Add pooling testcases

opencv · Oct 15, 2020 · 0089324 · 0089324
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commit 0089324
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diff --git a/testdata/dnn/onnx/data/input_average_pooling_dynamic_axes.npy b/testdata/dnn/onnx/data/input_average_pooling_dynamic_axes.npy
diff --git a/testdata/dnn/onnx/data/input_maxpooling_sigmoid_dynamic_axes.npy b/testdata/dnn/onnx/data/input_maxpooling_sigmoid_dynamic_axes.npy
diff --git a/testdata/dnn/onnx/data/output_average_pooling_dynamic_axes.npy b/testdata/dnn/onnx/data/output_average_pooling_dynamic_axes.npy
diff --git a/testdata/dnn/onnx/data/output_maxpooling_sigmoid_dynamic_axes.npy b/testdata/dnn/onnx/data/output_maxpooling_sigmoid_dynamic_axes.npy
diff --git a/testdata/dnn/onnx/generate_onnx_models.py b/testdata/dnn/onnx/generate_onnx_models.py
@@ -1070,3 +1070,16 @@ def forward(self, x):
 model = ResizeConv(2, 0, 2)
 save_data_and_model("resize_opset11_torch1.6_dynamic_axes", x, model, 11)
 postprocess_model("models/resize_opset11_torch1.6_dynamic_axes.onnx", [["batch_size", 2, 'height', 'width']])
+
+maxpooling_sigmoid = nn.Sequential(
+          nn.MaxPool2d(kernel_size=4, stride=2, padding=(1, 2), dilation=1),
+          nn.Sigmoid()
+        )
+input = Variable(torch.randn(2, 3, 12, 18))
+save_data_and_model("maxpooling_sigmoid_dynamic_axes", input, maxpooling_sigmoid)
+postprocess_model("models/maxpooling_sigmoid_dynamic_axes.onnx", [[2, 3, 'height', 'width']])
+
+ave_pool = nn.AvgPool2d(kernel_size=3, stride=2, padding=1)
+input = Variable(torch.randn(1, 3, 7, 5))
+save_data_and_model("average_pooling_dynamic_axes", input, ave_pool)
+postprocess_model("models/average_pooling_dynamic_axes.onnx", [[1, 3, 'height', 'width']])
diff --git a/testdata/dnn/onnx/models/average_pooling_dynamic_axes.onnx b/testdata/dnn/onnx/models/average_pooling_dynamic_axes.onnx
diff --git a/testdata/dnn/onnx/models/maxpooling_sigmoid_dynamic_axes.onnx b/testdata/dnn/onnx/models/maxpooling_sigmoid_dynamic_axes.onnx
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