diff --git a/timm/models/resnet.py b/timm/models/resnet.py index f012f797..7ba69d11 100644 --- a/timm/models/resnet.py +++ b/timm/models/resnet.py @@ -1,8 +1,6 @@ """PyTorch ResNet - This started as a copy of https://github.com/pytorch/vision 'resnet.py' (BSD-3-Clause) with additional dropout and dynamic global avg/max pool. - ResNeXt, SE-ResNeXt, SENet, and MXNet Gluon stem/downsample variants, tiered stems added by Ross Wightman Copyright 2020 Ross Wightman """ @@ -442,7 +440,7 @@ def drop_blocks(drop_block_rate=0.): def make_blocks( block_fn, channels, block_repeats, inplanes, reduce_first=1, output_stride=32, - down_kernel_size=1, avg_down=False, drop_block_rate=0., drop_path_rate=0., first_conv_stride=1, **kwargs): + down_kernel_size=1, avg_down=False, drop_block_rate=0., drop_path_rate=0., **kwargs): stages = [] feature_info = [] net_num_blocks = sum(block_repeats) @@ -451,7 +449,7 @@ def make_blocks( dilation = prev_dilation = 1 for stage_idx, (planes, num_blocks, db) in enumerate(zip(channels, block_repeats, drop_blocks(drop_block_rate))): stage_name = f'layer{stage_idx + 1}' # never liked this name, but weight compat requires it - stride = first_conv_stride if stage_idx == 0 else 2 + stride = 1 if stage_idx == 0 else 2 if net_stride >= output_stride: dilation *= stride stride = 1 @@ -494,7 +492,7 @@ class ResNet(nn.Module): This ResNet impl supports a number of stem and downsample options based on the v1c, v1d, v1e, and v1s variants included in the MXNet Gluon ResNetV1b model. The C and D variants are also discussed in the 'Bag of Tricks' paper: https://arxiv.org/pdf/1812.01187. The B variant is equivalent to torchvision default. - + ResNet variants (the same modifications can be used in SE/ResNeXt models as well): * normal, b - 7x7 stem, stem_width = 64, same as torchvision ResNet, NVIDIA ResNet 'v1.5', Gluon v1b * c - 3 layer deep 3x3 stem, stem_width = 32 (32, 32, 64) @@ -503,18 +501,18 @@ class ResNet(nn.Module): * s - 3 layer deep 3x3 stem, stem_width = 64 (64, 64, 128) * t - 3 layer deep 3x3 stem, stem width = 32 (24, 48, 64), average pool in downsample * tn - 3 layer deep 3x3 stem, stem width = 32 (24, 32, 64), average pool in downsample - + ResNeXt * normal - 7x7 stem, stem_width = 64, standard cardinality and base widths * same c,d, e, s variants as ResNet can be enabled - + SE-ResNeXt * normal - 7x7 stem, stem_width = 64 * same c, d, e, s variants as ResNet can be enabled - + SENet-154 - 3 layer deep 3x3 stem (same as v1c-v1s), stem_width = 64, cardinality=64, reduction by 2 on width of first bottleneck convolution, 3x3 downsample convs after first block - + Parameters ---------- block : Block @@ -558,12 +556,12 @@ class ResNet(nn.Module): cardinality=1, base_width=64, stem_width=64, stem_type='', 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, skip_stem_max_pool=False): + drop_block_rate=0., global_pool='avg', zero_init_last_bn=True, block_args=None, replace_stem_max_pool=False): block_args = block_args or dict() assert output_stride in (8, 16, 32) self.num_classes = num_classes self.drop_rate = drop_rate - self.skip_stem_max_pool = skip_stem_max_pool + self.replace_stem_max_pool = replace_stem_max_pool super(ResNet, self).__init__() # Stem @@ -588,8 +586,7 @@ class ResNet(nn.Module): self.feature_info = [dict(num_chs=inplanes, reduction=2, module='act1')] # Stem Pooling - if not self.skip_stem_max_pool: - first_conv_stride = 1 + if not self.replace_stem_max_pool: if aa_layer is not None: self.maxpool = nn.Sequential(*[ nn.MaxPool2d(kernel_size=3, stride=1, padding=1), @@ -597,8 +594,11 @@ class ResNet(nn.Module): else: self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) else: - self.maxpool = nn.Identity() - first_conv_stride = 2 + self.maxpool = nn.Sequential(*[ + nn.Conv2d(inplanes, inplanes, 3, stride=2, padding=1), + nn.BatchNorm2d(inplanes), + nn.ReLU() + ]) # Feature Blocks channels = [64, 128, 256, 512] @@ -606,7 +606,7 @@ class ResNet(nn.Module): block, channels, layers, inplanes, cardinality=cardinality, base_width=base_width, output_stride=output_stride, reduce_first=block_reduce_first, avg_down=avg_down, down_kernel_size=down_kernel_size, act_layer=act_layer, norm_layer=norm_layer, aa_layer=aa_layer, - drop_block_rate=drop_block_rate, drop_path_rate=drop_path_rate, first_conv_stride=first_conv_stride, **block_args) + drop_block_rate=drop_block_rate, drop_path_rate=drop_path_rate, **block_args) for stage in stage_modules: self.add_module(*stage) # layer1, layer2, etc self.feature_info.extend(stage_feature_info) @@ -1078,7 +1078,7 @@ def ecaresnet50d(pretrained=False, **kwargs): @register_model def resnetrs50(pretrained=False, **kwargs): model_args = dict( - block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', skip_stem_max_pool=True, + 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'), **kwargs) return _create_resnet('resnetrs50', pretrained, **model_args) @@ -1086,7 +1086,7 @@ def resnetrs50(pretrained=False, **kwargs): @register_model def resnetrs101(pretrained=False, **kwargs): model_args = dict( - block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', skip_stem_max_pool=True, + 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'), **kwargs) return _create_resnet('resnetrs101', pretrained, **model_args) @@ -1094,7 +1094,7 @@ def resnetrs101(pretrained=False, **kwargs): @register_model def resnetrs152(pretrained=False, **kwargs): model_args = dict( - block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', skip_stem_max_pool=True, + 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'), **kwargs) return _create_resnet('resnetrs152', pretrained, **model_args) @@ -1102,7 +1102,7 @@ def resnetrs152(pretrained=False, **kwargs): @register_model def resnetrs200(pretrained=False, **kwargs): model_args = dict( - block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep', skip_stem_max_pool=True, + 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'), **kwargs) return _create_resnet('resnetrs200', pretrained, **model_args) @@ -1110,7 +1110,7 @@ def resnetrs200(pretrained=False, **kwargs): @register_model def resnetrs270(pretrained=False, **kwargs): model_args = dict( - block=Bottleneck, layers=[4, 29, 53, 4], stem_width=32, stem_type='deep', skip_stem_max_pool=True, + 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'), **kwargs) return _create_resnet('resnetrs270', pretrained, **model_args) @@ -1119,7 +1119,7 @@ def resnetrs270(pretrained=False, **kwargs): @register_model def resnetrs350(pretrained=False, **kwargs): model_args = dict( - block=Bottleneck, layers=[4, 36, 72, 4], stem_width=32, stem_type='deep', skip_stem_max_pool=True, + 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'), **kwargs) return _create_resnet('resnetrs350', pretrained, **model_args) @@ -1127,7 +1127,7 @@ def resnetrs350(pretrained=False, **kwargs): @register_model def resnetrs420(pretrained=False, **kwargs): model_args = dict( - block=Bottleneck, layers=[4, 44, 87, 4], stem_width=32, stem_type='deep', skip_stem_max_pool=True, + 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'), **kwargs) return _create_resnet('resnetrs420', pretrained, **model_args) @@ -1373,4 +1373,4 @@ def senet154(pretrained=False, **kwargs): model_args = dict( block=Bottleneck, layers=[3, 8, 36, 3], cardinality=64, base_width=4, stem_type='deep', down_kernel_size=3, block_reduce_first=2, block_args=dict(attn_layer='se'), **kwargs) - return _create_resnet('senet154', pretrained, **model_args) + return _create_resnet('senet154', pretrained, **model_args) \ No newline at end of file