logo

image-embedding
/
timm
repo-copy-icon

copied

Browse Source

Update test 

Signed-off-by: Jael Gu <mengjia.gu@zilliz.com>
main
Jael Gu 3 years ago
parent
107529e868
commit
601022a74b
3 changed files with 135 additions and 135 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Unified View
Diff Options
Show Stats Download Patch File Download Diff File
  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 ## 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. and use the pre-trained ResNet50 model ('resnet50') to generate an image embedding.
*Write the pipeline in simplified style:* *Write the pipeline in simplified style:*
@ -23,7 +23,7 @@ and use the pre-trained ResNet50 model ('resnet50') to generate an image embeddi
```python ```python
import towhee import towhee
towhee.glob('./towhee.jpg') \ towhee.glob('./towhee.jpeg') \
.image_decode() \ .image_decode() \
.image_embedding.timm(model_name='resnet50') \ .image_embedding.timm(model_name='resnet50') \
.show() .show()
@ -35,7 +35,7 @@ towhee.glob('./towhee.jpg') \
```python ```python
import towhee import towhee
towhee.glob['path']('./towhee.jpg') \ towhee.glob['path']('./towhee.jpeg') \
.image_decode['path', 'img']() \ .image_decode['path', 'img']() \
.image_embedding.timm['img', 'vec'](model_name='resnet50') \ .image_embedding.timm['img', 'vec'](model_name='resnet50') \
.select['img', 'vec']() \ .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 = TimmImage.supported_model_names()[:1]
models = [ models = [
# 'vgg11', 'vgg11',
'resnet18', 'resnet18',
# 'resnetv2_50', 'resnetv2_50',
# 'seresnet33ts', 'seresnet33ts',
# 'skresnet18', 'skresnet18',
# 'resnext26ts', 'resnext26ts',
# 'seresnext26d_32x4d', 'seresnext26d_32x4d',
# 'skresnext50_32x4d', 'skresnext50_32x4d',
# 'convit_base', 'convit_base',
# 'inception_v4', 'inception_v4',
# 'efficientnet_b0', 'efficientnet_b0',
'tf_efficientnet_b0', 'tf_efficientnet_b0',
# 'swin_base_patch4_window7_224', 'swin_base_patch4_window7_224',
# 'vit_base_patch8_224', 'vit_base_patch8_224',
# 'beit_base_patch16_224', 'beit_base_patch16_224',
# 'convnext_base', 'convnext_base',
# 'crossvit_9_240', 'crossvit_9_240',
# 'convmixer_768_32', 'convmixer_768_32',
# 'coat_lite_mini', 'coat_lite_mini',
# 'inception_v3', 'inception_v3',
# 'cait_m36_384', 'cait_m36_384',
# 'cspdarknet53', 'cspdarknet53',
# 'deit_base_distilled_patch16_224', 'deit_base_distilled_patch16_224',
# 'densenet121', 'densenet121',
# 'dla34', 'dla34',
# 'dm_nfnet_f0', 'dm_nfnet_f0',
# 'nf_regnet_b1', 'nf_regnet_b1',
# 'nf_resnet50', 'nf_resnet50',
# 'dpn68', 'dpn68',
# 'ese_vovnet19b_dw', 'ese_vovnet19b_dw',
# 'fbnetc_100', 'fbnetc_100',
# 'fbnetv3_b', 'fbnetv3_b',
# 'halonet26t', 'halonet26t',
# 'eca_halonext26ts', 'eca_halonext26ts',
# 'sehalonet33ts', 'sehalonet33ts',
# 'hardcorenas_a', 'hardcorenas_a',
# 'hrnet_w18', 'hrnet_w18',
# 'jx_nest_base', 'jx_nest_base',
# 'lcnet_050', 'lcnet_050',
# 'levit_128', 'levit_128',
# 'mixer_b16_224', 'mixer_b16_224',
# 'mixnet_s', 'mixnet_s',
# 'mnasnet_100', 'mnasnet_100',
# 'mobilenetv2_050', 'mobilenetv2_050',
# 'mobilenetv3_large_100', 'mobilenetv3_large_100',
# 'nasnetalarge', 'nasnetalarge',
# 'pit_b_224', 'pit_b_224',
# 'pnasnet5large', 'pnasnet5large',
# 'regnetx_002', 'regnetx_002',
# 'repvgg_a2', 'repvgg_a2',
# 'res2net50_14w_8s', 'res2net50_14w_8s',
# 'res2next50', 'res2next50',
# 'resmlp_12_224', 'resmlp_12_224',
# 'resnest14d', 'resnest14d',
# 'rexnet_100', 'rexnet_100',
# 'selecsls42b', 'selecsls42b',
# 'semnasnet_075', 'semnasnet_075',
# 'tinynet_a', 'tinynet_a',
# 'tnt_s_patch16_224', 'tnt_s_patch16_224',
# 'tresnet_l', 'tresnet_l',
# 'twins_pcpvt_base', 'twins_pcpvt_base',
# 'visformer_small', 'visformer_small',
# 'xception', 'xception',
# 'xcit_large_24_p8_224', 'xcit_large_24_p8_224',
# 'ghostnet_100', 'ghostnet_100',
# 'gmlp_s16_224', 'gmlp_s16_224',
# 'lambda_resnet26rpt_256', 'lambda_resnet26rpt_256',
# 'spnasnet_100', 'spnasnet_100',
] ]
decoder = ops.image_decode() 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 = TimmImage.supported_model_names()[:1]
models = [ models = [
# 'vgg11', 'vgg11',
'resnet18', 'resnet18',
# 'resnetv2_50', 'resnetv2_50',
# 'seresnet33ts', 'seresnet33ts',
# 'skresnet18', 'skresnet18',
# 'resnext26ts', 'resnext26ts',
# 'seresnext26d_32x4d', 'seresnext26d_32x4d',
# 'skresnext50_32x4d', 'skresnext50_32x4d',
# 'convit_base', 'convit_base',
# 'inception_v4', 'inception_v4',
# 'efficientnet_b0', 'efficientnet_b0',
'tf_efficientnet_b0', 'tf_efficientnet_b0',
# 'swin_base_patch4_window7_224', 'swin_base_patch4_window7_224',
# 'vit_base_patch8_224', 'vit_base_patch8_224',
# 'beit_base_patch16_224', 'beit_base_patch16_224',
# 'convnext_base', 'convnext_base',
# 'crossvit_9_240', 'crossvit_9_240',
# 'convmixer_768_32', 'convmixer_768_32',
# 'coat_lite_mini', 'coat_lite_mini',
# 'inception_v3', 'inception_v3',
# 'cait_m36_384', 'cait_m36_384',
# 'cspdarknet53', 'cspdarknet53',
# 'deit_base_distilled_patch16_224', 'deit_base_distilled_patch16_224',
# 'densenet121', 'densenet121',
# 'dla34', 'dla34',
# 'dm_nfnet_f0', 'dm_nfnet_f0',
# 'nf_regnet_b1', 'nf_regnet_b1',
# 'nf_resnet50', 'nf_resnet50',
# 'dpn68', 'dpn68',
# 'ese_vovnet19b_dw', 'ese_vovnet19b_dw',
# 'fbnetc_100', 'fbnetc_100',
# 'fbnetv3_b', 'fbnetv3_b',
# 'halonet26t', 'halonet26t',
# 'eca_halonext26ts', 'eca_halonext26ts',
# 'sehalonet33ts', 'sehalonet33ts',
# 'hardcorenas_a', 'hardcorenas_a',
# 'hrnet_w18', 'hrnet_w18',
# 'jx_nest_base', 'jx_nest_base',
# 'lcnet_050', 'lcnet_050',
# 'levit_128', 'levit_128',
# 'mixer_b16_224', 'mixer_b16_224',
# 'mixnet_s', 'mixnet_s',
# 'mnasnet_100', 'mnasnet_100',
# 'mobilenetv2_050', 'mobilenetv2_050',
# 'mobilenetv3_large_100', 'mobilenetv3_large_100',
# 'nasnetalarge', 'nasnetalarge',
# 'pit_b_224', 'pit_b_224',
# 'pnasnet5large', 'pnasnet5large',
# 'regnetx_002', 'regnetx_002',
# 'repvgg_a2', 'repvgg_a2',
# 'res2net50_14w_8s', 'res2net50_14w_8s',
# 'res2next50', 'res2next50',
# 'resmlp_12_224', 'resmlp_12_224',
# 'resnest14d', 'resnest14d',
# 'rexnet_100', 'rexnet_100',
# 'selecsls42b', 'selecsls42b',
# 'semnasnet_075', 'semnasnet_075',
# 'tinynet_a', 'tinynet_a',
# 'tnt_s_patch16_224', 'tnt_s_patch16_224',
# 'tresnet_l', 'tresnet_l',
# 'twins_pcpvt_base', 'twins_pcpvt_base',
# 'visformer_small', 'visformer_small',
# 'xception', 'xception',
# 'xcit_large_24_p8_224', 'xcit_large_24_p8_224',
# 'ghostnet_100', 'ghostnet_100',
# 'gmlp_s16_224', 'gmlp_s16_224',
# 'lambda_resnet26rpt_256', 'lambda_resnet26rpt_256',
# 'spnasnet_100', 'spnasnet_100',
] ]
decoder = ops.image_decode() decoder = ops.image_decode()

Write Preview
Loading…
Cancel
Save