def main(args):
torch.device("cuda" if torch.cuda.is_available() else "cpu")
mode = args.mode
if mode == 'train':
epochs = args.epochs
evaluate_steps = args.evaluate_steps
save_steps = args.save_steps
save_dir = args.save_dir
train(model, train_dataloader, epochs, evaluate_steps, save_steps,
save_dir)
elif mode == 'evaluate':
model_path = args.model_path
if model_path is None:
logger.error('model_path is not set!')
return
model.load_state_dict(torch.load(model_path))
model.eval()
loss, acc = evaluate(model)
logger.info("loss=%f, accuracy=%f", loss, acc)
else:
logger.error('wrong mode: %s', mode)
transforms = Compose([Resize(resize_dim),ToTensor(),normalize])
convert_to = 'RGB'
if args.input_type == 'dicom':
dataset = iap.DicomSegment(args.img_path, transforms, convert_to)
elif args.input_type == 'png' and args.non_montgomery:
dataset = iap.LungTest(args.img_path, transforms, convert_to)
elif args.input_type == 'png':
dataset = iap.lungSegmentDataset(
os.path.join(args.img_path, "CXR_png"),
os.path.join(args.img_path, "ManualMask/leftMask/"),
os.path.join(args.img_path, "ManualMask/rightMask/"),
imagetransform=transforms,
labeltransform=Compose([Resize((224, 224)),ToTensor()]),
convert_to='RGB',
)
dataloader = torch.utils.data.DataLoader(dataset,batch_size=1,shuffle=False)
model = torch.nn.DataParallel(model)
model.load_state_dict(torch.load(args.resume_from))
show = iap.visualize(dataset)
with torch.no_grad():
for i, sample in enumerate(dataloader):
img = torch.autograd.Variable(sample['image']).cuda()
mask = model(img)
if not args.non_montgomery:
show.ImageWithGround(i,True,True,save=True)
show.ImageWithMask(i, sample['filename'][0], mask.squeeze().cpu().numpy(), True, True, save=True)
pred = [sum(pred) / len(pred)] * len(pred)
if not compute_metric:
return pred
pred = np.array(pred, dtype=np.float64)
ce = log_loss(gt, pred)
prauc = compute_prauc(pred, gt)
rce = compute_rce(pred, gt)
return ce, prauc, rce
if __name__ == '__main__':
if not os.path.exists('results'):
os.mkdir('results')
checkpoint = torch.load(
os.path.join(checkpoints_dir, model_name + '_best.pt'))
model.load_state_dict(checkpoint['model_state_dict'])
model.to(device)
if make_prediction:
data = pd.read_csv(test_file,
sep='\x01',
header=None,
names=all_features,
encoding='utf-8')
pred = test(model, compute_metric=False)
print('prediction finished')
pred = pd.concat([
data[['tweet_id', 'engaging_user_id']],
pd.DataFrame({'prediction': pred})
], 1)
pred.to_csv(os.path.join('results', arg.label + '_prediction.csv'),
header=False,
if args.model == 'resnet':
model = model.segmentNetwork().cuda()
resize_dim = (400, 400)
convert_to = 'L'
elif args.model == 'unet11':
model = unet11(out_filters=3).cuda()
resize_dim = (224, 224)
convert_to = 'RGB'
elif args.model == 'unet16':
model = unet16(out_filters=3).cuda()
resize_dim = (224, 224)
convert_to = 'RGB'
model = torch.nn.DataParallel(model)
model.load_state_dict(torch.load("unet16_100.pth"))
if args.no_normalize:
transforms = Compose([Resize(resize_dim),ToTensor()])
else:
transforms = Compose([Resize(resize_dim),ToTensor(),normalize])
convert_to = 'RGB'
source_path = '/home/enzo/data/Cohen/'#CORDA-dataset-v3/'
#mask_path = '/home/enzo/data/CORDA-dataset-v4-masks/'
target_path = '/home/enzo/data/Cohen-masks/'#'/home/enzo/data/CORDA-dataset-v3-masked/'
for this_folder in ["data"]:#["RX--COVID-", "RX--COVID+", "RX+-COVID-", "RX+-COVID+"]:
dataset = iap.LungTest(source_path+this_folder, transforms, convert_to)
dataloader = torch.utils.data.DataLoader(dataset,batch_size=1,shuffle=False)
MODELPATH = '/home/dxtien/dxtien_research/nmduy/pytorch-lung-segmentation/lung_segmentation/unet16_100.pth'
normalize = Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
model = unet16(out_filters=3).cuda()
resize_dim = (224, 224)
convert_to = 'RGB'
transforms = Compose([Resize(resize_dim), ToTensor(), normalize])
convert_to = 'RGB'
dataset = iap.MyLungTest(IMGPATH, IMGPATHTXT, transforms, convert_to)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False)
#model = torch.nn.DataParallel(model)
model.load_state_dict(torch.load(MODELPATH))
#show = iap.visualize(dataset)
with torch.no_grad():
for i, sample in enumerate(dataloader):
img = torch.autograd.Variable(sample['image']).cuda()
mask = model(img)
# if not args.non_montgomery:
# show.ImageWithGround(i,True,True,save=True)
# show.ImageWithMask(i, sample['filename'][0], mask.squeeze().cpu().numpy(), True, True, save=True)
mask_np = mask.squeeze().cpu().numpy()
filename = sample['filename']
filename = filename.split('/')[-1]
filename = filename[:-4]
save_mask(mask_np, OUTDIR, filename=filename + '_mask.png')
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
transform = transforms.Compose([
transforms.ToTensor(),
])
cfg_params = utils.read_data_cfg(args.cfgfile)
train_txt = cfg_params['train']
test_txt = cfg_params['test']
backup_dir = cfg_params['backup']
if args.load_model is not None:
print('Loading model from %s.' % args.load_model)
model = models.model.UNet(args.im_size, args.kernel_size)
model.load_state_dict(torch.load(args.load_model))
elif args.test:
print('Missing model file for evaluating test set.')
exit()
else:
model = models.model.UNet(args.im_size, args.kernel_size)
# Datasets and dataloaders.
if not args.test:
train_dataset = IGVCDataset(train_txt, im_size=args.im_size, split='train', transform=transform, val_samples=args.val_samples)
val_dataset = IGVCDataset(train_txt, im_size=args.im_size, split='val', transform=transform, val_samples=args.val_samples)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, **kwargs)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=args.batch_size, shuffle=True, **kwargs)
# Optmizer
lr = args.lr
def train(model, criterion, converter, device, pretrain=False):
imgdir_list = []
label_list = []
rootdir = '/home/chen-ubuntu/Desktop/checks_dataset/new_train_stamp_crop/'
for mode in ['new_crop', 'new_crop2', 'new_crop3']:
imgfile_dir = os.path.join(rootdir, mode)
imgs_files = sorted(os.listdir(imgfile_dir))
for img_file in imgs_files:
imgs_dir = os.path.join(imgfile_dir, img_file)
imgs = sorted(os.listdir(imgs_dir))
for img in imgs:
img_dir = os.path.join(imgs_dir, img)
imgdir_list.append(img_dir)
label_list.append(img[:-6])
if len(imgdir_list) != len(label_list):
print('dataset is wrong!')
np.random.seed(10)
state = np.random.get_state()
np.random.shuffle(imgdir_list)
np.random.set_state(state)
np.random.shuffle(label_list)
segment = len(imgdir_list) // 10
train_imgdirs = imgdir_list[:segment * 9]
train_labels = label_list[:segment * 9]
val_imgdirs = imgdir_list[segment * 9:]
val_labels = label_list[segment * 9:]
print('trainset: ', len(train_imgdirs))
print('validset: ', len(val_labels))
trainset = dataset_seal.BaseDataset(train_imgdirs,
train_labels,
transform=dataset_seal.img_enhancer,
_type='seal')
validset = dataset_seal.BaseDataset(val_imgdirs,
val_labels,
transform=dataset_seal.img_padder,
_type='seal')
print('Device:', device)
model = model.to(device)
if pretrain:
print("Using pretrained model")
'''
state_dict = torch.load("/home/chen-ubuntu/Desktop/checks_dataset/pths/crnn_pertrain.pth", map_location=device)
cnn_modules = {}
rnn_modules = {}
for module in state_dict:
if module.split('.')[1] == 'FeatureExtraction':
key = module.replace("module.FeatureExtraction.", "")
cnn_modules[key] = state_dict[module]
elif module.split('.')[1] == 'SequenceModeling':
key = module.replace("module.SequenceModeling.", "")
rnn_modules[key] = state_dict[module]
model.cnn.load_state_dict(cnn_modules)
model.rnn.load_state_dict(rnn_modules)
'''
model.load_state_dict(
torch.load(
'/home/chen-ubuntu/Desktop/checks_dataset/pths/seal_lr3_bat256_aug_epoch15_acc0.704862.pth'
))
dataloader = DataLoader(trainset,
batch_size=64,
shuffle=True,
num_workers=4,
drop_last=False)
'''
lr = 1e-3
params = model.parameters()
optimizer = optim.Adam(params, lr)
optimizer.zero_grad()
batch_cnt = 0
for epoch in range(config.epochs):
epoch_loss = 0
model.train()
train_acc = 0
train_acc_cnt = 0
for i, (img, label, _) in enumerate(dataloader):
n_correct = 0
batch_cnt += 1
train_acc_cnt += 1
img = img.to(device)
text, length = converter.encode(label)
preds = model(img)
preds_size = torch.IntTensor([preds.size(0)] * img.size(0))
preds = preds.to('cpu')
loss = criterion(preds, text, preds_size, length)
_, preds = preds.max(2)
preds = preds.transpose(1, 0).contiguous().view(-1)
sim_preds = converter.decode(preds.data, preds_size.data, raw=False)
list1 = [x for x in label]
for pred, target in zip(sim_preds, list1):
if pred == target:
n_correct += 1
# loss.backward()
# optimizer.step()
# model.zero_grad()
loss.backward()
if (i + 1) % 4:
optimizer.step()
optimizer.zero_grad()
epoch_loss += loss.item()
train_acc += n_correct / len(list1)
if (i + 1) % 4 == 0:
print("epoch: {:<3d}, batch: {:<3d}, batch loss: {:4f}, epoch loss: {:4f}, acc: {}". \
format(epoch, i, loss.item(), epoch_loss, n_correct / len(list1)))
writer.add_scalar('data/train_loss', loss.item(), batch_cnt)
writer.add_scalar('data/train_acc', n_correct / len(list1), batch_cnt)
'''
#print('train_average_acc is: {:.3f}'.format(train_acc / train_acc_cnt))
acc, valid_loss = valid(model, criterion, converter, device, validset)
'''
def train(model,
criterion,
converter,
device,
train_datasets,
pretrain=False): #valid_datasets=None, pretrain=False):
print('Device:', device)
model = model.to(device)
if pretrain:
print("Using pretrained model")
'''
state_dict = torch.load("/home/chen-ubuntu/Desktop/checks_dataset/pths/crnn_pertrain.pth", map_location=device)
cnn_modules = {}
rnn_modules = {}
for module in state_dict:
if module.split('.')[1] == 'FeatureExtraction':
key = module.replace("module.FeatureExtraction.", "")
cnn_modules[key] = state_dict[module]
elif module.split('.')[1] == 'SequenceModeling':
key = module.replace("module.SequenceModeling.", "")
rnn_modules[key] = state_dict[module]
model.cnn.load_state_dict(cnn_modules)
model.rnn.load_state_dict(rnn_modules)
'''
model.load_state_dict(
torch.load(
'/home/chen-ubuntu/Desktop/checks_dataset/pths/seal_lr3_bat256_aug_epoch15_acc0.704862.pth'
))
dataset_name = 'seal'
batch_dict = {
'print_word': 32,
'hand_num': 48,
'print_num': 48,
'symbol': 64,
'hand_word': 64,
'seal': 64
}
dataset = train_datasets.get(dataset_name)
dataloader = DataLoader(dataset,
batch_size=batch_dict.get(dataset_name),
shuffle=True,
num_workers=4,
drop_last=False)
lr = 1e-3
params = model.parameters()
optimizer = optim.Adam(params, lr)
optimizer.zero_grad()
batch_cnt = 0
for epoch in range(config.epochs):
epoch_loss = 0
model.train()
train_acc = 0
train_acc_cnt = 0
for i, (img, label, _) in enumerate(dataloader):
n_correct = 0
batch_cnt += 1
train_acc_cnt += 1
img = img.to(device)
text, length = converter.encode(label)
preds = model(img)
preds_size = torch.IntTensor([preds.size(0)] * img.size(0))
preds = preds.to('cpu')
loss = criterion(preds, text, preds_size, length)
_, preds = preds.max(2)
preds = preds.transpose(1, 0).contiguous().view(-1)
sim_preds = converter.decode(preds.data,
preds_size.data,
raw=False)
list1 = [x for x in label]
for pred, target in zip(sim_preds, list1):
if pred == target:
n_correct += 1
#loss.backward()
#optimizer.step()
#model.zero_grad()
loss.backward()
if (i + 1) % 4:
optimizer.step()
optimizer.zero_grad()
epoch_loss += loss.item()
train_acc += n_correct / len(list1)
if (i + 1) % 4 == 0:
print("epoch: {:<3d}, dataset:{:<8}, batch: {:<3d}, batch loss: {:4f}, epoch loss: {:4f}, acc: {}".\
format(epoch, dataset_name, i, loss.item(), epoch_loss, n_correct/len(list1)))
writer.add_scalar('data/train_loss', loss.item(), batch_cnt)
writer.add_scalar('data/train_acc', n_correct / len(list1),
batch_cnt)
print('train_average_acc is: {:.3f}'.format(train_acc / train_acc_cnt))
writer.add_scalar('data/valid_{}acc'.format(dataset_name),
train_acc / train_acc_cnt, batch_cnt)
'''
dataset_names = [dataset_name]
accs, valid_losses = valid(model, criterion, converter, device, valid_datasets, dataset_names)
acc, valid_loss = accs.get(dataset_name), valid_losses.get(dataset_name)
writer.add_scalar('data/valid_{}acc'.format(dataset_name), acc, batch_cnt)
writer.add_scalar('data/valid_{}loss'.format(dataset_name), valid_loss, batch_cnt)
'''
if epoch % 3 == 0:
torch.save(
model.state_dict(),
'/home/chen-ubuntu/Desktop/checks_dataset/tmp_pths/allseal_lr3_bat512_expaug_epoch_{}_acc{:4f}.pth'
.format(epoch + 1, train_acc / train_acc_cnt))
if train_acc / train_acc_cnt > 0.8:
torch.save(
model.state_dict(),
'/home/chen-ubuntu/Desktop/checks_dataset/tmp_pths/allseal_lr3_bat512_expaug_epoch{}_acc{:4f}.pth'
.format(epoch + 1, train_acc / train_acc_cnt))
],
key=lambda target: target.skeleton_type)
hf = h5py.File(os.path.join(cfg.INFERENCE.H5.PATH, 'source.h5'), 'w')
g1 = hf.create_group('group1')
source_pos = torch.stack([data.pos for data in test_data], dim=0)
g1.create_dataset('l_joint_pos_2', data=source_pos[:, :3])
g1.create_dataset('r_joint_pos_2', data=source_pos[:, 3:])
hf.close()
print('Source H5 file saved!')
# Create model
model = getattr(model, cfg.MODEL.NAME)().to(device)
# Load checkpoint
if cfg.MODEL.CHECKPOINT is not None:
model.load_state_dict(torch.load(cfg.MODEL.CHECKPOINT))
# store initial z
model.eval()
z_all = []
for batch_idx, data_list in enumerate(test_loader):
for target_idx, target in enumerate(test_target):
# fetch target
target_list = [target for data in data_list]
# forward
z = model.encode(
Batch.from_data_list(data_list).to(device)).detach()
# z = torch.empty(Batch.from_data_list(target_list).x.size(0), 64).normal_(mean=0, std=0.005).to(device)
z.requires_grad = True
z_all.append(z)
print("Using device {}".format(device))
transform = transforms.Compose([
transforms.ToTensor(),
])
cfg_params = utils.read_data_cfg(args.cfgfile)
train_txt = cfg_params["train"]
test_txt = cfg_params["test"]
backup_dir = cfg_params["backup"]
if args.load_model is not None:
print("Loading model from %s." % args.load_model)
model = models.model.UNet(args.im_size, args.kernel_size)
model.load_state_dict(torch.load(args.load_model))
elif args.test:
print("Missing model file for evaluating test set.")
exit()
else:
model = models.model.UNet(args.im_size, args.kernel_size)
# datasets and dataloaders.
if not args.test:
train_dataset = IGVCDataset(
train_txt,
im_size=args.im_size,
split="train",
transform=transform,
val_samples=args.val_samples,
)
# image_copy.show()
# image_copy.save(os.path.join(images_path, f"faster_rcnn/{attempt}/images/{name}.png"))
print(f"{name}, time: {elapsed_time}")
plt.imshow(image_copy)
plt.show()
break
if __name__ == "__main__":
# torch.manual_seed(4)
from models import model
attempt = 7
model_name = "faster_rcnn_7_30.pt"
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(f"Running on {device}...")
model.load_state_dict(
torch.load(os.path.join(models_path, "faster_rcnn_7_30.pt"),
map_location=device))
dataset = MyTestDataset(split='stage1_test',
transforms=get_test_transforms(rescale_size=(256,
256)))
test_loader = DataLoader(dataset,
batch_size=1,
num_workers=0,
shuffle=True)
predict(model, test_loader)
def train(epoch, model, criterion_cont, criterion_trip, criterion_sim,
criterion_l2, criterion_label, optimizer, scheduler, trainLoader,
device, cont_iter):
model.train(True)
losses = AverageMeter()
cont_losses = AverageMeter()
trip_losses = AverageMeter()
sim_losses = AverageMeter()
label_losses = AverageMeter()
scheduler.step()
print('lr:', optimizer.state_dict()['param_groups'][0]['lr'])
lr = optimizer.state_dict()['param_groups'][0]['lr']
if lr <= 0.0001:
checkpoint = torch.load('./' + args.save_dir + '/best_model.pth.tar')
model.load_state_dict(checkpoint['state_dict'])
total = 0.0
total_correct = 0.0
for batch_idx, (imgs, pids, bags_label) in tqdm(enumerate(trainLoader)):
#imgs, pids, bags_label = imgs.to(device=device, dtype=torch.float), \
# pids.to(device=device, dtype=torch.long), \
# bags_label.to(device=device, dtype=torch.long)
imgs, pids, bags_label = imgs.cuda(), pids.cuda(), bags_label.cuda()
imgs = imgs.float()
pids = pids.long()
bags_label = bags_label.long()
# identification
#if cont_iter == 100000:
# scheduler.step()
optimizer.zero_grad()
output_ident, output_all, ident_features, output_bag = model(imgs)
_, predicted = torch.max(output_bag, 1)
#print('predicted:')
#print(predicted)
#print('label:')
#print(bags_label)
correct = (predicted == bags_label).sum()
total += imgs.shape[0]
total_correct += correct
#print('total_correct: total:',total_correct,total)
#in the input find the image without bag
dict_hasBag = dict()
for i in range(len(pids)):
if bags_label[i] == 0:
dict_hasBag[pids[i].data.cpu().item()] = i
imgs_proj_without_bag = torch.zeros(imgs.shape, device=device)
for i in range(len(pids)):
imgs_proj_without_bag[i] = imgs[dict_hasBag[
pids[i].data.cpu().item()]]
# triplet loss
label_loss = criterion_label(output_bag, bags_label)
trip_loss, sim_loss = criterion_trip(ident_features, pids)
#sim_loss = criterion_sim(ident_features, pids)
cont_loss_withoutBag = criterion_l2(output_ident,
imgs_proj_without_bag)
cont_loss_withBag = criterion_l2(output_all, imgs)
cont_loss = cont_loss_withBag + cont_loss_withoutBag
if epoch < 10:
loss = trip_loss + sim_loss * 0.1 + cont_loss * 500 + label_loss * 0.05
else:
loss = trip_loss + sim_loss * 0.1 + cont_loss * 500 + label_loss * 0.05
loss.backward()
optimizer.step()
# loss to tensorboardx
losses.update(loss.item())
trip_losses.update(trip_loss.item())
sim_losses.update(sim_loss.item())
cont_losses.update(cont_loss.item())
label_losses.update(label_loss.item())
writer.add_scalar("Train/Loss", losses.val, cont_iter)
writer.add_scalar("Train/trip_Loss", trip_losses.val, cont_iter)
writer.add_scalar("Train/sim_Loss", sim_losses.val, cont_iter)
writer.add_scalar("Train/cont_Loss", cont_losses.val, cont_iter)
writer.add_scalar("Train/label_loss", label_losses.val, cont_iter)
cont_iter += 1
if (cont_iter + 1) % 50 == 0:
print("iter {}\t Loss {:.4f} ({:.4f}) "
"trip_loss {:.4f} ({:.4f}) "
"sim_loss {:.4f} ({:.4f}) "
"cont_loss {:.4f} ({:.4f})"
"label_loss {:.4f} ({:.4f})"
"total_correct_rate ({:.5f})".format(
cont_iter, losses.val, losses.avg, trip_losses.val,
trip_losses.avg, sim_losses.val, sim_losses.avg,
cont_losses.val, cont_losses.avg, label_losses.val,
label_losses.avg,
total_correct.float() / total))
train_f.write("iter {}\t Loss {:.4f} ({:.4f}) "
"trip_loss {:.4f} ({:.4f}) "
"sim_loss {:.4f} ({:.4f}) "
"cont_loss {:.4f} ({:.4f})"
"label_loss {:.4f} ({:.4f})"
"total_correct_rate ({:.5f})".format(
cont_iter, losses.val, losses.avg,
trip_losses.val, trip_losses.avg, sim_losses.val,
sim_losses.avg, cont_losses.val, cont_losses.avg,
label_losses.val, label_losses.avg,
total_correct.float() * 1.0 / total))
train_f.write('\n')
return cont_iter
root='G:/LJH/DATASETS/flower_photos', transform=train_transform)
train_dataset, valid_dataset = train_test_split(dataset,
test_size=0.2,
random_state=0)
print(len(train_dataset))
print(len(valid_dataset))
train_loader = DataLoader(
train_dataset, batch_size=BATCH_SIZE, shuffle=True,
num_workers=0) #Batch Size定义：一次训练所选取的样本数。 Batch Size的大小影响模型的优化程度和速度。
valid_loader = DataLoader(
valid_dataset, batch_size=BATCH_SIZE, shuffle=True,
num_workers=0) #Batch Size定义：一次训练所选取的样本数。 Batch Size的大小影响模型的优化程度和速度。
model = model.MobileNetV3_large()
model.load_state_dict(torch.load('weigths/best.pkl'))
model.conv4 = Conv2d(1280, 5, kernel_size=(1, 1),
stride=(1, 1)) #修改最后一层输出的分类个数
model.to(DEVICE)
optimizer = torch.optim.SGD(model.parameters(), lr=0.0001, momentum=0.9)
loss_func = torch.nn.CrossEntropyLoss()
avg_loss = []
avg_acc = []
def train(model, device, train_loader, optimizer, epoch):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
