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539 lines
33 KiB
539 lines
33 KiB
import torch
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from sotabencheval.image_classification import ImageNetEvaluator
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from sotabencheval.utils import is_server
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from timm import create_model
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from timm.data import resolve_data_config, create_loader, DatasetTar
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from timm.models import apply_test_time_pool
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from tqdm import tqdm
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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 _entry(model_name, paper_model_name, paper_arxiv_id, batch_size=BATCH_SIZE,
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ttp=False, args=dict(), model_desc=None):
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return dict(
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model=model_name,
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model_description=model_desc,
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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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ttp=ttp,
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args=args)
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# NOTE For any original PyTorch models, I'll remove from this list when you add to sotabench to
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# avoid overlap and confusion. Please contact me.
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model_list = [
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## Weights ported by myself from other frameworks or trained myself in PyTorch
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_entry('adv_inception_v3', 'Adversarial Inception V3', '1611.01236',
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model_desc='Ported from official Tensorflow weights'),
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_entry('ens_adv_inception_resnet_v2', 'Ensemble Adversarial Inception V3', '1705.07204',
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model_desc='Ported from official Tensorflow weights'),
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_entry('dpn68', 'DPN-68 (224x224)', '1707.01629'),
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_entry('dpn68b', 'DPN-68b (224x224)', '1707.01629'),
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_entry('dpn92', 'DPN-92 (224x224)', '1707.01629'),
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_entry('dpn98', 'DPN-98 (224x224)', '1707.01629'),
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_entry('dpn107', 'DPN-107 (224x224)', '1707.01629'),
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_entry('dpn131', 'DPN-131 (224x224)', '1707.01629'),
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_entry('dpn68', 'DPN-68 (320x320, Mean-Max Pooling)', '1707.01629', ttp=True, args=dict(img_size=320)),
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_entry('dpn68b', 'DPN-68b (320x320, Mean-Max Pooling)', '1707.01629', ttp=True, args=dict(img_size=320)),
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_entry('dpn92', 'DPN-92 (320x320, Mean-Max Pooling)', '1707.01629',
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ttp=True, args=dict(img_size=320), batch_size=BATCH_SIZE//2),
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_entry('dpn98', 'DPN-98 (320x320, Mean-Max Pooling)', '1707.01629',
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ttp=True, args=dict(img_size=320), batch_size=BATCH_SIZE//2),
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_entry('dpn107', 'DPN-107 (320x320, Mean-Max Pooling)', '1707.01629',
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ttp=True, args=dict(img_size=320), batch_size=BATCH_SIZE//4),
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_entry('dpn131', 'DPN-131 (320x320, Mean-Max Pooling)', '1707.01629',
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ttp=True, args=dict(img_size=320), batch_size=BATCH_SIZE//4),
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_entry('efficientnet_b0', 'EfficientNet-B0', '1905.11946'),
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_entry('efficientnet_b1', 'EfficientNet-B1', '1905.11946'),
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_entry('efficientnet_b2', 'EfficientNet-B2', '1905.11946',
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model_desc='Trained from scratch in PyTorch w/ RandAugment'),
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_entry('efficientnet_b2a', 'EfficientNet-B2 (288x288, 1.0 crop)', '1905.11946',
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model_desc='Trained from scratch in PyTorch w/ RandAugment'),
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_entry('efficientnet_b3', 'EfficientNet-B3', '1905.11946',
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model_desc='Trained from scratch in PyTorch w/ RandAugment'),
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_entry('efficientnet_b3a', 'EfficientNet-B3 (320x320, 1.0 crop)', '1905.11946',
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model_desc='Trained from scratch in PyTorch w/ RandAugment'),
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_entry('efficientnet_es', 'EfficientNet-EdgeTPU-S', '1905.11946',
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model_desc='Trained from scratch in PyTorch w/ RandAugment'),
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_entry('efficientnet_em', 'EfficientNet-EdgeTPU-M', '1905.11946',
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model_desc='Trained from scratch in PyTorch w/ RandAugment'),
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_entry('gluon_inception_v3', 'Inception V3', '1512.00567', model_desc='Ported from GluonCV Model Zoo'),
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_entry('gluon_resnet18_v1b', 'ResNet-18', '1812.01187', model_desc='Ported from GluonCV Model Zoo'),
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_entry('gluon_resnet34_v1b', 'ResNet-34', '1812.01187', model_desc='Ported from GluonCV Model Zoo'),
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_entry('gluon_resnet50_v1b', 'ResNet-50', '1812.01187', model_desc='Ported from GluonCV Model Zoo'),
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_entry('gluon_resnet50_v1c', 'ResNet-50-C', '1812.01187', model_desc='Ported from GluonCV Model Zoo'),
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_entry('gluon_resnet50_v1d', 'ResNet-50-D', '1812.01187', model_desc='Ported from GluonCV Model Zoo'),
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_entry('gluon_resnet50_v1s', 'ResNet-50-S', '1812.01187', model_desc='Ported from GluonCV Model Zoo'),
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_entry('gluon_resnet101_v1b', 'ResNet-101', '1812.01187', model_desc='Ported from GluonCV Model Zoo'),
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_entry('gluon_resnet101_v1c', 'ResNet-101-C', '1812.01187', model_desc='Ported from GluonCV Model Zoo'),
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_entry('gluon_resnet101_v1d', 'ResNet-101-D', '1812.01187', model_desc='Ported from GluonCV Model Zoo'),
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_entry('gluon_resnet101_v1s', 'ResNet-101-S', '1812.01187', model_desc='Ported from GluonCV Model Zoo'),
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_entry('gluon_resnet152_v1b', 'ResNet-152', '1812.01187', model_desc='Ported from GluonCV Model Zoo'),
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_entry('gluon_resnet152_v1c', 'ResNet-152-C', '1812.01187', model_desc='Ported from GluonCV Model Zoo'),
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_entry('gluon_resnet152_v1d', 'ResNet-152-D', '1812.01187', model_desc='Ported from GluonCV Model Zoo'),
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_entry('gluon_resnet152_v1s', 'ResNet-152-S', '1812.01187', model_desc='Ported from GluonCV Model Zoo'),
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_entry('gluon_resnext50_32x4d', 'ResNeXt-50 32x4d', '1812.01187', model_desc='Ported from GluonCV Model Zoo'),
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_entry('gluon_resnext101_32x4d', 'ResNeXt-101 32x4d', '1812.01187', model_desc='Ported from GluonCV Model Zoo'),
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_entry('gluon_resnext101_64x4d', 'ResNeXt-101 64x4d', '1812.01187', model_desc='Ported from GluonCV Model Zoo'),
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_entry('gluon_senet154', 'SENet-154', '1812.01187', model_desc='Ported from GluonCV Model Zoo'),
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_entry('gluon_seresnext50_32x4d', 'SE-ResNeXt-50 32x4d', '1812.01187', model_desc='Ported from GluonCV Model Zoo'),
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_entry('gluon_seresnext101_32x4d', 'SE-ResNeXt-101 32x4d', '1812.01187', model_desc='Ported from GluonCV Model Zoo'),
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_entry('gluon_seresnext101_64x4d', 'SE-ResNeXt-101 64x4d', '1812.01187', model_desc='Ported from GluonCV Model Zoo'),
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_entry('gluon_xception65', 'Modified Aligned Xception', '1802.02611', batch_size=BATCH_SIZE//2,
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model_desc='Ported from GluonCV Model Zoo'),
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_entry('mixnet_xl', 'MixNet-XL', '1907.09595', model_desc="My own scaling beyond paper's MixNet Large"),
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_entry('mixnet_l', 'MixNet-L', '1907.09595'),
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_entry('mixnet_m', 'MixNet-M', '1907.09595'),
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_entry('mixnet_s', 'MixNet-S', '1907.09595'),
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_entry('fbnetc_100', 'FBNet-C', '1812.03443',
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model_desc='Trained in PyTorch with RMSProp, exponential LR decay'),
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_entry('mnasnet_100', 'MnasNet-B1', '1807.11626'),
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_entry('semnasnet_100', 'MnasNet-A1', '1807.11626'),
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_entry('spnasnet_100', 'Single-Path NAS', '1904.02877',
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model_desc='Trained in PyTorch with SGD, cosine LR decay'),
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_entry('mobilenetv3_large_100', 'MobileNet V3-Large 1.0', '1905.02244',
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model_desc='Trained in PyTorch with RMSProp, exponential LR decay, and hyper-params matching '
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'paper as closely as possible.'),
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_entry('resnet18', 'ResNet-18', '1812.01187'),
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_entry('resnet26', 'ResNet-26', '1812.01187', model_desc='Block cfg of ResNet-34 w/ Bottleneck'),
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_entry('resnet26d', 'ResNet-26-D', '1812.01187',
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model_desc='Block cfg of ResNet-34 w/ Bottleneck, deep stem, and avg-pool in downsample layers.'),
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_entry('resnet34', 'ResNet-34', '1812.01187'),
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_entry('resnet50', 'ResNet-50', '1812.01187', model_desc='Trained with AugMix + JSD loss'),
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_entry('resnet50', 'ResNet-50 (288x288 Mean-Max Pooling)', '1812.01187',
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ttp=True, args=dict(img_size=288),
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model_desc='Trained with AugMix + JSD loss'),
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_entry('resnext50_32x4d', 'ResNeXt-50 32x4d', '1812.01187'),
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_entry('resnext50d_32x4d', 'ResNeXt-50-D 32x4d', '1812.01187',
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model_desc="'D' variant (3x3 deep stem w/ avg-pool downscale). Trained with "
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"SGD w/ cosine LR decay, random-erasing (gaussian per-pixel noise) and label-smoothing"),
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_entry('wide_resnet50_2', 'Wide-ResNet-50', '1605.07146'),
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_entry('seresnet50', 'SE-ResNet-50', '1709.01507'),
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_entry('seresnext26d_32x4d', 'SE-ResNeXt-26-D 32x4d', '1812.01187',
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model_desc='Block cfg of SE-ResNeXt-34 w/ Bottleneck, deep stem, and avg-pool in downsample layers.'),
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_entry('seresnext26t_32x4d', 'SE-ResNeXt-26-T 32x4d', '1812.01187',
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model_desc='Block cfg of SE-ResNeXt-34 w/ Bottleneck, deep tiered stem, and avg-pool in downsample layers.'),
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_entry('seresnext50_32x4d', 'SE-ResNeXt-50 32x4d', '1709.01507'),
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_entry('skresnet18', 'SK-ResNet-18', '1903.06586'),
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_entry('skresnet34', 'SK-ResNet-34', '1903.06586'),
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_entry('skresnext50_32x4d', 'SKNet-50', '1903.06586'),
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_entry('ecaresnetlight', 'ECA-ResNet-Light', '1910.03151',
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model_desc='A tweaked ResNet50d with ECA attn.'),
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_entry('ecaresnet50d', 'ECA-ResNet-50d', '1910.03151',
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model_desc='A ResNet50d with ECA attn'),
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_entry('ecaresnet101d', 'ECA-ResNet-101d', '1910.03151',
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model_desc='A ResNet101d with ECA attn'),
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_entry('resnetblur50', 'ResNet-Blur-50', '1904.11486'),
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_entry('densenet121', 'DenseNet-121', '1608.06993'),
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_entry('densenetblur121d', 'DenseNet-Blur-121D', '1904.11486',
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model_desc='DenseNet with blur pooling and deep stem'),
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_entry('ese_vovnet19b_dw', 'VoVNet-19-DW-V2', '1911.06667'),
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_entry('ese_vovnet39b', 'VoVNet-39-V2', '1911.06667'),
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_entry('cspresnet50', 'CSPResNet-50', '1911.11929'),
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_entry('cspresnext50', 'CSPResNeXt-50', '1911.11929'),
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_entry('cspdarknet53', 'CSPDarkNet-53', '1911.11929'),
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_entry('tf_efficientnet_b0', 'EfficientNet-B0 (AutoAugment)', '1905.11946',
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_b1', 'EfficientNet-B1 (AutoAugment)', '1905.11946',
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_b2', 'EfficientNet-B2 (AutoAugment)', '1905.11946',
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_b3', 'EfficientNet-B3 (AutoAugment)', '1905.11946', batch_size=BATCH_SIZE//2,
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_b4', 'EfficientNet-B4 (AutoAugment)', '1905.11946', batch_size=BATCH_SIZE//2,
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_b5', 'EfficientNet-B5 (RandAugment)', '1905.11946', batch_size=BATCH_SIZE//4,
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_b6', 'EfficientNet-B6 (AutoAugment)', '1905.11946', batch_size=BATCH_SIZE//8,
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_b7', 'EfficientNet-B7 (RandAugment)', '1905.11946', batch_size=BATCH_SIZE//8,
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_b8', 'EfficientNet-B8 (RandAugment)', '1905.11946', batch_size=BATCH_SIZE // 8,
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_b0_ap', 'EfficientNet-B0 (AdvProp)', '1911.09665',
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_b1_ap', 'EfficientNet-B1 (AdvProp)', '1911.09665',
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_b2_ap', 'EfficientNet-B2 (AdvProp)', '1911.09665',
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_b3_ap', 'EfficientNet-B3 (AdvProp)', '1911.09665', batch_size=BATCH_SIZE // 2,
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_b4_ap', 'EfficientNet-B4 (AdvProp)', '1911.09665', batch_size=BATCH_SIZE // 2,
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_b5_ap', 'EfficientNet-B5 (AdvProp)', '1911.09665', batch_size=BATCH_SIZE // 4,
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_b6_ap', 'EfficientNet-B6 (AdvProp)', '1911.09665', batch_size=BATCH_SIZE // 8,
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_b7_ap', 'EfficientNet-B7 (AdvProp)', '1911.09665', batch_size=BATCH_SIZE // 8,
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_b8_ap', 'EfficientNet-B8 (AdvProp)', '1911.09665', batch_size=BATCH_SIZE // 8,
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_b0_ns', 'EfficientNet-B0 (NoisyStudent)', '1911.04252',
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_b1_ns', 'EfficientNet-B1 (NoisyStudent)', '1911.04252',
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_b2_ns', 'EfficientNet-B2 (NoisyStudent)', '1911.04252',
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_b3_ns', 'EfficientNet-B3 (NoisyStudent)', '1911.04252', batch_size=BATCH_SIZE // 2,
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_b4_ns', 'EfficientNet-B4 (NoisyStudent)', '1911.04252', batch_size=BATCH_SIZE // 2,
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_b5_ns', 'EfficientNet-B5 (NoisyStudent)', '1911.04252', batch_size=BATCH_SIZE // 4,
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_b6_ns', 'EfficientNet-B6 (NoisyStudent)', '1911.04252', batch_size=BATCH_SIZE // 8,
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_b7_ns', 'EfficientNet-B7 (NoisyStudent)', '1911.04252', batch_size=BATCH_SIZE // 8,
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_l2_ns_475', 'EfficientNet-L2 475 (NoisyStudent)', '1911.04252', batch_size=BATCH_SIZE // 16,
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_l2_ns', 'EfficientNet-L2 (NoisyStudent)', '1911.04252', batch_size=BATCH_SIZE // 64,
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_cc_b0_4e', 'EfficientNet-CondConv-B0 4 experts', '1904.04971',
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_cc_b0_8e', 'EfficientNet-CondConv-B0 8 experts', '1904.04971',
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_cc_b1_8e', 'EfficientNet-CondConv-B1 8 experts', '1904.04971',
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_es', 'EfficientNet-EdgeTPU-S', '1905.11946',
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_em', 'EfficientNet-EdgeTPU-M', '1905.11946',
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_el', 'EfficientNet-EdgeTPU-L', '1905.11946', batch_size=BATCH_SIZE//2,
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_lite0', 'EfficientNet-Lite0', '1905.11946',
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_lite1', 'EfficientNet-Lite1', '1905.11946',
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_lite2', 'EfficientNet-Lite2', '1905.11946',
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_lite3', 'EfficientNet-Lite3', '1905.11946', batch_size=BATCH_SIZE // 2,
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_efficientnet_lite4', 'EfficientNet-Lite4', '1905.11946', batch_size=BATCH_SIZE // 2,
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_inception_v3', 'Inception V3', '1512.00567', model_desc='Ported from official Tensorflow weights'),
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_entry('tf_mixnet_l', 'MixNet-L', '1907.09595', model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_mixnet_m', 'MixNet-M', '1907.09595', model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_mixnet_s', 'MixNet-S', '1907.09595', model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_mobilenetv3_large_100', 'MobileNet V3-Large 1.0', '1905.02244',
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_mobilenetv3_large_075', 'MobileNet V3-Large 0.75', '1905.02244',
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_mobilenetv3_large_minimal_100', 'MobileNet V3-Large Minimal 1.0', '1905.02244',
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_mobilenetv3_small_100', 'MobileNet V3-Small 1.0', '1905.02244',
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_mobilenetv3_small_075', 'MobileNet V3-Small 0.75', '1905.02244',
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model_desc='Ported from official Google AI Tensorflow weights'),
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_entry('tf_mobilenetv3_small_minimal_100', 'MobileNet V3-Small Minimal 1.0', '1905.02244',
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model_desc='Ported from official Google AI Tensorflow weights'),
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## Cadene ported weights (to remove if Cadene adds sotabench)
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_entry('inception_resnet_v2', 'Inception ResNet V2', '1602.07261'),
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_entry('inception_v4', 'Inception V4', '1602.07261'),
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_entry('nasnetalarge', 'NASNet-A Large', '1707.07012', batch_size=BATCH_SIZE // 4),
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_entry('pnasnet5large', 'PNASNet-5', '1712.00559', batch_size=BATCH_SIZE // 4),
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_entry('xception', 'Xception', '1610.02357', batch_size=BATCH_SIZE//2),
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_entry('legacy_seresnet18', 'SE-ResNet-18', '1709.01507'),
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_entry('legacy_seresnet34', 'SE-ResNet-34', '1709.01507'),
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_entry('legacy_seresnet50', 'SE-ResNet-50', '1709.01507'),
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_entry('legacy_seresnet101', 'SE-ResNet-101', '1709.01507'),
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_entry('legacy_seresnet152', 'SE-ResNet-152', '1709.01507'),
|
|
_entry('legacy_seresnext26_32x4d', 'SE-ResNeXt-26 32x4d', '1709.01507',
|
|
model_desc='Block cfg of SE-ResNeXt-34 w/ Bottleneck'),
|
|
_entry('legacy_seresnext50_32x4d', 'SE-ResNeXt-50 32x4d', '1709.01507'),
|
|
_entry('legacy_seresnext101_32x4d', 'SE-ResNeXt-101 32x4d', '1709.01507'),
|
|
_entry('legacy_senet154', 'SENet-154', '1709.01507'),
|
|
|
|
## Torchvision weights
|
|
# _entry('densenet121'),
|
|
# _entry('densenet161'),
|
|
# _entry('densenet169'),
|
|
# _entry('densenet201'),
|
|
# _entry('inception_v3', paper_model_name='Inception V3', ),
|
|
# _entry('tv_resnet34', , ),
|
|
# _entry('tv_resnet50', , ),
|
|
# _entry('resnet101', , ),
|
|
# _entry('resnet152', , ),
|
|
# _entry('tv_resnext50_32x4d', , ),
|
|
# _entry('resnext101_32x8d', ),
|
|
# _entry('wide_resnet50_2' , ),
|
|
# _entry('wide_resnet101_2', , ),
|
|
|
|
## Facebook WSL weights
|
|
_entry('ig_resnext101_32x8d', 'ResNeXt-101 32x8d', '1805.00932',
|
|
model_desc='Weakly-Supervised pre-training on 1B Instagram hashtag dataset by Facebook Research'),
|
|
_entry('ig_resnext101_32x16d', 'ResNeXt-101 32x16d', '1805.00932',
|
|
model_desc='Weakly-Supervised pre-training on 1B Instagram hashtag dataset by Facebook Research'),
|
|
_entry('ig_resnext101_32x32d', 'ResNeXt-101 32x32d', '1805.00932', batch_size=BATCH_SIZE // 2,
|
|
model_desc='Weakly-Supervised pre-training on 1B Instagram hashtag dataset by Facebook Research'),
|
|
_entry('ig_resnext101_32x48d', 'ResNeXt-101 32x48d', '1805.00932', batch_size=BATCH_SIZE // 4,
|
|
model_desc='Weakly-Supervised pre-training on 1B Instagram hashtag dataset by Facebook Research'),
|
|
|
|
_entry('ig_resnext101_32x8d', 'ResNeXt-101 32x8d (288x288 Mean-Max Pooling)', '1805.00932',
|
|
ttp=True, args=dict(img_size=288),
|
|
model_desc='Weakly-Supervised pre-training on 1B Instagram hashtag dataset by Facebook Research'),
|
|
_entry('ig_resnext101_32x16d', 'ResNeXt-101 32x16d (288x288 Mean-Max Pooling)', '1805.00932',
|
|
ttp=True, args=dict(img_size=288), batch_size=BATCH_SIZE // 2,
|
|
model_desc='Weakly-Supervised pre-training on 1B Instagram hashtag dataset by Facebook Research'),
|
|
_entry('ig_resnext101_32x32d', 'ResNeXt-101 32x32d (288x288 Mean-Max Pooling)', '1805.00932',
|
|
ttp=True, args=dict(img_size=288), batch_size=BATCH_SIZE // 4,
|
|
model_desc='Weakly-Supervised pre-training on 1B Instagram hashtag dataset by Facebook Research'),
|
|
_entry('ig_resnext101_32x48d', 'ResNeXt-101 32x48d (288x288 Mean-Max Pooling)', '1805.00932',
|
|
ttp=True, args=dict(img_size=288), batch_size=BATCH_SIZE // 8,
|
|
model_desc='Weakly-Supervised pre-training on 1B Instagram hashtag dataset by Facebook Research'),
|
|
|
|
## Facebook SSL weights
|
|
_entry('ssl_resnet18', 'ResNet-18', '1905.00546',
|
|
model_desc='Semi-Supervised pre-training on YFCC100M dataset by Facebook Research'),
|
|
_entry('ssl_resnet50', 'ResNet-50', '1905.00546',
|
|
model_desc='Semi-Supervised pre-training on YFCC100M dataset by Facebook Research'),
|
|
_entry('ssl_resnext50_32x4d', 'ResNeXt-50 32x4d', '1905.00546',
|
|
model_desc='Semi-Supervised pre-training on YFCC100M dataset by Facebook Research'),
|
|
_entry('ssl_resnext101_32x4d', 'ResNeXt-101 32x4d', '1905.00546',
|
|
model_desc='Semi-Supervised pre-training on YFCC100M dataset by Facebook Research'),
|
|
_entry('ssl_resnext101_32x8d', 'ResNeXt-101 32x8d', '1905.00546',
|
|
model_desc='Semi-Supervised pre-training on YFCC100M dataset by Facebook Research'),
|
|
_entry('ssl_resnext101_32x16d', 'ResNeXt-101 32x16d', '1905.00546',
|
|
model_desc='Semi-Supervised pre-training on YFCC100M dataset by Facebook Research'),
|
|
|
|
_entry('ssl_resnet50', 'ResNet-50 (288x288 Mean-Max Pooling)', '1905.00546',
|
|
ttp=True, args=dict(img_size=288),
|
|
model_desc='Semi-Supervised pre-training on YFCC100M dataset by Facebook Research'),
|
|
_entry('ssl_resnext50_32x4d', 'ResNeXt-50 32x4d (288x288 Mean-Max Pooling)', '1905.00546',
|
|
ttp=True, args=dict(img_size=288),
|
|
model_desc='Semi-Supervised pre-training on YFCC100M dataset by Facebook Research'),
|
|
_entry('ssl_resnext101_32x4d', 'ResNeXt-101 32x4d (288x288 Mean-Max Pooling)', '1905.00546',
|
|
ttp=True, args=dict(img_size=288),
|
|
model_desc='Semi-Supervised pre-training on YFCC100M dataset by Facebook Research'),
|
|
_entry('ssl_resnext101_32x8d', 'ResNeXt-101 32x8d (288x288 Mean-Max Pooling)', '1905.00546',
|
|
ttp=True, args=dict(img_size=288),
|
|
model_desc='Semi-Supervised pre-training on YFCC100M dataset by Facebook Research'),
|
|
_entry('ssl_resnext101_32x16d', 'ResNeXt-101 32x16d (288x288 Mean-Max Pooling)', '1905.00546',
|
|
ttp=True, args=dict(img_size=288), batch_size=BATCH_SIZE // 2,
|
|
model_desc='Semi-Supervised pre-training on YFCC100M dataset by Facebook Research'),
|
|
|
|
## Facebook SWSL weights
|
|
_entry('swsl_resnet18', 'ResNet-18', '1905.00546',
|
|
model_desc='Semi-Weakly-Supervised pre-training on 1 billion unlabelled dataset by Facebook Research'),
|
|
_entry('swsl_resnet50', 'ResNet-50', '1905.00546',
|
|
model_desc='Semi-Weakly-Supervised pre-training on 1 billion unlabelled dataset by Facebook Research'),
|
|
_entry('swsl_resnext50_32x4d', 'ResNeXt-50 32x4d', '1905.00546',
|
|
model_desc='Semi-Weakly-Supervised pre-training on 1 billion unlabelled dataset by Facebook Research'),
|
|
_entry('swsl_resnext101_32x4d', 'ResNeXt-101 32x4d', '1905.00546',
|
|
model_desc='Semi-Weakly-Supervised pre-training on 1 billion unlabelled dataset by Facebook Research'),
|
|
_entry('swsl_resnext101_32x8d', 'ResNeXt-101 32x8d', '1905.00546',
|
|
model_desc='Semi-Weakly-Supervised pre-training on 1 billion unlabelled dataset by Facebook Research'),
|
|
_entry('swsl_resnext101_32x16d', 'ResNeXt-101 32x16d', '1905.00546',
|
|
model_desc='Semi-Weakly-Supervised pre-training on 1 billion unlabelled dataset by Facebook Research'),
|
|
|
|
_entry('swsl_resnet50', 'ResNet-50 (288x288 Mean-Max Pooling)', '1905.00546',
|
|
ttp=True, args=dict(img_size=288),
|
|
model_desc='Semi-Weakly-Supervised pre-training on 1 billion unlabelled dataset by Facebook Research'),
|
|
_entry('swsl_resnext50_32x4d', 'ResNeXt-50 32x4d (288x288 Mean-Max Pooling)', '1905.00546',
|
|
ttp=True, args=dict(img_size=288),
|
|
model_desc='Semi-Weakly-Supervised pre-training on 1 billion unlabelled dataset by Facebook Research'),
|
|
_entry('swsl_resnext101_32x4d', 'ResNeXt-101 32x4d (288x288 Mean-Max Pooling)', '1905.00546',
|
|
ttp=True, args=dict(img_size=288),
|
|
model_desc='Semi-Weakly-Supervised pre-training on 1 billion unlabelled dataset by Facebook Research'),
|
|
_entry('swsl_resnext101_32x8d', 'ResNeXt-101 32x8d (288x288 Mean-Max Pooling)', '1905.00546',
|
|
ttp=True, args=dict(img_size=288),
|
|
model_desc='Semi-Weakly-Supervised pre-training on 1 billion unlabelled dataset by Facebook Research'),
|
|
_entry('swsl_resnext101_32x16d', 'ResNeXt-101 32x16d (288x288 Mean-Max Pooling)', '1905.00546',
|
|
ttp=True, args=dict(img_size=288), batch_size=BATCH_SIZE // 2,
|
|
model_desc='Semi-Weakly-Supervised pre-training on 1 billion unlabelled dataset by Facebook Research'),
|
|
|
|
## DLA official impl weights (to remove if sotabench added to source)
|
|
_entry('dla34', 'DLA-34', '1707.06484'),
|
|
_entry('dla46_c', 'DLA-46-C', '1707.06484'),
|
|
_entry('dla46x_c', 'DLA-X-46-C', '1707.06484'),
|
|
_entry('dla60x_c', 'DLA-X-60-C', '1707.06484'),
|
|
_entry('dla60', 'DLA-60', '1707.06484'),
|
|
_entry('dla60x', 'DLA-X-60', '1707.06484'),
|
|
_entry('dla102', 'DLA-102', '1707.06484'),
|
|
_entry('dla102x', 'DLA-X-102', '1707.06484'),
|
|
_entry('dla102x2', 'DLA-X-102 64', '1707.06484'),
|
|
_entry('dla169', 'DLA-169', '1707.06484'),
|
|
|
|
## Res2Net official impl weights (to remove if sotabench added to source)
|
|
_entry('res2net50_26w_4s', 'Res2Net-50 26x4s', '1904.01169'),
|
|
_entry('res2net50_14w_8s', 'Res2Net-50 14x8s', '1904.01169'),
|
|
_entry('res2net50_26w_6s', 'Res2Net-50 26x6s', '1904.01169'),
|
|
_entry('res2net50_26w_8s', 'Res2Net-50 26x8s', '1904.01169'),
|
|
_entry('res2net50_48w_2s', 'Res2Net-50 48x2s', '1904.01169'),
|
|
_entry('res2net101_26w_4s', 'Res2NeXt-101 26x4s', '1904.01169'),
|
|
_entry('res2next50', 'Res2NeXt-50', '1904.01169'),
|
|
_entry('dla60_res2net', 'Res2Net-DLA-60', '1904.01169'),
|
|
_entry('dla60_res2next', 'Res2NeXt-DLA-60', '1904.01169'),
|
|
|
|
## HRNet official impl weights
|
|
_entry('hrnet_w18_small', 'HRNet-W18-C-Small-V1', '1908.07919'),
|
|
_entry('hrnet_w18_small_v2', 'HRNet-W18-C-Small-V2', '1908.07919'),
|
|
_entry('hrnet_w18', 'HRNet-W18-C', '1908.07919'),
|
|
_entry('hrnet_w30', 'HRNet-W30-C', '1908.07919'),
|
|
_entry('hrnet_w32', 'HRNet-W32-C', '1908.07919'),
|
|
_entry('hrnet_w40', 'HRNet-W40-C', '1908.07919'),
|
|
_entry('hrnet_w44', 'HRNet-W44-C', '1908.07919'),
|
|
_entry('hrnet_w48', 'HRNet-W48-C', '1908.07919'),
|
|
_entry('hrnet_w64', 'HRNet-W64-C', '1908.07919'),
|
|
|
|
|
|
## SelecSLS official impl weights
|
|
_entry('selecsls42b', 'SelecSLS-42_B', '1907.00837',
|
|
model_desc='Originally from https://github.com/mehtadushy/SelecSLS-Pytorch'),
|
|
_entry('selecsls60', 'SelecSLS-60', '1907.00837',
|
|
model_desc='Originally from https://github.com/mehtadushy/SelecSLS-Pytorch'),
|
|
_entry('selecsls60b', 'SelecSLS-60_B', '1907.00837',
|
|
model_desc='Originally from https://github.com/mehtadushy/SelecSLS-Pytorch'),
|
|
|
|
## ResNeSt official impl weights
|
|
_entry('resnest14d', 'ResNeSt-14', '2004.08955',
|
|
model_desc='Originally from GluonCV'),
|
|
_entry('resnest26d', 'ResNeSt-26', '2004.08955',
|
|
model_desc='Originally from GluonCV'),
|
|
_entry('resnest50d', 'ResNeSt-50', '2004.08955',
|
|
model_desc='Originally from https://github.com/zhanghang1989/ResNeSt'),
|
|
_entry('resnest101e', 'ResNeSt-101', '2004.08955',
|
|
model_desc='Originally from https://github.com/zhanghang1989/ResNeSt'),
|
|
_entry('resnest200e', 'ResNeSt-200', '2004.08955',
|
|
model_desc='Originally from https://github.com/zhanghang1989/ResNeSt'),
|
|
_entry('resnest269e', 'ResNeSt-269', '2004.08955', batch_size=BATCH_SIZE // 2,
|
|
model_desc='Originally from https://github.com/zhanghang1989/ResNeSt'),
|
|
_entry('resnest50d_4s2x40d', 'ResNeSt-50 4s2x40d', '2004.08955',
|
|
model_desc='Originally from https://github.com/zhanghang1989/ResNeSt'),
|
|
_entry('resnest50d_1s4x24d', 'ResNeSt-50 1s4x24d', '2004.08955',
|
|
model_desc='Originally from https://github.com/zhanghang1989/ResNeSt'),
|
|
|
|
## RegNet official impl weighs
|
|
_entry('regnetx_002', 'RegNetX-200MF', '2003.13678'),
|
|
_entry('regnetx_004', 'RegNetX-400MF', '2003.13678'),
|
|
_entry('regnetx_006', 'RegNetX-600MF', '2003.13678'),
|
|
_entry('regnetx_008', 'RegNetX-800MF', '2003.13678'),
|
|
_entry('regnetx_016', 'RegNetX-1.6GF', '2003.13678'),
|
|
_entry('regnetx_032', 'RegNetX-3.2GF', '2003.13678'),
|
|
_entry('regnetx_040', 'RegNetX-4.0GF', '2003.13678'),
|
|
_entry('regnetx_064', 'RegNetX-6.4GF', '2003.13678'),
|
|
_entry('regnetx_080', 'RegNetX-8.0GF', '2003.13678'),
|
|
_entry('regnetx_120', 'RegNetX-12GF', '2003.13678'),
|
|
_entry('regnetx_160', 'RegNetX-16GF', '2003.13678'),
|
|
_entry('regnetx_320', 'RegNetX-32GF', '2003.13678', batch_size=BATCH_SIZE // 2),
|
|
|
|
_entry('regnety_002', 'RegNetY-200MF', '2003.13678'),
|
|
_entry('regnety_004', 'RegNetY-400MF', '2003.13678'),
|
|
_entry('regnety_006', 'RegNetY-600MF', '2003.13678'),
|
|
_entry('regnety_008', 'RegNetY-800MF', '2003.13678'),
|
|
_entry('regnety_016', 'RegNetY-1.6GF', '2003.13678'),
|
|
_entry('regnety_032', 'RegNetY-3.2GF', '2003.13678'),
|
|
_entry('regnety_040', 'RegNetY-4.0GF', '2003.13678'),
|
|
_entry('regnety_064', 'RegNetY-6.4GF', '2003.13678'),
|
|
_entry('regnety_080', 'RegNetY-8.0GF', '2003.13678'),
|
|
_entry('regnety_120', 'RegNetY-12GF', '2003.13678'),
|
|
_entry('regnety_160', 'RegNetY-16GF', '2003.13678'),
|
|
_entry('regnety_320', 'RegNetY-32GF', '2003.13678', batch_size=BATCH_SIZE // 2),
|
|
|
|
_entry('rexnet_100', 'ReXNet-1.0x', '2007.00992'),
|
|
_entry('rexnet_130', 'ReXNet-1.3x', '2007.00992'),
|
|
_entry('rexnet_150', 'ReXNet-1.5x', '2007.00992'),
|
|
_entry('rexnet_200', 'ReXNet-2.0x', '2007.00992'),
|
|
|
|
_entry('vit_small_patch16_224', 'ViT-S/16', None),
|
|
_entry('vit_base_patch16_224', 'ViT-B/16', None),
|
|
]
|
|
|
|
if is_server():
|
|
DATA_ROOT = './.data/vision/imagenet'
|
|
else:
|
|
# local settings
|
|
DATA_ROOT = './'
|
|
DATA_FILENAME = 'ILSVRC2012_img_val.tar'
|
|
TAR_PATH = os.path.join(DATA_ROOT, DATA_FILENAME)
|
|
|
|
for m in model_list:
|
|
model_name = m['model']
|
|
# create model from name
|
|
model = create_model(model_name, pretrained=True)
|
|
param_count = sum([m.numel() for m in model.parameters()])
|
|
print('Model %s, %s created. Param count: %d' % (model_name, m['paper_model_name'], param_count))
|
|
|
|
dataset = DatasetTar(TAR_PATH)
|
|
filenames = [os.path.splitext(f)[0] for f in dataset.filenames()]
|
|
|
|
# get appropriate transform for model's default pretrained config
|
|
data_config = resolve_data_config(m['args'], model=model, verbose=True)
|
|
test_time_pool = False
|
|
if m['ttp']:
|
|
model, test_time_pool = apply_test_time_pool(model, data_config)
|
|
data_config['crop_pct'] = 1.0
|
|
|
|
batch_size = m['batch_size']
|
|
loader = create_loader(
|
|
dataset,
|
|
input_size=data_config['input_size'],
|
|
batch_size=batch_size,
|
|
use_prefetcher=True,
|
|
interpolation=data_config['interpolation'],
|
|
mean=data_config['mean'],
|
|
std=data_config['std'],
|
|
num_workers=6,
|
|
crop_pct=data_config['crop_pct'],
|
|
pin_memory=True)
|
|
|
|
evaluator = ImageNetEvaluator(
|
|
root=DATA_ROOT,
|
|
model_name=m['paper_model_name'],
|
|
paper_arxiv_id=m['paper_arxiv_id'],
|
|
model_description=m.get('model_description', None),
|
|
)
|
|
model.cuda()
|
|
model.eval()
|
|
with torch.no_grad():
|
|
# warmup
|
|
input = torch.randn((batch_size,) + data_config['input_size']).cuda()
|
|
model(input)
|
|
|
|
bar = tqdm(desc="Evaluation", mininterval=5, total=50000)
|
|
evaluator.reset_time()
|
|
sample_count = 0
|
|
for input, target in loader:
|
|
output = model(input)
|
|
num_samples = len(output)
|
|
image_ids = [filenames[i] for i in range(sample_count, sample_count + num_samples)]
|
|
output = output.cpu().numpy()
|
|
evaluator.add(dict(zip(image_ids, list(output))))
|
|
sample_count += num_samples
|
|
bar.update(num_samples)
|
|
if evaluator.cache_exists:
|
|
break
|
|
|
|
bar.close()
|
|
|
|
evaluator.save()
|
|
for k, v in evaluator.results.items():
|
|
print(k, v)
|
|
for k, v in evaluator.speed_mem_metrics.items():
|
|
print(k, v)
|
|
torch.cuda.empty_cache()
|
|
|
|
|