/usr/local/lib/python3.6/site-packages/transformers/models/vilt
Edit: /usr/local/lib/python3.6/site-packages/transformers/models/vilt/convert_vilt_original_to_pytorch.py (12827B)
# 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 ViLT checkpoints from the original Github repository."""
import argparse
import json
from pathlib import Path
import torch
from PIL import Image
import requests
from huggingface_hub import hf_hub_download
from transformers import (
BertTokenizer,
ViltConfig,
ViltFeatureExtractor,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltForQuestionAnswering,
ViltProcessor,
)
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, vqa_model=False, nlvr_model=False, irtr_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"transformer.blocks.{i}.norm1.weight", f"vilt.encoder.layer.{i}.layernorm_before.weight"))
rename_keys.append((f"transformer.blocks.{i}.norm1.bias", f"vilt.encoder.layer.{i}.layernorm_before.bias"))
rename_keys.append(
(f"transformer.blocks.{i}.attn.proj.weight", f"vilt.encoder.layer.{i}.attention.output.dense.weight")
)
rename_keys.append(
(f"transformer.blocks.{i}.attn.proj.bias", f"vilt.encoder.layer.{i}.attention.output.dense.bias")
)
rename_keys.append((f"transformer.blocks.{i}.norm2.weight", f"vilt.encoder.layer.{i}.layernorm_after.weight"))
rename_keys.append((f"transformer.blocks.{i}.norm2.bias", f"vilt.encoder.layer.{i}.layernorm_after.bias"))
rename_keys.append(
(f"transformer.blocks.{i}.mlp.fc1.weight", f"vilt.encoder.layer.{i}.intermediate.dense.weight")
)
rename_keys.append((f"transformer.blocks.{i}.mlp.fc1.bias", f"vilt.encoder.layer.{i}.intermediate.dense.bias"))
rename_keys.append((f"transformer.blocks.{i}.mlp.fc2.weight", f"vilt.encoder.layer.{i}.output.dense.weight"))
rename_keys.append((f"transformer.blocks.{i}.mlp.fc2.bias", f"vilt.encoder.layer.{i}.output.dense.bias"))
# embeddings
rename_keys.extend(
[
# text embeddings
("text_embeddings.word_embeddings.weight", "vilt.embeddings.text_embeddings.word_embeddings.weight"),
(
"text_embeddings.position_embeddings.weight",
"vilt.embeddings.text_embeddings.position_embeddings.weight",
),
("text_embeddings.position_ids", "vilt.embeddings.text_embeddings.position_ids"),
(
"text_embeddings.token_type_embeddings.weight",
"vilt.embeddings.text_embeddings.token_type_embeddings.weight",
),
("text_embeddings.LayerNorm.weight", "vilt.embeddings.text_embeddings.LayerNorm.weight"),
("text_embeddings.LayerNorm.bias", "vilt.embeddings.text_embeddings.LayerNorm.bias"),
# patch embeddings
("transformer.cls_token", "vilt.embeddings.cls_token"),
("transformer.patch_embed.proj.weight", "vilt.embeddings.patch_embeddings.projection.weight"),
("transformer.patch_embed.proj.bias", "vilt.embeddings.patch_embeddings.projection.bias"),
("transformer.pos_embed", "vilt.embeddings.position_embeddings"),
# token type embeddings
("token_type_embeddings.weight", "vilt.embeddings.token_type_embeddings.weight"),
]
)
# final layernorm + pooler
rename_keys.extend(
[
("transformer.norm.weight", "vilt.layernorm.weight"),
("transformer.norm.bias", "vilt.layernorm.bias"),
("pooler.dense.weight", "vilt.pooler.dense.weight"),
("pooler.dense.bias", "vilt.pooler.dense.bias"),
]
)
# classifier head(s)
if vqa_model:
# classification head
rename_keys.extend(
[
("vqa_classifier.0.weight", "classifier.0.weight"),
("vqa_classifier.0.bias", "classifier.0.bias"),
("vqa_classifier.1.weight", "classifier.1.weight"),
("vqa_classifier.1.bias", "classifier.1.bias"),
("vqa_classifier.3.weight", "classifier.3.weight"),
("vqa_classifier.3.bias", "classifier.3.bias"),
]
)
elif nlvr_model:
# classification head
rename_keys.extend(
[
("nlvr2_classifier.0.weight", "classifier.0.weight"),
("nlvr2_classifier.0.bias", "classifier.0.bias"),
("nlvr2_classifier.1.weight", "classifier.1.weight"),
("nlvr2_classifier.1.bias", "classifier.1.bias"),
("nlvr2_classifier.3.weight", "classifier.3.weight"),
("nlvr2_classifier.3.bias", "classifier.3.bias"),
]
)
else:
pass
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):
for i in range(config.num_hidden_layers):
prefix = "vilt."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
in_proj_weight = state_dict.pop(f"transformer.blocks.{i}.attn.qkv.weight")
in_proj_bias = state_dict.pop(f"transformer.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
@torch.no_grad()
def convert_vilt_checkpoint(checkpoint_url, pytorch_dump_folder_path):
"""
Copy/paste/tweak model's weights to our ViLT structure.
"""
# define configuration and initialize HuggingFace model
config = ViltConfig(image_size=384, patch_size=32, tie_word_embeddings=False)
mlm_model = False
vqa_model = False
nlvr_model = False
irtr_model = False
if "vqa" in checkpoint_url:
vqa_model = True
config.num_labels = 3129
repo_id = "datasets/huggingface/label-files"
filename = "vqa2-id2label.json"
id2label = json.load(open(hf_hub_download(repo_id, filename), "r"))
id2label = {int(k): v for k, v in id2label.items()}
config.id2label = id2label
config.label2id = {v: k for k, v in id2label.items()}
model = ViltForQuestionAnswering(config)
elif "nlvr" in checkpoint_url:
nlvr_model = True
config.num_labels = 2
config.id2label = {0: "False", 1: "True"}
config.label2id = {v: k for k, v in config.id2label.items()}
config.modality_type_vocab_size = 3
model = ViltForImagesAndTextClassification(config)
elif "irtr" in checkpoint_url:
irtr_model = True
model = ViltForImageAndTextRetrieval(config)
elif "mlm_itm" in checkpoint_url:
mlm_model = True
model = ViltForMaskedLM(config)
else:
raise ValueError("Unknown model type")
# 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")["state_dict"]
rename_keys = create_rename_keys(config, vqa_model, nlvr_model, irtr_model)
for src, dest in rename_keys:
rename_key(state_dict, src, dest)
read_in_q_k_v(state_dict, config)
if mlm_model or irtr_model:
ignore_keys = ["itm_score.fc.weight", "itm_score.fc.bias"]
for k in ignore_keys:
state_dict.pop(k, None)
# load state dict into HuggingFace model
model.eval()
if mlm_model:
missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
assert missing_keys == ["mlm_score.decoder.bias"]
else:
model.load_state_dict(state_dict)
# Define processor
feature_extractor = ViltFeatureExtractor(size=384)
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
processor = ViltProcessor(feature_extractor, tokenizer)
# Forward pass on example inputs (image + text)
if nlvr_model:
image1 = Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg", stream=True).raw)
image2 = Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg", stream=True).raw)
text = "The left image contains twice the number of dogs as the right image, and at least two dogs in total are standing."
encoding_1 = processor(image1, text, return_tensors="pt")
encoding_2 = processor(image2, text, return_tensors="pt")
outputs = model(
input_ids=encoding_1.input_ids,
pixel_values=encoding_1.pixel_values,
pixel_values_2=encoding_2.pixel_values,
)
else:
image = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw)
if mlm_model:
text = "a bunch of [MASK] laying on a [MASK]."
else:
text = "How many cats are there?"
encoding = processor(image, text, return_tensors="pt")
outputs = model(**encoding)
# Verify outputs
if mlm_model:
expected_shape = torch.Size([1, 11, 30522])
expected_slice = torch.tensor([-12.5061, -12.5123, -12.5174])
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3], expected_slice, atol=1e-4)
# verify masked token prediction equals "cats"
predicted_id = outputs.logits[0, 4, :].argmax(-1).item()
assert tokenizer.decode([predicted_id]) == "cats"
elif vqa_model:
expected_shape = torch.Size([1, 3129])
expected_slice = torch.tensor([-15.9495, -18.1472, -10.3041])
assert torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4)
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3], expected_slice, atol=1e-4)
# verify vqa prediction equals "2"
predicted_idx = outputs.logits.argmax(-1).item()
assert model.config.id2label[predicted_idx] == "2"
elif nlvr_model:
expected_shape = torch.Size([1, 2])
expected_slice = torch.tensor([-2.8721, 2.1291])
assert torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4)
assert outputs.logits.shape == expected_shape
Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
print(f"Saving model and processor to {pytorch_dump_folder_path}")
model.save_pretrained(pytorch_dump_folder_path)
processor.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt",
type=str,
help="URL of the checkpoint 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_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)