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train_all_dataset_weight_loss_coop_train.py
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from dataset import Ring_Cell_random_crop, collate_fn, Ring_Cell_all_dataset, Ring_Cell_random_crop_all
import torch
from torch.utils.data import Dataset, DataLoader
import model_all_dataset_weight_loss as model
import os
from tensorboardX import SummaryWriter
import numpy as np
from tqdm import tqdm
from metric import detection_metric, calculate_metric_final, calculate_metric_final_new
from lib_new.nms.gpu_nms import gpu_nms
import random
from lib_new.nms.nums_py import py_cpu_nms
def seed_torch(seed=0):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# set seed for torch and numpy
seed_torch(0)
def nms(dets, thresh):
"Dispatch to either CPU or GPU NMS implementations.\
Accept dets as tensor"""
dets = dets.cpu().detach().numpy()
# return gpu_nms(dets, thresh)
return py_cpu_nms(dets, thresh)
def get_lr(optimizer):
return optimizer.param_groups[0]['lr']
def main(params):
print('fold {}'.format(params['test_fold']))
description = 'resnet18_remove_some_da_cooperative_training'
if params['writer'] == True:
writer = SummaryWriter(comment='_4fold_{}_{}_all dataset(pos+neg)_weight_loss_{}_{}'.format(params['test_fold'], params['cooperative'], params['weight_loss'], description))
retinanet = model.resnet18(num_classes=2, pretrained=True, weight_loss=params['weight_loss'])
retinanet = torch.nn.DataParallel(retinanet).cuda()
if os.path.exists(params['model_path']) and params['resume']:
retinanet.module.load_state_dict(torch.load(params['model_path']))
print('resume training from {}'.format(params['model_path']))
optimizer = torch.optim.Adam(retinanet.parameters(), lr=params['learning_rate'])
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, verbose=True, factor=0.9)
train_dataset = Ring_Cell_random_crop_all(params['train_txt'], mixup=False)
train_dataloader = DataLoader(
train_dataset,
batch_size=params['batch_size'],
num_workers=4,
collate_fn=collate_fn,
shuffle=True
)
test_dataset = Ring_Cell_all_dataset(params['test_txt'])
# train
step = 1
best_recall = 0
best_recall_valid = 0
best_precision = 0
best_ap = 0
best_froc = 0
best_fps = 0
for epoch in range(params['max_epoch']):
retinanet.train()
retinanet.module.freeze_bn()
epoch_loss_train = []
epoch_loss = []
epoch_cls_loss_train = []
epoch_reg_loss_train = []
tq = tqdm(total=len(train_dataloader))
lr = get_lr(optimizer)
tq.set_description('epoch:{}, learning rate:{}'.format(epoch, lr))
for index, (data, label, _) in enumerate(train_dataloader):
optimizer.zero_grad()
classification_loss, regression_loss = retinanet([data.cuda().float(), label])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss.backward()
tq.update(1)
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
optimizer.step()
epoch_loss_train.append(float(loss))
epoch_loss.append(float(loss))
epoch_cls_loss_train.append(float(classification_loss))
epoch_reg_loss_train.append(float(regression_loss))
if index % 20 == 0 and params['writer'] == True:
writer.add_scalar('loss for train', float(np.mean(epoch_loss)), step)
step += 1
epoch_loss = []
torch.save(retinanet.module.state_dict(), 'ckpt_new/latest_fold_{}_{}_all_dataset_weight_loss_{}_{}.pth'.format(params['test_fold'], params['cooperative'], params['weight_loss'], description))
# if epoch == 50:
# torch.save(retinanet.module.state_dict(),
# 'ckpt_new/50_epoch_fold_{}_all_dataset_{}.pth'.format(
# params['test_fold'], params['weight_loss'], description))
tq.close()
scheduler.step(np.mean(epoch_loss_train))
if params['writer'] == True:
writer.add_scalar('epoch/loss for train', float(np.mean(epoch_loss_train)), epoch)
writer.add_scalar('epoch/cls loss for train', float(np.mean(epoch_cls_loss_train)), epoch)
writer.add_scalar('epoch/reg loss for train', float(np.mean(epoch_reg_loss_train)), epoch)
# test
# retinanet.eval()
# with torch.no_grad():
# test_image_size = params['test_image_size']
# stride_num = params['test_stride_num']
#
# pred_boxes_total = []
# pred_scores_total = []
# gt_boxes_total = []
#
# for i, (image, bbox, image_, image_name) in enumerate(tqdm(test_dataset)):
# h, w = image.size()[1:]
# stride_h = (h - test_image_size) / (stride_num - 1)
# stride_w = (w - test_image_size) / (stride_num - 1)
#
# pred_boxes = []
# pred_scores = []
#
# for h_index in range(stride_num):
# for w_index in range(stride_num):
# image_patch = image[:, int(h_index * stride_h): int(h_index * stride_h) + test_image_size,
# int(w_index * stride_w): int(w_index * stride_w) + test_image_size]
# # predict
# scores_patch, labels_patch, boxes_patch = retinanet(image_patch.unsqueeze(0).cuda().float(), score_threshold=0.05)
# scores_patch = scores_patch.cpu().detach().numpy() # size -> [num_box]
# # labels_patch = labels_patch.cpu().detach().numpy() # size -> [num_box]
# boxes_patch = boxes_patch.cpu().detach().numpy() # size -> [num_box, 4]
#
# # change bbox coordinates
#
# if boxes_patch.shape[0] != 0:
# start_x = int(w_index * stride_w)
# start_y = int(h_index * stride_h)
# box_index = (boxes_patch[:, 0] > 5) & (boxes_patch[:, 1] > 5) & (
# boxes_patch[:, 2] < test_image_size - 6) \
# & (boxes_patch[:, 3] < test_image_size - 6)
#
# boxes_patch = boxes_patch[box_index]
# scores_patch = scores_patch[box_index]
#
# boxes_patch[:, 0] = boxes_patch[:, 0] + start_x
# boxes_patch[:, 1] = boxes_patch[:, 1] + start_y
# boxes_patch[:, 2] = boxes_patch[:, 2] + start_x
# boxes_patch[:, 3] = boxes_patch[:, 3] + start_y
#
# boxes_patch = boxes_patch.tolist()
# scores_patch = scores_patch.tolist()
#
# pred_boxes.extend(boxes_patch)
# pred_scores.extend(scores_patch)
#
# # image = image_.permute(1, 2, 0).numpy()
# # for box in pred_boxes:
# # image = cv2.rectangle(image, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), (0, 0, 255), 2)
#
# # nms
# if len(pred_boxes) != 0:
# pred_boxes = torch.Tensor(pred_boxes).unsqueeze(0) # size -> [1, num_box, 4]
# pred_scores = torch.Tensor(pred_scores).unsqueeze(0).unsqueeze(-1) # size -> [1, num_box, 1]
#
# # pred_boxes_w = pred_boxes[0, :, 2] - pred_boxes[0, :, 0]
# # pred_boxes_h = pred_boxes[0, :, 3] - pred_boxes[0, :, 1]
#
# # wh_idx = (pred_boxes_w > 15) & (pred_boxes_h > 15)
# # pred_boxes = pred_boxes[:, wh_idx, :]
# # pred_scores = pred_scores[:, wh_idx, :]
#
# anchors_nms_idx = nms(torch.cat([pred_boxes, pred_scores], dim=2)[0, :, :], 0.4)
#
# pred_boxes = pred_boxes[0, anchors_nms_idx, :]
# pred_scores = pred_scores[0, anchors_nms_idx, 0]
#
# pred_boxes = pred_boxes.numpy().tolist()
# pred_scores = pred_scores.numpy().tolist()
#
# pred_boxes_total.append(pred_boxes)
# pred_scores_total.append(pred_scores)
# gt_boxes_total.append(bbox)
#
# else:
# pred_boxes_total.append([])
# pred_scores_total.append([])
# gt_boxes_total.append(bbox)
#
# recall, precision, froc, FPs, recall_record, precision_record, froc_record, FPs_record, score_record = \
# calculate_metric_final_new(pred_boxes_total, gt_boxes_total, pred_scores_total, score_threshold=0.2)
#
# if params['writer'] == True:
# writer.add_scalar('epoch/average froc', float(froc), epoch)
# writer.add_scalar('epoch/recall', float(recall[-1]), epoch)
# writer.add_scalar('epoch/precision', float(precision[-1]), epoch)
# writer.add_scalar('epoch/fps', float(FPs), epoch)
# writer.add_scalar('epoch/max valid recall', float(recall_record[-1]), epoch)
# writer.add_scalar('epoch/precision when max valid recall', float(precision_record[-1]), epoch)
# writer.add_scalar('epoch/score when max valid recall', float(score_record[-1]), epoch)
# writer.add_scalar('epoch/FPs when max valid recall', float(FPs_record), epoch)
#
# # if float(froc) > best_froc:
# # best_froc = float(froc)
# # torch.save(retinanet.module.state_dict(), 'ckpt/best_froc_resnet18_fold_{}_all_dataset.pth'.format(params['test_fold']))
#
# if float(recall_record[-1]) > best_recall_valid:
# best_recall_valid = float(recall_record[-1])
# torch.save(retinanet.module.state_dict(),
# 'ckpt_new/best_valid_recall_fold_{}_all_dataset_weight_loss_{}_{}.pth'.format(params['test_fold'], params['weight_loss'], description))
#
# if float(recall[-1]) > best_recall:
# best_recall = float(recall[-1])
# torch.save(retinanet.module.state_dict(),
# 'ckpt_new/best_recall_fold_{}_all_dataset_weight_loss_{}_{}.pth'.format(params['test_fold'], params['weight_loss'], description))
#
# # if float(precision[-1]) > best_precision:
# # best_precision = float(precision[-1])
# # torch.save(retinanet.module.state_dict(), 'ckpt/best_precision_resnet18_fold_{}_all_dataset.pth'.format(params['test_fold']))
# #
# # if float(FPs) > best_fps:
# # best_fps = float(FPs)
# # torch.save(retinanet.module.state_dict(), 'ckpt/best_fps_resnet18_fold_{}_all_dataset.pth'.format(params['test_fold']))
#
#
#
# print('froc: {}, recall: {}, precision: {}, fps: {}, best valid recall: {}'.format(froc, recall[-1], precision[-1], FPs, recall_record[-1]))
input()
if __name__ == '__main__':
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
test_fold = 0
cooperative = 0
params = {
'learning_rate': 1e-4,
'optim': 'adam',
'max_epoch': 300,
'test_fold': test_fold,
'cooperative': cooperative,
'train_txt': '../train_test_4/train_{}_{}.txt'.format(test_fold, cooperative),
'test_txt': '../train_test_4/test_{}.txt'.format(test_fold),
'batch_size': 32,
'writer': True,
'model_path': 'ckpt/best_precision_resnet101.pth',
'resume': False,
'test_image_size': 1024,
'test_stride_num': 3,
'weight_loss': 1
}
main(params)