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contextualized_object_detection_prediction.py
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contextualized_object_detection_prediction.py
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from PIL import ImageFile
import collections
from copy import deepcopy
from datasets import load_from_disk, set_caching_enabled
from detr import CocoEvaluator
from utils import data_utils, utils
from utils.args_helper import (
DataArguments,
ModelArguments,
TrainingArguments
)
from tqdm import tqdm
from trainer.detr_trainer import DetrTrainer
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
PretrainedConfig,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
EarlyStoppingCallback
)
from transformers.models.detr.modeling_detr import DetrHungarianMatcher
from transformers import HfArgumentParser, DataCollatorWithPadding
from transformers.trainer_utils import get_last_checkpoint, is_main_process
from typing import Dict, Union, Any, Optional, List, Tuple
from model.holy_detr import HolyDetrForObjectDetection
from simmc2.model.utils import ambiguous_candidates_evaluation as eval_utils
import datasets
import json
import logging
import numpy as np
import os
import pandas as pd
import sys
import torch
import torch.nn as nn
import transformers
set_caching_enabled(True)
logger = logging.getLogger(__name__)
#####
# Main Functions
#####
global split_name
split_name = 'traing'
def run(model_args, data_args, training_args):
training_args.output_dir="{}/{}".format(training_args.output_dir, model_args.model_name_or_path)
os.makedirs(training_args.output_dir, exist_ok=True)
cache_dir_path = "./{}/{}".format(data_args.cache_dir_name, model_args.model_name_or_path)
os.makedirs(cache_dir_path, exist_ok=True)
# Data loading
MAPPING = data_utils.load_categories()
scene_dset, MAPPING = data_utils.load_objects_in_scenes_dataset(mapping=MAPPING)
scene_dset = scene_dset.map(
data_utils.add_sitcom_detr_attr,
num_proc=data_args.preprocessing_num_workers,
desc="adding sitcom detr attribute",
load_from_cache_file=False,
remove_columns=None
)
conv_train_dset, train_gold_data = data_utils.load_sitcom_detr_dataset(
data_path=data_args.train_dataset_path,
mapping=MAPPING, return_gt_labels=True
)
conv_dev_dset, valid_gold_data = data_utils.load_sitcom_detr_dataset(
data_path=data_args.dev_dataset_path,
mapping=MAPPING, return_gt_labels=True
)
conv_test_dset, test_gold_data = data_utils.load_sitcom_detr_dataset(
data_path=data_args.devtest_dataset_path,
mapping=MAPPING, return_gt_labels=True
)
# Preprocessing
tokenizer = transformers.AutoTokenizer.from_pretrained(model_args.text_model_name_or_path)
feature_extractor = transformers.AutoFeatureExtractor.from_pretrained(model_args.model_name_or_path)
dataset = datasets.DatasetDict({
'scene': scene_dset,
'train': conv_train_dset,
'valid': conv_dev_dset,
'test': conv_test_dset,
})
dataset = dataset.map(
data_utils.convert_dialogue_to_caption,
num_proc=data_args.preprocessing_num_workers,
desc="convert object attributes to caption",
load_from_cache_file=False,
fn_kwargs={"num_utterances": data_args.num_utterances},
remove_columns=["dialogue"]
)
dataset = dataset.map(
data_utils.tokenize_text,
num_proc=data_args.preprocessing_num_workers,
desc="tokenize text data",
load_from_cache_file=False,
fn_kwargs={"tokenizer": tokenizer, "text_column_name": "caption"},
remove_columns=["caption"]
)
def transform(example_batch):
images = [image.convert("RGB") for image in example_batch["image"]]
# Preprocess target objects
targets = [
{"image_id": id_, "annotations": object_} \
for (id_, object_) in zip(example_batch["image_id"], example_batch["objects"])
]
features = feature_extractor(images=images, annotations=targets, return_tensors="pt")
for key, value in features.items():
example_batch[key] = value
for i, object_ in enumerate(example_batch["objects"]):
example_batch['labels'][i]['turn_id'] = torch.LongTensor([example_batch['turn_id'][i]])
example_batch['labels'][i]['dialog_id'] = torch.LongTensor([example_batch['dialog_id'][i]])
example_batch['labels'][i]['index'] = torch.LongTensor(list(map(lambda x: x['index'], object_)))
# Preprocess all objects
all_targets = [
{"image_id": idx, "annotations": object_} \
for idx, object_ in enumerate(example_batch["all_objects"])
]
features = feature_extractor(images=images, annotations=all_targets, return_tensors="pt")
for key in features['labels'][0].keys():
for i in range(len(features['labels'])):
example_batch['labels'][i][f"all_{key}"] = features['labels'][i][key]
for i, object_ in enumerate(example_batch["all_objects"]):
example_batch['labels'][i]['all_index'] = torch.LongTensor(list(map(lambda x: x['index'], object_)))
return example_batch
proc_datasets = deepcopy(dataset)
proc_datasets["train"] = proc_datasets["train"].with_transform(transform)
proc_datasets["valid"] = proc_datasets["valid"].with_transform(transform)
proc_datasets["test"] = proc_datasets["test"].with_transform(transform)
# Training and evaluation
text_collator = DataCollatorWithPadding(tokenizer)
def collate_fn(batch):
pixel_values = [item["pixel_values"] for item in batch]
encoding = feature_extractor.pad_and_create_pixel_mask(
pixel_values, return_tensors="pt"
)
labels = [item["labels"] for item in batch]
text_batch = text_collator({'input_ids': [item["input_ids"] for item in batch]})
batch = {}
batch["pixel_values"] = encoding["pixel_values"]
batch["pixel_mask"] = encoding["pixel_mask"]
batch["labels"] = labels
batch["input_ids"] = text_batch["input_ids"]
batch["attention_mask"] = text_batch["attention_mask"]
return batch
text_model = transformers.AutoModel.from_pretrained(model_args.text_model_name_or_path)
config = transformers.AutoConfig.from_pretrained(
model_args.model_name_or_path,
id2label=MAPPING["id2cat"],
label2id=MAPPING["cat2id"],
)
# detr_model = transformers.AutoModelForObjectDetection.from_pretrained(
# model_args.model_name_or_path,
# id2label=MAPPING["id2cat"],
# label2id=MAPPING["cat2id"],
# ignore_mismatched_sizes=True
# )
config.text_auxiliary_loss = False
holy_detr = HolyDetrForObjectDetection(config, text_model)
# holy_detr.load_state_dict(detr_model.state_dict(), strict=False)
holy_detr.load_state_dict(torch.load(f'{model_args.checkpoint_path}/pytorch_model.bin'), strict=True)
matcher = DetrHungarianMatcher(
class_cost=holy_detr.config.class_cost,
bbox_cost=holy_detr.config.bbox_cost,
giou_cost=holy_detr.config.giou_cost
)
@torch.inference_mode()
def compute_metrics(p: EvalPrediction):
def center_to_corners_format(x):
x_c, y_c, w, h = x.unbind(-1)
b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)]
return torch.stack(b, dim=-1)
def box_area(boxes):
return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
def box_iou(boxes1, boxes2):
area1 = box_area(boxes1)
area2 = box_area(boxes2)
left_top = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]
right_bottom = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]
width_height = (right_bottom - left_top).clamp(min=0) # [N,M,2]
inter = width_height[:, :, 0] * width_height[:, :, 1] # [N,M]
union = area1[:, None] + area2 - inter
iou = inter / union
return iou
# p.prediction: Dict{'pred_logits': Tensor, 'pred_boxes': Tensor}
# p.labels_ids: List[Dict{
# 'class_labels': Tensor, 'boxes': Tensor, 'image_id': Tensor, 'area': tensor,
# 'iscrowd': Tensor, 'orig_size': Tensor, 'size': Tensor
# }]
labels = p.label_ids
outputs = p.predictions
all_objects = []
for label in labels:
all_object = {}
for k, v in label.items():
if 'all_' in k:
all_object[k.replace('all_','')] = v
all_objects.append(all_object)
no_obj_idx = outputs['logits'].shape[-1] # index of no object prediction
probas = outputs['logits'].softmax(-1)
cls_preds = probas.argmax(dim=-1)
boxes_preds = outputs['pred_boxes']
match_indices = []
batch_size = 128
for i in range(0, len(all_objects), 128):
s_idx = i
e_idx = i + batch_size if i + batch_size < len(all_objects) else len(all_objects)
batch_outputs = {
'logits': outputs['logits'][s_idx:e_idx],
'pred_boxes': outputs['pred_boxes'][s_idx:e_idx]
}
batch_targets = all_objects[s_idx:e_idx]
match_indices += matcher(batch_outputs, batch_targets)
# probas: torch.Size([414, 100, 29])
# cls_preds: torch.Size([414, 100])
# boxes_preds: torch.Size([414, 100, 4])
# labels: List[Dict{'class_labels': Tensor, 'boxes': Tensor, 'index': Tensor}] (len 414)
# all_objects: List[Dict{'class_labels': Tensor, 'boxes'': Tensor, 'index': Tensor}] (len 414)
# indices: List[Tuple<pred_idxs, gt_idxs>] (len 414)
for threshold in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]:
results = collections.defaultdict(list)
for boxes_pred, label, all_object, (pred_idxs, gt_idxs) in zip(boxes_preds, labels, all_objects, match_indices):
tgt_boxes = all_object['boxes']
# iou_scores = box_iou(boxes_pred[pred_idxs], tgt_boxes[gt_idxs])
iou_scores = box_iou(
center_to_corners_format(boxes_pred[pred_idxs]),
center_to_corners_format(tgt_boxes[gt_idxs])
).diagonal()
valid_boxes = (iou_scores >= threshold)
turn_id = label['turn_id'].item()
dialog_id = label['dialog_id'].item()
# print('gt_idxs', gt_idxs)
# print('iou_scores', iou_scores)
# print('indices', all_object['index'])
pred_obj_ids = []
for j in range(len(valid_boxes)):
if valid_boxes[j]:
pred_obj_ids.append(all_object['index'][gt_idxs[j]].item())
new_instance = {
"turn_id": turn_id,
"disambiguation_candidates": pred_obj_ids
}
results[dialog_id].append(new_instance)
# Restructure results JSON and save.
print('Compariong predictions with ground truths...')
results = [{
"dialog_id": dialog_id,
"predictions": predictions,
} for dialog_id, predictions in results.items()]
global split_name
if split_name == 'train':
gold_data = train_gold_data
elif split_name == 'valid':
gold_data = valid_gold_data
elif split_name == 'test':
gold_data = test_gold_data
else:
raise ValueError(f'Unknown split name `{split_name}`')
metrics = eval_utils.evaluate_ambiguous_candidates(gold_data, results)
print(f'== Eval Metrics T={threshold} ==')
print('Recall: ', metrics["recall"])
print('Precision: ', metrics["precision"])
print('F1-Score: ', metrics["f1"])
return metrics
trainer = DetrTrainer(
model=holy_detr,
args=training_args,
data_collator=collate_fn,
train_dataset=proc_datasets["train"],
eval_dataset=proc_datasets["valid"],
# compute_metrics=compute_metrics, # Not sure why it doesn't work
tokenizer=feature_extractor,
callbacks=[transformers.EarlyStoppingCallback(early_stopping_patience=10)],
)
# # Training
# train_results = trainer.train()
# trainer.save_model()
# Evaluation
trainer.compute_metrics = compute_metrics
global split_name
# print('Running evaluation on the training data')
# split_name = "train"
# metrics = trainer.evaluate(proc_datasets["train"])
# trainer.log_metrics("train", metrics)
# trainer.save_metrics("train", metrics)
# print('Running evaluation on the validation data')
# split_name = "valid"
# metrics = trainer.evaluate(proc_datasets["valid"])
# trainer.log_metrics("valid", metrics)
# trainer.save_metrics("valid", metrics)
print('Running evaluation on the testing data')
split_name = "test"
metrics = trainer.evaluate(proc_datasets["test"])
trainer.log_metrics("test", metrics)
trainer.save_metrics("test", metrics)
# # Prediction
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# for idx, batch in enumerate(tqdm(proc_datasets["all"])):
# # forward pass
# outputs = model(
# pixel_values=batch["pixel_values"].unsqueeze(dim=0).to(device),
# pixel_mask=batch["pixel_mask"].unsqueeze(dim=0).to(device))
# print(outputs.pred_boxes.shape, outputs.last_hidden_state)
def main():
###
# Parsing & Initialization
###
# Parse argument
parser = HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
# Set random seed
utils.init_env(training_args.seed)
# Detect last checkpoint
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
elif last_checkpoint is not None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
###
# Prepare logger
###
# Init logging
os.makedirs("./log", exist_ok=True)
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout), logging.FileHandler(
"./log/log__{}".format(model_args.model_name_or_path.replace("/", "_")), mode="w")],
)
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
# Set the verbosity to warn of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank):
transformers.utils.logging.set_verbosity(transformers.logging.WARNING)
logger.info("Training/evaluation parameters %s", training_args)
###
# RUN RUN RUN!!!
###
run(model_args, data_args, training_args)
if __name__ == '__main__':
main()