update test scripts

Signed-off-by: chensuyue <[email protected]>

.azure-pipelines/template/docker-template.yml

@@ -27,7 +27,7 @@ steps:
       script: |
         docker ps -a
         if [[ $(docker ps -a | grep -i '${{ parameters.containerName }}'$) ]]; then
-            docker start $(docker ps -aq)
+            docker start $(docker ps -aq --filter "name=${{ parameters.containerName }}")
             echo "remove left files through container ..."
             docker exec ${{ parameters.containerName }} bash -c "ls -a /neural-compressor && rm -fr /neural-compressor/* && rm -fr /neural-compressor/.* && ls -a /neural-compressor  || true"
         fi
@@ -78,7 +78,7 @@ steps:
         displayName: "Pull habana docker image"
 
   - script: |
-      docker stop $(docker ps -aq)
+      docker stop $(docker ps -aq --filter "name=${{ parameters.containerName }}")
       docker rm -vf ${{ parameters.containerName }} || true
       env | sort
     displayName: "Clean docker container"

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

@@ -1,31 +1,29 @@
 import os
 import sys
+import torch
 import time
 
 import habana_frameworks.torch.core as htcore
-import torch
+
+from torch.utils.data import DataLoader
+from torchvision import transforms, datasets
 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"
@@ -38,25 +36,24 @@ def forward(self, x):
 model = model.to("hpu")
 
 
-model = torch.compile(model, backend="hpu_backend")
-
+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_dataset = datasets.MNIST(data_path, train=False, download=True, transform=transform)
-test_loader = torch.utils.data.DataLoader(test_dataset, 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)
 
 correct = 0
-with torch.no_grad():
-    for data, label in test_loader:
+for batch_idx, (data, label) in enumerate(test_loader):
+
+    data = data.to("hpu")
 
-        data = data.to("hpu")
+    output = model(data)
 
-        label = label.to("hpu")
+    htcore.mark_step()
 
-        output = model(data)
-        correct += output.argmax(1).eq(label).sum().item()
+    correct += output.max(1)[1].eq(label).sum()
 
-accuracy = correct / len(test_loader.dataset) * 100
-print("Inference with torch.compile Completed. Accuracy: {:.2f}%".format(accuracy))
+print('Accuracy: {:.2f}%'.format(100. * correct / (len(test_loader) * 32)))