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@TheCodez @ekagra-ranjan @taylanbil @Separius @pmeier @fmassa @eellison @apache2046 @hx89 @rayiooo
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from __future__ import division
import warnings
from collections import namedtuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.jit.annotations import Optional, Tuple
from torch import Tensor
from .utils import load_state_dict_from_url
__all__ = ['GoogLeNet', 'googlenet', "GoogLeNetOutputs", "_GoogLeNetOutputs"]
model_urls = {
# GoogLeNet ported from TensorFlow
'googlenet': 'https://download.pytorch.org/models/googlenet-1378be20.pth',
}
GoogLeNetOutputs = namedtuple('GoogLeNetOutputs', ['logits', 'aux_logits2', 'aux_logits1'])
GoogLeNetOutputs.__annotations__ = {'logits': Tensor, 'aux_logits2': Optional[Tensor],
'aux_logits1': Optional[Tensor]}
# Script annotations failed with _GoogleNetOutputs = namedtuple ...
# _GoogLeNetOutputs set here for backwards compat
_GoogLeNetOutputs = GoogLeNetOutputs
def googlenet(pretrained=False, progress=True, **kwargs):
r"""GoogLeNet (Inception v1) model architecture from
`"Going Deeper with Convolutions" <http://arxiv.org/abs/1409.4842>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
aux_logits (bool): If True, adds two auxiliary branches that can improve training.
Default: *False* when pretrained is True otherwise *True*
transform_input (bool): If True, preprocesses the input according to the method with which it
was trained on ImageNet. Default: *False*
"""
if pretrained:
if 'transform_input' not in kwargs:
kwargs['transform_input'] = True
if 'aux_logits' not in kwargs:
kwargs['aux_logits'] = False
if kwargs['aux_logits']:
warnings.warn('auxiliary heads in the pretrained googlenet model are NOT pretrained, '
'so make sure to train them')
original_aux_logits = kwargs['aux_logits']
kwargs['aux_logits'] = True
kwargs['init_weights'] = False
model = GoogLeNet(**kwargs)
state_dict = load_state_dict_from_url(model_urls['googlenet'],
progress=progress)
model.load_state_dict(state_dict)
if not original_aux_logits:
model.aux_logits = False
del model.aux1, model.aux2
return model
return GoogLeNet(**kwargs)
class GoogLeNet(nn.Module):
__constants__ = ['aux_logits', 'transform_input']
def __init__(self, num_classes=1000, aux_logits=True, transform_input=False, init_weights=True,
blocks=None):
super(GoogLeNet, self).__init__()
if blocks is None:
blocks = [BasicConv2d, Inception, InceptionAux]
assert len(blocks) == 3
conv_block = blocks[0]
inception_block = blocks[1]
inception_aux_block = blocks[2]
self.aux_logits = aux_logits
self.transform_input = transform_input
self.conv1 = conv_block(3, 64, kernel_size=7, stride=2, padding=3)
self.maxpool1 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
self.conv2 = conv_block(64, 64, kernel_size=1)
self.conv3 = conv_block(64, 192, kernel_size=3, padding=1)
self.maxpool2 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
self.inception3a = inception_block(192, 64, 96, 128, 16, 32, 32)
self.inception3b = inception_block(256, 128, 128, 192, 32, 96, 64)
self.maxpool3 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
self.inception4a = inception_block(480, 192, 96, 208, 16, 48, 64)
self.inception4b = inception_block(512, 160, 112, 224, 24, 64, 64)
self.inception4c = inception_block(512, 128, 128, 256, 24, 64, 64)
self.inception4d = inception_block(512, 112, 144, 288, 32, 64, 64)
self.inception4e = inception_block(528, 256, 160, 320, 32, 128, 128)
self.maxpool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.inception5a = inception_block(832, 256, 160, 320, 32, 128, 128)
self.inception5b = inception_block(832, 384, 192, 384, 48, 128, 128)
if aux_logits:
self.aux1 = inception_aux_block(512, num_classes)
self.aux2 = inception_aux_block(528, num_classes)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.dropout = nn.Dropout(0.2)
self.fc = nn.Linear(1024, num_classes)
if init_weights:
self._initialize_weights()
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
import scipy.stats as stats
X = stats.truncnorm(-2, 2, scale=0.01)
values = torch.as_tensor(X.rvs(m.weight.numel()), dtype=m.weight.dtype)
values = values.view(m.weight.size())
with torch.no_grad():
m.weight.copy_(values)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def _transform_input(self, x):
# type: (Tensor) -> Tensor
if self.transform_input:
x_ch0 = torch.unsqueeze(x[:, 0], 1) * (0.229 / 0.5) + (0.485 - 0.5) / 0.5
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)
return x
def _forward(self, x):
# type: (Tensor) -> Tuple[Tensor, Optional[Tensor], Optional[Tensor]]
# N x 3 x 224 x 224
x = self.conv1(x)
# N x 64 x 112 x 112
x = self.maxpool1(x)
# N x 64 x 56 x 56
x = self.conv2(x)
# N x 64 x 56 x 56
x = self.conv3(x)
# N x 192 x 56 x 56
x = self.maxpool2(x)
# N x 192 x 28 x 28
x = self.inception3a(x)
# N x 256 x 28 x 28
x = self.inception3b(x)
# N x 480 x 28 x 28
x = self.maxpool3(x)
# N x 480 x 14 x 14
x = self.inception4a(x)
# N x 512 x 14 x 14
aux_defined = self.training and self.aux_logits
if aux_defined:
aux1 = self.aux1(x)
else:
aux1 = None
x = self.inception4b(x)
# N x 512 x 14 x 14
x = self.inception4c(x)
# N x 512 x 14 x 14
x = self.inception4d(x)
# N x 528 x 14 x 14
if aux_defined:
aux2 = self.aux2(x)
else:
aux2 = None
x = self.inception4e(x)
# N x 832 x 14 x 14
x = self.maxpool4(x)
# N x 832 x 7 x 7
x = self.inception5a(x)
# N x 832 x 7 x 7
x = self.inception5b(x)
# N x 1024 x 7 x 7
x = self.avgpool(x)
# N x 1024 x 1 x 1
x = torch.flatten(x, 1)
# N x 1024
x = self.dropout(x)
x = self.fc(x)
# N x 1000 (num_classes)
return x, aux2, aux1
@torch.jit.unused
def eager_outputs(self, x, aux2, aux1):
# type: (Tensor, Optional[Tensor], Optional[Tensor]) -> GoogLeNetOutputs
if self.training and self.aux_logits:
return _GoogLeNetOutputs(x, aux2, aux1)
else:
return x
def forward(self, x):
# type: (Tensor) -> GoogLeNetOutputs
x = self._transform_input(x)
x, aux1, aux2 = self._forward(x)
aux_defined = self.training and self.aux_logits
if torch.jit.is_scripting():
if not aux_defined:
warnings.warn("Scripted GoogleNet always returns GoogleNetOutputs Tuple")
return GoogLeNetOutputs(x, aux2, aux1)
else:
return self.eager_outputs(x, aux2, aux1)
class Inception(nn.Module):
__constants__ = ['branch2', 'branch3', 'branch4']
def __init__(self, in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj,
conv_block=None):
super(Inception, self).__init__()
if conv_block is None:
conv_block = BasicConv2d
self.branch1 = conv_block(in_channels, ch1x1, kernel_size=1)
self.branch2 = nn.Sequential(
conv_block(in_channels, ch3x3red, kernel_size=1),
conv_block(ch3x3red, ch3x3, kernel_size=3, padding=1)
)
self.branch3 = nn.Sequential(
conv_block(in_channels, ch5x5red, kernel_size=1),
conv_block(ch5x5red, ch5x5, kernel_size=3, padding=1)
)
self.branch4 = nn.Sequential(
nn.MaxPool2d(kernel_size=3, stride=1, padding=1, ceil_mode=True),
conv_block(in_channels, pool_proj, kernel_size=1)
)
def _forward(self, x):
branch1 = self.branch1(x)
branch2 = self.branch2(x)
branch3 = self.branch3(x)
branch4 = self.branch4(x)
outputs = [branch1, branch2, branch3, branch4]
return outputs
def forward(self, x):
outputs = self._forward(x)
return torch.cat(outputs, 1)
class InceptionAux(nn.Module):
def __init__(self, in_channels, num_classes, conv_block=None):
super(InceptionAux, self).__init__()
if conv_block is None:
conv_block = BasicConv2d
self.conv = conv_block(in_channels, 128, kernel_size=1)
self.fc1 = nn.Linear(2048, 1024)
self.fc2 = nn.Linear(1024, num_classes)
def forward(self, x):
# aux1: N x 512 x 14 x 14, aux2: N x 528 x 14 x 14
x = F.adaptive_avg_pool2d(x, (4, 4))
# aux1: N x 512 x 4 x 4, aux2: N x 528 x 4 x 4
x = self.conv(x)
# N x 128 x 4 x 4
x = torch.flatten(x, 1)
# N x 2048
x = F.relu(self.fc1(x), inplace=True)
# N x 1024
x = F.dropout(x, 0.7, training=self.training)
# N x 1024
x = self.fc2(x)
# N x 1000 (num_classes)
return x
class BasicConv2d(nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
return F.relu(x, inplace=True)
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