test for 3dconv and 3dmaxpool

Author: s-westphal

tests/conv3d.py

@@ -0,0 +1,46 @@
+import numpy as np
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+from pytorch2keras.converter import pytorch_to_keras
+
+
+class TestConv3d(nn.Module):
+    """Module for Conv2d conversion testing
+    """
+
+    def __init__(self, inp=10, out=16, kernel_size=3, bias=True):
+        super(TestConv3d, self).__init__()
+        self.conv3d = nn.Conv3d(inp, out, kernel_size=kernel_size, bias=bias)
+
+    def forward(self, x):
+        x = self.conv3d(x)
+        return x
+
+
+if __name__ == '__main__':
+    max_error = 0
+    for i in range(100):
+        kernel_size = np.random.randint(1, 7)
+        inp = np.random.randint(kernel_size + 1, 30)
+        out = np.random.randint(1, 30)
+
+        model = TestConv3d(inp, out, kernel_size, inp % 2)
+
+        input_var = Variable(torch.randn(1, inp, inp, inp, inp))
+
+        output = model(input_var)
+
+        k_model = pytorch_to_keras(model,
+                                   input_var,
+                                   (inp, inp, inp, inp,),
+                                   verbose=True)
+
+        pytorch_output = output.data.numpy()
+        keras_output = k_model.predict(input_var.numpy())
+        error = np.max(pytorch_output - keras_output)
+        print("iteration: {}, error: {}".format(i, error))
+        if max_error < error:
+            max_error = error
+
+    print('Max error: {0}'.format(max_error))

tests/max_pool3d.py

@@ -0,0 +1,46 @@
+import numpy as np
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+from pytorch2keras.converter import pytorch_to_keras
+
+
+class MaxPool(nn.Module):
+    """Module for MaxPool conversion testing
+    """
+
+    def __init__(self, inp=10, out=16, kernel_size=3, bias=True):
+        super(MaxPool, self).__init__()
+        self.conv3d = nn.Conv3d(inp, out, kernel_size=kernel_size, bias=bias)
+        self.pool3d = nn.MaxPool3d(kernel_size=3, padding=1)
+
+    def forward(self, x):
+        x = self.conv3d(x)
+        x = self.pool3d(x)
+        return x
+
+
+if __name__ == '__main__':
+    max_error = 0
+    for i in range(100):
+        kernel_size = np.random.randint(1, 7)
+        inp = np.random.randint(kernel_size + 1, 30)
+        out = np.random.randint(1, 30)
+
+        model = MaxPool(inp, out, kernel_size, inp % 2)
+
+        input_np = np.random.uniform(0, 1, (1, inp, inp, inp, inp))
+        input_var = Variable(torch.FloatTensor(input_np))
+        output = model(input_var)
+
+        k_model = pytorch_to_keras(model, input_var, (inp, inp, inp, inp,), verbose=True)
+
+        pytorch_output = output.data.numpy()
+        keras_output = k_model.predict(input_np)
+
+        error = np.max(pytorch_output - keras_output)
+        print(error)
+        if max_error < error:
+            max_error = error
+
+    print('Max error: {0}'.format(max_error))