Update the pruning doc for tensorflow.
@@ -19,12 +19,12 @@ Filter/Channel pruning means pruning a larger part of the network, such as filte
## Pruning Algorithms supported by LPOT
-| Pruning Type | Algorithm | PyTorch |
-|------------------------|---------------------------------------------|---------|
-| unstructured pruning | basic_magnitude | Yes |
-| | pattern_lock | Yes |
-| structured pruning | pattern_lock | Yes |
-| filter/channel pruning | gradient_sensitivity | Yes |
+| Pruning Type | Algorithm | PyTorch | Tensorflow |
+|------------------------|---------------------------------------------|---------|------------|
+| unstructured pruning | basic_magnitude | Yes | Yes |
+| | pattern_lock | Yes | N/A |
+| structured pruning | pattern_lock | Yes | N/A |
+| filter/channel pruning | gradient_sensitivity | Yes | N/A |
LPOT also supports the two-shot execution of unstructured pruning and post-training quantization.
@@ -313,6 +313,7 @@ Following examples are supported in LPOT:
- CNN Examples:
- [resnet example](../examples/pytorch/eager/image_recognition/imagenet/cpu/prune/README.md): magnitude pruning on resnet.
- [pruning and post-training quantization](../examples/pytorch/eager/image_recognition/imagenet/cpu/prune_and_ptq/README.md): magnitude pruning and then post-training quantization on resnet.
+ - [resnet_v2 example](../examples/tensorflow/pruning/resnet_v2/README.md): magnitude pruning on resnet_v2 for tensorflow.
- NLP Examples:
- [BERT example](../examples/pytorch/eager/language_translation/prune/README.md): magnitude pruning on DistilBERT.
- [BERT example](../examples/pytorch/eager/huggingface_models/README.md): Pattern-lock and head-pruning on BERT-base.
