wehub-resource-sync
677 lines
20 KiB
Python
677 lines
20 KiB
Python
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# 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, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from op_test import (
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OpTest,
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get_device_place,
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is_custom_device,
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skip_check_grad_ci,
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)
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import paddle
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from paddle.base import core
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paddle.enable_static()
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def AffineGrid(theta, grid_shape):
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n = grid_shape[0]
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h = grid_shape[1]
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w = grid_shape[2]
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h_idx = np.repeat(np.linspace(-1, 1, h)[np.newaxis, :], w, axis=0).T[
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:, :, np.newaxis
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]
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w_idx = np.repeat(np.linspace(-1, 1, w)[np.newaxis, :], h, axis=0)[
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:, :, np.newaxis
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]
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grid = np.concatenate(
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[w_idx, h_idx, np.ones([h, w, 1])], axis=2
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) # h * w * 3
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grid = np.repeat(grid[np.newaxis, :], n, axis=0) # n * h * w *3
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ret = np.zeros([n, h * w, 2])
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theta = theta.transpose([0, 2, 1])
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for i in range(len(theta)):
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ret[i] = np.dot(grid[i].reshape([h * w, 3]), theta[i])
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return ret.reshape([n, h, w, 2]).astype("float64")
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def getGridPointValue(data, x, y):
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data_shape = data.shape
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N = data_shape[0]
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C = data_shape[1]
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in_H = data_shape[2]
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in_W = data_shape[3]
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out_H = x.shape[1]
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out_W = x.shape[2]
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# out = np.zeros(data_shape, dtype='float64')
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out = np.zeros([N, C, out_H, out_W], dtype='float64')
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for i in range(N):
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for j in range(out_H):
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for k in range(out_W):
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if (
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y[i, j, k] < 0
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or y[i, j, k] > in_H - 1
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or x[i, j, k] < 0
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or x[i, j, k] > in_W - 1
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):
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out[i, :, j, k] = 0
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else:
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out[i, :, j, k] = data[i, :, y[i, j, k], x[i, j, k]]
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return out
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def AffineGrid3D(theta, grid_shape):
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n = grid_shape[0]
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d = grid_shape[1]
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h = grid_shape[2]
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w = grid_shape[3]
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d_idx = np.repeat(
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np.repeat(np.linspace(-1, 1, d)[:, np.newaxis, np.newaxis], h, axis=1),
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w,
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axis=2,
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)[:, :, :, np.newaxis]
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h_idx = np.repeat(
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np.repeat(np.linspace(-1, 1, h)[np.newaxis, :, np.newaxis], w, axis=2),
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d,
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axis=0,
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)[:, :, :, np.newaxis]
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w_idx = np.repeat(
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np.repeat(np.linspace(-1, 1, w)[np.newaxis, np.newaxis, :], h, axis=1),
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d,
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axis=0,
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)[:, :, :, np.newaxis]
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grid = np.concatenate(
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[w_idx, h_idx, d_idx, np.ones([d, h, w, 1])], axis=3
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) # d * h * w * 4
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grid = np.repeat(grid[np.newaxis, :], n, axis=0) # n * d * h * w *4
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ret = np.zeros([n, d * h * w, 3])
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theta = theta.transpose([0, 2, 1])
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for i in range(len(theta)):
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ret[i] = np.dot(grid[i].reshape([d * h * w, 4]), theta[i])
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return ret.reshape([n, d, h, w, 3]).astype("float64")
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def getGridPointValue3D(data, x, y, z):
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data_shape = data.shape
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N = data_shape[0]
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C = data_shape[1]
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in_D = data_shape[2]
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in_H = data_shape[3]
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in_W = data_shape[4]
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out_D = x.shape[1]
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out_H = x.shape[2]
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out_W = x.shape[3]
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out = np.zeros([N, C, out_D, out_H, out_W], dtype='float64')
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for i in range(N):
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for j in range(out_D):
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for k in range(out_H):
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for l in range(out_W):
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if (
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y[i, j, k, l] < 0
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or y[i, j, k, l] > in_H - 1
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or x[i, j, k, l] < 0
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or x[i, j, k, l] > in_W - 1
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or z[i, j, k, l] < 0
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or z[i, j, k, l] > in_D - 1
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):
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out[i, :, j, k, l] = 0
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else:
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out[i, :, j, k, l] = data[
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i, :, z[i, j, k, l], y[i, j, k, l], x[i, j, k, l]
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]
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return out
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def clip(x, min_n, max_n):
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return np.maximum(np.minimum(x, max_n), min_n)
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def unnormalizeAndClip(grid_slice, max_val, align_corners, padding_mode):
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if align_corners:
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grid_slice = 0.5 * ((grid_slice.astype('float64') + 1.0) * max_val)
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else:
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grid_slice = (
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0.5 * ((grid_slice.astype('float64') + 1.0) * (max_val + 1)) - 0.5
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)
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if padding_mode == "border":
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grid_slice = clip(grid_slice, 0, max_val)
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elif padding_mode == "reflection":
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double_range = 2 * max_val if align_corners else (max_val + 1) * 2
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grid_abs = (
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np.abs(grid_slice) if align_corners else np.abs(grid_slice + 0.5)
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)
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extra = grid_abs - np.floor(grid_abs / double_range) * double_range
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grid_slice = np.minimum(extra, double_range - extra)
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grid_slice = (
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grid_slice if align_corners else clip(grid_slice - 0.5, 0, max_val)
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)
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return grid_slice
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def GridSampler(
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data, grid, align_corners=True, mode="bilinear", padding_mode="zeros"
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):
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dims = data.shape
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N = dims[0]
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in_C = dims[1]
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in_H = dims[2]
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in_W = dims[3]
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out_H = grid.shape[1]
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out_W = grid.shape[2]
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x = grid[:, :, :, 0]
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y = grid[:, :, :, 1]
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y_max = in_H - 1
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x_max = in_W - 1
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x = unnormalizeAndClip(x, x_max, align_corners, padding_mode)
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y = unnormalizeAndClip(y, y_max, align_corners, padding_mode)
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if mode == "bilinear":
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x0 = np.floor(x).astype('int32')
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x1 = x0 + 1
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y0 = np.floor(y).astype('int32')
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y1 = y0 + 1
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wa = np.tile(
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((x1 - x) * (y1 - y)).reshape((N, 1, out_H, out_W)), (1, in_C, 1, 1)
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)
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wb = np.tile(
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((x1 - x) * (y - y0)).reshape((N, 1, out_H, out_W)), (1, in_C, 1, 1)
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)
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wc = np.tile(
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((x - x0) * (y1 - y)).reshape((N, 1, out_H, out_W)), (1, in_C, 1, 1)
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)
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wd = np.tile(
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((x - x0) * (y - y0)).reshape((N, 1, out_H, out_W)), (1, in_C, 1, 1)
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)
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va = getGridPointValue(data, x0, y0)
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vb = getGridPointValue(data, x0, y1)
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vc = getGridPointValue(data, x1, y0)
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vd = getGridPointValue(data, x1, y1)
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out = (wa * va + wb * vb + wc * vc + wd * vd).astype('float64')
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elif mode == "nearest":
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x = np.round(x).astype('int32')
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y = np.round(y).astype('int32')
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out = getGridPointValue(data, x, y)
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return out
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def GridSampler3D(
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data, grid, align_corners=True, mode="bilinear", padding_mode="zeros"
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):
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dims = data.shape
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N = dims[0]
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in_C = dims[1]
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in_D = dims[2]
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in_H = dims[3]
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in_W = dims[4]
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out_D = grid.shape[1]
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out_H = grid.shape[2]
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out_W = grid.shape[3]
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x = grid[:, :, :, :, 0]
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y = grid[:, :, :, :, 1]
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z = grid[:, :, :, :, 2]
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z_max = in_D - 1
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y_max = in_H - 1
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x_max = in_W - 1
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x = unnormalizeAndClip(x, x_max, align_corners, padding_mode)
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y = unnormalizeAndClip(y, y_max, align_corners, padding_mode)
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z = unnormalizeAndClip(z, z_max, align_corners, padding_mode)
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if mode == "bilinear":
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x0 = np.floor(x).astype('int32')
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x1 = x0 + 1
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y0 = np.floor(y).astype('int32')
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y1 = y0 + 1
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z0 = np.floor(z).astype('int32')
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z1 = z0 + 1
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w_tnw = np.tile(
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((x1 - x) * (y1 - y) * (z1 - z)).reshape(
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(N, 1, out_D, out_H, out_W)
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),
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(1, in_C, 1, 1, 1),
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)
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w_tne = np.tile(
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((x - x0) * (y1 - y) * (z1 - z)).reshape(
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(N, 1, out_D, out_H, out_W)
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),
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(1, in_C, 1, 1, 1),
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)
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w_tsw = np.tile(
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((x1 - x) * (y - y0) * (z1 - z)).reshape(
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(N, 1, out_D, out_H, out_W)
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),
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(1, in_C, 1, 1, 1),
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)
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w_tse = np.tile(
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((x - x0) * (y - y0) * (z1 - z)).reshape(
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(N, 1, out_D, out_H, out_W)
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),
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(1, in_C, 1, 1, 1),
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)
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w_bnw = np.tile(
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((x1 - x) * (y1 - y) * (z - z0)).reshape(
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(N, 1, out_D, out_H, out_W)
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),
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(1, in_C, 1, 1, 1),
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)
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w_bne = np.tile(
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((x - x0) * (y1 - y) * (z - z0)).reshape(
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(N, 1, out_D, out_H, out_W)
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),
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(1, in_C, 1, 1, 1),
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)
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w_bsw = np.tile(
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((x1 - x) * (y - y0) * (z - z0)).reshape(
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(N, 1, out_D, out_H, out_W)
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),
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(1, in_C, 1, 1, 1),
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)
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w_bse = np.tile(
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((x - x0) * (y - y0) * (z - z0)).reshape(
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(N, 1, out_D, out_H, out_W)
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),
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(1, in_C, 1, 1, 1),
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)
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v_tnw = getGridPointValue3D(data, x0, y0, z0)
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v_tne = getGridPointValue3D(data, x1, y0, z0)
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v_tsw = getGridPointValue3D(data, x0, y1, z0)
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v_tse = getGridPointValue3D(data, x1, y1, z0)
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v_bnw = getGridPointValue3D(data, x0, y0, z1)
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v_bne = getGridPointValue3D(data, x1, y0, z1)
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v_bsw = getGridPointValue3D(data, x0, y1, z1)
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v_bse = getGridPointValue3D(data, x1, y1, z1)
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out = (
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w_tnw * v_tnw
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+ w_tne * v_tne
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+ w_tsw * v_tsw
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+ w_tse * v_tse
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+ w_bnw * v_bnw
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+ w_bne * v_bne
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+ w_bsw * v_bsw
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+ w_bse * v_bse
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).astype('float64')
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elif mode == "nearest":
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x = np.round(x).astype('int32')
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y = np.round(y).astype('int32')
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z = np.round(z).astype('int32')
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out = getGridPointValue3D(data, x, y, z)
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return out
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class TestGridSamplerOp(OpTest):
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def setUp(self):
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self.use_cudnn = False
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self.numeric_grad_delta = 0.0001
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self.op_type = 'grid_sampler'
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self.python_api = paddle.nn.functional.grid_sample
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self.align_corners = True
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self.padding_mode = "zeros"
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self.mode = "bilinear"
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self.initTestCase()
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x = np.random.randint(0, 255, self.x_shape).astype('float64')
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theta = np.zeros(self.theta_shape).astype('float64')
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if len(self.grid_shape) == 4:
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for i in range(self.theta_shape[0]):
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for j in range(2):
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for k in range(3):
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theta[i, j, k] = np.random.rand(1)[0]
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grid = AffineGrid(theta, self.grid_shape)
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self.inputs = {'X': x, 'Grid': grid}
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self.attrs = {
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'use_cudnn': self.use_cudnn,
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"align_corners": self.align_corners,
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"padding_mode": self.padding_mode,
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"mode": self.mode,
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}
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self.outputs = {
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'Output': GridSampler(
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x, grid, self.align_corners, self.mode, self.padding_mode
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)
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}
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else:
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for i in range(self.theta_shape[0]):
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for j in range(3):
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for k in range(4):
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theta[i, j, k] = np.random.rand(1)[0]
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grid = AffineGrid3D(theta, self.grid_shape)
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self.inputs = {'X': x, 'Grid': grid}
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self.attrs = {
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'use_cudnn': self.use_cudnn,
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"align_corners": self.align_corners,
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"padding_mode": self.padding_mode,
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"mode": self.mode,
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}
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self.outputs = {
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'Output': GridSampler3D(
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x, grid, self.align_corners, self.mode, self.padding_mode
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)
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}
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def test_check_output(self):
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self.check_output_with_place(core.CPUPlace(), check_pir=True)
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if core.is_compiled_with_cuda() or is_custom_device():
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self.check_output_with_place(get_device_place(), check_pir=True)
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self.check_output(check_pir=True)
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def test_check_grad_normal(self):
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self.check_grad_with_place(
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core.CPUPlace(),
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['X', 'Grid'],
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'Output',
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max_relative_error=0.01,
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numeric_grad_delta=self.numeric_grad_delta,
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check_pir=True,
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)
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if core.is_compiled_with_cuda() or is_custom_device():
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self.check_grad_with_place(
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get_device_place(),
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['X', 'Grid'],
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'Output',
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max_relative_error=0.01,
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numeric_grad_delta=self.numeric_grad_delta,
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check_pir=True,
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)
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def initTestCase(self):
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self.x_shape = (2, 3, 8, 8)
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self.grid_shape = (2, 7, 9, 2)
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self.theta_shape = (2, 2, 3)
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self.align_corners = True
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self.padding_mode = "zeros"
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self.mode = "bilinear"
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self.use_cudnn = False if core.is_compiled_with_rocm() else True
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class Case1(TestGridSamplerOp):
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def initTestCase(self):
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self.x_shape = (2, 3, 5, 6)
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self.grid_shape = (2, 8, 9, 2)
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self.theta_shape = (2, 2, 3)
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self.align_corners = False
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self.padding_mode = "zeros"
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self.mode = "bilinear"
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class Case1_(TestGridSamplerOp):
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def initTestCase(self):
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self.x_shape = (2, 3, 5, 6)
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self.grid_shape = (2, 8, 9, 2)
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self.theta_shape = (2, 2, 3)
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self.align_corners = False
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self.padding_mode = "border"
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self.mode = "bilinear"
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class Case2(TestGridSamplerOp):
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def initTestCase(self):
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self.x_shape = (2, 3, 5, 6)
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self.grid_shape = (2, 8, 9, 2)
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self.theta_shape = (2, 2, 3)
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self.align_corners = False
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self.padding_mode = "reflection"
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self.mode = "bilinear"
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class Case3(TestGridSamplerOp):
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def initTestCase(self):
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self.x_shape = (2, 3, 5, 6)
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self.grid_shape = (2, 8, 9, 2)
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self.theta_shape = (2, 2, 3)
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self.align_corners = True
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self.padding_mode = "reflection"
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self.mode = "bilinear"
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class Case4(TestGridSamplerOp):
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def initTestCase(self):
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self.x_shape = (2, 3, 5, 6)
|
|
self.grid_shape = (2, 8, 9, 2)
|
|
self.theta_shape = (2, 2, 3)
|
|
self.align_corners = False
|
|
self.padding_mode = "reflection"
|
|
self.mode = "nearest"
|
|
self.numeric_grad_delta = 0.0001
|
|
|
|
|
|
class Case_ZeroSize(TestGridSamplerOp):
|
|
def initTestCase(self):
|
|
self.x_shape = (2, 0, 5, 6)
|
|
self.grid_shape = (2, 8, 9, 2)
|
|
self.theta_shape = (2, 2, 3)
|
|
self.align_corners = False
|
|
self.padding_mode = "zeros"
|
|
self.mode = "bilinear"
|
|
|
|
|
|
@skip_check_grad_ci(
|
|
reason="'check_grad' on large inputs is too slow, "
|
|
+ "however it is desirable to cover the forward pass"
|
|
)
|
|
class LargeInputCase(TestGridSamplerOp):
|
|
def get_places(self):
|
|
places = []
|
|
if core.is_compiled_with_cuda() or is_custom_device():
|
|
places.append(get_device_place())
|
|
return places
|
|
|
|
def initTestCase(self):
|
|
self.no_need_check_grad = True
|
|
self.x_shape = (2, 3, 128, 128)
|
|
self.grid_shape = (2, 130, 130, 2)
|
|
self.theta_shape = (2, 2, 3)
|
|
self.align_corners = False
|
|
self.padding_mode = "reflection"
|
|
self.mode = "bilinear"
|
|
|
|
def test_check_grad_normal(self):
|
|
pass
|
|
|
|
|
|
@skip_check_grad_ci(
|
|
reason="'check_grad' on large inputs is too slow, "
|
|
+ "however it is desirable to cover the forward pass"
|
|
)
|
|
class Case5(LargeInputCase):
|
|
def initTestCase(self):
|
|
self.no_need_check_grad = True
|
|
self.x_shape = (2, 3, 128, 128)
|
|
self.grid_shape = (2, 130, 130, 2)
|
|
self.theta_shape = (2, 2, 3)
|
|
self.align_corners = True
|
|
self.padding_mode = "zeros"
|
|
self.mode = "bilinear"
|
|
self.use_cudnn = False if core.is_compiled_with_rocm() else True
|
|
|
|
|
|
class Case6(TestGridSamplerOp):
|
|
def initTestCase(self):
|
|
self.x_shape = (2, 3, 5, 6, 7)
|
|
self.grid_shape = (2, 8, 9, 10, 3)
|
|
self.theta_shape = (2, 3, 4)
|
|
self.align_corners = False
|
|
self.padding_mode = "zeros"
|
|
self.mode = "bilinear"
|
|
self.numeric_grad_delta = 0.000001
|
|
|
|
|
|
class Case6_(TestGridSamplerOp):
|
|
def initTestCase(self):
|
|
self.x_shape = (2, 3, 4, 5, 6)
|
|
self.grid_shape = (2, 7, 8, 9, 3)
|
|
self.theta_shape = (2, 3, 4)
|
|
self.align_corners = False
|
|
self.padding_mode = "border"
|
|
self.mode = "bilinear"
|
|
self.numeric_grad_delta = 0.000001
|
|
|
|
|
|
class Case7(TestGridSamplerOp):
|
|
def initTestCase(self):
|
|
self.x_shape = (2, 3, 4, 5, 6)
|
|
self.grid_shape = (2, 7, 8, 9, 3)
|
|
self.theta_shape = (2, 3, 4)
|
|
self.align_corners = False
|
|
self.padding_mode = "reflection"
|
|
self.mode = "bilinear"
|
|
self.numeric_grad_delta = 0.000001
|
|
|
|
|
|
class Case8(TestGridSamplerOp):
|
|
def initTestCase(self):
|
|
self.x_shape = (2, 3, 4, 5, 6)
|
|
self.grid_shape = (2, 7, 8, 9, 3)
|
|
self.theta_shape = (2, 3, 4)
|
|
self.align_corners = True
|
|
self.padding_mode = "reflection"
|
|
self.mode = "bilinear"
|
|
self.numeric_grad_delta = 0.000001
|
|
|
|
|
|
class Case9(TestGridSamplerOp):
|
|
def initTestCase(self):
|
|
self.x_shape = (2, 3, 4, 5, 6)
|
|
self.grid_shape = (2, 7, 8, 9, 3)
|
|
self.theta_shape = (2, 3, 4)
|
|
self.align_corners = False
|
|
self.padding_mode = "reflection"
|
|
self.mode = "nearest"
|
|
self.numeric_grad_delta = 0.000001
|
|
|
|
|
|
@skip_check_grad_ci(
|
|
reason="'check_grad' on large inputs is too slow, "
|
|
+ "however it is desirable to cover the forward pass"
|
|
)
|
|
class LargeInput3DCase(TestGridSamplerOp):
|
|
def get_places(self):
|
|
places = []
|
|
if core.is_compiled_with_cuda() or is_custom_device():
|
|
places.append(get_device_place())
|
|
return places
|
|
|
|
def initTestCase(self):
|
|
self.no_need_check_grad = True
|
|
self.x_shape = (2, 3, 24, 24, 12)
|
|
self.grid_shape = (2, 25, 25, 12, 3)
|
|
self.theta_shape = (2, 3, 4)
|
|
self.align_corners = False
|
|
self.padding_mode = "reflection"
|
|
self.mode = "bilinear"
|
|
self.numeric_grad_delta = 0.000001
|
|
self.use_cudnn = False
|
|
|
|
def test_check_grad_normal(self):
|
|
pass
|
|
|
|
|
|
@skip_check_grad_ci(
|
|
reason="'check_grad' on large inputs is too slow, "
|
|
+ "however it is desirable to cover the forward pass"
|
|
)
|
|
class Case10(LargeInput3DCase):
|
|
def initTestCase(self):
|
|
self.no_need_check_grad = True
|
|
self.x_shape = (2, 3, 24, 24, 12)
|
|
self.grid_shape = (2, 25, 25, 12, 3)
|
|
self.theta_shape = (2, 3, 4)
|
|
self.align_corners = True
|
|
self.padding_mode = "zeros"
|
|
self.mode = "bilinear"
|
|
self.numeric_grad_delta = 0.000001
|
|
|
|
|
|
class TestGridSampleErrorMode1(unittest.TestCase):
|
|
def _test_case(self):
|
|
paddle.nn.functional.grid_sample(
|
|
paddle.randn([2, 3, 4, 5, 6], dtype="float32"),
|
|
paddle.randn([2, 7, 8, 9, 3], dtype="float32"),
|
|
mode="error_mode",
|
|
padding_mode="zeros",
|
|
align_corners=False,
|
|
)
|
|
|
|
def test_error(self):
|
|
self.assertRaises(ValueError, self._test_case)
|
|
|
|
|
|
class TestGridSampleErrorMode2(unittest.TestCase):
|
|
def _test_case(self):
|
|
paddle.nn.functional.grid_sample(
|
|
paddle.randn([2, 3, 4, 5, 6], dtype="float32"),
|
|
paddle.randn([2, 7, 8, 9, 3], dtype="float32"),
|
|
mode="nearest",
|
|
padding_mode="error_mode",
|
|
align_corners=False,
|
|
)
|
|
|
|
def test_error(self):
|
|
self.assertRaises(ValueError, self._test_case)
|
|
|
|
|
|
class TestGridSampleErrorMode3(unittest.TestCase):
|
|
def _test_case(self):
|
|
paddle.nn.functional.grid_sample(
|
|
paddle.randn([2, 3, 4, 5, 6], dtype="float32"),
|
|
paddle.randn([2, 7, 8, 9, 3], dtype="float32"),
|
|
mode="error_mode",
|
|
padding_mode="error_mode",
|
|
align_corners=False,
|
|
)
|
|
|
|
def test_error(self):
|
|
self.assertRaises(ValueError, self._test_case)
|
|
|
|
|
|
class TestGridSampleErrorMode4(unittest.TestCase):
|
|
def _test_case(self):
|
|
paddle.nn.functional.grid_sample(
|
|
paddle.randn([2, 3, 4, 5, 6], dtype="float32"),
|
|
paddle.randn([2, 7, 8, 9, 3], dtype="float32"),
|
|
mode="nearest",
|
|
padding_mode="zeros",
|
|
align_corners=1,
|
|
)
|
|
|
|
def test_error(self):
|
|
self.assertRaises(TypeError, self._test_case)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|