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""" Inception-V3 | |
Originally from torchvision Inception3 model | |
Licensed BSD-Clause 3 https://github.com/pytorch/vision/blob/master/LICENSE | |
""" | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
from timm.data import IMAGENET_DEFAULT_STD, IMAGENET_DEFAULT_MEAN, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD | |
from .helpers import build_model_with_cfg | |
from .registry import register_model | |
from .layers import trunc_normal_, create_classifier, Linear | |
def _cfg(url='', **kwargs): | |
return { | |
'url': url, | |
'num_classes': 1000, 'input_size': (3, 299, 299), 'pool_size': (8, 8), | |
'crop_pct': 0.875, 'interpolation': 'bicubic', | |
'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD, | |
'first_conv': 'Conv2d_1a_3x3.conv', 'classifier': 'fc', | |
**kwargs | |
} | |
default_cfgs = { | |
# original PyTorch weights, ported from Tensorflow but modified | |
'inception_v3': _cfg( | |
url='https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth', | |
has_aux=True), # checkpoint has aux logit layer weights | |
# my port of Tensorflow SLIM weights (http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz) | |
'tf_inception_v3': _cfg( | |
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_inception_v3-e0069de4.pth', | |
num_classes=1001, has_aux=False), | |
# my port of Tensorflow adversarially trained Inception V3 from | |
# http://download.tensorflow.org/models/adv_inception_v3_2017_08_18.tar.gz | |
'adv_inception_v3': _cfg( | |
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/adv_inception_v3-9e27bd63.pth', | |
num_classes=1001, has_aux=False), | |
# from gluon pretrained models, best performing in terms of accuracy/loss metrics | |
# https://gluon-cv.mxnet.io/model_zoo/classification.html | |
'gluon_inception_v3': _cfg( | |
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_inception_v3-9f746940.pth', | |
mean=IMAGENET_DEFAULT_MEAN, # also works well with inception defaults | |
std=IMAGENET_DEFAULT_STD, # also works well with inception defaults | |
has_aux=False, | |
) | |
} | |
class InceptionA(nn.Module): | |
def __init__(self, in_channels, pool_features, conv_block=None): | |
super(InceptionA, self).__init__() | |
if conv_block is None: | |
conv_block = BasicConv2d | |
self.branch1x1 = conv_block(in_channels, 64, kernel_size=1) | |
self.branch5x5_1 = conv_block(in_channels, 48, kernel_size=1) | |
self.branch5x5_2 = conv_block(48, 64, kernel_size=5, padding=2) | |
self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1) | |
self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1) | |
self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, padding=1) | |
self.branch_pool = conv_block(in_channels, pool_features, kernel_size=1) | |
def _forward(self, x): | |
branch1x1 = self.branch1x1(x) | |
branch5x5 = self.branch5x5_1(x) | |
branch5x5 = self.branch5x5_2(branch5x5) | |
branch3x3dbl = self.branch3x3dbl_1(x) | |
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) | |
branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl) | |
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1) | |
branch_pool = self.branch_pool(branch_pool) | |
outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool] | |
return outputs | |
def forward(self, x): | |
outputs = self._forward(x) | |
return torch.cat(outputs, 1) | |
class InceptionB(nn.Module): | |
def __init__(self, in_channels, conv_block=None): | |
super(InceptionB, self).__init__() | |
if conv_block is None: | |
conv_block = BasicConv2d | |
self.branch3x3 = conv_block(in_channels, 384, kernel_size=3, stride=2) | |
self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1) | |
self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1) | |
self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, stride=2) | |
def _forward(self, x): | |
branch3x3 = self.branch3x3(x) | |
branch3x3dbl = self.branch3x3dbl_1(x) | |
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) | |
branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl) | |
branch_pool = F.max_pool2d(x, kernel_size=3, stride=2) | |
outputs = [branch3x3, branch3x3dbl, branch_pool] | |
return outputs | |
def forward(self, x): | |
outputs = self._forward(x) | |
return torch.cat(outputs, 1) | |
class InceptionC(nn.Module): | |
def __init__(self, in_channels, channels_7x7, conv_block=None): | |
super(InceptionC, self).__init__() | |
if conv_block is None: | |
conv_block = BasicConv2d | |
self.branch1x1 = conv_block(in_channels, 192, kernel_size=1) | |
c7 = channels_7x7 | |
self.branch7x7_1 = conv_block(in_channels, c7, kernel_size=1) | |
self.branch7x7_2 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3)) | |
self.branch7x7_3 = conv_block(c7, 192, kernel_size=(7, 1), padding=(3, 0)) | |
self.branch7x7dbl_1 = conv_block(in_channels, c7, kernel_size=1) | |
self.branch7x7dbl_2 = conv_block(c7, c7, kernel_size=(7, 1), padding=(3, 0)) | |
self.branch7x7dbl_3 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3)) | |
self.branch7x7dbl_4 = conv_block(c7, c7, kernel_size=(7, 1), padding=(3, 0)) | |
self.branch7x7dbl_5 = conv_block(c7, 192, kernel_size=(1, 7), padding=(0, 3)) | |
self.branch_pool = conv_block(in_channels, 192, kernel_size=1) | |
def _forward(self, x): | |
branch1x1 = self.branch1x1(x) | |
branch7x7 = self.branch7x7_1(x) | |
branch7x7 = self.branch7x7_2(branch7x7) | |
branch7x7 = self.branch7x7_3(branch7x7) | |
branch7x7dbl = self.branch7x7dbl_1(x) | |
branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl) | |
branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl) | |
branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl) | |
branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl) | |
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1) | |
branch_pool = self.branch_pool(branch_pool) | |
outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool] | |
return outputs | |
def forward(self, x): | |
outputs = self._forward(x) | |
return torch.cat(outputs, 1) | |
class InceptionD(nn.Module): | |
def __init__(self, in_channels, conv_block=None): | |
super(InceptionD, self).__init__() | |
if conv_block is None: | |
conv_block = BasicConv2d | |
self.branch3x3_1 = conv_block(in_channels, 192, kernel_size=1) | |
self.branch3x3_2 = conv_block(192, 320, kernel_size=3, stride=2) | |
self.branch7x7x3_1 = conv_block(in_channels, 192, kernel_size=1) | |
self.branch7x7x3_2 = conv_block(192, 192, kernel_size=(1, 7), padding=(0, 3)) | |
self.branch7x7x3_3 = conv_block(192, 192, kernel_size=(7, 1), padding=(3, 0)) | |
self.branch7x7x3_4 = conv_block(192, 192, kernel_size=3, stride=2) | |
def _forward(self, x): | |
branch3x3 = self.branch3x3_1(x) | |
branch3x3 = self.branch3x3_2(branch3x3) | |
branch7x7x3 = self.branch7x7x3_1(x) | |
branch7x7x3 = self.branch7x7x3_2(branch7x7x3) | |
branch7x7x3 = self.branch7x7x3_3(branch7x7x3) | |
branch7x7x3 = self.branch7x7x3_4(branch7x7x3) | |
branch_pool = F.max_pool2d(x, kernel_size=3, stride=2) | |
outputs = [branch3x3, branch7x7x3, branch_pool] | |
return outputs | |
def forward(self, x): | |
outputs = self._forward(x) | |
return torch.cat(outputs, 1) | |
class InceptionE(nn.Module): | |
def __init__(self, in_channels, conv_block=None): | |
super(InceptionE, self).__init__() | |
if conv_block is None: | |
conv_block = BasicConv2d | |
self.branch1x1 = conv_block(in_channels, 320, kernel_size=1) | |
self.branch3x3_1 = conv_block(in_channels, 384, kernel_size=1) | |
self.branch3x3_2a = conv_block(384, 384, kernel_size=(1, 3), padding=(0, 1)) | |
self.branch3x3_2b = conv_block(384, 384, kernel_size=(3, 1), padding=(1, 0)) | |
self.branch3x3dbl_1 = conv_block(in_channels, 448, kernel_size=1) | |
self.branch3x3dbl_2 = conv_block(448, 384, kernel_size=3, padding=1) | |
self.branch3x3dbl_3a = conv_block(384, 384, kernel_size=(1, 3), padding=(0, 1)) | |
self.branch3x3dbl_3b = conv_block(384, 384, kernel_size=(3, 1), padding=(1, 0)) | |
self.branch_pool = conv_block(in_channels, 192, kernel_size=1) | |
def _forward(self, x): | |
branch1x1 = self.branch1x1(x) | |
branch3x3 = self.branch3x3_1(x) | |
branch3x3 = [ | |
self.branch3x3_2a(branch3x3), | |
self.branch3x3_2b(branch3x3), | |
] | |
branch3x3 = torch.cat(branch3x3, 1) | |
branch3x3dbl = self.branch3x3dbl_1(x) | |
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) | |
branch3x3dbl = [ | |
self.branch3x3dbl_3a(branch3x3dbl), | |
self.branch3x3dbl_3b(branch3x3dbl), | |
] | |
branch3x3dbl = torch.cat(branch3x3dbl, 1) | |
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1) | |
branch_pool = self.branch_pool(branch_pool) | |
outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool] | |
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.conv0 = conv_block(in_channels, 128, kernel_size=1) | |
self.conv1 = conv_block(128, 768, kernel_size=5) | |
self.conv1.stddev = 0.01 | |
self.fc = Linear(768, num_classes) | |
self.fc.stddev = 0.001 | |
def forward(self, x): | |
# N x 768 x 17 x 17 | |
x = F.avg_pool2d(x, kernel_size=5, stride=3) | |
# N x 768 x 5 x 5 | |
x = self.conv0(x) | |
# N x 128 x 5 x 5 | |
x = self.conv1(x) | |
# N x 768 x 1 x 1 | |
# Adaptive average pooling | |
x = F.adaptive_avg_pool2d(x, (1, 1)) | |
# N x 768 x 1 x 1 | |
x = torch.flatten(x, 1) | |
# N x 768 | |
x = self.fc(x) | |
# N x 1000 | |
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) | |
class InceptionV3(nn.Module): | |
"""Inception-V3 with no AuxLogits | |
FIXME two class defs are redundant, but less screwing around with torchsript fussyness and inconsistent returns | |
""" | |
def __init__(self, num_classes=1000, in_chans=3, drop_rate=0., global_pool='avg', aux_logits=False): | |
super(InceptionV3, self).__init__() | |
self.num_classes = num_classes | |
self.drop_rate = drop_rate | |
self.aux_logits = aux_logits | |
self.Conv2d_1a_3x3 = BasicConv2d(in_chans, 32, kernel_size=3, stride=2) | |
self.Conv2d_2a_3x3 = BasicConv2d(32, 32, kernel_size=3) | |
self.Conv2d_2b_3x3 = BasicConv2d(32, 64, kernel_size=3, padding=1) | |
self.Pool1 = nn.MaxPool2d(kernel_size=3, stride=2) | |
self.Conv2d_3b_1x1 = BasicConv2d(64, 80, kernel_size=1) | |
self.Conv2d_4a_3x3 = BasicConv2d(80, 192, kernel_size=3) | |
self.Pool2 = nn.MaxPool2d(kernel_size=3, stride=2) | |
self.Mixed_5b = InceptionA(192, pool_features=32) | |
self.Mixed_5c = InceptionA(256, pool_features=64) | |
self.Mixed_5d = InceptionA(288, pool_features=64) | |
self.Mixed_6a = InceptionB(288) | |
self.Mixed_6b = InceptionC(768, channels_7x7=128) | |
self.Mixed_6c = InceptionC(768, channels_7x7=160) | |
self.Mixed_6d = InceptionC(768, channels_7x7=160) | |
self.Mixed_6e = InceptionC(768, channels_7x7=192) | |
if aux_logits: | |
self.AuxLogits = InceptionAux(768, num_classes) | |
else: | |
self.AuxLogits = None | |
self.Mixed_7a = InceptionD(768) | |
self.Mixed_7b = InceptionE(1280) | |
self.Mixed_7c = InceptionE(2048) | |
self.feature_info = [ | |
dict(num_chs=64, reduction=2, module='Conv2d_2b_3x3'), | |
dict(num_chs=192, reduction=4, module='Conv2d_4a_3x3'), | |
dict(num_chs=288, reduction=8, module='Mixed_5d'), | |
dict(num_chs=768, reduction=16, module='Mixed_6e'), | |
dict(num_chs=2048, reduction=32, module='Mixed_7c'), | |
] | |
self.num_features = 2048 | |
self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool) | |
for m in self.modules(): | |
if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear): | |
stddev = m.stddev if hasattr(m, 'stddev') else 0.1 | |
trunc_normal_(m.weight, std=stddev) | |
elif isinstance(m, nn.BatchNorm2d): | |
nn.init.constant_(m.weight, 1) | |
nn.init.constant_(m.bias, 0) | |
def forward_preaux(self, x): | |
# N x 3 x 299 x 299 | |
x = self.Conv2d_1a_3x3(x) | |
# N x 32 x 149 x 149 | |
x = self.Conv2d_2a_3x3(x) | |
# N x 32 x 147 x 147 | |
x = self.Conv2d_2b_3x3(x) | |
# N x 64 x 147 x 147 | |
x = self.Pool1(x) | |
# N x 64 x 73 x 73 | |
x = self.Conv2d_3b_1x1(x) | |
# N x 80 x 73 x 73 | |
x = self.Conv2d_4a_3x3(x) | |
# N x 192 x 71 x 71 | |
x = self.Pool2(x) | |
# N x 192 x 35 x 35 | |
x = self.Mixed_5b(x) | |
# N x 256 x 35 x 35 | |
x = self.Mixed_5c(x) | |
# N x 288 x 35 x 35 | |
x = self.Mixed_5d(x) | |
# N x 288 x 35 x 35 | |
x = self.Mixed_6a(x) | |
# N x 768 x 17 x 17 | |
x = self.Mixed_6b(x) | |
# N x 768 x 17 x 17 | |
x = self.Mixed_6c(x) | |
# N x 768 x 17 x 17 | |
x = self.Mixed_6d(x) | |
# N x 768 x 17 x 17 | |
x = self.Mixed_6e(x) | |
# N x 768 x 17 x 17 | |
return x | |
def forward_postaux(self, x): | |
x = self.Mixed_7a(x) | |
# N x 1280 x 8 x 8 | |
x = self.Mixed_7b(x) | |
# N x 2048 x 8 x 8 | |
x = self.Mixed_7c(x) | |
# N x 2048 x 8 x 8 | |
return x | |
def forward_features(self, x): | |
x = self.forward_preaux(x) | |
x = self.forward_postaux(x) | |
return x | |
def get_classifier(self): | |
return self.fc | |
def reset_classifier(self, num_classes, global_pool='avg'): | |
self.num_classes = num_classes | |
self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool) | |
def forward(self, x): | |
x = self.forward_features(x) | |
x = self.global_pool(x) | |
if self.drop_rate > 0: | |
x = F.dropout(x, p=self.drop_rate, training=self.training) | |
x = self.fc(x) | |
return x | |
class InceptionV3Aux(InceptionV3): | |
"""InceptionV3 with AuxLogits | |
""" | |
def __init__(self, num_classes=1000, in_chans=3, drop_rate=0., global_pool='avg', aux_logits=True): | |
super(InceptionV3Aux, self).__init__( | |
num_classes, in_chans, drop_rate, global_pool, aux_logits) | |
def forward_features(self, x): | |
x = self.forward_preaux(x) | |
aux = self.AuxLogits(x) if self.training else None | |
x = self.forward_postaux(x) | |
return x, aux | |
def forward(self, x): | |
x, aux = self.forward_features(x) | |
x = self.global_pool(x) | |
if self.drop_rate > 0: | |
x = F.dropout(x, p=self.drop_rate, training=self.training) | |
x = self.fc(x) | |
return x, aux | |
def _create_inception_v3(variant, pretrained=False, **kwargs): | |
default_cfg = default_cfgs[variant] | |
aux_logits = kwargs.pop('aux_logits', False) | |
if aux_logits: | |
assert not kwargs.pop('features_only', False) | |
model_cls = InceptionV3Aux | |
load_strict = default_cfg['has_aux'] | |
else: | |
model_cls = InceptionV3 | |
load_strict = not default_cfg['has_aux'] | |
return build_model_with_cfg( | |
model_cls, variant, pretrained, default_cfg=default_cfgs[variant], | |
pretrained_strict=load_strict, **kwargs) | |
def inception_v3(pretrained=False, **kwargs): | |
# original PyTorch weights, ported from Tensorflow but modified | |
model = _create_inception_v3('inception_v3', pretrained=pretrained, **kwargs) | |
return model | |
def tf_inception_v3(pretrained=False, **kwargs): | |
# my port of Tensorflow SLIM weights (http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz) | |
model = _create_inception_v3('tf_inception_v3', pretrained=pretrained, **kwargs) | |
return model | |
def adv_inception_v3(pretrained=False, **kwargs): | |
# my port of Tensorflow adversarially trained Inception V3 from | |
# http://download.tensorflow.org/models/adv_inception_v3_2017_08_18.tar.gz | |
model = _create_inception_v3('adv_inception_v3', pretrained=pretrained, **kwargs) | |
return model | |
def gluon_inception_v3(pretrained=False, **kwargs): | |
# from gluon pretrained models, best performing in terms of accuracy/loss metrics | |
# https://gluon-cv.mxnet.io/model_zoo/classification.html | |
model = _create_inception_v3('gluon_inception_v3', pretrained=pretrained, **kwargs) | |
return model | |