table-transformer
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microsoft
Convert .pth to safetensors
Below is a comment on how to convert training weights to use with the Transformers library.
From source: https://gitlab.scai.fraunhofer.de/sergei.kuznetsov/transformers-mod/-/blob/master/src/transformers/models/table_transformer/convert_table_transformer_original_pytorch_checkpoint_to_pytorch.py
How to run:
python pth_to_hf.py --checkpoint_path=./model.pth --pytorch_dump_folder_path=model --model_type=detection
Bellow the code (modified):
# 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 Table Transformer checkpoints.
URL: https://github.com/microsoft/table-transformer
"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrFeatureExtractor, TableTransformerConfig, TableTransformerForObjectDetection
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)
rename_keys = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(f"transformer.encoder.layers.{i}.self_attn.out_proj.weight", f"encoder.layers.{i}.self_attn.out_proj.weight")
)
rename_keys.append(
(f"transformer.encoder.layers.{i}.self_attn.out_proj.bias", f"encoder.layers.{i}.self_attn.out_proj.bias")
)
rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"encoder.layers.{i}.fc1.weight"))
rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"encoder.layers.{i}.fc1.bias"))
rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"encoder.layers.{i}.fc2.weight"))
rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"encoder.layers.{i}.fc2.bias"))
rename_keys.append(
(f"transformer.encoder.layers.{i}.norm1.weight", f"encoder.layers.{i}.self_attn_layer_norm.weight")
)
rename_keys.append((f"transformer.encoder.layers.{i}.norm1.bias", f"encoder.layers.{i}.self_attn_layer_norm.bias"))
rename_keys.append((f"transformer.encoder.layers.{i}.norm2.weight", f"encoder.layers.{i}.final_layer_norm.weight"))
rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"encoder.layers.{i}.final_layer_norm.bias"))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(f"transformer.decoder.layers.{i}.self_attn.out_proj.weight", f"decoder.layers.{i}.self_attn.out_proj.weight")
)
rename_keys.append(
(f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"decoder.layers.{i}.self_attn.out_proj.bias")
)
rename_keys.append(
(
f"transformer.decoder.layers.{i}.multihead_attn.out_proj.weight",
f"decoder.layers.{i}.encoder_attn.out_proj.weight",
)
)
rename_keys.append(
(
f"transformer.decoder.layers.{i}.multihead_attn.out_proj.bias",
f"decoder.layers.{i}.encoder_attn.out_proj.bias",
)
)
rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"decoder.layers.{i}.fc1.weight"))
rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"decoder.layers.{i}.fc1.bias"))
rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"decoder.layers.{i}.fc2.weight"))
rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"decoder.layers.{i}.fc2.bias"))
rename_keys.append(
(f"transformer.decoder.layers.{i}.norm1.weight", f"decoder.layers.{i}.self_attn_layer_norm.weight")
)
rename_keys.append((f"transformer.decoder.layers.{i}.norm1.bias", f"decoder.layers.{i}.self_attn_layer_norm.bias"))
rename_keys.append(
(f"transformer.decoder.layers.{i}.norm2.weight", f"decoder.layers.{i}.encoder_attn_layer_norm.weight")
)
rename_keys.append(
(f"transformer.decoder.layers.{i}.norm2.bias", f"decoder.layers.{i}.encoder_attn_layer_norm.bias")
)
rename_keys.append((f"transformer.decoder.layers.{i}.norm3.weight", f"decoder.layers.{i}.final_layer_norm.weight"))
rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"decoder.layers.{i}.final_layer_norm.bias"))
# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
("input_proj.weight", "input_projection.weight"),
("input_proj.bias", "input_projection.bias"),
("query_embed.weight", "query_position_embeddings.weight"),
("transformer.encoder.norm.weight", "encoder.layernorm.weight"),
("transformer.encoder.norm.bias", "encoder.layernorm.bias"),
("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
("class_embed.weight", "class_labels_classifier.weight"),
("class_embed.bias", "class_labels_classifier.bias"),
("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
]
)
def rename_key(state_dict, old, new):
val = state_dict.pop(old)
state_dict[new] = val
def rename_backbone_keys(state_dict):
new_state_dict = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
new_key = key.replace("backbone.0.body", "backbone.conv_encoder.model")
new_state_dict[new_key] = value
else:
new_state_dict[key] = value
return new_state_dict
def read_in_q_k_v(state_dict):
prefix = ""
# first: transformer encoder
for i in range(6):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
in_proj_weight = state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight")
in_proj_bias = state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias")
# next, add query, keys and values (in that order) to the state dict
state_dict[f"encoder.layers.{i}.self_attn.q_proj.weight"] = in_proj_weight[:256, :]
state_dict[f"encoder.layers.{i}.self_attn.q_proj.bias"] = in_proj_bias[:256]
state_dict[f"encoder.layers.{i}.self_attn.k_proj.weight"] = in_proj_weight[256:512, :]
state_dict[f"encoder.layers.{i}.self_attn.k_proj.bias"] = in_proj_bias[256:512]
state_dict[f"encoder.layers.{i}.self_attn.v_proj.weight"] = in_proj_weight[-256:, :]
state_dict[f"encoder.layers.{i}.self_attn.v_proj.bias"] = in_proj_bias[-256:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6):
# read in weights + bias of input projection layer of self-attention
in_proj_weight = state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight")
in_proj_bias = state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias")
# next, add query, keys and values (in that order) to the state dict
state_dict[f"decoder.layers.{i}.self_attn.q_proj.weight"] = in_proj_weight[:256, :]
state_dict[f"decoder.layers.{i}.self_attn.q_proj.bias"] = in_proj_bias[:256]
state_dict[f"decoder.layers.{i}.self_attn.k_proj.weight"] = in_proj_weight[256:512, :]
state_dict[f"decoder.layers.{i}.self_attn.k_proj.bias"] = in_proj_bias[256:512]
state_dict[f"decoder.layers.{i}.self_attn.v_proj.weight"] = in_proj_weight[-256:, :]
state_dict[f"decoder.layers.{i}.self_attn.v_proj.bias"] = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
in_proj_weight_cross_attn = state_dict.pop(
f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"
)
in_proj_bias_cross_attn = state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias")
# next, add query, keys and values (in that order) of cross-attention to the state dict
state_dict[f"decoder.layers.{i}.encoder_attn.q_proj.weight"] = in_proj_weight_cross_attn[:256, :]
state_dict[f"decoder.layers.{i}.encoder_attn.q_proj.bias"] = in_proj_bias_cross_attn[:256]
state_dict[f"decoder.layers.{i}.encoder_attn.k_proj.weight"] = in_proj_weight_cross_attn[256:512, :]
state_dict[f"decoder.layers.{i}.encoder_attn.k_proj.bias"] = in_proj_bias_cross_attn[256:512]
state_dict[f"decoder.layers.{i}.encoder_attn.v_proj.weight"] = in_proj_weight_cross_attn[-256:, :]
state_dict[f"decoder.layers.{i}.encoder_attn.v_proj.bias"] = in_proj_bias_cross_attn[-256:]
def resize(image, checkpoint_path, model_type):
width, height = image.size
current_max_size = max(width, height)
target_max_size = 800 if model_type == 'detection' else 1000
scale = target_max_size / current_max_size
resized_image = image.resize((int(round(scale * width)), int(round(scale * height))))
return resized_image
def normalize(image):
image = F.to_tensor(image)
image = F.normalize(image, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
return image
@torch.no_grad()
def convert_table_transformer_checkpoint(checkpoint_path, model_type, pytorch_dump_folder_path, push_to_hub):
"""
Copy/paste/tweak model's weights to our DETR structure.
"""
logger.info("Converting model...")
# load original state dict
if 'https' in checkpoint_path:
state_dict = torch.hub.load_state_dict_from_url(checkpoint_path, map_location="cpu")
else:
state_dict = torch.load(checkpoint_path, map_location="cpu")
# rename keys
for src, dest in rename_keys:
rename_key(state_dict, src, dest)
state_dict = rename_backbone_keys(state_dict)
# query, key and value matrices need special treatment
read_in_q_k_v(state_dict)
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
prefix = "model."
for key in state_dict.copy().keys():
if not key.startswith("class_labels_classifier") and not key.startswith("bbox_predictor"):
val = state_dict.pop(key)
state_dict[prefix + key] = val
# create HuggingFace model and load state dict
config = TableTransformerConfig(
backbone="resnet18",
mask_loss_coefficient=1,
dice_loss_coefficient=1,
ce_loss_coefficient=1,
bbox_loss_coefficient=5,
giou_loss_coefficient=2,
eos_coefficient=0.4,
class_cost=1,
bbox_cost=5,
giou_cost=2,
)
if model_type == 'detection':
config.num_queries = 15
config.num_labels = 2
id2label = {0: "table", 1: "table rotated"}
config.id2label = id2label
config.label2id = {v: k for k, v in id2label.items()}
else:
config.num_queries = 125
config.num_labels = 6
id2label = {
0: "table",
1: "table column",
2: "table row",
3: "table column header",
4: "table projected row header",
5: "table spanning cell",
}
config.id2label = id2label
config.label2id = {v: k for k, v in id2label.items()}
feature_extractor = DetrFeatureExtractor(
format="coco_detection", max_size=800 if model_type == 'detection' else 1000
)
model = TableTransformerForObjectDetection(config)
model.load_state_dict(state_dict)
model.eval()
# verify our conversion
filename = "example_pdf.png" if model_type == 'detection' else "example_table.png"
file_path = hf_hub_download(repo_id="nielsr/example-pdf", repo_type="dataset", filename=filename)
image = Image.open(file_path).convert("RGB")
pixel_values = normalize(resize(image, checkpoint_path, model_type)).unsqueeze(0)
outputs = model(pixel_values)
if model_type == 'detection':
expected_shape = (1, 15, 3)
expected_logits = torch.tensor(
[[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]]
)
expected_boxes = torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]])
else:
expected_shape = (1, 125, 7)
expected_logits = torch.tensor(
[[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]
)
expected_boxes = torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]])
assert outputs.logits.shape == expected_shape
print("Looks ok!")
if pytorch_dump_folder_path is not None:
# Save model and feature extractor
logger.info(f"Saving PyTorch model and feature extractor to {pytorch_dump_folder_path}...")
Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
model.save_pretrained(pytorch_dump_folder_path)
feature_extractor.save_pretrained(pytorch_dump_folder_path)
if push_to_hub:
# Push model to HF hub
logger.info("Pushing model to the hub...")
model_name = (
"microsoft/table-transformer-detection"
if model_type == 'detection'
else "microsoft/table-transformer-structure-recognition"
)
model.push_to_hub(model_name)
feature_extractor.push_to_hub(model_name)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path",
default="https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth",
type=str,
help="Path or URL of the Table Transformer checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
parser.add_argument(
"--model_type", type=str, help="Type of model."
)
args = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_path, args.model_type, args.pytorch_dump_folder_path, args.push_to_hub)