From 828a8422a83fdf664339b8ebeb3b4a2f377e7307 Mon Sep 17 00:00:00 2001
From: ChairC <974833488@qq.com>
Date: Wed, 18 Sep 2024 08:08:47 +0800
Subject: [PATCH 1/3] Fix: Remove two imshow() duplicate functions.

---
 utils/utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/utils/utils.py b/utils/utils.py
index a0cd7ca..bfa6241 100644
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -43,7 +43,7 @@ def plot_one_image_in_images(images, fig_size=(64, 64)):
     plt.figure(figsize=fig_size)
     for i in images.cpu():
         plt.imshow(X=i)
-        plt.imshow()
+        plt.show()
 
 
 def save_images(images, path, **kwargs):

From 95f4bf1a0fe698c2a68ebab845dbc634d3ac2de7 Mon Sep 17 00:00:00 2001
From: ChairC <974833488@qq.com>
Date: Fri, 20 Sep 2024 22:48:38 +0800
Subject: [PATCH 2/3] Update: Remove magic transforms and eval function.

---
 sr/train.py  |  3 ---
 webui/web.py | 12 ------------
 2 files changed, 15 deletions(-)

diff --git a/sr/train.py b/sr/train.py
index a3ff9a6..92fe37f 100644
--- a/sr/train.py
+++ b/sr/train.py
@@ -257,9 +257,6 @@ def train(rank=None, args=None):
 
         # Saving and validating models in the main process
         if save_models:
-            # Val
-            # model.eval()
-
             # Saving model, set the checkpoint name
             save_name = f"ckpt_{str(epoch).zfill(3)}"
             # Init ckpt params
diff --git a/webui/web.py b/webui/web.py
index 75144e6..f87d71d 100644
--- a/webui/web.py
+++ b/webui/web.py
@@ -102,18 +102,6 @@ def train(self, seed, conditional, sample, network, run_name, epochs, batch_size
         results_dir = results_logging[1]
         results_vis_dir = results_logging[2]
         results_tb_dir = results_logging[3]
-        # Dataloader
-        transforms = torchvision.transforms.Compose([
-            # Resize input size
-            # torchvision.transforms.Resize(80), image_size + 1/4 * image_size
-            torchvision.transforms.Resize(size=int(image_size + image_size / 4)),
-            # Random adjustment cropping
-            torchvision.transforms.RandomResizedCrop(size=image_size, scale=RANDOM_RESIZED_CROP_SCALE),
-            # To Tensor Format
-            torchvision.transforms.ToTensor(),
-            # For standardization, the mean and standard deviation
-            torchvision.transforms.Normalize(mean=MEAN, std=STD)
-        ])
         # Load the folder data under the current path,
         # and automatically divide the labels according to the dataset under each file name
         dataloader = get_dataset(image_size=image_size, dataset_path=dataset_path, batch_size=batch_size,

From 595f9126fb9b2128dcffb7db989e457687bbb5a0 Mon Sep 17 00:00:00 2001
From: ChairC <974833488@qq.com>
Date: Fri, 20 Sep 2024 22:49:19 +0800
Subject: [PATCH 3/3] Update: Replace images.shape[0] to batch_size.

---
 tools/train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/train.py b/tools/train.py
index 5dd8d6a..5ce1fc0 100644
--- a/tools/train.py
+++ b/tools/train.py
@@ -265,7 +265,7 @@ def train(rank=None, args=None):
                 # Enable visualization
                 if vis:
                     # images.shape[0] is the number of images in the current batch
-                    n = num_vis if num_vis > 0 else images.shape[0]
+                    n = num_vis if num_vis > 0 else batch_size
                     sampled_images = diffusion.sample(model=model, n=n)
                     save_images(images=sampled_images,
                                 path=os.path.join(results_vis_dir, f"{save_name}.{image_format}"))