Skip to content

#192 include top #208

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
lukemelas merged 3 commits into from
Aug 25, 2020
Merged

#192 include top #208

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
477f307
Added include_top option
Jul 15, 2020
f522b18
Changed include_top to match keras implementation
Jul 15, 2020
a78e84e
Merge branch 'master' into #192_include_top
lukemelas Aug 25, 2020
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
13 changes: 7 additions & 6 deletions efficientnet_pytorch/model.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -152,7 +152,9 @@ class EfficientNet(nn.Module):
[1] https://arxiv.org/abs/1905.11946 (EfficientNet)

Example:
>>> import torch


import torch
>>> from efficientnet.model import EfficientNet
>>> inputs = torch.rand(1, 3, 224, 224)
>>> model = EfficientNet.from_pretrained('efficientnet-b0')
Expand Down Expand Up @@ -307,13 +309,12 @@ def forward(self, inputs):
"""
# Convolution layers
x = self.extract_features(inputs)

# Pooling and final linear layer
x = self._avg_pooling(x)
x = x.flatten(start_dim=1)
x = self._dropout(x)
x = self._fc(x)

if self._global_params.include_top:
x = x.flatten(start_dim=1)
x = self._dropout(x)
x = self._fc(x)
return x

@classmethod
Expand Down
5 changes: 3 additions & 2 deletions efficientnet_pytorch/utils.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -39,7 +39,7 @@
GlobalParams = collections.namedtuple('GlobalParams', [
'width_coefficient', 'depth_coefficient', 'image_size', 'dropout_rate',
'num_classes', 'batch_norm_momentum', 'batch_norm_epsilon',
'drop_connect_rate', 'depth_divisor', 'min_depth'])
'drop_connect_rate', 'depth_divisor', 'min_depth', 'include_top'])

# Parameters for an individual model block
BlockArgs = collections.namedtuple('BlockArgs', [
Expand Down Expand Up @@ -475,7 +475,7 @@ def efficientnet_params(model_name):


def efficientnet(width_coefficient=None, depth_coefficient=None, image_size=None,
dropout_rate=0.2, drop_connect_rate=0.2, num_classes=1000):
dropout_rate=0.2, drop_connect_rate=0.2, num_classes=1000, include_top=True):
"""Create BlockArgs and GlobalParams for efficientnet model.

Args:
Expand Down Expand Up @@ -517,6 +517,7 @@ def efficientnet(width_coefficient=None, depth_coefficient=None, image_size=None
drop_connect_rate=drop_connect_rate,
depth_divisor=8,
min_depth=None,
include_top=include_top,
)

return blocks_args, global_params
Expand Down