def compute_time():
"""
计算１00次，取平均值
:return:
"""
model_googlenet_bn = googlenet_bn.GoogLeNet_BN(num_classes=1000)
model_googlenet = googlenet.GoogLeNet(num_classes=1000)
model_googlenet_bn.eval()
model_googlenet.eval()
total_time_googlenet_bn = 0.0
total_time_googlenet = 0.0
epoch = 100
for i in range(epoch):
data = torch.randn((1, 3, 224, 224))
start = time.time()
outputs = model_googlenet_bn.forward(data)
end = time.time()
total_time_googlenet_bn += (end - start)
start = time.time()
outputs = model_googlenet.forward(data)
end = time.time()
total_time_googlenet += (end - start)
print('[googlenet_bn] time: {:.4f}'.format(total_time_googlenet_bn /
epoch))
print('[googlenet] time: {:.4f}'.format(total_time_googlenet / epoch))
print('time_googlenet / time_googlenet_bn: {:.3f}'.format(
total_time_googlenet / total_time_googlenet_bn))
def compute_param():
model_googlenet_bn = googlenet_bn.GoogLeNet_BN(num_classes=1000)
model_googlenet = googlenet.GoogLeNet(num_classes=1000)
model_googlenet_bn.eval()
model_googlenet.eval()
num_googlenet_bn = util.num_model(model_googlenet_bn)
num_googlenet = util.num_model(model_googlenet)
print('[googlenet_bn] param num: {}'.format(num_googlenet_bn))
print('[googlenet] param num: {}'.format(num_googlenet))
print('num_googlenet_bn / num_googlenet: {:.2f}'.format(num_googlenet_bn /
num_googlenet))
def test():
model_googlenet_bn = googlenet_bn.GoogLeNet_BN(num_classes=1000)
# print(model_googlenet_bn)
# 训练阶段
model_googlenet_bn.train()
data = torch.randn((1, 3, 224, 224))
outputs, aux2, aux1 = model_googlenet_bn.forward(data)
print(outputs.shape)
print(aux2.shape)
print(aux1.shape)
# 测试阶段
model_googlenet_bn.eval()
data = torch.randn((1, 3, 224, 224))
outputs = model_googlenet_bn.forward(data)
print(outputs.shape)
return model, loss_dict, acc_dict
if __name__ == '__main__':
device = util.get_device()
# device = 'cpu'
data_loaders, data_sizes = load_data('./data/pascal-voc')
print(data_loaders)
print(data_sizes)
res_loss = dict()
res_acc = dict()
for name in ['googlenet_bn', 'googlenet']:
if name == 'googlenet_bn':
model = googlenet_bn.GoogLeNet_BN(num_classes=20)
else:
model = googlenet.GoogLeNet(num_classes=20)
model.eval()
# print(model)
model = model.to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=1e-3)
lr_schduler = optim.lr_scheduler.StepLR(optimizer,
step_size=8,
gamma=0.96)
util.check_dir('./data/models/')
best_model, loss_dict, acc_dict = train_model(data_loaders,
data_sizes,