[Feature] Add ResNetV1c. (#692)
* add ResNetV1c * add unit tests * fix lint * update docstring * fix lintpull/710/head
Ezra-Yu
GitHub
parent
43024cda73
commit
7fcaedcbfb
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@ -0,0 +1,17 @@
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# model settings
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model = dict(
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type='ImageClassifier',
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backbone=dict(
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type='ResNetV1c',
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depth=50,
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num_stages=4,
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out_indices=(3, ),
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style='pytorch'),
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neck=dict(type='GlobalAveragePooling'),
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head=dict(
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type='LinearClsHead',
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num_classes=1000,
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in_channels=2048,
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loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
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topk=(1, 5),
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))
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@ -40,6 +40,9 @@ The depth of representations is of central importance for many visual recognitio
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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@ -298,3 +298,42 @@ Models:
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Top 5 Accuracy: 93.80
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Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a3-100e_in1k_20211228-3493673c.pth
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Config: configs/resnet/resnet50_8xb256-rsb-a3-100e_in1k.py
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- Name: resnetv1c50_8xb32_in1k
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Metadata:
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FLOPs: 4360000000
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Parameters: 25580000
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In Collection: ResNet
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Results:
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- Dataset: ImageNet-1k
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Metrics:
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Top 1 Accuracy: 77.01
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Top 5 Accuracy: 93.58
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Task: Image Classification
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Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c50_8xb32_in1k_20220214-3343eccd.pth
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Config: configs/resnet/resnetv1c50_8xb32_in1k.py
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- Name: resnetv1c101_8xb32_in1k
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Metadata:
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FLOPs: 8090000000
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Parameters: 44570000
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In Collection: ResNet
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Results:
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- Dataset: ImageNet-1k
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Metrics:
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Top 1 Accuracy: 78.30
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Top 5 Accuracy: 94.27
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Task: Image Classification
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Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c101_8xb32_in1k_20220214-434fe45f.pth
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Config: configs/resnet/resnetv1c101_8xb32_in1k.py
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- Name: resnetv1c152_8xb32_in1k
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Metadata:
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FLOPs: 11820000000
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Parameters: 60210000
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In Collection: ResNet
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Results:
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- Dataset: ImageNet-1k
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Metrics:
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Top 1 Accuracy: 78.76
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Top 5 Accuracy: 94.41
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Task: Image Classification
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Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c152_8xb32_in1k_20220214-c013291f.pth
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Config: configs/resnet/resnetv1c152_8xb32_in1k.py
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@ -0,0 +1,7 @@
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_base_ = [
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'../_base_/models/resnetv1c50.py',
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'../_base_/datasets/imagenet_bs32_pil_resize.py',
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'../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
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]
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model = dict(backbone=dict(depth=101))
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_base_ = [
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'../_base_/models/resnetv1c50.py',
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'../_base_/datasets/imagenet_bs32_pil_resize.py',
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'../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
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]
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model = dict(backbone=dict(depth=152))
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_base_ = [
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'../_base_/models/resnetv1c50.py',
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'../_base_/datasets/imagenet_bs32_pil_resize.py',
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'../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
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]
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@ -13,7 +13,7 @@ from .regnet import RegNet
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from .repvgg import RepVGG
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from .res2net import Res2Net
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from .resnest import ResNeSt
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from .resnet import ResNet, ResNetV1d
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from .resnet import ResNet, ResNetV1c, ResNetV1d
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from .resnet_cifar import ResNet_CIFAR
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from .resnext import ResNeXt
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from .seresnet import SEResNet
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@ -34,5 +34,5 @@ __all__ = [
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'ShuffleNetV2', 'MobileNetV2', 'MobileNetV3', 'VisionTransformer',
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'SwinTransformer', 'TNT', 'TIMMBackbone', 'T2T_ViT', 'Res2Net', 'RepVGG',
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'Conformer', 'MlpMixer', 'DistilledVisionTransformer', 'PCPVT', 'SVT',
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'EfficientNet', 'ConvNeXt', 'HRNet'
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'EfficientNet', 'ConvNeXt', 'HRNet', 'ResNetV1c'
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]
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@ -653,6 +653,22 @@ class ResNet(BaseBackbone):
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m.eval()
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@BACKBONES.register_module()
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class ResNetV1c(ResNet):
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"""ResNetV1c backbone.
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This variant is described in `Bag of Tricks.
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<https://arxiv.org/pdf/1812.01187.pdf>`_.
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Compared with default ResNet(ResNetV1b), ResNetV1c replaces the 7x7 conv
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in the input stem with three 3x3 convs.
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"""
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def __init__(self, **kwargs):
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super(ResNetV1c, self).__init__(
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deep_stem=True, avg_down=False, **kwargs)
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@BACKBONES.register_module()
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class ResNetV1d(ResNet):
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"""ResNetV1d backbone.
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@ -5,7 +5,7 @@ import torch.nn as nn
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from mmcv.cnn import ConvModule
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from mmcv.utils.parrots_wrapper import _BatchNorm
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from mmcls.models.backbones import ResNet, ResNetV1d
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from mmcls.models.backbones import ResNet, ResNetV1c, ResNetV1d
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from mmcls.models.backbones.resnet import (BasicBlock, Bottleneck, ResLayer,
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get_expansion)
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@ -526,6 +526,45 @@ def test_resnet():
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assert not all_zeros(m.norm2)
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def test_resnet_v1c():
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model = ResNetV1c(depth=50, out_indices=(0, 1, 2, 3))
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model.init_weights()
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model.train()
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assert len(model.stem) == 3
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for i in range(3):
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assert isinstance(model.stem[i], ConvModule)
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imgs = torch.randn(1, 3, 224, 224)
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feat = model.stem(imgs)
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assert feat.shape == (1, 64, 112, 112)
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feat = model(imgs)
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assert len(feat) == 4
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assert feat[0].shape == (1, 256, 56, 56)
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assert feat[1].shape == (1, 512, 28, 28)
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assert feat[2].shape == (1, 1024, 14, 14)
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assert feat[3].shape == (1, 2048, 7, 7)
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# Test ResNet50V1d with first stage frozen
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frozen_stages = 1
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model = ResNetV1d(depth=50, frozen_stages=frozen_stages)
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assert len(model.stem) == 3
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for i in range(3):
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assert isinstance(model.stem[i], ConvModule)
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model.init_weights()
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model.train()
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check_norm_state(model.stem, False)
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for param in model.stem.parameters():
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assert param.requires_grad is False
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for i in range(1, frozen_stages + 1):
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layer = getattr(model, f'layer{i}')
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for mod in layer.modules():
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if isinstance(mod, _BatchNorm):
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assert mod.training is False
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for param in layer.parameters():
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assert param.requires_grad is False
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def test_resnet_v1d():
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model = ResNetV1d(depth=50, out_indices=(0, 1, 2, 3))
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model.init_weights()
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@ -4,6 +4,7 @@ import copy
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import os
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import os.path as osp
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import time
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import warnings
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import mmcv
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import torch
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