mirror of
https://github.com/huggingface/pytorch-image-models.git
synced 2025-06-03 15:01:08 +08:00
Make 'regnetz' model experiments closer to actual RegNetZ, bottleneck expansion, expand from in_chs, no shortcut on stride 2, tweak model sizes
This commit is contained in:
Ross Wightman
parent
b81e79aae9
commit
0ca687f224
@ -57,8 +57,14 @@ default_cfgs = {
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input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256)),
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'lambda_resnet26t': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/lambda_resnet26t_256-b040fce6.pth',
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url='',
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min_input_size=(3, 128, 128), input_size=(3, 256, 256), pool_size=(8, 8)),
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'lambda_resnet50ts': _cfg(
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url='',
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min_input_size=(3, 128, 128), input_size=(3, 256, 256), pool_size=(8, 8)),
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'lambda_resnet26rt_256': _cfg(
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url='',
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fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
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}
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@ -198,6 +204,33 @@ model_cfgs = dict(
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self_attn_layer='lambda',
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self_attn_kwargs=dict(r=9)
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),
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lambda_resnet50ts=ByoModelCfg(
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blocks=(
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ByoBlockCfg(type='bottle', d=3, c=256, s=1, gs=0, br=0.25),
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interleave_blocks(types=('bottle', 'self_attn'), every=4, d=4, c=512, s=2, gs=0, br=0.25),
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interleave_blocks(types=('bottle', 'self_attn'), d=6, c=1024, s=2, gs=0, br=0.25),
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interleave_blocks(types=('bottle', 'self_attn'), d=3, c=2048, s=2, gs=0, br=0.25),
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),
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stem_chs=64,
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stem_type='tiered',
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stem_pool='maxpool',
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act_layer='silu',
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self_attn_layer='lambda',
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self_attn_kwargs=dict(r=9)
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),
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lambda_resnet26rt_256=ByoModelCfg(
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blocks=(
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ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
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ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=0, br=0.25),
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interleave_blocks(types=('bottle', 'self_attn'), d=2, c=1024, s=2, gs=0, br=0.25),
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ByoBlockCfg(type='self_attn', d=2, c=2048, s=2, gs=0, br=0.25),
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),
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stem_chs=64,
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stem_type='tiered',
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stem_pool='maxpool',
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self_attn_layer='lambda',
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self_attn_kwargs=dict(r=None)
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),
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)
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@ -275,6 +308,21 @@ def eca_halonext26ts(pretrained=False, **kwargs):
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@register_model
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def lambda_resnet26t(pretrained=False, **kwargs):
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""" Lambda-ResNet-26T. Lambda layers in last two stages.
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""" Lambda-ResNet-26-T. Lambda layers w/ conv pos in last two stages.
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"""
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return _create_byoanet('lambda_resnet26t', pretrained=pretrained, **kwargs)
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@register_model
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def lambda_resnet50ts(pretrained=False, **kwargs):
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""" Lambda-ResNet-50-TS. SiLU act. Lambda layers w/ conv pos in last two stages.
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"""
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return _create_byoanet('lambda_resnet50ts', pretrained=pretrained, **kwargs)
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@register_model
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def lambda_resnet26rt_256(pretrained=False, **kwargs):
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""" Lambda-ResNet-26-R-T. Lambda layers w/ rel pos embed in last two stages.
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"""
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kwargs.setdefault('img_size', 256)
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return _create_byoanet('lambda_resnet26rt_256', pretrained=pretrained, **kwargs)
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@ -51,6 +51,16 @@ def _cfg(url='', **kwargs):
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}
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def _cfgr(url='', **kwargs):
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return {
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'url': url, 'num_classes': 1000, 'input_size': (3, 256, 256), 'pool_size': (8, 8),
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'crop_pct': 0.9, 'interpolation': 'bicubic',
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'stem.conv1.conv', 'classifier': 'head.fc',
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**kwargs
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}
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default_cfgs = {
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# GPU-Efficient (ResNet) weights
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'gernet_s': _cfg(
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@ -92,65 +102,50 @@ default_cfgs = {
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet51q_ra2-d47dcc76.pth',
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first_conv='stem.conv1', input_size=(3, 256, 256), pool_size=(8, 8),
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test_input_size=(3, 288, 288), crop_pct=1.0),
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'resnet61q': _cfg(
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'resnet61q': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet61q_ra2-6afc536c.pth',
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first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8),
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test_input_size=(3, 288, 288), crop_pct=1.0, interpolation='bicubic'),
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test_input_size=(3, 288, 288), crop_pct=1.0),
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'resnext26ts': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/resnext26ts_256_ra2-8bbd9106.pth',
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first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
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'gcresnext26ts': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnext26ts_256-e414378b.pth',
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first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
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'seresnext26ts': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/seresnext26ts_256-6f0d74a3.pth',
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first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
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'eca_resnext26ts': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/eca_resnext26ts_256-5a1d030f.pth',
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first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
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'bat_resnext26ts': _cfg(
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'resnext26ts': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/resnext26ts_256_ra2-8bbd9106.pth'),
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'gcresnext26ts': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnext26ts_256-e414378b.pth'),
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'seresnext26ts': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/seresnext26ts_256-6f0d74a3.pth'),
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'eca_resnext26ts': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/eca_resnext26ts_256-5a1d030f.pth'),
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'bat_resnext26ts': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/bat_resnext26ts_256-fa6fd595.pth',
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first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic',
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min_input_size=(3, 256, 256)),
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'resnet32ts': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/resnet32ts_256-aacf5250.pth',
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first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
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'resnet33ts': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/resnet33ts_256-e91b09a4.pth',
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first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
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'gcresnet33ts': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnet33ts_256-0e0cd345.pth',
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first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
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'seresnet33ts': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/seresnet33ts_256-f8ad44d9.pth',
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first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
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'eca_resnet33ts': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/eca_resnet33ts_256-8f98face.pth',
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first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
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'resnet32ts': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/resnet32ts_256-aacf5250.pth'),
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'resnet33ts': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/resnet33ts_256-e91b09a4.pth'),
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'gcresnet33ts': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnet33ts_256-0e0cd345.pth'),
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'seresnet33ts': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/seresnet33ts_256-f8ad44d9.pth'),
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'eca_resnet33ts': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/eca_resnet33ts_256-8f98face.pth'),
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'gcresnet50t': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnet50t_256-96374d1c.pth',
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first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
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'gcresnet50t': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnet50t_256-96374d1c.pth'),
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'gcresnext50ts': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnext50ts_256-3e0f515e.pth',
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first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
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'gcresnext50ts': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnext50ts_256-3e0f515e.pth'),
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# experimental models
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'regnetz_b': _cfg(
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# experimental models, likely to change ot be removed
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'regnetz_b': _cfgr(
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url='',
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mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
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input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
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'regnetz_c': _cfg(
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input_size=(3, 224, 224), pool_size=(7, 7), first_conv='stem.conv'),
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'regnetz_c': _cfgr(
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url='',
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mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
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input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
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'regnetz_d': _cfg(
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imean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), first_conv='stem.conv'),
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'regnetz_d': _cfgr(
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url='',
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mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
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first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
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mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
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}
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@ -507,46 +502,52 @@ model_cfgs = dict(
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# experimental models, closer to a RegNetZ than a ResNet. Similar to EfficientNets but w/ groups instead of DW
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regnetz_b=ByoModelCfg(
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blocks=(
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ByoBlockCfg(type='bottle', d=2, c=192, s=2, gs=24, br=0.25, block_kwargs=dict(linear_out=True)),
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ByoBlockCfg(type='bottle', d=6, c=384, s=2, gs=24, br=0.25, block_kwargs=dict(linear_out=True)),
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ByoBlockCfg(type='bottle', d=12, c=768, s=2, gs=24, br=0.25, block_kwargs=dict(linear_out=True)),
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ByoBlockCfg(type='bottle', d=2, c=1536, s=2, gs=24, br=0.25, block_kwargs=dict(linear_out=True)),
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ByoBlockCfg(type='bottle', d=2, c=48, s=2, gs=16, br=3),
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ByoBlockCfg(type='bottle', d=6, c=96, s=2, gs=16, br=3),
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ByoBlockCfg(type='bottle', d=12, c=192, s=2, gs=16, br=3),
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ByoBlockCfg(type='bottle', d=2, c=288, s=2, gs=16, br=3),
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),
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stem_chs=32,
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stem_pool='',
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num_features=1792,
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downsample='',
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num_features=1536,
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act_layer='silu',
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attn_layer='se',
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attn_kwargs=dict(rd_ratio=0.25),
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block_kwargs=dict(bottle_in=True, linear_out=True),
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),
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regnetz_c=ByoModelCfg(
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blocks=(
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ByoBlockCfg(type='bottle', d=2, c=128, s=2, gs=16, br=0.5, block_kwargs=dict(linear_out=True)),
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ByoBlockCfg(type='bottle', d=6, c=512, s=2, gs=32, br=0.25, block_kwargs=dict(linear_out=True)),
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ByoBlockCfg(type='bottle', d=12, c=768, s=2, gs=32, br=0.25, block_kwargs=dict(linear_out=True)),
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ByoBlockCfg(type='bottle', d=2, c=1536, s=2, gs=64, br=0.25, block_kwargs=dict(linear_out=True)),
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ByoBlockCfg(type='bottle', d=2, c=48, s=2, gs=16, br=4),
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ByoBlockCfg(type='bottle', d=6, c=96, s=2, gs=16, br=4),
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ByoBlockCfg(type='bottle', d=12, c=192, s=2, gs=16, br=4),
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ByoBlockCfg(type='bottle', d=2, c=288, s=2, gs=16, br=4),
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),
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stem_chs=32,
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stem_pool='',
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num_features=1792,
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downsample='',
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num_features=1536,
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act_layer='silu',
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attn_layer='se',
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attn_kwargs=dict(rd_ratio=0.25),
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block_kwargs=dict(bottle_in=True, linear_out=True),
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),
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regnetz_d=ByoModelCfg(
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blocks=(
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ByoBlockCfg(type='bottle', d=3, c=256, s=1, gs=64, br=0.25, block_kwargs=dict(linear_out=True)),
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ByoBlockCfg(type='bottle', d=6, c=512, s=2, gs=64, br=0.25, block_kwargs=dict(linear_out=True)),
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ByoBlockCfg(type='bottle', d=12, c=768, s=2, gs=64, br=0.25, block_kwargs=dict(linear_out=True)),
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ByoBlockCfg(type='bottle', d=3, c=1536, s=2, gs=64, br=0.25, block_kwargs=dict(linear_out=True)),
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ByoBlockCfg(type='bottle', d=3, c=64, s=1, gs=32, br=4),
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ByoBlockCfg(type='bottle', d=6, c=128, s=2, gs=32, br=4),
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ByoBlockCfg(type='bottle', d=12, c=256, s=2, gs=32, br=4),
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ByoBlockCfg(type='bottle', d=3, c=384, s=2, gs=32, br=4),
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),
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stem_chs=128,
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stem_type='quad',
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stem_chs=64,
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stem_type='tiered',
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stem_pool='',
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downsample='',
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num_features=1792,
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act_layer='silu',
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attn_layer='se',
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attn_kwargs=dict(rd_ratio=0.25),
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block_kwargs=dict(bottle_in=True, linear_out=True),
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),
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)
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@ -802,11 +803,17 @@ class DownsampleAvg(nn.Module):
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return self.conv(self.pool(x))
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def create_downsample(downsample_type, layers: LayerFn, **kwargs):
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if downsample_type == 'avg':
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return DownsampleAvg(**kwargs)
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def create_shortcut(downsample_type, layers: LayerFn, in_chs, out_chs, stride, dilation, **kwargs):
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assert downsample_type in ('avg', 'conv1x1', '')
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if in_chs != out_chs or stride != 1 or dilation[0] != dilation[1]:
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if not downsample_type:
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return None # no shortcut
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elif downsample_type == 'avg':
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return DownsampleAvg(in_chs, out_chs, stride=stride, dilation=dilation[0], **kwargs)
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else:
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return layers.conv_norm_act(in_chs, out_chs, kernel_size=1, stride=stride, dilation=dilation[0], **kwargs)
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else:
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return layers.conv_norm_act(kwargs.pop('in_chs'), kwargs.pop('out_chs'), kernel_size=1, **kwargs)
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return nn.Identity() # identity shortcut
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class BasicBlock(nn.Module):
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@ -822,12 +829,9 @@ class BasicBlock(nn.Module):
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mid_chs = make_divisible(out_chs * bottle_ratio)
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groups = num_groups(group_size, mid_chs)
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if in_chs != out_chs or stride != 1 or dilation[0] != dilation[1]:
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self.shortcut = create_downsample(
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downsample, in_chs=in_chs, out_chs=out_chs, stride=stride, dilation=dilation[0],
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apply_act=False, layers=layers)
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else:
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self.shortcut = nn.Identity()
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self.shortcut = create_shortcut(
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downsample, in_chs=in_chs, out_chs=out_chs, stride=stride, dilation=dilation,
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apply_act=False, layers=layers)
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self.conv1_kxk = layers.conv_norm_act(in_chs, mid_chs, kernel_size, stride=stride, dilation=dilation[0])
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self.attn = nn.Identity() if attn_last or layers.attn is None else layers.attn(mid_chs)
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@ -838,23 +842,21 @@ class BasicBlock(nn.Module):
|
||||
self.act = nn.Identity() if linear_out else layers.act(inplace=True)
|
||||
|
||||
def init_weights(self, zero_init_last: bool = False):
|
||||
if zero_init_last:
|
||||
if zero_init_last and self.shortcut is not None:
|
||||
nn.init.zeros_(self.conv2_kxk.bn.weight)
|
||||
for attn in (self.attn, self.attn_last):
|
||||
if hasattr(attn, 'reset_parameters'):
|
||||
attn.reset_parameters()
|
||||
|
||||
def forward(self, x):
|
||||
shortcut = self.shortcut(x)
|
||||
|
||||
# residual path
|
||||
shortcut = x
|
||||
x = self.conv1_kxk(x)
|
||||
x = self.conv2_kxk(x)
|
||||
x = self.attn(x)
|
||||
x = self.drop_path(x)
|
||||
|
||||
x = self.act(x + shortcut)
|
||||
return x
|
||||
if self.shortcut is not None:
|
||||
x = x + self.shortcut(shortcut)
|
||||
return self.act(x)
|
||||
|
||||
|
||||
class BottleneckBlock(nn.Module):
|
||||
@ -862,24 +864,18 @@ class BottleneckBlock(nn.Module):
|
||||
"""
|
||||
|
||||
def __init__(self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), bottle_ratio=1., group_size=None,
|
||||
downsample='avg', attn_last=False, linear_out=False, extra_conv=False, layers: LayerFn = None,
|
||||
drop_block=None, drop_path_rate=0.):
|
||||
downsample='avg', attn_last=False, linear_out=False, extra_conv=False, bottle_in=False,
|
||||
layers: LayerFn = None, drop_block=None, drop_path_rate=0.):
|
||||
super(BottleneckBlock, self).__init__()
|
||||
layers = layers or LayerFn()
|
||||
mid_chs = make_divisible(out_chs * bottle_ratio)
|
||||
mid_chs = make_divisible((in_chs if bottle_in else out_chs) * bottle_ratio)
|
||||
groups = num_groups(group_size, mid_chs)
|
||||
|
||||
if in_chs != out_chs or stride != 1 or dilation[0] != dilation[1]:
|
||||
self.shortcut = create_downsample(
|
||||
downsample, in_chs=in_chs, out_chs=out_chs, stride=stride, dilation=dilation[0],
|
||||
apply_act=False, layers=layers)
|
||||
else:
|
||||
self.shortcut = nn.Identity()
|
||||
self.shortcut = create_shortcut(
|
||||
downsample, in_chs=in_chs, out_chs=out_chs, stride=stride, dilation=dilation,
|
||||
apply_act=False, layers=layers)
|
||||
|
||||
self.conv1_1x1 = layers.conv_norm_act(in_chs, mid_chs, 1)
|
||||
self.conv2_kxk = layers.conv_norm_act(
|
||||
mid_chs, mid_chs, kernel_size, stride=stride, dilation=dilation[0],
|
||||
groups=groups, drop_block=drop_block)
|
||||
self.conv2_kxk = layers.conv_norm_act(
|
||||
mid_chs, mid_chs, kernel_size, stride=stride, dilation=dilation[0],
|
||||
groups=groups, drop_block=drop_block)
|
||||
@ -895,15 +891,14 @@ class BottleneckBlock(nn.Module):
|
||||
self.act = nn.Identity() if linear_out else layers.act(inplace=True)
|
||||
|
||||
def init_weights(self, zero_init_last: bool = False):
|
||||
if zero_init_last:
|
||||
if zero_init_last and self.shortcut is not None:
|
||||
nn.init.zeros_(self.conv3_1x1.bn.weight)
|
||||
for attn in (self.attn, self.attn_last):
|
||||
if hasattr(attn, 'reset_parameters'):
|
||||
attn.reset_parameters()
|
||||
|
||||
def forward(self, x):
|
||||
shortcut = self.shortcut(x)
|
||||
|
||||
shortcut = x
|
||||
x = self.conv1_1x1(x)
|
||||
x = self.conv2_kxk(x)
|
||||
x = self.conv2b_kxk(x)
|
||||
@ -911,9 +906,9 @@ class BottleneckBlock(nn.Module):
|
||||
x = self.conv3_1x1(x)
|
||||
x = self.attn_last(x)
|
||||
x = self.drop_path(x)
|
||||
|
||||
x = self.act(x + shortcut)
|
||||
return x
|
||||
if self.shortcut is not None:
|
||||
x = x + self.shortcut(shortcut)
|
||||
return self.act(x)
|
||||
|
||||
|
||||
class DarkBlock(nn.Module):
|
||||
@ -935,12 +930,9 @@ class DarkBlock(nn.Module):
|
||||
mid_chs = make_divisible(out_chs * bottle_ratio)
|
||||
groups = num_groups(group_size, mid_chs)
|
||||
|
||||
if in_chs != out_chs or stride != 1 or dilation[0] != dilation[1]:
|
||||
self.shortcut = create_downsample(
|
||||
downsample, in_chs=in_chs, out_chs=out_chs, stride=stride, dilation=dilation[0],
|
||||
apply_act=False, layers=layers)
|
||||
else:
|
||||
self.shortcut = nn.Identity()
|
||||
self.shortcut = create_shortcut(
|
||||
downsample, in_chs=in_chs, out_chs=out_chs, stride=stride, dilation=dilation,
|
||||
apply_act=False, layers=layers)
|
||||
|
||||
self.conv1_1x1 = layers.conv_norm_act(in_chs, mid_chs, 1)
|
||||
self.attn = nn.Identity() if attn_last or layers.attn is None else layers.attn(mid_chs)
|
||||
@ -952,22 +944,22 @@ class DarkBlock(nn.Module):
|
||||
self.act = nn.Identity() if linear_out else layers.act(inplace=True)
|
||||
|
||||
def init_weights(self, zero_init_last: bool = False):
|
||||
if zero_init_last:
|
||||
if zero_init_last and self.shortcut is not None:
|
||||
nn.init.zeros_(self.conv2_kxk.bn.weight)
|
||||
for attn in (self.attn, self.attn_last):
|
||||
if hasattr(attn, 'reset_parameters'):
|
||||
attn.reset_parameters()
|
||||
|
||||
def forward(self, x):
|
||||
shortcut = self.shortcut(x)
|
||||
|
||||
shortcut = x
|
||||
x = self.conv1_1x1(x)
|
||||
x = self.attn(x)
|
||||
x = self.conv2_kxk(x)
|
||||
x = self.attn_last(x)
|
||||
x = self.drop_path(x)
|
||||
x = self.act(x + shortcut)
|
||||
return x
|
||||
if self.shortcut is not None:
|
||||
x = x + self.shortcut(shortcut)
|
||||
return self.act(x)
|
||||
|
||||
|
||||
class EdgeBlock(nn.Module):
|
||||
@ -988,12 +980,9 @@ class EdgeBlock(nn.Module):
|
||||
mid_chs = make_divisible(out_chs * bottle_ratio)
|
||||
groups = num_groups(group_size, mid_chs)
|
||||
|
||||
if in_chs != out_chs or stride != 1 or dilation[0] != dilation[1]:
|
||||
self.shortcut = create_downsample(
|
||||
downsample, in_chs=in_chs, out_chs=out_chs, stride=stride, dilation=dilation[0],
|
||||
apply_act=False, layers=layers)
|
||||
else:
|
||||
self.shortcut = nn.Identity()
|
||||
self.shortcut = create_shortcut(
|
||||
downsample, in_chs=in_chs, out_chs=out_chs, stride=stride, dilation=dilation,
|
||||
apply_act=False, layers=layers)
|
||||
|
||||
self.conv1_kxk = layers.conv_norm_act(
|
||||
in_chs, mid_chs, kernel_size, stride=stride, dilation=dilation[0],
|
||||
@ -1005,22 +994,22 @@ class EdgeBlock(nn.Module):
|
||||
self.act = nn.Identity() if linear_out else layers.act(inplace=True)
|
||||
|
||||
def init_weights(self, zero_init_last: bool = False):
|
||||
if zero_init_last:
|
||||
if zero_init_last and self.shortcut is not None:
|
||||
nn.init.zeros_(self.conv2_1x1.bn.weight)
|
||||
for attn in (self.attn, self.attn_last):
|
||||
if hasattr(attn, 'reset_parameters'):
|
||||
attn.reset_parameters()
|
||||
|
||||
def forward(self, x):
|
||||
shortcut = self.shortcut(x)
|
||||
|
||||
shortcut = x
|
||||
x = self.conv1_kxk(x)
|
||||
x = self.attn(x)
|
||||
x = self.conv2_1x1(x)
|
||||
x = self.attn_last(x)
|
||||
x = self.drop_path(x)
|
||||
x = self.act(x + shortcut)
|
||||
return x
|
||||
if self.shortcut is not None:
|
||||
x = x + self.shortcut(shortcut)
|
||||
return self.act(x)
|
||||
|
||||
|
||||
class RepVggBlock(nn.Module):
|
||||
@ -1065,8 +1054,7 @@ class RepVggBlock(nn.Module):
|
||||
x = self.drop_path(x) # not in the paper / official impl, experimental
|
||||
x = x + identity
|
||||
x = self.attn(x) # no attn in the paper / official impl, experimental
|
||||
x = self.act(x)
|
||||
return x
|
||||
return self.act(x)
|
||||
|
||||
|
||||
class SelfAttnBlock(nn.Module):
|
||||
@ -1074,19 +1062,16 @@ class SelfAttnBlock(nn.Module):
|
||||
"""
|
||||
|
||||
def __init__(self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), bottle_ratio=1., group_size=None,
|
||||
downsample='avg', extra_conv=False, linear_out=False, post_attn_na=True, feat_size=None,
|
||||
layers: LayerFn = None, drop_block=None, drop_path_rate=0.):
|
||||
downsample='avg', extra_conv=False, linear_out=False, bottle_in=False, post_attn_na=True,
|
||||
feat_size=None, layers: LayerFn = None, drop_block=None, drop_path_rate=0.):
|
||||
super(SelfAttnBlock, self).__init__()
|
||||
assert layers is not None
|
||||
mid_chs = make_divisible(out_chs * bottle_ratio)
|
||||
mid_chs = make_divisible((in_chs if bottle_in else out_chs) * bottle_ratio)
|
||||
groups = num_groups(group_size, mid_chs)
|
||||
|
||||
if in_chs != out_chs or stride != 1 or dilation[0] != dilation[1]:
|
||||
self.shortcut = create_downsample(
|
||||
downsample, in_chs=in_chs, out_chs=out_chs, stride=stride, dilation=dilation[0],
|
||||
apply_act=False, layers=layers)
|
||||
else:
|
||||
self.shortcut = nn.Identity()
|
||||
self.shortcut = create_shortcut(
|
||||
downsample, in_chs=in_chs, out_chs=out_chs, stride=stride, dilation=dilation,
|
||||
apply_act=False, layers=layers)
|
||||
|
||||
self.conv1_1x1 = layers.conv_norm_act(in_chs, mid_chs, 1)
|
||||
if extra_conv:
|
||||
@ -1105,7 +1090,7 @@ class SelfAttnBlock(nn.Module):
|
||||
self.act = nn.Identity() if linear_out else layers.act(inplace=True)
|
||||
|
||||
def init_weights(self, zero_init_last: bool = False):
|
||||
if zero_init_last:
|
||||
if zero_init_last and self.shortcut is not None:
|
||||
nn.init.zeros_(self.conv3_1x1.bn.weight)
|
||||
if hasattr(self.self_attn, 'reset_parameters'):
|
||||
self.self_attn.reset_parameters()
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user