model_name = sys.argv[1]
if model_name == "vgg16":
model = Vgg16Net()
elif model_name == "mobile":
model = MobileNet()
elif model_name == "alexnet":
model = AlexNet()
elif model_name == "res50":
model = ResNet50()
elif model_name == "res34":
model = ResNet34()
elif model_name == "vgg11":
model = Vgg11Net()
else:
print("Moddel Wrong")
model.to(device)
# train/test
loss_name = sys.argv[2]
batch = sys.argv[3]
model.eval()
model.load_state_dict(
torch.load("/home/lxd/checkpoints/{}/{}_{}_VeRI_{}.pt".format(
date, model_name, loss_name, batch)))
print("model load {}".format(model_name))
#def trajectory_reader():
# trajectory_path = "/home/lxd/datasets/VeRi/test_track_VeRi.txt"
def get_trajectorys():
def main():
# define empty list to store the losses and accuracy for ploting
train_all_losses2 = []
train_all_acc2 = []
val_all_losses2 = []
val_all_acc2 = []
test_all_losses2 = 0.0
# define the training epoches
epochs = 100
# instantiate Net class
mobilenet = MobileNet()
# use cuda to train the network
mobilenet.to('cuda')
#loss function and optimizer
criterion = nn.BCELoss()
learning_rate = 1e-3
optimizer = torch.optim.Adam(mobilenet.parameters(), lr=learning_rate, betas=(0.9, 0.999))
%load_ext memory_profiler
best_acc = 0.0
for epoch in range(epochs):
train(mobilenet, epoch, train_all_losses2, train_all_acc2)
acc = validation(mobilenet, val_all_losses2, val_all_acc2, best_acc)
# record the best model
if acc > best_acc:
checkpoint_path = './model_checkpoint.pth'
best_acc = acc
# save the model and optimizer
torch.save({'model_state_dict': mobilenet.state_dict(),
'optimizer_state_dict': optimizer.state_dict()}, checkpoint_path)
print('new best model saved')
print("========================================================================")
checkpoint_path = './model_checkpoint.pth'
model = MobileNet().to('cuda')
checkpoint = torch.load(checkpoint_path)
print("model load successfully.")
model.load_state_dict(checkpoint['model_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
model.eval()
attr_acc = []
test(model, attr_acc=attr_acc)
# plot results
plt.figure(figsize=(8, 10))
plt.barh(range(40), [100 * acc for acc in attr_acc], tick_label = attributes, fc = 'brown')
plt.show()
plt.figure(figsize=(8, 6))
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.title('Loss')
plt.grid(True, linestyle='-.')
plt.plot(train_all_losses2, c='salmon', label = 'Training Loss')
plt.plot(val_all_losses2, c='brown', label = 'Validation Loss')
plt.legend(fontsize='12', loc='upper right')
plt.show()
plt.figure(figsize=(8, 6))
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.title('Accuracy')
plt.grid(True, linestyle='-.')
plt.plot(train_all_acc2, c='salmon', label = 'Training Accuracy')
plt.plot(val_all_acc2, c='brown', label = 'Validation Accuracy')
plt.legend(fontsize='12', loc='lower right')
plt.show()