name)) + config.data_params.dataset = config.task.override_dataset_name + if hasattr(config.task, "override_dataset_data_size"): + print(" [!] Override dataset raw resolution to {} with specification in test-config!".format( + config.task.override_dataset_data_size)) + config.train_params.data_size = config.task.override_dataset_data_size + if hasattr(config.task, "override_dataset_full_size"): + print(" [!] Override dataset full-image resolution to {} with specification in test-config!".format( + config.task.override_dataset_full_size)) + config.train_params.full_size = config.task.override_dataset_full_size + + if args.seed is not None: + print(" [!] Forcingly use seed from cmdline!") + cur_seed = args.seed + elif config.task.seed is not None: + print(" [!] Use default seed in task-config!!") + cur_seed = config.task.seed + else: + print(" [!] Seed not specified, randomly assign one now!") + cur_seed = np.random.randint(0, 9487) + print(" [!] Current seed: {}".format(cur_seed)) + manually_seed(cur_seed) + + """ + Batch size calibration + """ + if config.task.num_gen % config.task.batch_size != 0: + bs = config.task.batch_size + config.task.num_gen = math.ceil(config.task.num_gen / bs) * bs + print(" [!] Force number of generated images to a multiple of batch size => {}".format(config.task.num_gen)) + config.train_params.batch_size = config.task.batch_size + + """ + Trait generation has specific num_gen + """ + if hasattr(config.task, "is_trait_figure") and config.task.is_trait_figure: + if hasattr(config.task, "n_trait_x"): + config.task.num_gen = config.task.n_trait_x * config.task.n_trait_y + else: + config.task.num_gen = 1 + + """ + Archive mode + """ + if args.archive_mode: + config.var.log_dir = "../../" # We are running in ./logs//codes/ + else: + config.var.log_dir = "./logs/" + + """ + Error file writing handling + Remove previous error file (will make confusion on log synchronizing) + """ + error_f = os.path.join(config.var.log_dir, config.var.exp_name, "error-log.txt") + if os.path.exists(error_f): + os.remove(error_f) + + if "CUDA_VISIBLE_DEVICES" in os.environ: + n_gpu = len(os.environ["CUDA_VISIBLE_DEVICES"].split(",")) + config.var.dataparallel = n_gpu > 1 + config.var.n_gpu = n_gpu + if n_gpu > 1: + torch.backends.cudnn.benchmark = True + else: + torch.backends.cudnn.benchmark = False + else: + raise ValueError(" [!] Please specify CUDA_VISIBLE_DEVICES!") + + # [NOTE] In debug mode: + # 1. Will not write any logs + # 2. Exit after first full iteration + # 3. Force eval FID with one batch of fake samples; will not write FID cache if real stats are not exist + if args.debug: + print(" [Warning] Debug mode; Do not use this unless you know what you are doing!") + bs = 1 + config.task.batch_size = bs * n_gpu + config.log_params.n_save_sample = bs * n_gpu + + """ + Build G + """ + g_ema = import_func(config.train_params.g_arch)(config=config) + + """ + Multi-GPU + """ + if config.var.dataparallel: + device = "cpu" # torch will auto do the GPU partitioning in backend + g_ema = nn.DataParallel(g_ema).cuda() + else: + device = "cuda" + g_ema = g_ema.to(device) + g_ema.eval() + + + """ + Load checkpoint + """ + if args.ckpt is None: + ckpt_dir = os.path.join(config.var.log_dir, config.var.exp_name, "ckpt") + best_ckpt = os.path.join(ckpt_dir, "best_fid.pth.tar") + + assert os.path.exists(best_ckpt), "Cannot find checkpoint at {}!".format(best_ckpt) + print(" [*] Found ckpt, load model from:", best_ckpt) + ckpt = torch.load(best_ckpt, map_location=lambda storage, loc: storage) + else: + ckpt = torch.load(args.ckpt, map_location=lambda storage, loc: storage) + safe_load_state_dict(g_ema, ckpt["g_ema"]) #, strict=False) + print(" [*] Loaded ckpt at {} iter with FID {:.4f}".format(ckpt["iter"], ckpt["best_fid"])) + + with torch.no_grad(): + inference(g_ema, device, config, args) + + except Exception as e: + if e is not KeyboardInterrupt: + error_f = os.path.join(config.var.log_dir, config.var.exp_name, "test-error-log.txt") + with FileLock(error_f, timeout=10, delay=0.1) as lock: + with open(error_f, "w+") as f: + f.write(str(e) + "\n") + f.write(" *** stack trace *** \n") + f.write(traceback.format_exc()) + raise e diff --git a/test_distr.py b/test_distr.py new file mode 100644 index 0000000..33977ae --- /dev/null +++ b/test_distr.py @@ -0,0 +1,228 @@ +import os +import time +import argparse +import traceback +import subprocess +import urllib.request +import numpy as np +import pickle as pkl +from PIL import Image +from glob import glob +from multiprocessing import Pool + +from env_config import REMOTE_CKPT_URL +from utils import FileLock + +GPU_MIN_MEM = 300 +GPU_IN_USE = "-gpu_in_use.pkl" +ERROR_LOGS = "-error_logs.txt" + +CKPTS = { + "InfinityGAN-IOF": "http://vllab1.ucmerced.edu/~hubert/shared_files/infinityGAN/ckpt/IOF/best_fid.pth.tar", + "InfinityGAN-IOP": "http://vllab1.ucmerced.edu/~hubert/shared_files/infinityGAN/ckpt/IOP/best_fid.pth.tar", +} + + +def new_experiment(kwargs): + args = kwargs["args"] + sample_idx = str(kwargs["idx"]) + test_name = os.path.basename(args.test_config).split(".yaml")[0] + + while True: + with FileLock(test_name+GPU_IN_USE, timeout=np.inf, delay=1) as lock: + gpu_usage = pkl.load(open(test_name+GPU_IN_USE, "rb")) + found_gpu = False + for k,v in gpu_usage.items(): + if v < args.exp_per_gpu: + gpu_usage[k] += 1 + gpu_id = k + found_gpu = True + break + if found_gpu: + pkl.dump(gpu_usage, open(test_name+GPU_IN_USE, "wb")) + break + else: + time.sleep(1) + + job_env = os.environ.copy() + job_env["CUDA_VISIBLE_DEVICES"] = gpu_id + + mem_ratio = 1 / args.exp_per_gpu + print(" [*] Submitting a sample {} to GPU id = {}".format(sample_idx, gpu_id)) + # So run subprocess.Popen, which creates legit process, which has access to GPUs + FNULL = open(os.devnull, 'w') # suppress all outputs + proc = subprocess.Popen([ + "python", "test.py", + "--test-config", args.test_config, + "--model-config", args.model_config, + "--inv-start-idx", sample_idx, + "--try-restrict-memory", str(mem_ratio), + ], env=job_env, stdout=FNULL, stderr=subprocess.PIPE) + output, error = proc.communicate() # Blocking + + if proc.returncode != 0: + print("[!] Error at sample_id {} (return {}):".format(sample_idx, proc.returncode)) + print(error.decode("utf-8")) + with FileLock(test_name+ERROR_LOGS, timeout=np.inf, delay=1) as lock: + with open(test_name+ERROR_LOGS, "a") as f: + f.write("[!] Error at sample_id {}\n".format(sample_idx)) + f.write(error.decode("utf-8") + "\n") + f.write("\n") + + with FileLock(test_name+GPU_IN_USE, timeout=np.inf, delay=1) as lock: + gpu_usage = pkl.load(open(test_name+GPU_IN_USE, "rb")) + gpu_usage[gpu_id] -= 1 + pkl.dump(gpu_usage, open(test_name+GPU_IN_USE, "wb")) + + +def create_jobs(args, gpu_id): + exp_name = os.path.basename(args.model_config).split(".yaml")[0] + test_name = os.path.basename(args.test_config).split(".yaml")[0] + + if args.idx is not None: + args.idx = [int(v) for v in args.idx.split(",")] + all_jobs = [ + { + "args": args, + "idx": str(i), + } for i in args.idx] + else: + save_root = os.path.join("logs", exp_name, "test", test_name, "stats") + prev_result_files = glob(os.path.join(save_root, "*.pkl")) + + if args.validate: + for path in prev_result_files: + try: + pkl.load(open(path, "rb")) + except: + os.remove(path) + prev_result_files = glob(os.path.join(save_root, "*.pkl")) + + completed_exp_ids = [ + int(os.path.basename(path).split(".pkl")[0]) + for path in prev_result_files] + + job_ids = range(args.num) if args.st is None else range(args.st, args.num) + + all_jobs = [ + { + "args": args, + "idx": str(i), + } for i in job_ids if i not in completed_exp_ids] + + return all_jobs + + +def get_gpu_memory_map(): + result = subprocess.check_output( + [ + 'nvidia-smi', '--query-gpu=memory.used,memory.free', + '--format=csv,nounits,noheader' + ], encoding='utf-8') + # Convert lines into a dictionary + gpu_memory = [ + { + "used": int(x.split(", ")[0]), + "free": int(x.split(", ")[1]), + } for x in result.strip().split('\n')] + gpu_memory_map = dict(zip(range(len(gpu_memory)), gpu_memory)) + return gpu_memory_map + + +def check_gpu_availability(): + empty_gpus = [] + mem_map = get_gpu_memory_map() + is_all_empty = True + for gpu_id in list(mem_map.keys()): + #used_ratio = mem_map[gpu_id]["used"] / (mem_map[gpu_id]["free"]+mem_map[gpu_id]["used"]) + #if used_ratio > 0.3: + if mem_map[gpu_id]["used"] > GPU_MIN_MEM: # sometimes GPUs has awkawrd memory reservation + mem_map.pop(gpu_id) + is_all_empty = False + else: + empty_gpus.append(gpu_id) + return empty_gpus, is_all_empty + + +def emission_func(a): + img = Image.open("assets/lena.png") + H, W = img.height, img.width + nH, nW = 10240, 10240 + while True: + img.resize((nH, nW), resample=Image.BICUBIC) + img.resize((H, W), resample=Image.BICUBIC) + time.sleep(1) + _, is_all_empty = check_gpu_availability() + if is_all_empty: + return + + +if __name__ == "__main__": + + parser = argparse.ArgumentParser() + parser.add_argument("--model-config", type=str) + parser.add_argument("--test-config", type=str) + + parser.add_argument("--st", type=int, default=None) + parser.add_argument("--num", type=int, default=50000) + parser.add_argument("--idx", type=str, default=None) + + parser.add_argument("--exp-per-gpu", type=int, default=2) + parser.add_argument("--validate", action="store_true") + parser.add_argument("--update-ckpt", action="store_true") + parser.add_argument("--recur", action="store_true") + parser.add_argument("--emit-carbon-dioxide", action="store_true", + help="GCP automatically closes instances with low CPU usage, which forces me to emit more CO2 than they need to...") + args = parser.parse_args() + + assert "CUDA_VISIBLE_DEVICES" in os.environ, "Must specify GPUs with CUDA_VISIBLE_DEVICES!" + gpu_ids = os.environ["CUDA_VISIBLE_DEVICES"].split(",") + n_gpus = len(gpu_ids) + + # # Get ckpt from remote + exp_name = os.path.basename(args.model_config).split(".yaml")[0] + exp_ckpt = os.path.join("logs", exp_name, "ckpt", "best_fid.pth.tar") + if args.update_ckpt or (not os.path.exists(exp_ckpt)): + # remote_url = os.path.join(REMOTE_CKPT_URL, exp_name+".pth.tar") + remote_url = CKPTS[exp_name] + ckpt_dir = os.path.dirname(exp_ckpt) + if (not os.path.exists(ckpt_dir)): + os.makedirs(ckpt_dir) + print(" [*] Downloading checkpoint from {}".format(remote_url)) + urllib.request.urlretrieve(remote_url, exp_ckpt) + assert os.path.exists(exp_ckpt), \ + "Cannot find ckpt at {}".format(exp_ckpt) + + # Remove prev GPU usage map, and create new GPU usage profile + test_name = os.path.basename(args.test_config).split(".yaml")[0] + if os.path.exists(test_name+GPU_IN_USE): + os.remove(test_name+GPU_IN_USE) + if os.path.exists(test_name+GPU_IN_USE + ".lock"): + os.remove(test_name+GPU_IN_USE + ".lock") + pkl.dump({i: 0 for i in gpu_ids}, open(test_name+GPU_IN_USE, "wb")) + + # Create dir in master thread, avoid conflicts in subprocess + test_folder = os.path.join("logs", exp_name, "test", test_name) + if not os.path.exists(test_folder): + os.makedirs(test_folder) + + if args.emit_carbon_dioxide: + print(" [*] Emiting CO2 to keep GCP instance alive...") + with Pool(4) as pool: + pool.map(emission_func, range(16)) + + while True: + # Create job templates and submit + all_jobs = create_jobs(args, gpu_ids) + n_workers = n_gpus * args.exp_per_gpu + with Pool(n_workers) as pool: + pool.map(new_experiment, all_jobs, chunksize=1) + + if args.recur: + if len(all_jobs) == 0: + break + else: + break + + print(" [*] Complete!") + diff --git a/test_managers/base_test_manager.py b/test_managers/base_test_manager.py new file mode 100644 index 0000000..a157ab5 --- /dev/null +++ b/test_managers/base_test_manager.py @@ -0,0 +1,282 @@ +import math +import time +import numpy as np + +import torch + +from coord_handler import CoordHandler +from latent_sampler import LatentSampler + + +class BaseTestManager(): + + def __init__(self, g_ema, device, save_root, config): + self.g_ema = g_ema.eval() + self.device = device + self.save_root = save_root + self.config = config + + self.accum_exec_times = [] + + if config.var.dataparallel: + self.g_ema_module = g_ema.module + else: + self.g_ema_module = g_ema + + self.ss_unfold_size = self.config.train_params.ss_n_layers * self.config.train_params.ss_unfold_radius + + if hasattr(config.task, "parallel_batch_size") and config.task.parallel_batch_size != config.task.batch_size: + print(" [!] Parallel batched generation is enabled!") + assert config.task.batch_size == 1, "Though should supported, but never tested." + self.enable_parallel_batching = True + self.parallel_stack = [] + else: + self.enable_parallel_batching = False + + self.coord_handler = CoordHandler(config) + self.latent_sampler = LatentSampler(g_ema, config) + + self.coord_handler.eval() + + if hasattr(self.config.task, "init_index") and self.config.task.init_index is not None: + self.cur_global_id = self.config.task.init_index + else: + self.cur_global_id = 0 + ts_input_size = config.train_params.ts_input_size + + # One can easily confused here (even I frequently confuse myself...). The + # two images generated with two consecutive blocks of spatial_latent is NOT + # direct neighbors of each other. There is a missing region between them that + # was omitted due to the no-padding "odd-architecture" of the generator. More + # specifically, some of the intermediate features are thrown away, as it is only + # used to avoid the generator padding. + # + # Here we manually compute how many pixels does it displaced + # while moving one pixel in the latent space. Then calculate + # how many pixels shall we displace in practice in order to + # generate spatially consecutive patches. + # + # [Example] + # Original arch: 5 => 7 => 11 => 19 => 17 (assume end here) + # Doubled arch: 10 => 17 => 31 => 59 => 57 + # pixel_space_disp = 57 - 17 = 40 + # + # Z: | 5 | (omitted_area) | 5 | + # X: | 17 | 23 | 17 | + # G1: | 17 | + # G2: (16) | 17 | + # G3: (16) (16) | 17 | + # + # pixelspace_disp_unit = 40 // 5 = 8 + # (N pixels disp. in pixel space per 1 pixel disp. in latent space) + # latentspace_step_size = 17 // 8 = 2 + # pixelspace_step_size = 2 * 8 = 16 + # + # [Notes] + # 1. The overlapping area between G1, G2 and G3 shall be consistent with our calculation + # 2. The GNN part "MAY" not truely-fully-convolutional, need to specifically dealt with later. + # 3. The GNN padding is is a constant, and automatically discounted while computing `outfeat_disps`. + self.outfeat_sizes_list = \ + self._get_feature_sizes(side="output", multiplier=1, include_ss=False) + self.outfeat_sizes_list_doubled = \ + self._get_feature_sizes(side="output", multiplier=2, include_ss=False) + outfeat_disps = self.outfeat_sizes_list_doubled - self.outfeat_sizes_list + assert (outfeat_disps % ts_input_size == 0).all(), \ + "By definition, ts_input_size {} should be a divisor of {}".format( + ts_input_size, outfeat_disps.tolist()) + + # Similarly, process input space + # (actually, it is just `np.roll(outfeat_disps, -1)[:-1]`, but I am too lazy to impl. `include_ss` in TS...) + self.infeat_sizes_list = self._get_feature_sizes( + side="input", + output_size=self.outfeat_sizes_list[-1], + include_ss=False) + self.in_size_doubled_list = self._get_feature_sizes( + side="input", + output_size=self.outfeat_sizes_list_doubled[-1], + include_ss=False) + infeat_disps = self.in_size_doubled_list - self.infeat_sizes_list + assert (infeat_disps % ts_input_size == 0).all(), \ + "By definition, ts_input_size {} should be a divisor of {}".format( + ts_input_size, infeat_disps.tolist()) + + # Get the image space / latent space step size + pixelspace_disp_unit = outfeat_disps[-1] // ts_input_size + self.pixelspace_step_size = (self.outfeat_sizes_list[-1] // pixelspace_disp_unit) * pixelspace_disp_unit + self.latentspace_step_size = self.pixelspace_step_size // pixelspace_disp_unit + + # Consequently, also records the step sizes in the feature space (input-side) for further noise/latent handling + infeat_disp_units = infeat_disps // ts_input_size + self.infeat_step_sizes = self.latentspace_step_size * infeat_disp_units + + # Consequently, also records the step sizes in the feature space (output-side) for further noise/latent handling + outfeat_disp_units = outfeat_disps // ts_input_size + self.outfeat_step_sizes = self.latentspace_step_size * outfeat_disp_units + + # Double-check the correctness of the spec in the config + if config.train_params.training_modality == "patch": + out_res_spec = config.train_params.patch_size + else: + raise ValueError("Unknown training modality {}".format(config.train_params.training_modality)) + assert self.outfeat_sizes_list[-1] == out_res_spec, \ + "In general, in terms of efficiency, I should've set generator output" +\ + " resolution config ({}) equals to the true generator output resolution ({}).".format( + out_res_spec, self.outfeat_sizes_list[-1]) + + def _get_feature_sizes(self, side=None, multiplier=1, output_size=None, include_ss=False): + assert side in {"input", "output"} + if side == "input": + assert output_size is not None + assert multiplier == 1 + size_list = self.g_ema_module.calc_in_spatial_size( + output_size, include_ss=include_ss, return_list=True) + elif side == "output": + assert include_ss == False, "SS is never used, some logics are not implemented/verified." + size_list = self.g_ema_module.calc_out_spatial_size( + self.config.train_params.ts_input_size*multiplier, include_ss=include_ss, return_list=True) + size_list = np.array(size_list).astype(np.uint32) + return size_list + + def task_specific_init(self): + # Task-specific initialization + raise NotImplementedError() + + def run_next(self): + # Run the complete task once + raise NotImplementedError() + + def save_results(self, path): + raise NotImplementedError() + + def exit(self): + return # Usually does nothing + + def get_exec_time_stats(self): + mean = np.mean(self.accum_exec_times) + std = np.std(self.accum_exec_times) + return mean, std + + def pretty_print_flops(self, flops): + ret = "" + if flops > 1e12: + ret += str(int((flops//1e12) % 1000)).zfill(3) + "T " + if flops > 1e9: + ret += str(int((flops//1e9) % 1000)).zfill(3) + "G " + if flops > 1e6: + ret += str(int((flops//1e6) % 1000)).zfill(3) + "M " + if flops > 1e3: + ret += str(int((flops//1e3) % 1000)).zfill(3) + "K " + if flops > 1: + ret += str(int((flops//1e1) % 1000)).zfill(3) + return ret + + """ + Parallel batching + """ + def agg_args(self, args): + if isinstance(args[0], dict): + raise NotImplementedError("Unused") + # return {k: self.parallel_agg_kwargs(v) for k,v in kwargs.items()} + elif isinstance(args[0], list): + # return [self.parallel_agg_kwargs(v) for v in kwargs] + ret = [] + for i in range(len(args[0])): + ret.append(self.agg_args([v[i] for v in args])) + return ret + elif isinstance(args[0], torch.Tensor): + return torch.cat(args, 0) + elif isinstance(args[0], bool): + return args[0] + else: # Other scalar types, assume all shares the same value + assert all([v==args[0] for v in args]), \ + "Assume all params within the parallel batch share the same scalar value, but got {}".format(args) + return args[0] + + def parallel_agg_kwargs(self, kwargs_list): + all_keys = [k for k in kwargs_list[0].keys()] + ret = {} + for k in all_keys: + ret[k] = self.agg_args([kwargs[k] for kwargs in kwargs_list]) + return ret + + def ensure_contiguous(self, kwargs): + if isinstance(kwargs, dict): + return {k: self.ensure_contiguous(v) for k,v in kwargs.items()} + elif isinstance(kwargs, list): + return [self.ensure_contiguous(v) for v in kwargs] + elif isinstance(kwargs, torch.Tensor): + return kwargs.contiguous() + else: + return kwargs + + def maybe_parallel_inference(self, testing_vars, g_ema_kwargs=None, index_tuple=None, + flush=False, return_exec_time=False, calc_flops=False): + exec_time = -1 + flops = {"all": 0, "ss": 0, "ts": 0} + if self.enable_parallel_batching: + if g_ema_kwargs is not None: + self.parallel_stack.append({ + "g_ema_kwargs": g_ema_kwargs, + "index_tuple": index_tuple + }) + else: + assert flush, "Only expected use case." + + if flush and len(self.parallel_stack) == 0: + return exec_time, flops + elif flush or len(self.parallel_stack) == self.config.task.parallel_batch_size: + all_g_ema_kwargs = [d["g_ema_kwargs"] for d in self.parallel_stack] + agg_g_ema_kwargs = self.parallel_agg_kwargs(all_g_ema_kwargs) + agg_g_ema_kwargs = self.ensure_contiguous(agg_g_ema_kwargs) + if return_exec_time: + torch.cuda.synchronize() + start_time = time.time() + output = self.g_ema(**agg_g_ema_kwargs) + if return_exec_time: + torch.cuda.synchronize() + exec_time = time.time() - start_time + if calc_flops: + assert self.config.train_params.batch_size == 1, \ + "Does not consider batch_size!=1 and parallel batching happens at the same time!" + pbatch_size = output["gen"].shape[0] + flops = { + "all": output["flops_all"].sum() * pbatch_size, + "ss": output["flops_ss"].sum() * pbatch_size, + "ts": output["flops_ts"].sum() * pbatch_size, + } + patch = output["gen"] + for i,d in enumerate(self.parallel_stack): + img_x_st, img_x_ed, img_y_st, img_y_ed = d["index_tuple"] + testing_vars.meta_img[:, :, img_x_st:img_x_ed, img_y_st:img_y_ed] = patch[i:i+1].detach().cpu() + self.parallel_stack = [] + else: + exec_time = 0 + pass # Wait until queue is full or forced flush + else: + if flush: + return exec_time, flops # Normal mode does not have flushing issue + img_x_st, img_x_ed, img_y_st, img_y_ed = index_tuple + g_ema_kwargs = self.ensure_contiguous(g_ema_kwargs) + if return_exec_time: + torch.cuda.synchronize() + start_time = time.time() + output = self.g_ema(**g_ema_kwargs) + if return_exec_time: + torch.cuda.synchronize() + exec_time = time.time() - start_time + if calc_flops: + flops = { + "all": output["flops_all"], + "ss": output["flops_ss"], + "ts": output["flops_ts"], + } + patch = output["gen"] + testing_vars.meta_img[:, :, img_x_st:img_x_ed, img_y_st:img_y_ed] = patch.detach().cpu() + return exec_time, flops + + """ + Interactive utils + """ + def is_overlaping_update_map(self, update_map, xst, xed, yst, yed): + return (update_map[:, :, xst:xed, yst:yed] > 0).any() + diff --git a/test_managers/direct_generation.py b/test_managers/direct_generation.py new file mode 100644 index 0000000..d66fd3a --- /dev/null +++ b/test_managers/direct_generation.py @@ -0,0 +1,157 @@ +import os +import time +import torch +import datetime +import traceback +import numpy as np +import matplotlib.pyplot as plt +from skimage.transform import resize as sk_resize + +from test_managers.base_test_manager import BaseTestManager +from latent_sampler import LatentSampler + + +class DirectGenerationManager(BaseTestManager): + + def __init__(self, g_ema, device, save_root, config): + self.g_ema = g_ema + self.device = device + self.save_root = save_root + self.config = config + self.cur_global_id = 0 + + if config.var.dataparallel: + self.g_ema_module = g_ema.module + else: + self.g_ema_module = g_ema + + self.accum_exec_times = [] + self.target_height = self.config.task.height + self.target_width = self.config.task.width + + self.latent_sampler = LatentSampler(g_ema, config) + + # assert config.train_params.styleGAN2_baseline + + assert self.target_height == self.target_width, \ + "Didn't spend time to make it flexible." + + if hasattr(config.train_params, "styleGAN2_baseline") and config.train_params.styleGAN2_baseline: + mult = int(np.ceil(self.target_height / self.config.train_params.patch_size)) + self.full_ts_input_size = self.config.train_params.ts_input_size * mult + else: + self.full_ts_input_size = self.g_ema_module.calc_in_spatial_size( + self.target_height, include_ss=False, return_list=False) + print(" [*] Full local latent input size:", self.full_ts_input_size) + + def task_specific_init(self): + pass + + def run_next(self, save=True, write_gpu_time=False, calc_flops=False, **kwargs): + if len(kwargs) > 0: + for k,v in kwargs.items(): + if k in {"inv_records", "inv_placements"}: continue + print(" [Warning] task manager receives untracked arg {} with value {}".format(k ,v)) + meta_img = self.generate(write_gpu_time=write_gpu_time, calc_flops=calc_flops) + if save: + self.save_results(meta_img) + return meta_img + + def generate(self, write_gpu_time=False, calc_flops=False): + + accum_exec_time = 0 + + assert self.target_height == self.target_width, \ + "Didn't spend time to make it flexible." + + + global_latent = self.latent_sampler.sample_global_latent( + self.config.train_params.batch_size, self.device) + local_latent = self.latent_sampler.sample_local_latent( + self.config.train_params.batch_size, + self.device, + specific_shape=(self.full_ts_input_size, self.full_ts_input_size)) + + if write_gpu_time: + torch.cuda.synchronize() + start_time = time.time() + + try: + output = self.g_ema( + global_latent=global_latent, + local_latent=local_latent, + disable_dual_latents=True, + calc_flops=calc_flops).detach().cpu() + meta_img = output["gen"] + except RuntimeError as e: # Out of memory + if "CUDA out of memory" in traceback.format_exc(): + exec_time = -1 + meta_img = None + output = None + print(" [!] OOM!") + else: + raise e + + if meta_img is not None and write_gpu_time: + torch.cuda.synchronize() + exec_time = time.time() - start_time + + if calc_flops and (output is not None): + if "flops_ss" in output: + print(" [*] Total FLOPs: {} (SS {}, TS {})".format( + self.pretty_print_flops(output["flops_all"]), + self.pretty_print_flops(output["flops_ss"]), + self.pretty_print_flops(output["flops_ts"]))) + else: + print(" [*] Total FLOPs: {}.".format( + self.pretty_print_flops(output["flops_all"]))) + + if write_gpu_time: + accum_exec_time += exec_time + print(" [*] GPU time {}".format(accum_exec_time)) + self.accum_exec_times.append(accum_exec_time) + fmt_date = datetime.date.today().strftime("%d-%m-%Y") + benchmark_file = os.path.join(self.save_root, "speed_benchmark_{}.txt".format(fmt_date)) + with open(benchmark_file, "a") as f: + f.write("{:.6f}".format(accum_exec_time)) + + return meta_img + + def get_exec_time_stats(self): + mean = np.mean(self.accum_exec_times) + std = np.std(self.accum_exec_times) + return mean, std + + def save_results(self, meta_img): + print(" [*] Saving results...") + if meta_img is None: + return + self.save_meta_imgs(meta_img) + self.cur_global_id += self.config.train_params.batch_size + + def save_meta_imgs(self, meta_img): + + # Center crop + pad_h = (meta_img.shape[2] - self.target_height) // 2 + pad_w = (meta_img.shape[3] - self.target_width) // 2 + meta_img = meta_img[:, :, pad_h:pad_h+self.target_height, pad_w:pad_w+self.target_width] + + # Save the full image and the low-resolution image (for visualization) + meta_img = meta_img.clamp(-1, 1).permute(0, 2, 3, 1) + meta_img = (meta_img + 1) / 2 + meta_img_np = meta_img.numpy() + + for i in range(self.config.train_params.batch_size): + global_id = self.cur_global_id + i + + save_path = os.path.join(self.save_root, str(global_id).zfill(6)+".png") + plt.imsave(save_path, meta_img_np[i]) + + if hasattr(self.config.task, "lowres_height") and self.config.task.lowres_height > 0: + lr_save_path = os.path.join(self.save_root, str(global_id).zfill(6)+"_lr.png") + resize_ratio = self.config.task.lowres_height / self.config.task.height + resize_shape = ( + int(round(self.target_height*resize_ratio)), + int(round(self.target_width*resize_ratio))) + lr_img = sk_resize((meta_img_np[i]*255).astype(np.uint8), resize_shape) + plt.imsave(lr_save_path, lr_img) diff --git a/test_managers/fused_generation.py b/test_managers/fused_generation.py new file mode 100644 index 0000000..2be4c59 --- /dev/null +++ b/test_managers/fused_generation.py @@ -0,0 +1,438 @@ +import os +import math +import datetime +import numpy as np +import pickle as pkl +import matplotlib.pyplot as plt +from tqdm import tqdm +from glob import glob +from itertools import product as iter_product + +import torch + +from models.ops import Blur +from test_managers.base_test_manager import BaseTestManager +from test_managers.testing_vars_wrapper import TestingVars +from test_managers.global_config import test_meta_extra_pad + + +class FusedGenerationManager(BaseTestManager): + + def task_specific_init(self): + + self.target_height = self.config.task.height + self.target_width = self.config.task.width + self.ss_unfold_size = self.config.train_params.ss_n_layers * self.config.train_params.ss_unfold_radius + + self._init_starting_points() + self.noise_heights = self.outfeat_step_sizes * (self.num_steps_h-1) + self.outfeat_sizes_list + self.noise_widths = self.outfeat_step_sizes * (self.num_steps_w-1) + self.outfeat_sizes_list + + self.meta_local_latent_shape = ( + # Does not account GNN padding here, it is handled within the latent_sampler + self.g_ema_module.calc_in_spatial_size(self.meta_height, include_ss=False), + self.g_ema_module.calc_in_spatial_size(self.meta_width, include_ss=False), + ) + + # The meta size is different from the direct output size + self.x_meta_pad_h = (self.meta_height - self.target_height) // 2 + self.x_meta_pad_w = (self.meta_width - self.target_width) // 2 + self.meta_style_center_indices = [ + [ + center_f[0] * self.target_height + self.x_meta_pad_h, + center_f[1] * self.target_width + self.x_meta_pad_w, + ] for center_f in self.config.task.style_centers] + + self.blur_kernel = Blur( + kernel=self.config.task.blur_kernel, + pad=self.config.task.blur_kernel//2, + padding_mode="replicate", + prior=self.config.task.blur_type) + + # This is used to select which style to use at each spatial index + print(" [*] Creating fusion map...") + self.meta_fusion_map_ss, self.meta_fusion_map_ts = self._create_fusion_map( + self.meta_style_center_indices) + + if hasattr(self.config.task, "gen_from_inv_stats") and self.config.task.gen_from_inv_stats: + self.inv_roots = self.compose_inv_root() + self.inv_rec_files = [sorted(glob(os.path.join(root, "*"))) for root in self.inv_roots] + self.gen_from_inv_stats = True + else: + self.gen_from_inv_stats = False + + def run_next(self, save=True, write_gpu_time=False, inv_records=None, inv_placements=None, calc_flops=False, disable_pbar=True, **kwargs): + if len(kwargs) > 0: + for k,v in kwargs.items(): + if v is not None: + print(" [Warning] task manager receives untracked arg {} with value {}".format(k ,v)) + testing_vars = self.create_vars(inv_records=inv_records, inv_placements=inv_placements) + self.generate(testing_vars, write_gpu_time=write_gpu_time, calc_flops=calc_flops) + if save: + self.save_results(testing_vars.meta_img) + return testing_vars.meta_img + + def create_vars(self, inv_records=None, inv_placements=None): + + mixing = False + assert mixing == False, "Otherwise, an injection index must be specified and fed into g_ema." + + # Allocate memory for the final output, starts iterating and filling in the generated results. + # Can be reused + meta_img = torch.empty( + self.config.train_params.batch_size, + 3, + int(self.meta_height), + int(self.meta_width)).float() + + # [Note] + # 1. One may implement a sophisticated version that does not required to + # generate all the latents at once, as most of the info are not reusing + # during the inference. However, the author is just lazy and abusing his + # CPU memory OuO + all_global_latents = [] + all_styles = [] + for i in range(len(self.config.task.style_centers)): + # [Hubert] Actually, can use mixing here, but disabled by default for simplicity + global_latent = self.latent_sampler.sample_global_latent( + self.config.train_params.batch_size, mixing=mixing, device=self.device) + # cur_ts_style = self.g_ema_module.texture_synthesizer.get_style(global_latent) + cur_ts_style = self.g_ema( + call_internal_method="get_style", + internal_method_kwargs={"global_latent": global_latent}) + if mixing: + raise NotImplementedError("Sample and mix here, but i'm lazy copying the codes.") + else: + cur_ts_style[:, 1] = cur_ts_style[:, 0] # Forcingly and explicitly disable style fusion + global_latent = global_latent + all_global_latents.append(global_latent) + all_styles.append(cur_ts_style) + local_latent = self.latent_sampler.sample_local_latent( + self.config.train_params.batch_size, + device="cpu", # Store in CPU anyway, it can be INFINITLY LARGE! + specific_shape=self.meta_local_latent_shape) + meta_coords = self.coord_handler.sample_coord_grid( + local_latent, + is_training=False) + + # Create randomized noises + randomized_noises = [ + torch.randn(self.config.train_params.batch_size, 1, int(h), int(w)) + for (h,w) in zip(self.noise_heights, self.noise_widths)] + + testing_vars = TestingVars( + meta_img=meta_img, + global_latent=all_global_latents, + local_latent=local_latent, + meta_coords=meta_coords, + styles=all_styles, + noises=randomized_noises, + device=self.device) + + if self.gen_from_inv_stats and len(self.inv_roots) > 0: + assert inv_records is None, \ + "`gen_from_inv_stats` already specified, should not receive `inv_records` from command!" + assert self.config.train_params.batch_size == 1, \ + "Inverted parameters loading for batch is not yet implemeted! " + \ + "Please use parallel-batching instead, which provides a similar inference speed." + num_style_centers = len(self.config.task.style_centers) + num_inv_records = len(self.inv_roots) + assert num_style_centers >= num_inv_records, "Got {} < {}".format(num_style_centers, num_inv_records) + + inv_records, inv_placements = [], [] + for i in range(num_style_centers): + if i < num_inv_records: + inv_records.append(self.inv_rec_files[i][self.cur_global_id]) + inv_placements.append(self.config.task.gen_from_inv_placement[i]) + else: + inv_records.append(None) + inv_placements.append(None) + + if inv_records is not None: + testing_vars.replace_by_records(self.g_ema_module, inv_records, inv_placements) + + return testing_vars + + def generate(self, testing_vars, tkinter_pbar=None, update_by_ss_map=None, update_by_ts_map=None, write_gpu_time=False, calc_flops=False): + + # I don't mind bruteforce casting combination here, cuz you should worry about the meta_img size first + idx_tuples = list(iter_product(range(self.start_pts_mesh_z.shape[0]), range(self.start_pts_mesh_z.shape[1]))) + + if tkinter_pbar is not None: + pbar = tkinter_pbar(idx_tuples) + else: + pbar = tqdm(idx_tuples) + + accum_exec_time = 0 + accum_flops_all, accum_flops_ss, accum_flops_ts = 0, 0, 0 + for iiter, (idx_x,idx_y) in enumerate(pbar): + + zx_st, zy_st = self.start_pts_mesh_z[idx_x, idx_y] + zx_ed = zx_st + self.config.train_params.ts_input_size + zy_ed = zy_st + self.config.train_params.ts_input_size + + # Deal with SS unfolding here + zx_st -= self.ss_unfold_size + zy_st -= self.ss_unfold_size + zx_ed += self.ss_unfold_size + zy_ed += self.ss_unfold_size + + # Handle the randomized noise input of the texture_synthesizer... + outfeat_x_st = [start_pts_mesh[idx_x,idx_y,0] for start_pts_mesh in self.start_pts_mesh_outfeats] + outfeat_y_st = [start_pts_mesh[idx_x,idx_y,1] for start_pts_mesh in self.start_pts_mesh_outfeats] + outfeat_x_ed = [ + x_st + out_size for (x_st, out_size) in zip(outfeat_x_st, self.outfeat_sizes_list)] + outfeat_y_ed = [ + y_st + out_size for (y_st, out_size) in zip(outfeat_y_st, self.outfeat_sizes_list)] + noises = [] + for i, (fx_st, fy_st, fx_ed, fy_ed) in enumerate(zip(outfeat_x_st, outfeat_y_st, outfeat_x_ed, outfeat_y_ed)): + noises.append(testing_vars.noises[i][:, :, fx_st:fx_ed, fy_st:fy_ed].to(self.device)) + + # Get the pixel-space cursors + img_x_st, img_y_st = outfeat_x_st[-1], outfeat_y_st[-1] + img_x_ed, img_y_ed = outfeat_x_ed[-1], outfeat_y_ed[-1] + + # Handle the spatially-shaped styles in TS + infeat_x_st = [start_pts_mesh[idx_x,idx_y,0] for start_pts_mesh in self.start_pts_mesh_infeats] + infeat_y_st = [start_pts_mesh[idx_x,idx_y,1] for start_pts_mesh in self.start_pts_mesh_infeats] + infeat_x_ed = [ + x_st + out_size for (x_st, out_size) in zip(infeat_x_st, self.infeat_sizes_list)] + infeat_y_ed = [ + y_st + out_size for (y_st, out_size) in zip(infeat_y_st, self.infeat_sizes_list)] + cur_fusion_map_ts = [] + for i, (fx_st, fy_st, fx_ed, fy_ed) in enumerate(zip(infeat_x_st, infeat_y_st, infeat_x_ed, infeat_y_ed)): + cur_fusion_map_ts.append(self.meta_fusion_map_ts[i][:, :, fx_st:fx_ed, fy_st:fy_ed]) + + # One extra style for the last ToRgb layer, which is in pixel-space shape + cur_fusion_map_ts.append(self.meta_fusion_map_ts[-1][:, :, img_x_st:img_x_ed, img_y_st:img_y_ed]) + + # Handle the spatially-shaped global_latents in SS + cur_fusion_map_ss = self.meta_fusion_map_ss[:, :, zx_st:zx_ed, zy_st:zy_ed].to(self.device) + + # Handle other variables + cur_local_latent = testing_vars.local_latent[:, :, zx_st:zx_ed, zy_st:zy_ed].to(self.device) + cur_coords = testing_vars.meta_coords[:, :, zx_st:zx_ed, zy_st:zy_ed].to(self.device) + + # [Interactive] Decide whether the region will be updated, otherwise no need to generate + if update_by_ss_map is not None: + ss_cursors = zx_st, zx_ed, zy_st, zy_ed + if not self.is_overlaping_update_map(update_by_ss_map, *ss_cursors): + continue + if update_by_ts_map is not None: + # For TS regional selection, we only select noises + ts_cursors = outfeat_x_st[0], outfeat_x_ed[0], outfeat_y_st[0], outfeat_y_ed[0] + if not self.is_overlaping_update_map(update_by_ts_map, *ts_cursors): + continue + + # Hard to define global/style mixing while fusion, viable, but I'm lazy + single_global_latents = [ + v[:, 0] for v in testing_vars.global_latent] + + if hasattr(testing_vars, "wplus_styles"): + g_ema_kwargs = { + "global_latent": single_global_latents, + "local_latent": cur_local_latent, + "wplus_styles": testing_vars.wplus_styles, + "override_coords": cur_coords, + "noises": noises, + "disable_dual_latents": True, + "style_fusion_map_ss": cur_fusion_map_ss, + "style_fusion_map_ts": cur_fusion_map_ts, + "calc_flops": calc_flops, + } + else: + single_styles = [ + s[:, 0] for s in testing_vars.styles] + g_ema_kwargs = { + "global_latent": single_global_latents, + "local_latent": cur_local_latent, + "styles": single_styles, + "override_coords": cur_coords, + "noises": noises, + "disable_dual_latents": True, + "style_fusion_map_ss": cur_fusion_map_ss, + "style_fusion_map_ts": cur_fusion_map_ts, + "calc_flops": calc_flops, + } + index_tuple = (img_x_st, img_x_ed, img_y_st, img_y_ed) + exec_time, flops = self.maybe_parallel_inference( + testing_vars, g_ema_kwargs=g_ema_kwargs, index_tuple=index_tuple, return_exec_time=write_gpu_time, calc_flops=calc_flops) + accum_exec_time += exec_time + if calc_flops: + accum_flops_all += flops["all"] + accum_flops_ss += flops["ss"] + accum_flops_ts += flops["ts"] + + exec_time, flops = self.maybe_parallel_inference(testing_vars, flush=True, return_exec_time=write_gpu_time, calc_flops=calc_flops) + if calc_flops: + accum_flops_all += flops["all"] + accum_flops_ss += flops["ss"] + accum_flops_ts += flops["ts"] + + if write_gpu_time: + accum_exec_time += exec_time + print(" [*] GPU time {}".format(accum_exec_time)) + self.accum_exec_times.append(accum_exec_time) + fmt_date = datetime.date.today().strftime("%d-%m-%Y") + benchmark_file = os.path.join(self.save_root, "speed_benchmark_{}.txt".format(fmt_date)) + with open(benchmark_file, "a") as f: + f.write("{:.6f}".format(accum_exec_time)) + + if calc_flops: + print(" [*] Total FLOPs: {} (SS {}, TS {})".format( + self.pretty_print_flops(accum_flops_all), + self.pretty_print_flops(accum_flops_ss), + self.pretty_print_flops(accum_flops_ts))) + + def save_results(self, meta_img, dump_vars=None): + print(" [*] Saving results...") + self.save_meta_imgs(meta_img) + if dump_vars is not None: + self.save_testing_vars(dump_vars) + self.cur_global_id += self.config.train_params.batch_size + + + def save_testing_vars(self, testing_vars): + assert self.config.train_params.batch_size == 1, \ + "This is only designed to be used with the interactive tool." + save_path = os.path.join(self.save_root, str(self.cur_global_id).zfill(6)+".pkl") + pkl.dump(testing_vars, open(save_path, "wb")) + + + def save_meta_imgs(self, meta_img): + + # Center crop + pad_h = (self.meta_height - self.target_height) // 2 + pad_w = (self.meta_width - self.target_width) // 2 + meta_img = meta_img[:, :, pad_h:pad_h+self.target_height, pad_w:pad_w+self.target_width] + + # Save the full image and the low-resolution image (for visualization) + meta_img = meta_img.clamp(-1, 1).permute(0, 2, 3, 1) + meta_img = (meta_img + 1) / 2 + meta_img_np = meta_img.numpy() + + for i in range(self.config.train_params.batch_size): + global_id = self.cur_global_id + i + save_path = os.path.join(self.save_root, str(global_id).zfill(6)+".png") + plt.imsave(save_path, meta_img_np[i]) + + + def _create_start_pts_mesh(self, step_size, num_steps_h, num_steps_w): + start_pts_x = np.arange(num_steps_h) * step_size + start_pts_y = np.arange(num_steps_w) * step_size + start_pts_mesh = np.stack([ + np.repeat(start_pts_x.reshape(num_steps_h, 1), num_steps_w, axis=1), + np.repeat(start_pts_y.reshape(1, num_steps_w), num_steps_h, axis=0), + ], 2).astype(np.uint32) # shape: (H, W, 2) + return start_pts_mesh + + + def _create_fusion_map(self, style_center_list): + """ + The generator involves cropping to deal with zero-paddings. + As the spatially-shaped styles will face resizing + """ + # input: (h_i, w_i) + # style: (h_i, w_i) + # noise: (h_(i+1), w_(i+1)) + # output = conv_trans(input, style) + noise + + self.x_meta_pad_h = (self.meta_height - self.target_height) // 2 + self.x_meta_pad_w = (self.meta_width - self.target_width) // 2 + + num_centers = len(style_center_list) + meta_dist_to_center = self._create_idx_grid(num_centers, self.meta_height, self.meta_width) + for i,style_center in enumerate(style_center_list): + meta_dist_to_center[i, 0] = meta_dist_to_center[i, 0] - style_center[0] + meta_dist_to_center[i, 1] = meta_dist_to_center[i, 1] - style_center[1] + + # Use simple L2 dist + meta_dist_to_center = meta_dist_to_center.pow(2).sum(1) # output shape: (N, H, W), sqrt omitted + meta_fusion_idx = meta_dist_to_center.topk(1, largest=False, dim=0).indices[0] # (H, W) + + # Convert (H, W) index to (N, H, W) mask, for later blur filtering + batch_size = self.config.train_params.batch_size + meta_fusion_map = torch.zeros(num_centers, self.meta_height*self.meta_width) # (N, H*W) + meta_fusion_idx = meta_fusion_idx.view(1, self.meta_height*self.meta_width) # (1, H*W) + meta_fusion_map.scatter_(0, meta_fusion_idx, 1) + meta_fusion_map = meta_fusion_map.unsqueeze(0).repeat(batch_size, 1, 1) # (B, N, H*W) + meta_fusion_map = meta_fusion_map.reshape(batch_size, num_centers, self.meta_height, self.meta_width) + + meta_fusion_map = self.blur_kernel(meta_fusion_map) + + # Save initial fusion map for future visualization + for i in range(num_centers): + plt.imsave( + os.path.join(self.save_root, "_init_fusion_map_{}.png".format(i)), + meta_fusion_map[0, i].cpu().detach().clamp(0, 1).numpy()) + + # Need to carefully account the upsampling and no-padding involved in the generator + # [Note] All the latents returned are on the input-side + meta_fusion_map_ss, meta_fusion_map_ts, _, _ = \ + self.g_ema_module.calibrate_spatial_shape( + meta_fusion_map, direction="backward", padding_mode="replicate", verbose=True) + + # Special case handling for the last ToRgb layer, which is in pixel-space shape + meta_fusion_map_ts.append(meta_fusion_map) + + # We only use the first (as well as the largest) meta_fusion_map_ss + meta_fusion_map_ss = meta_fusion_map_ss[0] + + return meta_fusion_map_ss, meta_fusion_map_ts + + def _create_idx_grid(self, num, h, w): + x_idx = torch.arange(h).reshape(h, 1).repeat(1, w) # (h, w) + y_idx = torch.arange(w).reshape(1, w).repeat(h, 1) # (h, w) + grid = torch.stack([x_idx, y_idx]) + gird = grid.unsqueeze(0).repeat(num, 1, 1, 1) # (num, 2, h, w) + return gird + + def _init_starting_points(self): + + # Get all the starting points of the patches to generate + self.num_steps_h = \ + math.ceil((self.target_height - self.outfeat_sizes_list[-1]) / self.pixelspace_step_size) + test_meta_extra_pad + self.num_steps_w = \ + math.ceil((self.target_width - self.outfeat_sizes_list[-1]) / self.pixelspace_step_size) + test_meta_extra_pad + + self.start_pts_mesh_z = self._create_start_pts_mesh( + step_size=self.latentspace_step_size, + num_steps_h=self.num_steps_h, + num_steps_w=self.num_steps_w) + self.start_pts_mesh_z += self.ss_unfold_size + + # Create this for: + # (1) Final image pixels assignment + # (2) Randomized noise handling within the texture synthesizer + self.start_pts_mesh_outfeats = [ + self._create_start_pts_mesh( + step_size=step_size, + num_steps_h=self.num_steps_h, + num_steps_w=self.num_steps_w, + ) for step_size in self.outfeat_step_sizes] + # start_pts_mesh_x = \ + # (start_pts_mesh_z - ss_unfold_size) // latentspace_step_size * pixelspace_step_size # shape: (H, W, 2) + # start_pts_mesh_x = start_pts_mesh_x.astype(np.uint32) + + # Consequently, create for input space + self.start_pts_mesh_infeats = [ + self._create_start_pts_mesh( + step_size=step_size, + num_steps_h=self.num_steps_h, + num_steps_w=self.num_steps_w, + ) for step_size in self.infeat_step_sizes] + + # To avoid edge-condition on the image edge, we generate an image slightly larger than + # requested, then center-crop to the requested resolution. + self.meta_height = self.pixelspace_step_size * (self.num_steps_h-1) + self.outfeat_sizes_list[-1] + self.meta_width = self.pixelspace_step_size * (self.num_steps_w-1) + self.outfeat_sizes_list[-1] + + + def compose_inv_root(self): + if isinstance(self.config.task.prev_inv_config, list): + return [ + os.path.join("./logs/", self.config.var.exp_name, "test", cfg, "stats") + for cfg in self.config.task.prev_inv_config] + else: + return [os.path.join("./logs/", self.config.var.exp_name, "test", self.config.task.prev_inv_config, "stats")] + diff --git a/test_managers/fused_seq_connecting_generation.py b/test_managers/fused_seq_connecting_generation.py new file mode 100644 index 0000000..46097ec --- /dev/null +++ b/test_managers/fused_seq_connecting_generation.py @@ -0,0 +1,442 @@ +import os +import math +import datetime +import numpy as np +import pickle as pkl +import matplotlib.pyplot as plt +from torch._C import device +from tqdm import tqdm +from glob import glob +from itertools import product as iter_product + +import torch + +from models.ops import Blur +from test_managers.base_test_manager import BaseTestManager +from test_managers.testing_vars_wrapper import TestingVars +from test_managers.global_config import test_meta_extra_pad + + +class FusedSeqConnectingGenerationManager(BaseTestManager): + + def task_specific_init(self): + + self.local_latent_shape_single = ( + self.config.train_params.ts_input_size, + self.config.train_params.ts_input_size, + ) + self.local_latent_shape_connect = ( + self.config.train_params.ts_input_size, + self.config.train_params.ts_input_size*2 + self.config.task.anchor_gap, + ) + + self.noise_heights_single = self.g_ema_module.calc_out_spatial_size( + self.local_latent_shape_single[0], + include_ss=False, return_list=True) + self.noise_widths_single = self.g_ema_module.calc_out_spatial_size( + self.local_latent_shape_single[1], + include_ss=False, return_list=True) + self.noise_widths_connect = self.g_ema_module.calc_out_spatial_size( + self.local_latent_shape_connect[1], + include_ss=False, return_list=True) + + self.target_height = self.config.task.height + self.target_width = self.noise_widths_connect[-1] + self.anchor_patch_width = self.noise_widths_single[-1] + self.fusion_patch_width = self.target_width - self.anchor_patch_width * 2 + self.ss_unfold_pad = self.config.train_params.ss_n_layers * self.config.train_params.ss_unfold_radius + + self.noise_gap_sizes = [b-a*2 for a,b in zip(self.noise_widths_single, self.noise_widths_connect)] + + self.meta_style_center_indices = [ + [ + center_f[0] * self.target_height, + center_f[1] * self.target_width, + ] for center_f in self.config.task.style_centers] + + self.blur_kernel = Blur( + kernel=self.config.task.blur_kernel, + pad=self.config.task.blur_kernel//2, + padding_mode="replicate", + prior=self.config.task.blur_type) + + # This is used to select which style to use at each spatial index + print(" [*] Creating fusion map...") + self.meta_fusion_map_ss, self.meta_fusion_map_ts = \ + self._create_fusion_map(self.meta_style_center_indices) + + + # Create an initial stack of anchors + assert len(self.config.task.style_centers) % 2 == 0, \ + "Expect an even number of anchors in the stack (so that we connect the middle two), but got {}".format(len(self.config.task.style_centers)) + + self.output_stack = [] # The manager produces an anchor patch and a fusion patch at each step. Buffer the results to simulate an iterator. + self.anchor_stack = [] + num_style_centers = len(self.config.task.style_centers) + num_preceding_centers = num_style_centers // 2 - 1 + init_anchor_idx = num_style_centers - num_preceding_centers + for i in range(-num_preceding_centers, init_anchor_idx): + cur_anchor = self.create_vars(i) + if len(self.anchor_stack) > 1: # Make the overlapped region (if happens) value consistent + self.unify_overlapped_local_latent(self.anchor_stack[-1].local_latent, cur_anchor.local_latent) + self.anchor_stack.append(cur_anchor) + + self.cur_anchor_idx = init_anchor_idx + 1 + + def run_next(self, save=True, write_gpu_time=False, inv_records=None, inv_placements=None, calc_flops=False, disable_pbar=True, **kwargs): + assert (inv_records is None) and (inv_placements is None), "Not implemented, we won't have that many inverted latents to inbetween." + if len(kwargs) > 0: + for k,v in kwargs.items(): + if v is not None: + print(" [Warning] task manager receives untracked arg {} with value {}".format(k ,v)) + + if (len(self.output_stack) > 0) and (self.config.task.save_type != "all-in-one"): + cur_output = self.output_stack.pop(0) + if save: + self.save_meta_imgs(cur_output) + return cur_output + + testing_vars = self.fuse_anchors(self.anchor_stack) + self.generate(testing_vars, write_gpu_time=write_gpu_time, calc_flops=calc_flops) + + new_anchor = self.create_vars(self.cur_anchor_idx) + self.unify_overlapped_local_latent(self.anchor_stack[-1].local_latent, new_anchor.local_latent) + self.anchor_stack.append(new_anchor) + self.anchor_stack = self.anchor_stack[1:] + self.cur_anchor_idx += 1 + + # We throw away the right anchor patch every step. + # It is possible to implement a more efficient function, but it will make the codes unreadable. + anchor_patch = testing_vars.meta_img[:, :, :, :self.anchor_patch_width] + fusion_patch = testing_vars.meta_img[:, :, :, self.anchor_patch_width:self.anchor_patch_width+self.fusion_patch_width] + + if self.config.task.save_type == "patches": + cur_output = anchor_patch + self.output_stack.append(fusion_patch) + elif self.config.task.save_type == "patches-centercrop": + cur_output = anchor_patch + fusion_patch = self._center_crop(src=fusion_patch, tgt=anchor_patch) + self.output_stack.append(fusion_patch) + elif self.config.task.save_type == "anchors": + cur_output = testing_vars.meta_img + elif self.config.task.save_type == "all-in-one": + save = False + cur_output = None + self.output_stack += [anchor_patch, fusion_patch] + else: + raise NotImplementedError("Unknown save type {}".format(self.config.task.save_type)) + + if save and (cur_output is not None): + self.save_meta_imgs(cur_output) + return cur_output + + def create_vars(self, cur_anchor_idx): + + mixing = False + assert mixing == False, "Otherwise, an injection index must be specified and fed into g_ema." + + # Allocate memory for the final output, starts iterating and filling in the generated results. + # Can be reused + meta_img = None # No need to create + + global_latent = self.latent_sampler.sample_global_latent( + self.config.task.batch_size, mixing=mixing, device=self.device) + ts_style = self.g_ema( + call_internal_method="get_style", + internal_method_kwargs={"global_latent": global_latent}) + if mixing: + raise NotImplementedError("Sample and mix here, but i'm lazy copying the codes.") + else: + ts_style[:, 1] = ts_style[:, 0] # Forcingly and explicitly disable style fusion + + coord_disp = ( + 0, cur_anchor_idx * (self.config.train_params.ts_input_size + self.config.task.anchor_gap) + ) + coord_shape = ( + self.config.train_params.ts_input_size + self.ss_unfold_pad*2, + self.config.train_params.ts_input_size*2 + self.config.task.anchor_gap + self.ss_unfold_pad*2, + ) + local_latent = self.latent_sampler.sample_local_latent( + self.config.task.batch_size, + device="cpu", # Store in CPU anyway, it can be INFINITLY LARGE! + specific_shape=self.local_latent_shape_single) + meta_coords = self.coord_handler.sample_coord_grid( + spatial_latent=None, + specific_shape=coord_shape, + is_training=False, + coord_init=coord_disp, + batch_size=self.config.task.batch_size, + device=self.device) + + # Create randomized noises + randomized_noises = [ + torch.randn(self.config.task.batch_size, 1, int(h), int(w)) + for (h,w) in zip(self.noise_heights_single, self.noise_widths_single)] + + testing_vars = TestingVars( + meta_img=meta_img, + global_latent=global_latent, + local_latent=local_latent, + meta_coords=meta_coords, + styles=ts_style, + noises=randomized_noises, + device=self.device) + + return testing_vars + + + def fuse_anchors(self, anchor_stack): + num_centers = len(self.config.task.style_centers) + left_anchor = anchor_stack[num_centers//2-1] + right_anchor = anchor_stack[num_centers//2] + + joint_local_latent = self.connect_local_latents(left_anchor.local_latent, right_anchor.local_latent) + noise_gaps = [torch.randn(self.config.task.batch_size, 1, h, wgap) for h,wgap in zip(self.noise_heights_single, self.noise_gap_sizes)] + joint_noises = [torch.cat([l,g,r], 3) for l,g,r in zip(left_anchor.noises, noise_gaps, right_anchor.noises)] + return TestingVars( + meta_img=left_anchor.meta_img, + global_latent=[anchor.global_latent for anchor in anchor_stack], + local_latent=joint_local_latent, + meta_coords=left_anchor.meta_coords, + styles=[anchor.styles for anchor in anchor_stack], + noises=joint_noises, + device=self.device + ) + + def generate(self, testing_vars, tkinter_pbar=None, update_by_ss_map=None, update_by_ts_map=None, write_gpu_time=False, calc_flops=False): + + cur_fusion_map_ts = list(self.meta_fusion_map_ts) + cur_fusion_map_ts.append(self.meta_fusion_map_ts[-1]) + + # Handle the spatially-shaped global_latents in SS + cur_fusion_map_ss = self.meta_fusion_map_ss.to(self.device) + + # Handle other variables + cur_local_latent = testing_vars.local_latent.to(self.device) + cur_coords = testing_vars.meta_coords.to(self.device) + cur_noises = [n.to(self.device) for n in testing_vars.noises] + + # Hard to define global/style mixing while fusion, viable, but I'm lazy + single_global_latents = [ + v[:, 0] for v in testing_vars.global_latent] + + single_styles = [s[:, 0] for s in testing_vars.styles] + g_ema_kwargs = { + "global_latent": single_global_latents, + "local_latent": cur_local_latent, + "styles": single_styles, + "override_coords": cur_coords, + "noises": cur_noises, + "disable_dual_latents": True, + "style_fusion_map_ss": cur_fusion_map_ss, + "style_fusion_map_ts": cur_fusion_map_ts, + "calc_flops": calc_flops, + } + output = self.g_ema(**g_ema_kwargs) + testing_vars.meta_img = output["gen"].cpu().detach() + + # Somehow the cache is not released + del output + torch.cuda.empty_cache() + + def save_meta_imgs(self, meta_img): + + # Save the full image and the low-resolution image (for visualization) + meta_img = meta_img.clamp(-1, 1).permute(0, 2, 3, 1) + meta_img = (meta_img + 1) / 2 + meta_img_np = meta_img.numpy() + + assert self.config.task.batch_size == 1 + for i in range(self.config.task.batch_size): + save_path = os.path.join(self.save_root, str(self.cur_global_id).zfill(6)+".png") + plt.imsave(save_path, meta_img_np[i]) + + self.cur_global_id += self.config.train_params.batch_size + + def exit(self): + if self.config.task.save_type == "all-in-one": + meta_img = torch.cat(self.output_stack, 3) + self.save_meta_imgs(meta_img) + + def _create_start_pts_mesh(self, step_size, num_steps_h, num_steps_w): + start_pts_x = np.arange(num_steps_h) * step_size + start_pts_y = np.arange(num_steps_w) * step_size + start_pts_mesh = np.stack([ + np.repeat(start_pts_x.reshape(num_steps_h, 1), num_steps_w, axis=1), + np.repeat(start_pts_y.reshape(1, num_steps_w), num_steps_h, axis=0), + ], 2).astype(np.uint32) # shape: (H, W, 2) + return start_pts_mesh + + def _create_idx_grid(self, num, h, w): + x_idx = torch.arange(h).reshape(h, 1).repeat(1, w) # (h, w) + y_idx = torch.arange(w).reshape(1, w).repeat(h, 1) # (h, w) + grid = torch.stack([x_idx, y_idx]) + gird = grid.unsqueeze(0).repeat(num, 1, 1, 1) # (num, 2, h, w) + return gird + + def _create_fusion_map(self, style_center_list): + """ + The generator involves cropping to deal with zero-paddings. + As the spatially-shaped styles will face resizing + """ + # input: (h_i, w_i) + # style: (h_i, w_i) + # noise: (h_(i+1), w_(i+1)) + # output = conv_trans(input, style) + noise + + fusion_map_pad = self.config.task.fusion_map_pad + padded_height = self.target_height + fusion_map_pad[0] * 2 + padded_width = self.target_width + fusion_map_pad[1] * 2 + + num_centers = len(style_center_list) + meta_dist_to_center = self._create_idx_grid( + num_centers, padded_height, padded_width) + meta_dist_to_center[:, 0, :, :] = meta_dist_to_center[:, 0, :, :] - fusion_map_pad[0] + meta_dist_to_center[:, 1, :, :] = meta_dist_to_center[:, 1, :, :] - fusion_map_pad[1] + for i,style_center in enumerate(style_center_list): + meta_dist_to_center[i, 0] = meta_dist_to_center[i, 0] - style_center[0] + meta_dist_to_center[i, 1] = meta_dist_to_center[i, 1] - style_center[1] + + # Use simple L2 dist + meta_dist_to_center = meta_dist_to_center.pow(2).sum(1) # output shape: (N, H, W), sqrt omitted + meta_fusion_idx = meta_dist_to_center.topk(1, largest=False, dim=0).indices[0] # (H, W) + + # Convert (H, W) index to (N, H, W) mask, for later blur filtering + batch_size = self.config.task.batch_size + meta_fusion_map = torch.zeros(num_centers, padded_height*padded_width) # (N, H*W) + meta_fusion_idx = meta_fusion_idx.view(1, padded_height*padded_width) # (1, H*W) + meta_fusion_map.scatter_(0, meta_fusion_idx, 1) + meta_fusion_map = meta_fusion_map.unsqueeze(0).repeat(batch_size, 1, 1) # (B, N, H*W) + meta_fusion_map = meta_fusion_map.reshape(batch_size, num_centers, padded_height, padded_width) + + meta_fusion_map = self.blur_kernel(meta_fusion_map) + + # Save initial fusion map for future visualization + if self.save_root is not None: + for c in range(num_centers): + plt.imsave( + os.path.join(self.save_root, "_init_fusion_map_{}.png".format(c)), + meta_fusion_map[0, c].cpu().detach().clamp(0, 1).numpy()) + + + # Need to carefully account the upsampling and no-padding involved in the generator + # [Note] All the latents returned are on the input-side + meta_fusion_map_ss, meta_fusion_map_ts, _, _ = \ + self.g_ema_module.calibrate_spatial_shape( + meta_fusion_map, direction="backward", padding_mode="replicate", verbose=False) + + # Remove fusion_map_pad, here we just want to get the shape of intermediate feature maps in each layer + dummy_tensor = torch.zeros(1, 1, self.target_height, self.target_width) + dummy_fusion_map_ss, dummy_fusion_map_ts, _, _ = \ + self.g_ema_module.calibrate_spatial_shape( + dummy_tensor, direction="backward", padding_mode="replicate", verbose=True) + meta_fusion_map_ss = [ + self._center_crop(src=feat_meta, tgt=feat_dummy) + for feat_meta, feat_dummy in zip(meta_fusion_map_ss, dummy_fusion_map_ss)] + meta_fusion_map_ts = [ + self._center_crop(src=feat_meta, tgt=feat_dummy) + for feat_meta, feat_dummy in zip(meta_fusion_map_ts, dummy_fusion_map_ts)] + + # Special case handling for the last ToRgb layer, which is in pixel-space shape + meta_fusion_map_ts.append(meta_fusion_map) + + # We only use the first (as well as the largest) meta_fusion_map_ss + meta_fusion_map_ss = meta_fusion_map_ss[0] + + return meta_fusion_map_ss, meta_fusion_map_ts + + def _center_crop(self, src, tgt): + _, _, sh, sw = src.shape + _, _, th, tw = tgt.shape + assert sh>=th and sw>=tw, \ + "src should always >= to tgt shape, got src {} and tgt {}".format(src.shape, tgt.shape) + pad_h = (sh - th) // 2 + pad_w = (sw - tw) // 2 + return src[:, :, pad_h:pad_h+th, pad_w:pad_w+tw] + + def connect_local_latents(self, local_latent_prev, local_latent_next): + # | prev | gap | next | + # | prev | gap | next | + # | prev | gap | next | + # + # ss_pad | anchor | ss_pad + # |-> ss_disp <-|-> ss_disp <-|-> ss_disp <-|-> ss_disp <-| + + # [EX] suppose we have ss_pad=4: + # + # anchor_gap=0 + # prev: ... A B C D|E F G H + # next: A B C D|E F G H ... + # anchor_gap=1 + # prev: ... A B C D|E F G H + # next: B C D E|F G H ... + # anchor_gap=2 + # prev: ... A B C D|E F G H + # next: C D E F|G H ... + # + # (...) + # + # anchor_gap=8 + # prev: ... A B C D|E F G H + # next: I J K L|M N O P ... + # anchor_gap=9 + # prev: ... A B C D|E F G H + # new : X + # next: J K L M|N O P ... + + ss_pad = self.ss_unfold_pad + ts_input_size = self.config.train_params.ts_input_size + + assert local_latent_prev.shape[3] == ss_pad*2 + ts_input_size, \ + "Got {} != {}*2+{} (={})".format(local_latent_prev.shape[3], ss_pad, ts_input_size, ss_pad*2+ts_input_size) + + overlap_size = ss_pad*2 - self.config.task.anchor_gap + if overlap_size == 0: # ss_pad accidentally equals to gap, no new values to fill in, no need to overwtite values as well + prev_slice = local_latent_prev + gap_slice = None + next_slice = local_latent_next + elif overlap_size < 0: # ss_pad does not cover the gap, add new local_latent in + B, C, _, _ = local_latent_prev.shape + prev_slice = local_latent_prev + gap_slice = torch.randn(B, C, ts_input_size, -overlap_size).to(self.device) + next_slice = local_latent_next + else: # ss_pad overlaps + prev_slice = local_latent_prev + gap_slice = None + next_slice = local_latent_next[:, :, :, overlap_size:] + + if gap_slice is None: + return torch.cat([prev_slice, next_slice], 3) + else: + return torch.cat([prev_slice, gap_slice, next_slice], 3) + + def unify_overlapped_local_latent(self, local_latent_prev, local_latent_next): + ss_pad = self.ss_unfold_pad + overlap_size = ss_pad*2 - self.config.task.anchor_gap + if overlap_size > 0: + prev_overlap = local_latent_prev[:, :, :, -overlap_size:] + local_latent_next[:, :, :, :overlap_size] = prev_overlap + + # def dispute_local_latents(self, local_latent): + # ss_disp = self.config.train_params.ts_input_size + self.config.task.anchor_gap + # assert ss_disp > 0, "Got unexpected displacement {}".format(ss_disp) + # return local_latent[:, :, :, ss_disp:] + + # def verify_overlapped_anchors(self, anchor_prev, anchor_next): + # ss_pad = self.config.train_params.ss_n_layers * self.config.train_params.ss_unfold_radius + # ts_input_size = self.config.train_params.ts_input_size + # assert anchor_prev.shape[3] == ss_pad*2 + ts_input_size, \ + # "Got {} != {}*2+{} (={})".format(anchor_prev.shape[3], ss_pad, ts_input_size, ss_pad*2+ts_input_size) + + # overlap_slice = anchor_prev[:, :, :, -ss_pad:] + # assert ((anchor_next[:, :, :, :ss_pad] - overlap_slice).abs() < 1e-2).all(), "Got overlapped achors have in consistent values!" + + + def compose_inv_root(self): + if isinstance(self.config.task.prev_inv_config, list): + return [ + os.path.join("./logs/", self.config.var.exp_name, "test", cfg, "stats") + for cfg in self.config.task.prev_inv_config] + else: + return [os.path.join("./logs/", self.config.var.exp_name, "test", self.config.task.prev_inv_config, "stats")] + diff --git a/test_managers/global_config.py b/test_managers/global_config.py new file mode 100644 index 0000000..5255683 --- /dev/null +++ b/test_managers/global_config.py @@ -0,0 +1 @@ +test_meta_extra_pad = 3 \ No newline at end of file diff --git a/test_managers/infinite_generation.py b/test_managers/infinite_generation.py new file mode 100644 index 0000000..d0e5511 --- /dev/null +++ b/test_managers/infinite_generation.py @@ -0,0 +1,298 @@ +import os +import math +import datetime +import numpy as np +import pickle as pkl +import matplotlib.pyplot as plt +from tqdm import tqdm +from glob import glob +from itertools import product as iter_product + +import torch + +from test_managers.base_test_manager import BaseTestManager +from test_managers.testing_vars_wrapper import TestingVars +from test_managers.global_config import test_meta_extra_pad + + +class InfiniteGenerationManager(BaseTestManager): + + def task_specific_init(self, output_size=None): + + if output_size is None: + self.target_height = self.config.task.height + self.target_width = self.config.task.width + else: + self.target_height, self.target_width = output_size + + self._init_starting_points() + self.noise_heights = self.outfeat_step_sizes * (self.num_steps_h-1) + self.outfeat_sizes_list + self.noise_widths = self.outfeat_step_sizes * (self.num_steps_w-1) + self.outfeat_sizes_list + + if hasattr(self.config.task, "gen_from_inv_stats") and self.config.task.gen_from_inv_stats: + self.inv_root = self.compose_inv_root() + self.inv_rec_files = sorted(glob(os.path.join(self.inv_root, "*"))) + self.gen_from_inv_stats = True + else: + self.gen_from_inv_stats = False + + def run_next(self, save=True, write_gpu_time=False, inv_records=None, inv_placements=None, calc_flops=False, disable_pbar=False, **kwargs): + if len(kwargs) > 0: + for k,v in kwargs.items(): + if v is not None: + print(" [Warning] task manager receives untracked arg {} with value {}".format(k ,v)) + testing_vars = self.create_vars(inv_records=inv_records, inv_placements=inv_placements) + self.generate(testing_vars, write_gpu_time=write_gpu_time, calc_flops=calc_flops, disable_pbar=disable_pbar) + if save: + self.save_results(testing_vars.meta_img) + return testing_vars.meta_img + + def create_vars(self, inv_records=None, inv_placements=None): + mixing = False + assert mixing == False, "Otherwise, an injection index must be specified and fed into g_ema." + + # Allocate memory for the final output, starts iterating and filling in the generated results. + # Can be reused + meta_img = torch.empty( + self.config.train_params.batch_size, + 3, + int(self.meta_height), + int(self.meta_width)).float() + + # [Note] + # 1. One may implement a sophisticated version that does not required to + # generate all the latents at once, as most of the info are not reusing + # during the inference. However, the author is just lazy and abusing his + # CPU memory OuO + global_latent = self.latent_sampler.sample_global_latent( + self.config.train_params.batch_size, mixing=mixing, device=self.device) + full_local_latent_shape = ( + # Does not account GNN padding here, it is handled within the latent_sampler + int(self.g_ema_module.calc_in_spatial_size(self.meta_height, include_ss=False)), + int(self.g_ema_module.calc_in_spatial_size(self.meta_width, include_ss=False)), + ) + local_latent = self.latent_sampler.sample_local_latent( + self.config.train_params.batch_size, + device="cpu", # Store in CPU anyway, it can be INFINITLY LARGE! + specific_shape=full_local_latent_shape) + + meta_coords = self.coord_handler.sample_coord_grid( + local_latent, + is_training=False) + + # Create randomized noises + randomized_noises = [ + torch.randn(self.config.train_params.batch_size, 1, int(h), int(w)) + for (h,w) in zip(self.noise_heights, self.noise_widths)] + + testing_vars = TestingVars( + meta_img=meta_img, + global_latent=global_latent, + local_latent=local_latent, + meta_coords=meta_coords, + noises=randomized_noises, + device=self.device) + + if self.gen_from_inv_stats: + assert inv_records is None, \ + "`gen_from_inv_stats` already specified, should not receive `inv_records` from command!" + assert self.config.train_params.batch_size == 1, \ + "Inverted parameters loading for batch is not yet implemeted! " + \ + "Please use parallel-batching instead, which provides a similar inference speed." + inv_records = [self.inv_rec_files[self.cur_global_id]] + inv_placements = [self.config.task.gen_from_inv_placement] + + if inv_records is not None: + testing_vars.replace_by_records( + self.g_ema_module, inv_records, inv_placements, assert_no_style=True) + + return testing_vars + + def generate(self, testing_vars, tkinter_pbar=None, update_by_ss_map=None, update_by_ts_map=None, + write_gpu_time=False, calc_flops=False, disable_pbar=False): + + # I don't mind bruteforce casting combination here, cuz you should worry about the meta_img size first + idx_tuples = list(iter_product(range(self.start_pts_mesh_z.shape[0]), range(self.start_pts_mesh_z.shape[1]))) + + if disable_pbar: + pbar = idx_tuples + elif tkinter_pbar is not None: + pbar = tkinter_pbar(idx_tuples) + else: + pbar = tqdm(idx_tuples) + + accum_exec_time = 0 + accum_flops_all, accum_flops_ss, accum_flops_ts = 0, 0, 0 + for iiter, (idx_x,idx_y) in enumerate(pbar): + zx_st, zy_st = self.start_pts_mesh_z[idx_x, idx_y] + zx_ed = zx_st + self.config.train_params.ts_input_size + zy_ed = zy_st + self.config.train_params.ts_input_size + + # Handle the randomized noise input of the texture_synthesizer... + outfeat_x_st = [start_pts_mesh[idx_x,idx_y,0] for start_pts_mesh in self.start_pts_mesh_outfeats] + outfeat_y_st = [start_pts_mesh[idx_x,idx_y,1] for start_pts_mesh in self.start_pts_mesh_outfeats] + outfeat_x_ed = [ + x_st + out_size for (x_st, out_size) in zip(outfeat_x_st, self.outfeat_sizes_list)] + outfeat_y_ed = [ + y_st + out_size for (y_st, out_size) in zip(outfeat_y_st, self.outfeat_sizes_list)] + noises = [] + for i, (fx_st, fy_st, fx_ed, fy_ed) in enumerate(zip(outfeat_x_st, outfeat_y_st, outfeat_x_ed, outfeat_y_ed)): + noises.append(testing_vars.noises[i][:, :, fx_st:fx_ed, fy_st:fy_ed].to(self.device)) + + # Deal with SS unfolding here + zx_st -= self.ss_unfold_size + zy_st -= self.ss_unfold_size + zx_ed += self.ss_unfold_size + zy_ed += self.ss_unfold_size + + # [Interactive] Decide whether the region will be updated, otherwise no need to generate + if update_by_ss_map is not None: + ss_cursors = zx_st, zx_ed, zy_st, zy_ed + if not self.is_overlaping_update_map(update_by_ss_map, *ss_cursors): + continue + if update_by_ts_map is not None: + # For TS regional selection, we only select noises + ts_cursors = outfeat_x_st[0], outfeat_x_ed[0], outfeat_y_st[0], outfeat_y_ed[0] + if not self.is_overlaping_update_map(update_by_ts_map, *ts_cursors): + continue + + cur_local_latent = testing_vars.local_latent[:, :, zx_st:zx_ed, zy_st:zy_ed].to(self.device) + cur_coords = testing_vars.meta_coords[:, :, zx_st:zx_ed, zy_st:zy_ed].to(self.device) + g_ema_kwargs = { + "global_latent": testing_vars.global_latent, + "local_latent": cur_local_latent, + "override_coords": cur_coords, + "noises": noises, + "disable_dual_latents": True, + "calc_flops": calc_flops, + } + if hasattr(testing_vars, "wplus_styles") and testing_vars.wplus_styles is not None: + g_ema_kwargs["wplus_styles"] = testing_vars.wplus_styles + img_x_st, img_y_st = outfeat_x_st[-1], outfeat_y_st[-1] + img_x_ed, img_y_ed = outfeat_x_ed[-1], outfeat_y_ed[-1] + index_tuple = (img_x_st, img_x_ed, img_y_st, img_y_ed) + + exec_time, flops = self.maybe_parallel_inference( + testing_vars, g_ema_kwargs=g_ema_kwargs, index_tuple=index_tuple, return_exec_time=write_gpu_time, calc_flops=calc_flops) + accum_exec_time += exec_time + if calc_flops: + accum_flops_all += flops["all"] + accum_flops_ss += flops["ss"] + accum_flops_ts += flops["ts"] + + exec_time, flops = self.maybe_parallel_inference( + testing_vars, flush=True, return_exec_time=write_gpu_time, calc_flops=calc_flops) + if calc_flops: + accum_flops_all += flops["all"] + accum_flops_ss += flops["ss"] + accum_flops_ts += flops["ts"] + + if write_gpu_time: + accum_exec_time += exec_time + print(" [*] GPU time {}".format(accum_exec_time)) + self.accum_exec_times.append(accum_exec_time) + fmt_date = datetime.date.today().strftime("%d-%m-%Y") + benchmark_file = os.path.join(self.save_root, "speed_benchmark_{}.txt".format(fmt_date)) + with open(benchmark_file, "a") as f: + f.write("{:.6f}".format(accum_exec_time)) + + if calc_flops: + print(" [*] Total FLOPs: {} (SS {}, TS {})".format( + self.pretty_print_flops(accum_flops_all), + self.pretty_print_flops(accum_flops_ss), + self.pretty_print_flops(accum_flops_ts))) + + + def save_results(self, meta_img, dump_vars=None): + print(" [*] Saving results...") + self.save_meta_imgs(meta_img) + if dump_vars is not None: + self.save_testing_vars(dump_vars) + self.cur_global_id += self.config.train_params.batch_size + + def save_testing_vars(self, testing_vars): + assert self.config.train_params.batch_size == 1, \ + "This is only designed to be used with the interactive tool." + save_path = os.path.join(self.save_root, str(self.cur_global_id).zfill(6)+".pkl") + pkl.dump(testing_vars, open(save_path, "wb")) + + def _wrap_feature(self, feat, wrap_size, dim): + assert wrap_size < (feat.shape[dim] - 2*wrap_size), \ + "Does not expect the wrapping area is larger than a full period." + if dim == 2: + valid_st = feat[:, :, wrap_size:2*wrap_size] + valid_ed = feat[:, :, -2*wrap_size:-wrap_size] + feat[:, :, :wrap_size] = valid_ed + feat[:, :, -wrap_size:] = valid_st + elif dim == 3: + valid_st = feat[:, :, :, wrap_size:2*wrap_size] + valid_ed = feat[:, :, :, -2*wrap_size:-wrap_size] + feat[:, :, :, :wrap_size] = valid_ed + feat[:, :, :, -wrap_size:] = valid_st + else: + raise NotImplementedError( + "I don't expect this function will be used other than spatial dims, but got {}.".format(dim)) + + def save_meta_imgs(self, meta_img): + + # Center crop + pad_h = (self.meta_height - self.target_height) // 2 + pad_w = (self.meta_width - self.target_width) // 2 + meta_img = meta_img[:, :, pad_h:pad_h+self.target_height, pad_w:pad_w+self.target_width] + + # Save the full image and the low-resolution image (for visualization) + meta_img = meta_img.clamp(-1, 1).permute(0, 2, 3, 1) + meta_img = (meta_img + 1) / 2 + meta_img_np = meta_img.numpy() + + for i in range(self.config.train_params.batch_size): + global_id = self.cur_global_id + i + save_path = os.path.join(self.save_root, str(global_id).zfill(6)+".png") + plt.imsave(save_path, meta_img_np[i]) + + def _create_start_pts_mesh(self, step_size, num_steps_h, num_steps_w): + start_pts_x = np.arange(num_steps_h) * step_size + start_pts_y = np.arange(num_steps_w) * step_size + start_pts_mesh = np.stack([ + np.repeat(start_pts_x.reshape(num_steps_h, 1), num_steps_w, axis=1), + np.repeat(start_pts_y.reshape(1, num_steps_w), num_steps_h, axis=0), + ], 2).astype(np.uint32) # shape: (H, W, 2) + return start_pts_mesh + + + def _init_starting_points(self): + + # Get all the starting points of the patches to generate + self.num_steps_h = \ + math.ceil((self.target_height - self.outfeat_sizes_list[-1]) / self.pixelspace_step_size) + test_meta_extra_pad + self.num_steps_w = \ + math.ceil((self.target_width - self.outfeat_sizes_list[-1]) / self.pixelspace_step_size) + test_meta_extra_pad + + self.start_pts_mesh_z = self._create_start_pts_mesh( + step_size=self.latentspace_step_size, + num_steps_h=self.num_steps_h, + num_steps_w=self.num_steps_w) + self.start_pts_mesh_z += self.ss_unfold_size + + # Create this for: + # (1) Final image pixels assignment + # (2) Randomized noise handling within the texture synthesizer + self.start_pts_mesh_outfeats = [ + self._create_start_pts_mesh( + step_size=step_size, + num_steps_h=self.num_steps_h, + num_steps_w=self.num_steps_w, + ) for step_size in self.outfeat_step_sizes] + # start_pts_mesh_x = \ + # (start_pts_mesh_z - ss_unfold_size) // latentspace_step_size * pixelspace_step_size # shape: (H, W, 2) + # start_pts_mesh_x = start_pts_mesh_x.astype(np.uint32) + + # To avoid edge-condition on the image edge, we generate an image slightly larger than + # requested, then center-crop to the requested resolution. + self.meta_height = self.pixelspace_step_size * (self.num_steps_h-1) + self.outfeat_sizes_list[-1] + self.meta_width = self.pixelspace_step_size * (self.num_steps_w-1) + self.outfeat_sizes_list[-1] + + + def compose_inv_root(self): + return os.path.join("./logs/", self.config.var.exp_name, "test", self.config.task.prev_inv_config, "stats") + diff --git a/test_managers/interactive_sampler.py b/test_managers/interactive_sampler.py new file mode 100644 index 0000000..06c5c67 --- /dev/null +++ b/test_managers/interactive_sampler.py @@ -0,0 +1,487 @@ +import numpy as np +import tkinter +import tkinter.ttk +from tkinter.filedialog import askopenfilename +from PIL import Image, ImageTk, ImageOps + +import torch + +from latent_sampler import LatentSampler +from utils import manually_seed + + +class TkinterProgressBarWrapper(): + """ + A tkinter pbar wrapper that simulates tqdm behavior + """ + def __init__(self, root, tkinter_pbar): + self.root = root + self.pbar = tkinter_pbar + + self.counter = 0 + self.length = 0 + self.iter_target = None + + def __iter__(self): + return self + + def __next__(self): + self.counter += 1 + percentage = self.counter / self.length + self.pbar['value'] = int(round(percentage*100)) + self.pbar.update() + return next(self.iter_target) + + def __call__(self, target): + self.counter = 0 + self.length = len(target) + self.iter_target = iter(target) + self.pbar.stop() + self.pbar['value'] = 0 + self.pbar.update() + return self + +class InteractiveSampler(): + + def __init__(self, task_manager, testing_vars, config): + + assert config.task.batch_size == 1, \ + "Bro, you have only one mouse, why you need a batch more than one image???" + + # In case the image is too large, setting this value to N will downsample your image by N times + self.fov_rescale = 1 + + self.task_manager = task_manager + self.g_ema_module = task_manager.g_ema_module + self.latent_sampler = LatentSampler(self.g_ema_module, config) + + self.root = tkinter.Tk() + self.root.title('Interactive InfinityGAN') + self.root.iconphoto(False, tkinter.PhotoImage(file="assets/favicon.ico")) + + # Create canvas with scroll bar + self.addi_img_padding = 20 + _, _, meta_height, meta_width = testing_vars.meta_img.shape + self.frame_height = int(meta_height / self.fov_rescale) + self.addi_img_padding * 2 + self.frame_width = int(meta_width / self.fov_rescale) + self.addi_img_padding * 2 + frame = tkinter.Frame(self.root, width=self.frame_width, height=self.frame_height, relief=tkinter.SUNKEN) + frame.pack(expand=True, fill=tkinter.BOTH) + # frame.grid_rowconfigure(0, weight=1) + # frame.grid_columnconfigure(0, weight=1) + hscroll = tkinter.Scrollbar(frame, orient=tkinter.HORIZONTAL) + hscroll.pack(side=tkinter.BOTTOM, fill=tkinter.X) + # hscroll.grid(row=1, column=0, sticky=tkinter.E+tkinter.W) + vscroll = tkinter.Scrollbar(frame) + vscroll.pack(side=tkinter.RIGHT, fill=tkinter.Y) + # vscroll.grid(row=0, column=1, sticky=tkinter.N+tkinter.S) + + btn_height = 2 # 120 + canvas_width = min(self.root.winfo_screenwidth(), self.frame_width) + canvas_height = min(self.root.winfo_screenheight()-btn_height*60, self.frame_height) + print(" [*] Canvas size: ({}, {})".format(canvas_width, canvas_height)) + self.canvas = tkinter.Canvas( + frame, width=canvas_width, height=canvas_height, bd=0, + xscrollcommand=hscroll.set, yscrollcommand=vscroll.set, + scrollregion=(0, 0, self.frame_width, self.frame_height)) + self.canvas.pack(side=tkinter.LEFT, expand=True, fill=tkinter.BOTH) + hscroll.config(command=self.canvas.xview) + vscroll.config(command=self.canvas.yview) + + # Bind buttons + # pixel = tkinter.PhotoImage(width=1, height=1) + btn_common_args = { + # "image": pixel, + "height": btn_height + } + extra_btns = [] + btn_ssg = tkinter.Button(self.root, text="SS Global (all)", command=self.run_global_resample, **btn_common_args) + btn_ssg.pack(side=tkinter.LEFT, padx=5) + extra_btns.append(btn_ssg) + if "FusedGenerationManager" in config.task.task_manager: + for i in range(len(config.task.style_centers)): + btn_ssg = tkinter.Button( + self.root, text="SS Global {}".format(i), **btn_common_args, + command=lambda index=i: self.run_global_resample(update_index=index)) # Dereference `i` with default arg + btn_ssg.pack(side=tkinter.LEFT, padx=5) + extra_btns.append(btn_ssg) + btn_ssl = tkinter.Button(self.root, text="SS Local", command=self.run_local_resample, **btn_common_args) + btn_ssl.pack(side=tkinter.LEFT, padx=5) + btn_tsn = tkinter.Button(self.root, text="TS Noise", command=self.run_noises_resample, **btn_common_args) + btn_tsn.pack(side=tkinter.LEFT, padx=5) + btn_undo = tkinter.Button(self.root, text="Undo", command=self.run_undo, **btn_common_args) + btn_undo.pack(side=tkinter.LEFT, padx=5) + btn_redo = tkinter.Button(self.root, text="Redo", command=self.run_redo, **btn_common_args) + btn_redo.pack(side=tkinter.LEFT, padx=5) + btn_clean_bbox = tkinter.Button(self.root, text="Clean Bbox", command=self.clean_bbox, **btn_common_args) + btn_clean_bbox.pack(side=tkinter.LEFT, padx=5) + btn_comp_task = tkinter.Button(self.root, text="Save", command=self.dump_current, **btn_common_args) + btn_comp_task.pack(side=tkinter.LEFT, padx=55) + btn_comp_task = tkinter.Button(self.root, text="Complete & Save", command=self.set_task_complete, **btn_common_args) + btn_comp_task.pack(side=tkinter.LEFT, padx=15) + self.all_btns = [btn_ssg, btn_ssl, btn_tsn, btn_comp_task, btn_undo, btn_redo, btn_clean_bbox] + extra_btns + + + self.pbar = tkinter.ttk.Progressbar(self.root, orient=tkinter.HORIZONTAL, length=200, mode='determinate') + self.pbar.pack(side=tkinter.RIGHT, padx=5) + self.pbar_caller = TkinterProgressBarWrapper(self.root, self.pbar) + + # Bind mouse click event + self.canvas.bind("

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