# 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 ViT hybrid checkpoints from the timm library.""" import argparse import json from pathlib import Path import requests import timm import torch from huggingface_hub import hf_hub_download from PIL import Image from timm.data import resolve_data_config from timm.data.transforms_factory import create_transform from transformers import ( BitConfig, ViTHybridConfig, ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel, ) 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, base_model=False): rename_keys = [] # fmt: off # stem: rename_keys.append(("cls_token", "vit.embeddings.cls_token")) rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings")) rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight")) rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias")) # backbone rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight")) rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight")) rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias")) for stage_idx in range(len(config.backbone_config.depths)): for layer_idx in range(config.backbone_config.depths[stage_idx]): rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight")) rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight")) rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias")) rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight")) rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight")) rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias")) rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight")) rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight")) rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias")) rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight")) rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight")) rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias")) # transformer encoder 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")) if base_model: # layernorm + pooler rename_keys.extend( [ ("norm.weight", "layernorm.weight"), ("norm.bias", "layernorm.bias"), ("pre_logits.fc.weight", "pooler.dense.weight"), ("pre_logits.fc.bias", "pooler.dense.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"), ] ) # fmt: on 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, push_to_hub=False): """ Copy/paste/tweak model's weights to our ViT structure. """ # define default ViT hybrid configuration backbone_config = BitConfig( global_padding="same", layer_type="bottleneck", depths=(3, 4, 9), out_features=["stage3"], embedding_dynamic_padding=True, ) config = ViTHybridConfig(backbone_config=backbone_config, image_size=384, num_labels=1000) base_model = False # load original model from timm timm_model = timm.create_model(vit_name, pretrained=True) timm_model.eval() # load state_dict of original model, remove and rename some keys state_dict = timm_model.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) repo_id = "huggingface/label-files" 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()} # load HuggingFace model if vit_name[-5:] == "in21k": model = ViTHybridModel(config).eval() else: model = ViTHybridForImageClassification(config).eval() model.load_state_dict(state_dict) # create image processor transform = create_transform(**resolve_data_config({}, model=timm_model)) timm_transforms = transform.transforms pillow_resamplings = { "bilinear": PILImageResampling.BILINEAR, "bicubic": PILImageResampling.BICUBIC, "nearest": PILImageResampling.NEAREST, } processor = ViTHybridImageProcessor( do_resize=True, size={"shortest_edge": timm_transforms[0].size}, resample=pillow_resamplings[timm_transforms[0].interpolation.value], do_center_crop=True, crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}, do_normalize=True, image_mean=timm_transforms[-1].mean.tolist(), image_std=timm_transforms[-1].std.tolist(), ) image = prepare_img() timm_pixel_values = transform(image).unsqueeze(0) pixel_values = processor(image, return_tensors="pt").pixel_values # verify pixel values assert torch.allclose(timm_pixel_values, pixel_values) # verify logits with torch.no_grad(): outputs = model(pixel_values) logits = outputs.logits print("Predicted class:", logits.argmax(-1).item()) if base_model: timm_pooled_output = timm_model.forward_features(pixel_values) assert timm_pooled_output.shape == outputs.pooler_output.shape assert torch.allclose(timm_pooled_output, outputs.pooler_output, atol=1e-3) else: timm_logits = timm_model(pixel_values) assert timm_logits.shape == outputs.logits.shape assert torch.allclose(timm_logits, outputs.logits, atol=1e-3) print("Looks ok!") if pytorch_dump_folder_path is not None: 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 processor to {pytorch_dump_folder_path}") processor.save_pretrained(pytorch_dump_folder_path) if push_to_hub: print(f"Pushing model and processor to the hub {vit_name}") model.push_to_hub(f"ybelkada/{vit_name}") processor.push_to_hub(f"ybelkada/{vit_name}") if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--vit_name", default="vit_base_r50_s16_384", type=str, help="Name of the hybrid 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." ) parser.add_argument( "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub." ) args = parser.parse_args() convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)