gromero commented on a change in pull request #10452:
URL: https://github.com/apache/tvm/pull/10452#discussion_r818650000
##########
File path: include/tvm/relay/attrs/image.h
##########
@@ -291,8 +292,25 @@ struct GridSampleAttrs : public
tvm::AttrsNode<GridSampleAttrs> {
TVM_ATTR_FIELD(padding_mode)
.set_default("zeros")
.describe(
- "Specify the padding mode to use."
- "zeros, border etc.");
+ "If :attr:'grid' has values outside the range of '[-1, 1]', the
corresponding"
+ "outputs are handled as defined by padding_mode. Options are"
+ "padding_mode='zeros': use '0' for out-of-bound grid locations,"
+ "padding_mode='border': use border values for out-of-bound grid
locations"
+ "padding_mode='reflection': use values at locations reflected by"
+ "the border for out-of-bound grid locations. For location far away"
+ "from the border, it will keep being reflected until becoming in
bound,"
+ "e.g., (normalized) pixel location 'x = -3.5' reflects by border
'-1'"
+ "and becomes 'x' = 1.5, then reflects by border '1' and becomes"
+ "'x' = -0.5");
+ TVM_ATTR_FIELD(align_corners)
+ .set_default(true)
+ .describe(
+ "Geometrically, we consider the pixels of the"
+ "input as squares rather than points."
Review comment:
There is a double space here, hence one should be removed.
##########
File path: python/tvm/relay/op/image/_image.py
##########
@@ -366,19 +366,41 @@ def compute_grid_sample(attrs, inputs, out_dtype):
method = attrs.method
layout = attrs.layout
padding_mode = attrs.padding_mode
- return [topi.image.grid_sample(inputs[0], inputs[1], method, layout,
padding_mode)]
+ align_corners = attrs.align_corners
+ return [
+ topi.image.grid_sample(
+ inputs[0],
+ inputs[1],
+ method,
+ layout,
+ padding_mode,
+ align_corners
+ )
+ ]
reg.register_injective_schedule("image.grid_sample")
@script
def _grid_sample_func(data, grid):
- out = output_tensor((4,), "int64")
- out[0] = int64(data[0])
- out[1] = int64(data[1])
- out[2] = int64(grid[2])
- out[3] = int64(grid[3])
+ if len(data) == 4:
+ out = output_tensor((4,), "int64")
+ out[0] = int64(data[0])
+ out[1] = int64(data[1])
+ out[2] = int64(grid[2])
+ out[3] = int64(grid[3])
+ elif len(data) == 5:
+ out = output_tensor((5,), "int64")
+ out[0] = int64(data[0])
+ out[1] = int64(data[1])
+ out[2] = int64(grid[2])
+ out[3] = int64(grid[3])
+ out[4] = int64(grid[4])
+ else:
+ msg = f"dimension is not surpported"
Review comment:
s/surpported/supported/ here
##########
File path: python/tvm/relay/frontend/pytorch.py
##########
@@ -2913,23 +2913,47 @@ def mv(self, inputs, _):
return _op.transform.squeeze(dense_result)
def grid_sampler(self, inputs, input_types):
- if inputs[2] == 0:
- mode = "bilinear"
- else:
- msg = "Only bilinear mode is supported in grid_sampler"
- raise NotImplementedError(msg)
-
- if inputs[3] == 0:
- padding_mode = "zeros"
- elif inputs[3] == 1:
- padding_mode = "border"
- else:
- msg = "Only zeros and border padding mode are supported in
grid_sampler"
- raise NotImplementedError(msg)
+ interpolate_mode = inputs[2]
+ padding_mode = inputs[3]
+ align_corners = inputs[4]
+ data_shape = self.infer_shape_with_prelude(inputs[0])
+
+ if len(data_shape) == 4:
+ layout = "NCHW"
+ axes = [0, 3, 1, 2]
+ grid = _op.transform.transpose(inputs[1], axes)
+ elif len(data_shape) == 5:
+ layout = "NCDHW"
+ axes = [0, 4, 1, 2, 3]
+ grid = _op.transform.transpose(inputs[1], axes)
+ else:
+ msg = f"only 4D and 5D are surpported."
+ raise ValueError(msg)
+
+ if interpolate_mode == 0:
+ interpolate_str = "bilinear"
+ elif interpolate_mode == 1:
+ interpolate_str = "nearest"
+ elif interpolate_mode == 2:
+ interpolate_str = "bicubic"
+ else:
+ msg = f"interpolation method {interpolate_mode} is not surpported"
Review comment:
s/surpported/supported/ here
##########
File path: include/tvm/relay/attrs/image.h
##########
@@ -291,8 +292,25 @@ struct GridSampleAttrs : public
tvm::AttrsNode<GridSampleAttrs> {
TVM_ATTR_FIELD(padding_mode)
.set_default("zeros")
.describe(
- "Specify the padding mode to use."
- "zeros, border etc.");
+ "If :attr:'grid' has values outside the range of '[-1, 1]', the
corresponding"
+ "outputs are handled as defined by padding_mode. Options are"
+ "padding_mode='zeros': use '0' for out-of-bound grid locations,"
+ "padding_mode='border': use border values for out-of-bound grid
locations"
+ "padding_mode='reflection': use values at locations reflected by"
+ "the border for out-of-bound grid locations. For location far away"
+ "from the border, it will keep being reflected until becoming in
bound,"
+ "e.g., (normalized) pixel location 'x = -3.5' reflects by border
'-1'"
+ "and becomes 'x' = 1.5, then reflects by border '1' and becomes"
+ "'x' = -0.5");
+ TVM_ATTR_FIELD(align_corners)
+ .set_default(true)
+ .describe(
+ "Geometrically, we consider the pixels of the"
+ "input as squares rather than points."
Review comment:
Shouldn't also the options `nearest` and `bicubic` be
added/listed/explained for the `method` attribute field? Similarly to what you
did for `padding_mode` and `align_corners` options?
##########
File path: python/tvm/relay/frontend/pytorch.py
##########
@@ -2913,23 +2913,47 @@ def mv(self, inputs, _):
return _op.transform.squeeze(dense_result)
def grid_sampler(self, inputs, input_types):
- if inputs[2] == 0:
- mode = "bilinear"
- else:
- msg = "Only bilinear mode is supported in grid_sampler"
- raise NotImplementedError(msg)
-
- if inputs[3] == 0:
- padding_mode = "zeros"
- elif inputs[3] == 1:
- padding_mode = "border"
- else:
- msg = "Only zeros and border padding mode are supported in
grid_sampler"
- raise NotImplementedError(msg)
+ interpolate_mode = inputs[2]
+ padding_mode = inputs[3]
+ align_corners = inputs[4]
+ data_shape = self.infer_shape_with_prelude(inputs[0])
+
+ if len(data_shape) == 4:
+ layout = "NCHW"
+ axes = [0, 3, 1, 2]
+ grid = _op.transform.transpose(inputs[1], axes)
+ elif len(data_shape) == 5:
+ layout = "NCDHW"
+ axes = [0, 4, 1, 2, 3]
+ grid = _op.transform.transpose(inputs[1], axes)
+ else:
+ msg = f"only 4D and 5D are surpported."
+ raise ValueError(msg)
+
+ if interpolate_mode == 0:
+ interpolate_str = "bilinear"
+ elif interpolate_mode == 1:
+ interpolate_str = "nearest"
+ elif interpolate_mode == 2:
+ interpolate_str = "bicubic"
+ else:
+ msg = f"interpolation method {interpolate_mode} is not surpported"
+ raise ValueError(msg)
+
+ if padding_mode == 0:
+ padding_mode_str = "zeros"
+ elif padding_mode == 1:
+ padding_mode_str = "border"
+ elif padding_mode == 2:
+ padding_mode_str = 'reflection'
+ else:
+ msg = f"padding_mode {padding_mode} is not surpportted"
Review comment:
s/surpported/supported/ here
##########
File path: python/tvm/relay/frontend/pytorch.py
##########
@@ -2913,23 +2913,47 @@ def mv(self, inputs, _):
return _op.transform.squeeze(dense_result)
def grid_sampler(self, inputs, input_types):
- if inputs[2] == 0:
- mode = "bilinear"
- else:
- msg = "Only bilinear mode is supported in grid_sampler"
- raise NotImplementedError(msg)
-
- if inputs[3] == 0:
- padding_mode = "zeros"
- elif inputs[3] == 1:
- padding_mode = "border"
- else:
- msg = "Only zeros and border padding mode are supported in
grid_sampler"
- raise NotImplementedError(msg)
+ interpolate_mode = inputs[2]
+ padding_mode = inputs[3]
+ align_corners = inputs[4]
+ data_shape = self.infer_shape_with_prelude(inputs[0])
+
+ if len(data_shape) == 4:
+ layout = "NCHW"
+ axes = [0, 3, 1, 2]
+ grid = _op.transform.transpose(inputs[1], axes)
+ elif len(data_shape) == 5:
+ layout = "NCDHW"
+ axes = [0, 4, 1, 2, 3]
+ grid = _op.transform.transpose(inputs[1], axes)
+ else:
+ msg = f"only 4D and 5D are surpported."
Review comment:
s/surpported/supported/ here
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