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test/3x/torch/algorithms/fp8_quant/test_basic.py

@@ -1,29 +1,31 @@
 import os
 import sys
-import torch
 import time
 
 import habana_frameworks.torch.core as htcore
-
-from torch.utils.data import DataLoader
-from torchvision import transforms, datasets
+import torch
 import torch.nn as nn
 import torch.nn.functional as F
+from torch.utils.data import DataLoader
+from torchvision import datasets, transforms
+
 
 class Net(nn.Module):
     def __init__(self):
         super(Net, self).__init__()
-        self.fc1   = nn.Linear(784, 256)
-        self.fc2   = nn.Linear(256, 64)
-        self.fc3   = nn.Linear(64, 10)
+        self.fc1 = nn.Linear(784, 256)
+        self.fc2 = nn.Linear(256, 64)
+        self.fc3 = nn.Linear(64, 10)
+
     def forward(self, x):
-        out = x.view(-1,28*28)
+        out = x.view(-1, 28 * 28)
         out = F.relu(self.fc1(out))
         out = F.relu(self.fc2(out))
         out = self.fc3(out)
         out = F.log_softmax(out, dim=1)
         return out
 
+
 model = Net()
 model_link = "https://vault.habana.ai/artifactory/misc/inference/mnist/mnist-epoch_20.pth"
 model_path = "/tmp/.neural_compressor/mnist-epoch_20.pth"
@@ -36,14 +38,12 @@ def forward(self, x):
 model = model.to("hpu")
 
 
-transform=transforms.Compose([
-        transforms.ToTensor(),
-        transforms.Normalize((0.1307,), (0.3081,))])
+transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
 
-data_path = './data'
-test_kwargs = {'batch_size': 32}
+data_path = "./data"
+test_kwargs = {"batch_size": 32}
 dataset1 = datasets.MNIST(data_path, train=False, download=True, transform=transform)
-test_loader = torch.utils.data.DataLoader(dataset1,**test_kwargs)
+test_loader = torch.utils.data.DataLoader(dataset1, **test_kwargs)
 
 correct = 0
 for batch_idx, (data, label) in enumerate(test_loader):
@@ -56,4 +56,4 @@ def forward(self, x):
 
     correct += output.max(1)[1].eq(label).sum()
 
-print('Accuracy: {:.2f}%'.format(100. * correct / (len(test_loader) * 32)))
+print("Accuracy: {:.2f}%".format(100.0 * correct / (len(test_loader) * 32)))