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engine_sptsv2.py
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engine_sptsv2.py
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# ------------------------------------------------------------------------
# Copyright (2023) Bytedance Ltd. and/or its affiliates
# ------------------------------------------------------------------------
# ------------------------------------------------------------------------
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------
import os
import sys
import cv2
import math
import json
import torch
import numpy as np
from typing import Iterable
from tqdm import tqdm
import util.misc_sptsv2 as utils
from util.visualize import vis_output_seqs, extract_result_from_output_seqs, convert_rec_to_str
def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
data_loader: Iterable, optimizer: torch.optim.Optimizer,
device: torch.device, epoch: int, max_norm: float = 0,
lr_scheduler: list = [0], print_freq: int = 10, text_length: int = 25):
model.train()
criterion.train()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
header = 'Epoch: [{}]'.format(epoch)
optimizer.param_groups[0]['lr'] = lr_scheduler[epoch]
optimizer.param_groups[1]['lr'] = lr_scheduler[epoch] * 0.1
for samples, input_box_seqs, input_label_seqs, output_box_seqs, output_label_seqs in metric_logger.log_every(data_loader, print_freq, header):
samples = samples.to(device); input_box_seqs = input_box_seqs.to(device); input_label_seqs = input_label_seqs.to(device); output_box_seqs = output_box_seqs.to(device)
output_label_seqs = output_label_seqs.to(device)
if not all(input_label_seqs.tolist()):
continue
output_seqs = torch.cat([output_box_seqs.flatten(),output_label_seqs.flatten() ])
outputs_box, outputs_label = model(samples, input_box_seqs, input_label_seqs, text_length)
outputs_box = outputs_box.reshape(-1, outputs_box.shape[-1])
outputs_label = outputs_label.reshape(-1, outputs_label.shape[-1])
outputs = torch.cat([outputs_box,outputs_label],0)
loss = criterion(outputs, output_seqs.flatten())
loss_dict = {'at':loss}
weight_dict = {'at':1}
losses = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict)
# reduce losses over all GPUs for logging purposes
loss_dict_reduced = utils.reduce_dict(loss_dict)
loss_dict_reduced_unscaled = {f'{k}_unscaled': v
for k, v in loss_dict_reduced.items()}
loss_dict_reduced_scaled = {k: v * weight_dict[k]
for k, v in loss_dict_reduced.items() if k in weight_dict}
losses_reduced_scaled = sum(loss_dict_reduced_scaled.values())
loss_value = losses_reduced_scaled.item()
if not math.isfinite(loss_value):
print("Loss is {}, stopping training".format(loss_value))
print(loss_dict_reduced)
sys.exit(1)
optimizer.zero_grad()
losses.backward()
if max_norm > 0:
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
optimizer.step()
metric_logger.update(loss=loss_value, **loss_dict_reduced_scaled, **loss_dict_reduced_unscaled)
metric_logger.update(lr=optimizer.param_groups[0]["lr"])
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
@torch.no_grad()
def evaluate(model, criterion, data_loader, device, output_dir, chars, start_index, visualize=False, text_length=25):
model.eval()
criterion.eval()
chars = list(chars)
import time
cnt = 0
total = 0
results = []
for samples, targets in tqdm(data_loader):
batch = len(targets)
targets = targets[: batch // 2]
samples.mask = samples.mask[: batch // 2, :, :]
samples.tensors = samples.tensors[: batch // 2, :, :, :]
samples = samples.to(device)
dataset_names = [target['dataset_name'] for target in targets]
targets = [{k: v.to(device) for k, v in t.items() if k != 'dataset_name'} for t in targets]
seq = torch.ones(len(targets), 1).to(samples.mask) * start_index
torch.cuda.synchronize()
t0 = time.time()
outputs = model(samples, seq,seq, text_length)
torch.cuda.synchronize()
t1 = time.time()
cnt += 1
total += t1-t0
print(total/cnt)
if outputs == None:
continue
outputs, values, rec_scores = outputs
if visualize:
samples_ = samples.to(torch.device('cpu')); outputs_ = outputs.cpu()
vis_images = vis_output_seqs(samples_, outputs_, rec_scores, False, True, text_length, chars)
for vis_image, target, dataset_name in zip(vis_images, targets, dataset_names):
save_path = os.path.join(output_dir, 'vis', dataset_name, '{:06d}.jpg'.format(target['image_id'].item()))
os.makedirs(os.path.dirname(save_path), exist_ok=True)
cv2.imwrite(save_path, vis_image)
outputs = outputs.cpu(); values = values.cpu(); rec_scores = rec_scores.cpu()
for target, output, value, rec_score in zip(targets, outputs, values, rec_scores):
image_id = target['image_id'].item()
output, split_index = extract_result_from_output_seqs(output, rec_score, return_index=True, text_length=text_length, chars=chars)
split_values = [value[split_index[i]:split_index[i+1]] for i in range(0, len(split_index)-1)]
center_pts = output['center_pts']; rec_labels = output['rec']; rec_scores = output['key_rec_score']
rec_labels = convert_rec_to_str(rec_labels, chars)
orig_h, orig_w = target['orig_size']#; img_h, img_w = target['size']
for center_pt, rec_label, rec_score, split_value in zip(center_pts, rec_labels, rec_scores, split_values):
if center_pt.numel() != 2:
continue
center_pt = center_pt.numpy().reshape(-1, 2).astype(np.float64)
center_pt[:, 0] *= (float(orig_w.item()) / 1000); center_pt[:, 1] *= (float(orig_h.item()) / 1000)
polygon_pts = center_pt.tolist()
result = {
'image_id': image_id,
'category_id': 1,
'polys': polygon_pts,
'rec': rec_label,
'score': split_value.mean().item(),
'value': split_value.numpy().tolist(),
'rec_score': rec_score.numpy().tolist()
}
results.append(result)
json_path = os.path.join(output_dir, 'results', dataset_name+'.json')
os.makedirs(os.path.dirname(json_path), exist_ok=True)
results_json = json.dumps(results, indent=4)
with open(json_path, 'w') as f:
f.write(results_json)