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
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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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