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test.py
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test.py
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# coding: utf-8
__author__ = 'Roman Solovyev: https://github.com/ZFTurbo'
import timm_3d
import torch
if __name__ == '__main__':
if 1:
resnet_models = [
'resnet10t.c3_in1k',
'resnet14t.c3_in1k',
'resnet18.a1_in1k',
'resnet18.a2_in1k',
'resnet18.a3_in1k',
'resnet18d.ra2_in1k',
'resnet34.a1_in1k',
'resnet34.a2_in1k',
'resnet34.a3_in1k',
'resnet34.bt_in1k',
'resnet34d.ra2_in1k',
'resnet26.bt_in1k',
'resnet26d.bt_in1k',
'resnet26t.ra2_in1k',
'resnet50.a1_in1k',
'resnet50.a1h_in1k',
'resnet50.a2_in1k',
'resnet50.a3_in1k',
'resnet50.b1k_in1k',
'resnet50.b2k_in1k',
'resnet50.c1_in1k',
'resnet50.c2_in1k',
'resnet50.d_in1k',
'resnet50.ram_in1k',
'resnet50.am_in1k',
'resnet50.ra_in1k',
'resnet50.bt_in1k',
'resnet50d.ra2_in1k',
'resnet50d.a1_in1k',
'resnet50d.a2_in1k',
'resnet50d.a3_in1k',
'resnet50t.untrained',
'resnet101.a1_in1k',
'resnet101.a2_in1k',
'resnet101.a3_in1k',
'resnet101d.ra2_in1k',
'resnet152.a1h_in1k',
'resnet152.a1_in1k',
'resnet152.a2_in1k',
'resnet152.a3_in1k',
'resnet152d.ra2_in1k',
'resnet200.untrained',
'resnet200d.ra2_in1k',
'wide_resnet50_2.racm_in1k',
'resnet18.tv_in1k',
'resnet34.tv_in1k',
'resnet50.tv_in1k',
'resnet50.tv2_in1k',
'resnet101.tv_in1k',
'resnet101.tv2_in1k',
'resnet152.tv_in1k',
'resnet152.tv2_in1k',
'wide_resnet50_2.tv_in1k',
'wide_resnet50_2.tv2_in1k',
'wide_resnet101_2.tv_in1k',
'wide_resnet101_2.tv2_in1k',
'resnet50_gn.a1h_in1k',
'resnext50_32x4d.a1h_in1k',
'resnext50_32x4d.a1_in1k',
'resnext50_32x4d.a2_in1k',
'resnext50_32x4d.a3_in1k',
'resnext50_32x4d.ra_in1k',
'resnext50d_32x4d.bt_in1k',
'resnext101_32x4d.untrained',
'resnext101_64x4d.c1_in1k',
'resnext50_32x4d.tv_in1k',
'resnext101_32x8d.tv_in1k',
'resnext101_64x4d.tv_in1k',
'resnext50_32x4d.tv2_in1k',
'resnext101_32x8d.tv2_in1k',
'resnext101_32x8d.fb_wsl_ig1b_ft_in1k',
'resnext101_32x16d.fb_wsl_ig1b_ft_in1k',
'resnext101_32x32d.fb_wsl_ig1b_ft_in1k',
# 'resnext101_32x48d.fb_wsl_ig1b_ft_in1k',
'resnet18.fb_ssl_yfcc100m_ft_in1k',
'resnet50.fb_ssl_yfcc100m_ft_in1k',
'resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k',
'resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k',
'resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k',
'resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k',
'resnet18.fb_swsl_ig1b_ft_in1k',
'resnet50.fb_swsl_ig1b_ft_in1k',
'resnext50_32x4d.fb_swsl_ig1b_ft_in1k',
'resnext101_32x4d.fb_swsl_ig1b_ft_in1k',
'resnext101_32x8d.fb_swsl_ig1b_ft_in1k',
'resnext101_32x16d.fb_swsl_ig1b_ft_in1k',
'ecaresnet26t.ra2_in1k',
'ecaresnetlight.miil_in1k',
'ecaresnet50d.miil_in1k',
# 'ecaresnet50d_pruned.miil_in1k',
# 'ecaresnet50t.ra2_in1k',
'ecaresnet50t.a1_in1k',
'ecaresnet50t.a2_in1k',
'ecaresnet50t.a3_in1k',
'ecaresnet101d.miil_in1k',
# 'ecaresnet101d_pruned.miil_in1k',
'ecaresnet200d.untrained',
'ecaresnet269d.ra2_in1k',
'ecaresnext26t_32x4d.untrained',
'ecaresnext50t_32x4d.untrained',
'seresnet18.untrained',
'seresnet34.untrained',
'seresnet50.a1_in1k',
'seresnet50.a2_in1k',
'seresnet50.a3_in1k',
'seresnet50.ra2_in1k',
'seresnet50t.untrained',
'seresnet101.untrained',
'seresnet152.untrained',
'seresnet152d.ra2_in1k',
'seresnet200d.untrained',
'seresnet269d.untrained',
'seresnext26d_32x4d.bt_in1k',
'seresnext26t_32x4d.bt_in1k',
'seresnext50_32x4d.racm_in1k',
'seresnext101_32x4d.untrained',
'seresnext101_32x8d.ah_in1k',
'seresnext101d_32x8d.ah_in1k',
# 'resnetaa50d.sw_in12k_ft_in1k',
# 'resnetaa101d.sw_in12k_ft_in1k',
# 'seresnextaa101d_32x8d.sw_in12k_ft_in1k_288',
# 'seresnextaa101d_32x8d.sw_in12k_ft_in1k',
# 'seresnextaa201d_32x8d.sw_in12k_ft_in1k_384',
# 'seresnextaa201d_32x8d.sw_in12k',
# 'resnetaa50d.sw_in12k',
# 'resnetaa50d.d_in12k',
# 'resnetaa101d.sw_in12k',
# 'seresnextaa101d_32x8d.sw_in12k',
'resnetblur18.untrained',
'resnetblur50.bt_in1k',
'resnetblur50d.untrained',
'resnetblur101d.untrained',
# 'resnetaa34d.untrained',
# 'resnetaa50.a1h_in1k',
# 'seresnetaa50d.untrained',
# 'seresnextaa101d_32x8d.ah_in1k',
'resnetrs50.tf_in1k',
'resnetrs101.tf_in1k',
'resnetrs152.tf_in1k',
'resnetrs200.tf_in1k',
'resnetrs270.tf_in1k',
'resnetrs350.tf_in1k',
'resnetrs420.tf_in1k',
'resnet18.gluon_in1k',
'resnet34.gluon_in1k',
'resnet50.gluon_in1k',
'resnet101.gluon_in1k',
'resnet152.gluon_in1k',
'resnet50c.gluon_in1k',
'resnet101c.gluon_in1k',
'resnet152c.gluon_in1k',
'resnet50d.gluon_in1k',
'resnet101d.gluon_in1k',
'resnet152d.gluon_in1k',
'resnet50s.gluon_in1k',
'resnet101s.gluon_in1k',
'resnet152s.gluon_in1k',
'resnext50_32x4d.gluon_in1k',
'resnext101_32x4d.gluon_in1k',
'resnext101_64x4d.gluon_in1k',
'seresnext50_32x4d.gluon_in1k',
'seresnext101_32x4d.gluon_in1k',
'seresnext101_64x4d.gluon_in1k',
'senet154.gluon_in1k',
]
if 1:
for model_name in resnet_models[:1]:
print('Go: {}'.format(model_name))
m = timm_3d.create_model(
model_name,
pretrained=False,
num_classes=0,
global_pool=''
)
o = m(torch.randn(2, 3, 128, 128, 128))
print(f'Unpooled shape: {o.shape}')
if 1:
maxvit_models = [
('coatnet_nano_rw_224.sw_in1k', (3, 128, 128, 128)),
('coatnet_0_rw_224.sw_in1k', (3, 128, 128, 128)),
('coatnet_1_rw_224.sw_in1k', (3, 128, 128, 128)),
('coatnet_2_rw_224.sw_in12k_ft_in1k', (3, 128, 128, 128)),
('coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k', (3, 128, 128, 128)),
('coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k', (3, 128, 128, 128)),
('coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k', (3, 192, 192, 192)),
('coatnet_bn_0_rw_224.sw_in1k', (3, 128, 128, 128)),
('coatnet_rmlp_nano_rw_224.sw_in1k', (3, 128, 128, 128)),
('coatnet_rmlp_0_rw_224.untrained', (3, 128, 128, 128)),
('coatnet_rmlp_1_rw_224.sw_in1k', (3, 128, 128, 128)),
('coatnet_rmlp_2_rw_224.sw_in1k', (3, 128, 128, 128)),
('coatnet_rmlp_3_rw_224.untrained', (3, 128, 128, 128)),
('coatnet_nano_cc_224.untrained', (3, 128, 128, 128)),
('coatnext_nano_rw_224.sw_in1k', (3, 128, 128, 128)),
('coatnet_2_rw_224.sw_in12k', (3, 128, 128, 128)),
('coatnet_3_rw_224.sw_in12k', (3, 128, 128, 128)),
('coatnet_rmlp_2_rw_224.sw_in12k', (3, 128, 128, 128)),
('coatnet_0_224.untrained', (3, 128, 128, 128)),
('coatnet_1_224.untrained', (3, 128, 128, 128)),
('coatnet_2_224.untrained', (3, 128, 128, 128)),
('coatnet_3_224.untrained', (3, 128, 128, 128)),
('coatnet_4_224.untrained', (3, 128, 128, 128)),
('coatnet_5_224.untrained', (3, 128, 128, 128)),
('maxvit_tiny_tf_224.in1k', (3, 64, 64, 64)),
('maxvit_tiny_tf_384.in1k', (3, 96, 96, 96)),
('maxvit_tiny_tf_512.in1k', (3, 128, 128, 128)),
('maxvit_small_tf_224.in1k', (3, 64, 64, 64)),
('maxvit_small_tf_384.in1k', (3, 96, 96, 96)),
('maxvit_small_tf_512.in1k', (3, 128, 128, 128)),
('maxvit_base_tf_224.in1k', (3, 64, 64, 64)),
('maxvit_base_tf_384.in1k', (3, 96, 96, 96)),
('maxvit_base_tf_512.in1k', (3, 128, 128, 128)),
('maxvit_large_tf_224.in1k', (3, 64, 64, 64)),
('maxvit_large_tf_384.in1k', (3, 96, 96, 96)),
('maxvit_large_tf_512.in1k', (3, 128, 128, 128)),
('maxvit_base_tf_224.in21k', (3, 64, 64, 64)),
('maxvit_base_tf_384.in21k_ft_in1k', (3, 96, 96, 96)),
('maxvit_base_tf_512.in21k_ft_in1k', (3, 128, 128, 128)),
('maxvit_large_tf_224.in21k', (3, 64, 64, 64)),
('maxvit_large_tf_384.in21k_ft_in1k', (3, 96, 96, 96)),
('maxvit_large_tf_512.in21k_ft_in1k', (3, 128, 128, 128)),
('maxvit_xlarge_tf_224.in21k', (3, 64, 64, 64)),
('maxvit_xlarge_tf_384.in21k_ft_in1k', (3, 96, 96, 96)),
('maxvit_xlarge_tf_512.in21k_ft_in1k', (3, 128, 128, 128)),
# ('maxxvitv2_rmlp_base_rw_224.sw_in12k', (3, 128, 128, 128)),
# ('maxxvitv2_rmlp_large_rw_224.untrained', (3, 128, 128, 128)),
# ('maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k', (3, 384, 384, 384)),
# ('maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k', (3, 128, 128, 128)),
# ('maxxvitv2_nano_rw_256.sw_in1k', (3, 128, 128, 128)),
# ('maxxvit_rmlp_small_rw_256.sw_in1k', (3, 128, 128, 128)),
# ('maxxvit_rmlp_tiny_rw_256.untrained', (3, 128, 128, 128)),
# ('maxxvit_rmlp_nano_rw_256.sw_in1k', (3, 128, 256, 256)),
# ('maxvit_rmlp_base_rw_224.sw_in12k', (3, 128, 128, 384)),
# ('maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k', (3, 64, 64, 64)),
# ('maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k', (3, 112, 112, 112)),
# ('maxvit_rmlp_small_rw_256.untrained', (3, 128, 128, 128)),
# ('maxvit_rmlp_small_rw_224.sw_in1k', (3, 128, 128, 128)),
# ('maxvit_rmlp_tiny_rw_256.sw_in1k', (3, 128, 128, 128)),
# ('maxvit_rmlp_nano_rw_256.sw_in1k', (3, 128, 128, 128)),
# ('maxvit_rmlp_pico_rw_256.sw_in1k', (3, 128, 128, 128)),
# ('maxvit_tiny_pm_256.untrained', (3, 128, 128, 128)), # unsupported
# ('maxvit_tiny_rw_256', (3, 128, 128, 128)), # unsupported
# ('maxvit_tiny_rw_224.sw_in1k', (3, 128, 128, 128)), # unsupported
# ('maxvit_nano_rw_256.sw_in1k', (3, 128, 128, 128)), # unsupported
# ('maxvit_pico_rw_256.untrained', (3, 128, 128, 128)), # unsupported
]
for model_name, shape in maxvit_models[:1]:
print('Go for: {}'.format(model_name))
m = timm_3d.create_model(
model_name,
pretrained=False,
num_classes=0,
global_pool=''
)
o = m(torch.randn((2,) + shape))
print(f'Unpooled shape: {o.shape}')
if 1:
effnet_models = [
('mnasnet_100.rmsp_in1k', (3, 128, 128, 128)),
('mnasnet_050.untrained', (3, 128, 128, 128)),
('mnasnet_075.untrained', (3, 128, 128, 128)),
('mnasnet_100.rmsp_in1k', (3, 128, 128, 128)),
('mnasnet_140.untrained', (3, 128, 128, 128)),
('semnasnet_050.untrained', (3, 128, 128, 128)),
('semnasnet_075.rmsp_in1k', (3, 128, 128, 128)),
('semnasnet_100.rmsp_in1k', (3, 128, 128, 128)),
('semnasnet_140.untrained', (3, 128, 128, 128)),
('mnasnet_small.lamb_in1k', (3, 128, 128, 128)),
('mobilenetv2_035.untrained', (3, 128, 128, 128)),
('mobilenetv2_050.lamb_in1k', (3, 128, 128, 128)),
('mobilenetv2_075.untrained', (3, 128, 128, 128)),
('mobilenetv2_100.ra_in1k', (3, 128, 128, 128)),
('mobilenetv2_110d.ra_in1k', (3, 128, 128, 128)),
('mobilenetv2_120d.ra_in1k', (3, 128, 128, 128)),
('mobilenetv2_140.ra_in1k', (3, 128, 128, 128)),
('fbnetc_100.rmsp_in1k', (3, 128, 128, 128)),
('spnasnet_100.rmsp_in1k', (3, 128, 128, 128)),
('efficientnet_b0.ra_in1k', (3, 128, 128, 128)),
('efficientnet_b1.ft_in1k', (3, 128, 128, 128)),
('efficientnet_b2.ra_in1k', (3, 128, 128, 128)),
('efficientnet_b3.ra2_in1k', (3, 128, 128, 128)),
('efficientnet_b4.ra2_in1k', (3, 128, 128, 128)),
('efficientnet_b5.sw_in12k_ft_in1k', (3, 128, 128, 128)),
('efficientnet_b5.sw_in12k', (3, 128, 128, 128)),
('efficientnet_b6.untrained', (3, 128, 128, 128)),
('efficientnet_b7.untrained', (3, 128, 128, 128)),
('efficientnet_b8.untrained', (3, 128, 128, 128)),
('efficientnet_l2.untrained', (3, 128, 128, 128)),
('efficientnet_b0_gn.untrained', (3, 128, 128, 128)),
('efficientnet_b0_g8_gn.untrained', (3, 128, 128, 128)),
('efficientnet_b0_g16_evos.untrained', (3, 128, 128, 128)),
('efficientnet_b3_gn.untrained', (3, 128, 128, 128)),
('efficientnet_b3_g8_gn.untrained', (3, 128, 128, 128)),
('efficientnet_es.ra_in1k', (3, 128, 128, 128)),
('efficientnet_em.ra2_in1k', (3, 128, 128, 128)),
('efficientnet_el.ra_in1k', (3, 128, 128, 128)),
('efficientnet_es_pruned.in1k', (3, 128, 128, 128)),
('efficientnet_el_pruned.in1k', (3, 128, 128, 128)),
('efficientnet_cc_b0_4e.untrained', (3, 128, 128, 128)),
('efficientnet_cc_b0_8e.untrained', (3, 128, 128, 128)),
('efficientnet_cc_b1_8e.untrained', (3, 128, 128, 128)),
('efficientnet_lite0.ra_in1k', (3, 128, 128, 128)),
('efficientnet_lite1.untrained', (3, 128, 128, 128)),
('efficientnet_lite2.untrained', (3, 128, 128, 128)),
('efficientnet_lite3.untrained', (3, 128, 128, 128)),
('efficientnet_lite4.untrained', (3, 128, 128, 128)),
('efficientnet_b1_pruned.in1k', (3, 128, 128, 128)),
('efficientnet_b2_pruned.in1k', (3, 128, 128, 128)),
('efficientnet_b3_pruned.in1k', (3, 128, 128, 128)),
('efficientnetv2_rw_t.ra2_in1k', (3, 128, 128, 128)),
('gc_efficientnetv2_rw_t.agc_in1k', (3, 128, 128, 128)),
('efficientnetv2_rw_s.ra2_in1k', (3, 128, 128, 128)),
('efficientnetv2_rw_m.agc_in1k', (3, 128, 128, 128)),
('efficientnetv2_s.untrained', (3, 128, 128, 128)),
('efficientnetv2_m.untrained', (3, 128, 128, 128)),
('efficientnetv2_l.untrained', (3, 128, 128, 128)),
('efficientnetv2_xl.untrained', (3, 128, 128, 128)),
('tf_efficientnet_b0.ns_jft_in1k', (3, 128, 128, 128)),
('tf_efficientnet_b1.ns_jft_in1k', (3, 128, 128, 128)),
('tf_efficientnet_b2.ns_jft_in1k', (3, 128, 128, 128)),
('tf_efficientnet_b3.ns_jft_in1k', (3, 128, 128, 128)),
('tf_efficientnet_b4.ns_jft_in1k', (3, 128, 128, 128)),
('tf_efficientnet_b5.ns_jft_in1k', (3, 128, 128, 128)),
('tf_efficientnet_b6.ns_jft_in1k', (3, 128, 128, 128)),
('tf_efficientnet_b7.ns_jft_in1k', (3, 128, 128, 128)),
('tf_efficientnet_l2.ns_jft_in1k_475', (3, 128, 128, 128)),
('tf_efficientnet_l2.ns_jft_in1k', (3, 128, 128, 128)),
('tf_efficientnet_b0.ap_in1k', (3, 128, 128, 128)),
('tf_efficientnet_b1.ap_in1k', (3, 128, 128, 128)),
('tf_efficientnet_b2.ap_in1k', (3, 128, 128, 128)),
('tf_efficientnet_b3.ap_in1k', (3, 128, 128, 128)),
('tf_efficientnet_b4.ap_in1k', (3, 128, 128, 128)),
('tf_efficientnet_b5.ap_in1k', (3, 128, 128, 128)),
('tf_efficientnet_b6.ap_in1k', (3, 128, 128, 128)),
('tf_efficientnet_b7.ap_in1k', (3, 128, 128, 128)),
('tf_efficientnet_b8.ap_in1k', (3, 128, 128, 128)),
('tf_efficientnet_b5.ra_in1k', (3, 128, 128, 128)),
('tf_efficientnet_b7.ra_in1k', (3, 128, 128, 128)),
('tf_efficientnet_b8.ra_in1k', (3, 128, 128, 128)),
('tf_efficientnet_b0.aa_in1k', (3, 128, 128, 128)),
('tf_efficientnet_b1.aa_in1k', (3, 128, 128, 128)),
('tf_efficientnet_b2.aa_in1k', (3, 128, 128, 128)),
('tf_efficientnet_b3.aa_in1k', (3, 128, 128, 128)),
('tf_efficientnet_b4.aa_in1k', (3, 128, 128, 128)),
('tf_efficientnet_b5.aa_in1k', (3, 128, 128, 128)),
('tf_efficientnet_b6.aa_in1k', (3, 128, 128, 128)),
('tf_efficientnet_b7.aa_in1k', (3, 128, 128, 128)),
('tf_efficientnet_b0.in1k', (3, 128, 128, 128)),
('tf_efficientnet_b1.in1k', (3, 128, 128, 128)),
('tf_efficientnet_b2.in1k', (3, 128, 128, 128)),
('tf_efficientnet_b3.in1k', (3, 128, 128, 128)),
('tf_efficientnet_b4.in1k', (3, 128, 128, 128)),
('tf_efficientnet_b5.in1k', (3, 128, 128, 128)),
('tf_efficientnet_es.in1k', (3, 128, 128, 128)),
('tf_efficientnet_em.in1k', (3, 128, 128, 128)),
('tf_efficientnet_el.in1k', (3, 128, 128, 128)),
('tf_efficientnet_cc_b0_4e.in1k', (3, 128, 128, 128)),
('tf_efficientnet_cc_b0_8e.in1k', (3, 128, 128, 128)),
('tf_efficientnet_cc_b1_8e.in1k', (3, 128, 128, 128)),
('tf_efficientnet_lite0.in1k', (3, 128, 128, 128)),
('tf_efficientnet_lite1.in1k', (3, 128, 128, 128)),
('tf_efficientnet_lite2.in1k', (3, 128, 128, 128)),
('tf_efficientnet_lite3.in1k', (3, 128, 128, 128)),
('tf_efficientnet_lite4.in1k', (3, 128, 128, 128)),
('tf_efficientnetv2_s.in21k_ft_in1k', (3, 128, 128, 128)),
('tf_efficientnetv2_m.in21k_ft_in1k', (3, 128, 128, 128)),
('tf_efficientnetv2_l.in21k_ft_in1k', (3, 128, 128, 128)),
('tf_efficientnetv2_xl.in21k_ft_in1k', (3, 128, 128, 128)),
('tf_efficientnetv2_s.in1k', (3, 128, 128, 128)),
('tf_efficientnetv2_m.in1k', (3, 128, 128, 128)),
('tf_efficientnetv2_l.in1k', (3, 128, 128, 128)),
('tf_efficientnetv2_s.in21k', (3, 128, 128, 128)),
('tf_efficientnetv2_m.in21k', (3, 128, 128, 128)),
('tf_efficientnetv2_l.in21k', (3, 128, 128, 128)),
('tf_efficientnetv2_xl.in21k', (3, 128, 128, 128)),
('tf_efficientnetv2_b0.in1k', (3, 128, 128, 128)),
('tf_efficientnetv2_b1.in1k', (3, 128, 128, 128)),
('tf_efficientnetv2_b2.in1k', (3, 128, 128, 128)),
('tf_efficientnetv2_b3.in21k_ft_in1k', (3, 128, 128, 128)),
('tf_efficientnetv2_b3.in1k', (3, 128, 128, 128)),
('tf_efficientnetv2_b3.in21k', (3, 128, 128, 128)),
('mixnet_s.ft_in1k', (3, 128, 128, 128)),
('mixnet_m.ft_in1k', (3, 128, 128, 128)),
('mixnet_l.ft_in1k', (3, 128, 128, 128)),
('mixnet_xl.ra_in1k', (3, 128, 128, 128)),
('mixnet_xxl.untrained', (3, 128, 128, 128)),
('tf_mixnet_s.in1k', (3, 128, 128, 128)),
('tf_mixnet_m.in1k', (3, 128, 128, 128)),
('tf_mixnet_l.in1k', (3, 128, 128, 128)),
("tinynet_a.in1k", (3, 128, 128, 128)),
("tinynet_b.in1k", (3, 128, 128, 128)),
("tinynet_c.in1k", (3, 128, 128, 128)),
("tinynet_d.in1k", (3, 128, 128, 128)),
("tinynet_e.in1k", (3, 128, 128, 128)),
]
for eff, shape in effnet_models[:1]:
print('Go for: {}'.format(eff))
m = timm_3d.create_model(
eff,
pretrained=False,
num_classes=0,
global_pool=''
)
o = m(torch.randn((2,) + shape))
print(f'Unpooled shape: {o.shape}')
# random pretrain check
effnet_models = [
('fbnetc_100.rmsp_in1k', (3, 128, 128, 128)),
]
for eff, shape in effnet_models:
print('Go for: {}'.format(eff))
m = timm_3d.create_model(
eff,
pretrained=True,
num_classes=0,
global_pool=''
)
o = m(torch.randn((2,) + shape))
print(f'Unpooled shape: {o.shape}')
if 1:
from timm_3d.models.convnext import default_cfgs
convnext_models = list(default_cfgs.keys())
print(convnext_models)
shape = (3, 64, 64, 64)
for model_name in convnext_models:
print('Go for: {}'.format(model_name))
m = timm_3d.create_model(
model_name,
pretrained=False,
num_classes=0,
global_pool=''
)
o = m(torch.randn((2,) + shape))
print(f'Unpooled shape: {o.shape}')
if 1:
from timm_3d.models.densenet import default_cfgs
models_list = list(default_cfgs.keys())
print(models_list)
shape = (3, 64, 64, 64)
for model_name in models_list:
print('Go for: {}'.format(model_name))
m = timm_3d.create_model(
model_name,
pretrained=False,
num_classes=0,
global_pool=''
)
o = m(torch.randn((2,) + shape))
print(f'Unpooled shape: {o.shape}')
if 1:
from timm_3d.models.vgg import default_cfgs
models_list = list(default_cfgs.keys())
print(models_list)
shape = (3, 64, 64, 64)
for model_name in models_list:
print('Go for: {}'.format(model_name))
m = timm_3d.create_model(
model_name,
pretrained=False,
num_classes=0,
global_pool=''
)
o = m(torch.randn((2,) + shape))
print(f'Unpooled shape: {o.shape}')