Merge pull request #146 from rwightman/inceptionv3_fix

Remove annoying torchvision InceptionV3 dependency on scipy and insanely slow tru…

.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: |

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': {
+    'inception_v3': _cfg(
-        'url': 'https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth',
+        url='https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth',
-        'input_size': (3, 299, 299),
+        has_aux=True),  # checkpoint has aux logit layer weights
-        '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'
-    },
     # my port of Tensorflow SLIM weights (http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz)
-    'tf_inception_v3': {
+    'tf_inception_v3': _cfg(
-        'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_inception_v3-e0069de4.pth',
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_inception_v3-e0069de4.pth',
-        'input_size': (3, 299, 299),
+        num_classes=1001, has_aux=False),
-        'crop_pct': 0.875,
-        'interpolation': 'bicubic',
-        'mean': IMAGENET_INCEPTION_MEAN,
-        'std': IMAGENET_INCEPTION_STD,
-        'num_classes': 1001,
-        'first_conv': 'conv0',
-        'classifier': 'fc'
-    },
     # 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': {
+    'adv_inception_v3': _cfg(
-        'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/adv_inception_v3-9e27bd63.pth',
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/adv_inception_v3-9e27bd63.pth',
-        'input_size': (3, 299, 299),
+        num_classes=1001, has_aux=False),
-        'crop_pct': 0.875,
-        'interpolation': 'bicubic',
-        'mean': IMAGENET_INCEPTION_MEAN,
-        'std': IMAGENET_INCEPTION_STD,
-        'num_classes': 1001,
-        'first_conv': 'conv0',
-        'classifier': 'fc'
-    },
     # from gluon pretrained models, best performing in terms of accuracy/loss metrics
     # https://gluon-cv.mxnet.io/model_zoo/classification.html
-    'gluon_inception_v3': {
+    'gluon_inception_v3': _cfg(
-        'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_inception_v3-9f746940.pth',
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_inception_v3-9f746940.pth',
-        'input_size': (3, 299, 299),
+        mean=IMAGENET_DEFAULT_MEAN,  # also works well with inception defaults
-        'crop_pct': 0.875,
+        std=IMAGENET_DEFAULT_STD,  # also works well with inception defaults
-        'interpolation': 'bicubic',
+        has_aux=False,
-        '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'
-    }
 }
 
 
-def _assert_default_kwargs(kwargs):
+class InceptionV3Aux(nn.Module):
-    # for imported models (ie torchvision) without capability to change these params,
+    """InceptionV3 with AuxLogits
-    # make sure they aren't being set to non-defaults
+    """
-    assert kwargs.pop('global_pool', 'avg') == 'avg'
+
-    assert kwargs.pop('drop_rate', 0.) == 0.
+    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']
+    model = _inception_v3('inception_v3', pretrained=pretrained, **kwargs)
-    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
     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']
+    model = _inception_v3('tf_inception_v3', pretrained=pretrained, **kwargs)
-    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
     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']
+    model = _inception_v3('adv_inception_v3', pretrained=pretrained, **kwargs)
-    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
     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']
+    model = _inception_v3('gluon_inception_v3', pretrained=pretrained, **kwargs)
-    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
     return model

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_

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)