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241 lines
7.5 KiB
241 lines
7.5 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, 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(
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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, :], 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 y[i, j, k] < 0 or y[i, j, k] > in_H - 1 or x[
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i, j, k] < 0 or x[i, j, k] > in_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 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 = 0.5 * (
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(grid_slice.astype('float64') + 1.0) * (max_val + 1)) - 0.5
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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 = np.abs(grid_slice) if align_corners else np.abs(grid_slice +
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0.5)
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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 = grid_slice if align_corners else clip(grid_slice - 0.5, 0,
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max_val)
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return grid_slice
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def GridSampler(data,
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grid,
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align_corners=True,
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mode="bilinear",
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padding_mode="zeros"):
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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(((x1 - x) * (y1 - y)).reshape((N, 1, out_H, out_W)),
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(1, in_C, 1, 1))
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wb = np.tile(((x1 - x) * (y - y0)).reshape((N, 1, out_H, out_W)),
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(1, in_C, 1, 1))
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wc = np.tile(((x - x0) * (y1 - y)).reshape((N, 1, out_H, out_W)),
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(1, in_C, 1, 1))
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wd = np.tile(((x - x0) * (y - y0)).reshape((N, 1, out_H, out_W)),
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(1, in_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('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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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.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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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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# print("X: {}".format(x))
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self.outputs = {
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'Output': GridSampler(x, grid, self.align_corners, self.mode,
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self.padding_mode)
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}
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def test_check_output(self):
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self.check_output()
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def test_check_grad_normal(self):
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self.check_grad(
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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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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 = 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)
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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 = "nearest"
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self.numeric_grad_delta = 0.0001
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if __name__ == "__main__":
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unittest.main()
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