Semantic Segmentation with OG Img Resolution ultralytics#17

models/common.py

@@ -851,10 +851,12 @@ class Seg(nn.Module):
 
     def __init__(self, in_channels):
         super().__init__()
+        print('SEG in channels: ', in_channels)
         self.cv1 = Conv(in_channels, 32, k=3)
         self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
         self.cv2 = Conv(32, 64, k=3)
-        self.cv3 = Conv(64, 1, act=False)
+        self.cv3 = Conv(64, 64, k=3)
+        self.cv4 = Conv(64, 1, act=False)
         self.relu = nn.ReLU()
 
         # self.dropout_weak = nn.Dropout(0.25)
@@ -873,8 +875,11 @@ def forward(self, x):
         # x = self.relu(x)
         # x = self.dropout_normal(x)
         x = self.cv3(x)
+        x = self.upsample(x)
         # print('----out shape', x.shape, '---\n')
         # x = self.sigmoid(x)
+
+        x = self.cv4(x)
         return x
 
 

train.py

@@ -550,7 +550,7 @@ def parse_opt(known=False):
 
 
 def main(opt, callbacks=Callbacks()):
-    print('\n---------- VERSION:', '#0016', '----------\n')
+    print('\n---------- VERSION:', '#0017', '----------\n')
     # Checks
     if RANK in {-1, 0}:
         print_args(vars(opt))

utils/dataloaders.py

@@ -677,7 +677,8 @@ def __getitem__(self, index):
             # Letterbox
             shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size  # final letterboxed shape
             img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment)
-            mask_shape = [shape[0] // 2, shape[1] // 2] if self.rect else self.img_size // 2
+            # mask_shape = [shape[0] // 2, shape[1] // 2] if self.rect else self.img_size // 2
+            mask_shape = [shape[0], shape[1]] if self.rect else self.img_size
             seg, _, _ = letterbox(seg, mask_shape, auto=False, scaleup=self.augment, color=(0, 0, 0))
             shapes = (h0, w0), ((h / h0, w / w0), pad)  # for COCO mAP rescaling