convert pytorch model to paddlepaddle model

Update README.md

.gitignore

@@ -1,3 +1,4 @@
+.idea
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

README.md

@@ -23,19 +23,19 @@ This repo is the official implementation of ["Swin Transformer: Hierarchical Vis
 
 Initial commits:
 
-1. Pretrained models on ImageNet-1K ([Swin-T-IN1K](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth), [Swin-S-IN1K](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth), [Swin-B-IN1K](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224.pth)) and ImageNet-22K ([Swin-B-IN22K](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22k.pth), [Swin-L-IN22K](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22k.pth)) are provided.
+1. Pretrained models on ImageNet-1K ([Swin-T-IN1K](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth), [Swin-S-IN1K](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth), [Swin-B-IN1K](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224.pth)) and ImageNet-22K ([Swin-B-IN22K](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22k.pth), [Swin-L-IN22K](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22k.pth)) are provided.
 2. The supported code and models for ImageNet-1K image classification, COCO object detection and ADE20K semantic segmentation are provided.
 3. The cuda kernel implementation for the [local relation layer](https://arxiv.org/pdf/1904.11491.pdf) is provided in branch [LR-Net](https://github.com/microsoft/Swin-Transformer/tree/LR-Net).
 
 ## Introduction
 
-**Swin Transformer** is initially described in [arxiv](https://arxiv.org/abs/2103.14030), which capably serves as a
+**Swin Transformer** (the name `Swin` stands for **S**hifted **win**dow) is initially described in [arxiv](https://arxiv.org/abs/2103.14030), which capably serves as a
 general-purpose backbone for computer vision. It is basically a hierarchical Transformer whose representation is
 computed with shifted windows. The shifted windowing scheme brings greater efficiency by limiting self-attention
 computation to non-overlapping local windows while also allowing for cross-window connection.
 
 Swin Transformer achieves strong performance on COCO object detection (`58.7 box AP` and `51.1 mask AP` on test-dev) and
-ADE20K semantic segmentatiion (`53.5 mIoU` on val), surpassing previous models by a large margin.
+ADE20K semantic segmentation (`53.5 mIoU` on val), surpassing previous models by a large margin.
 
 ![teaser](figures/teaser.png)
 
@@ -102,6 +102,18 @@ Note: <sup>*</sup> indicates multi-scale testing.
 - For **Object Detection and Instance Segmentation**, please see [Swin Transformer for Object Detection](https://github.com/SwinTransformer/Swin-Transformer-Object-Detection).
 - For **Semantic Segmentation**, please see [Swin Transformer for Semantic Segmentation](https://github.com/SwinTransformer/Swin-Transformer-Semantic-Segmentation).
 
+## Third-party Usage and Experiments
+
+***In this pargraph, we cross link third-party repositories which use Swin and report results. You can let us know by raising an issue*** 
+
+(`Note please report accuracy numbers and provide trained models in your new repository to facilitate others to get sense of correctness and model behavior`)
+
+[04/14/2021] Swin for RetinaNet in Detectron: https://github.com/xiaohu2015/SwinT_detectron2.
+
+[04/16/2021] Included in a famous model zoo: https://github.com/rwightman/pytorch-image-models.
+
+[04/20/2021] Swin-Transformer classifier inference using TorchServe: https://github.com/kamalkraj/Swin-Transformer-Serve
+
 ## Contributing
 
 This project welcomes contributions and suggestions.  Most contributions require you to agree to a

main.py

@@ -110,7 +110,9 @@ def main(config):
         if resume_file:
             if config.MODEL.RESUME:
                 logger.warning(f"auto-resume changing resume file from {config.MODEL.RESUME} to {resume_file}")
+            config.defrost()
             config.MODEL.RESUME = resume_file
+            config.freeze()
             logger.info(f'auto resuming from {resume_file}')
         else:
             logger.info(f'no checkpoint found in {config.OUTPUT}, ignoring auto resume')

tools/pytorch2paddlepaddle.py

@@ -0,0 +1,28 @@
+import torch
+import numpy as np
+from models.swin_transformer import SwinTransformer
+# 构建输入
+input_data = np.random.rand(1, 3, 224, 224).astype("float32")
+
+
+swin_model_cfg_map = {
+    "swin_tiny_patch4_window7_224": {
+        "EMBED_DIM": 96,
+        "DEPTHS": [ 2, 2, 6, 2 ],
+        "NUM_HEADS": [ 3, 6, 12, 24 ],
+        "WINDOW_SIZE": 7,
+    }
+}
+
+model_name = "swin_tiny_patch4_window7_224"
+torch_module = SwinTransformer(**swin_model_cfg_map[model_name])
+torch_state_dict = torch.load("/home/andy/data/pretrained_models/{}.pth".format(model_name))["model"]
+torch_module.load_state_dict(torch_state_dict)
+# 设置为eval模式
+torch_module.eval()
+# 进行转换
+from x2paddle.convert import pytorch2paddle
+pytorch2paddle(torch_module,
+               save_dir="pd_{}".format(model_name),
+               jit_type="trace",
+               input_examples=[torch.tensor(input_data)])

utils.py

@@ -29,7 +29,9 @@ def load_checkpoint(config, model, optimizer, lr_scheduler, logger):
     if not config.EVAL_MODE and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint:
         optimizer.load_state_dict(checkpoint['optimizer'])
         lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
+        config.defrost()
         config.TRAIN.START_EPOCH = checkpoint['epoch'] + 1
+        config.freeze()
         if 'amp' in checkpoint and config.AMP_OPT_LEVEL != "O0" and checkpoint['config'].AMP_OPT_LEVEL != "O0":
             amp.load_state_dict(checkpoint['amp'])
         logger.info(f"=> loaded successfully '{config.MODEL.RESUME}' (epoch {checkpoint['epoch']})")