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Implement reference nGraph implementation for "Interpolate-4" with 5D tensor support in the "linear_onnx" mode (#3948)

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* Commit.

* Written the structure InfoForLinearONNXMode5D that contains info to perform interpolation in 'linear_onnx' mode for 5D tensors.

* Started to write the method get_info_for_linear_onnx_mode5D() that returns info for calculations of 'linear_onnx' mode in 5D case.

* Written the method InterpolateEvalHelper::get_info_for_linear_onnx_mode5D().

* Code style fix.

* Started to write calculation of 5D case of 'linear_onnx' mode.

* Written the method void InterpolateEval<T>::linear_onnx5D_func(const T* input_data, T* out).

* Added dispatching of 4D/5D cases of the mode 'linear_onnx'.

* Fixed code style.

* Some fixes.

* Code style fixes.

* Now linear_onnx_func throws an exception for incorrect input rank.

* Code style fix.

* Started to write tests for evaluation of 'linear_onnx' mode in the 5D case.

* Added first test for linear_onnx 5D.

* Small fixes.

* Written tests for evaluation of Interpolate-4 in linear_onnx 5D case.

* Some code style fixes.

* Small fix.

* Corrected documentation.

* Started to write generic implementation of 'linear_onnx' mode, for any ranks.

* Written the draft of a generic (for all ranks) implementation of 'linear_onnx' mode.

* Small fixes.

* Small fix.

* Small fix.

* Small fix.

* Code style fix.

* Small fix.

* Code style fix.

* Some fixes.

* Some fix.

* Small fix.

* Small fix.

* Code style fix.

* Added check for axes correctness into a generic implementation of the 'linear_onnx' mode.

* Now 5D case of the 'linear_onnx' mode is calculated using generic function.

* Code style fix.

* Deleted unused variable.

* Added debug prints.

* Small fix.

* Some fixes.

* Code style fix.

* Now all ranks are processed by a generic implementation in the 'linear_onnx' mode.

* Deleted name of missed test.

* Deleted 4D case implementation of the 'linear_onnx' mode.

* Reverted change in tests.

* Added needed 'const' modifiers and added a comment about the variable 'axis_idx_offset'.

* Small fixes.
This commit is contained in:
Vladimir Gavrilov authored and GitHub committed 2021-02-09 14:23:50 +03:00
1 parent deca4fc443
commit 2c4c3a777a
4 files changed
+429 -136
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docs/ops/image/Interpolate_4.md
+123 -2
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@@ -15,7 +15,7 @@
* **Type**: string
* **Default value**: none
* **Required**: *yes*
* **Note**: Only 2D and 4D tensors with `axes = {0, 1}` and `axes = {2, 3}` respectively are supported for `"mode" == "linear_onnx"`.
**Note**: Only 2D, 3D, 4D, 5D tensors with `axes = {0, 1}`, `axes = {0, 1, 2}`, `axes = {2, 3}`, `axes = {2, 3, 4}` respectively are supported for `"mode" == "linear_onnx"`.
* *shape_calculation_mode*
@@ -366,7 +366,119 @@ class InterpolateCalculation:
return result
def onnx_linear_interpolation(self, input_data):
def onnx_linear_interpolation5D(self, input_data):
rank = len(self.input_shape)
assert rank in [3, 5], "mode 'linear_onnx' supports only 3D or 5D tensors"
assert set(self.axes) == {2, 3, 4} or set(self.axes) == {0, 1, 2}, \
"mode 'linear_onnx' supports only case when axes = {2, 3, 4} or axes = {0, 1, 2}"
result = np.zeros(self.output_shape)
if rank == 3:
reshaped_data = np.reshape(input_data, (1, 1, self.input_shape[0], self.input_shape[1], self.input_shape[2]))
result = np.reshape(result, (1, 1, self.output_shape[0], self.output_shape[1], self.output_shape[2]))
else:
reshaped_data = input_data
input_shape = np.array(reshaped_data.shape).astype(np.int64)
output_shape = np.array(result.shape).astype(np.int64)
batch_size = input_shape[0];
num_channels = input_shape[1];
input_depth = input_shape[2];
input_height = input_shape[3];
input_width = input_shape[4];
output_depth = output_shape[2];
output_height = output_shape[3];
output_width = output_shape[4];
depth_scale = self.scales[0];
height_scale = self.scales[1];
width_scale = self.scales[2];
z_original = np.zeros(output_depth).astype(np.float)
y_original = np.zeros(output_height).astype(np.float)
x_original = np.zeros(output_width).astype(np.float)
in_z1 = np.zeros(output_depth).astype(np.int64)
in_z2 = np.zeros(output_depth).astype(np.int64)
in_y1 = np.zeros(output_height).astype(np.int64)
in_y2 = np.zeros(output_height).astype(np.int64)
in_x1 = np.zeros(output_width).astype(np.int64)
in_x2 = np.zeros(output_width).astype(np.int64)
dz1 = np.zeros(output_depth).astype(np.float)
dz2 = np.zeros(output_depth).astype(np.float)
dy1 = np.zeros(output_height).astype(np.float)
dy2 = np.zeros(output_height).astype(np.float)
dx1 = np.zeros(output_width).astype(np.float)
dx2 = np.zeros(output_width).astype(np.float)
for z in range(0, output_depth):
in_z = self.get_original_coordinate(z, depth_scale, output_depth, input_depth)
z_original[z] = in_z
in_z = max(0, min(in_z, input_depth - 1))
in_z1[z] = max(0, min(int(in_z), input_depth - 1))
in_z2[z] = min(in_z1[z] + 1, input_depth - 1)
dz1[z] = abs(in_z - in_z1[z])
dz2[z] = abs(in_z - in_z2[z])
if in_z1[z] == in_z2[z]:
dz1[z] = 0.5
dz2[z] = 0.5
for y in range(0, output_height):
in_y = self.get_original_coordinate(y, height_scale, output_height, input_height)
y_original[y] = in_y
in_y = max(0, min(in_y, input_height - 1))
in_y1[y] = max(0, min(int(in_y), input_height - 1))
in_y2[y] = min(in_y1[y] + 1, input_height - 1)
dy1[y] = abs(in_y - in_y1[y])
dy2[y] = abs(in_y - in_y2[y])
if in_y1[y] == in_y2[y]:
dy1[y] = 0.5
dy2[y] = 0.5
for x in range(0, output_width):
in_x = self.get_original_coordinate(x, width_scale, output_width, input_width);
x_original[x] = in_x
in_x = max(0.0, min(in_x, input_width - 1));
in_x1[x] = min(in_x, input_width - 1);
in_x2[x] = min(in_x1[x] + 1, input_width - 1);
dx1[x] = abs(in_x - in_x1[x]);
dx2[x] = abs(in_x - in_x2[x]);
if in_x1[x] == in_x2[x]:
dx1[x] = 0.5
dx2[x] = 0.5
for n in range(0, batch_size):
for c in range(0, num_channels):
for z in range(0, output_depth):
for y in range(0, output_height):
for x in range(0, output_width):
x111 = reshaped_data[n, c, in_z1[z], in_y1[y], in_x1[x]]
x211 = reshaped_data[n, c, in_z1[z], in_y1[y], in_x2[x]]
x121 = reshaped_data[n, c, in_z1[z], in_y2[y], in_x1[x]]
x221 = reshaped_data[n, c, in_z1[z], in_y2[y], in_x2[x]]
x112 = reshaped_data[n, c, in_z2[z], in_y1[y], in_x1[x]]
x212 = reshaped_data[n, c, in_z2[z], in_y1[y], in_x2[x]]
x122 = reshaped_data[n, c, in_z2[z], in_y2[y], in_x1[x]]
x222 = reshaped_data[n, c, in_z2[z], in_y2[y], in_x2[x]]
temp = dx2[x] * dy2[y] * dz2[z] * x111 + dx1[x] * dy2[y] * dz2[z] * x211
temp += dx2[x] * dy1[y] * dz2[z] * x121 + dx1[x] * dy1[y] * dz2[z] * x221
temp += dx2[x] * dy2[y] * dz1[z] * x112 + dx1[x] * dy2[y] * dz1[z] * x212
temp += dx2[x] * dy1[y] * dz1[z] * x122 + dx1[x] * dy1[y] * dz1[z] * x222
result[n, c, z, y, x] = temp
return np.reshape(result, self.output_shape)
def onnx_linear_interpolation4D(self, input_data):
rank = len(self.input_shape)
assert rank in [2, 4], "mode 'linear_onnx' supports only 2D or 4D tensors"
assert set(self.axes) == {2, 3} or set(self.axes) == {0, 1}, \
@@ -446,6 +558,15 @@ class InterpolateCalculation:
return np.reshape(result, self.output_shape)
def onnx_linear_interpolation(self, input_data):
rank = len(self.input_shape)
assert rank in [2, 3, 4, 5], "mode 'linear_onnx' supports only 2D, 3D, 4D, or 5D tensors"
if rank in [2, 4]:
self.onnx_linear_interpolation4D(input_data)
else:
self.onnx_linear_interpolation5D(input_data)
def nearest_interpolation(self, input_data):
result = np.zeros(self.output_shape)