def __init__(self):
##The top config
#self.data_root = '/media/hhy/data/USdata/MergePhase1/test_0.3'
#self.log_dir = '/media/hhy/data/code_results/MILs/MIL_H_Attention'
self.root = '/remote-home/my/Ultrasound_CV/data/Ruijin/clean'
self.log_dir = '/remote-home/my/hhy/Ultrasound_MIL/experiments/weighted_sampler/'
if not os.path.exists(self.log_dir):
os.makedirs(self.log_dir)
##training config
self.lr = 1e-4
self.epoch = 50
self.resume = -1
self.batch_size = 1
self.net = Attention()
self.net.cuda()
self.optimizer = Adam(self.net.parameters(), lr=self.lr)
self.lrsch = torch.optim.lr_scheduler.MultiStepLR(self.optimizer, milestones=[10, 30, 50, 70], gamma=0.5)
self.logger = Logger(self.log_dir)
self.train_transform = transforms.Compose([
transforms.Resize((224,224)),
transforms.RandomResizedCrop((224,224)),
transforms.RandomHorizontalFlip(0.5),
transforms.RandomVerticalFlip(0.5),
transforms.ColorJitter(0.25,0.25,0.25,0.25),
transforms.ToTensor()
])
self.test_transform = transforms.Compose([
transforms.Resize((224,224)),
transforms.ToTensor()
])
self.trainbag = RuijinBags(self.root, [0,1,2,3],self.train_transform)
self.testbag = RuijinBags(self.root, [4], self.test_transform)
train_label_list = list(map(lambda x: int(x['label']), self.trainbag.patient_info))
pos_ratio = sum(train_label_list) / len(train_label_list)
print(pos_ratio)
train_weight = [(1-pos_ratio) if x>0 else pos_ratio for x in train_label_list]
self.train_sampler = WeightedRandomSampler(weights=train_weight, num_samples=len(self.trainbag))
self.train_loader = DataLoader(self.trainbag, batch_size=self.batch_size, num_workers=8,
sampler=self.train_sampler)
self.val_loader = DataLoader(self.testbag, batch_size=self.batch_size, shuffle=False, num_workers=8)
if self.resume > 0:
self.net, self.optimizer, self.lrsch, self.loss, self.global_step = self.logger.load(self.net, self.optimizer, self.lrsch, self.loss, self.resume)
else:
self.global_step = 0
# self.trainer = MTTrainer(self.net, self.optimizer, self.lrsch, self.loss, self.train_loader, self.val_loader, self.logger, self.global_step, mode=2)
self.trainer = MILTrainer_batch1(self.net, self.optimizer, self.lrsch, None, self.train_loader, self.val_loader, self.logger,
self.global_step)
class Config(object):
'''
This config is for single modality BM classification
'''
def __init__(self):
##The top config
#self.data_root = '/media/hhy/data/USdata/MergePhase1/test_0.3'
#self.log_dir = '/media/hhy/data/code_results/MILs/MIL_H_Attention'
self.root = '/remote-home/my/Ultrasound_CV/data/Ruijin/clean'
self.log_dir = '/remote-home/my/hhy/Ultrasound_MIL/experiments/weighted_sampler/'
if not os.path.exists(self.log_dir):
os.makedirs(self.log_dir)
##training config
self.lr = 1e-4
self.epoch = 50
self.resume = -1
self.batch_size = 1
self.net = Attention()
self.net.cuda()
self.optimizer = Adam(self.net.parameters(), lr=self.lr)
self.lrsch = torch.optim.lr_scheduler.MultiStepLR(self.optimizer, milestones=[10, 30, 50, 70], gamma=0.5)
self.logger = Logger(self.log_dir)
self.train_transform = transforms.Compose([
transforms.Resize((224,224)),
transforms.RandomResizedCrop((224,224)),
transforms.RandomHorizontalFlip(0.5),
transforms.RandomVerticalFlip(0.5),
transforms.ColorJitter(0.25,0.25,0.25,0.25),
transforms.ToTensor()
])
self.test_transform = transforms.Compose([
transforms.Resize((224,224)),
transforms.ToTensor()
])
self.trainbag = RuijinBags(self.root, [0,1,2,3],self.train_transform)
self.testbag = RuijinBags(self.root, [4], self.test_transform)
train_label_list = list(map(lambda x: int(x['label']), self.trainbag.patient_info))
pos_ratio = sum(train_label_list) / len(train_label_list)
print(pos_ratio)
train_weight = [(1-pos_ratio) if x>0 else pos_ratio for x in train_label_list]
self.train_sampler = WeightedRandomSampler(weights=train_weight, num_samples=len(self.trainbag))
self.train_loader = DataLoader(self.trainbag, batch_size=self.batch_size, num_workers=8,
sampler=self.train_sampler)
self.val_loader = DataLoader(self.testbag, batch_size=self.batch_size, shuffle=False, num_workers=8)
if self.resume > 0:
self.net, self.optimizer, self.lrsch, self.loss, self.global_step = self.logger.load(self.net, self.optimizer, self.lrsch, self.loss, self.resume)
else:
self.global_step = 0
# self.trainer = MTTrainer(self.net, self.optimizer, self.lrsch, self.loss, self.train_loader, self.val_loader, self.logger, self.global_step, mode=2)
self.trainer = MILTrainer_batch1(self.net, self.optimizer, self.lrsch, None, self.train_loader, self.val_loader, self.logger,
self.global_step)
def update(self,dicts):
for k,v in dicts.items():
self.__setattr__(k,v)
@staticmethod
def auto_argparser(cfg,description=None):
"""Generate argparser from config file automatically (experimental)
"""
# partial_parser = ArgumentParser(description=description)
# partial_parser.add_argument('config', help='config file path')
# parsed, unparsed_args = partial_parser.parse_known_args()
# cfg = Config.fromfile(parsed.config)
parser = ArgumentParser(description=description)
add_args(parser, cfg)
parsed_args = parser.parse_args()
update_cfg(cfg, parsed_args)
return cfg
class Config(object):
'''
This config is for single modality BM classification
'''
def __init__(self):
##The top config
#self.data_root = '/media/hhy/data/USdata/MergePhase1/test_0.3'
#self.log_dir = '/media/hhy/data/code_results/MILs/MIL_H_Attention'
self.root = '/remote-home/my/Ultrasound_CV/data/Ruijin/clean'
self.log_dir = '/remote-home/my/hhy/Ultrasound_MIL/code_results'
if not os.path.exists(self.log_dir):
os.mkdir(self.log_dir)
##training config
self.lr = 1e-4
self.epoch = 50
self.resume = -1
self.batch_size = 1
self.net = Attention()
self.net.cuda()
self.optimizer = Adam(self.net.parameters(), lr=self.lr)
self.lrsch = torch.optim.lr_scheduler.MultiStepLR(self.optimizer, milestones=[10, 30, 50, 70], gamma=0.5)
self.logger = Logger(self.log_dir)
self.train_transform = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.ColorJitter(brightness = 0.25),
transforms.RandomHorizontalFlip(0.5),
transforms.RandomVerticalFlip(0.5),
# transforms.ColorJitter(0.25, 0.25, 0.25, 0.25),
transforms.ToTensor()
])
self.test_transform = transforms.Compose([
transforms.Resize((224,224)),
transforms.ToTensor()
])
self.trainbag = RuijinBags(self.root, [0,1,2,3],self.test_transform)
self.testbag = RuijinBags(self.root, [4], self.test_transform)
# patient bags
#self.trainbag = PatientBags(self.data_root+'/train', self.train_transform)
#self.testbag = PatientBags(self.data_root+'/test', self.test_transform)
'''
# random
self.train_set = BMDataset(self.data_root+'/train',self.train_transform)
self.test_set = BMDataset(self.data_root+'/test',self.test_transform)
self.trainbag = BMBags(dataset=self.train_set)
self.testbag = BMBags(dataset=self.test_set)
'''
#self.train_loader = DataLoader(self.trainbag, batch_size=1, shuffle=True, num_workers=8)
#self.val_loader = DataLoader(self.testbag, batch_size=1, shuffle=False, num_workers=8)
self.train_loader = DataLoader(self.trainbag, batch_size=4, collate_fn = my_collate, shuffle=True, num_workers=8)
#print (len(self.train_loader))
self.val_loader = DataLoader(self.testbag, batch_size=4, collate_fn = my_collate, shuffle=False, num_workers=8)
if self.resume > 0:
self.net, self.optimizer, self.lrsch, self.loss, self.global_step = self.logger.load(self.net, self.optimizer, self.lrsch, self.loss, self.resume)
else:
self.global_step = 0
# self.trainer = MTTrainer(self.net, self.optimizer, self.lrsch, self.loss, self.train_loader, self.val_loader, self.logger, self.global_step, mode=2)
self.trainer = MILTrainer(self.net, self.optimizer, self.lrsch, None, self.train_loader, self.val_loader, self.logger,
self.global_step)
def update(self,dicts):
for k,v in dicts.items():
self.__setattr__(k,v)
@staticmethod
def auto_argparser(cfg,description=None):
"""Generate argparser from config file automatically (experimental)
"""
# partial_parser = ArgumentParser(description=description)
# partial_parser.add_argument('config', help='config file path')
# parsed, unparsed_args = partial_parser.parse_known_args()
# cfg = Config.fromfile(parsed.config)
parser = ArgumentParser(description=description)
add_args(parser, cfg)
parsed_args = parser.parse_args()
update_cfg(cfg, parsed_args)
return cfg
def __init__(self):
##The top config
#self.data_root = '/media/hhy/data/USdata/MergePhase1/test_0.3'
#self.log_dir = '/media/hhy/data/code_results/MILs/MIL_H_Attention'
self.root = '/remote-home/my/Ultrasound_CV/data/Ruijin/clean'
self.log_dir = '/remote-home/my/hhy/Ultrasound_MIL/code_results'
if not os.path.exists(self.log_dir):
os.mkdir(self.log_dir)
##training config
self.lr = 1e-4
self.epoch = 50
self.resume = -1
self.batch_size = 1
self.net = Attention()
self.net.cuda()
self.optimizer = Adam(self.net.parameters(), lr=self.lr)
self.lrsch = torch.optim.lr_scheduler.MultiStepLR(self.optimizer, milestones=[10, 30, 50, 70], gamma=0.5)
self.logger = Logger(self.log_dir)
self.train_transform = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.ColorJitter(brightness = 0.25),
transforms.RandomHorizontalFlip(0.5),
transforms.RandomVerticalFlip(0.5),
# transforms.ColorJitter(0.25, 0.25, 0.25, 0.25),
transforms.ToTensor()
])
self.test_transform = transforms.Compose([
transforms.Resize((224,224)),
transforms.ToTensor()
])
self.trainbag = RuijinBags(self.root, [0,1,2,3],self.test_transform)
self.testbag = RuijinBags(self.root, [4], self.test_transform)
# patient bags
#self.trainbag = PatientBags(self.data_root+'/train', self.train_transform)
#self.testbag = PatientBags(self.data_root+'/test', self.test_transform)
'''
# random
self.train_set = BMDataset(self.data_root+'/train',self.train_transform)
self.test_set = BMDataset(self.data_root+'/test',self.test_transform)
self.trainbag = BMBags(dataset=self.train_set)
self.testbag = BMBags(dataset=self.test_set)
'''
#self.train_loader = DataLoader(self.trainbag, batch_size=1, shuffle=True, num_workers=8)
#self.val_loader = DataLoader(self.testbag, batch_size=1, shuffle=False, num_workers=8)
self.train_loader = DataLoader(self.trainbag, batch_size=4, collate_fn = my_collate, shuffle=True, num_workers=8)
#print (len(self.train_loader))
self.val_loader = DataLoader(self.testbag, batch_size=4, collate_fn = my_collate, shuffle=False, num_workers=8)
if self.resume > 0:
self.net, self.optimizer, self.lrsch, self.loss, self.global_step = self.logger.load(self.net, self.optimizer, self.lrsch, self.loss, self.resume)
else:
self.global_step = 0
# self.trainer = MTTrainer(self.net, self.optimizer, self.lrsch, self.loss, self.train_loader, self.val_loader, self.logger, self.global_step, mode=2)
self.trainer = MILTrainer(self.net, self.optimizer, self.lrsch, None, self.train_loader, self.val_loader, self.logger,
self.global_step)
plt.hist(pos_bags, range=[0,1],density=False)
plt.show()
plt.hist(neg_bags,range=[0,1], color='g',density=False)
plt.show()
'''
return pos_bags, neg_bags
if __name__ == '__main__':
model_path = '/media/hhy/data/code_results/MILs/MIL_MixedBags/ckp/net.ckpt50.pth'
net = Attention()
inference_path = '/media/hhy/data/USdata/MergePhase1/test_0.3/test'
pre_transform = transforms.Compose([
transforms.Resize((224,224)),
transforms.ToTensor()
])
A = inferencer(model_path,net,inference_path, pre_transform)
A.inference()
A.stats()
'''
for i in range (len(A.total_weights)):
print('Predicted label is {}'.format(A.pred_label[i].item()))
print('Weights are {}'.format(A.total_weights[i].squeeze(-2).tolist()))
#print(A.total_weights)
'''