Dev se resnet

mmcls/models/backbones/__init__.py

@@ -1,10 +1,11 @@
 from .mobilenet_v2 import MobileNetV2
 from .resnet import ResNet, ResNetV1d
 from .resnext import ResNeXt
+from .seresnet import SEResNet
 from .shufflenet_v1 import ShuffleNetV1
 from .shufflenet_v2 import ShuffleNetV2
 
 __all__ = [
-    'ResNet', 'ResNeXt', 'ResNetV1d', 'ResNetV1d', 'ShuffleNetV1',
+    'ResNet', 'ResNeXt', 'ResNetV1d', 'ResNetV1d', 'SEResNet', 'ShuffleNetV1',
     'ShuffleNetV2', 'MobileNetV2'
 ]

mmcls/models/backbones/seresnet.py

@@ -0,0 +1,115 @@
+import torch.utils.checkpoint as cp
+
+from ..builder import BACKBONES
+from ..utils.se_layer import SELayer
+from .resnet import Bottleneck, ResLayer, ResNet
+
+
+class SEBottleneck(Bottleneck):
+    """SEBottleneck block for SEResNet.
+
+    Args:
+        inplanes (int): The input channels of the SEBottleneck block.
+        planes (int): The output channel base of the SEBottleneck block.
+        se_ratio (int): Squeeze ratio in SELayer. Default: 16
+    """
+    expansion = 4
+
+    def __init__(self, inplanes, planes, se_ratio=16, **kwargs):
+        super(SEBottleneck, self).__init__(inplanes, planes, **kwargs)
+        self.se_layer = SELayer(planes * self.expansion, ratio=se_ratio)
+
+    def forward(self, x):
+
+        def _inner_forward(x):
+            identity = x
+
+            out = self.conv1(x)
+            out = self.norm1(out)
+            out = self.relu(out)
+
+            out = self.conv2(out)
+            out = self.norm2(out)
+            out = self.relu(out)
+
+            out = self.conv3(out)
+            out = self.norm3(out)
+
+            out = self.se_layer(out)
+
+            if self.downsample is not None:
+                identity = self.downsample(x)
+
+            out += identity
+
+            return out
+
+        if self.with_cp and x.requires_grad:
+            out = cp.checkpoint(_inner_forward, x)
+        else:
+            out = _inner_forward(x)
+
+        out = self.relu(out)
+
+        return out
+
+
+@BACKBONES.register_module()
+class SEResNet(ResNet):
+    """SEResNet backbone.
+
+    Args:
+        depth (int): Depth of seresnet, from {50, 101, 152}.
+        in_channels (int): Number of input image channels. Normally 3.
+        base_channels (int): Number of base channels of hidden layer.
+        num_stages (int): Resnet stages, normally 4.
+        strides (Sequence[int]): Strides of the first block of each stage.
+        dilations (Sequence[int]): Dilation of each stage.
+        out_indices (Sequence[int]): Output from which stages.
+        se_ratio (int): Squeeze ratio in SELayer. Default: 16
+        style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
+            layer is the 3x3 conv layer, otherwise the stride-two layer is
+            the first 1x1 conv layer.
+        deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv
+        avg_down (bool): Use AvgPool instead of stride conv when
+            downsampling in the bottleneck.
+        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+            -1 means not freezing any parameters.
+        norm_cfg (dict): Dictionary to construct and config norm layer.
+        norm_eval (bool): Whether to set norm layers to eval mode, namely,
+            freeze running stats (mean and var). Note: Effect on Batch Norm
+            and its variants only.
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed.
+        zero_init_residual (bool): Whether to use zero init for last norm layer
+            in resblocks to let them behave as identity.
+
+    Example:
+        >>> from mmcls.models import SEResNet
+        >>> import torch
+        >>> self = SEResNet(depth=50)
+        >>> self.eval()
+        >>> inputs = torch.rand(1, 3, 224, 224)
+        >>> level_outputs = self.forward(inputs)
+        >>> for level_out in level_outputs:
+        ...     print(tuple(level_out.shape))
+        (1, 64, 56, 56)
+        (1, 128, 28, 28)
+        (1, 256, 14, 14)
+        (1, 512, 7, 7)
+    """
+
+    arch_settings = {
+        50: (SEBottleneck, (3, 4, 6, 3)),
+        101: (SEBottleneck, (3, 4, 23, 3)),
+        152: (SEBottleneck, (3, 8, 36, 3))
+    }
+
+    def __init__(self, depth, se_ratio=16, **kwargs):
+        if depth not in self.arch_settings:
+            raise KeyError(f'invalid depth {depth} for resnet')
+        self.se_ratio = se_ratio
+        super(SEResNet, self).__init__(depth, **kwargs)
+
+    def make_res_layer(self, **kwargs):
+        return ResLayer(se_ratio=self.se_ratio, **kwargs)

mmcls/models/utils/se_layer.py

@@ -0,0 +1,30 @@
+import torch.nn as nn
+
+
+class SELayer(nn.Module):
+    """Squeeze-and-Excitation Module.
+
+    Args:
+        inplanes (int): The input channels of the SEBottleneck block.
+        ratio (int): Squeeze ratio in SELayer. Default: 16
+    """
+
+    def __init__(self, inplanes, ratio=16):
+        super(SELayer, self).__init__()
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.conv1 = nn.Conv2d(
+            inplanes, int(inplanes / ratio), kernel_size=1, stride=1)
+        self.conv2 = nn.Conv2d(
+            int(inplanes / ratio), inplanes, kernel_size=1, stride=1)
+        self.relu = nn.ReLU(inplace=True)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        out = self.global_avgpool(x)
+
+        out = self.conv1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.sigmoid(out)
+        return x * out

tests/test_backbones/test_seresnet.py

@@ -0,0 +1,259 @@
+import pytest
+import torch
+from torch.nn.modules import AvgPool2d
+from torch.nn.modules.batchnorm import _BatchNorm
+
+from mmcls.models.backbones import SEResNet
+from mmcls.models.backbones.resnet import ResLayer
+from mmcls.models.backbones.seresnet import SEBottleneck, SELayer
+
+
+def is_block(modules):
+    """Check if is ResNet building block."""
+    if isinstance(modules, (SEBottleneck, )):
+        return True
+    return False
+
+
+def is_norm(modules):
+    """Check if is one of the norms."""
+    if isinstance(modules, (_BatchNorm, )):
+        return True
+    return False
+
+
+def all_zeros(modules):
+    """Check if the weight(and bias) is all zero."""
+    weight_zero = torch.equal(modules.weight.data,
+                              torch.zeros_like(modules.weight.data))
+    if hasattr(modules, 'bias'):
+        bias_zero = torch.equal(modules.bias.data,
+                                torch.zeros_like(modules.bias.data))
+    else:
+        bias_zero = True
+
+    return weight_zero and bias_zero
+
+
+def check_norm_state(modules, train_state):
+    """Check if norm layer is in correct train state."""
+    for mod in modules:
+        if isinstance(mod, _BatchNorm):
+            if mod.training != train_state:
+                return False
+    return True
+
+
+def test_serenet_selayer():
+    # Test selayer forward
+    layer = SELayer(64)
+    x = torch.randn(1, 64, 56, 56)
+    x_out = layer(x)
+    assert x_out.shape == torch.Size([1, 64, 56, 56])
+
+    # Test selayer forward with different ratio
+    layer = SELayer(64, ratio=8)
+    x = torch.randn(1, 64, 56, 56)
+    x_out = layer(x)
+    assert x_out.shape == torch.Size([1, 64, 56, 56])
+
+
+def test_seresnet_bottleneckse():
+
+    with pytest.raises(AssertionError):
+        # Style must be in ['pytorch', 'caffe']
+        SEBottleneck(64, 64, style='tensorflow')
+
+    # Test SEBottleneck with checkpoint forward
+    block = SEBottleneck(64, 16, with_cp=True)
+    assert block.with_cp
+    x = torch.randn(1, 64, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([1, 64, 56, 56])
+
+    # Test Bottleneck style
+    block = SEBottleneck(64, 64, stride=2, style='pytorch')
+    assert block.conv1.stride == (1, 1)
+    assert block.conv2.stride == (2, 2)
+    block = SEBottleneck(64, 64, stride=2, style='caffe')
+    assert block.conv1.stride == (2, 2)
+    assert block.conv2.stride == (1, 1)
+
+    # Test Bottleneck forward
+    block = SEBottleneck(64, 16)
+    x = torch.randn(1, 64, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([1, 64, 56, 56])
+
+
+def test_seresnet_res_layer():
+    # Test ResLayer of 3 Bottleneck w\o downsample
+    layer = ResLayer(SEBottleneck, 64, 16, 3, se_ratio=16)
+    assert len(layer) == 3
+    assert layer[0].conv1.in_channels == 64
+    assert layer[0].conv1.out_channels == 16
+    for i in range(1, len(layer)):
+        assert layer[i].conv1.in_channels == 64
+        assert layer[i].conv1.out_channels == 16
+    for i in range(len(layer)):
+        assert layer[i].downsample is None
+    x = torch.randn(1, 64, 56, 56)
+    x_out = layer(x)
+    assert x_out.shape == torch.Size([1, 64, 56, 56])
+
+    # Test ResLayer of 3 SEBottleneck with downsample
+    layer = ResLayer(SEBottleneck, 64, 64, 3, se_ratio=16)
+    assert layer[0].downsample[0].out_channels == 256
+    for i in range(1, len(layer)):
+        assert layer[i].downsample is None
+    x = torch.randn(1, 64, 56, 56)
+    x_out = layer(x)
+    assert x_out.shape == torch.Size([1, 256, 56, 56])
+
+    # Test ResLayer of 3 SEBottleneck with stride=2
+    layer = ResLayer(SEBottleneck, 64, 64, 3, stride=2, se_ratio=8)
+    assert layer[0].downsample[0].out_channels == 256
+    assert layer[0].downsample[0].stride == (2, 2)
+    for i in range(1, len(layer)):
+        assert layer[i].downsample is None
+    x = torch.randn(1, 64, 56, 56)
+    x_out = layer(x)
+    assert x_out.shape == torch.Size([1, 256, 28, 28])
+
+    # Test ResLayer of 3 SEBottleneck with stride=2 and average downsample
+    layer = ResLayer(
+        SEBottleneck, 64, 64, 3, stride=2, avg_down=True, se_ratio=8)
+    assert isinstance(layer[0].downsample[0], AvgPool2d)
+    assert layer[0].downsample[1].out_channels == 256
+    assert layer[0].downsample[1].stride == (1, 1)
+    for i in range(1, len(layer)):
+        assert layer[i].downsample is None
+    x = torch.randn(1, 64, 56, 56)
+    x_out = layer(x)
+    assert x_out.shape == torch.Size([1, 256, 28, 28])
+
+
+def test_seresnet_backbone():
+    """Test resnet backbone"""
+    with pytest.raises(KeyError):
+        # SEResNet depth should be in [50, 101, 152]
+        SEResNet(20)
+
+    with pytest.raises(AssertionError):
+        # In SEResNet: 1 <= num_stages <= 4
+        SEResNet(50, num_stages=0)
+
+    with pytest.raises(AssertionError):
+        # In SEResNet: 1 <= num_stages <= 4
+        SEResNet(50, num_stages=5)
+
+    with pytest.raises(AssertionError):
+        # len(strides) == len(dilations) == num_stages
+        SEResNet(50, strides=(1, ), dilations=(1, 1), num_stages=3)
+
+    with pytest.raises(TypeError):
+        # pretrained must be a string path
+        model = SEResNet(50)
+        model.init_weights(pretrained=0)
+
+    with pytest.raises(AssertionError):
+        # Style must be in ['pytorch', 'caffe']
+        SEResNet(50, style='tensorflow')
+
+    # Test SEResNet50 norm_eval=True
+    model = SEResNet(50, norm_eval=True)
+    model.init_weights()
+    model.train()
+    assert check_norm_state(model.modules(), False)
+
+    # Test SEResNet50 with torchvision pretrained weight
+    model = SEResNet(depth=50, norm_eval=True)
+    model.init_weights('torchvision://resnet50')
+    model.train()
+    assert check_norm_state(model.modules(), False)
+
+    # Test SEResNet50 with first stage frozen
+    frozen_stages = 1
+    model = SEResNet(50, frozen_stages=frozen_stages)
+    model.init_weights()
+    model.train()
+    assert model.norm1.training is False
+    for layer in [model.conv1, model.norm1]:
+        for param in layer.parameters():
+            assert param.requires_grad is False
+    for i in range(1, frozen_stages + 1):
+        layer = getattr(model, f'layer{i}')
+        for mod in layer.modules():
+            if isinstance(mod, _BatchNorm):
+                assert mod.training is False
+        for param in layer.parameters():
+            assert param.requires_grad is False
+
+    # Test SEResNet50 with BatchNorm forward
+    model = SEResNet(50, out_indices=(0, 1, 2, 3))
+    for m in model.modules():
+        if is_norm(m):
+            assert isinstance(m, _BatchNorm)
+    model.init_weights()
+    model.train()
+
+    imgs = torch.randn(1, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 4
+    assert feat[0].shape == torch.Size([1, 256, 56, 56])
+    assert feat[1].shape == torch.Size([1, 512, 28, 28])
+    assert feat[2].shape == torch.Size([1, 1024, 14, 14])
+    assert feat[3].shape == torch.Size([1, 2048, 7, 7])
+
+    # Test SEResNet50 with layers 1, 2, 3 out forward
+    model = SEResNet(50, out_indices=(0, 1, 2))
+    model.init_weights()
+    model.train()
+
+    imgs = torch.randn(1, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 3
+    assert feat[0].shape == torch.Size([1, 256, 56, 56])
+    assert feat[1].shape == torch.Size([1, 512, 28, 28])
+    assert feat[2].shape == torch.Size([1, 1024, 14, 14])
+
+    # Test SEResNet50 with layers 3 (top feature maps) out forward
+    model = SEResNet(50, out_indices=(3, ))
+    model.init_weights()
+    model.train()
+
+    imgs = torch.randn(1, 3, 224, 224)
+    feat = model(imgs)
+    assert feat.shape == torch.Size([1, 2048, 7, 7])
+
+    # Test SEResNet50 with checkpoint forward
+    model = SEResNet(50, out_indices=(0, 1, 2, 3), with_cp=True)
+    for m in model.modules():
+        if is_block(m):
+            assert m.with_cp
+    model.init_weights()
+    model.train()
+
+    imgs = torch.randn(1, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 4
+    assert feat[0].shape == torch.Size([1, 256, 56, 56])
+    assert feat[1].shape == torch.Size([1, 512, 28, 28])
+    assert feat[2].shape == torch.Size([1, 1024, 14, 14])
+    assert feat[3].shape == torch.Size([1, 2048, 7, 7])
+
+    # Test SEResNet50 zero initialization of residual
+    model = SEResNet(50, out_indices=(0, 1, 2, 3), zero_init_residual=True)
+    model.init_weights()
+    for m in model.modules():
+        if isinstance(m, SEBottleneck):
+            assert all_zeros(m.norm3)
+    model.train()
+
+    imgs = torch.randn(1, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 4
+    assert feat[0].shape == torch.Size([1, 256, 56, 56])
+    assert feat[1].shape == torch.Size([1, 512, 28, 28])
+    assert feat[2].shape == torch.Size([1, 1024, 14, 14])
+    assert feat[3].shape == torch.Size([1, 2048, 7, 7])