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193 lines
6.2 KiB
193 lines
6.2 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 max_pool3D_forward_naive(x, ksize, strides, paddings, global_pool=False):
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N, C, D, H, W = x.shape
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if global_pool:
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ksize = [D, H, W]
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paddings = [0, 0, 0]
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D_out = (D - ksize[0] + 2 * paddings[0]) / strides[0] + 1
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H_out = (H - ksize[1] + 2 * paddings[1]) / strides[1] + 1
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W_out = (W - ksize[2] + 2 * paddings[2]) / strides[2] + 1
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out = np.zeros((N, C, D_out, H_out, W_out))
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mask = np.zeros((N, C, D_out, H_out, W_out))
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for k in range(D_out):
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d_start = np.max((k * strides[0] - paddings[0], 0))
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d_end = np.min((k * strides[0] + ksize[0] - paddings[0], D))
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for i in range(H_out):
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h_start = np.max((i * strides[0] - paddings[0], 0))
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h_end = np.min((i * strides[0] + ksize[0] - paddings[0], H))
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for j in range(W_out):
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w_start = np.max((j * strides[1] - paddings[1], 0))
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w_end = np.min((j * strides[1] + ksize[1] - paddings[1], W))
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x_masked = x[:, :, d_start:d_end, h_start:h_end, w_start:w_end]
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out[:, :, k, i, j] = np.max(x_masked, axis=(2, 3, 4))
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for n in range(N):
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for c in range(C):
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arr = x_masked[n, c, :, :, :]
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index = np.where(arr == np.max(arr))
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sub_deep = index[0][0]
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sub_row = index[1][0]
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sub_col = index[2][0]
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index = ((d_start + sub_deep) * H +
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(h_start + sub_row)) * W + w_start + sub_col
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mask[n, c, k, i, j] = index
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return out, mask
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def max_pool2D_forward_naive(x, ksize, strides, paddings, global_pool=False):
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N, C, H, W = x.shape
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if global_pool:
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ksize = [H, W]
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paddings = [0, 0]
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H_out = (H - ksize[0] + 2 * paddings[0]) / strides[0] + 1
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W_out = (W - ksize[1] + 2 * paddings[1]) / strides[1] + 1
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out = np.zeros((N, C, H_out, W_out))
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mask = np.zeros((N, C, H_out, W_out))
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for i in range(H_out):
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for j in range(W_out):
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r_start = np.max((i * strides[0] - paddings[0], 0))
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r_end = np.min((i * strides[0] + ksize[0] - paddings[0], H))
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c_start = np.max((j * strides[1] - paddings[1], 0))
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c_end = np.min((j * strides[1] + ksize[1] - paddings[1], W))
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x_masked = x[:, :, r_start:r_end, c_start:c_end]
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out[:, :, i, j] = np.max(x_masked, axis=(2, 3))
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for n in range(N):
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for c in range(C):
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arr = x_masked[n, c, :, :]
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index = np.where(arr == np.max(arr))
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sub_row = index[0][0]
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sub_col = index[1][0]
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index = (r_start + sub_row) * W + c_start + sub_col
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mask[n, c, i, j] = index
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return out, mask
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class TestMaxPoolWithIndex_Op(OpTest):
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def setUp(self):
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self.init_test_case()
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self.init_global()
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input = np.random.random(self.shape).astype("float32")
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output, mask = self.pool_forward_naive(input, self.ksize, self.strides,
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self.paddings, self.global_pool)
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output = output.astype("float32")
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mask = mask.astype("int32")
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self.attrs = {
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'strides': self.strides,
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'paddings': self.paddings,
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'ksize': self.ksize,
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'global_pooling': self.global_pool,
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}
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self.inputs = {'X': input}
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self.outputs = {'Out': output, "Mask": mask}
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def test_check_output(self):
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self.check_output()
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# def test_check_grad(self):
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# self.check_grad(set(['X']), ['Out'], max_relative_error=0.07)
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def init_test_case(self):
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self.op_type = "max_pool3d_with_index"
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self.pool_forward_naive = max_pool3D_forward_naive
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self.shape = [2, 3, 5, 5, 5]
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self.ksize = [3, 3, 3]
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self.strides = [1, 1, 1]
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self.paddings = [1, 1, 1]
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def init_global(self):
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self.global_pool = False
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class TestCase1(TestMaxPoolWithIndex_Op):
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def init_global(self):
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self.global_pool = True
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class TestCase2(TestMaxPoolWithIndex_Op):
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def init_test_case(self):
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self.op_type = "max_pool3d_with_index"
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self.pool_forward_naive = max_pool3D_forward_naive
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self.shape = [2, 3, 7, 7, 7]
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self.ksize = [3, 3, 3]
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self.strides = [2, 2, 2]
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self.paddings = [0, 0, 0]
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def init_global(self):
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self.global_pool = True
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class TestCase3(TestCase2):
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def init_global(self):
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self.global_pool = False
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#----------------max_pool2d_with_index----------------
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class TestCase4(TestMaxPoolWithIndex_Op):
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def init_test_case(self):
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self.op_type = "max_pool2d_with_index"
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self.pool_forward_naive = max_pool2D_forward_naive
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self.shape = [2, 3, 7, 7]
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self.ksize = [3, 3]
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self.strides = [1, 1]
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self.paddings = [1, 1]
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def init_global(self):
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self.global_pool = True
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class TestCase5(TestCase4):
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def init_global(self):
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self.global_pool = False
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class TestCase6(TestMaxPoolWithIndex_Op):
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def init_test_case(self):
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self.op_type = "max_pool2d_with_index"
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self.pool_forward_naive = max_pool2D_forward_naive
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self.shape = [2, 3, 7, 7]
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self.ksize = [3, 3]
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self.strides = [2, 2]
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self.paddings = [0, 0]
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def init_global(self):
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self.global_pool = True
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class TestCase7(TestCase6):
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def init_global(self):
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self.global_pool = False
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if __name__ == '__main__':
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unittest.main()
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