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Update a bunch of weights with external links to timm release assets. Fixes issue with *aliyuncs.com returning forbidden. Did pickle scan / verify and re-hash. Add TresNet-V2-L weights.

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pull/1471/head
Ross Wightman 2 years ago
parent fa8c84eede
commit e11efa872d
11 changed files with 77 additions and 58 deletions
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20
timm/models/dla.py
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@ -32,16 +32,16 @@ def _cfg(url='', **kwargs):
default_cfgs = { default_cfgs = {
'dla34': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla34-ba72cf86.pth'), 'dla34': _cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/dla34-2b83ff04.pth'),
'dla46_c': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla46_c-2bfd52c3.pth'), 'dla46_c': _cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/dla46_c-9b68d685.pth'),
'dla46x_c': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla46x_c-d761bae7.pth'), 'dla46x_c': _cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/dla46x_c-6bc5b5c8.pth'),
'dla60x_c': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla60x_c-b870c45c.pth'), 'dla60x_c': _cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/dla60x_c-a38e054a.pth'),
'dla60': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla60-24839fc4.pth'), 'dla60': _cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/dla60-9e91bd4d.pth'),
'dla60x': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla60x-d15cacda.pth'), 'dla60x': _cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/dla60x-6818f6bb.pth'),
'dla102': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla102-d94d9790.pth'), 'dla102': _cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/dla102-21f57b54.pth'),
'dla102x': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla102x-ad62be81.pth'), 'dla102x': _cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/dla102x-7ec0aa2a.pth'),
'dla102x2': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla102x2-262837b6.pth'), 'dla102x2': _cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/dla102x2-ac4239c4.pth'),
'dla169': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla169-0914e092.pth'), 'dla169': _cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/dla169-7c767967.pthh'),
'dla60_res2net': _cfg( 'dla60_res2net': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net_dla60_4s-d88db7f9.pth'), url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net_dla60_4s-d88db7f9.pth'),
'dla60_res2next': _cfg( 'dla60_res2next': _cfg(

12
timm/models/efficientnet.py
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@ -151,13 +151,13 @@ default_cfgs = {
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_em_ra2-66250f76.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_em_ra2-66250f76.pth',
input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882), input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882),
'efficientnet_el': _cfg( 'efficientnet_el': _cfg(
url='https://github.com/DeGirum/pruned-models/releases/download/efficientnet_v1.0/efficientnet_el.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_el-3b455510.pth',
input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904), input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
'efficientnet_es_pruned': _cfg( 'efficientnet_es_pruned': _cfg(
url='https://github.com/DeGirum/pruned-models/releases/download/efficientnet_v1.0/efficientnet_es_pruned75.pth'), url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_es_pruned75-1b7248cf.pth'),
'efficientnet_el_pruned': _cfg( 'efficientnet_el_pruned': _cfg(
url='https://github.com/DeGirum/pruned-models/releases/download/efficientnet_v1.0/efficientnet_el_pruned70.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_el_pruned70-ef2a2ccf.pth',
input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904), input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
'efficientnet_cc_b0_4e': _cfg(url=''), 'efficientnet_cc_b0_4e': _cfg(url=''),
@ -179,13 +179,13 @@ default_cfgs = {
url='', input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922), url='', input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922),
'efficientnet_b1_pruned': _cfg( 'efficientnet_b1_pruned': _cfg(
url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45403/outputs/effnetb1_pruned_9ebb3fe6.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/effnetb1_pruned-bea43a3a.pth',
input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882, mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), 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( 'efficientnet_b2_pruned': _cfg(
url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45403/outputs/effnetb2_pruned_203f55bc.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/effnetb2_pruned-08c1b27c.pth',
input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890, mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), 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( 'efficientnet_b3_pruned': _cfg(
url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45403/outputs/effnetb3_pruned_5abcc29f.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/effnetb3_pruned-59ecf72d.pth',
input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904, mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904, mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
'efficientnetv2_rw_t': _cfg( 'efficientnetv2_rw_t': _cfg(

12
timm/models/hardcorenas.py
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@ -22,12 +22,12 @@ def _cfg(url='', **kwargs):
default_cfgs = { default_cfgs = {
'hardcorenas_a': _cfg(url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/public/HardCoReNAS/HardCoreNAS_A_Green_38ms_75.9_23474aeb.pth'), 'hardcorenas_a': _cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/hardcorenas_a_green_38ms_75_9-31dc7186.pth'),
'hardcorenas_b': _cfg(url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/public/HardCoReNAS/HardCoreNAS_B_Green_40ms_76.5_1f882d1e.pth'), 'hardcorenas_b': _cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/hardcorenas_b_green_40ms_76_5-32d91ff2.pth'),
'hardcorenas_c': _cfg(url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/public/HardCoReNAS/HardCoreNAS_C_Green_44ms_77.1_d4148c9e.pth'), 'hardcorenas_c': _cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/hardcorenas_c_green_44ms_77_1-631a0983.pth'),
'hardcorenas_d': _cfg(url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/public/HardCoReNAS/HardCoreNAS_D_Green_50ms_77.4_23e3cdde.pth'), 'hardcorenas_d': _cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/hardcorenas_d_green_50ms_77_4-998d9d7a.pth'),
'hardcorenas_e': _cfg(url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/public/HardCoReNAS/HardCoreNAS_E_Green_55ms_77.9_90f20e8a.pth'), 'hardcorenas_e': _cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/hardcorenas_e_green_55ms_77_9-482886a3.pth'),
'hardcorenas_f': _cfg(url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/public/HardCoReNAS/HardCoreNAS_F_Green_60ms_78.1_2855edf1.pth'), 'hardcorenas_f': _cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/hardcorenas_f_green_60ms_78_1-14b9e780.pth'),
} }

4
timm/models/mlp_mixer.py
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@ -84,11 +84,11 @@ default_cfgs = dict(
# Mixer ImageNet-21K-P pretraining # Mixer ImageNet-21K-P pretraining
mixer_b16_224_miil_in21k=_cfg( mixer_b16_224_miil_in21k=_cfg(
url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/mixer_b16_224_miil_in21k.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/mixer_b16_224_miil_in21k-2a558a71.pth',
mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear', num_classes=11221, mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear', num_classes=11221,
), ),
mixer_b16_224_miil=_cfg( mixer_b16_224_miil=_cfg(
url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/mixer_b16_224_miil.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/mixer_b16_224_miil-9229a591.pth',
mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear', mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear',
), ),

6
timm/models/mobilenetv3.py
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@ -42,10 +42,10 @@ default_cfgs = {
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_large_100_ra-f55367f5.pth'), url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_large_100_ra-f55367f5.pth'),
'mobilenetv3_large_100_miil': _cfg( 'mobilenetv3_large_100_miil': _cfg(
interpolation='bilinear', mean=(0., 0., 0.), std=(1., 1., 1.), interpolation='bilinear', mean=(0., 0., 0.), std=(1., 1., 1.),
url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/mobilenetv3_large_100_1k_miil_78_0.pth'), url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/mobilenetv3_large_100_1k_miil_78_0-66471c13.pth'),
'mobilenetv3_large_100_miil_in21k': _cfg( 'mobilenetv3_large_100_miil_in21k': _cfg(
interpolation='bilinear', mean=(0., 0., 0.), std=(1., 1., 1.), url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/mobilenetv3_large_100_in21k_miil-d71cc17b.pth',
url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/mobilenetv3_large_100_in21k_miil.pth', num_classes=11221), interpolation='bilinear', mean=(0., 0., 0.), std=(1., 1., 1.), num_classes=11221),
'mobilenetv3_small_050': _cfg( 'mobilenetv3_small_050': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_small_050_lambc-4b7bbe87.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_small_050_lambc-4b7bbe87.pth',

2
timm/models/nasnet.py
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@ -16,7 +16,7 @@ __all__ = ['NASNetALarge']
default_cfgs = { default_cfgs = {
'nasnetalarge': { 'nasnetalarge': {
'url': 'http://data.lip6.fr/cadene/pretrainedmodels/nasnetalarge-a1897284.pth', 'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/nasnetalarge-dc4a7b8b.pth',
'input_size': (3, 331, 331), 'input_size': (3, 331, 331),
'pool_size': (11, 11), 'pool_size': (11, 11),
'crop_pct': 0.911, 'crop_pct': 0.911,

10
timm/models/resnet.py
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@ -164,14 +164,14 @@ default_cfgs = {
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
crop_pct=0.95, test_input_size=(3, 320, 320)), crop_pct=0.95, test_input_size=(3, 320, 320)),
'ecaresnetlight': _cfg( 'ecaresnetlight': _cfg(
url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45402/outputs/ECAResNetLight_4f34b35b.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/ecaresnetlight-75a9c627.pth',
interpolation='bicubic'), interpolation='bicubic'),
'ecaresnet50d': _cfg( 'ecaresnet50d': _cfg(
url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45402/outputs/ECAResNet50D_833caf58.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/ecaresnet50d-93c81e3b.pth',
interpolation='bicubic', interpolation='bicubic',
first_conv='conv1.0'), first_conv='conv1.0'),
'ecaresnet50d_pruned': _cfg( 'ecaresnet50d_pruned': _cfg(
url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45899/outputs/ECAResNet50D_P_9c67f710.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/ecaresnet50d_p-e4fa23c2.pth',
interpolation='bicubic', interpolation='bicubic',
first_conv='conv1.0'), first_conv='conv1.0'),
'ecaresnet50t': _cfg( 'ecaresnet50t': _cfg(
@ -179,10 +179,10 @@ default_cfgs = {
interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
crop_pct=0.95, test_input_size=(3, 320, 320)), crop_pct=0.95, test_input_size=(3, 320, 320)),
'ecaresnet101d': _cfg( 'ecaresnet101d': _cfg(
url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45402/outputs/ECAResNet101D_281c5844.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/ecaresnet101d-153dad65.pth',
interpolation='bicubic', first_conv='conv1.0'), interpolation='bicubic', first_conv='conv1.0'),
'ecaresnet101d_pruned': _cfg( 'ecaresnet101d_pruned': _cfg(
url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45610/outputs/ECAResNet101D_P_75a3370e.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/ecaresnet101d_p-9e74cb91.pth',
interpolation='bicubic', interpolation='bicubic',
first_conv='conv1.0'), first_conv='conv1.0'),
'ecaresnet200d': _cfg( 'ecaresnet200d': _cfg(

12
timm/models/senet.py
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@ -37,8 +37,8 @@ def _cfg(url='', **kwargs):
default_cfgs = { default_cfgs = {
'legacy_senet154': 'legacy_senet154': _cfg(
_cfg(url='http://data.lip6.fr/cadene/pretrainedmodels/senet154-c7b49a05.pth'), url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/legacy_senet154-e9eb9fe6.pth'),
'legacy_seresnet18': _cfg( 'legacy_seresnet18': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet18-4bb0ce65.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet18-4bb0ce65.pth',
interpolation='bicubic'), interpolation='bicubic'),
@ -53,10 +53,10 @@ default_cfgs = {
'legacy_seresnext26_32x4d': _cfg( 'legacy_seresnext26_32x4d': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26_32x4d-65ebdb501.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26_32x4d-65ebdb501.pth',
interpolation='bicubic'), interpolation='bicubic'),
'legacy_seresnext50_32x4d': 'legacy_seresnext50_32x4d': _cfg(
_cfg(url='http://data.lip6.fr/cadene/pretrainedmodels/se_resnext50_32x4d-a260b3a4.pth'), url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/legacy_se_resnext50_32x4d-f3651bad.pth'),
'legacy_seresnext101_32x4d': 'legacy_seresnext101_32x4d': _cfg(
_cfg(url='http://data.lip6.fr/cadene/pretrainedmodels/se_resnext101_32x4d-3b2fe3d8.pth'), url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/legacy_se_resnext101_32x4d-37725eac.pth'),
} }

6
timm/models/swin_transformer.py
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@ -87,13 +87,13 @@ default_cfgs = {
num_classes=21841), num_classes=21841),
'swin_s3_tiny_224': _cfg( 'swin_s3_tiny_224': _cfg(
url='https://github.com/silent-chen/AutoFormerV2-model-zoo/releases/download/v1.0.0/S3-T.pth' url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/s3_t-1d53f6a8.pth'
), ),
'swin_s3_small_224': _cfg( 'swin_s3_small_224': _cfg(
url='https://github.com/silent-chen/AutoFormerV2-model-zoo/releases/download/v1.0.0/S3-S.pth' url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/s3_s-3bb4c69d.pth'
), ),
'swin_s3_base_224': _cfg( 'swin_s3_base_224': _cfg(
url='https://github.com/silent-chen/AutoFormerV2-model-zoo/releases/download/v1.0.0/S3-B.pth' url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/s3_b-a1e95db4.pth'
) )
} }

40
timm/models/tresnet.py
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@ -29,9 +29,9 @@ def _cfg(url='', **kwargs):
default_cfgs = { default_cfgs = {
'tresnet_m': _cfg( 'tresnet_m': _cfg(
url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/tresnet_m_1k_miil_83_1.pth'), url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_m_1k_miil_83_1-d236afcb.pth'),
'tresnet_m_miil_in21k': _cfg( 'tresnet_m_miil_in21k': _cfg(
url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/tresnet_m_miil_in21k.pth', num_classes=11221), url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_m_miil_in21k-901b6ed4.pth', num_classes=11221),
'tresnet_l': _cfg( 'tresnet_l': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_l_81_5-235b486c.pth'), url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_l_81_5-235b486c.pth'),
'tresnet_xl': _cfg( 'tresnet_xl': _cfg(
@ -44,7 +44,10 @@ default_cfgs = {
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_l_448-940d0cd1.pth'), url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_l_448-940d0cd1.pth'),
'tresnet_xl_448': _cfg( 'tresnet_xl_448': _cfg(
input_size=(3, 448, 448), pool_size=(14, 14), input_size=(3, 448, 448), pool_size=(14, 14),
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_xl_448-8c1815de.pth') url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_xl_448-8c1815de.pth'),
'tresnet_v2_l': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_l_v2_83_9-f36e4445.pth'),
} }
@ -99,7 +102,7 @@ class BasicBlock(nn.Module):
if self.se is not None: if self.se is not None:
out = self.se(out) out = self.se(out)
out += shortcut out = out + shortcut
out = self.relu(out) out = self.relu(out)
return out return out
@ -153,7 +156,16 @@ class Bottleneck(nn.Module):
class TResNet(nn.Module): class TResNet(nn.Module):
def __init__(self, layers, in_chans=3, num_classes=1000, width_factor=1.0, global_pool='fast', drop_rate=0.): def __init__(
self,
layers,
in_chans=3,
num_classes=1000,
width_factor=1.0,
v2=False,
global_pool='fast',
drop_rate=0.,
):
self.num_classes = num_classes self.num_classes = num_classes
self.drop_rate = drop_rate self.drop_rate = drop_rate
super(TResNet, self).__init__() super(TResNet, self).__init__()
@ -163,15 +175,19 @@ class TResNet(nn.Module):
# TResnet stages # TResnet stages
self.inplanes = int(64 * width_factor) self.inplanes = int(64 * width_factor)
self.planes = int(64 * width_factor) self.planes = int(64 * width_factor)
if v2:
self.inplanes = self.inplanes // 8 * 8
self.planes = self.planes // 8 * 8
conv1 = conv2d_iabn(in_chans * 16, self.planes, stride=1, kernel_size=3) conv1 = conv2d_iabn(in_chans * 16, self.planes, stride=1, kernel_size=3)
layer1 = self._make_layer( layer1 = self._make_layer(
BasicBlock, self.planes, layers[0], stride=1, use_se=True, aa_layer=aa_layer) # 56x56 Bottleneck if v2 else BasicBlock, self.planes, layers[0], stride=1, use_se=True, aa_layer=aa_layer)
layer2 = self._make_layer( layer2 = self._make_layer(
BasicBlock, self.planes * 2, layers[1], stride=2, use_se=True, aa_layer=aa_layer) # 28x28 Bottleneck if v2 else BasicBlock, self.planes * 2, layers[1], stride=2, use_se=True, aa_layer=aa_layer)
layer3 = self._make_layer( layer3 = self._make_layer(
Bottleneck, self.planes * 4, layers[2], stride=2, use_se=True, aa_layer=aa_layer) # 14x14 Bottleneck, self.planes * 4, layers[2], stride=2, use_se=True, aa_layer=aa_layer)
layer4 = self._make_layer( layer4 = self._make_layer(
Bottleneck, self.planes * 8, layers[3], stride=2, use_se=False, aa_layer=aa_layer) # 7x7 Bottleneck, self.planes * 8, layers[3], stride=2, use_se=False, aa_layer=aa_layer)
# body # body
self.body = nn.Sequential(OrderedDict([ self.body = nn.Sequential(OrderedDict([
@ -285,6 +301,12 @@ def tresnet_l(pretrained=False, **kwargs):
return _create_tresnet('tresnet_l', pretrained=pretrained, **model_kwargs) return _create_tresnet('tresnet_l', pretrained=pretrained, **model_kwargs)
@register_model
def tresnet_v2_l(pretrained=False, **kwargs):
model_kwargs = dict(layers=[3, 4, 23, 3], width_factor=1.0, v2=True, **kwargs)
return _create_tresnet('tresnet_v2_l', pretrained=pretrained, **model_kwargs)
@register_model @register_model
def tresnet_xl(pretrained=False, **kwargs): def tresnet_xl(pretrained=False, **kwargs):
model_kwargs = dict(layers=[4, 5, 24, 3], width_factor=1.3, **kwargs) model_kwargs = dict(layers=[4, 5, 24, 3], width_factor=1.3, **kwargs)

11
timm/models/vision_transformer.py
Unescape View File

@ -162,14 +162,11 @@ default_cfgs = {
# ViT ImageNet-21K-P pretraining by MILL # ViT ImageNet-21K-P pretraining by MILL
'vit_base_patch16_224_miil_in21k': _cfg( 'vit_base_patch16_224_miil_in21k': _cfg(
url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/vit_base_patch16_224_in21k_miil.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/vit_base_patch16_224_in21k_miil-887286df.pth',
mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear', num_classes=11221, mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear', num_classes=11221),
),
'vit_base_patch16_224_miil': _cfg( 'vit_base_patch16_224_miil': _cfg(
url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm' url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/vit_base_patch16_224_1k_miil_84_4-2deb18e3.pth',
'/vit_base_patch16_224_1k_miil_84_4.pth', mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear'),
mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear',
),
'vit_base_patch16_rpn_224': _cfg( 'vit_base_patch16_rpn_224': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_base_patch16_rpn_224-sw-3b07e89d.pth'), url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_base_patch16_rpn_224-sw-3b07e89d.pth'),

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