[*] image resizing in dataset

src/train/dataset.py

@@ -2,7 +2,7 @@ import os
 import glob
 import cv2
 from pathlib import Path
-from typing import Callable, List, Optional, Tuple, Union, Dict
+from typing import Tuple, Union
 
 import torch
 import torchvision
@@ -16,34 +16,44 @@ class SegDataset(Dataset):
     """
 
     def __init__(
-            self,
+        self,
-            frame_dir: Union[str, Path],
+        frame_dir: Union[str, Path],
-            mask_dir: Union[str, Path]
+        mask_dir: Union[str, Path]
     ) -> None:
 
         self.frame_dir = Path(frame_dir)
         self.mask_dir = Path(mask_dir)
         self.image_names = glob.glob(f"{self.frame_dir}/*.jpg") 
-        self.mask_names = [os.path.join(self.mask_dir,"mask"+x.split('/')[-1][:-4][5:]+".png") for x in self.image_names] 
+        self.mask_names = [os.path.join(self.mask_dir,(x.split('/')[-1])[:-4]+".png") for x in self.image_names] 
-        print(self.mask_names)
+        #print(self.mask_names)
         self.transform = torchvision.transforms.Compose([
-        torchvision.transforms.ToTensor(),
+            torchvision.transforms.ToPILImage(),
-        torchvision.transforms.Normalize( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
+            torchvision.transforms.Resize((512,512)),
-
+            torchvision.transforms.ToTensor(),
+            torchvision.transforms.Normalize( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])        
+        ])
+        self.transform2 = torchvision.transforms.Compose([
+            torchvision.transforms.ToPILImage(),
+            torchvision.transforms.Resize((512,512)),
+            torchvision.transforms.ToTensor()
+        ])
 
     def __getitem__(self, index: int) -> Tuple[torch.Tensor, torch.Tensor]:
         frame_pth = self.image_names[index]
         mask_pth = self.mask_names[index]
 
-        frame =  cv2.imread(frame_pth)
+        frame = cv2.imread(frame_pth)
         frame = self.transform(frame)
 
-        mask =  cv2.imread(mask_pth,cv2.IMREAD_GRAYSCALE)
+        mask = cv2.imread(mask_pth,cv2.IMREAD_GRAYSCALE)
         trimap = torch.from_numpy(makeTrimap(mask)).float()
-        trimap =  torch.unsqueeze(trimap,0)
+        trimap = torch.unsqueeze(trimap,0)
         mask = torch.from_numpy(mask)
         mask = torch.unsqueeze(mask,0).float()
 
+        mask = self.transform2(mask)
+        trimap = self.transform2(trimap)
+        
         return frame, trimap, mask
 
     def __len__(self):

src/train/train.py

@@ -60,7 +60,7 @@ neptuneRun = neptune.init(project = project,
 
 for epoch in range(0, epochs):
     for idx, (image, trimap, gt_matte) in enumerate(dataloader):
-        semantic_loss, detail_loss, matte_loss, semantic_iou = supervised_training_iter(modnet, optimizer, image, trimap, gt_matte, semantic_scale=1)
+        semantic_loss, detail_loss, matte_loss, semantic_iou = supervised_training_iter(modnet, optimizer, image, trimap, gt_matte) # , semantic_scale=1, detail_scale=10, matte_scale=1)
         if idx % 100 == 0:
             logger.info(f'idx: {idx}, semantic_loss: {semantic_loss:.5f}, detail_loss: {detail_loss:.5f}, matte_loss: {matte_loss:.5f}, semantic_iou: {semantic_iou:.5f}')
     logger.info(f'Epoch: {epoch}, semantic_loss: {semantic_loss:.5f}, detail_loss: {detail_loss:.5f}, matte_loss: {matte_loss:.5f}, semantic_iou: {semantic_iou:.5f}')