remove pycache

image-inpainting/.gitignore

@@ -1,4 +1,5 @@
 data/*
 *.zip
 *.jpg
-*.pt
+*.pt
+__pycache__/

image-inpainting/src/architecture.py

@@ -18,20 +18,6 @@ def init_weights(m):
     elif isinstance(m, nn.BatchNorm2d):
         nn.init.constant_(m.weight, 1)
         nn.init.constant_(m.bias, 0)
-    elif isinstance(m, nn.GroupNorm):
-        if m.weight is not None:
-            nn.init.constant_(m.weight, 1)
-        if m.bias is not None:
-            nn.init.constant_(m.bias, 0)
-
-
-def _make_norm(num_channels: int) -> nn.Module:
-    """Batch-size independent normalization (works well for batch_size=1 eval)."""
-    # Choose a group count that divides num_channels.
-    num_groups = min(32, num_channels)
-    while num_groups > 1 and (num_channels % num_groups) != 0:
-        num_groups //= 2
-    return nn.GroupNorm(num_groups=num_groups, num_channels=num_channels)
 
 
 class ChannelAttention(nn.Module):
@@ -87,7 +73,7 @@ class ConvBlock(nn.Module):
     def __init__(self, in_channels, out_channels, kernel_size=3, padding=1, dropout=0.0):
         super().__init__()
         self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, padding=padding)
-        self.bn = _make_norm(out_channels)
+        self.bn = nn.BatchNorm2d(out_channels)
         self.relu = nn.LeakyReLU(0.1, inplace=True)
         self.dropout = nn.Dropout2d(dropout) if dropout > 0 else nn.Identity()
     
@@ -99,9 +85,9 @@ class ResidualConvBlock(nn.Module):
     def __init__(self, channels, dropout=0.0):
         super().__init__()
         self.conv1 = nn.Conv2d(channels, channels, 3, padding=1)
-        self.bn1 = _make_norm(channels)
+        self.bn1 = nn.BatchNorm2d(channels)
         self.conv2 = nn.Conv2d(channels, channels, 3, padding=1)
-        self.bn2 = _make_norm(channels)
+        self.bn2 = nn.BatchNorm2d(channels)
         self.relu = nn.LeakyReLU(0.1, inplace=True)
         self.dropout = nn.Dropout2d(dropout) if dropout > 0 else nn.Identity()
     
@@ -114,26 +100,6 @@ class ResidualConvBlock(nn.Module):
         return self.relu(out)
 
 
-class GatedConvBlock(nn.Module):
-    """Gated convolution block (helps the network condition on the mask channel)."""
-    def __init__(self, in_channels, out_channels, kernel_size=3, padding=1, dropout=0.0):
-        super().__init__()
-        self.feature = nn.Conv2d(in_channels, out_channels, kernel_size, padding=padding)
-        self.gate = nn.Conv2d(in_channels, out_channels, kernel_size, padding=padding)
-        self.norm = _make_norm(out_channels)
-        self.act = nn.LeakyReLU(0.1, inplace=True)
-        self.dropout = nn.Dropout2d(dropout) if dropout > 0 else nn.Identity()
-
-    def forward(self, x):
-        feat = self.feature(x)
-        gate = torch.sigmoid(self.gate(x))
-        out = feat * gate
-        out = self.norm(out)
-        out = self.act(out)
-        out = self.dropout(out)
-        return out
-
-
 class DownBlock(nn.Module):
     """Downsampling block with conv blocks, residual connection, attention, and max pooling"""
     def __init__(self, in_channels, out_channels, dropout=0.1):
@@ -181,7 +147,7 @@ class MyModel(nn.Module):
         
         # Initial convolution with larger receptive field
         self.init_conv = nn.Sequential(
-            GatedConvBlock(n_in_channels, base_channels, kernel_size=7, padding=3, dropout=dropout),
+            ConvBlock(n_in_channels, base_channels, kernel_size=7, padding=3),
             ConvBlock(base_channels, base_channels),
             ResidualConvBlock(base_channels)
         )

image-inpainting/src/datasets.py

@@ -32,25 +32,6 @@ def resize(img: Image):
         transforms.CenterCrop((IMAGE_DIMENSION, IMAGE_DIMENSION))
     ])
     return resize_transforms(img)
-
-
-def augment_geometric(img: Image.Image) -> Image.Image:
-    """Lightweight, label-preserving augmentation (safe for train/val/test splits)."""
-    # Horizontal flip
-    if random.random() < 0.5:
-        img = img.transpose(Image.Transpose.FLIP_LEFT_RIGHT)
-    # Vertical flip (less frequent)
-    if random.random() < 0.2:
-        img = img.transpose(Image.Transpose.FLIP_TOP_BOTTOM)
-    # 90-degree rotations (no interpolation artifacts)
-    r = random.random()
-    if r < 0.25:
-        img = img.transpose(Image.Transpose.ROTATE_90)
-    elif r < 0.5:
-        img = img.transpose(Image.Transpose.ROTATE_180)
-    elif r < 0.75:
-        img = img.transpose(Image.Transpose.ROTATE_270)
-    return img
 def preprocess(input_array: np.ndarray):
     input_array = np.asarray(input_array, dtype=np.float32) / 255.0
     return input_array
@@ -69,17 +50,13 @@ class ImageDataset(torch.utils.data.Dataset):
     def __getitem__(self, idx:int):
         index = int(idx)
         
-        image = Image.open(self.imagefiles[index]).convert("RGB")
+        image = Image.open(self.imagefiles[index])
-        image = augment_geometric(image)
         image = np.asarray(resize(image))
         image = preprocess(image)
-
+        spacing_x = random.randint(2,6)
-        # Sample a grid-mask similar in density to the challenge testset (~8% known pixels).
+        spacing_y = random.randint(2,6)
-        # IMPORTANT: offset ranges must be tied to spacing to avoid accidental distribution shift.
+        offset_x = random.randint(0,8)
-        spacing_x = random.randint(4, 6)
+        offset_y = random.randint(0,8)
-        spacing_y = random.randint(2, 4)
-        offset_x = random.randint(0, spacing_x - 1)
-        offset_y = random.randint(0, spacing_y - 1)
         spacing = (spacing_x, spacing_y)
         offset = (offset_x, offset_y)
         input_array, known_array = create_arrays_from_image(image.copy(), offset, spacing)