# 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 ViT and non-distilled DeiT checkpoints from the timm library.""" import argparse from pathlib import Path import requests import timm import torch from PIL import Image from timm.data import ImageNetInfo, infer_imagenet_subset from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel 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, base_model=False): 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"blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight")) rename_keys.append((f"blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias")) rename_keys.append((f"blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight")) rename_keys.append((f"blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias")) rename_keys.append((f"blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight")) rename_keys.append((f"blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias")) rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight")) rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias")) rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight")) rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias")) # projection layer + position embeddings rename_keys.extend( [ ("cls_token", "vit.embeddings.cls_token"), ("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"), ("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"), ("pos_embed", "vit.embeddings.position_embeddings"), ] ) if base_model: # layernorm rename_keys.extend( [ ("norm.weight", "layernorm.weight"), ("norm.bias", "layernorm.bias"), ] ) # if just the base model, we should remove "vit" from all keys that start with "vit" rename_keys = [(pair[0], pair[1][4:]) if pair[1].startswith("vit") else pair for pair in rename_keys] else: # layernorm + classification head rename_keys.extend( [ ("norm.weight", "vit.layernorm.weight"), ("norm.bias", "vit.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, base_model=False): for i in range(config.num_hidden_layers): if base_model: prefix = "" else: prefix = "vit." # read in weights + bias of input projection layer (in timm, this is a single matrix + bias) in_proj_weight = state_dict.pop(f"blocks.{i}.attn.qkv.weight") in_proj_bias = state_dict.pop(f"blocks.{i}.attn.qkv.bias") # next, add query, keys and values (in that order) to the state dict state_dict[f"{prefix}encoder.layer.{i}.attention.attention.query.weight"] = in_proj_weight[ : config.hidden_size, : ] state_dict[f"{prefix}encoder.layer.{i}.attention.attention.query.bias"] = in_proj_bias[: config.hidden_size] state_dict[f"{prefix}encoder.layer.{i}.attention.attention.key.weight"] = in_proj_weight[ config.hidden_size : config.hidden_size * 2, : ] state_dict[f"{prefix}encoder.layer.{i}.attention.attention.key.bias"] = in_proj_bias[ config.hidden_size : config.hidden_size * 2 ] state_dict[f"{prefix}encoder.layer.{i}.attention.attention.value.weight"] = in_proj_weight[ -config.hidden_size :, : ] state_dict[f"{prefix}encoder.layer.{i}.attention.attention.value.bias"] = in_proj_bias[-config.hidden_size :] def remove_classification_head_(state_dict): ignore_keys = ["head.weight", "head.bias"] for k in ignore_keys: state_dict.pop(k, None) 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_vit_checkpoint(vit_name, pytorch_dump_folder_path): """ Copy/paste/tweak model's weights to our ViT structure. """ # define default ViT configuration config = ViTConfig() base_model = False # load original model from timm timm_model = timm.create_model(vit_name, pretrained=True) timm_model.eval() # detect unsupported ViT models in transformers # fc_norm is present if not isinstance(getattr(timm_model, "fc_norm", None), torch.nn.Identity): raise ValueError(f"{vit_name} is not supported in transformers because of the presence of fc_norm.") # use of global average pooling in combination (or without) class token if getattr(timm_model, "global_pool", None) == "avg": raise ValueError(f"{vit_name} is not supported in transformers because of use of global average pooling.") # CLIP style vit with norm_pre layer present if "clip" in vit_name and not isinstance(getattr(timm_model, "norm_pre", None), torch.nn.Identity): raise ValueError( f"{vit_name} is not supported in transformers because it's a CLIP style ViT with norm_pre layer." ) # SigLIP style vit with attn_pool layer present if "siglip" in vit_name and getattr(timm_model, "global_pool", None) == "map": raise ValueError( f"{vit_name} is not supported in transformers because it's a SigLIP style ViT with attn_pool." ) # use of layer scale in ViT model blocks if not isinstance(getattr(timm_model.blocks[0], "ls1", None), torch.nn.Identity) or not isinstance( getattr(timm_model.blocks[0], "ls2", None), torch.nn.Identity ): raise ValueError(f"{vit_name} is not supported in transformers because it uses a layer scale in its blocks.") # Hybrid ResNet-ViTs if not isinstance(timm_model.patch_embed, timm.layers.PatchEmbed): raise ValueError(f"{vit_name} is not supported in transformers because it is a hybrid ResNet-ViT.") # get patch size and image size from the patch embedding submodule config.patch_size = timm_model.patch_embed.patch_size[0] config.image_size = timm_model.patch_embed.img_size[0] # retrieve architecture-specific parameters from the timm model config.hidden_size = timm_model.embed_dim config.intermediate_size = timm_model.blocks[0].mlp.fc1.out_features config.num_hidden_layers = len(timm_model.blocks) config.num_attention_heads = timm_model.blocks[0].attn.num_heads # check whether the model has a classification head or not if timm_model.num_classes != 0: config.num_labels = timm_model.num_classes # infer ImageNet subset from timm model imagenet_subset = infer_imagenet_subset(timm_model) dataset_info = ImageNetInfo(imagenet_subset) config.id2label = {i: dataset_info.index_to_label_name(i) for i in range(dataset_info.num_classes())} config.label2id = {v: k for k, v in config.id2label.items()} else: print(f"{vit_name} is going to be converted as a feature extractor only.") base_model = True # load state_dict of original model state_dict = timm_model.state_dict() # remove and rename some keys in the state dict if base_model: remove_classification_head_(state_dict) rename_keys = create_rename_keys(config, base_model) for src, dest in rename_keys: rename_key(state_dict, src, dest) read_in_q_k_v(state_dict, config, base_model) # load HuggingFace model if base_model: model = ViTModel(config, add_pooling_layer=False).eval() else: model = ViTForImageClassification(config).eval() model.load_state_dict(state_dict) # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor if "deit" in vit_name: image_processor = DeiTImageProcessor(size=config.image_size) else: image_processor = ViTImageProcessor(size=config.image_size) encoding = image_processor(images=prepare_img(), return_tensors="pt") pixel_values = encoding["pixel_values"] outputs = model(pixel_values) if base_model: timm_pooled_output = timm_model.forward_features(pixel_values) assert timm_pooled_output.shape == outputs.last_hidden_state.shape assert torch.allclose(timm_pooled_output, outputs.last_hidden_state, atol=1e-1) else: timm_logits = timm_model(pixel_values) assert timm_logits.shape == outputs.logits.shape assert torch.allclose(timm_logits, outputs.logits, atol=1e-3) Path(pytorch_dump_folder_path).mkdir(exist_ok=True) print(f"Saving model {vit_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 __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--vit_name", default="vit_base_patch16_224", type=str, help="Name of the ViT timm model you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." ) args = parser.parse_args() convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)