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* add efficientnet_v2.py * add efficientnet_v2 in __init__.py * add efficientnet_v2_s base config file * add efficientnet_v2 config file * add efficientnet_v2 config file * update tuple output * update config file * update model file * update model file * update model file * update config file * update model file * update config file * update model file * update model file * update model file * update model file * update model file * update config file * update config file * update model file * update model file * update model file * update model file * update model config file * Update efficientnet_v2.py * add config file and modify arch * add config file and modify arch * add the file about convert_pth from timm to mmcls * update efficientnetv2 model file with mmcls style * add the file about convert_pth from timm to mmcls * add the file about convert_pth from timm to mmcls * update convert file * update model file * update convert file * update model file * update model file * update model file * add metefile and README * Update tools/model_converters/efficientnetv2-timm_to_mmcls.py Co-authored-by: Ezra-Yu <18586273+Ezra-Yu@users.noreply.github.com> * update model file and convert file * update model file and convert file * update model file and convert file * update model file and convert file * update model file * update model file * update model file * update config file and README file * update metafile * Update efficientnetv2_to_mmcls.py * update model-index.yml * update metafile.yml * update b0 and s train pipeline * update b0 and s train pipeline * update b0 and s train pipeline * add test_efficientnet_v2 * update test_efficientnet_v2 * update model file docs * update test_efficientnet_v2 * update test_efficientnet_v2 * add efficientnet_v2.py * add efficientnet_v2 in __init__.py * add efficientnet_v2_s base config file * add efficientnet_v2 config file * add efficientnet_v2 config file * update tuple output * update config file * update model file * update model file * update model file * update model file * update config file * update config file * update model file * update model file * update model file * update model file * update model file * update config file * update config file * update model file * update model file * update model file * update model file * update model config file * Update efficientnet_v2.py * add config file and modify arch * add config file and modify arch * add the file about convert_pth from timm to mmcls * update efficientnetv2 model file with mmcls style * add the file about convert_pth from timm to mmcls * add the file about convert_pth from timm to mmcls * update convert file * update model file * update convert file * update model file * update model file * update model file * add metefile and README * Update tools/model_converters/efficientnetv2-timm_to_mmcls.py Co-authored-by: Ezra-Yu <18586273+Ezra-Yu@users.noreply.github.com> * update model file and convert file * update model file and convert file * update model file and convert file * update model file and convert file * update model file * update model file * update model file * update config file and README file * update metafile * Update efficientnetv2_to_mmcls.py * update model-index.yml * update metafile.yml * update b0 and s train pipeline * update b0 and s train pipeline * update b0 and s train pipeline * add test_efficientnet_v2 * update test_efficientnet_v2 * update model file docs * update test_efficientnet_v2 * update test_efficientnet_v2 * pass pre-commit hook * refactor efficientnetv2 * refactor efficientnetv2 * update readme, metafile and weight links * update model-index.yml * fix lint * fix typo * Update efficientnetv2-b1_8xb32_in1k.py * Update efficientnetv2-b2_8xb32_in1k.py * Update efficientnetv2-b3_8xb32_in1k.py * update two moduals and model file * update modual file * update accuracys * update accuracys * update metafile * fix build docs * update links * update README.md Co-authored-by: qingtian <459291290@qq.com> Co-authored-by: Ezra-Yu <18586273+Ezra-Yu@users.noreply.github.com>
151 lines
5.7 KiB
Python
151 lines
5.7 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import pytest
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import torch
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from torch.nn.modules import GroupNorm
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from torch.nn.modules.batchnorm import _BatchNorm
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from mmcls.models.backbones import EfficientNetV2
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def is_norm(modules):
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"""Check if is one of the norms."""
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if isinstance(modules, (GroupNorm, _BatchNorm)):
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return True
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return False
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def check_norm_state(modules, train_state):
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"""Check if norm layer is in correct train state."""
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for mod in modules:
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if isinstance(mod, _BatchNorm):
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if mod.training != train_state:
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return False
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return True
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def test_efficientnet_v2_backbone():
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with pytest.raises(TypeError):
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# pretrained must be a string path
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model = EfficientNetV2()
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model.init_weights(pretrained=0)
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with pytest.raises(AssertionError):
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# arch must in arc_settings
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EfficientNetV2(arch='others')
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with pytest.raises(ValueError):
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# frozen_stages must less than 8
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EfficientNetV2(arch='b1', frozen_stages=12)
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# Test EfficientNetV2
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model = EfficientNetV2()
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model.init_weights()
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model.train()
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x = torch.rand((1, 3, 224, 224))
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model(x)
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# Test EfficientNetV2 with first stage frozen
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frozen_stages = 7
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model = EfficientNetV2(arch='b0', frozen_stages=frozen_stages)
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model.init_weights()
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model.train()
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for i in range(frozen_stages):
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layer = model.layers[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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# Test EfficientNetV2 with norm eval
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model = EfficientNetV2(norm_eval=True)
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model.init_weights()
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model.train()
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assert check_norm_state(model.modules(), False)
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# Test EfficientNetV2 forward with 'b0' arch
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out_channels = [32, 16, 32, 48, 96, 112, 192, 1280]
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model = EfficientNetV2(arch='b0', out_indices=(0, 1, 2, 3, 4, 5, 6, 7))
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model.init_weights()
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model.train()
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imgs = torch.randn(1, 3, 224, 224)
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feat = model(imgs)
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assert len(feat) == 8
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assert feat[0].shape == torch.Size([1, out_channels[0], 112, 112])
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assert feat[1].shape == torch.Size([1, out_channels[1], 112, 112])
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assert feat[2].shape == torch.Size([1, out_channels[2], 56, 56])
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assert feat[3].shape == torch.Size([1, out_channels[3], 28, 28])
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assert feat[4].shape == torch.Size([1, out_channels[4], 14, 14])
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assert feat[5].shape == torch.Size([1, out_channels[5], 14, 14])
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assert feat[6].shape == torch.Size([1, out_channels[6], 7, 7])
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assert feat[6].shape == torch.Size([1, out_channels[6], 7, 7])
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# Test EfficientNetV2 forward with 'b0' arch and GroupNorm
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out_channels = [32, 16, 32, 48, 96, 112, 192, 1280]
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model = EfficientNetV2(
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arch='b0',
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out_indices=(0, 1, 2, 3, 4, 5, 6, 7),
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norm_cfg=dict(type='GN', num_groups=2, requires_grad=True))
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for m in model.modules():
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if is_norm(m):
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assert isinstance(m, GroupNorm)
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model.init_weights()
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model.train()
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imgs = torch.randn(1, 3, 64, 64)
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feat = model(imgs)
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assert len(feat) == 8
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assert feat[0].shape == torch.Size([1, out_channels[0], 32, 32])
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assert feat[1].shape == torch.Size([1, out_channels[1], 32, 32])
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assert feat[2].shape == torch.Size([1, out_channels[2], 16, 16])
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assert feat[3].shape == torch.Size([1, out_channels[3], 8, 8])
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assert feat[4].shape == torch.Size([1, out_channels[4], 4, 4])
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assert feat[5].shape == torch.Size([1, out_channels[5], 4, 4])
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assert feat[6].shape == torch.Size([1, out_channels[6], 2, 2])
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assert feat[7].shape == torch.Size([1, out_channels[7], 2, 2])
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# Test EfficientNetV2 forward with 'm' arch
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out_channels = [24, 24, 48, 80, 160, 176, 304, 512, 1280]
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model = EfficientNetV2(arch='m', out_indices=(0, 1, 2, 3, 4, 5, 6, 7, 8))
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model.init_weights()
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model.train()
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imgs = torch.randn(1, 3, 64, 64)
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feat = model(imgs)
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assert len(feat) == 9
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assert feat[0].shape == torch.Size([1, out_channels[0], 32, 32])
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assert feat[1].shape == torch.Size([1, out_channels[1], 32, 32])
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assert feat[2].shape == torch.Size([1, out_channels[2], 16, 16])
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assert feat[3].shape == torch.Size([1, out_channels[3], 8, 8])
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assert feat[4].shape == torch.Size([1, out_channels[4], 4, 4])
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assert feat[5].shape == torch.Size([1, out_channels[5], 4, 4])
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assert feat[6].shape == torch.Size([1, out_channels[6], 2, 2])
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assert feat[7].shape == torch.Size([1, out_channels[7], 2, 2])
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assert feat[8].shape == torch.Size([1, out_channels[8], 2, 2])
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# Test EfficientNetV2 forward with 'm' arch and GroupNorm
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out_channels = [24, 24, 48, 80, 160, 176, 304, 512, 1280]
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model = EfficientNetV2(
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arch='m',
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out_indices=(0, 1, 2, 3, 4, 5, 6, 7, 8),
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norm_cfg=dict(type='GN', num_groups=2, requires_grad=True))
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for m in model.modules():
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if is_norm(m):
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assert isinstance(m, GroupNorm)
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model.init_weights()
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model.train()
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imgs = torch.randn(1, 3, 64, 64)
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feat = model(imgs)
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assert len(feat) == 9
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assert feat[0].shape == torch.Size([1, out_channels[0], 32, 32])
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assert feat[1].shape == torch.Size([1, out_channels[1], 32, 32])
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assert feat[2].shape == torch.Size([1, out_channels[2], 16, 16])
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assert feat[3].shape == torch.Size([1, out_channels[3], 8, 8])
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assert feat[4].shape == torch.Size([1, out_channels[4], 4, 4])
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assert feat[5].shape == torch.Size([1, out_channels[5], 4, 4])
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assert feat[6].shape == torch.Size([1, out_channels[6], 2, 2])
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assert feat[7].shape == torch.Size([1, out_channels[7], 2, 2])
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assert feat[8].shape == torch.Size([1, out_channels[8], 2, 2])
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