diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 68fa4741..ef43f0a2 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -36,7 +36,6 @@ jobs: - name: Install requirements run: | if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - pip install scipy pip install git+https://github.com/mapillary/inplace_abn.git@v1.0.11 - name: Run tests run: | diff --git a/timm/models/inception_v3.py b/timm/models/inception_v3.py index a0ea784f..0997e024 100644 --- a/timm/models/inception_v3.py +++ b/timm/models/inception_v3.py @@ -1,120 +1,562 @@ -from torchvision.models import Inception3 +import torch +import torch.nn as nn +import torch.nn.functional as F + from .registry import register_model from .helpers import load_pretrained +from .layers import trunc_normal_, SelectAdaptivePool2d from timm.data import IMAGENET_DEFAULT_STD, IMAGENET_DEFAULT_MEAN, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD __all__ = [] +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': 'conv1', 'classifier': 'fc', + **kwargs + } + + default_cfgs = { # original PyTorch weights, ported from Tensorflow but modified - 'inception_v3': { - 'url': 'https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth', - 'input_size': (3, 299, 299), - 'crop_pct': 0.875, - 'interpolation': 'bicubic', - 'mean': IMAGENET_INCEPTION_MEAN, # also works well enough with resnet defaults - 'std': IMAGENET_INCEPTION_STD, # also works well enough with resnet defaults - 'num_classes': 1000, - 'first_conv': 'conv0', - 'classifier': 'fc' - }, + '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': { - 'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_inception_v3-e0069de4.pth', - 'input_size': (3, 299, 299), - 'crop_pct': 0.875, - 'interpolation': 'bicubic', - 'mean': IMAGENET_INCEPTION_MEAN, - 'std': IMAGENET_INCEPTION_STD, - 'num_classes': 1001, - 'first_conv': 'conv0', - 'classifier': 'fc' - }, + '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': { - 'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/adv_inception_v3-9e27bd63.pth', - 'input_size': (3, 299, 299), - 'crop_pct': 0.875, - 'interpolation': 'bicubic', - 'mean': IMAGENET_INCEPTION_MEAN, - 'std': IMAGENET_INCEPTION_STD, - 'num_classes': 1001, - 'first_conv': 'conv0', - 'classifier': 'fc' - }, + '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': { - 'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_inception_v3-9f746940.pth', - 'input_size': (3, 299, 299), - 'crop_pct': 0.875, - 'interpolation': 'bicubic', - 'mean': IMAGENET_DEFAULT_MEAN, # also works well with inception defaults - 'std': IMAGENET_DEFAULT_STD, # also works well with inception defaults - 'num_classes': 1000, - 'first_conv': 'conv0', - 'classifier': 'fc' - } + '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, + ) } -def _assert_default_kwargs(kwargs): - # for imported models (ie torchvision) without capability to change these params, - # make sure they aren't being set to non-defaults - assert kwargs.pop('global_pool', 'avg') == 'avg' - assert kwargs.pop('drop_rate', 0.) == 0. +class InceptionV3Aux(nn.Module): + """InceptionV3 with AuxLogits + """ + + def __init__(self, inception_blocks=None, num_classes=1000, in_chans=3, drop_rate=0., global_pool='avg'): + super(InceptionV3Aux, self).__init__() + self.num_classes = num_classes + self.drop_rate = drop_rate + + if inception_blocks is None: + inception_blocks = [ + BasicConv2d, InceptionA, InceptionB, InceptionC, + InceptionD, InceptionE, InceptionAux + ] + assert len(inception_blocks) == 7 + conv_block = inception_blocks[0] + inception_a = inception_blocks[1] + inception_b = inception_blocks[2] + inception_c = inception_blocks[3] + inception_d = inception_blocks[4] + inception_e = inception_blocks[5] + inception_aux = inception_blocks[6] + + self.Conv2d_1a_3x3 = conv_block(in_chans, 32, kernel_size=3, stride=2) + self.Conv2d_2a_3x3 = conv_block(32, 32, kernel_size=3) + self.Conv2d_2b_3x3 = conv_block(32, 64, kernel_size=3, padding=1) + self.Conv2d_3b_1x1 = conv_block(64, 80, kernel_size=1) + self.Conv2d_4a_3x3 = conv_block(80, 192, kernel_size=3) + self.Mixed_5b = inception_a(192, pool_features=32) + self.Mixed_5c = inception_a(256, pool_features=64) + self.Mixed_5d = inception_a(288, pool_features=64) + self.Mixed_6a = inception_b(288) + self.Mixed_6b = inception_c(768, channels_7x7=128) + self.Mixed_6c = inception_c(768, channels_7x7=160) + self.Mixed_6d = inception_c(768, channels_7x7=160) + self.Mixed_6e = inception_c(768, channels_7x7=192) + self.AuxLogits = inception_aux(768, num_classes) + self.Mixed_7a = inception_d(768) + self.Mixed_7b = inception_e(1280) + self.Mixed_7c = inception_e(2048) + + self.num_features = 2048 + self.global_pool = SelectAdaptivePool2d(pool_type=global_pool) + self.fc = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes) + + 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_features(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 = F.max_pool2d(x, kernel_size=3, stride=2) + # 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 = F.max_pool2d(x, kernel_size=3, stride=2) + # 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 + aux = self.AuxLogits(x) if self.training else None + # N x 768 x 17 x 17 + 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, aux + + def get_classifier(self): + return self.fc + + def reset_classifier(self, num_classes, global_pool='avg'): + self.global_pool = SelectAdaptivePool2d(pool_type=global_pool) + self.num_classes = num_classes + if self.num_classes > 0: + self.fc = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes) + else: + self.fc = nn.Identity() + + def forward(self, x): + x, aux = self.forward_features(x) + x = self.global_pool(x).flatten(1) + if self.drop_rate > 0: + x = F.dropout(x, p=self.drop_rate, training=self.training) + x = self.fc(x) + return x, aux + + +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, inception_blocks=None, num_classes=1000, in_chans=3, drop_rate=0., global_pool='avg'): + super(InceptionV3, self).__init__() + self.num_classes = num_classes + self.drop_rate = drop_rate + + if inception_blocks is None: + inception_blocks = [ + BasicConv2d, InceptionA, InceptionB, InceptionC, InceptionD, InceptionE] + assert len(inception_blocks) >= 6 + conv_block = inception_blocks[0] + inception_a = inception_blocks[1] + inception_b = inception_blocks[2] + inception_c = inception_blocks[3] + inception_d = inception_blocks[4] + inception_e = inception_blocks[5] + + self.Conv2d_1a_3x3 = conv_block(in_chans, 32, kernel_size=3, stride=2) + self.Conv2d_2a_3x3 = conv_block(32, 32, kernel_size=3) + self.Conv2d_2b_3x3 = conv_block(32, 64, kernel_size=3, padding=1) + self.Conv2d_3b_1x1 = conv_block(64, 80, kernel_size=1) + self.Conv2d_4a_3x3 = conv_block(80, 192, kernel_size=3) + self.Mixed_5b = inception_a(192, pool_features=32) + self.Mixed_5c = inception_a(256, pool_features=64) + self.Mixed_5d = inception_a(288, pool_features=64) + self.Mixed_6a = inception_b(288) + self.Mixed_6b = inception_c(768, channels_7x7=128) + self.Mixed_6c = inception_c(768, channels_7x7=160) + self.Mixed_6d = inception_c(768, channels_7x7=160) + self.Mixed_6e = inception_c(768, channels_7x7=192) + self.Mixed_7a = inception_d(768) + self.Mixed_7b = inception_e(1280) + self.Mixed_7c = inception_e(2048) + + self.num_features = 2048 + self.global_pool = SelectAdaptivePool2d(pool_type=global_pool) + self.fc = nn.Linear(2048, num_classes) + + 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_features(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 = F.max_pool2d(x, kernel_size=3, stride=2) + # 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 = F.max_pool2d(x, kernel_size=3, stride=2) + # 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 + 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 get_classifier(self): + return self.fc + + def reset_classifier(self, num_classes, global_pool='avg'): + self.global_pool = SelectAdaptivePool2d(pool_type=global_pool) + self.num_classes = num_classes + if self.num_classes > 0: + self.fc = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes) + else: + self.fc = nn.Identity() + + def forward(self, x): + x = self.forward_features(x) + x = self.global_pool(x).flatten(1) + if self.drop_rate > 0: + x = F.dropout(x, p=self.drop_rate, training=self.training) + x = self.fc(x) + return x + + +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 = nn.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) + + +def _inception_v3(variant, pretrained=False, **kwargs): + default_cfg = default_cfgs[variant] + if kwargs.pop('features_only', False): + assert False, 'Not Implemented' # TODO + load_strict = False + model_kwargs.pop('num_classes', 0) + model_class = InceptionV3 + else: + aux_logits = kwargs.pop('aux_logits', False) + if aux_logits: + model_class = InceptionV3Aux + load_strict = default_cfg['has_aux'] + else: + model_class = InceptionV3 + load_strict = not default_cfg['has_aux'] + + model = model_class(**kwargs) + model.default_cfg = default_cfg + if pretrained: + load_pretrained( + model, + num_classes=kwargs.get('num_classes', 0), + in_chans=kwargs.get('in_chans', 3), + strict=load_strict) + return model @register_model -def inception_v3(pretrained=False, num_classes=1000, in_chans=3, **kwargs): +def inception_v3(pretrained=False, **kwargs): # original PyTorch weights, ported from Tensorflow but modified - default_cfg = default_cfgs['inception_v3'] - assert in_chans == 3 - _assert_default_kwargs(kwargs) - model = Inception3(num_classes=num_classes, aux_logits=True, transform_input=False) - if pretrained: - load_pretrained(model, default_cfg, num_classes, in_chans) - model.default_cfg = default_cfg + model = _inception_v3('inception_v3', pretrained=pretrained, **kwargs) return model @register_model -def tf_inception_v3(pretrained=False, num_classes=1000, in_chans=3, **kwargs): +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) - default_cfg = default_cfgs['tf_inception_v3'] - assert in_chans == 3 - _assert_default_kwargs(kwargs) - model = Inception3(num_classes=num_classes, aux_logits=False, transform_input=False) - if pretrained: - load_pretrained(model, default_cfg, num_classes, in_chans) - model.default_cfg = default_cfg + model = _inception_v3('tf_inception_v3', pretrained=pretrained, **kwargs) return model @register_model -def adv_inception_v3(pretrained=False, num_classes=1000, in_chans=3, **kwargs): +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 - default_cfg = default_cfgs['adv_inception_v3'] - assert in_chans == 3 - _assert_default_kwargs(kwargs) - model = Inception3(num_classes=num_classes, aux_logits=False, transform_input=False) - if pretrained: - load_pretrained(model, default_cfg, num_classes, in_chans) - model.default_cfg = default_cfg + model = _inception_v3('adv_inception_v3', pretrained=pretrained, **kwargs) return model @register_model -def gluon_inception_v3(pretrained=False, num_classes=1000, in_chans=3, **kwargs): +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 - default_cfg = default_cfgs['gluon_inception_v3'] - assert in_chans == 3 - _assert_default_kwargs(kwargs) - model = Inception3(num_classes=num_classes, aux_logits=False, transform_input=False) - if pretrained: - load_pretrained(model, default_cfg, num_classes, in_chans) - model.default_cfg = default_cfg + model = _inception_v3('gluon_inception_v3', pretrained=pretrained, **kwargs) return model diff --git a/timm/models/layers/__init__.py b/timm/models/layers/__init__.py index 4f84bb9e..667e7ea1 100644 --- a/timm/models/layers/__init__.py +++ b/timm/models/layers/__init__.py @@ -19,3 +19,4 @@ from .split_batchnorm import SplitBatchNorm2d, convert_splitbn_model from .anti_aliasing import AntiAliasDownsampleLayer from .space_to_depth import SpaceToDepthModule from .blur_pool import BlurPool2d +from .weight_init import trunc_normal_ diff --git a/timm/models/layers/weight_init.py b/timm/models/layers/weight_init.py new file mode 100644 index 00000000..d731029f --- /dev/null +++ b/timm/models/layers/weight_init.py @@ -0,0 +1,60 @@ +import torch +import math +import warnings + + +def _no_grad_trunc_normal_(tensor, mean, std, a, b): + # Cut & paste from PyTorch official master until it's in a few official releases - RW + # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1. + math.erf(x / math.sqrt(2.))) / 2. + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " + "The distribution of values may be incorrect.", + stacklevel=2) + + with torch.no_grad(): + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + l = norm_cdf((a - mean) / std) + u = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [l, u], then translate to + # [2l-1, 2u-1]. + tensor.uniform_(2 * l - 1, 2 * u - 1) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + tensor.erfinv_() + + # Transform to proper mean, std + tensor.mul_(std * math.sqrt(2.)) + tensor.add_(mean) + + # Clamp to ensure it's in the proper range + tensor.clamp_(min=a, max=b) + return tensor + + +def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): + # type: (Tensor, float, float, float, float) -> Tensor + r"""Fills the input Tensor with values drawn from a truncated + normal distribution. The values are effectively drawn from the + normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` + with values outside :math:`[a, b]` redrawn until they are within + the bounds. The method used for generating the random values works + best when :math:`a \leq \text{mean} \leq b`. + Args: + tensor: an n-dimensional `torch.Tensor` + mean: the mean of the normal distribution + std: the standard deviation of the normal distribution + a: the minimum cutoff value + b: the maximum cutoff value + Examples: + >>> w = torch.empty(3, 5) + >>> nn.init.trunc_normal_(w) + """ + return _no_grad_trunc_normal_(tensor, mean, std, a, b)