Mei Ye
98fcca1720
* [relay] Fix stack overflow in device_planner observed on windows due to recursive function calls. * Revert "[relay] Fix stack overflow in device_planner observed on windows due to recursive function calls." This reverts commit 70581364771e2415b37b202a9fa6a937f275cfc6. * [PyTorch] Add grid_sample with zeros and border padding mode for PyTorch.
100 lines
4.2 KiB
Python
100 lines
4.2 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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# pylint: disable=invalid-name, line-too-long, unused-variable, too-many-locals
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"""affine_grid and grid_sample operators in python"""
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import math
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import numpy as np
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def affine_grid_python(data, target_shape):
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yv, xv = np.meshgrid(np.arange(target_shape[0]), np.arange(target_shape[1]))
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yv = yv.T * 2 / (target_shape[0] - 1) - 1
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xv = xv.T * 2 / (target_shape[1] - 1) - 1
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ones = np.ones_like(xv)
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grid = np.stack([xv, yv, ones]).reshape(3, -1)
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return data.reshape(-1, 3).dot(grid).reshape(data.shape[0], 2, *target_shape)
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def _bilinear_sample_nchw_python(data, grid, padding_mode):
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batch, in_channel, in_height, in_width = data.shape
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_, _, out_height, out_width = grid.shape
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out = np.zeros((batch, in_channel, out_height, out_width), dtype=data.dtype)
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def _within_bound(y, x):
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return 0 <= y < in_height and 0 <= x < in_width
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def compute_padding_mode_zeros():
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for n in range(0, batch):
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for h in range(0, out_height):
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for w in range(0, out_width):
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x, y = grid[n, :, h, w]
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y = (y + 1) * (in_height - 1) / 2
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x = (x + 1) * (in_width - 1) / 2
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y0 = int(math.floor(y))
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x0 = int(math.floor(x))
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y1 = y0 + 1
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x1 = x0 + 1
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if _within_bound(y0, x0):
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out[n, :, h, w] += data[n, :, y0, x0] * (1.0 - (y - y0)) * (1.0 - (x - x0))
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if _within_bound(y0, x1):
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out[n, :, h, w] += data[n, :, y0, x1] * (1.0 - (y - y0)) * (x - x0)
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if _within_bound(y1, x0):
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out[n, :, h, w] += data[n, :, y1, x0] * (y - y0) * (1.0 - (x - x0))
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if _within_bound(y1, x1):
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out[n, :, h, w] += data[n, :, y1, x1] * (y - y0) * (x - x0)
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return out
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def get_pixel_value(x, x_max):
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return max(min(x, x_max - 1), 0)
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def compute_padding_mode_border():
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for n in range(0, batch):
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for h in range(0, out_height):
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for w in range(0, out_width):
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x, y = grid[n, :, h, w]
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y = (y + 1) * (in_height - 1) / 2
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x = (x + 1) * (in_width - 1) / 2
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y0 = int(math.floor(y))
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x0 = int(math.floor(x))
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y1 = y0 + 1
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x1 = x0 + 1
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y0 = get_pixel_value(y0, in_height)
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y1 = get_pixel_value(y1, in_height)
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x0 = get_pixel_value(x0, in_width)
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x1 = get_pixel_value(x1, in_width)
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out[n, :, h, w] = data[n, :, y0, x0] * (1.0 - (y - y0)) * (1.0 - (x - x0))
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out[n, :, h, w] += data[n, :, y0, x1] * (1.0 - (y - y0)) * (x - x0)
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out[n, :, h, w] += data[n, :, y1, x0] * (y - y0) * (1.0 - (x - x0))
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out[n, :, h, w] += data[n, :, y1, x1] * (y - y0) * (x - x0)
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return out
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if padding_mode == "zeros":
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return compute_padding_mode_zeros()
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if padding_mode == "border":
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return compute_padding_mode_border()
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raise ValueError("invalid padding_mode")
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def grid_sample_nchw_python(data, grid, method="bilinear", padding_mode="zeros"):
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if method == "bilinear":
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return _bilinear_sample_nchw_python(data, grid, padding_mode)
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raise ValueError("invalid method")
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