diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 00000000..4f2d1584 --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,2 @@ +include timm/models/pruned/*.txt + diff --git a/README.md b/README.md index e375e1bb..70e2a701 100644 --- a/README.md +++ b/README.md @@ -2,11 +2,14 @@ ## What's New +### May 3, 2020 +* Pruned EfficientNet B1, B2, and B3 (https://arxiv.org/abs/2002.08258) contributed by [Yonathan Aflalo](https://github.com/yoniaflalo) + ### May 1, 2020 * Merged a number of execellent contributions in the ResNet model family over the past month * BlurPool2D and resnetblur models initiated by [Chris Ha](https://github.com/VRandme), I trained resnetblur50 to 79.3. * TResNet models and SpaceToDepth, AntiAliasDownsampleLayer layers by [mrT23](https://github.com/mrT23) - * ecaresnet (50d, 101d, light) models and two pruned variants using pruning as per (https://arxiv.org/abs/2002.08258) by [yoniaflalo](https://github.com/yoniaflalo) + * ecaresnet (50d, 101d, light) models and two pruned variants using pruning as per (https://arxiv.org/abs/2002.08258) by [Yonathan Aflalo](https://github.com/yoniaflalo) * 200 pretrained models in total now with updated results csv in results folder ### April 5, 2020 diff --git a/results/results-imagenet-a.csv b/results/results-imagenet-a.csv index c260234f..9f54457c 100644 --- a/results/results-imagenet-a.csv +++ b/results/results-imagenet-a.csv @@ -66,11 +66,13 @@ efficientnet_b2,6.0933,93.9067,21.9333,78.0667,9.11,260,0.875,bicubic gluon_resnext101_32x4d,6.04,93.96,21.1333,78.8667,44.18,224,0.875,bicubic gluon_resnet101_v1d,5.92,94.08,19.9467,80.0533,44.57,224,0.875,bicubic gluon_seresnext50_32x4d,5.7867,94.2133,21.4267,78.5733,27.56,224,0.875,bicubic +efficientnet_b3_pruned,5.7333,94.2667,21.36,78.64,9.86,300,0.904,bicubic gluon_inception_v3,5.5067,94.4933,19.9467,80.0533,23.83,299,0.875,bicubic mixnet_xl,5.48,94.52,21.0933,78.9067,11.9,224,0.875,bicubic tresnet_m,5.44,94.56,19.96,80.04,31.39,224,0.875,bilinear gluon_resnet101_v1s,5.28,94.72,19.5467,80.4533,44.67,224,0.875,bicubic hrnet_w64,5.1333,94.8667,19.4533,80.5467,128.06,224,0.875,bilinear +efficientnet_b2_pruned,4.9467,95.0533,19.3467,80.6533,8.31,260,0.89,bicubic dpn107,4.88,95.12,17.6133,82.3867,86.92,224,0.875,bicubic gluon_resnet152_v1c,4.8667,95.1333,17.7733,82.2267,60.21,224,0.875,bicubic adv_inception_v3,4.7467,95.2533,17.5467,82.4533,23.83,299,0.875,bicubic @@ -116,6 +118,7 @@ seresnet101,3.2533,96.7467,15.4533,84.5467,49.33,224,0.875,bilinear dla60_res2next,3.04,96.96,14.4533,85.5467,17.33,224,0.875,bilinear gluon_resnet50_v1d,3.0133,96.9867,14.6267,85.3733,25.58,224,0.875,bicubic wide_resnet101_2,2.96,97.04,13.9467,86.0533,126.89,224,0.875,bilinear +efficientnet_b1_pruned,2.9333,97.0667,14.4133,85.5867,6.33,240,0.882,bicubic gluon_resnet50_v1s,2.92,97.08,13.12,86.88,25.68,224,0.875,bicubic tf_efficientnet_b1,2.8667,97.1333,13.5067,86.4933,7.79,240,0.882,bicubic res2net50_26w_6s,2.84,97.16,12.6,87.4,37.05,224,0.875,bilinear diff --git a/results/results-imagenet.csv b/results/results-imagenet.csv index 6ecdecb2..75310e45 100644 --- a/results/results-imagenet.csv +++ b/results/results-imagenet.csv @@ -48,6 +48,7 @@ gluon_resnet152_v1s,81.012,18.988,95.416,4.584,60.32,224,0.875,bicubic ssl_resnext101_32x4d,80.928,19.072,95.728,4.272,44.18,224,0.875,bilinear gluon_seresnext101_32x4d,80.902,19.098,95.294,4.706,48.96,224,0.875,bicubic gluon_seresnext101_64x4d,80.89,19.11,95.304,4.696,88.23,224,0.875,bicubic +efficientnet_b3_pruned,80.856,19.144,95.24,4.76,9.86,300,0.904,bicubic ecaresnet101d_pruned,80.808,19.192,95.628,4.372,24.88,224,0.875,bicubic tresnet_m,80.796,19.204,94.856,5.144,31.39,224,0.875,bilinear efficientnet_b2a,80.608,19.392,95.31,4.69,9.11,288,1,bicubic @@ -71,6 +72,7 @@ skresnext50_32x4d,80.15,19.85,94.644,5.356,27.48,224,0.875,bicubic tf_efficientnet_b2,80.09,19.91,94.906,5.094,9.11,260,0.89,bicubic dpn92,80.016,19.984,94.838,5.162,37.67,224,0.875,bicubic ens_adv_inception_resnet_v2,79.976,20.024,94.946,5.054,55.84,299,0.8975,bicubic +efficientnet_b2_pruned,79.918,20.082,94.848,5.152,8.31,260,0.89,bicubic gluon_resnet152_v1c,79.916,20.084,94.842,5.158,60.21,224,0.875,bicubic gluon_seresnext50_32x4d,79.912,20.088,94.818,5.182,27.56,224,0.875,bicubic dpn131,79.828,20.172,94.704,5.296,79.25,224,0.875,bicubic @@ -120,6 +122,7 @@ hrnet_w32,78.448,21.552,94.188,5.812,41.23,224,0.875,bilinear selecsls60b,78.418,21.582,94.166,5.834,32.77,224,0.875,bicubic seresnet101,78.396,21.604,94.258,5.742,49.33,224,0.875,bilinear resnet152,78.312,21.688,94.046,5.954,60.19,224,0.875,bilinear +efficientnet_b1_pruned,78.242,21.758,93.832,6.168,6.33,240,0.882,bicubic dla60x,78.242,21.758,94.022,5.978,17.65,224,0.875,bilinear res2next50,78.242,21.758,93.892,6.108,24.67,224,0.875,bilinear hrnet_w30,78.196,21.804,94.218,5.782,37.71,224,0.875,bilinear diff --git a/results/results-imagenetv2-matched-frequency.csv b/results/results-imagenetv2-matched-frequency.csv index 580ef030..55e5cd95 100644 --- a/results/results-imagenetv2-matched-frequency.csv +++ b/results/results-imagenetv2-matched-frequency.csv @@ -55,6 +55,7 @@ ecaresnet50d,69.83,30.17,89.37,10.63,25.58,224,0.875,bicubic gluon_resnext101_64x4d,69.69,30.31,88.26,11.74,83.46,224,0.875,bicubic ssl_resnext50_32x4d,69.69,30.31,89.42,10.58,25.03,224,0.875,bilinear tresnet_m,69.65,30.35,88,12,31.39,224,0.875,bilinear +efficientnet_b3_pruned,69.58,30.42,88.97,11.03,9.86,300,0.904,bicubic ens_adv_inception_resnet_v2,69.52,30.48,88.5,11.5,55.84,299,0.8975,bicubic efficientnet_b2a,69.49,30.51,88.68,11.32,9.11,288,1,bicubic inception_v4,69.35,30.65,88.78,11.22,42.68,299,0.875,bicubic @@ -81,6 +82,7 @@ ssl_resnet50,68.42,31.58,88.58,11.42,25.56,224,0.875,bilinear ecaresnet50d_pruned,68.39,31.61,88.37,11.63,19.94,224,0.875,bicubic skresnext50_32x4d,68.39,31.61,87.59,12.41,27.48,224,0.875,bicubic dla102x2,68.34,31.66,87.87,12.13,41.75,224,0.875,bilinear +efficientnet_b2_pruned,68.3,31.7,88.1,11.9,8.31,260,0.89,bicubic gluon_resnext50_32x4d,68.28,31.72,87.32,12.68,25.03,224,0.875,bicubic tf_efficientnet_lite3,68.23,31.77,87.72,12.28,8.2,300,0.904,bilinear tf_efficientnet_el,68.18,31.82,88.35,11.65,10.59,300,0.904,bicubic @@ -134,6 +136,7 @@ tf_efficientnet_cc_b0_8e,66.21,33.79,86.22,13.78,24.01,224,0.875,bicubic tv_resnext50_32x4d,66.18,33.82,86.04,13.96,25.03,224,0.875,bilinear res2net50_26w_4s,66.17,33.83,86.6,13.4,25.7,224,0.875,bilinear inception_v3,66.12,33.88,86.34,13.66,27.16,299,0.875,bicubic +efficientnet_b1_pruned,66.08,33.92,86.58,13.42,6.33,240,0.882,bicubic gluon_resnet50_v1b,66.04,33.96,86.27,13.73,25.56,224,0.875,bicubic res2net50_14w_8s,66.02,33.98,86.24,13.76,25.06,224,0.875,bilinear densenet161,65.85,34.15,86.46,13.54,28.68,224,0.875,bicubic diff --git a/results/results-sketch.csv b/results/results-sketch.csv index f9f3ddb9..078659b2 100644 --- a/results/results-sketch.csv +++ b/results/results-sketch.csv @@ -32,6 +32,7 @@ tf_efficientnet_b1_ns,34.1528,65.8472,55.4894,44.5106,7.79,240,0.882,bicubic tf_efficientnet_b4,34.0624,65.9376,54.216,45.784,19.34,380,0.922,bicubic ssl_resnext101_32x8d,34.0211,65.9789,55.5935,44.4065,88.79,224,0.875,bilinear tf_efficientnet_b6,34.0054,65.9946,54.5403,45.4597,43.04,528,0.942,bicubic +efficientnet_b3_pruned,33.9956,66.0044,54.1099,45.8901,9.86,300,0.904,bicubic tresnet_xl,33.2587,66.7413,52.2962,47.7038,78.44,224,0.875,bilinear tf_efficientnet_b3,32.8637,67.1363,52.9623,47.0377,12.23,300,0.904,bicubic inception_resnet_v2,32.736,67.264,50.6396,49.3604,55.84,299,0.8975,bicubic @@ -96,6 +97,7 @@ skresnet34,28.629,71.371,47.9573,52.0427,22.28,224,0.875,bicubic resnet152,28.5347,71.4653,47.1143,52.8857,60.19,224,0.875,bilinear hrnet_w48,28.4069,71.5931,47.5741,52.4259,77.47,224,0.875,bilinear gluon_resnext50_32x4d,28.3833,71.6167,45.3261,54.6739,25.03,224,0.875,bicubic +efficientnet_b2_pruned,28.3657,71.6343,47.0593,52.9407,8.31,260,0.89,bicubic tf_efficientnet_b0_ap,28.3499,71.6501,47.5309,52.4691,5.29,224,0.875,bicubic dla102x2,28.3185,71.6815,46.7567,53.2433,41.75,224,0.875,bilinear dla169,28.3106,71.6894,47.3992,52.6008,53.99,224,0.875,bilinear @@ -114,6 +116,7 @@ hrnet_w30,27.3851,72.6149,46.5425,53.4575,37.71,224,0.875,bilinear hrnet_w32,27.3772,72.6228,45.9903,54.0097,41.23,224,0.875,bilinear gluon_resnet50_v1s,27.3281,72.6719,45.2141,54.7859,25.68,224,0.875,bicubic densenet201,27.2613,72.7387,46.2241,53.7759,20.01,224,0.875,bicubic +efficientnet_b1_pruned,27.1945,72.8055,45.8724,54.1276,6.33,240,0.882,bicubic res2net50_26w_8s,27.0726,72.9274,44.432,55.568,48.4,224,0.875,bilinear dla102x,27.0235,72.9765,45.4951,54.5049,26.77,224,0.875,bilinear resnet101,26.9685,73.0315,45.2357,54.7643,44.55,224,0.875,bilinear diff --git a/setup.py b/setup.py index ce493e70..bc940810 100644 --- a/setup.py +++ b/setup.py @@ -41,6 +41,7 @@ setup( # Note that this is a string of words separated by whitespace, not a list. keywords='pytorch pretrained models efficientnet mobilenetv3 mnasnet', packages=find_packages(exclude=['convert']), + include_package_data=True, install_requires=['torch >= 1.0', 'torchvision'], python_requires='>=3.6', ) diff --git a/timm/models/efficientnet.py b/timm/models/efficientnet.py index c7a362c2..69bfad26 100644 --- a/timm/models/efficientnet.py +++ b/timm/models/efficientnet.py @@ -24,12 +24,14 @@ An implementation of EfficienNet that covers variety of related models with effi Hacked together by Ross Wightman """ -from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD from .efficientnet_builder import * from .feature_hooks import FeatureHooks -from .helpers import load_pretrained -from .layers import SelectAdaptivePool2d from .registry import register_model +from .helpers import load_pretrained, adapt_model_from_file +from .layers import SelectAdaptivePool2d +from timm.models.layers import create_conv2d +from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD + __all__ = ['EfficientNet'] @@ -97,6 +99,19 @@ default_cfgs = { 'efficientnet_lite3': _cfg(input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904), 'efficientnet_lite4': _cfg(input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922), + 'efficientnet_b1_pruned': _cfg( + url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45403/outputs/effnetb1_pruned_9ebb3fe6.pth', + input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882, mean=IMAGENET_INCEPTION_MEAN, + std=IMAGENET_INCEPTION_STD), + 'efficientnet_b2_pruned': _cfg( + url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45403/outputs/effnetb2_pruned_203f55bc.pth', + input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890, mean=IMAGENET_INCEPTION_MEAN, + std=IMAGENET_INCEPTION_STD), + 'efficientnet_b3_pruned': _cfg( + url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45403/outputs/effnetb3_pruned_5abcc29f.pth', + input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904, mean=IMAGENET_INCEPTION_MEAN, + std=IMAGENET_INCEPTION_STD), + 'tf_efficientnet_b0': _cfg(url=url_weight_dir + 'tf_efficientnet_b0_aa-827b6e33.pth', input_size=(3, 224, 224)), 'tf_efficientnet_b1': _cfg(url=url_weight_dir + 'tf_efficientnet_b1_aa-ea7a6ee0.pth', @@ -410,9 +425,11 @@ def _create_model(model_kwargs, default_cfg, pretrained=False): else: load_strict = True model_class = EfficientNet - + variant = model_kwargs.pop('variant', '') model = model_class(**model_kwargs) model.default_cfg = default_cfg + if '_pruned' in variant: + model = adapt_model_from_file(model, variant) if pretrained: load_pretrained( model, @@ -658,6 +675,7 @@ def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pre channel_multiplier=channel_multiplier, act_layer=Swish, norm_kwargs=resolve_bn_args(kwargs), + variant=variant, **kwargs, ) model = _create_model(model_kwargs, default_cfgs[variant], pretrained) @@ -1158,6 +1176,39 @@ def efficientnet_lite4(pretrained=False, **kwargs): return model +@register_model +def efficientnet_b1_pruned(pretrained=False, **kwargs): + """ EfficientNet-B1 Pruned. The pruning has been obtained using https://arxiv.org/pdf/2002.08258.pdf """ + kwargs['bn_eps'] = BN_EPS_TF_DEFAULT + kwargs['pad_type'] = 'same' + variant = 'efficientnet_b1_pruned' + model = _gen_efficientnet( + variant, channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs) + return model + + +@register_model +def efficientnet_b2_pruned(pretrained=False, **kwargs): + """ EfficientNet-B2 Pruned. The pruning has been obtained using https://arxiv.org/pdf/2002.08258.pdf """ + kwargs['bn_eps'] = BN_EPS_TF_DEFAULT + kwargs['pad_type'] = 'same' + model = _gen_efficientnet( + 'efficientnet_b2_pruned', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs) + return model + + +@register_model +def efficientnet_b3_pruned(pretrained=False, **kwargs): + """ EfficientNet-B3 Pruned. The pruning has been obtained using https://arxiv.org/pdf/2002.08258.pdf """ + kwargs['bn_eps'] = BN_EPS_TF_DEFAULT + kwargs['pad_type'] = 'same' + model = _gen_efficientnet( + 'efficientnet_b3_pruned', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs) + return model + + + + @register_model def tf_efficientnet_b0(pretrained=False, **kwargs): """ EfficientNet-B0. Tensorflow compatible variant """ diff --git a/timm/models/pruned/efficientnet_b1_pruned.txt b/timm/models/pruned/efficientnet_b1_pruned.txt new file mode 100644 index 00000000..0972b527 --- /dev/null +++ b/timm/models/pruned/efficientnet_b1_pruned.txt @@ -0,0 +1 @@ +conv_stem.weight:[32, 3, 3, 3]***bn1.weight:[32]***bn1.bias:[32]***bn1.running_mean:[32]***bn1.running_var:[32]***bn1.num_batches_tracked:[]***blocks.0.0.conv_dw.weight:[32, 1, 3, 3]***blocks.0.0.bn1.weight:[32]***blocks.0.0.bn1.bias:[32]***blocks.0.0.bn1.running_mean:[32]***blocks.0.0.bn1.running_var:[32]***blocks.0.0.bn1.num_batches_tracked:[]***blocks.0.0.se.conv_reduce.weight:[8, 32, 1, 1]***blocks.0.0.se.conv_reduce.bias:[8]***blocks.0.0.se.conv_expand.weight:[32, 8, 1, 1]***blocks.0.0.se.conv_expand.bias:[32]***blocks.0.0.conv_pw.weight:[16, 32, 1, 1]***blocks.0.0.bn2.weight:[16]***blocks.0.0.bn2.bias:[16]***blocks.0.0.bn2.running_mean:[16]***blocks.0.0.bn2.running_var:[16]***blocks.0.0.bn2.num_batches_tracked:[]***blocks.0.1.conv_dw.weight:[16, 1, 3, 3]***blocks.0.1.bn1.weight:[16]***blocks.0.1.bn1.bias:[16]***blocks.0.1.bn1.running_mean:[16]***blocks.0.1.bn1.running_var:[16]***blocks.0.1.bn1.num_batches_tracked:[]***blocks.0.1.se.conv_reduce.weight:[4, 16, 1, 1]***blocks.0.1.se.conv_reduce.bias:[4]***blocks.0.1.se.conv_expand.weight:[16, 4, 1, 1]***blocks.0.1.se.conv_expand.bias:[16]***blocks.0.1.conv_pw.weight:[16, 16, 1, 1]***blocks.0.1.bn2.weight:[16]***blocks.0.1.bn2.bias:[16]***blocks.0.1.bn2.running_mean:[16]***blocks.0.1.bn2.running_var:[16]***blocks.0.1.bn2.num_batches_tracked:[]***blocks.1.0.conv_pw.weight:[48, 16, 1, 1]***blocks.1.0.bn1.weight:[48]***blocks.1.0.bn1.bias:[48]***blocks.1.0.bn1.running_mean:[48]***blocks.1.0.bn1.running_var:[48]***blocks.1.0.bn1.num_batches_tracked:[]***blocks.1.0.conv_dw.weight:[48, 1, 3, 3]***blocks.1.0.bn2.weight:[48]***blocks.1.0.bn2.bias:[48]***blocks.1.0.bn2.running_mean:[48]***blocks.1.0.bn2.running_var:[48]***blocks.1.0.bn2.num_batches_tracked:[]***blocks.1.0.se.conv_reduce.weight:[4, 48, 1, 1]***blocks.1.0.se.conv_reduce.bias:[4]***blocks.1.0.se.conv_expand.weight:[48, 4, 1, 1]***blocks.1.0.se.conv_expand.bias:[48]***blocks.1.0.conv_pwl.weight:[12, 48, 1, 1]***blocks.1.0.bn3.weight:[12]***blocks.1.0.bn3.bias:[12]***blocks.1.0.bn3.running_mean:[12]***blocks.1.0.bn3.running_var:[12]***blocks.1.0.bn3.num_batches_tracked:[]***blocks.1.1.conv_pw.weight:[62, 12, 1, 1]***blocks.1.1.bn1.weight:[62]***blocks.1.1.bn1.bias:[62]***blocks.1.1.bn1.running_mean:[62]***blocks.1.1.bn1.running_var:[62]***blocks.1.1.bn1.num_batches_tracked:[]***blocks.1.1.conv_dw.weight:[62, 1, 3, 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