From 5c6da1c55a99d7d188b1631e91ed83185ec8aa04 Mon Sep 17 00:00:00 2001 From: Ross Wightman Date: Wed, 28 Aug 2019 21:34:39 -0700 Subject: [PATCH] Experiment with sotabench model_description field. --- sotabench.py | 52 +++++++++++++++++++++++++++++++++++++--------------- 1 file changed, 37 insertions(+), 15 deletions(-) diff --git a/sotabench.py b/sotabench.py index e2a8fcb0..2eca7ff8 100644 --- a/sotabench.py +++ b/sotabench.py @@ -8,9 +8,11 @@ NUM_GPU = 1 BATCH_SIZE = 256 * NUM_GPU -def _entry(model_name, paper_model_name, paper_arxiv_id, batch_size=BATCH_SIZE, ttp=False, args=dict()): +def _entry(model_name, paper_model_name, paper_arxiv_id, batch_size=BATCH_SIZE, + ttp=False, args=dict(), model_desc=None): return dict( model=model_name, + model_description=model_desc, paper_model_name=paper_model_name, paper_arxiv_id=paper_arxiv_id, batch_size=batch_size, @@ -46,7 +48,7 @@ model_list = [ #_entry('ens_adv_inception_resnet_v2', 'Ensemble Adversarial Inception V3'), _entry('fbnetc_100', 'FBNet-C', '1812.03443'), _entry('gluon_inception_v3', 'Inception V3', '1512.00567'), - _entry('gluon_resnet18_v1b', 'ResNet-18', '1812.01187'), + _entry('gluon_resnet18_v1b', 'ResNet-18', '1812.01187', model_desc='Ported from GluonCV Model Zoo'), _entry('gluon_resnet34_v1b', 'ResNet-34', '1812.01187'), _entry('gluon_resnet50_v1b', 'ResNet-50', '1812.01187'), _entry('gluon_resnet50_v1c', 'ResNet-50-C', '1812.01187'), @@ -79,7 +81,9 @@ model_list = [ _entry('mixnet_m', 'MixNet-M', '1907.09595'), _entry('mixnet_s', 'MixNet-S', '1907.09595'), _entry('mnasnet_100', 'MnasNet-B1', '1807.11626'), - _entry('mobilenetv3_100', 'MobileNet V3(1.0)', '1905.02244'), + _entry('mobilenetv3_100', 'MobileNet V3(1.0)', '1905.02244', + model_desc='Trained from scratch in PyTorch with RMSProp, exponential LR decay, and hyper-params matching' + ' paper as closely as possible.'), _entry('nasnetalarge', 'NASNet-A Large', '1707.07012', batch_size=BATCH_SIZE//4), _entry('pnasnet5large', 'PNASNet-5', '1712.00559', batch_size=BATCH_SIZE//4), _entry('resnet18', 'ResNet-18', '1812.01187'), @@ -90,7 +94,13 @@ model_list = [ #_entry('resnet101', , ), # same weights as torchvision #_entry('resnet152', , ), # same weights as torchvision _entry('resnext50_32x4d', 'ResNeXt-50 32x4d', '1812.01187'), - _entry('resnext50d_32x4d', 'ResNeXt-50-D 32x4d', '1812.01187'), + _entry('resnext50d_32x4d', 'ResNeXt-50-D 32x4d', '1812.01187', + model_desc="""'D' variant (3x3 deep stem w/ avg-pool downscale) + Trained with: + * SGD w/ cosine LR decay + * Random-erasing (gaussian per-pixel noise) + * Label-smoothing + """), #_entry('resnext101_32x8d', ), # same weights as torchvision _entry('semnasnet_100', 'MnasNet-A1', '1807.11626'), _entry('senet154', 'SENet-154', '1709.01507'), @@ -103,17 +113,28 @@ model_list = [ _entry('seresnext50_32x4d', 'SE-ResNeXt-50 32x4d', '1709.01507'), _entry('seresnext101_32x4d', 'SE-ResNeXt-101 32x4d', '1709.01507'), _entry('spnasnet_100', 'Single-Path NAS', '1904.02877'), - _entry('tf_efficientnet_b0', 'EfficientNet-B0 (AutoAugment)', '1905.11946'), - _entry('tf_efficientnet_b1', 'EfficientNet-B1 (AutoAugment)', '1905.11946'), - _entry('tf_efficientnet_b2', 'EfficientNet-B2 (AutoAugment)', '1905.11946'), - _entry('tf_efficientnet_b3', 'EfficientNet-B3 (AutoAugment)', '1905.11946', batch_size=BATCH_SIZE//2), - _entry('tf_efficientnet_b4', 'EfficientNet-B4 (AutoAugment)', '1905.11946', batch_size=BATCH_SIZE//2), - _entry('tf_efficientnet_b5', 'EfficientNet-B5 (AutoAugment)', '1905.11946', batch_size=BATCH_SIZE//4), - _entry('tf_efficientnet_b6', 'EfficientNet-B6 (AutoAugment)', '1905.11946', batch_size=BATCH_SIZE//8), - _entry('tf_efficientnet_b7', 'EfficientNet-B7 (AutoAugment)', '1905.11946', batch_size=BATCH_SIZE//8), - _entry('tf_efficientnet_es', 'EfficientNet-EdgeTPU-S', '1905.11946'), - _entry('tf_efficientnet_em', 'EfficientNet-EdgeTPU-M', '1905.11946'), - _entry('tf_efficientnet_el', 'EfficientNet-EdgeTPU-L', '1905.11946', batch_size=BATCH_SIZE//2), + _entry('tf_efficientnet_b0', 'EfficientNet-B0 (AutoAugment)', '1905.11946', + model_desc='Ported from official Google AI Tensorflow weights'), + _entry('tf_efficientnet_b1', 'EfficientNet-B1 (AutoAugment)', '1905.11946', + model_desc='Ported from official Google AI Tensorflow weights'), + _entry('tf_efficientnet_b2', 'EfficientNet-B2 (AutoAugment)', '1905.11946', + model_desc='Ported from official Google AI Tensorflow weights'), + _entry('tf_efficientnet_b3', 'EfficientNet-B3 (AutoAugment)', '1905.11946', batch_size=BATCH_SIZE//2, + model_desc='Ported from official Google AI Tensorflow weights'), + _entry('tf_efficientnet_b4', 'EfficientNet-B4 (AutoAugment)', '1905.11946', batch_size=BATCH_SIZE//2, + model_desc='Ported from official Google AI Tensorflow weights'), + _entry('tf_efficientnet_b5', 'EfficientNet-B5 (AutoAugment)', '1905.11946', batch_size=BATCH_SIZE//4, + model_desc='Ported from official Google AI Tensorflow weights'), + _entry('tf_efficientnet_b6', 'EfficientNet-B6 (AutoAugment)', '1905.11946', batch_size=BATCH_SIZE//8, + model_desc='Ported from official Google AI Tensorflow weights'), + _entry('tf_efficientnet_b7', 'EfficientNet-B7 (AutoAugment)', '1905.11946', batch_size=BATCH_SIZE//8, + model_desc='Ported from official Google AI Tensorflow weights'), + _entry('tf_efficientnet_es', 'EfficientNet-EdgeTPU-S', '1905.11946', + model_desc='Ported from official Google AI Tensorflow weights'), + _entry('tf_efficientnet_em', 'EfficientNet-EdgeTPU-M', '1905.11946', + model_desc='Ported from official Google AI Tensorflow weights'), + _entry('tf_efficientnet_el', 'EfficientNet-EdgeTPU-L', '1905.11946', batch_size=BATCH_SIZE//2, + model_desc='Ported from official Google AI Tensorflow weights'), _entry('tf_inception_v3', 'Inception V3', '1512.00567'), _entry('tf_mixnet_l', 'MixNet-L', '1907.09595'), _entry('tf_mixnet_m', 'MixNet-M', '1907.09595'), @@ -144,6 +165,7 @@ for m in model_list: # Run the benchmark ImageNet.benchmark( model=model, + model_description=m.get('model_description', None), paper_model_name=m['paper_model_name'], paper_arxiv_id=m['paper_arxiv_id'], input_transform=input_transform,