def human_cnn(self):
human_cnn = Lenet()
human_cnn.load_state_dict(
torch.load(
'utils/cifar10_2class/trained_models/humancnn/lenet_weights_0.5_normalisation',
map_location=self.device))
human_cnn.to(self.device)
return human_cnn
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes
print("class_names", class_names)
model_ft = Lenet()
model_ft.conv1 = nn.Conv2d(3, 6, 3) # 这里是彩色图像
num_ftrs = model_ft.fc3.in_features
# Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)).
model_ft.fc = nn.Linear(num_ftrs, len(class_names))
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
# dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs
model_ft = nn.DataParallel(model_ft)
model_ft = model_ft.to(device)
criterion = nn.CrossEntropyLoss()
# Observe that all parameters are being optimized
# optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
optimizer_ft = optim.Adam(model_ft.parameters())
# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft,
step_size=7,
gamma=0.1)
model_ft = train_model(model_ft,
criterion,
optimizer_ft,
