-
Notifications
You must be signed in to change notification settings - Fork 186
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
【Hackathon 7th PPSCI No.7】No.7 AI-aided geometric design of anti-infe…
…ction catheters 论文复现 (#986) * 黑客松No.7 * update * NO7 * update * fix config * fix code * Update examples/catheter/conf/catheter.yaml Co-authored-by: HydrogenSulfate <[email protected]> * update config * update code * update code * add inference * add inference * add inference * codestyle * codestyle * bugfix * fix codestyle * fix bug * codestyle * Complex GradNode bug fix * codestyle * add docs * add docs 2 * add docs 3 * add docs 4 * add docs 5 * add docs 6 * add docs 7 * add docs 7 * add docs 8 * add docs 9 * add docs 10 * add docs 10 * add docs 11 * add docs 11 * add docs 11 * add docs 12 * add docs 13 * add docs 13 * add docs 14 * add docs 14 * add docs 15 * docfix for transovler * finally * finally * fix codestyle * fix codestyle * fix codestyle * fix codestyle * fix codestyle * fix codestyle * fix * fix * fix * fix * fix * fix --------- Co-authored-by: HydrogenSulfate <[email protected]> Co-authored-by: wangguan <[email protected]>
- Loading branch information
3 people
authored
Dec 31, 2024
1 parent
c4979df
commit ee2d77c
Showing
9 changed files
with
1,170 additions
and
0 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -18,6 +18,7 @@ | |
| - DGMR | ||
| - Discriminator | ||
| - ExtFormerMoECuboid | ||
| - FNO1d | ||
| - Generator | ||
| - HEDeepONets | ||
| - LorenzEmbedding | ||
|
|
||
Large diffs are not rendered by default.
Oops, something went wrong.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,303 @@ | ||
| # Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
|
||
| import os | ||
| from os import path as osp | ||
|
|
||
| import hydra | ||
| import matplotlib.pyplot as plt | ||
| import numpy as np | ||
| import paddle | ||
| from omegaconf import DictConfig | ||
|
|
||
| import ppsci | ||
| from ppsci.loss import L2RelLoss | ||
| from ppsci.optimizer import Adam | ||
| from ppsci.optimizer import lr_scheduler | ||
| from ppsci.utils import logger | ||
|
|
||
|
|
||
| # build data | ||
| def getdata( | ||
| x_path, | ||
| y_path, | ||
| para_path, | ||
| output_path, | ||
| n_data, | ||
| n, | ||
| s, | ||
| is_train=True, | ||
| is_inference=False, | ||
| ): | ||
| # load data | ||
| inputX_raw = np.load(x_path)[:, 0:n_data] | ||
| inputY_raw = np.load(y_path)[:, 0:n_data] | ||
| inputPara_raw = np.load(para_path)[:, 0:n_data] | ||
| output_raw = np.load(output_path)[:, 0:n_data] | ||
|
|
||
| # preprocess data | ||
| inputX = inputX_raw[:, 0::3] | ||
| inputY = inputY_raw[:, 0::3] | ||
| inputPara = inputPara_raw[:, 0::3] | ||
| label = (output_raw[:, 0::3] + output_raw[:, 1::3] + output_raw[:, 2::3]) / 3.0 | ||
|
|
||
| if is_inference: | ||
| inputX = np.transpose(inputX, (1, 0)) | ||
| inputY = np.transpose(inputY, (1, 0)) | ||
| input = np.stack(arrays=[inputX, inputY], axis=-1).astype(np.float32) | ||
| input = input.reshape(n, s, 2) | ||
| return input | ||
|
|
||
| inputX = paddle.to_tensor(data=inputX, dtype="float32").transpose(perm=[1, 0]) | ||
| inputY = paddle.to_tensor(data=inputY, dtype="float32").transpose(perm=[1, 0]) | ||
| input = paddle.stack(x=[inputX, inputY], axis=-1) | ||
| label = paddle.to_tensor(data=label, dtype="float32").transpose(perm=[1, 0]) | ||
| if is_train: | ||
| index = paddle.randperm(n=n) | ||
| index = index[:n] | ||
| input = paddle.index_select(input, index) | ||
| label = paddle.index_select(label, index) | ||
| input = input.reshape([n, s, 2]) | ||
| else: | ||
| input = input.reshape([n, s, 2]) | ||
| label = label.unsqueeze(axis=-1) | ||
| return input, label, inputPara | ||
|
|
||
|
|
||
| def plot(input: np.ndarray, out_pred: np.ndarray, output_dir: str): | ||
| os.makedirs(output_dir, exist_ok=True) | ||
| fig_path = osp.join(output_dir, "inference.png") | ||
|
|
||
| xx = np.linspace(-500, 0, 2001) | ||
| fig = plt.figure(figsize=(5, 4)) | ||
| plt.plot(input[:, 0], input[:, 1], color="C1", label="Channel geometry") | ||
| plt.plot(input[:, 0], 100 - input[:, 1], color="C1") | ||
| plt.plot( | ||
| xx, | ||
| out_pred, | ||
| "--*", | ||
| color="C2", | ||
| fillstyle="none", | ||
| markevery=len(xx) // 10, | ||
| label="Predicted bacteria distribution", | ||
| ) | ||
| plt.xlabel(r"x") | ||
| plt.legend() | ||
| plt.tight_layout() | ||
| fig.savefig(fig_path, bbox_inches="tight", dpi=400) | ||
| plt.close() | ||
| ppsci.utils.logger.info(f"Saving figure to {fig_path}") | ||
|
|
||
|
|
||
| def train(cfg: DictConfig): | ||
| # generate training dataset | ||
| inputs_train, labels_train, _ = getdata(**cfg.TRAIN_DATA, is_train=True) | ||
|
|
||
| # set constraints | ||
| sup_constraint = ppsci.constraint.SupervisedConstraint( | ||
| { | ||
| "dataset": { | ||
| "name": "NamedArrayDataset", | ||
| "input": {"input": inputs_train}, | ||
| "label": {"output": labels_train}, | ||
| }, | ||
| "batch_size": cfg.TRAIN.batch_size, | ||
| "sampler": { | ||
| "name": "BatchSampler", | ||
| "drop_last": False, | ||
| "shuffle": True, | ||
| }, | ||
| }, | ||
| L2RelLoss(reduction="sum"), | ||
| name="sup_constraint", | ||
| ) | ||
| constraint = {sup_constraint.name: sup_constraint} | ||
|
|
||
| # set model | ||
| model = ppsci.arch.FNO1d(**cfg.MODEL) | ||
| if cfg.TRAIN.use_pretrained_model is True: | ||
| logger.info( | ||
| "Loading pretrained model from {}".format(cfg.TRAIN.pretrained_model_path) | ||
| ) | ||
| model.set_state_dict(paddle.load(cfg.TRAIN.pretrained_model_path)) | ||
|
|
||
| # set optimizer | ||
| ITERS_PER_EPOCH = int(cfg.TRAIN_DATA.n / cfg.TRAIN.batch_size) | ||
| scheduler = lr_scheduler.Step( | ||
| **cfg.TRAIN.lr_scheduler, iters_per_epoch=ITERS_PER_EPOCH | ||
| ) | ||
| optimizer = Adam(scheduler(), weight_decay=cfg.TRAIN.weight_decay)(model) | ||
|
|
||
| # generate test dataset | ||
| inputs_test, labels_test, _ = getdata(**cfg.TEST_DATA, is_train=False) | ||
|
|
||
| # set validator | ||
| l2rel_validator = { | ||
| "validator1": ppsci.validate.SupervisedValidator( | ||
| { | ||
| "dataset": { | ||
| "name": "NamedArrayDataset", | ||
| "input": {"input": inputs_test}, | ||
| "label": {"output": labels_test}, | ||
| }, | ||
| "batch_size": cfg.TRAIN.batch_size, | ||
| }, | ||
| L2RelLoss(reduction="sum"), | ||
| metric={"L2Rel": ppsci.metric.L2Rel()}, | ||
| name="L2Rel_Validator", | ||
| ) | ||
| } | ||
|
|
||
| # initialize solver | ||
| solver = ppsci.solver.Solver( | ||
| model, | ||
| constraint, | ||
| cfg.output_dir, | ||
| optimizer, | ||
| epochs=cfg.TRAIN.epochs, | ||
| iters_per_epoch=ITERS_PER_EPOCH, | ||
| eval_with_no_grad=True, | ||
| eval_during_train=cfg.TRAIN.eval_during_train, | ||
| validator=l2rel_validator, | ||
| save_freq=cfg.TRAIN.save_freq, | ||
| ) | ||
|
|
||
| # train model | ||
| solver.train() | ||
| # plot losses | ||
| solver.plot_loss_history(by_epoch=True, smooth_step=1) | ||
|
|
||
|
|
||
| def evaluate(cfg: DictConfig): | ||
| # set model | ||
| model = ppsci.arch.FNO1d(**cfg.MODEL) | ||
| ppsci.utils.save_load.load_pretrain( | ||
| model, | ||
| cfg.EVAL.pretrained_model_path, | ||
| ) | ||
|
|
||
| # set data | ||
| x_test, y_test, para = getdata(**cfg.TEST_DATA, is_train=False) | ||
| y_test = y_test.numpy() | ||
|
|
||
| for sample_id in [0, 8]: | ||
| sample, uf, L_p, x1, x2, x3, h = para[:, sample_id] | ||
| mesh = x_test[sample_id, :, :] | ||
| mesh = mesh.numpy() | ||
|
|
||
| y_test_pred = ( | ||
| paddle.exp( | ||
| model({"input": x_test[sample_id : sample_id + 1, :, :]})["output"] | ||
| ) | ||
| .numpy() | ||
| .flatten() | ||
| ) | ||
| logger.info( | ||
| "rel. error is ", | ||
| np.linalg.norm(y_test_pred - y_test[sample_id, :].flatten()) | ||
| / np.linalg.norm(y_test[sample_id, :].flatten()), | ||
| ) | ||
| xx = np.linspace(-500, 0, 2001) | ||
| plt.figure(figsize=(5, 4)) | ||
|
|
||
| plt.plot(mesh[:, 0], mesh[:, 1], color="C1", label="Channel geometry") | ||
| plt.plot(mesh[:, 0], 100 - mesh[:, 1], color="C1") | ||
|
|
||
| plt.plot( | ||
| xx, | ||
| y_test[sample_id, :], | ||
| "--o", | ||
| color="red", | ||
| markevery=len(xx) // 10, | ||
| label="Reference", | ||
| ) | ||
| plt.plot( | ||
| xx, | ||
| y_test_pred, | ||
| "--*", | ||
| color="C2", | ||
| fillstyle="none", | ||
| markevery=len(xx) // 10, | ||
| label="Predicted bacteria distribution", | ||
| ) | ||
|
|
||
| plt.xlabel(r"x") | ||
|
|
||
| plt.legend() | ||
| plt.tight_layout() | ||
| plt.savefig(f"Validation.{sample_id}.pdf") | ||
|
|
||
|
|
||
| def export(cfg: DictConfig): | ||
| # set model | ||
| model = ppsci.arch.FNO1d(**cfg.MODEL) | ||
| # initialize solver | ||
| solver = ppsci.solver.Solver( | ||
| model, | ||
| pretrained_model_path=cfg.INFER.pretrained_model_path, | ||
| ) | ||
| # export model | ||
| from paddle.static import InputSpec | ||
|
|
||
| input_spec = [ | ||
| { | ||
| key: InputSpec([None, 2001, 2], "float32", name=key) | ||
| for key in model.input_keys | ||
| }, | ||
| ] | ||
| solver.export(input_spec, cfg.INFER.export_path) | ||
|
|
||
|
|
||
| def inference(cfg: DictConfig): | ||
| from deploy import python_infer | ||
|
|
||
| predictor = python_infer.GeneralPredictor(cfg) | ||
|
|
||
| # evaluate | ||
| input = getdata(**cfg.TEST_DATA, is_train=False, is_inference=True) | ||
| input_dict = {"input": input} | ||
|
|
||
| output_dict = predictor.predict(input_dict, cfg.INFER.batch_size) | ||
| # mapping data to cfg.INFER.output_keys | ||
| output_keys = ["output"] | ||
| output_dict = { | ||
| store_key: paddle.exp(paddle.to_tensor(output_dict[infer_key])) | ||
| .numpy() | ||
| .flatten() | ||
| for store_key, infer_key in zip(output_keys, output_dict.keys()) | ||
| } | ||
|
|
||
| mesh = input_dict["input"][5, :, :] | ||
| yy = output_dict["output"][5] | ||
| plot(mesh, yy, cfg.output_dir) | ||
|
|
||
|
|
||
| @hydra.main(version_base=None, config_path="./conf", config_name="catheter.yaml") | ||
| def main(cfg: DictConfig): | ||
| if cfg.mode == "train": | ||
| train(cfg) | ||
| elif cfg.mode == "eval": | ||
| evaluate(cfg) | ||
| elif cfg.mode == "export": | ||
| export(cfg) | ||
| elif cfg.mode == "infer": | ||
| inference(cfg) | ||
| else: | ||
| raise ValueError( | ||
| f"cfg.mode should in ['train', 'eval', 'export', 'infer], but got '{cfg.mode}'" | ||
| ) | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
| main() |
Oops, something went wrong.