Create torch_test.py

apps/deeplearning/PyTorch/torch_test.py

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+import torch
+import time
+
+# 테스트용 간단한 CNN 모델 정의
+class SimpleCNN(torch.nn.Module):
+    def __init__(self):
+        super(SimpleCNN, self).__init__()
+        self.conv1 = torch.nn.Conv2d(3, 16, 3, stride=1, padding=1)
+        self.pool = torch.nn.MaxPool2d(2, 2)
+        self.fc1 = torch.nn.Linear(16 * 16 * 16, 10)
+
+    def forward(self, x):
+        x = self.pool(torch.nn.functional.relu(self.conv1(x)))
+        x = x.view(-1, 16 * 16 * 16)
+        x = self.fc1(x)
+        return x
+
+# 모델 생성 및 임의 데이터 생성
+model = SimpleCNN()
+dummy_input = torch.randn(1, 3, 32, 32)
+
+# 모델 성능 테스트
+start_time = time.time()
+with torch.no_grad():
+    for _ in range(100):
+        output = model(dummy_input)
+end_time = time.time()
+
+# 평균 처리 시간 계산
+avg_time = (end_time - start_time) / 100
+print(f"Average inference time per batch: {avg_time:.6f} seconds")