[Feature] Add ResNetV1c. (#692)

* add ResNetV1c

* add unit tests

* fix lint

* update docstring

* fix lint

configs/_base_/models/resnetv1c50.py

@@ -0,0 +1,17 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='ResNetV1c',
+        depth=50,
+        num_stages=4,
+        out_indices=(3, ),
+        style='pytorch'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=2048,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

configs/resnet/README.md

@@ -40,6 +40,9 @@ The depth of representations is of central importance for many visual recognitio
 | ResNet-50             | 25.56     | 4.12     | 76.55 | 93.06 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.log.json) |
 | ResNet-101            | 44.55     | 7.85     | 77.97 | 94.06 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_8xb32_in1k_20210831-539c63f8.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_8xb32_in1k_20210831-539c63f8.log.json) |
 | ResNet-152            | 60.19     | 11.58    | 78.48 | 94.13 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet152_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_8xb32_in1k_20210901-4d7582fa.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_8xb32_in1k_20210901-4d7582fa.log.json) |
+| ResNetV1C-50          | 25.58     | 4.36     | 77.01 | 93.58 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1c50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c50_8xb32_in1k_20220214-3343eccd.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c50_8xb32_in1k_20220214-3343eccd.log.json) |
+| ResNetV1C-101         | 44.57     | 8.09     | 78.30 | 94.27 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1c101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c101_8xb32_in1k_20220214-434fe45f.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c101_8xb32_in1k_20220214-434fe45f.log.json) |
+| ResNetV1C-152         | 60.21     | 11.82    | 78.76 | 94.41 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1c152_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c152_8xb32_in1k_20220214-c013291f.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c152_8xb32_in1k_20220214-c013291f.log.json) |
 | ResNetV1D-50          | 25.58     | 4.36     | 77.54  | 93.57 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_b32x8_imagenet_20210531-db14775a.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_b32x8_imagenet_20210531-db14775a.log.json) |
 | ResNetV1D-101         | 44.57     | 8.09     | 78.93 | 94.48 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_b32x8_imagenet_20210531-6e13bcd3.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_b32x8_imagenet_20210531-6e13bcd3.log.json) |
 | ResNetV1D-152         | 60.21     | 11.82    | 79.41 | 94.70 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d152_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_b32x8_imagenet_20210531-278cf22a.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_b32x8_imagenet_20210531-278cf22a.log.json) |

configs/resnet/metafile.yml

@@ -298,3 +298,42 @@ Models:
           Top 5 Accuracy: 93.80
     Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a3-100e_in1k_20211228-3493673c.pth
     Config: configs/resnet/resnet50_8xb256-rsb-a3-100e_in1k.py
+  - Name: resnetv1c50_8xb32_in1k
+    Metadata:
+      FLOPs: 4360000000
+      Parameters: 25580000
+    In Collection: ResNet
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 77.01
+          Top 5 Accuracy: 93.58
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c50_8xb32_in1k_20220214-3343eccd.pth
+    Config: configs/resnet/resnetv1c50_8xb32_in1k.py
+  - Name: resnetv1c101_8xb32_in1k
+    Metadata:
+      FLOPs: 8090000000
+      Parameters: 44570000
+    In Collection: ResNet
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 78.30
+          Top 5 Accuracy: 94.27
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c101_8xb32_in1k_20220214-434fe45f.pth
+    Config: configs/resnet/resnetv1c101_8xb32_in1k.py
+  - Name: resnetv1c152_8xb32_in1k
+    Metadata:
+      FLOPs: 11820000000
+      Parameters: 60210000
+    In Collection: ResNet
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 78.76
+          Top 5 Accuracy: 94.41
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c152_8xb32_in1k_20220214-c013291f.pth
+    Config: configs/resnet/resnetv1c152_8xb32_in1k.py

configs/resnet/resnetv1c101_8xb32_in1k.py

@@ -0,0 +1,7 @@
+_base_ = [
+    '../_base_/models/resnetv1c50.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]
+
+model = dict(backbone=dict(depth=101))

configs/resnet/resnetv1c152_8xb32_in1k.py

@@ -0,0 +1,7 @@
+_base_ = [
+    '../_base_/models/resnetv1c50.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]
+
+model = dict(backbone=dict(depth=152))

configs/resnet/resnetv1c50_8xb32_in1k.py

@@ -0,0 +1,5 @@
+_base_ = [
+    '../_base_/models/resnetv1c50.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

mmcls/models/backbones/__init__.py

@@ -13,7 +13,7 @@ from .regnet import RegNet
 from .repvgg import RepVGG
 from .res2net import Res2Net
 from .resnest import ResNeSt
-from .resnet import ResNet, ResNetV1d
+from .resnet import ResNet, ResNetV1c, ResNetV1d
 from .resnet_cifar import ResNet_CIFAR
 from .resnext import ResNeXt
 from .seresnet import SEResNet
@@ -34,5 +34,5 @@ __all__ = [
     'ShuffleNetV2', 'MobileNetV2', 'MobileNetV3', 'VisionTransformer',
     'SwinTransformer', 'TNT', 'TIMMBackbone', 'T2T_ViT', 'Res2Net', 'RepVGG',
     'Conformer', 'MlpMixer', 'DistilledVisionTransformer', 'PCPVT', 'SVT',
-    'EfficientNet', 'ConvNeXt', 'HRNet'
+    'EfficientNet', 'ConvNeXt', 'HRNet', 'ResNetV1c'
 ]

mmcls/models/backbones/resnet.py

@@ -653,6 +653,22 @@ class ResNet(BaseBackbone):
                     m.eval()
 
 
+@BACKBONES.register_module()
+class ResNetV1c(ResNet):
+    """ResNetV1c backbone.
+
+    This variant is described in `Bag of Tricks.
+    <https://arxiv.org/pdf/1812.01187.pdf>`_.
+
+    Compared with default ResNet(ResNetV1b), ResNetV1c replaces the 7x7 conv
+    in the input stem with three 3x3 convs.
+    """
+
+    def __init__(self, **kwargs):
+        super(ResNetV1c, self).__init__(
+            deep_stem=True, avg_down=False, **kwargs)
+
+
 @BACKBONES.register_module()
 class ResNetV1d(ResNet):
     """ResNetV1d backbone.

tests/test_models/test_backbones/test_resnet.py

@@ -5,7 +5,7 @@ import torch.nn as nn
 from mmcv.cnn import ConvModule
 from mmcv.utils.parrots_wrapper import _BatchNorm
 
-from mmcls.models.backbones import ResNet, ResNetV1d
+from mmcls.models.backbones import ResNet, ResNetV1c, ResNetV1d
 from mmcls.models.backbones.resnet import (BasicBlock, Bottleneck, ResLayer,
                                            get_expansion)
 
@@ -526,6 +526,45 @@ def test_resnet():
             assert not all_zeros(m.norm2)
 
 
+def test_resnet_v1c():
+    model = ResNetV1c(depth=50, out_indices=(0, 1, 2, 3))
+    model.init_weights()
+    model.train()
+
+    assert len(model.stem) == 3
+    for i in range(3):
+        assert isinstance(model.stem[i], ConvModule)
+
+    imgs = torch.randn(1, 3, 224, 224)
+    feat = model.stem(imgs)
+    assert feat.shape == (1, 64, 112, 112)
+    feat = model(imgs)
+    assert len(feat) == 4
+    assert feat[0].shape == (1, 256, 56, 56)
+    assert feat[1].shape == (1, 512, 28, 28)
+    assert feat[2].shape == (1, 1024, 14, 14)
+    assert feat[3].shape == (1, 2048, 7, 7)
+
+    # Test ResNet50V1d with first stage frozen
+    frozen_stages = 1
+    model = ResNetV1d(depth=50, frozen_stages=frozen_stages)
+    assert len(model.stem) == 3
+    for i in range(3):
+        assert isinstance(model.stem[i], ConvModule)
+    model.init_weights()
+    model.train()
+    check_norm_state(model.stem, False)
+    for param in model.stem.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
+
+
 def test_resnet_v1d():
     model = ResNetV1d(depth=50, out_indices=(0, 1, 2, 3))
     model.init_weights()

tools/train.py

@@ -4,6 +4,7 @@ import copy
 import os
 import os.path as osp
 import time
+import warnings
 
 import mmcv
 import torch