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Commit d2d176bf authored by Frisinghelli Daniel's avatar Frisinghelli Daniel
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Stable refactored version.

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with 53 additions and 21 deletions
pysegcnn/core/layers.py
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......@@ -216,9 +216,9 @@ class EncoderBlock(Block):
Returns
-------
y : `torch.Tensor`
y : `torch.Tensor`, shape=(batch, channel, height, width)
Output of the encoder block.
x : `torch.Tensor`
x : `torch.Tensor`, shape=(batch, channel, height, width)
Intermediate output before applying downsampling. Useful to
implement skip connections.
indices : `torch.Tensor` or `None`
......@@ -239,7 +239,7 @@ class EncoderBlock(Block):
def downsample(self, x):
"""Define the downsampling method.
The `~pysegcnn.core.layers.EncoderBlock.downsample` `method should
The `~pysegcnn.core.layers.EncoderBlock.downsample` method should
implement the spatial pooling operation.
Use one of the following functions to downsample:
......@@ -251,7 +251,7 @@ class EncoderBlock(Block):
Parameters
----------
x : `torch.Tensor`
x : `torch.Tensor`, shape=(batch, channel, height, width)
Input tensor, e.g. output of a convolutional block.
Raises
......@@ -261,7 +261,7 @@ class EncoderBlock(Block):
Returns
-------
x : `torch.Tensor`
x : `torch.Tensor`, shape=(batch, channel, height, width)
The spatially downsampled tensor.
indices : `torch.Tensor` or `None`
Optional indices of the downsampling method, e.g. indices of the
......@@ -299,7 +299,7 @@ class DecoderBlock(Block):
Parameters
----------
x : `torch.Tensor`
x : `torch.Tensor`, shape=(batch, channel, height, width)
Input tensor.
feature : `torch.Tensor`, shape=(batch, channel, height, width)
Intermediate output of a layer in the encoder.
......@@ -313,7 +313,7 @@ class DecoderBlock(Block):
Returns
-------
x : `torch.Tensor`
x : `torch.Tensor`, shape=(batch, channel, height, width)
Output of the decoder block.
"""
......@@ -334,7 +334,7 @@ class DecoderBlock(Block):
def upsample(self, x, feature, indices):
"""Define the upsampling method.
The `~pysegcnn.core.layers.DecoderBlock.upsample` `method should
The `~pysegcnn.core.layers.DecoderBlock.upsample` method should
implement the spatial upsampling operation.
Use one of the following functions to upsample:
......@@ -347,7 +347,7 @@ class DecoderBlock(Block):
Parameters
----------
x : `torch.Tensor`
x : `torch.Tensor`, shape=(batch, channel, height, width)
Input tensor, e.g. output of a convolutional block.
feature : `torch.Tensor`, shape=(batch, channel, height, width)
Intermediate output of a layer in the encoder. Used to implement
......@@ -362,7 +362,7 @@ class DecoderBlock(Block):
Returns
-------
x : `torch.Tensor`
x : `torch.Tensor`, shape=(batch, channel, height, width)
The spatially upsampled tensor.
"""
......@@ -433,12 +433,12 @@ class Encoder(nn.Module):
Parameters
----------
x : `torch.Tensor`
x : `torch.Tensor`, shape=(batch, channel, height, width)
Input image.
Returns
-------
x : `torch.Tensor`
x : `torch.Tensor`, shape=(batch, channel, height, width)
Output of the encoder.
"""
......@@ -520,7 +520,7 @@ class Decoder(nn.Module):
Parameters
----------
x : `torch.Tensor`
x : `torch.Tensor`, shape=(batch, channel, height, width)
Output of the encoder.
enc_cache : `dict` [`dict`]
Cache dictionary. The keys of the dictionary are the number of the
......@@ -533,7 +533,7 @@ class Decoder(nn.Module):
Returns
-------
x : `torch.Tensor`
x : `torch.Tensor`, shape=(batch, channel, height, width)
Output of the decoder.
"""
......@@ -591,12 +591,12 @@ class ConvBnReluMaxPool(EncoderBlock):
Parameters
----------
x : `torch.Tensor`
x : `torch.Tensor`, shape=(batch, channel, height, width)
Input tensor.
Returns
-------
x : `torch.Tensor`
x : `torch.Tensor`, shape=(batch, channel, height // 2, width // 2)
The 2x2 max pooled tensor.
indices : `torch.Tensor` or `None`
The indices of the maxima. Useful for upsampling with
......@@ -605,6 +605,17 @@ class ConvBnReluMaxPool(EncoderBlock):
x, indices = F.max_pool2d(x, kernel_size=2, return_indices=True)
return x, indices
def extra_repr(self):
"""Define optional extra information about this module.
Returns
-------
`str`
Extra representation string.
"""
return ('(pool): MaxPool2d(kernel_size=2, stride=2, padding=0, '
'dilation=1, ceil_mode=False)')
class ConvBnReluMaxUnpool(DecoderBlock):
"""Block of convolution, batchnorm, relu and 2x2 max unpool.
......@@ -646,7 +657,7 @@ class ConvBnReluMaxUnpool(DecoderBlock):
Parameters
----------
x : `torch.Tensor`
x : `torch.Tensor`, shape=(batch, channel, height, width)
Input tensor.
feature : `torch.Tensor`, shape=(batch, channel, height, width)
Intermediate output of a layer in the encoder. Used to determine
......@@ -657,13 +668,24 @@ class ConvBnReluMaxUnpool(DecoderBlock):
Returns
-------
x : `torch.Tensor`
x : `torch.Tensor`, shape=(batch, channel, height * 2, width * 2)
The 2x2 max unpooled tensor.
"""
return F.max_unpool2d(x, indices, kernel_size=2,
output_size=feature.shape[2:])
def extra_repr(self):
"""Define optional extra information about this module.
Returns
-------
`str`
Extra representation string.
"""
return ('(pool): MaxUnpool2d(kernel_size=(2, 2), stride=(2, 2), '
'padding=(0, 0))')
class ConvBnReluUpsample(DecoderBlock):
"""Block of convolution, batchnorm, relu and nearest neighbor upsampling.
......@@ -704,7 +726,7 @@ class ConvBnReluUpsample(DecoderBlock):
Parameters
----------
x : `torch.Tensor`
x : `torch.Tensor`, shape=(batch, channel, height, width)
Input tensor.
feature : `torch.Tensor`, shape=(batch, channel, height, width)
Intermediate output of a layer in the encoder. Used to determine
......@@ -716,8 +738,18 @@ class ConvBnReluUpsample(DecoderBlock):
Returns
-------
x : `torch.Tensor`
The 2x2 max unpooled tensor.
x : `torch.Tensor`, shape=(batch, channel, height, width)
The 2x2 upsampled tensor.
"""
return F.interpolate(x, size=feature.shape[2:], mode='nearest')
def extra_repr(self):
"""Define optional extra information about this module.
Returns
-------
`str`
Extra representation string.
"""
return '(pool): Upsample(mode="nearest")'
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