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Commit a323fce6 authored by WenmuZhou's avatar WenmuZhou
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vqa code integrated into ppocr training system

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ppstructure/vqa/infer_ser_e2e.py deleted 100644 → 0
View file @ 1ded2ac4
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import os
import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
import json
import cv2
import numpy as np
from copy import deepcopy
from PIL import Image
import paddle
from paddlenlp.transformers import LayoutXLMModel, LayoutXLMTokenizer, LayoutXLMForTokenClassification
from paddlenlp.transformers import LayoutLMModel, LayoutLMTokenizer, LayoutLMForTokenClassification
# relative reference
from vqa_utils import parse_args, get_image_file_list, draw_ser_results, get_bio_label_maps
from vqa_utils import pad_sentences, split_page, preprocess, postprocess, merge_preds_list_with_ocr_info
MODELS = {
'LayoutXLM':
(LayoutXLMTokenizer, LayoutXLMModel, LayoutXLMForTokenClassification),
'LayoutLM':
(LayoutLMTokenizer, LayoutLMModel, LayoutLMForTokenClassification)
}
def trans_poly_to_bbox(poly):
x1 = np.min([p[0] for p in poly])
x2 = np.max([p[0] for p in poly])
y1 = np.min([p[1] for p in poly])
y2 = np.max([p[1] for p in poly])
return [x1, y1, x2, y2]
def parse_ocr_info_for_ser(ocr_result):
ocr_info = []
for res in ocr_result:
ocr_info.append({
"text": res[1][0],
"bbox": trans_poly_to_bbox(res[0]),
"poly": res[0],
})
return ocr_info
class SerPredictor(object):
def __init__(self, args):
self.args = args
self.max_seq_length = args.max_seq_length
# init ser token and model
tokenizer_class, base_model_class, model_class = MODELS[
args.ser_model_type]
self.tokenizer = tokenizer_class.from_pretrained(
args.model_name_or_path)
self.model = model_class.from_pretrained(args.model_name_or_path)
self.model.eval()
# init ocr_engine
from paddleocr import PaddleOCR
self.ocr_engine = PaddleOCR(
rec_model_dir=args.rec_model_dir,
det_model_dir=args.det_model_dir,
use_angle_cls=False,
show_log=False)
# init dict
label2id_map, self.id2label_map = get_bio_label_maps(
args.label_map_path)
self.label2id_map_for_draw = dict()
for key in label2id_map:
if key.startswith("I-"):
self.label2id_map_for_draw[key] = label2id_map["B" + key[1:]]
else:
self.label2id_map_for_draw[key] = label2id_map[key]
def __call__(self, img):
ocr_result = self.ocr_engine.ocr(img, cls=False)
ocr_info = parse_ocr_info_for_ser(ocr_result)
inputs = preprocess(
tokenizer=self.tokenizer,
ori_img=img,
ocr_info=ocr_info,
max_seq_len=self.max_seq_length)
if self.args.ser_model_type == 'LayoutLM':
preds = self.model(
input_ids=inputs["input_ids"],
bbox=inputs["bbox"],
token_type_ids=inputs["token_type_ids"],
attention_mask=inputs["attention_mask"])
elif self.args.ser_model_type == 'LayoutXLM':
preds = self.model(
input_ids=inputs["input_ids"],
bbox=inputs["bbox"],
image=inputs["image"],
token_type_ids=inputs["token_type_ids"],
attention_mask=inputs["attention_mask"])
preds = preds[0]
preds = postprocess(inputs["attention_mask"], preds, self.id2label_map)
ocr_info = merge_preds_list_with_ocr_info(
ocr_info, inputs["segment_offset_id"], preds,
self.label2id_map_for_draw)
return ocr_info, inputs
if __name__ == "__main__":
args = parse_args()
os.makedirs(args.output_dir, exist_ok=True)
# get infer img list
infer_imgs = get_image_file_list(args.infer_imgs)
# loop for infer
ser_engine = SerPredictor(args)
with open(
os.path.join(args.output_dir, "infer_results.txt"),
"w",
encoding='utf-8') as fout:
for idx, img_path in enumerate(infer_imgs):
save_img_path = os.path.join(
args.output_dir,
os.path.splitext(os.path.basename(img_path))[0] + "_ser.jpg")
print("process: [{}/{}], save result to {}".format(
idx, len(infer_imgs), save_img_path))
img = cv2.imread(img_path)
result, _ = ser_engine(img)
fout.write(img_path + "\t" + json.dumps(
{
"ser_resule": result,
}, ensure_ascii=False) + "\n")
img_res = draw_ser_results(img, result)
cv2.imwrite(save_img_path, img_res)
ppstructure/vqa/infer_ser_re_e2e.py deleted 100644 → 0
View file @ 1ded2ac4
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import os
import sys
import json
import cv2
import numpy as np
from copy import deepcopy
from PIL import Image
import paddle
from paddlenlp.transformers import LayoutXLMModel, LayoutXLMTokenizer, LayoutXLMForRelationExtraction
# relative reference
from vqa_utils import parse_args, get_image_file_list, draw_re_results
from infer_ser_e2e import SerPredictor
def make_input(ser_input, ser_result, max_seq_len=512):
entities_labels = {'HEADER': 0, 'QUESTION': 1, 'ANSWER': 2}
entities = ser_input['entities'][0]
assert len(entities) == len(ser_result)
# entities
start = []
end = []
label = []
entity_idx_dict = {}
for i, (res, entity) in enumerate(zip(ser_result, entities)):
if res['pred'] == 'O':
continue
entity_idx_dict[len(start)] = i
start.append(entity['start'])
end.append(entity['end'])
label.append(entities_labels[res['pred']])
entities = dict(start=start, end=end, label=label)
# relations
head = []
tail = []
for i in range(len(entities["label"])):
for j in range(len(entities["label"])):
if entities["label"][i] == 1 and entities["label"][j] == 2:
head.append(i)
tail.append(j)
relations = dict(head=head, tail=tail)
batch_size = ser_input["input_ids"].shape[0]
entities_batch = []
relations_batch = []
for b in range(batch_size):
entities_batch.append(entities)
relations_batch.append(relations)
ser_input['entities'] = entities_batch
ser_input['relations'] = relations_batch
ser_input.pop('segment_offset_id')
return ser_input, entity_idx_dict
class SerReSystem(object):
def __init__(self, args):
self.ser_engine = SerPredictor(args)
self.tokenizer = LayoutXLMTokenizer.from_pretrained(
args.re_model_name_or_path)
self.model = LayoutXLMForRelationExtraction.from_pretrained(
args.re_model_name_or_path)
self.model.eval()
def __call__(self, img):
ser_result, ser_inputs = self.ser_engine(img)
re_input, entity_idx_dict = make_input(ser_inputs, ser_result)
re_result = self.model(**re_input)
pred_relations = re_result['pred_relations'][0]
# 进行 relations 到 ocr信息的转换
result = []
used_tail_id = []
for relation in pred_relations:
if relation['tail_id'] in used_tail_id:
continue
used_tail_id.append(relation['tail_id'])
ocr_info_head = ser_result[entity_idx_dict[relation['head_id']]]
ocr_info_tail = ser_result[entity_idx_dict[relation['tail_id']]]
result.append((ocr_info_head, ocr_info_tail))
return result
if __name__ == "__main__":
args = parse_args()
os.makedirs(args.output_dir, exist_ok=True)
# get infer img list
infer_imgs = get_image_file_list(args.infer_imgs)
# loop for infer
ser_re_engine = SerReSystem(args)
with open(
os.path.join(args.output_dir, "infer_results.txt"),
"w",
encoding='utf-8') as fout:
for idx, img_path in enumerate(infer_imgs):
save_img_path = os.path.join(
args.output_dir,
os.path.splitext(os.path.basename(img_path))[0] + "_re.jpg")
print("process: [{}/{}], save result to {}".format(
idx, len(infer_imgs), save_img_path))
img = cv2.imread(img_path)
result = ser_re_engine(img)
fout.write(img_path + "\t" + json.dumps(
{
"result": result,
}, ensure_ascii=False) + "\n")
img_res = draw_re_results(img, result)
cv2.imwrite(save_img_path, img_res)
ppstructure/vqa/metric.py deleted 100644 → 0
View file @ 1ded2ac4
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import os
import re
import numpy as np
import logging
logger = logging.getLogger(__name__)
PREFIX_CHECKPOINT_DIR = "checkpoint"
_re_checkpoint = re.compile(r"^" + PREFIX_CHECKPOINT_DIR + r"\-(\d+)$")
def get_last_checkpoint(folder):
content = os.listdir(folder)
checkpoints = [
path for path in content
if _re_checkpoint.search(path) is not None and os.path.isdir(
os.path.join(folder, path))
]
if len(checkpoints) == 0:
return
return os.path.join(
folder,
max(checkpoints,
key=lambda x: int(_re_checkpoint.search(x).groups()[0])))
def re_score(pred_relations, gt_relations, mode="strict"):
"""Evaluate RE predictions
Args:
pred_relations (list) : list of list of predicted relations (several relations in each sentence)
gt_relations (list) : list of list of ground truth relations
rel = { "head": (start_idx (inclusive), end_idx (exclusive)),
"tail": (start_idx (inclusive), end_idx (exclusive)),
"head_type": ent_type,
"tail_type": ent_type,
"type": rel_type}
vocab (Vocab) : dataset vocabulary
mode (str) : in 'strict' or 'boundaries'"""
assert mode in ["strict", "boundaries"]
relation_types = [v for v in [0, 1] if not v == 0]
scores = {
rel: {
"tp": 0,
"fp": 0,
"fn": 0
}
for rel in relation_types + ["ALL"]
}
# Count GT relations and Predicted relations
n_sents = len(gt_relations)
n_rels = sum([len([rel for rel in sent]) for sent in gt_relations])
n_found = sum([len([rel for rel in sent]) for sent in pred_relations])
# Count TP, FP and FN per type
for pred_sent, gt_sent in zip(pred_relations, gt_relations):
for rel_type in relation_types:
# strict mode takes argument types into account
if mode == "strict":
pred_rels = {(rel["head"], rel["head_type"], rel["tail"],
rel["tail_type"])
for rel in pred_sent if rel["type"] == rel_type}
gt_rels = {(rel["head"], rel["head_type"], rel["tail"],
rel["tail_type"])
for rel in gt_sent if rel["type"] == rel_type}
# boundaries mode only takes argument spans into account
elif mode == "boundaries":
pred_rels = {(rel["head"], rel["tail"])
for rel in pred_sent if rel["type"] == rel_type}
gt_rels = {(rel["head"], rel["tail"])
for rel in gt_sent if rel["type"] == rel_type}
scores[rel_type]["tp"] += len(pred_rels & gt_rels)
scores[rel_type]["fp"] += len(pred_rels - gt_rels)
scores[rel_type]["fn"] += len(gt_rels - pred_rels)
# Compute per entity Precision / Recall / F1
for rel_type in scores.keys():
if scores[rel_type]["tp"]:
scores[rel_type]["p"] = scores[rel_type]["tp"] / (
scores[rel_type]["fp"] + scores[rel_type]["tp"])
scores[rel_type]["r"] = scores[rel_type]["tp"] / (
scores[rel_type]["fn"] + scores[rel_type]["tp"])
else:
scores[rel_type]["p"], scores[rel_type]["r"] = 0, 0
if not scores[rel_type]["p"] + scores[rel_type]["r"] == 0:
scores[rel_type]["f1"] = (
2 * scores[rel_type]["p"] * scores[rel_type]["r"] /
(scores[rel_type]["p"] + scores[rel_type]["r"]))
else:
scores[rel_type]["f1"] = 0
# Compute micro F1 Scores
tp = sum([scores[rel_type]["tp"] for rel_type in relation_types])
fp = sum([scores[rel_type]["fp"] for rel_type in relation_types])
fn = sum([scores[rel_type]["fn"] for rel_type in relation_types])
if tp:
precision = tp / (tp + fp)
recall = tp / (tp + fn)
f1 = 2 * precision * recall / (precision + recall)
else:
precision, recall, f1 = 0, 0, 0
scores["ALL"]["p"] = precision
scores["ALL"]["r"] = recall
scores["ALL"]["f1"] = f1
scores["ALL"]["tp"] = tp
scores["ALL"]["fp"] = fp
scores["ALL"]["fn"] = fn
# Compute Macro F1 Scores
scores["ALL"]["Macro_f1"] = np.mean(
[scores[ent_type]["f1"] for ent_type in relation_types])
scores["ALL"]["Macro_p"] = np.mean(
[scores[ent_type]["p"] for ent_type in relation_types])
scores["ALL"]["Macro_r"] = np.mean(
[scores[ent_type]["r"] for ent_type in relation_types])
# logger.info(f"RE Evaluation in *** {mode.upper()} *** mode")
# logger.info(
# "processed {} sentences with {} relations; found: {} relations; correct: {}.".format(
# n_sents, n_rels, n_found, tp
# )
# )
# logger.info(
# "\tALL\t TP: {};\tFP: {};\tFN: {}".format(scores["ALL"]["tp"], scores["ALL"]["fp"], scores["ALL"]["fn"])
# )
# logger.info("\t\t(m avg): precision: {:.2f};\trecall: {:.2f};\tf1: {:.2f} (micro)".format(precision, recall, f1))
# logger.info(
# "\t\t(M avg): precision: {:.2f};\trecall: {:.2f};\tf1: {:.2f} (Macro)\n".format(
# scores["ALL"]["Macro_p"], scores["ALL"]["Macro_r"], scores["ALL"]["Macro_f1"]
# )
# )
# for rel_type in relation_types:
# logger.info(
# "\t{}: \tTP: {};\tFP: {};\tFN: {};\tprecision: {:.2f};\trecall: {:.2f};\tf1: {:.2f};\t{}".format(
# rel_type,
# scores[rel_type]["tp"],
# scores[rel_type]["fp"],
# scores[rel_type]["fn"],
# scores[rel_type]["p"],
# scores[rel_type]["r"],
# scores[rel_type]["f1"],
# scores[rel_type]["tp"] + scores[rel_type]["fp"],
# )
# )
return scores
ppstructure/vqa/requirements.txt
View file @ a323fce6
sentencepiece sentencepiece
yacs yacs
seqeval seqeval
paddlenlp>=2.2.1
\ No newline at end of file
ppstructure/vqa/train_re.py deleted 100644 → 0
View file @ 1ded2ac4
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import os
import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
import random
import time
import numpy as np
import paddle
from paddlenlp.transformers import LayoutXLMTokenizer, LayoutXLMModel, LayoutXLMForRelationExtraction
from xfun import XFUNDataset
from vqa_utils import parse_args, get_bio_label_maps, print_arguments, set_seed
from data_collator import DataCollator
from eval_re import evaluate
from ppocr.utils.logging import get_logger
def train(args):
logger = get_logger(log_file=os.path.join(args.output_dir, "train.log"))
rank = paddle.distributed.get_rank()
distributed = paddle.distributed.get_world_size() > 1
print_arguments(args, logger)
# Added here for reproducibility (even between python 2 and 3)
set_seed(args.seed)
label2id_map, id2label_map = get_bio_label_maps(args.label_map_path)
pad_token_label_id = paddle.nn.CrossEntropyLoss().ignore_index
# dist mode
if distributed:
paddle.distributed.init_parallel_env()
tokenizer = LayoutXLMTokenizer.from_pretrained(args.model_name_or_path)
if not args.resume:
model = LayoutXLMModel.from_pretrained(args.model_name_or_path)
model = LayoutXLMForRelationExtraction(model, dropout=None)
logger.info('train from scratch')
else:
logger.info('resume from {}'.format(args.model_name_or_path))
model = LayoutXLMForRelationExtraction.from_pretrained(
args.model_name_or_path)
# dist mode
if distributed:
model = paddle.DataParallel(model)
train_dataset = XFUNDataset(
tokenizer,
data_dir=args.train_data_dir,
label_path=args.train_label_path,
label2id_map=label2id_map,
img_size=(224, 224),
max_seq_len=args.max_seq_length,
pad_token_label_id=pad_token_label_id,
contains_re=True,
add_special_ids=False,
return_attention_mask=True,
load_mode='all')
eval_dataset = XFUNDataset(
tokenizer,
data_dir=args.eval_data_dir,
label_path=args.eval_label_path,
label2id_map=label2id_map,
img_size=(224, 224),
max_seq_len=args.max_seq_length,
pad_token_label_id=pad_token_label_id,
contains_re=True,
add_special_ids=False,
return_attention_mask=True,
load_mode='all')
train_sampler = paddle.io.DistributedBatchSampler(
train_dataset, batch_size=args.per_gpu_train_batch_size, shuffle=True)
train_dataloader = paddle.io.DataLoader(
train_dataset,
batch_sampler=train_sampler,
num_workers=args.num_workers,
use_shared_memory=True,
collate_fn=DataCollator())
eval_dataloader = paddle.io.DataLoader(
eval_dataset,
batch_size=args.per_gpu_eval_batch_size,
num_workers=args.num_workers,
shuffle=False,
collate_fn=DataCollator())
t_total = len(train_dataloader) * args.num_train_epochs
# build linear decay with warmup lr sch
lr_scheduler = paddle.optimizer.lr.PolynomialDecay(
learning_rate=args.learning_rate,
decay_steps=t_total,
end_lr=0.0,
power=1.0)
if args.warmup_steps > 0:
lr_scheduler = paddle.optimizer.lr.LinearWarmup(
lr_scheduler,
args.warmup_steps,
start_lr=0,
end_lr=args.learning_rate, )
grad_clip = paddle.nn.ClipGradByNorm(clip_norm=10)
optimizer = paddle.optimizer.Adam(
learning_rate=args.learning_rate,
parameters=model.parameters(),
epsilon=args.adam_epsilon,
grad_clip=grad_clip,
weight_decay=args.weight_decay)
# Train!
logger.info("***** Running training *****")
logger.info(" Num examples = {}".format(len(train_dataset)))
logger.info(" Num Epochs = {}".format(args.num_train_epochs))
logger.info(" Instantaneous batch size per GPU = {}".format(
args.per_gpu_train_batch_size))
logger.info(
" Total train batch size (w. parallel, distributed & accumulation) = {}".
format(args.per_gpu_train_batch_size *
paddle.distributed.get_world_size()))
logger.info(" Total optimization steps = {}".format(t_total))
global_step = 0
model.clear_gradients()
train_dataloader_len = len(train_dataloader)
best_metirc = {'f1': 0}
model.train()
train_reader_cost = 0.0
train_run_cost = 0.0
total_samples = 0
reader_start = time.time()
print_step = 1
for epoch in range(int(args.num_train_epochs)):
for step, batch in enumerate(train_dataloader):
train_reader_cost += time.time() - reader_start
train_start = time.time()
outputs = model(**batch)
train_run_cost += time.time() - train_start
# model outputs are always tuple in ppnlp (see doc)
loss = outputs['loss']
loss = loss.mean()
loss.backward()
optimizer.step()
optimizer.clear_grad()
# lr_scheduler.step() # Update learning rate schedule
global_step += 1
total_samples += batch['image'].shape[0]
if rank == 0 and step % print_step == 0:
logger.info(
"epoch: [{}/{}], iter: [{}/{}], global_step:{}, train loss: {:.6f}, lr: {:.6f}, avg_reader_cost: {:.5f} sec, avg_batch_cost: {:.5f} sec, avg_samples: {:.5f}, ips: {:.5f} images/sec".
format(epoch, args.num_train_epochs, step,
train_dataloader_len, global_step,
np.mean(loss.numpy()),
optimizer.get_lr(), train_reader_cost / print_step, (
train_reader_cost + train_run_cost) / print_step,
total_samples / print_step, total_samples / (
train_reader_cost + train_run_cost)))
train_reader_cost = 0.0
train_run_cost = 0.0
total_samples = 0
if rank == 0 and args.eval_steps > 0 and global_step % args.eval_steps == 0 and args.evaluate_during_training:
# Log metrics
# Only evaluate when single GPU otherwise metrics may not average well
results = evaluate(model, eval_dataloader, logger)
if results['f1'] >= best_metirc['f1']:
best_metirc = results
output_dir = os.path.join(args.output_dir, "best_model")
os.makedirs(output_dir, exist_ok=True)
if distributed:
model._layers.save_pretrained(output_dir)
else:
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
paddle.save(args,
os.path.join(output_dir, "training_args.bin"))
logger.info("Saving model checkpoint to {}".format(
output_dir))
logger.info("eval results: {}".format(results))
logger.info("best_metirc: {}".format(best_metirc))
reader_start = time.time()
if rank == 0:
# Save model checkpoint
output_dir = os.path.join(args.output_dir, "latest_model")
os.makedirs(output_dir, exist_ok=True)
if distributed:
model._layers.save_pretrained(output_dir)
else:
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
paddle.save(args, os.path.join(output_dir, "training_args.bin"))
logger.info("Saving model checkpoint to {}".format(output_dir))
logger.info("best_metirc: {}".format(best_metirc))
if __name__ == "__main__":
args = parse_args()
os.makedirs(args.output_dir, exist_ok=True)
train(args)
ppstructure/vqa/train_ser.py deleted 100644 → 0
View file @ 1ded2ac4
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import os
import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
import random
import time
import copy
import logging
import argparse
import paddle
import numpy as np
from seqeval.metrics import classification_report, f1_score, precision_score, recall_score
from paddlenlp.transformers import LayoutXLMModel, LayoutXLMTokenizer, LayoutXLMForTokenClassification
from paddlenlp.transformers import LayoutLMModel, LayoutLMTokenizer, LayoutLMForTokenClassification
from xfun import XFUNDataset
from vqa_utils import parse_args, get_bio_label_maps, print_arguments, set_seed
from eval_ser import evaluate
from losses import SERLoss
from ppocr.utils.logging import get_logger
MODELS = {
'LayoutXLM':
(LayoutXLMTokenizer, LayoutXLMModel, LayoutXLMForTokenClassification),
'LayoutLM':
(LayoutLMTokenizer, LayoutLMModel, LayoutLMForTokenClassification)
}
def train(args):
os.makedirs(args.output_dir, exist_ok=True)
rank = paddle.distributed.get_rank()
distributed = paddle.distributed.get_world_size() > 1
logger = get_logger(log_file=os.path.join(args.output_dir, "train.log"))
print_arguments(args, logger)
label2id_map, id2label_map = get_bio_label_maps(args.label_map_path)
loss_class = SERLoss(len(label2id_map))
pad_token_label_id = loss_class.ignore_index
# dist mode
if distributed:
paddle.distributed.init_parallel_env()
tokenizer_class, base_model_class, model_class = MODELS[args.ser_model_type]
tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
if not args.resume:
base_model = base_model_class.from_pretrained(args.model_name_or_path)
model = model_class(
base_model, num_classes=len(label2id_map), dropout=None)
logger.info('train from scratch')
else:
logger.info('resume from {}'.format(args.model_name_or_path))
model = model_class.from_pretrained(args.model_name_or_path)
# dist mode
if distributed:
model = paddle.DataParallel(model)
train_dataset = XFUNDataset(
tokenizer,
data_dir=args.train_data_dir,
label_path=args.train_label_path,
label2id_map=label2id_map,
img_size=(224, 224),
pad_token_label_id=pad_token_label_id,
contains_re=False,
add_special_ids=False,
return_attention_mask=True,
load_mode='all')
eval_dataset = XFUNDataset(
tokenizer,
data_dir=args.eval_data_dir,
label_path=args.eval_label_path,
label2id_map=label2id_map,
img_size=(224, 224),
pad_token_label_id=pad_token_label_id,
contains_re=False,
add_special_ids=False,
return_attention_mask=True,
load_mode='all')
train_sampler = paddle.io.DistributedBatchSampler(
train_dataset, batch_size=args.per_gpu_train_batch_size, shuffle=True)
train_dataloader = paddle.io.DataLoader(
train_dataset,
batch_sampler=train_sampler,
num_workers=args.num_workers,
use_shared_memory=True,
collate_fn=None, )
eval_dataloader = paddle.io.DataLoader(
eval_dataset,
batch_size=args.per_gpu_eval_batch_size,
num_workers=args.num_workers,
use_shared_memory=True,
collate_fn=None, )
t_total = len(train_dataloader) * args.num_train_epochs
# build linear decay with warmup lr sch
lr_scheduler = paddle.optimizer.lr.PolynomialDecay(
learning_rate=args.learning_rate,
decay_steps=t_total,
end_lr=0.0,
power=1.0)
if args.warmup_steps > 0:
lr_scheduler = paddle.optimizer.lr.LinearWarmup(
lr_scheduler,
args.warmup_steps,
start_lr=0,
end_lr=args.learning_rate, )
optimizer = paddle.optimizer.AdamW(
learning_rate=lr_scheduler,
parameters=model.parameters(),
epsilon=args.adam_epsilon,
weight_decay=args.weight_decay)
# Train!
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_dataset))
logger.info(" Num Epochs = %d", args.num_train_epochs)
logger.info(" Instantaneous batch size per GPU = %d",
args.per_gpu_train_batch_size)
logger.info(
" Total train batch size (w. parallel, distributed) = %d",
args.per_gpu_train_batch_size * paddle.distributed.get_world_size(), )
logger.info(" Total optimization steps = %d", t_total)
global_step = 0
tr_loss = 0.0
set_seed(args.seed)
best_metrics = None
train_reader_cost = 0.0
train_run_cost = 0.0
total_samples = 0
reader_start = time.time()
print_step = 1
model.train()
for epoch_id in range(args.num_train_epochs):
for step, batch in enumerate(train_dataloader):
train_reader_cost += time.time() - reader_start
if args.ser_model_type == 'LayoutLM':
if 'image' in batch:
batch.pop('image')
labels = batch.pop('labels')
train_start = time.time()
outputs = model(**batch)
train_run_cost += time.time() - train_start
if args.ser_model_type == 'LayoutXLM':
outputs = outputs[0]
loss = loss_class(labels, outputs, batch['attention_mask'])
# model outputs are always tuple in ppnlp (see doc)
loss = loss.mean()
loss.backward()
tr_loss += loss.item()
optimizer.step()
lr_scheduler.step() # Update learning rate schedule
optimizer.clear_grad()
global_step += 1
total_samples += batch['input_ids'].shape[0]
if rank == 0 and step % print_step == 0:
logger.info(
"epoch: [{}/{}], iter: [{}/{}], global_step:{}, train loss: {:.6f}, lr: {:.6f}, avg_reader_cost: {:.5f} sec, avg_batch_cost: {:.5f} sec, avg_samples: {:.5f}, ips: {:.5f} images/sec".
format(epoch_id, args.num_train_epochs, step,
len(train_dataloader), global_step,
loss.numpy()[0],
lr_scheduler.get_lr(), train_reader_cost /
print_step, (train_reader_cost + train_run_cost) /
print_step, total_samples / print_step, total_samples
/ (train_reader_cost + train_run_cost)))
train_reader_cost = 0.0
train_run_cost = 0.0
total_samples = 0
if rank == 0 and args.eval_steps > 0 and global_step % args.eval_steps == 0 and args.evaluate_during_training:
# Log metrics
# Only evaluate when single GPU otherwise metrics may not average well
results, _ = evaluate(args, model, tokenizer, loss_class,
eval_dataloader, label2id_map,
id2label_map, pad_token_label_id, logger)
if best_metrics is None or results["f1"] >= best_metrics["f1"]:
best_metrics = copy.deepcopy(results)
output_dir = os.path.join(args.output_dir, "best_model")
os.makedirs(output_dir, exist_ok=True)
if distributed:
model._layers.save_pretrained(output_dir)
else:
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
paddle.save(args,
os.path.join(output_dir, "training_args.bin"))
logger.info("Saving model checkpoint to {}".format(
output_dir))
logger.info("[epoch {}/{}][iter: {}/{}] results: {}".format(
epoch_id, args.num_train_epochs, step,
len(train_dataloader), results))
if best_metrics is not None:
logger.info("best metrics: {}".format(best_metrics))
reader_start = time.time()
if rank == 0:
# Save model checkpoint
output_dir = os.path.join(args.output_dir, "latest_model")
os.makedirs(output_dir, exist_ok=True)
if distributed:
model._layers.save_pretrained(output_dir)
else:
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
paddle.save(args, os.path.join(output_dir, "training_args.bin"))
logger.info("Saving model checkpoint to {}".format(output_dir))
return global_step, tr_loss / global_step
if __name__ == "__main__":
args = parse_args()
train(args)
ppstructure/vqa/vqa_utils.py deleted 100644 → 0
View file @ 1ded2ac4
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import os
import argparse
import cv2
import random
import numpy as np
import imghdr
from copy import deepcopy
import paddle
from PIL import Image, ImageDraw, ImageFont
def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
paddle.seed(seed)
def get_bio_label_maps(label_map_path):
with open(label_map_path, "r", encoding='utf-8') as fin:
lines = fin.readlines()
lines = [line.strip() for line in lines]
if "O" not in lines:
lines.insert(0, "O")
labels = []
for line in lines:
if line == "O":
labels.append("O")
else:
labels.append("B-" + line)
labels.append("I-" + line)
label2id_map = {label: idx for idx, label in enumerate(labels)}
id2label_map = {idx: label for idx, label in enumerate(labels)}
return label2id_map, id2label_map
def get_image_file_list(img_file):
imgs_lists = []
if img_file is None or not os.path.exists(img_file):
raise Exception("not found any img file in {}".format(img_file))
img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif', 'GIF'}
if os.path.isfile(img_file) and imghdr.what(img_file) in img_end:
imgs_lists.append(img_file)
elif os.path.isdir(img_file):
for single_file in os.listdir(img_file):
file_path = os.path.join(img_file, single_file)
if os.path.isfile(file_path) and imghdr.what(file_path) in img_end:
imgs_lists.append(file_path)
if len(imgs_lists) == 0:
raise Exception("not found any img file in {}".format(img_file))
imgs_lists = sorted(imgs_lists)
return imgs_lists
def draw_ser_results(image,
ocr_results,
font_path="../../doc/fonts/simfang.ttf",
font_size=18):
np.random.seed(2021)
color = (np.random.permutation(range(255)),
np.random.permutation(range(255)),
np.random.permutation(range(255)))
color_map = {
idx: (color[0][idx], color[1][idx], color[2][idx])
for idx in range(1, 255)
}
if isinstance(image, np.ndarray):
image = Image.fromarray(image)
img_new = image.copy()
draw = ImageDraw.Draw(img_new)
font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
for ocr_info in ocr_results:
if ocr_info["pred_id"] not in color_map:
continue
color = color_map[ocr_info["pred_id"]]
text = "{}: {}".format(ocr_info["pred"], ocr_info["text"])
draw_box_txt(ocr_info["bbox"], text, draw, font, font_size, color)
img_new = Image.blend(image, img_new, 0.5)
return np.array(img_new)
def draw_box_txt(bbox, text, draw, font, font_size, color):
# draw ocr results outline
bbox = ((bbox[0], bbox[1]), (bbox[2], bbox[3]))
draw.rectangle(bbox, fill=color)
# draw ocr results
start_y = max(0, bbox[0][1] - font_size)
tw = font.getsize(text)[0]
draw.rectangle(
[(bbox[0][0] + 1, start_y), (bbox[0][0] + tw + 1, start_y + font_size)],
fill=(0, 0, 255))
draw.text((bbox[0][0] + 1, start_y), text, fill=(255, 255, 255), font=font)
def draw_re_results(image,
result,
font_path="../../doc/fonts/simfang.ttf",
font_size=18):
np.random.seed(0)
if isinstance(image, np.ndarray):
image = Image.fromarray(image)
img_new = image.copy()
draw = ImageDraw.Draw(img_new)
font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
color_head = (0, 0, 255)
color_tail = (255, 0, 0)
color_line = (0, 255, 0)
for ocr_info_head, ocr_info_tail in result:
draw_box_txt(ocr_info_head["bbox"], ocr_info_head["text"], draw, font,
font_size, color_head)
draw_box_txt(ocr_info_tail["bbox"], ocr_info_tail["text"], draw, font,
font_size, color_tail)
center_head = (
(ocr_info_head['bbox'][0] + ocr_info_head['bbox'][2]) // 2,
(ocr_info_head['bbox'][1] + ocr_info_head['bbox'][3]) // 2)
center_tail = (
(ocr_info_tail['bbox'][0] + ocr_info_tail['bbox'][2]) // 2,
(ocr_info_tail['bbox'][1] + ocr_info_tail['bbox'][3]) // 2)
draw.line([center_head, center_tail], fill=color_line, width=5)
img_new = Image.blend(image, img_new, 0.5)
return np.array(img_new)
# pad sentences
def pad_sentences(tokenizer,
encoded_inputs,
max_seq_len=512,
pad_to_max_seq_len=True,
return_attention_mask=True,
return_token_type_ids=True,
return_overflowing_tokens=False,
return_special_tokens_mask=False):
# Padding with larger size, reshape is carried out
max_seq_len = (
len(encoded_inputs["input_ids"]) // max_seq_len + 1) * max_seq_len
needs_to_be_padded = pad_to_max_seq_len and \
max_seq_len and len(encoded_inputs["input_ids"]) < max_seq_len
if needs_to_be_padded:
difference = max_seq_len - len(encoded_inputs["input_ids"])
if tokenizer.padding_side == 'right':
if return_attention_mask:
encoded_inputs["attention_mask"] = [1] * len(encoded_inputs[
"input_ids"]) + [0] * difference
if return_token_type_ids:
encoded_inputs["token_type_ids"] = (
encoded_inputs["token_type_ids"] +
[tokenizer.pad_token_type_id] * difference)
if return_special_tokens_mask:
encoded_inputs["special_tokens_mask"] = encoded_inputs[
"special_tokens_mask"] + [1] * difference
encoded_inputs["input_ids"] = encoded_inputs[
"input_ids"] + [tokenizer.pad_token_id] * difference
encoded_inputs["bbox"] = encoded_inputs["bbox"] + [[0, 0, 0, 0]
] * difference
else:
if return_attention_mask:
encoded_inputs["attention_mask"] = [1] * len(encoded_inputs[
"input_ids"])
return encoded_inputs
def split_page(encoded_inputs, max_seq_len=512):
"""
truncate is often used in training process
"""
for key in encoded_inputs:
if key == 'entities':
encoded_inputs[key] = [encoded_inputs[key]]
continue
encoded_inputs[key] = paddle.to_tensor(encoded_inputs[key])
if encoded_inputs[key].ndim <= 1: # for input_ids, att_mask and so on
encoded_inputs[key] = encoded_inputs[key].reshape([-1, max_seq_len])
else: # for bbox
encoded_inputs[key] = encoded_inputs[key].reshape(
[-1, max_seq_len, 4])
return encoded_inputs
def preprocess(
tokenizer,
ori_img,
ocr_info,
img_size=(224, 224),
pad_token_label_id=-100,
max_seq_len=512,
add_special_ids=False,
return_attention_mask=True, ):
ocr_info = deepcopy(ocr_info)
height = ori_img.shape[0]
width = ori_img.shape[1]
img = cv2.resize(ori_img, img_size).transpose([2, 0, 1]).astype(np.float32)
segment_offset_id = []
words_list = []
bbox_list = []
input_ids_list = []
token_type_ids_list = []
entities = []
for info in ocr_info:
# x1, y1, x2, y2
bbox = info["bbox"]
bbox[0] = int(bbox[0] * 1000.0 / width)
bbox[2] = int(bbox[2] * 1000.0 / width)
bbox[1] = int(bbox[1] * 1000.0 / height)
bbox[3] = int(bbox[3] * 1000.0 / height)
text = info["text"]
encode_res = tokenizer.encode(
text, pad_to_max_seq_len=False, return_attention_mask=True)
if not add_special_ids:
# TODO: use tok.all_special_ids to remove
encode_res["input_ids"] = encode_res["input_ids"][1:-1]
encode_res["token_type_ids"] = encode_res["token_type_ids"][1:-1]
encode_res["attention_mask"] = encode_res["attention_mask"][1:-1]
# for re
entities.append({
"start": len(input_ids_list),
"end": len(input_ids_list) + len(encode_res["input_ids"]),
"label": "O",
})
input_ids_list.extend(encode_res["input_ids"])
token_type_ids_list.extend(encode_res["token_type_ids"])
bbox_list.extend([bbox] * len(encode_res["input_ids"]))
words_list.append(text)
segment_offset_id.append(len(input_ids_list))
encoded_inputs = {
"input_ids": input_ids_list,
"token_type_ids": token_type_ids_list,
"bbox": bbox_list,
"attention_mask": [1] * len(input_ids_list),
"entities": entities
}
encoded_inputs = pad_sentences(
tokenizer,
encoded_inputs,
max_seq_len=max_seq_len,
return_attention_mask=return_attention_mask)
encoded_inputs = split_page(encoded_inputs)
fake_bs = encoded_inputs["input_ids"].shape[0]
encoded_inputs["image"] = paddle.to_tensor(img).unsqueeze(0).expand(
[fake_bs] + list(img.shape))
encoded_inputs["segment_offset_id"] = segment_offset_id
return encoded_inputs
def postprocess(attention_mask, preds, id2label_map):
if isinstance(preds, paddle.Tensor):
preds = preds.numpy()
preds = np.argmax(preds, axis=2)
preds_list = [[] for _ in range(preds.shape[0])]
# keep batch info
for i in range(preds.shape[0]):
for j in range(preds.shape[1]):
if attention_mask[i][j] == 1:
preds_list[i].append(id2label_map[preds[i][j]])
return preds_list
def merge_preds_list_with_ocr_info(ocr_info, segment_offset_id, preds_list,
label2id_map_for_draw):
# must ensure the preds_list is generated from the same image
preds = [p for pred in preds_list for p in pred]
id2label_map = dict()
for key in label2id_map_for_draw:
val = label2id_map_for_draw[key]
if key == "O":
id2label_map[val] = key
if key.startswith("B-") or key.startswith("I-"):
id2label_map[val] = key[2:]
else:
id2label_map[val] = key
for idx in range(len(segment_offset_id)):
if idx == 0:
start_id = 0
else:
start_id = segment_offset_id[idx - 1]
end_id = segment_offset_id[idx]
curr_pred = preds[start_id:end_id]
curr_pred = [label2id_map_for_draw[p] for p in curr_pred]
if len(curr_pred) <= 0:
pred_id = 0
else:
counts = np.bincount(curr_pred)
pred_id = np.argmax(counts)
ocr_info[idx]["pred_id"] = int(pred_id)
ocr_info[idx]["pred"] = id2label_map[int(pred_id)]
return ocr_info
def print_arguments(args, logger=None):
print_func = logger.info if logger is not None else print
"""print arguments"""
print_func('----------- Configuration Arguments -----------')
for arg, value in sorted(vars(args).items()):
print_func('%s: %s' % (arg, value))
print_func('------------------------------------------------')
def parse_args():
parser = argparse.ArgumentParser()
# Required parameters
# yapf: disable
parser.add_argument("--model_name_or_path",
default=None, type=str, required=True,)
parser.add_argument("--ser_model_type",
default='LayoutXLM', type=str)
parser.add_argument("--re_model_name_or_path",
default=None, type=str, required=False,)
parser.add_argument("--train_data_dir", default=None,
type=str, required=False,)
parser.add_argument("--train_label_path", default=None,
type=str, required=False,)
parser.add_argument("--eval_data_dir", default=None,
type=str, required=False,)
parser.add_argument("--eval_label_path", default=None,
type=str, required=False,)
parser.add_argument("--output_dir", default=None, type=str, required=True,)
parser.add_argument("--max_seq_length", default=512, type=int,)
parser.add_argument("--evaluate_during_training", action="store_true",)
parser.add_argument("--num_workers", default=8, type=int,)
parser.add_argument("--per_gpu_train_batch_size", default=8,
type=int, help="Batch size per GPU/CPU for training.",)
parser.add_argument("--per_gpu_eval_batch_size", default=8,
type=int, help="Batch size per GPU/CPU for eval.",)
parser.add_argument("--learning_rate", default=5e-5,
type=float, help="The initial learning rate for Adam.",)
parser.add_argument("--weight_decay", default=0.0,
type=float, help="Weight decay if we apply some.",)
parser.add_argument("--adam_epsilon", default=1e-8,
type=float, help="Epsilon for Adam optimizer.",)
parser.add_argument("--max_grad_norm", default=1.0,
type=float, help="Max gradient norm.",)
parser.add_argument("--num_train_epochs", default=3, type=int,
help="Total number of training epochs to perform.",)
parser.add_argument("--warmup_steps", default=0, type=int,
help="Linear warmup over warmup_steps.",)
parser.add_argument("--eval_steps", type=int, default=10,
help="eval every X updates steps.",)
parser.add_argument("--seed", type=int, default=2048,
help="random seed for initialization",)
parser.add_argument("--rec_model_dir", default=None, type=str, )
parser.add_argument("--det_model_dir", default=None, type=str, )
parser.add_argument(
"--label_map_path", default="./labels/labels_ser.txt", type=str, required=False, )
parser.add_argument("--infer_imgs", default=None, type=str, required=False)
parser.add_argument("--resume", action='store_true')
parser.add_argument("--ocr_json_path", default=None,
type=str, required=False, help="ocr prediction results")
# yapf: enable
args = parser.parse_args()
return args
ppstructure/vqa/xfun.py deleted 100644 → 0
View file @ 1ded2ac4
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import json
import os
import cv2
import numpy as np
import paddle
import copy
from paddle.io import Dataset
__all__ = ["XFUNDataset"]
class XFUNDataset(Dataset):
"""
Example:
print("=====begin to build dataset=====")
from paddlenlp.transformers import LayoutXLMTokenizer
tokenizer = LayoutXLMTokenizer.from_pretrained("/paddle/models/transformers/layoutxlm-base-paddle/")
tok_res = tokenizer.tokenize("Maribyrnong")
# res = tokenizer.convert_ids_to_tokens(val_data["input_ids"][0])
dataset = XfunDatasetForSer(
tokenizer,
data_dir="./zh.val/",
label_path="zh.val/xfun_normalize_val.json",
img_size=(224,224))
print(len(dataset))
data = dataset[0]
print(data.keys())
print("input_ids: ", data["input_ids"])
print("labels: ", data["labels"])
print("token_type_ids: ", data["token_type_ids"])
print("words_list: ", data["words_list"])
print("image shape: ", data["image"].shape)
"""
def __init__(self,
tokenizer,
data_dir,
label_path,
contains_re=False,
label2id_map=None,
img_size=(224, 224),
pad_token_label_id=None,
add_special_ids=False,
return_attention_mask=True,
load_mode='all',
max_seq_len=512):
super().__init__()
self.tokenizer = tokenizer
self.data_dir = data_dir
self.label_path = label_path
self.contains_re = contains_re
self.label2id_map = label2id_map
self.img_size = img_size
self.pad_token_label_id = pad_token_label_id
self.add_special_ids = add_special_ids
self.return_attention_mask = return_attention_mask
self.load_mode = load_mode
self.max_seq_len = max_seq_len
if self.pad_token_label_id is None:
self.pad_token_label_id = paddle.nn.CrossEntropyLoss().ignore_index
self.all_lines = self.read_all_lines()
self.entities_labels = {'HEADER': 0, 'QUESTION': 1, 'ANSWER': 2}
self.return_keys = {
'bbox': {
'type': 'np',
'dtype': 'int64'
},
'input_ids': {
'type': 'np',
'dtype': 'int64'
},
'labels': {
'type': 'np',
'dtype': 'int64'
},
'attention_mask': {
'type': 'np',
'dtype': 'int64'
},
'image': {
'type': 'np',
'dtype': 'float32'
},
'token_type_ids': {
'type': 'np',
'dtype': 'int64'
},
'entities': {
'type': 'dict'
},
'relations': {
'type': 'dict'
}
}
if load_mode == "all":
self.encoded_inputs_all = self._parse_label_file_all()
def pad_sentences(self,
encoded_inputs,
max_seq_len=512,
pad_to_max_seq_len=True,
return_attention_mask=True,
return_token_type_ids=True,
truncation_strategy="longest_first",
return_overflowing_tokens=False,
return_special_tokens_mask=False):
# Padding
needs_to_be_padded = pad_to_max_seq_len and \
max_seq_len and len(encoded_inputs["input_ids"]) < max_seq_len
if needs_to_be_padded:
difference = max_seq_len - len(encoded_inputs["input_ids"])
if self.tokenizer.padding_side == 'right':
if return_attention_mask:
encoded_inputs["attention_mask"] = [1] * len(encoded_inputs[
"input_ids"]) + [0] * difference
if return_token_type_ids:
encoded_inputs["token_type_ids"] = (
encoded_inputs["token_type_ids"] +
[self.tokenizer.pad_token_type_id] * difference)
if return_special_tokens_mask:
encoded_inputs["special_tokens_mask"] = encoded_inputs[
"special_tokens_mask"] + [1] * difference
encoded_inputs["input_ids"] = encoded_inputs[
"input_ids"] + [self.tokenizer.pad_token_id] * difference
encoded_inputs["labels"] = encoded_inputs[
"labels"] + [self.pad_token_label_id] * difference
encoded_inputs["bbox"] = encoded_inputs[
"bbox"] + [[0, 0, 0, 0]] * difference
elif self.tokenizer.padding_side == 'left':
if return_attention_mask:
encoded_inputs["attention_mask"] = [0] * difference + [
1
] * len(encoded_inputs["input_ids"])
if return_token_type_ids:
encoded_inputs["token_type_ids"] = (
[self.tokenizer.pad_token_type_id] * difference +
encoded_inputs["token_type_ids"])
if return_special_tokens_mask:
encoded_inputs["special_tokens_mask"] = [
1
] * difference + encoded_inputs["special_tokens_mask"]
encoded_inputs["input_ids"] = [
self.tokenizer.pad_token_id
] * difference + encoded_inputs["input_ids"]
encoded_inputs["labels"] = [
self.pad_token_label_id
] * difference + encoded_inputs["labels"]
encoded_inputs["bbox"] = [
[0, 0, 0, 0]
] * difference + encoded_inputs["bbox"]
else:
if return_attention_mask:
encoded_inputs["attention_mask"] = [1] * len(encoded_inputs[
"input_ids"])
return encoded_inputs
def truncate_inputs(self, encoded_inputs, max_seq_len=512):
for key in encoded_inputs:
if key == "sample_id":
continue
length = min(len(encoded_inputs[key]), max_seq_len)
encoded_inputs[key] = encoded_inputs[key][:length]
return encoded_inputs
def read_all_lines(self, ):
with open(self.label_path, "r", encoding='utf-8') as fin:
lines = fin.readlines()
return lines
def _parse_label_file_all(self):
"""
parse all samples
"""
encoded_inputs_all = []
for line in self.all_lines:
encoded_inputs_all.extend(self._parse_label_file(line))
return encoded_inputs_all
def _parse_label_file(self, line):
"""
parse single sample
"""
image_name, info_str = line.split("\t")
image_path = os.path.join(self.data_dir, image_name)
def add_imgge_path(x):
x['image_path'] = image_path
return x
encoded_inputs = self._read_encoded_inputs_sample(info_str)
if self.contains_re:
encoded_inputs = self._chunk_re(encoded_inputs)
else:
encoded_inputs = self._chunk_ser(encoded_inputs)
encoded_inputs = list(map(add_imgge_path, encoded_inputs))
return encoded_inputs
def _read_encoded_inputs_sample(self, info_str):
"""
parse label info
"""
# read text info
info_dict = json.loads(info_str)
height = info_dict["height"]
width = info_dict["width"]
words_list = []
bbox_list = []
input_ids_list = []
token_type_ids_list = []
gt_label_list = []
if self.contains_re:
# for re
entities = []
relations = []
id2label = {}
entity_id_to_index_map = {}
empty_entity = set()
for info in info_dict["ocr_info"]:
if self.contains_re:
# for re
if len(info["text"]) == 0:
empty_entity.add(info["id"])
continue
id2label[info["id"]] = info["label"]
relations.extend([tuple(sorted(l)) for l in info["linking"]])
# x1, y1, x2, y2
bbox = info["bbox"]
label = info["label"]
bbox[0] = int(bbox[0] * 1000.0 / width)
bbox[2] = int(bbox[2] * 1000.0 / width)
bbox[1] = int(bbox[1] * 1000.0 / height)
bbox[3] = int(bbox[3] * 1000.0 / height)
text = info["text"]
encode_res = self.tokenizer.encode(
text, pad_to_max_seq_len=False, return_attention_mask=True)
gt_label = []
if not self.add_special_ids:
# TODO: use tok.all_special_ids to remove
encode_res["input_ids"] = encode_res["input_ids"][1:-1]
encode_res["token_type_ids"] = encode_res["token_type_ids"][1:
-1]
encode_res["attention_mask"] = encode_res["attention_mask"][1:
-1]
if label.lower() == "other":
gt_label.extend([0] * len(encode_res["input_ids"]))
else:
gt_label.append(self.label2id_map[("b-" + label).upper()])
gt_label.extend([self.label2id_map[("i-" + label).upper()]] *
(len(encode_res["input_ids"]) - 1))
if self.contains_re:
if gt_label[0] != self.label2id_map["O"]:
entity_id_to_index_map[info["id"]] = len(entities)
entities.append({
"start": len(input_ids_list),
"end":
len(input_ids_list) + len(encode_res["input_ids"]),
"label": label.upper(),
})
input_ids_list.extend(encode_res["input_ids"])
token_type_ids_list.extend(encode_res["token_type_ids"])
bbox_list.extend([bbox] * len(encode_res["input_ids"]))
gt_label_list.extend(gt_label)
words_list.append(text)
encoded_inputs = {
"input_ids": input_ids_list,
"labels": gt_label_list,
"token_type_ids": token_type_ids_list,
"bbox": bbox_list,
"attention_mask": [1] * len(input_ids_list),
# "words_list": words_list,
}
encoded_inputs = self.pad_sentences(
encoded_inputs,
max_seq_len=self.max_seq_len,
return_attention_mask=self.return_attention_mask)
encoded_inputs = self.truncate_inputs(encoded_inputs)
if self.contains_re:
relations = self._relations(entities, relations, id2label,
empty_entity, entity_id_to_index_map)
encoded_inputs['relations'] = relations
encoded_inputs['entities'] = entities
return encoded_inputs
def _chunk_ser(self, encoded_inputs):
encoded_inputs_all = []
seq_len = len(encoded_inputs['input_ids'])
chunk_size = 512
for chunk_id, index in enumerate(range(0, seq_len, chunk_size)):
chunk_beg = index
chunk_end = min(index + chunk_size, seq_len)
encoded_inputs_example = {}
for key in encoded_inputs:
encoded_inputs_example[key] = encoded_inputs[key][chunk_beg:
chunk_end]
encoded_inputs_all.append(encoded_inputs_example)
return encoded_inputs_all
def _chunk_re(self, encoded_inputs):
# prepare data
entities = encoded_inputs.pop('entities')
relations = encoded_inputs.pop('relations')
encoded_inputs_all = []
chunk_size = 512
for chunk_id, index in enumerate(
range(0, len(encoded_inputs["input_ids"]), chunk_size)):
item = {}
for k in encoded_inputs:
item[k] = encoded_inputs[k][index:index + chunk_size]
# select entity in current chunk
entities_in_this_span = []
global_to_local_map = {} #
for entity_id, entity in enumerate(entities):
if (index <= entity["start"] < index + chunk_size and
index <= entity["end"] < index + chunk_size):
entity["start"] = entity["start"] - index
entity["end"] = entity["end"] - index
global_to_local_map[entity_id] = len(entities_in_this_span)
entities_in_this_span.append(entity)
# select relations in current chunk
relations_in_this_span = []
for relation in relations:
if (index <= relation["start_index"] < index + chunk_size and
index <= relation["end_index"] < index + chunk_size):
relations_in_this_span.append({
"head": global_to_local_map[relation["head"]],
"tail": global_to_local_map[relation["tail"]],
"start_index": relation["start_index"] - index,
"end_index": relation["end_index"] - index,
})
item.update({
"entities": reformat(entities_in_this_span),
"relations": reformat(relations_in_this_span),
})
item['entities']['label'] = [
self.entities_labels[x] for x in item['entities']['label']
]
encoded_inputs_all.append(item)
return encoded_inputs_all
def _relations(self, entities, relations, id2label, empty_entity,
entity_id_to_index_map):
"""
build relations
"""
relations = list(set(relations))
relations = [
rel for rel in relations
if rel[0] not in empty_entity and rel[1] not in empty_entity
]
kv_relations = []
for rel in relations:
pair = [id2label[rel[0]], id2label[rel[1]]]
if pair == ["question", "answer"]:
kv_relations.append({
"head": entity_id_to_index_map[rel[0]],
"tail": entity_id_to_index_map[rel[1]]
})
elif pair == ["answer", "question"]:
kv_relations.append({
"head": entity_id_to_index_map[rel[1]],
"tail": entity_id_to_index_map[rel[0]]
})
else:
continue
relations = sorted(
[{
"head": rel["head"],
"tail": rel["tail"],
"start_index": get_relation_span(rel, entities)[0],
"end_index": get_relation_span(rel, entities)[1],
} for rel in kv_relations],
key=lambda x: x["head"], )
return relations
def load_img(self, image_path):
# read img
img = cv2.imread(image_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
resize_h, resize_w = self.img_size
im_shape = img.shape[0:2]
im_scale_y = resize_h / im_shape[0]
im_scale_x = resize_w / im_shape[1]
img_new = cv2.resize(
img, None, None, fx=im_scale_x, fy=im_scale_y, interpolation=2)
mean = np.array([0.485, 0.456, 0.406])[np.newaxis, np.newaxis, :]
std = np.array([0.229, 0.224, 0.225])[np.newaxis, np.newaxis, :]
img_new = img_new / 255.0
img_new -= mean
img_new /= std
img = img_new.transpose((2, 0, 1))
return img
def __getitem__(self, idx):
if self.load_mode == "all":
data = copy.deepcopy(self.encoded_inputs_all[idx])
else:
data = self._parse_label_file(self.all_lines[idx])[0]
image_path = data.pop('image_path')
data["image"] = self.load_img(image_path)
return_data = {}
for k, v in data.items():
if k in self.return_keys:
if self.return_keys[k]['type'] == 'np':
v = np.array(v, dtype=self.return_keys[k]['dtype'])
return_data[k] = v
return return_data
def __len__(self, ):
if self.load_mode == "all":
return len(self.encoded_inputs_all)
else:
return len(self.all_lines)
def get_relation_span(rel, entities):
bound = []
for entity_index in [rel["head"], rel["tail"]]:
bound.append(entities[entity_index]["start"])
bound.append(entities[entity_index]["end"])
return min(bound), max(bound)
def reformat(data):
new_data = {}
for item in data:
for k, v in item.items():
if k not in new_data:
new_data[k] = []
new_data[k].append(v)
return new_data
requirements.txt
View file @ a323fce6
...@@ -13,4 +13,3 @@ lxml ...@@ -13,4 +13,3 @@ lxml
premailer premailer
openpyxl openpyxl
fasttext==0.9.1 fasttext==0.9.1
paddlenlp>=2.2.1
tools/eval.py
View file @ a323fce6
...@@ -61,7 +61,8 @@ def main(): ...@@ -61,7 +61,8 @@ def main():
else: else:
model_type = None model_type = None
best_model_dict = load_model(config, model) best_model_dict = load_model(
config, model, model_type=config['Architecture']["model_type"])
if len(best_model_dict): if len(best_model_dict):
logger.info('metric in ckpt ***************') logger.info('metric in ckpt ***************')
for k, v in best_model_dict.items(): for k, v in best_model_dict.items():
......
tools/infer_vqa_token_ser.py 0 → 100755
View file @ a323fce6
This diff is collapsed.
tools/infer_vqa_token_ser_re.py 0 → 100755
View file @ a323fce6
This diff is collapsed.
tools/program.py
View file @ a323fce6
This diff is collapsed.
tools/train.py
View file @ a323fce6
...@@ -97,7 +97,8 @@ def main(config, device, logger, vdl_writer): ...@@ -97,7 +97,8 @@ def main(config, device, logger, vdl_writer):
# build metric # build metric
eval_class = build_metric(config['Metric']) eval_class = build_metric(config['Metric'])
# load pretrain model # load pretrain model
pre_best_model_dict = load_model(config, model, optimizer) pre_best_model_dict = load_model(config, model, optimizer,
config['Architecture']["model_type"])
logger.info('train dataloader has {} iters'.format(len(train_dataloader))) logger.info('train dataloader has {} iters'.format(len(train_dataloader)))
if valid_dataloader is not None: if valid_dataloader is not None:
logger.info('valid dataloader has {} iters'.format( logger.info('valid dataloader has {} iters'.format(
......
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