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409 lines
13 KiB
409 lines
13 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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from __future__ import print_function
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from __future__ import division
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import unittest
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import numpy as np
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import paddle.fluid.core as core
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from op_test import OpTest
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def adaptive_start_index(index, input_size, output_size):
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return int(np.floor(index * input_size / output_size))
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def adaptive_end_index(index, input_size, output_size):
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return int(np.ceil((index + 1) * input_size / output_size))
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def max_pool3D_forward_naive(x,
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ksize,
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strides,
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paddings,
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global_pool=0,
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ceil_mode=False,
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exclusive=True,
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adaptive=False):
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N, C, D, H, W = x.shape
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if global_pool == 1:
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ksize = [D, H, W]
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if adaptive:
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D_out, H_out, W_out = ksize
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else:
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D_out = (D - ksize[0] + 2 * paddings[0] + strides[0] - 1
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) // strides[0] + 1 if ceil_mode else (
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H - 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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) // strides[1] + 1 if ceil_mode else (
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W - 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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) // strides[2] + 1 if ceil_mode else (
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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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for k in range(D_out):
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if adaptive:
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d_start = adaptive_start_index(k, D, ksize[0])
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d_end = adaptive_end_index(k, D, ksize[0])
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else:
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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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if adaptive:
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h_start = adaptive_start_index(i, H, ksize[1])
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h_end = adaptive_end_index(i, H, ksize[1])
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else:
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h_start = np.max((i * strides[1] - paddings[1], 0))
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h_end = np.min((i * strides[1] + ksize[1] - paddings[1], H))
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for j in range(W_out):
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if adaptive:
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w_start = adaptive_start_index(j, W, ksize[2])
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w_end = adaptive_end_index(j, W, ksize[2])
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else:
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w_start = np.max((j * strides[2] - paddings[2], 0))
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w_end = np.min((j * strides[2] + ksize[2] - paddings[2], 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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return out
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def avg_pool3D_forward_naive(x,
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ksize,
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strides,
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paddings,
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global_pool=0,
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ceil_mode=False,
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exclusive=True,
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adaptive=False):
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N, C, D, H, W = x.shape
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if global_pool == 1:
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ksize = [D, H, W]
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if adaptive:
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D_out, H_out, W_out = ksize
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else:
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D_out = (D - ksize[0] + 2 * paddings[0] + strides[0] - 1
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) // strides[0] + 1 if ceil_mode else (
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H - 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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) // strides[1] + 1 if ceil_mode else (
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W - 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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) // strides[2] + 1 if ceil_mode else (
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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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for k in range(D_out):
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if adaptive:
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d_start = adaptive_start_index(k, D, ksize[0])
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d_end = adaptive_end_index(k, D, ksize[0])
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else:
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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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if adaptive:
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h_start = adaptive_start_index(i, H, ksize[1])
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h_end = adaptive_end_index(i, H, ksize[1])
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else:
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h_start = np.max((i * strides[1] - paddings[1], 0))
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h_end = np.min((i * strides[1] + ksize[1] - paddings[1], H))
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for j in range(W_out):
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if adaptive:
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w_start = adaptive_start_index(j, W, ksize[2])
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w_end = adaptive_end_index(j, W, ksize[2])
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else:
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w_start = np.max((j * strides[2] - paddings[2], 0))
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w_end = np.min((j * strides[2] + ksize[2] - paddings[2], W))
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x_masked = x[:, :, d_start:d_end, h_start:h_end, w_start:w_end]
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field_size = (d_end - d_start) * (h_end - h_start) * (w_end - w_start) \
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if (exclusive or adaptive) else ksize[0] * ksize[1] * ksize[2]
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out[:, :, k, i, j] = np.sum(x_masked, axis=(2, 3,
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4)) / field_size
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return out
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class TestPool3d_Op(OpTest):
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def setUp(self):
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self.op_type = "pool3d"
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self.use_cudnn = False
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self.dtype = np.float32
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self.init_test_case()
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self.init_global_pool()
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self.init_kernel_type()
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self.init_pool_type()
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self.init_ceil_mode()
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self.init_exclusive()
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self.init_adaptive()
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if self.global_pool:
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self.paddings = [0 for _ in range(len(self.paddings))]
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input = np.random.random(self.shape).astype(self.dtype)
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output = self.pool3D_forward_naive(
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input, self.ksize, self.strides, self.paddings, self.global_pool,
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self.ceil_mode, self.exclusive, self.adaptive).astype(self.dtype)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(input)}
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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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'pooling_type': self.pool_type,
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'global_pooling': self.global_pool,
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'use_cudnn': self.use_cudnn,
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'ceil_mode': self.ceil_mode,
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'data_format':
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'AnyLayout', # TODO(dzhwinter) : should be fix latter
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'exclusive': self.exclusive,
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'adaptive': self.adaptive
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}
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self.outputs = {'Out': output}
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def has_cudnn(self):
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return core.is_compiled_with_cuda() and self.use_cudnn
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def test_check_output(self):
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if self.has_cudnn():
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place = core.CUDAPlace(0)
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self.check_output_with_place(place, atol=1e-5)
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else:
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self.check_output()
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def test_check_grad(self):
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if self.dtype == np.float16:
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return
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if self.has_cudnn() and self.pool_type != "max":
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place = core.CUDAPlace(0)
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self.check_grad_with_place(
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place, set(['X']), 'Out', max_relative_error=0.07)
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elif self.pool_type != "max":
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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.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 = [0, 0, 0]
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def init_kernel_type(self):
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pass
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def init_pool_type(self):
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self.pool_type = "avg"
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self.pool3D_forward_naive = avg_pool3D_forward_naive
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def init_global_pool(self):
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self.global_pool = True
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def init_ceil_mode(self):
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self.ceil_mode = False
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def init_exclusive(self):
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self.exclusive = True
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def init_adaptive(self):
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self.adaptive = False
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class TestCase1(TestPool3d_Op):
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def init_test_case(self):
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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 = [1, 1, 1]
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self.paddings = [0, 0, 0]
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def init_pool_type(self):
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self.pool_type = "avg"
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self.pool3D_forward_naive = avg_pool3D_forward_naive
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def init_global_pool(self):
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self.global_pool = False
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class TestCase2(TestPool3d_Op):
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def init_test_case(self):
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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 = [1, 1, 1]
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self.paddings = [1, 1, 1]
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def init_pool_type(self):
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self.pool_type = "avg"
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self.pool3D_forward_naive = avg_pool3D_forward_naive
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def init_global_pool(self):
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self.global_pool = False
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class TestCase3(TestPool3d_Op):
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def init_pool_type(self):
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self.pool_type = "max"
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self.pool3D_forward_naive = max_pool3D_forward_naive
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class TestCase4(TestCase1):
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def init_pool_type(self):
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self.pool_type = "max"
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self.pool3D_forward_naive = max_pool3D_forward_naive
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class TestCase5(TestCase2):
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def init_pool_type(self):
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self.pool_type = "max"
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self.pool3D_forward_naive = max_pool3D_forward_naive
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#--------------------test pool3d--------------------
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class TestCUDNNCase1(TestPool3d_Op):
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def init_kernel_type(self):
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self.use_cudnn = True
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class TestFP16CUDNNCase1(TestPool3d_Op):
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def init_kernel_type(self):
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self.use_cudnn = True
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self.dtype = np.float16
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def test_check_output(self):
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_output_with_place(place, atol=1e-3)
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class TestCUDNNCase2(TestCase1):
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def init_kernel_type(self):
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self.use_cudnn = True
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class TestFP16CUDNNCase2(TestCase1):
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def init_kernel_type(self):
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self.use_cudnn = True
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self.dtype = np.float16
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def test_check_output(self):
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_output_with_place(place, atol=1e-3)
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class TestCUDNNCase3(TestCase2):
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def init_kernel_type(self):
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self.use_cudnn = True
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class TestFP16CUDNNCase3(TestCase2):
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def init_kernel_type(self):
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self.use_cudnn = True
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self.dtype = np.float16
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def test_check_output(self):
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_output_with_place(place, atol=1e-3)
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class TestCUDNNCase4(TestCase3):
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def init_kernel_type(self):
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self.use_cudnn = True
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class TestFP16CUDNNCase4(TestCase3):
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def init_kernel_type(self):
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self.use_cudnn = True
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self.dtype = np.float16
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def test_check_output(self):
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_output_with_place(place, atol=1e-3)
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class TestCUDNNCase5(TestCase4):
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def init_kernel_type(self):
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self.use_cudnn = True
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class TestFP16CUDNNCase5(TestCase4):
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def init_kernel_type(self):
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self.use_cudnn = True
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self.dtype = np.float16
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def test_check_output(self):
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_output_with_place(place, atol=1e-3)
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class TestCUDNNCase6(TestCase5):
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def init_kernel_type(self):
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self.use_cudnn = True
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class TestFP16CUDNNCase6(TestCase5):
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def init_kernel_type(self):
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self.use_cudnn = True
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self.dtype = np.float16
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def test_check_output(self):
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_output_with_place(place, atol=1e-3)
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class TestCeilModeCase1(TestCUDNNCase1):
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def init_ceil_mode(self):
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self.ceil_mode = True
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class TestCeilModeCase2(TestCUDNNCase2):
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def init_ceil_mode(self):
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self.ceil_mode = True
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class TestCeilModeCase3(TestCase1):
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def init_ceil_mode(self):
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self.ceil_mode = True
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class TestCeilModeCase4(TestCase2):
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def init_ceil_mode(self):
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self.ceil_mode = True
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class TestAvgInclude(TestCase2):
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def init_exclusive(self):
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self.exclusive = False
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class TestCUDNNAvgInclude(TestCUDNNCase3):
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def init_exclusive(self):
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self.exclusive = False
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class TestAvgPoolAdaptive(TestCase1):
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def init_adaptive(self):
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self.adaptive = True
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if __name__ == '__main__':
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unittest.main()
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