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# 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 DeiT distilled 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 transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor | |
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"deit.encoder.layer.{i}.layernorm_before.weight")) | |
rename_keys.append((f"blocks.{i}.norm1.bias", f"deit.encoder.layer.{i}.layernorm_before.bias")) | |
rename_keys.append((f"blocks.{i}.attn.proj.weight", f"deit.encoder.layer.{i}.attention.output.dense.weight")) | |
rename_keys.append((f"blocks.{i}.attn.proj.bias", f"deit.encoder.layer.{i}.attention.output.dense.bias")) | |
rename_keys.append((f"blocks.{i}.norm2.weight", f"deit.encoder.layer.{i}.layernorm_after.weight")) | |
rename_keys.append((f"blocks.{i}.norm2.bias", f"deit.encoder.layer.{i}.layernorm_after.bias")) | |
rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"deit.encoder.layer.{i}.intermediate.dense.weight")) | |
rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"deit.encoder.layer.{i}.intermediate.dense.bias")) | |
rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"deit.encoder.layer.{i}.output.dense.weight")) | |
rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"deit.encoder.layer.{i}.output.dense.bias")) | |
# projection layer + position embeddings | |
rename_keys.extend( | |
[ | |
("cls_token", "deit.embeddings.cls_token"), | |
("dist_token", "deit.embeddings.distillation_token"), | |
("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"), | |
("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"), | |
("pos_embed", "deit.embeddings.position_embeddings"), | |
] | |
) | |
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 "deit" from all keys that start with "deit" | |
rename_keys = [(pair[0], pair[1][4:]) if pair[1].startswith("deit") else pair for pair in rename_keys] | |
else: | |
# layernorm + classification heads | |
rename_keys.extend( | |
[ | |
("norm.weight", "deit.layernorm.weight"), | |
("norm.bias", "deit.layernorm.bias"), | |
("head.weight", "cls_classifier.weight"), | |
("head.bias", "cls_classifier.bias"), | |
("head_dist.weight", "distillation_classifier.weight"), | |
("head_dist.bias", "distillation_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 = "deit." | |
# 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 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 | |
def convert_deit_checkpoint(deit_name, pytorch_dump_folder_path): | |
""" | |
Copy/paste/tweak model's weights to our DeiT structure. | |
""" | |
# define default DeiT configuration | |
config = DeiTConfig() | |
# all deit models have fine-tuned heads | |
base_model = False | |
# dataset (fine-tuned on ImageNet 2012), patch_size and image_size | |
config.num_labels = 1000 | |
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()} | |
config.patch_size = int(deit_name[-6:-4]) | |
config.image_size = int(deit_name[-3:]) | |
# size of the architecture | |
if deit_name[9:].startswith("tiny"): | |
config.hidden_size = 192 | |
config.intermediate_size = 768 | |
config.num_hidden_layers = 12 | |
config.num_attention_heads = 3 | |
elif deit_name[9:].startswith("small"): | |
config.hidden_size = 384 | |
config.intermediate_size = 1536 | |
config.num_hidden_layers = 12 | |
config.num_attention_heads = 6 | |
if deit_name[9:].startswith("base"): | |
pass | |
elif deit_name[4:].startswith("large"): | |
config.hidden_size = 1024 | |
config.intermediate_size = 4096 | |
config.num_hidden_layers = 24 | |
config.num_attention_heads = 16 | |
# load original model from timm | |
timm_model = timm.create_model(deit_name, pretrained=True) | |
timm_model.eval() | |
# load state_dict of original model, remove and rename some keys | |
state_dict = timm_model.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 | |
model = DeiTForImageClassificationWithTeacher(config).eval() | |
model.load_state_dict(state_dict) | |
# Check outputs on an image, prepared by DeiTImageProcessor | |
size = int( | |
(256 / 224) * config.image_size | |
) # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103 | |
image_processor = DeiTImageProcessor(size=size, crop_size=config.image_size) | |
encoding = image_processor(images=prepare_img(), return_tensors="pt") | |
pixel_values = encoding["pixel_values"] | |
outputs = model(pixel_values) | |
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 {deit_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( | |
"--deit_name", | |
default="vit_deit_base_distilled_patch16_224", | |
type=str, | |
help="Name of the DeiT 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_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path) | |