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Modifying the comments of inceptionV3 dimensions #748

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Feb 14, 2019
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Modifying the comments of inceptionV3 dimensions #748

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597e02d
Modifying the comments of inceptionV3 dimensions
ekagra-ranjan Feb 14, 2019
a92edc7
Added Batch size in comment
ekagra-ranjan Feb 14, 2019
21da0ee
Update inception.py
ekagra-ranjan Feb 14, 2019
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64 changes: 32 additions & 32 deletions torchvision/models/inception.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,7 @@ def inception_v3(pretrained=False, **kwargs):

.. note::
**Important**: In contrast to the other models the inception_v3 expects tensors with a size of
299x299x3, so ensure your images are sized accordingly.
N x 3 x 299 x 299, so ensure your images are sized accordingly.

Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
Expand Down Expand Up @@ -78,55 +78,55 @@ def forward(self, x):
x_ch1 = torch.unsqueeze(x[:, 1], 1) * (0.224 / 0.5) + (0.456 - 0.5) / 0.5
x_ch2 = torch.unsqueeze(x[:, 2], 1) * (0.225 / 0.5) + (0.406 - 0.5) / 0.5
x = torch.cat((x_ch0, x_ch1, x_ch2), 1)
# 299 x 299 x 3
# N x 3 x 299 x 299
x = self.Conv2d_1a_3x3(x)
# 149 x 149 x 32
# N x 32 x 149 x 149
x = self.Conv2d_2a_3x3(x)
# 147 x 147 x 32
# N x 32 x 147 x 147
x = self.Conv2d_2b_3x3(x)
# 147 x 147 x 64
# N x 64 x 147 x 147
x = F.max_pool2d(x, kernel_size=3, stride=2)
# 73 x 73 x 64
# N x 64 x 73 x 73
x = self.Conv2d_3b_1x1(x)
# 73 x 73 x 80
# N x 80 x 73 x 73
x = self.Conv2d_4a_3x3(x)
# 71 x 71 x 192
# N x 192 x 71 x 71
x = F.max_pool2d(x, kernel_size=3, stride=2)
# 35 x 35 x 192
# N x 192 x 35 x 35
x = self.Mixed_5b(x)
# 35 x 35 x 256
# N x 256 x 35 x 35
x = self.Mixed_5c(x)
# 35 x 35 x 288
# N x 288 x 35 x 35
x = self.Mixed_5d(x)
# 35 x 35 x 288
# N x 288 x 35 x 35
x = self.Mixed_6a(x)
# 17 x 17 x 768
# N x 768 x 17 x 17
x = self.Mixed_6b(x)
# 17 x 17 x 768
# N x 768 x 17 x 17
x = self.Mixed_6c(x)
# 17 x 17 x 768
# N x 768 x 17 x 17
x = self.Mixed_6d(x)
# 17 x 17 x 768
# N x 768 x 17 x 17
x = self.Mixed_6e(x)
# 17 x 17 x 768
# N x 768 x 17 x 17
if self.training and self.aux_logits:
aux = self.AuxLogits(x)
# 17 x 17 x 768
# N x 768 x 17 x 17
x = self.Mixed_7a(x)
# 8 x 8 x 1280
# N x 1280 x 8 x 8
x = self.Mixed_7b(x)
# 8 x 8 x 2048
# N x 2048 x 8 x 8
x = self.Mixed_7c(x)
# 8 x 8 x 2048
# N x 2048 x 8 x 8
# Adaptive average pooling
x = F.adaptive_avg_pool2d(x, (1, 1))
# 1 x 1 x 2048
# N x 2048 x 1 x 1
x = F.dropout(x, training=self.training)
# 1 x 1 x 2048
# N x 2048 x 1 x 1
x = x.view(x.size(0), -1)
# 2048
# N x 2048
x = self.fc(x)
# 1000 (num_classes)
# N x 1000 (num_classes)
if self.training and self.aux_logits:
return x, aux
return x
Expand Down Expand Up @@ -305,20 +305,20 @@ def __init__(self, in_channels, num_classes):
self.fc.stddev = 0.001

def forward(self, x):
# 17 x 17 x 768
# N x 768 x 17 x 17
x = F.avg_pool2d(x, kernel_size=5, stride=3)
# 5 x 5 x 768
# N x 768 x 5 x 5
x = self.conv0(x)
# 5 x 5 x 128
# N x 128 x 5 x 5
x = self.conv1(x)
# 1 x 1 x 768
# N x 768 x 1 x 1
# Adaptive average pooling
x = F.adaptive_avg_pool2d(x, (1, 1))
# 1 x 1 x 768
# N x 768 x 1 x 1
x = x.view(x.size(0), -1)
# 768
# N x 768
x = self.fc(x)
# 1000
# N x 1000
return x


Expand Down