# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert Swin SimMIM checkpoints from the original repository. URL: https://github.com/microsoft/Swin-Transformer/blob/main/MODELHUB.md#simmim-pretrained-swin-v1-models""" import argparse import requests import torch from PIL import Image from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor def get_swin_config(model_name): config = SwinConfig(image_size=192) if "base" in model_name: window_size = 6 embed_dim = 128 depths = (2, 2, 18, 2) num_heads = (4, 8, 16, 32) elif "large" in model_name: window_size = 12 embed_dim = 192 depths = (2, 2, 18, 2) num_heads = (6, 12, 24, 48) else: raise ValueError("Model not supported, only supports base and large variants") config.window_size = window_size config.embed_dim = embed_dim config.depths = depths config.num_heads = num_heads return config def rename_key(name): if "encoder.mask_token" in name: name = name.replace("encoder.mask_token", "embeddings.mask_token") if "encoder.patch_embed.proj" in name: name = name.replace("encoder.patch_embed.proj", "embeddings.patch_embeddings.projection") if "encoder.patch_embed.norm" in name: name = name.replace("encoder.patch_embed.norm", "embeddings.norm") if "attn.proj" in name: name = name.replace("attn.proj", "attention.output.dense") if "attn" in name: name = name.replace("attn", "attention.self") if "norm1" in name: name = name.replace("norm1", "layernorm_before") if "norm2" in name: name = name.replace("norm2", "layernorm_after") if "mlp.fc1" in name: name = name.replace("mlp.fc1", "intermediate.dense") if "mlp.fc2" in name: name = name.replace("mlp.fc2", "output.dense") if name == "encoder.norm.weight": name = "layernorm.weight" if name == "encoder.norm.bias": name = "layernorm.bias" if "decoder" in name: pass else: name = "swin." + name return name def convert_state_dict(orig_state_dict, model): for key in orig_state_dict.copy().keys(): val = orig_state_dict.pop(key) if "attn_mask" in key: pass elif "qkv" in key: key_split = key.split(".") layer_num = int(key_split[2]) block_num = int(key_split[4]) dim = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size if "weight" in key: orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.query.weight"] = ( val[:dim, :] ) orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.key.weight"] = val[ dim : dim * 2, : ] orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.value.weight"] = ( val[-dim:, :] ) else: orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.query.bias"] = val[ :dim ] orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.key.bias"] = val[ dim : dim * 2 ] orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.value.bias"] = val[ -dim: ] else: orig_state_dict[rename_key(key)] = val return orig_state_dict def convert_swin_checkpoint(model_name, checkpoint_path, pytorch_dump_folder_path, push_to_hub): state_dict = torch.load(checkpoint_path, map_location="cpu")["model"] config = get_swin_config(model_name) model = SwinForMaskedImageModeling(config) model.eval() new_state_dict = convert_state_dict(state_dict, model) model.load_state_dict(new_state_dict) url = "http://images.cocodataset.org/val2017/000000039769.jpg" image_processor = ViTImageProcessor(size={"height": 192, "width": 192}) image = Image.open(requests.get(url, stream=True).raw) inputs = image_processor(images=image, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs).logits print(outputs.keys()) print("Looks ok!") if pytorch_dump_folder_path is not None: print(f"Saving model {model_name} to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) print(f"Saving image processor to {pytorch_dump_folder_path}") image_processor.save_pretrained(pytorch_dump_folder_path) if push_to_hub: print(f"Pushing model and image processor for {model_name} to hub") model.push_to_hub(f"microsoft/{model_name}") image_processor.push_to_hub(f"microsoft/{model_name}") if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--model_name", default="swin-base-simmim-window6-192", type=str, choices=["swin-base-simmim-window6-192", "swin-large-simmim-window12-192"], help="Name of the Swin SimMIM model you'd like to convert.", ) parser.add_argument( "--checkpoint_path", default="/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth", type=str, help="Path to the original PyTorch checkpoint (.pth file).", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." ) parser.add_argument( "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub." ) args = parser.parse_args() convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)