# 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 DiT checkpoints from the unilm repository.""" import argparse import json from pathlib import Path import requests import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor from transformers.image_utils import PILImageResampling from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) # here we list all keys to be renamed (original name on the left, our name on the right) def create_rename_keys(config, has_lm_head=False, is_semantic=False): prefix = "backbone." if is_semantic else "" rename_keys = [] for i in range(config.num_hidden_layers): # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms rename_keys.append((f"{prefix}blocks.{i}.norm1.weight", f"beit.encoder.layer.{i}.layernorm_before.weight")) rename_keys.append((f"{prefix}blocks.{i}.norm1.bias", f"beit.encoder.layer.{i}.layernorm_before.bias")) rename_keys.append( (f"{prefix}blocks.{i}.attn.proj.weight", f"beit.encoder.layer.{i}.attention.output.dense.weight") ) rename_keys.append( (f"{prefix}blocks.{i}.attn.proj.bias", f"beit.encoder.layer.{i}.attention.output.dense.bias") ) rename_keys.append((f"{prefix}blocks.{i}.norm2.weight", f"beit.encoder.layer.{i}.layernorm_after.weight")) rename_keys.append((f"{prefix}blocks.{i}.norm2.bias", f"beit.encoder.layer.{i}.layernorm_after.bias")) rename_keys.append((f"{prefix}blocks.{i}.mlp.fc1.weight", f"beit.encoder.layer.{i}.intermediate.dense.weight")) rename_keys.append((f"{prefix}blocks.{i}.mlp.fc1.bias", f"beit.encoder.layer.{i}.intermediate.dense.bias")) rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.weight", f"beit.encoder.layer.{i}.output.dense.weight")) rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.bias", f"beit.encoder.layer.{i}.output.dense.bias")) # projection layer + position embeddings rename_keys.extend( [ (f"{prefix}cls_token", "beit.embeddings.cls_token"), (f"{prefix}patch_embed.proj.weight", "beit.embeddings.patch_embeddings.projection.weight"), (f"{prefix}patch_embed.proj.bias", "beit.embeddings.patch_embeddings.projection.bias"), (f"{prefix}pos_embed", "beit.embeddings.position_embeddings"), ] ) if has_lm_head: # mask token + layernorm rename_keys.extend( [ ("mask_token", "beit.embeddings.mask_token"), ("norm.weight", "layernorm.weight"), ("norm.bias", "layernorm.bias"), ] ) else: # layernorm + classification head rename_keys.extend( [ ("fc_norm.weight", "beit.pooler.layernorm.weight"), ("fc_norm.bias", "beit.pooler.layernorm.bias"), ("head.weight", "classifier.weight"), ("head.bias", "classifier.bias"), ] ) return rename_keys # we split up the matrix of each encoder layer into queries, keys and values def read_in_q_k_v(state_dict, config, has_lm_head=False, is_semantic=False): for i in range(config.num_hidden_layers): prefix = "backbone." if is_semantic else "" # queries, keys and values in_proj_weight = state_dict.pop(f"{prefix}blocks.{i}.attn.qkv.weight") q_bias = state_dict.pop(f"{prefix}blocks.{i}.attn.q_bias") v_bias = state_dict.pop(f"{prefix}blocks.{i}.attn.v_bias") state_dict[f"beit.encoder.layer.{i}.attention.attention.query.weight"] = in_proj_weight[ : config.hidden_size, : ] state_dict[f"beit.encoder.layer.{i}.attention.attention.query.bias"] = q_bias state_dict[f"beit.encoder.layer.{i}.attention.attention.key.weight"] = in_proj_weight[ config.hidden_size : config.hidden_size * 2, : ] state_dict[f"beit.encoder.layer.{i}.attention.attention.value.weight"] = in_proj_weight[ -config.hidden_size :, : ] state_dict[f"beit.encoder.layer.{i}.attention.attention.value.bias"] = v_bias # gamma_1 and gamma_2 # we call them lambda because otherwise they are renamed when using .from_pretrained gamma_1 = state_dict.pop(f"{prefix}blocks.{i}.gamma_1") gamma_2 = state_dict.pop(f"{prefix}blocks.{i}.gamma_2") state_dict[f"beit.encoder.layer.{i}.lambda_1"] = gamma_1 state_dict[f"beit.encoder.layer.{i}.lambda_2"] = gamma_2 def rename_key(dct, old, new): val = dct.pop(old) dct[new] = val # We will verify our results on an image of cute cats def prepare_img(): url = "http://images.cocodataset.org/val2017/000000039769.jpg" im = Image.open(requests.get(url, stream=True).raw) return im @torch.no_grad() def convert_dit_checkpoint(checkpoint_url, pytorch_dump_folder_path, push_to_hub=False): """ Copy/paste/tweak model's weights to our BEiT structure. """ # define default BEiT configuration has_lm_head = False if "rvlcdip" in checkpoint_url else True config = BeitConfig(use_absolute_position_embeddings=True, use_mask_token=has_lm_head) # size of the architecture if "large" in checkpoint_url or "dit-l" in checkpoint_url: config.hidden_size = 1024 config.intermediate_size = 4096 config.num_hidden_layers = 24 config.num_attention_heads = 16 # labels if "rvlcdip" in checkpoint_url: config.num_labels = 16 repo_id = "huggingface/label-files" filename = "rvlcdip-id2label.json" id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) id2label = {int(k): v for k, v in id2label.items()} config.id2label = id2label config.label2id = {v: k for k, v in id2label.items()} # load state_dict of original model, remove and rename some keys state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")["model"] rename_keys = create_rename_keys(config, has_lm_head=has_lm_head) for src, dest in rename_keys: rename_key(state_dict, src, dest) read_in_q_k_v(state_dict, config, has_lm_head=has_lm_head) # load HuggingFace model model = BeitForMaskedImageModeling(config) if has_lm_head else BeitForImageClassification(config) model.eval() model.load_state_dict(state_dict) # Check outputs on an image image_processor = BeitImageProcessor( size=config.image_size, resample=PILImageResampling.BILINEAR, do_center_crop=False ) image = prepare_img() encoding = image_processor(images=image, return_tensors="pt") pixel_values = encoding["pixel_values"] outputs = model(pixel_values) logits = outputs.logits # verify logits expected_shape = [1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8192] assert logits.shape == torch.Size(expected_shape), "Shape of logits not as expected" Path(pytorch_dump_folder_path).mkdir(exist_ok=True) print(f"Saving model 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: if has_lm_head: model_name = "dit-base" if "base" in checkpoint_url else "dit-large" else: model_name = "dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip" image_processor.push_to_hub( repo_path_or_name=Path(pytorch_dump_folder_path, model_name), organization="nielsr", commit_message="Add image processor", use_temp_dir=True, ) model.push_to_hub( repo_path_or_name=Path(pytorch_dump_folder_path, model_name), organization="nielsr", commit_message="Add model", use_temp_dir=True, ) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--checkpoint_url", default="https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth", type=str, help="URL to the original PyTorch checkpoint (.pth file).", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model." ) parser.add_argument( "--push_to_hub", action="store_true", ) args = parser.parse_args() convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)