diff --git a/tests/test_models.py b/tests/test_models.py index 3da0f872..0d3fde76 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -15,16 +15,16 @@ if hasattr(torch._C, '_jit_set_profiling_executor'): torch._C._jit_set_profiling_mode(False) # transformer models don't support many of the spatial / feature based model functionalities -NON_STD_FILTERS = ['vit_*', 'tnt_*', 'pit_*', 'swin_*','coat_*'] +NON_STD_FILTERS = ['vit_*', 'tnt_*', 'pit_*', 'swin_*', 'coat_*'] NUM_NON_STD = len(NON_STD_FILTERS) # exclude models that cause specific test failures if 'GITHUB_ACTIONS' in os.environ: # and 'Linux' in platform.system(): # GitHub Linux runner is slower and hits memory limits sooner than MacOS, exclude bigger models EXCLUDE_FILTERS = [ - '*efficientnet_l2*', '*resnext101_32x48d', '*in21k', '*152x4_bitm', + '*efficientnet_l2*', '*resnext101_32x48d', '*in21k', '*152x4_bitm', '*101x3_bitm', '*nfnet_f3*', '*nfnet_f4*', '*nfnet_f5*', '*nfnet_f6*', '*nfnet_f7*', - '*resnetrs200*', '*resnetrs270*', '*resnetrs350*', '*resnetrs420*'] + NON_STD_FILTERS + '*resnetrs350*', '*resnetrs420*'] + NON_STD_FILTERS else: EXCLUDE_FILTERS = NON_STD_FILTERS diff --git a/timm/models/resnet.py b/timm/models/resnet.py index 377d2d97..5355d61d 100644 --- a/timm/models/resnet.py +++ b/timm/models/resnet.py @@ -7,6 +7,7 @@ ResNeXt, SE-ResNeXt, SENet, and MXNet Gluon stem/downsample variants, tiered ste Copyright 2020 Ross Wightman """ import math +from functools import partial import torch import torch.nn as nn @@ -14,7 +15,7 @@ import torch.nn.functional as F from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD from .helpers import build_model_with_cfg -from .layers import DropBlock2d, DropPath, AvgPool2dSame, BlurPool2d, create_attn, create_classifier +from .layers import DropBlock2d, DropPath, AvgPool2dSame, BlurPool2d, create_attn, get_attn, create_classifier from .registry import register_model __all__ = ['ResNet', 'BasicBlock', 'Bottleneck'] # model_registry will add each entrypoint fn to this @@ -240,18 +241,32 @@ default_cfgs = { # ResNet-RS models 'resnetrs50': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs50-7c9728e2.pth', + input_size=(3, 160, 160), pool_size=(4, 4), crop_pct=0.91, test_input_size=(3, 224, 224), interpolation='bicubic', first_conv='conv1.0'), 'resnetrs101': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs101-3e4bb55c.pth', + input_size=(3, 192, 192), pool_size=(6, 6), crop_pct=0.94, test_input_size=(3, 288, 288), interpolation='bicubic', first_conv='conv1.0'), 'resnetrs152': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs152-b1efe56d.pth', + input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, test_input_size=(3, 320, 320), interpolation='bicubic', first_conv='conv1.0'), 'resnetrs200': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs200-b455b791.pth', + input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, test_input_size=(3, 320, 320), interpolation='bicubic', first_conv='conv1.0'), 'resnetrs270': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs270-cafcfbc7.pth', + input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, test_input_size=(3, 320, 320), interpolation='bicubic', first_conv='conv1.0'), 'resnetrs350': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs350-06d9bfac.pth', + input_size=(3, 288, 288), pool_size=(9, 9), crop_pct=1.0, test_input_size=(3, 384, 384), interpolation='bicubic', first_conv='conv1.0'), 'resnetrs420': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs420-d26764a5.pth', + input_size=(3, 320, 320), pool_size=(10, 10), crop_pct=1.0, test_input_size=(3, 416, 416), interpolation='bicubic', first_conv='conv1.0'), } @@ -334,7 +349,7 @@ class Bottleneck(nn.Module): def __init__(self, inplanes, planes, stride=1, downsample=None, cardinality=1, base_width=64, reduce_first=1, dilation=1, first_dilation=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, - attn_layer=None, aa_layer=None, drop_block=None, drop_path=None, **kwargs): + attn_layer=None, aa_layer=None, drop_block=None, drop_path=None): super(Bottleneck, self).__init__() width = int(math.floor(planes * (base_width / 64)) * cardinality) @@ -357,7 +372,7 @@ class Bottleneck(nn.Module): self.conv3 = nn.Conv2d(width, outplanes, kernel_size=1, bias=False) self.bn3 = norm_layer(outplanes) - self.se = create_attn(attn_layer, outplanes, **kwargs) + self.se = create_attn(attn_layer, outplanes) self.act3 = act_layer(inplace=True) self.downsample = downsample @@ -558,15 +573,14 @@ class ResNet(nn.Module): """ def __init__(self, block, layers, num_classes=1000, in_chans=3, - cardinality=1, base_width=64, stem_width=64, stem_type='', + cardinality=1, base_width=64, stem_width=64, stem_type='', replace_stem_pool=False, output_stride=32, block_reduce_first=1, down_kernel_size=1, avg_down=False, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, aa_layer=None, drop_rate=0.0, drop_path_rate=0., - drop_block_rate=0., global_pool='avg', zero_init_last_bn=True, block_args=None, replace_stem_max_pool=False): + drop_block_rate=0., global_pool='avg', zero_init_last_bn=True, block_args=None): block_args = block_args or dict() assert output_stride in (8, 16, 32) self.num_classes = num_classes self.drop_rate = drop_rate - self.replace_stem_max_pool = replace_stem_max_pool super(ResNet, self).__init__() # Stem @@ -591,19 +605,20 @@ class ResNet(nn.Module): self.feature_info = [dict(num_chs=inplanes, reduction=2, module='act1')] # Stem Pooling - if not self.replace_stem_max_pool: + if replace_stem_pool: + self.maxpool = nn.Sequential(*filter(None, [ + nn.Conv2d(inplanes, inplanes, 3, stride=1 if aa_layer else 2, padding=1, bias=False), + aa_layer(channels=inplanes, stride=2) if aa_layer else None, + norm_layer(inplanes), + act_layer(inplace=True) + ])) + else: if aa_layer is not None: self.maxpool = nn.Sequential(*[ nn.MaxPool2d(kernel_size=3, stride=1, padding=1), aa_layer(channels=inplanes, stride=2)]) else: self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) - else: - self.maxpool = nn.Sequential(*[ - nn.Conv2d(inplanes, inplanes, 3, stride=2, padding=1, bias=False), - norm_layer(inplanes), - act_layer(inplace=True) - ]) # Feature Blocks channels = [64, 128, 256, 512] @@ -1091,58 +1106,93 @@ def ecaresnet50d(pretrained=False, **kwargs): @register_model def resnetrs50(pretrained=False, **kwargs): + """Constructs a ResNet-RS-50 model. + Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579 + Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs + """ + attn_layer = partial(get_attn('se'), reduction_ratio=0.25) model_args = dict( - block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', replace_stem_max_pool=True, - avg_down=True, block_args=dict(attn_layer='se', reduction_ratio=0.25), **kwargs) + block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', replace_stem_pool=True, + avg_down=True, block_args=dict(attn_layer=attn_layer), **kwargs) return _create_resnet('resnetrs50', pretrained, **model_args) @register_model def resnetrs101(pretrained=False, **kwargs): + """Constructs a ResNet-RS-101 model. + Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579 + Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs + """ + attn_layer = partial(get_attn('se'), reduction_ratio=0.25) model_args = dict( - block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', replace_stem_max_pool=True, - avg_down=True, block_args=dict(attn_layer='se', reduction_ratio=0.25), **kwargs) + block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', replace_stem_pool=True, + avg_down=True, block_args=dict(attn_layer=attn_layer), **kwargs) return _create_resnet('resnetrs101', pretrained, **model_args) @register_model def resnetrs152(pretrained=False, **kwargs): + """Constructs a ResNet-RS-152 model. + Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579 + Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs + """ + attn_layer = partial(get_attn('se'), reduction_ratio=0.25) model_args = dict( - block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', replace_stem_max_pool=True, - avg_down=True, block_args=dict(attn_layer='se', reduction_ratio=0.25), **kwargs) + block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', replace_stem_pool=True, + avg_down=True, block_args=dict(attn_layer=attn_layer), **kwargs) return _create_resnet('resnetrs152', pretrained, **model_args) @register_model def resnetrs200(pretrained=False, **kwargs): + """Constructs a ResNet-RS-200 model. + Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579 + Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs + """ + attn_layer = partial(get_attn('se'), reduction_ratio=0.25) model_args = dict( - block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep', replace_stem_max_pool=True, - avg_down=True, block_args=dict(attn_layer='se', reduction_ratio=0.25), **kwargs) + block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep', replace_stem_pool=True, + avg_down=True, block_args=dict(attn_layer=attn_layer), **kwargs) return _create_resnet('resnetrs200', pretrained, **model_args) @register_model def resnetrs270(pretrained=False, **kwargs): + """Constructs a ResNet-RS-270 model. + Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579 + Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs + """ + attn_layer = partial(get_attn('se'), reduction_ratio=0.25) model_args = dict( - block=Bottleneck, layers=[4, 29, 53, 4], stem_width=32, stem_type='deep', replace_stem_max_pool=True, - avg_down=True, block_args=dict(attn_layer='se', reduction_ratio=0.25), **kwargs) + block=Bottleneck, layers=[4, 29, 53, 4], stem_width=32, stem_type='deep', replace_stem_pool=True, + avg_down=True, block_args=dict(attn_layer=attn_layer), **kwargs) return _create_resnet('resnetrs270', pretrained, **model_args) @register_model def resnetrs350(pretrained=False, **kwargs): + """Constructs a ResNet-RS-350 model. + Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579 + Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs + """ + attn_layer = partial(get_attn('se'), reduction_ratio=0.25) model_args = dict( - block=Bottleneck, layers=[4, 36, 72, 4], stem_width=32, stem_type='deep', replace_stem_max_pool=True, - avg_down=True, block_args=dict(attn_layer='se', reduction_ratio=0.25), **kwargs) + block=Bottleneck, layers=[4, 36, 72, 4], stem_width=32, stem_type='deep', replace_stem_pool=True, + avg_down=True, block_args=dict(attn_layer=attn_layer), **kwargs) return _create_resnet('resnetrs350', pretrained, **model_args) @register_model def resnetrs420(pretrained=False, **kwargs): + """Constructs a ResNet-RS-420 model + Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579 + Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs + """ + attn_layer = partial(get_attn('se'), reduction_ratio=0.25) model_args = dict( - block=Bottleneck, layers=[4, 44, 87, 4], stem_width=32, stem_type='deep', replace_stem_max_pool=True, - avg_down=True, block_args=dict(attn_layer='se', reduction_ratio=0.25), **kwargs) + block=Bottleneck, layers=[4, 44, 87, 4], stem_width=32, stem_type='deep', replace_stem_pool=True, + avg_down=True, block_args=dict(attn_layer=attn_layer), **kwargs) return _create_resnet('resnetrs420', pretrained, **model_args)