import copy
import torch.nn as nn
def weights_init(m):
if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight.data)
nn.init.constant_(m.bias, 0.1)
train_path = '../data_preprocessing/anno_store/imglist_anno_24.txt'
val_path = '../data_preprocessing/anno_store/imglist_anno_24_val.txt'
batch_size = 32
dataloaders = {
'train':
torch.utils.data.DataLoader(ListDataset(train_path),
batch_size=batch_size,
shuffle=True),
'val':
torch.utils.data.DataLoader(ListDataset(val_path),
batch_size=batch_size,
shuffle=True)
}
dataset_sizes = {
'train': len(ListDataset(train_path)),
'val': len(ListDataset(val_path))
}
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# load the model and weights for initialization
from Data_Loading import ListDataset
from MTCNN_nets import RNet
import time
import copy
import torch.nn as nn
from tqdm import tqdm
def weights_init(m):
if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight.data)
nn.init.constant_(m.bias, 0.1)
train_path = '../data_preprocessing/anno_store/imglist_anno_24.txt'
val_path = '../data_preprocessing/anno_store/imglist_anno_24.txt'
batch_size = 128
dataloaders = {'train': torch.utils.data.DataLoader(ListDataset(train_path), batch_size=batch_size, shuffle=True),
'val': torch.utils.data.DataLoader(ListDataset(val_path), batch_size=batch_size, shuffle=True)}
dataset_sizes = {'train': len(ListDataset(train_path)), 'val': len(ListDataset(val_path))}
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# load the model and weights for initialization
model = RNet(is_train=True).to(device)
model.apply(weights_init)
optimizer = torch.optim.Adam(model.parameters())
since = time.time()
best_model_wts = copy.deepcopy(model.state_dict())
best_accuracy = 0.0
best_loss = 100
from torch.utils.data import Dataset
from Data_Loading import ListDataset
from model.MTCNN_nets import PNet
import time
import copy
import torch.nn as nn
def weights_init(m):
if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight.data)
nn.init.constant_(m.bias, 0.1)
train_path = '../data_preprocessing/anno_store/imglist_anno_12_train.txt'
val_path = '../data_preprocessing/anno_store/imglist_anno_12_val.txt'
batch_size = 64
dataloaders = {'train': torch.utils.data.DataLoader(ListDataset(train_path), batch_size=batch_size, shuffle=True),
'val': torch.utils.data.DataLoader(ListDataset(val_path), batch_size=batch_size, shuffle=True)}
dataset_sizes = {'train': len(ListDataset(train_path)), 'val': len(ListDataset(val_path))}
print('training dataset loaded with length : {}'.format(len(ListDataset(train_path))))
print('validation dataset loaded with length : {}'.format(len(ListDataset(val_path))))
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print(device)
# load the model and weights for initialization
model = PNet(is_train=True).to(device)
model.apply(weights_init)
print("Pnet loaded")
train_logging_file = 'Pnet_train_logging.txt'
optimizer = torch.optim.Adam(model.parameters())