[GitHub] [incubator-tvm] anijain2305 commented on a change in pull request #4961: [Torch] Upsampling op support and enable registering a user defined op conversion map

[GitBox <gi...@apache.org>]

anijain2305 commented on a change in pull request #4961: [Torch] Upsampling op support and enable registering a user defined op conversion map
URL: https://github.com/apache/incubator-tvm/pull/4961#discussion_r386057076
 
 

 ##########
 File path: tests/python/frontend/pytorch/test_forward.py
 ##########
 @@ -730,6 +752,55 @@ def test_vgg11_bn():
 """
 
 
+def test_custom_conversion_map():
+    def get_roi_align():
+        pool_size = 5
+        n_channels = 2 * (pool_size ** 2)
+        x = torch.rand(2, n_channels, 10, 10)
+        rois = torch.tensor([[0, 0, 0, 9, 9],  # format is (xyxy)
+                             [0, 0, 5, 4, 9],
+                             [0, 5, 5, 9, 9],
+                             [1, 0, 0, 9, 9]], dtype=torch.float)
+        roi_align = torchvision.ops.RoIAlign(pool_size, spatial_scale=1,
+                                             sampling_ratio=-1)
+        return roi_align.eval(), [x, rois]
+
+    def convert_roi_align():
+        def _impl(inputs, input_types):
+            spatial_scale = inputs[2]
+            pooled_size = (inputs[3], inputs[4])
+            sampling_ratio = inputs[5]
+            return relay.op.vision.roi_align(inputs[0], inputs[1],
+                                             pooled_size, spatial_scale,
+                                             sampling_ratio)
+        return _impl
+
+    custom_map = {'torchvision::roi_align': convert_roi_align()}
+    model, inputs = get_roi_align()
+
+    verify_model(model, inputs, custom_map)
+
+
+def test_segmentaton_models():
+    class SegmentationModelWrapper(Module):
+        def __init__(self, model):
+            super().__init__()
+            self.model = model
+
+        def forward(self, inp):
+            out = self.model(inp)
+            return out["out"]
+
+    fcn = torchvision.models.segmentation.fcn_resnet101(pretrained=True)
+    deeplab = torchvision.models.segmentation.deeplabv3_resnet101(pretrained=True)
+
+    inp = [torch.rand((1, 3, 300, 300), dtype=torch.float)]
+
+    for model in [fcn, deeplab]:
+        verify_model(SegmentationModelWrapper(model.eval()), inp,
+                     ctx_list=[("cuda", tvm.gpu(0))])  # dilated covolution not supported on x86
 
 Review comment:
   Maybe adding the issue number is helpful here?

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