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Signed-off-by: Jael Gu <mengjia.gu@zilliz.com>
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Jael Gu 2 years ago
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  1. 6
      README.md
  2. 132
      test_onnx.py
  3. 132
      test_torchscript.py

6
README.md
View File

@ -15,7 +15,7 @@ who maintains SOTA deep-learning models and tools in computer vision.
## Code Example
Load an image from path './towhee.jpg'
Load an image from path './towhee.jpeg'
and use the pre-trained ResNet50 model ('resnet50') to generate an image embedding.
*Write the pipeline in simplified style:*
@ -23,7 +23,7 @@ and use the pre-trained ResNet50 model ('resnet50') to generate an image embeddi
```python
import towhee
towhee.glob('./towhee.jpg') \
towhee.glob('./towhee.jpeg') \
.image_decode() \
.image_embedding.timm(model_name='resnet50') \
.show()
@ -35,7 +35,7 @@ towhee.glob('./towhee.jpg') \
```python
import towhee
towhee.glob['path']('./towhee.jpg') \
towhee.glob['path']('./towhee.jpeg') \
.image_decode['path', 'img']() \
.image_embedding.timm['img', 'vec'](model_name='resnet50') \
.select['img', 'vec']() \

132
test_onnx.py
View File

@ -7,74 +7,74 @@ f.write('model_name, run_op, save_onnx, check_onnx\n')
# models = TimmImage.supported_model_names()[:1]
models = [
# 'vgg11',
'vgg11',
'resnet18',
# 'resnetv2_50',
# 'seresnet33ts',
# 'skresnet18',
# 'resnext26ts',
# 'seresnext26d_32x4d',
# 'skresnext50_32x4d',
# 'convit_base',
# 'inception_v4',
# 'efficientnet_b0',
'resnetv2_50',
'seresnet33ts',
'skresnet18',
'resnext26ts',
'seresnext26d_32x4d',
'skresnext50_32x4d',
'convit_base',
'inception_v4',
'efficientnet_b0',
'tf_efficientnet_b0',
# 'swin_base_patch4_window7_224',
# 'vit_base_patch8_224',
# 'beit_base_patch16_224',
# 'convnext_base',
# 'crossvit_9_240',
# 'convmixer_768_32',
# 'coat_lite_mini',
# 'inception_v3',
# 'cait_m36_384',
# 'cspdarknet53',
# 'deit_base_distilled_patch16_224',
# 'densenet121',
# 'dla34',
# 'dm_nfnet_f0',
# 'nf_regnet_b1',
# 'nf_resnet50',
# 'dpn68',
# 'ese_vovnet19b_dw',
# 'fbnetc_100',
# 'fbnetv3_b',
# 'halonet26t',
# 'eca_halonext26ts',
# 'sehalonet33ts',
# 'hardcorenas_a',
# 'hrnet_w18',
# 'jx_nest_base',
# 'lcnet_050',
# 'levit_128',
# 'mixer_b16_224',
# 'mixnet_s',
# 'mnasnet_100',
# 'mobilenetv2_050',
# 'mobilenetv3_large_100',
# 'nasnetalarge',
# 'pit_b_224',
# 'pnasnet5large',
# 'regnetx_002',
# 'repvgg_a2',
# 'res2net50_14w_8s',
# 'res2next50',
# 'resmlp_12_224',
# 'resnest14d',
# 'rexnet_100',
# 'selecsls42b',
# 'semnasnet_075',
# 'tinynet_a',
# 'tnt_s_patch16_224',
# 'tresnet_l',
# 'twins_pcpvt_base',
# 'visformer_small',
# 'xception',
# 'xcit_large_24_p8_224',
# 'ghostnet_100',
# 'gmlp_s16_224',
# 'lambda_resnet26rpt_256',
# 'spnasnet_100',
'swin_base_patch4_window7_224',
'vit_base_patch8_224',
'beit_base_patch16_224',
'convnext_base',
'crossvit_9_240',
'convmixer_768_32',
'coat_lite_mini',
'inception_v3',
'cait_m36_384',
'cspdarknet53',
'deit_base_distilled_patch16_224',
'densenet121',
'dla34',
'dm_nfnet_f0',
'nf_regnet_b1',
'nf_resnet50',
'dpn68',
'ese_vovnet19b_dw',
'fbnetc_100',
'fbnetv3_b',
'halonet26t',
'eca_halonext26ts',
'sehalonet33ts',
'hardcorenas_a',
'hrnet_w18',
'jx_nest_base',
'lcnet_050',
'levit_128',
'mixer_b16_224',
'mixnet_s',
'mnasnet_100',
'mobilenetv2_050',
'mobilenetv3_large_100',
'nasnetalarge',
'pit_b_224',
'pnasnet5large',
'regnetx_002',
'repvgg_a2',
'res2net50_14w_8s',
'res2next50',
'resmlp_12_224',
'resnest14d',
'rexnet_100',
'selecsls42b',
'semnasnet_075',
'tinynet_a',
'tnt_s_patch16_224',
'tresnet_l',
'twins_pcpvt_base',
'visformer_small',
'xception',
'xcit_large_24_p8_224',
'ghostnet_100',
'gmlp_s16_224',
'lambda_resnet26rpt_256',
'spnasnet_100',
]
decoder = ops.image_decode()

132
test_torchscript.py
View File

@ -7,74 +7,74 @@ f.write('model_name,run_op,save_torchscript,check_result\n')
# models = TimmImage.supported_model_names()[:1]
models = [
# 'vgg11',
'vgg11',
'resnet18',
# 'resnetv2_50',
# 'seresnet33ts',
# 'skresnet18',
# 'resnext26ts',
# 'seresnext26d_32x4d',
# 'skresnext50_32x4d',
# 'convit_base',
# 'inception_v4',
# 'efficientnet_b0',
'resnetv2_50',
'seresnet33ts',
'skresnet18',
'resnext26ts',
'seresnext26d_32x4d',
'skresnext50_32x4d',
'convit_base',
'inception_v4',
'efficientnet_b0',
'tf_efficientnet_b0',
# 'swin_base_patch4_window7_224',
# 'vit_base_patch8_224',
# 'beit_base_patch16_224',
# 'convnext_base',
# 'crossvit_9_240',
# 'convmixer_768_32',
# 'coat_lite_mini',
# 'inception_v3',
# 'cait_m36_384',
# 'cspdarknet53',
# 'deit_base_distilled_patch16_224',
# 'densenet121',
# 'dla34',
# 'dm_nfnet_f0',
# 'nf_regnet_b1',
# 'nf_resnet50',
# 'dpn68',
# 'ese_vovnet19b_dw',
# 'fbnetc_100',
# 'fbnetv3_b',
# 'halonet26t',
# 'eca_halonext26ts',
# 'sehalonet33ts',
# 'hardcorenas_a',
# 'hrnet_w18',
# 'jx_nest_base',
# 'lcnet_050',
# 'levit_128',
# 'mixer_b16_224',
# 'mixnet_s',
# 'mnasnet_100',
# 'mobilenetv2_050',
# 'mobilenetv3_large_100',
# 'nasnetalarge',
# 'pit_b_224',
# 'pnasnet5large',
# 'regnetx_002',
# 'repvgg_a2',
# 'res2net50_14w_8s',
# 'res2next50',
# 'resmlp_12_224',
# 'resnest14d',
# 'rexnet_100',
# 'selecsls42b',
# 'semnasnet_075',
# 'tinynet_a',
# 'tnt_s_patch16_224',
# 'tresnet_l',
# 'twins_pcpvt_base',
# 'visformer_small',
# 'xception',
# 'xcit_large_24_p8_224',
# 'ghostnet_100',
# 'gmlp_s16_224',
# 'lambda_resnet26rpt_256',
# 'spnasnet_100',
'swin_base_patch4_window7_224',
'vit_base_patch8_224',
'beit_base_patch16_224',
'convnext_base',
'crossvit_9_240',
'convmixer_768_32',
'coat_lite_mini',
'inception_v3',
'cait_m36_384',
'cspdarknet53',
'deit_base_distilled_patch16_224',
'densenet121',
'dla34',
'dm_nfnet_f0',
'nf_regnet_b1',
'nf_resnet50',
'dpn68',
'ese_vovnet19b_dw',
'fbnetc_100',
'fbnetv3_b',
'halonet26t',
'eca_halonext26ts',
'sehalonet33ts',
'hardcorenas_a',
'hrnet_w18',
'jx_nest_base',
'lcnet_050',
'levit_128',
'mixer_b16_224',
'mixnet_s',
'mnasnet_100',
'mobilenetv2_050',
'mobilenetv3_large_100',
'nasnetalarge',
'pit_b_224',
'pnasnet5large',
'regnetx_002',
'repvgg_a2',
'res2net50_14w_8s',
'res2next50',
'resmlp_12_224',
'resnest14d',
'rexnet_100',
'selecsls42b',
'semnasnet_075',
'tinynet_a',
'tnt_s_patch16_224',
'tresnet_l',
'twins_pcpvt_base',
'visformer_small',
'xception',
'xcit_large_24_p8_224',
'ghostnet_100',
'gmlp_s16_224',
'lambda_resnet26rpt_256',
'spnasnet_100',
]
decoder = ops.image_decode()

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