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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,101 +1,101 @@ | ||
| # Tensorflow slim "Resnet V2" to PyTorch conversion example | ||
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| Model: ["ResNet V2 152" for Imagenet](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v2.py) | ||
|
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| Source: TensorFlow | ||
|
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||
| Destination: PyTorch | ||
|
|
||
| --- | ||
|
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||
| ## Prepare the TensorFlow model | ||
|
|
||
| You need to prepare your pre-trained TensorFlow model firstly. And there is a pre-trained model extractor for frameworks to help you. You can refer it to extract your TensorFlow model checkpoint files. | ||
|
|
||
| ```bash | ||
| $ mmdownload -f tensorflow -n resnet_v2_152 | ||
|
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||
| Downloading file [./resnet_v2_152_2017_04_14.tar.gz] from [http://download.tensorflow.org/models/resnet_v2_152_2017_04_14.tar.gz] | ||
| 100% [......................................................................] 675629399 / 675629399 | ||
|
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| Model saved in file: ./imagenet_resnet_v2_152.ckpt | ||
| ``` | ||
|
|
||
| Then you got the TensorFlow checkpoint files for *ResNet V2 152* model which is in your current working directory, including *imagenet_resnet_v2_152.ckpt.meta* for architecture , *imagenet_resnet_v2_152.ckpt.data-00000-of-00001* and *imagenet_resnet_v2_152.ckpt.index* for weights. | ||
|
|
||
| ## Find the output node of the model | ||
|
|
||
| TensorFlow original checkpoint files contain many operators (if you tried tensorboard to visualize the graph) which is not used in our toolkits. we should prune them with specifying the output node of your model. | ||
|
|
||
| ```bash | ||
| $ mmvismeta imagenet_resnet_v2_152.ckpt.meta ./log/ | ||
| . | ||
| . | ||
| . | ||
| TensorBoard 1.5.1 at http://kit-station:6006 (Press CTRL+C to quit) | ||
| ``` | ||
|
|
||
| Then you can open URL above to find the output node of your model, | ||
|
|
||
|  | ||
|
|
||
| like the squeeze node named *MMdnn_Output* we set up in our tensorflow model extractor. Detail information is in [TensorFlow README](https://github.com/Microsoft/MMdnn/blob/master/mmdnn/conversion/tensorflow/README.md) | ||
|
|
||
| ## Convert TensorFlow Model to PyTorch | ||
|
|
||
| We provide two ways to convert models. | ||
|
|
||
| ### **One-step Command** | ||
|
|
||
| Above MMdnn@0.1.4, we provide one command to achieve the conversion | ||
|
|
||
| ```bash | ||
| $ mmconvert -sf tensorflow -in imagenet_resnet_v2_152.ckpt.meta -iw imagenet_resnet_v2_152.ckpt --dstNode MMdnn_Output -df pytorch -om tf_to_pytorch_resnet_152.pth | ||
| . | ||
| . | ||
| . | ||
| PyTorch model file is saved as [tf_to_pytorch_resnet_152.pth], generated by [052eb72db9934edc90d8e1ffa48144d7.py] and [052eb72db9934edc90d8e1ffa48144d7.npy]. | ||
| ``` | ||
|
|
||
| Then you get the PyTorch original model *tf_to_pytorch_resnet_152.pth* converted from TensorFlow. **052eb72db9934edc90d8e1ffa48144d7.py** and **052eb72db9934edc90d8e1ffa48144d7.npy** are temporal files which will be removed automatically. | ||
|
|
||
|
|
||
| ### Step-by-step Command for debugging | ||
|
|
||
| #### Convert the pre-trained model files to intermediate representation | ||
|
|
||
| ```bash | ||
| $ mmtoir -f tensorflow -n imagenet_resnet_v2_152.ckpt.meta -w imagenet_resnet_v2_152.ckpt --dstNode MMdnn_Output -o converted | ||
|
|
||
| Parse file [imagenet_resnet_v2_152.ckpt.meta] with binary format successfully. | ||
| Tensorflow model file [imagenet_resnet_v2_152.ckpt.meta] loaded successfully. | ||
| Tensorflow checkpoint file [imagenet_resnet_v2_152.ckpt] loaded successfully. [816] variables loaded. | ||
| IR network structure is saved as [converted.json]. | ||
| IR network structure is saved as [converted.pb]. | ||
| IR weights are saved as [converted.npy]. | ||
| ``` | ||
|
|
||
| Then you got the **intermediate representation** files *converted.json* for visualization, *converted.proto* and *converted.npy* for next steps. | ||
|
|
||
| #### Convert the IR files to PyTorch code | ||
|
|
||
| ```bash | ||
| $ mmtocode -f pytorch -n converted.pb -w converted.npy -d converted_pytorch.py -dw converted_pytorch.npy | ||
|
|
||
| Parse file [converted.pb] with binary format successfully. | ||
| Target network code snippet is saved as [converted_pytorch.py]. | ||
| Target weights are saved as [converted_pytorch.npy]. | ||
| ``` | ||
|
|
||
| And you will get a filename *converted_pytorch.py*, which contains the **original PyTorch** codes to build the *ResNet V2 152* network and *converted_pytorch.npy* which is used to set weights in the network building process. | ||
|
|
||
| With the three steps, you have already converted the pre-trained TensorFlow *ResNet V2 152* models to PyTorch network building file *converted_pytorch.py* and weights file *converted_pytorch.npy*. You can use these two files to fine-tune training or inference. | ||
|
|
||
| #### Dump the original PyTorch model | ||
|
|
||
| ```bash | ||
| $ mmtomodel -f pytorch -in converted_pytorch.py -iw converted_pytorch.npy -o converted_pytorch.pth | ||
|
|
||
| PyTorch model file is saved as [converted_pytorch.pth], generated by [converted_pytorch.py] and [converted_pytorch.npy]. | ||
| ``` | ||
| The file *converted_pytorch.pth* can be loaded by PyTorch directly. | ||
| # Tensorflow slim "Resnet V2" to PyTorch conversion example | ||
|
|
||
| Model: ["ResNet V2 152" for Imagenet](https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v2.py) | ||
|
|
||
| Source: TensorFlow | ||
|
|
||
| Destination: PyTorch | ||
|
|
||
| --- | ||
|
|
||
| ## Prepare the TensorFlow model | ||
|
|
||
| You need to prepare your pre-trained TensorFlow model firstly. And there is a pre-trained model extractor for frameworks to help you. You can refer it to extract your TensorFlow model checkpoint files. | ||
|
|
||
| ```bash | ||
| $ mmdownload -f tensorflow -n resnet_v2_152 | ||
|
|
||
| Downloading file [./resnet_v2_152_2017_04_14.tar.gz] from [http://download.tensorflow.org/models/resnet_v2_152_2017_04_14.tar.gz] | ||
| 100% [......................................................................] 675629399 / 675629399 | ||
|
|
||
| Model saved in file: ./imagenet_resnet_v2_152.ckpt | ||
| ``` | ||
|
|
||
| Then you got the TensorFlow checkpoint files for *ResNet V2 152* model which is in your current working directory, including *imagenet_resnet_v2_152.ckpt.meta* for architecture , *imagenet_resnet_v2_152.ckpt.data-00000-of-00001* and *imagenet_resnet_v2_152.ckpt.index* for weights. | ||
|
|
||
| ## Find the output node of the model | ||
|
|
||
| TensorFlow original checkpoint files contain many operators (if you tried tensorboard to visualize the graph) which is not used in our toolkits. we should prune them with specifying the output node of your model. | ||
|
|
||
| ```bash | ||
| $ mmvismeta imagenet_resnet_v2_152.ckpt.meta ./log/ | ||
| . | ||
| . | ||
| . | ||
| TensorBoard 1.5.1 at http://kit-station:6006 (Press CTRL+C to quit) | ||
| ``` | ||
|
|
||
| Then you can open URL above to find the output node of your model, | ||
|
|
||
|  | ||
|
|
||
| like the squeeze node named *MMdnn_Output* we set up in our tensorflow model extractor. Detail information is in [TensorFlow README](https://github.com/Microsoft/MMdnn/blob/master/mmdnn/conversion/tensorflow/README.md) | ||
|
|
||
| ## Convert TensorFlow Model to PyTorch | ||
|
|
||
| We provide two ways to convert models. | ||
|
|
||
| ### **One-step Command** | ||
|
|
||
| Above MMdnn@0.1.4, we provide one command to achieve the conversion | ||
|
|
||
| ```bash | ||
| $ mmconvert -sf tensorflow -in imagenet_resnet_v2_152.ckpt.meta -iw imagenet_resnet_v2_152.ckpt --dstNode MMdnn_Output -df pytorch -om tf_to_pytorch_resnet_152.pth | ||
| . | ||
| . | ||
| . | ||
| PyTorch model file is saved as [tf_to_pytorch_resnet_152.pth], generated by [052eb72db9934edc90d8e1ffa48144d7.py] and [052eb72db9934edc90d8e1ffa48144d7.npy]. | ||
| ``` | ||
|
|
||
| Then you get the PyTorch original model *tf_to_pytorch_resnet_152.pth* converted from TensorFlow. **052eb72db9934edc90d8e1ffa48144d7.py** and **052eb72db9934edc90d8e1ffa48144d7.npy** are temporal files which will be removed automatically. | ||
|
|
||
|
|
||
| ### Step-by-step Command for debugging | ||
|
|
||
| #### Convert the pre-trained model files to intermediate representation | ||
|
|
||
| ```bash | ||
| $ mmtoir -f tensorflow -n imagenet_resnet_v2_152.ckpt.meta -w imagenet_resnet_v2_152.ckpt --dstNode MMdnn_Output -o converted | ||
|
|
||
| Parse file [imagenet_resnet_v2_152.ckpt.meta] with binary format successfully. | ||
| Tensorflow model file [imagenet_resnet_v2_152.ckpt.meta] loaded successfully. | ||
| Tensorflow checkpoint file [imagenet_resnet_v2_152.ckpt] loaded successfully. [816] variables loaded. | ||
| IR network structure is saved as [converted.json]. | ||
| IR network structure is saved as [converted.pb]. | ||
| IR weights are saved as [converted.npy]. | ||
| ``` | ||
|
|
||
| Then you got the **intermediate representation** files *converted.json* for visualization, *converted.proto* and *converted.npy* for next steps. | ||
|
|
||
| #### Convert the IR files to PyTorch code | ||
|
|
||
| ```bash | ||
| $ mmtocode -f pytorch -n converted.pb -w converted.npy -d converted_pytorch.py -dw converted_pytorch.npy | ||
|
|
||
| Parse file [converted.pb] with binary format successfully. | ||
| Target network code snippet is saved as [converted_pytorch.py]. | ||
| Target weights are saved as [converted_pytorch.npy]. | ||
| ``` | ||
|
|
||
| And you will get a filename *converted_pytorch.py*, which contains the **original PyTorch** codes to build the *ResNet V2 152* network and *converted_pytorch.npy* which is used to set weights in the network building process. | ||
|
|
||
| With the three steps, you have already converted the pre-trained TensorFlow *ResNet V2 152* models to PyTorch network building file *converted_pytorch.py* and weights file *converted_pytorch.npy*. You can use these two files to fine-tune training or inference. | ||
|
|
||
| #### Dump the original PyTorch model | ||
|
|
||
| ```bash | ||
| $ mmtomodel -f pytorch -in converted_pytorch.py -iw converted_pytorch.npy -o converted_pytorch.pth | ||
|
|
||
| PyTorch model file is saved as [converted_pytorch.pth], generated by [converted_pytorch.py] and [converted_pytorch.npy]. | ||
| ``` | ||
| The file *converted_pytorch.pth* can be loaded by PyTorch directly. |
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