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222 lines
11 KiB
222 lines
11 KiB
from torchbench.image_classification import ImageNet
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from timm import create_model, list_models
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from timm.data import resolve_data_config, create_transform
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import os
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NUM_GPU = 1
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BATCH_SIZE = 256 * NUM_GPU
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def _attrib(paper_model_name='', paper_arxiv_id='', batch_size=BATCH_SIZE):
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return dict(
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paper_model_name=paper_model_name,
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paper_arxiv_id=paper_arxiv_id,
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batch_size=batch_size)
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model_map = dict(
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#adv_inception_v3=_attrib(paper_model_name='Adversarial Inception V3', paper_arxiv_id=),
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#densenet121=_attrib(paper_model_name=, paper_arxiv_id=), # same weights as torchvision
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#densenet161=_attrib(paper_model_name=, paper_arxiv_id=), # same weights as torchvision
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#densenet169=_attrib(paper_model_name=, paper_arxiv_id=), # same weights as torchvision
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#densenet201=_attrib(paper_model_name=, paper_arxiv_id=), # same weights as torchvision
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dpn68=_attrib(
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paper_model_name='DPN-68', paper_arxiv_id='1707.01629'),
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dpn68b=_attrib(
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paper_model_name='DPN-68b', paper_arxiv_id='1707.01629'),
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dpn92=_attrib(
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paper_model_name='DPN-92', paper_arxiv_id='1707.01629'),
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dpn98=_attrib(
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paper_model_name='DPN-98', paper_arxiv_id='1707.01629'),
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dpn107=_attrib(
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paper_model_name='DPN-107', paper_arxiv_id='1707.01629'),
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dpn131=_attrib(
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paper_model_name='DPN-131', paper_arxiv_id='1707.01629'),
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efficientnet_b0=_attrib(
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paper_model_name='EfficientNet-B0', paper_arxiv_id='1905.11946'),
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efficientnet_b1=_attrib(
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paper_model_name='EfficientNet-B1', paper_arxiv_id='1905.11946'),
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efficientnet_b2=_attrib(
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paper_model_name='EfficientNet-B2', paper_arxiv_id='1905.11946'),
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#ens_adv_inception_resnet_v2=_attrib(paper_model_name=, paper_arxiv_id=),
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fbnetc_100=_attrib(
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paper_model_name='FBNet-C', paper_arxiv_id='1812.03443'),
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gluon_inception_v3=_attrib(
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paper_model_name='Inception V3', paper_arxiv_id='1512.00567'),
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gluon_resnet18_v1b=_attrib(
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paper_model_name='ResNet-18', paper_arxiv_id='1812.01187'),
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gluon_resnet34_v1b=_attrib(
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paper_model_name='ResNet-34', paper_arxiv_id='1812.01187'),
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gluon_resnet50_v1b=_attrib(
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paper_model_name='ResNet-50', paper_arxiv_id='1812.01187'),
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gluon_resnet50_v1c=_attrib(
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paper_model_name='ResNet-50-C', paper_arxiv_id='1812.01187'),
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gluon_resnet50_v1d=_attrib(
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paper_model_name='ResNet-50-D', paper_arxiv_id='1812.01187'),
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gluon_resnet50_v1s=_attrib(
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paper_model_name='ResNet-50-S', paper_arxiv_id='1812.01187'),
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gluon_resnet101_v1b=_attrib(
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paper_model_name='ResNet-101', paper_arxiv_id='1812.01187'),
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gluon_resnet101_v1c=_attrib(
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paper_model_name='ResNet-101-C', paper_arxiv_id='1812.01187'),
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gluon_resnet101_v1d=_attrib(
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paper_model_name='ResNet-101-D', paper_arxiv_id='1812.01187'),
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gluon_resnet101_v1s=_attrib(
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paper_model_name='ResNet-101-S', paper_arxiv_id='1812.01187'),
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gluon_resnet152_v1b=_attrib(
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paper_model_name='ResNet-152', paper_arxiv_id='1812.01187'),
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gluon_resnet152_v1c=_attrib(
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paper_model_name='ResNet-152-C', paper_arxiv_id='1812.01187'),
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gluon_resnet152_v1d=_attrib(
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paper_model_name='ResNet-152-D', paper_arxiv_id='1812.01187'),
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gluon_resnet152_v1s=_attrib(
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paper_model_name='ResNet-152-S', paper_arxiv_id='1812.01187'),
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gluon_resnext50_32x4d=_attrib(
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paper_model_name='ResNeXt-50 32x4d', paper_arxiv_id='1812.01187'),
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gluon_resnext101_32x4d=_attrib(
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paper_model_name='ResNeXt-101 32x4d', paper_arxiv_id='1812.01187'),
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gluon_resnext101_64x4d=_attrib(
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paper_model_name='ResNeXt-101 64x4d', paper_arxiv_id='1812.01187'),
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gluon_senet154=_attrib(
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paper_model_name='SENet-154', paper_arxiv_id='1812.01187'),
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gluon_seresnext50_32x4d=_attrib(
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paper_model_name='SE-ResNeXt-50 32x4d', paper_arxiv_id='1812.01187'),
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gluon_seresnext101_32x4d=_attrib(
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paper_model_name='SE-ResNeXt-101 32x4d', paper_arxiv_id='1812.01187'),
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gluon_seresnext101_64x4d=_attrib(
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paper_model_name='SE-ResNeXt-101 64x4d', paper_arxiv_id='1812.01187'),
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gluon_xception65=_attrib(
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paper_model_name='Modified Aligned Xception', paper_arxiv_id='1802.02611', batch_size=BATCH_SIZE//2),
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ig_resnext101_32x8d=_attrib(
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paper_model_name='ResNeXt-101 32x8d', paper_arxiv_id='1805.00932'),
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ig_resnext101_32x16d=_attrib(
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paper_model_name='ResNeXt-101 32x16d', paper_arxiv_id='1805.00932'),
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ig_resnext101_32x32d=_attrib(
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paper_model_name='ResNeXt-101 32x32d', paper_arxiv_id='1805.00932', batch_size=BATCH_SIZE//2),
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ig_resnext101_32x48d=_attrib(
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paper_model_name='ResNeXt-101 32x48d', paper_arxiv_id='1805.00932', batch_size=BATCH_SIZE//4),
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inception_resnet_v2=_attrib(
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paper_model_name='Inception ResNet V2', paper_arxiv_id='1602.07261'),
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#inception_v3=dict(paper_model_name='Inception V3', paper_arxiv_id=), # same weights as torchvision
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inception_v4=_attrib(
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paper_model_name='Inception V4', paper_arxiv_id='1602.07261'),
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mixnet_l=_attrib(
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paper_model_name='MixNet-L', paper_arxiv_id='1907.09595'),
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mixnet_m=_attrib(
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paper_model_name='MixNet-M', paper_arxiv_id='1907.09595'),
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mixnet_s=_attrib(
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paper_model_name='MixNet-S', paper_arxiv_id='1907.09595'),
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mnasnet_100=_attrib(
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paper_model_name='MnasNet-B1', paper_arxiv_id='1807.11626'),
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mobilenetv3_100=_attrib(
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paper_model_name='MobileNet V3(1.0)', paper_arxiv_id='1905.02244'),
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nasnetalarge=_attrib(
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paper_model_name='NASNet-A Large', paper_arxiv_id='1707.07012', batch_size=BATCH_SIZE//4),
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pnasnet5large=_attrib(
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paper_model_name='PNASNet-5', paper_arxiv_id='1712.00559', batch_size=BATCH_SIZE//4),
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resnet18=_attrib(
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paper_model_name='ResNet-18', paper_arxiv_id='1812.01187'),
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resnet26=_attrib(
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paper_model_name='ResNet-26', paper_arxiv_id='1812.01187'),
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resnet26d=_attrib(
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paper_model_name='ResNet-26-D', paper_arxiv_id='1812.01187'),
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resnet34=_attrib(
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paper_model_name='ResNet-34', paper_arxiv_id='1812.01187'),
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resnet50=_attrib(
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paper_model_name='ResNet-50', paper_arxiv_id='1812.01187'),
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#resnet101=_attrib(paper_model_name=, paper_arxiv_id=), # same weights as torchvision
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#resnet152=_attrib(paper_model_name=, paper_arxiv_id=), # same weights as torchvision
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resnext50_32x4d=_attrib(
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paper_model_name='ResNeXt-50 32x4d', paper_arxiv_id='1812.01187'),
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resnext50d_32x4d=_attrib(
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paper_model_name='ResNeXt-50-D 32x4d', paper_arxiv_id='1812.01187'),
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#resnext101_32x8d=_attrib(paper_model_name=, paper_arxiv_id=), # same weights as torchvision
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semnasnet_100=_attrib(
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paper_model_name='MnasNet-A1', paper_arxiv_id='1807.11626'),
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senet154=_attrib(
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paper_model_name='SENet-154', paper_arxiv_id='1709.01507'),
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seresnet18=_attrib(
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paper_model_name='SE-ResNet-18', paper_arxiv_id='1709.01507'),
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seresnet34=_attrib(
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paper_model_name='SE-ResNet-34', paper_arxiv_id='1709.01507'),
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seresnet50=_attrib(
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paper_model_name='SE-ResNet-50', paper_arxiv_id='1709.01507'),
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seresnet101=_attrib(
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paper_model_name='SE-ResNet-101', paper_arxiv_id='1709.01507'),
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seresnet152=_attrib(
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paper_model_name='SE-ResNet-152', paper_arxiv_id='1709.01507'),
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seresnext26_32x4d=_attrib(
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paper_model_name='SE-ResNeXt-26 32x4d', paper_arxiv_id='1709.01507'),
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seresnext50_32x4d=_attrib(
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paper_model_name='SE-ResNeXt-50 32x4d', paper_arxiv_id='1709.01507'),
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seresnext101_32x4d=_attrib(
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paper_model_name='SE-ResNeXt-101 32x4d', paper_arxiv_id='1709.01507'),
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spnasnet_100=_attrib(
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paper_model_name='Single-Path NAS', paper_arxiv_id='1904.02877'),
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tf_efficientnet_b0=_attrib(
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paper_model_name='EfficientNet-B0', paper_arxiv_id='1905.11946'),
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tf_efficientnet_b1=_attrib(
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paper_model_name='EfficientNet-B1', paper_arxiv_id='1905.11946'),
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tf_efficientnet_b2=_attrib(
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paper_model_name='EfficientNet-B2', paper_arxiv_id='1905.11946'),
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tf_efficientnet_b3=_attrib(
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paper_model_name='EfficientNet-B3', paper_arxiv_id='1905.11946', batch_size=BATCH_SIZE//2),
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tf_efficientnet_b4=_attrib(
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paper_model_name='EfficientNet-B4', paper_arxiv_id='1905.11946', batch_size=BATCH_SIZE//2),
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tf_efficientnet_b5=_attrib(
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paper_model_name='EfficientNet-B5', paper_arxiv_id='1905.11946', batch_size=BATCH_SIZE//4),
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tf_efficientnet_b6=_attrib(
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paper_model_name='EfficientNet-B6', paper_arxiv_id='1905.11946', batch_size=BATCH_SIZE//8),
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tf_efficientnet_b7=_attrib(
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paper_model_name='EfficientNet-B7', paper_arxiv_id='1905.11946', batch_size=BATCH_SIZE//8),
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tf_efficientnet_es=_attrib(
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paper_model_name='EfficientNet-EdgeTPU-S', paper_arxiv_id='1905.11946'),
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tf_efficientnet_em=_attrib(
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paper_model_name='EfficientNet-EdgeTPU-M', paper_arxiv_id='1905.11946'),
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tf_efficientnet_el=_attrib(
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paper_model_name='EfficientNet-EdgeTPU-L', paper_arxiv_id='1905.11946', batch_size=BATCH_SIZE//2),
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tf_inception_v3=_attrib(
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paper_model_name='Inception V3', paper_arxiv_id='1512.00567'),
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tf_mixnet_l=_attrib(
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paper_model_name='MixNet-L', paper_arxiv_id='1907.09595'),
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tf_mixnet_m=_attrib(
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paper_model_name='MixNet-M', paper_arxiv_id='1907.09595'),
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tf_mixnet_s=_attrib(
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paper_model_name='MixNet-S', paper_arxiv_id='1907.09595'),
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#tv_resnet34=_attrib(paper_model_name=, paper_arxiv_id=), # same weights as torchvision
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#tv_resnet50=_attrib(paper_model_name=, paper_arxiv_id=), # same weights as torchvision
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#tv_resnext50_32x4d=_attrib(paper_model_name=, paper_arxiv_id=), # same weights as torchvision
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#wide_resnet50_2=_attrib(paper_model_name=, paper_arxiv_id=), # same weights as torchvision
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#wide_resnet101_2=_attrib(paper_model_name=, paper_arxiv_id=), # same weights as torchvision
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xception=_attrib(
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paper_model_name='Xception', paper_arxiv_id='1610.02357'),
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)
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model_names = list_models(pretrained=True)
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for model_name in model_names:
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if model_name not in model_map:
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print('Skipping %s' % model_name)
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continue
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# create model from name
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model = create_model(model_name, pretrained=True)
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param_count = sum([m.numel() for m in model.parameters()])
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print('Model %s created, param count: %d' % (model_name, param_count))
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# get appropriate transform for model's default pretrained config
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data_config = resolve_data_config(dict(), model=model, verbose=True)
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input_transform = create_transform(**data_config)
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# Run the benchmark
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ImageNet.benchmark(
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model=model,
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paper_model_name=model_map[model_name]['paper_model_name'],
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paper_arxiv_id=model_map[model_name]['paper_arxiv_id'],
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input_transform=input_transform,
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batch_size=model_map[model_name]['batch_size'],
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num_gpu=NUM_GPU,
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data_root=os.environ.get('IMAGENET_DIR', './imagenet')
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)
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