# coding=utf-8 # Copyright 2021 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 BEiT checkpoints from the unilm repository.""" import argparse import json from pathlib import Path import requests import torch from datasets import load_dataset from huggingface_hub import hf_hub_download from PIL import Image from transformers import ( BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation, 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"), ] ) if has_lm_head: # mask token + shared relative position bias + layernorm rename_keys.extend( [ ("mask_token", "beit.embeddings.mask_token"), ( "rel_pos_bias.relative_position_bias_table", "beit.encoder.relative_position_bias.relative_position_bias_table", ), ( "rel_pos_bias.relative_position_index", "beit.encoder.relative_position_bias.relative_position_index", ), ("norm.weight", "layernorm.weight"), ("norm.bias", "layernorm.bias"), ] ) elif is_semantic: # semantic segmentation classification heads rename_keys.extend( [ ("decode_head.conv_seg.weight", "decode_head.classifier.weight"), ("decode_head.conv_seg.bias", "decode_head.classifier.bias"), ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"), ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.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 # relative_position bias table + index if not has_lm_head: # each layer has its own relative position bias table = state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_bias_table") index = state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_index") state_dict[ f"beit.encoder.layer.{i}.attention.attention.relative_position_bias.relative_position_bias_table" ] = table state_dict[ f"beit.encoder.layer.{i}.attention.attention.relative_position_bias.relative_position_index" ] = index 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_beit_checkpoint(checkpoint_url, pytorch_dump_folder_path): """ Copy/paste/tweak model's weights to our BEiT structure. """ # define default BEiT configuration config = BeitConfig() has_lm_head = False is_semantic = False repo_id = "huggingface/label-files" # set config parameters based on URL if checkpoint_url[-9:-4] == "pt22k": # masked image modeling config.use_shared_relative_position_bias = True config.use_mask_token = True has_lm_head = True elif checkpoint_url[-9:-4] == "ft22k": # intermediate fine-tuning on ImageNet-22k config.use_relative_position_bias = True config.num_labels = 21841 filename = "imagenet-22k-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()} # this dataset contains 21843 labels but the model only has 21841 # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18 del id2label[9205] del id2label[15027] config.id2label = id2label config.label2id = {v: k for k, v in id2label.items()} elif checkpoint_url[-8:-4] == "to1k": # fine-tuning on ImageNet-1k config.use_relative_position_bias = True config.num_labels = 1000 filename = "imagenet-1k-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()} if "384" in checkpoint_url: config.image_size = 384 if "512" in checkpoint_url: config.image_size = 512 elif "ade20k" in checkpoint_url: # fine-tuning config.use_relative_position_bias = True config.num_labels = 150 filename = "ade20k-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()} config.image_size = 640 is_semantic = True else: raise ValueError("Checkpoint not supported, URL should either end with 'pt22k', 'ft22k', 'to1k' or 'ade20k'") # size of the architecture if "base" in checkpoint_url: pass elif "large" in checkpoint_url: config.hidden_size = 1024 config.intermediate_size = 4096 config.num_hidden_layers = 24 config.num_attention_heads = 16 if "ade20k" in checkpoint_url: config.image_size = 640 config.out_indices = [7, 11, 15, 23] else: raise ValueError("Should either find 'base' or 'large' in checkpoint URL") # 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", check_hash=True) state_dict = state_dict["model"] if "ade20k" not in checkpoint_url else state_dict["state_dict"] rename_keys = create_rename_keys(config, has_lm_head=has_lm_head, is_semantic=is_semantic) 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, is_semantic=is_semantic) if is_semantic: # add prefix to decoder keys for key, val in state_dict.copy().items(): val = state_dict.pop(key) if key.startswith("backbone.fpn"): key = key.replace("backbone.fpn", "fpn") state_dict[key] = val # load HuggingFace model if checkpoint_url[-9:-4] == "pt22k": model = BeitForMaskedImageModeling(config) elif "ade20k" in checkpoint_url: model = BeitForSemanticSegmentation(config) else: model = BeitForImageClassification(config) model.eval() model.load_state_dict(state_dict) # Check outputs on an image if is_semantic: image_processor = BeitImageProcessor(size=config.image_size, do_center_crop=False) ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test", trust_remote_code=True) image = Image.open(ds[0]["file"]) else: 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 = torch.Size([1, 1000]) if checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k"): expected_shape = torch.Size([1, 196, 8192]) elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k"): expected_shape = torch.Size([1, 196, 8192]) elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22k"): expected_shape = torch.Size([1, 21841]) expected_logits = torch.tensor([2.2288, 2.4671, 0.7395]) expected_class_idx = 2397 elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22k"): expected_shape = torch.Size([1, 21841]) expected_logits = torch.tensor([1.6881, -0.2787, 0.5901]) expected_class_idx = 2396 elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft1k"): expected_logits = torch.tensor([0.1241, 0.0798, -0.6569]) expected_class_idx = 285 elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22kto1k"): expected_logits = torch.tensor([-1.2385, -1.0987, -1.0108]) expected_class_idx = 281 elif checkpoint_url[:-4].endswith("beit_base_patch16_384_pt22k_ft22kto1k"): expected_logits = torch.tensor([-1.5303, -0.9484, -0.3147]) expected_class_idx = 761 elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft1k"): expected_logits = torch.tensor([0.4610, -0.0928, 0.2086]) expected_class_idx = 761 elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22kto1k"): expected_logits = torch.tensor([-0.4804, 0.6257, -0.1837]) expected_class_idx = 761 elif checkpoint_url[:-4].endswith("beit_large_patch16_384_pt22k_ft22kto1k"): expected_logits = torch.tensor([[-0.5122, 0.5117, -0.2113]]) expected_class_idx = 761 elif checkpoint_url[:-4].endswith("beit_large_patch16_512_pt22k_ft22kto1k"): expected_logits = torch.tensor([-0.3062, 0.7261, 0.4852]) expected_class_idx = 761 elif checkpoint_url[:-4].endswith("beit_base_patch16_640_pt22k_ft22ktoade20k"): expected_shape = (1, 150, 160, 160) expected_logits = torch.tensor( [ [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]], [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]], [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]], ] ) elif checkpoint_url[:-4].endswith("beit_large_patch16_640_pt22k_ft22ktoade20k"): expected_shape = (1, 150, 160, 160) expected_logits = torch.tensor( [ [[-4.3305, -2.3049, -3.0161], [-2.9591, -1.5305, -2.2251], [-3.4198, -1.8004, -2.9062]], [[-5.8922, -3.7435, -4.3978], [-4.2063, -2.7872, -3.4755], [-4.2791, -3.1874, -4.1681]], [[0.9895, 4.3467, 4.7663], [4.2476, 5.6830, 6.1518], [4.5550, 6.2495, 6.5154]], ] ) else: raise ValueError("Can't verify logits as model is not supported") if logits.shape != expected_shape: raise ValueError(f"Shape of logits not as expected. {logits.shape=}, {expected_shape=}") if not has_lm_head: if is_semantic: if not torch.allclose(logits[0, :3, :3, :3], expected_logits, atol=1e-3): raise ValueError("First elements of logits not as expected") else: print("Predicted class idx:", logits.argmax(-1).item()) if not torch.allclose(logits[0, :3], expected_logits, atol=1e-3): raise ValueError("First elements of logits not as expected") if logits.argmax(-1).item() != expected_class_idx: raise ValueError("Predicted class index 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 __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--checkpoint_url", default="https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.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." ) args = parser.parse_args() convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)