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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 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 | |
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") | |
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) | |