[Feature] support modified resnet structure used in oCLIP

mmocr/models/common/backbones/__init__.py

@@ -1,4 +1,5 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+from .clip_resnet import CLIPResNet
 from .unet import UNet
 
-__all__ = ['UNet']
+__all__ = ['UNet', 'CLIPResNet']

mmocr/models/common/backbones/clip_resnet.py

@@ -0,0 +1,100 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+
+import torch.nn as nn
+from mmdet.models.backbones import ResNet
+from mmdet.models.backbones.resnet import Bottleneck
+
+from mmocr.registry import MODELS
+
+
+class CLIPBottleneck(Bottleneck):
+    """Bottleneck for CLIPResNet.
+
+    It is a Bottleneck variant used in the ResNet variant of CLIP. After the
+    second convolution layer, there is an additional average pooling layer with
+    kernel_size 2 and stride 2, which is added as a plugin when the
+    input stride > 1. The stride of each convolution layer is always set to 1.
+
+    Args:
+        **kwargs: Keyword arguments for
+            :class:``mmdet.models.backbones.resnet.Bottleneck``.
+    """
+
+    def __init__(self, **kwargs):
+        stride = kwargs.get('stride', 1)
+        kwargs['stride'] = 1
+        plugins = kwargs.get('plugins', None)
+        if stride > 1:
+            if plugins is None:
+                plugins = []
+
+            plugins.insert(
+                0,
+                dict(
+                    cfg=dict(type='mmocr.AvgPool2d', kernel_size=2),
+                    position='after_conv2'))
+            kwargs['plugins'] = plugins
+        super().__init__(**kwargs)
+
+
+@MODELS.register_module()
+class CLIPResNet(ResNet):
+    """Implement the ResNet variant used in `oCLIP.
+
+    <https://github.com/bytedance/oclip>`_.
+
+    It is also the official structure in
+    `CLIP <https://github.com/openai/CLIP>`_.
+
+    Compared with ResNetV1d structure, CLIPResNet replaces the
+    max pooling layer with an average pooling layer at the end
+    of the input stem.
+
+    In the Bottleneck of CLIPResNet, after the second convolution
+    layer, there is an additional average pooling layer with
+    kernel_size 2 and stride 2, which is added as a plugin
+    when the input stride > 1.
+    The stride of each convolution layer is always set to 1.
+
+    Args:
+        depth (int): Depth of resnet, options are [50]. Defaults to 50.
+        strides (sequence(int)): Strides of the first block of each stage.
+            Defaults to (1, 2, 2, 2).
+        deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv.
+            Defaults to True.
+        avg_down (bool): Use AvgPool instead of stride conv at
+            the downsampling stage in the bottleneck. Defaults to True.
+        **kwargs: Keyword arguments for
+            :class:``mmdet.models.backbones.resnet.ResNet``.
+    """
+    arch_settings = {
+        50: (CLIPBottleneck, (3, 4, 6, 3)),
+    }
+
+    def __init__(self,
+                 depth=50,
+                 strides=(1, 2, 2, 2),
+                 deep_stem=True,
+                 avg_down=True,
+                 **kwargs):
+        super().__init__(
+            depth=depth,
+            strides=strides,
+            deep_stem=deep_stem,
+            avg_down=avg_down,
+            **kwargs)
+
+    def _make_stem_layer(self, in_channels: int, stem_channels: int):
+        """Build stem layer for CLIPResNet used in `CLIP
+        https://github.com/openai/CLIP>`_.
+
+        It uses an average pooling layer rather than a max pooling
+        layer at the end of the input stem.
+
+        Args:
+            in_channels (int): Number of input channels.
+            stem_channels (int): Number of output channels.
+        """
+        super()._make_stem_layer(in_channels, stem_channels)
+        if self.deep_stem:
+            self.maxpool = nn.AvgPool2d(kernel_size=2)

mmocr/models/common/plugins/__init__.py

@@ -0,0 +1,4 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .common import AvgPool2d
+
+__all__ = ['AvgPool2d']

mmocr/models/common/plugins/common.py

@@ -0,0 +1,40 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+
+from mmocr.registry import MODELS
+
+
+@MODELS.register_module()
+class AvgPool2d(nn.Module):
+    """Applies a 2D average pooling over an input signal composed of several
+    input planes.
+
+    It can also be used as a network plugin.
+
+    Args:
+        kernel_size (int or tuple(int)): the size of the window.
+        stride (int or tuple(int), optional): the stride of the window.
+            Defaults to None.
+        padding (int or tuple(int)): implicit zero padding. Defaults to 0.
+    """
+
+    def __init__(self,
+                 kernel_size: Union[int, Tuple[int]],
+                 stride: Optional[Union[int, Tuple[int]]] = None,
+                 padding: Union[int, Tuple[int]] = 0,
+                 **kwargs) -> None:
+        super().__init__()
+        self.model = nn.AvgPool2d(kernel_size, stride, padding)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Forward function.
+        Args:
+            x (Tensor): Input feature map.
+
+        Returns:
+            Tensor: Output tensor after Avgpooling layer.
+        """
+        return self.model(x)

tests/test_models/test_common/test_backbones/test_clip_resnet.py

@@ -0,0 +1,66 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from unittest import TestCase
+
+import torch
+import torch.nn as nn
+from mmcv.cnn import build_conv_layer, build_norm_layer
+
+from mmocr.models.common.backbones import CLIPResNet
+from mmocr.models.common.backbones.clip_resnet import CLIPBottleneck
+
+
+class TestCLIPResNet(TestCase):
+
+    def test_forward(self):
+        model = CLIPResNet()
+        model.eval()
+
+        imgs = torch.randn(1, 3, 32, 32)
+        feat = model(imgs)
+        assert len(feat) == 4
+        assert feat[0].shape == torch.Size([1, 256, 8, 8])
+        assert feat[1].shape == torch.Size([1, 512, 4, 4])
+        assert feat[2].shape == torch.Size([1, 1024, 2, 2])
+        assert feat[3].shape == torch.Size([1, 2048, 1, 1])
+
+
+class TestCLIPBottleneck(TestCase):
+
+    def test_forward(self):
+        stride = 2
+        inplanes = 256
+        planes = 128
+        conv_cfg = None
+        norm_cfg = {'type': 'BN', 'requires_grad': True}
+
+        downsample = []
+        downsample.append(
+            nn.AvgPool2d(
+                kernel_size=stride,
+                stride=stride,
+                ceil_mode=True,
+                count_include_pad=False))
+        downsample.extend([
+            build_conv_layer(
+                conv_cfg,
+                inplanes,
+                planes * CLIPBottleneck.expansion,
+                kernel_size=1,
+                stride=1,
+                bias=False),
+            build_norm_layer(norm_cfg, planes * CLIPBottleneck.expansion)[1]
+        ])
+        downsample = nn.Sequential(*downsample)
+
+        model = CLIPBottleneck(
+            inplanes=inplanes,
+            planes=planes,
+            stride=stride,
+            downsample=downsample,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg)
+        model.eval()
+
+        input_feat = torch.randn(1, 256, 8, 8)
+        output_feat = model(input_feat)
+        assert output_feat.shape == torch.Size([1, 512, 4, 4])

tests/test_models/test_common/test_plugins/test_avgpool.py

@@ -0,0 +1,16 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from unittest import TestCase
+
+import torch
+
+from mmocr.models.common.plugins import AvgPool2d
+
+
+class TestAvgPool2d(TestCase):
+
+    def setUp(self) -> None:
+        self.img = torch.rand(1, 3, 32, 100)
+
+    def test_avgpool2d(self):
+        avgpool2d = AvgPool2d(kernel_size=2, stride=2)
+        self.assertEqual(avgpool2d(self.img).shape, torch.Size([1, 3, 16, 50]))