def train(self, epochs, start_epoch=0):
max_test_acc = 0.0
for epoch in range(start_epoch, epochs):
Tools.print()
Tools.print("Start Epoch {}".format(epoch))
self._lr(epoch)
Tools.print('Epoch:{:02d},lr={:.4f}'.format(
epoch, self.optimizer.param_groups[0]['lr']))
epoch_loss, epoch_train_acc, epoch_train_acc_k = self._train_epoch(
)
self._save_checkpoint(self.model, self.root_ckpt_dir, epoch)
test_loss, epoch_test_acc, epoch_test_acc_k = self.test()
result_str = 'Epoch:{:02d}, Train:{:.4f}-{:.4f}/{:.4f} Test:{:.4f}-{:.4f}/{:.4f}'.format(
epoch, epoch_train_acc, epoch_train_acc_k, epoch_loss,
epoch_test_acc, epoch_test_acc_k, test_loss)
Tools.print(result_str)
if epoch_test_acc > max_test_acc:
max_test_acc = epoch_test_acc
Tools.write_to_txt(param.log_path, result_str + "\n")
pass
pass
pass
def __init__(self):
self.device = param.device
self.root_ckpt_dir = param.root_ckpt_dir
self.lr_s = param.lr
self.train_dataset = MyDataset(data_root_path=param.data_root,
is_train=True,
image_size=param.image_size,
sp_size=param.sp_size,
padding=param.padding)
self.test_dataset = MyDataset(data_root_path=param.data_root,
is_train=False,
image_size=param.image_size,
sp_size=param.sp_size,
padding=param.padding)
self.train_loader = DataLoader(
self.train_dataset,
batch_size=param.batch_size,
shuffle=True,
num_workers=param.num_workers,
collate_fn=self.train_dataset.collate_fn)
self.test_loader = DataLoader(self.test_dataset,
batch_size=param.batch_size,
shuffle=False,
num_workers=param.num_workers,
collate_fn=self.test_dataset.collate_fn)
self.model = MyGCNNet().to(self.device)
if param.is_sgd:
self.optimizer = torch.optim.SGD(self.model.parameters(),
lr=self.lr_s[0][1],
momentum=0.9,
weight_decay=param.weight_decay)
else:
self.optimizer = torch.optim.Adam(self.model.parameters(),
lr=self.lr_s[0][1],
weight_decay=param.weight_decay)
self.loss_class = nn.CrossEntropyLoss().to(self.device)
param_num = "Total param: {}".format(self._view_model_param(
self.model))
Tools.print(param_num)
Tools.write_to_txt(param.log_path, param_num + "\n")
pass
import os
import random
from alisuretool.Tools import Tools
split_num = 4
data_dir = "/home/ubuntu/data1.5TB/C3D/UCF-101"
train_list, test_list = [], []
video_dir = [os.path.join(data_dir, _) for _ in sorted(os.listdir(data_dir))]
for video_index, video_one in enumerate(video_dir):
video_one_dir = [
os.path.join(video_one, _) for _ in sorted(os.listdir(video_one))
]
for video_one_dir_one in video_one_dir:
if random.randint(0, split_num - 1) > 0:
train_list.append("{} {}\n".format(video_one_dir_one, video_index))
else:
test_list.append("{} {}\n".format(video_one_dir_one, video_index))
pass
pass
pass
Tools.write_to_txt("train.list", train_list, reset=True)
Tools.write_to_txt("test.list", test_list, reset=True)
def print(self):
print(self.name)
print(self.__dict__)
Tools.write_to_txt(self.log_path, self.name + "\n")
pass
mask_path = "/mnt/4T/Data/SOD/DUTS/DUTS-TE/DUTS-TE-Mask"
result_path = "/mnt/4T/ALISURE/GCN/PyTorchGCN_Result/PYG_ChangeGCN_GCNAtt_NoAddGCN_NoAttRes/DUTS-TE/SOD"
mask_path = "/media/ubuntu/data1/ALISURE/DUTS/DUTS-TE/DUTS-TE-Mask"
result_path = "/media/ubuntu/data1/ALISURE/PyTorchGCN_Result/PYG_GCNAtt_NoAddGCN_NoAttRes/DUTS-TE/SOD"
Max F-measre: 0.894474
Precision: 0.920642
Recall: 0.817062
MAE: 0.0368984
mask_path = "/media/ubuntu/data1/ALISURE/DUTS/DUTS-TE/DUTS-TE-Mask"
result_path = "/media/ubuntu/data1/ALISURE/PyTorchGCN_Result/PYG_GCNAtt_NoAddGCN_NoAttRes_NewPool/DUTS-TE/SOD"
Max F-measre: 0.894308
Precision: 0.916229
Recall: 0.828253
MAE: 0.0370773
mask_path = "/media/ubuntu/data1/ALISURE/DUTS/DUTS-TE/DUTS-TE-Mask"
result_path = "/media/ubuntu/data1/ALISURE/PyTorchGCN_Result/PYG_GCNAtt_NoAddGCN_NoAttRes_Sigmoid/DUTS-TE/SOD"
"""
if __name__ == '__main__':
mask_path = "/media/ubuntu/data1/ALISURE/DUTS/DUTS-TE/DUTS-TE-Mask"
result_path = "/media/ubuntu/data1/ALISURE/PyTorchGCN_Result/PYG_GCNAtt_NoAddGCN_NoAttRes_Sigmoid/DUTS-TE/SOD"
_result_files = get_file(mask_path, result_path)
_txt = "\n".join(_result_files)
Tools.write_to_txt("salmetric.txt", _txt, reset=True)
pass
