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124 lines
3.7 KiB
124 lines
3.7 KiB
# 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 OpTest
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def AffineGrid(theta, size):
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n = size[0]
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h = size[2]
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w = size[3]
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h_idx = np.repeat(
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np.linspace(-1, 1, h)[np.newaxis, :], w, axis=0).T[:, :, np.newaxis]
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w_idx = np.repeat(
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np.linspace(-1, 1, w)[np.newaxis, :], h, axis=0)[:, :, np.newaxis]
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grid = np.concatenate(
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[w_idx, h_idx, np.ones([h, w, 1])], axis=2) # h * w * 3
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grid = np.repeat(grid[np.newaxis, :], size[0], 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("float32")
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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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H = data_shape[2]
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W = data_shape[3]
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out = np.zeros(data_shape, dtype='float')
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for i in range(N):
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for j in range(H):
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for k in range(W):
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if y[i, j, k] < 0 or y[i, j, k] > H - 1 or x[i, j, k] < 0 or x[
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i, j, k] > W - 1:
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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 GridSampler(data, grid):
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dims = data.shape
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N = dims[0]
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C = dims[1]
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H = dims[2]
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W = dims[3]
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x = grid[:, :, :, 0]
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y = grid[:, :, :, 1]
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y_max = H - 1
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x_max = W - 1
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x = 0.5 * ((x.astype('float32') + 1.0) * x_max)
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y = 0.5 * ((y.astype('float32') + 1.0) * y_max)
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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(((x1 - x) * (y1 - y)).reshape((N, 1, H, W)), (1, C, 1, 1))
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wb = np.tile(((x1 - x) * (y - y0)).reshape((N, 1, H, W)), (1, C, 1, 1))
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wc = np.tile(((x - x0) * (y1 - y)).reshape((N, 1, H, W)), (1, C, 1, 1))
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wd = np.tile(((x - x0) * (y - y0)).reshape((N, 1, H, W)), (1, C, 1, 1))
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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('float32')
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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.initTestCase()
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self.op_type = 'grid_sampler'
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x = np.random.randint(0, 255, self.x_shape).astype('float32')
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theta = np.zeros(self.theta_shape).astype('float32')
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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.x_shape)
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self.inputs = {'X': x, 'Grid': grid}
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self.attrs = {'use_cudnn': True}
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self.outputs = {'Output': GridSampler(x, grid)}
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def test_check_output(self):
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self.check_output(atol=1e-3)
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def test_check_grad_normal(self):
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self.check_grad(['X', 'Grid'], 'Output', max_relative_error=0.61)
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def initTestCase(self):
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self.x_shape = (2, 5, 7, 3)
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self.grid_shape = (2, 7, 3, 2)
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self.theta_shape = (2, 2, 3)
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if __name__ == "__main__":
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unittest.main()
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