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Add BatchNorm and Dropout to DoubleConv for improved training stability #535

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Add BatchNorm and Dropout to DoubleConv for improved training stability #535

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16 changes: 8 additions & 8 deletions unet/unet_parts.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,17 +8,17 @@
class DoubleConv(nn.Module):
"""(convolution => [BN] => ReLU) * 2"""

def __init__(self, in_channels, out_channels, mid_channels=None):
super().__init__()
if not mid_channels:
mid_channels = out_channels
def __init__(self, in_channels, out_channels, dropout_prob=0.1):
super(DoubleConv, self).__init__()
self.double_conv = nn.Sequential(
nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(mid_channels),
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(out_channels), # <- اضافه شدن BatchNorm بعد از کانولوشن
nn.ReLU(inplace=True),
nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False),
nn.Dropout2d(dropout_prob), # <- اضافه شدن Dropout برای جلوگیری از بیش‌برازش
nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
nn.ReLU(inplace=True),
nn.Dropout2d(dropout_prob)
)

def forward(self, x):
Expand Down