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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,89 @@ | ||
| import torch | ||
| import torch.nn.functional as F | ||
| from torchvision import datasets, transforms | ||
| from nni.compression.torch import DoReFaQuantizer | ||
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| class Mnist(torch.nn.Module): | ||
| def __init__(self): | ||
| super().__init__() | ||
| self.conv1 = torch.nn.Conv2d(1, 20, 5, 1) | ||
| self.conv2 = torch.nn.Conv2d(20, 50, 5, 1) | ||
| self.fc1 = torch.nn.Linear(4 * 4 * 50, 500) | ||
| self.fc2 = torch.nn.Linear(500, 10) | ||
| self.relu1 = torch.nn.ReLU6() | ||
| self.relu2 = torch.nn.ReLU6() | ||
| self.relu3 = torch.nn.ReLU6() | ||
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| def forward(self, x): | ||
| x = self.relu1(self.conv1(x)) | ||
| x = F.max_pool2d(x, 2, 2) | ||
| x = self.relu2(self.conv2(x)) | ||
| x = F.max_pool2d(x, 2, 2) | ||
| x = x.view(-1, 4 * 4 * 50) | ||
| x = self.relu3(self.fc1(x)) | ||
| x = self.fc2(x) | ||
| return F.log_softmax(x, dim=1) | ||
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| def train(model, quantizer, device, train_loader, optimizer): | ||
| model.train() | ||
| for batch_idx, (data, target) in enumerate(train_loader): | ||
| data, target = data.to(device), target.to(device) | ||
| optimizer.zero_grad() | ||
| output = model(data) | ||
| loss = F.nll_loss(output, target) | ||
| loss.backward() | ||
| optimizer.step() | ||
| if batch_idx % 100 == 0: | ||
| print('{:2.0f}% Loss {}'.format(100 * batch_idx / len(train_loader), loss.item())) | ||
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| def test(model, device, test_loader): | ||
| model.eval() | ||
| test_loss = 0 | ||
| correct = 0 | ||
| with torch.no_grad(): | ||
| for data, target in test_loader: | ||
| data, target = data.to(device), target.to(device) | ||
| output = model(data) | ||
| test_loss += F.nll_loss(output, target, reduction='sum').item() | ||
| pred = output.argmax(dim=1, keepdim=True) | ||
| correct += pred.eq(target.view_as(pred)).sum().item() | ||
| test_loss /= len(test_loader.dataset) | ||
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| print('Loss: {} Accuracy: {}%)\n'.format( | ||
| test_loss, 100 * correct / len(test_loader.dataset))) | ||
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| def main(): | ||
| torch.manual_seed(0) | ||
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | ||
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| trans = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) | ||
| train_loader = torch.utils.data.DataLoader( | ||
| datasets.MNIST('data', train=True, download=True, transform=trans), | ||
| batch_size=64, shuffle=True) | ||
| test_loader = torch.utils.data.DataLoader( | ||
| datasets.MNIST('data', train=False, transform=trans), | ||
| batch_size=1000, shuffle=True) | ||
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| model = Mnist() | ||
| model = model.to(device) | ||
| configure_list = [{ | ||
| 'quant_types': ['weight'], | ||
| 'quant_bits': { | ||
| 'weight': 8, | ||
| }, # you can just use `int` here because all `quan_types` share same bits length, see config for `ReLu6` below. | ||
| 'op_types':['Conv2d', 'Linear'] | ||
| }] | ||
| quantizer = DoReFaQuantizer(model, configure_list) | ||
| quantizer.compress() | ||
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| optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.5) | ||
| for epoch in range(10): | ||
| print('# Epoch {} #'.format(epoch)) | ||
| train(model, quantizer, device, train_loader, optimizer) | ||
| test(model, device, test_loader) | ||
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| if __name__ == '__main__': | ||
| main() |
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