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641 lines
21 KiB
641 lines
21 KiB
# Copyright (c) 2019 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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import unittest
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import numpy as np
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from op_test import OpTest
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import paddle.fluid.core as core
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import paddle.fluid as fluid
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def trilinear_interp_np(input,
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out_d,
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out_h,
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out_w,
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out_size=None,
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actual_shape=None,
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align_corners=True,
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align_mode=0,
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data_layout='NCDHW'):
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"""trilinear interpolation implement in shape [N, C, D, H, W]"""
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if data_layout == "NDHWC":
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input = np.transpose(input, (0, 4, 1, 2, 3)) # NDHWC => NCDHW
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if out_size is not None:
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out_d = out_size[0]
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out_h = out_size[1]
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out_w = out_size[2]
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if actual_shape is not None:
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out_d = actual_shape[0]
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out_h = actual_shape[1]
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out_w = actual_shape[2]
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batch_size, channel, in_d, in_h, in_w = input.shape
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ratio_d = ratio_h = ratio_w = 0.0
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if out_d > 1:
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if (align_corners):
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ratio_d = (in_d - 1.0) / (out_d - 1.0)
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else:
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ratio_d = 1.0 * in_d / out_d
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if out_h > 1:
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if (align_corners):
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ratio_h = (in_h - 1.0) / (out_h - 1.0)
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else:
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ratio_h = 1.0 * in_h / out_h
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if out_w > 1:
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if (align_corners):
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ratio_w = (in_w - 1.0) / (out_w - 1.0)
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else:
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ratio_w = 1.0 * in_w / out_w
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out = np.zeros((batch_size, channel, out_d, out_h, out_w))
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for i in range(out_d):
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if (align_mode == 0 and not align_corners):
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d = int(ratio_d * (i + 0.5) - 0.5)
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else:
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d = int(ratio_d * i)
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d = max(0, d)
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did = 1 if d < in_d - 1 else 0
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if (align_mode == 0 and not align_corners):
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idx_src_d = max(ratio_d * (i + 0.5) - 0.5, 0)
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d1lambda = idx_src_d - d
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else:
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d1lambda = ratio_d * i - d
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d2lambda = 1.0 - d1lambda
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for j in range(out_h):
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if (align_mode == 0 and not align_corners):
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h = int(ratio_h * (j + 0.5) - 0.5)
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else:
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h = int(ratio_h * j)
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h = max(0, h)
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hid = 1 if h < in_h - 1 else 0
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if (align_mode == 0 and not align_corners):
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idx_src_h = max(ratio_h * (j + 0.5) - 0.5, 0)
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h1lambda = idx_src_h - h
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else:
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h1lambda = ratio_h * j - h
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h2lambda = 1.0 - h1lambda
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for k in range(out_w):
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if (align_mode == 0 and not align_corners):
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w = int(ratio_w * (k + 0.5) - 0.5)
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else:
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w = int(ratio_w * k)
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w = max(0, w)
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wid = 1 if w < in_w - 1 else 0
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if (align_mode == 0 and not align_corners):
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idx_src_w = max(ratio_w * (k + 0.5) - 0.5, 0)
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w1lambda = idx_src_w - w
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else:
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w1lambda = ratio_w * k - w
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w2lambda = 1.0 - w1lambda
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out[:, :, i, j, k] = \
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d2lambda * \
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(h2lambda * (w2lambda * input[:, :, d, h, w] + \
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w1lambda * input[:, :, d, h, w+wid]) + \
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h1lambda * (w2lambda * input[:, :, d, h+hid, w] + \
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w1lambda * input[:, :, d, h+hid, w+wid])) + \
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d1lambda * \
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(h2lambda * (w2lambda * input[:, :, d+did, h, w] + \
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w1lambda * input[:, :, d+did, h, w+wid]) + \
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h1lambda * (w2lambda * input[:, :, d+did, h+hid, w] + \
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w1lambda * input[:, :, d+did, h+hid, w+wid]))
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if data_layout == "NDHWC":
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out = np.transpose(out, (0, 2, 3, 4, 1)) # NCDHW => NDHWC
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return out.astype(input.dtype)
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class TestTrilinearInterpOp(OpTest):
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def setUp(self):
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self.out_size = None
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self.actual_shape = None
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self.data_layout = 'NCDHW'
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self.init_test_case()
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self.op_type = "trilinear_interp"
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input_np = np.random.random(self.input_shape).astype("float32")
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if self.data_layout == "NCDHW":
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in_d = self.input_shape[2]
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in_h = self.input_shape[3]
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in_w = self.input_shape[4]
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else:
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in_d = self.input_shape[1]
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in_h = self.input_shape[2]
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in_w = self.input_shape[3]
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if self.scale > 0:
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out_d = int(in_d * self.scale)
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out_h = int(in_h * self.scale)
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out_w = int(in_w * self.scale)
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else:
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out_d = self.out_d
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out_h = self.out_h
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out_w = self.out_w
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output_np = trilinear_interp_np(
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input_np, out_d, out_h, out_w, self.out_size, self.actual_shape,
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self.align_corners, self.align_mode, self.data_layout)
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self.inputs = {'X': input_np}
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if self.out_size is not None:
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self.inputs['OutSize'] = self.out_size
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if self.actual_shape is not None:
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self.inputs['OutSize'] = self.actual_shape
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# c++ end treat NCDHW the same way as NCHW
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if self.data_layout == 'NCDHW':
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data_layout = 'NCHW'
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else:
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data_layout = 'NHWC'
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self.attrs = {
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'out_d': self.out_d,
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'out_h': self.out_h,
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'out_w': self.out_w,
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'scale': self.scale,
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'interp_method': self.interp_method,
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'align_corners': self.align_corners,
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'align_mode': self.align_mode,
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'data_layout': data_layout
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}
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self.outputs = {'Out': output_np}
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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(['X'], 'Out', in_place=True)
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def init_test_case(self):
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self.interp_method = 'trilinear'
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self.input_shape = [2, 3, 4, 4, 4]
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self.out_d = 2
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self.out_h = 2
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self.out_w = 2
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self.scale = 0.
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self.out_size = np.array([3, 3, 3]).astype("int32")
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self.align_corners = True
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self.align_mode = 1
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class TestTrilinearInterpCase1(TestTrilinearInterpOp):
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def init_test_case(self):
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self.interp_method = 'trilinear'
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self.input_shape = [2, 1, 7, 8, 9]
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self.out_d = 1
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self.out_h = 1
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self.out_w = 1
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self.scale = 0.
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self.align_corners = True
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self.align_mode = 1
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class TestTrilinearInterpCase2(TestTrilinearInterpOp):
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def init_test_case(self):
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self.interp_method = 'trilinear'
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self.input_shape = [2, 3, 9, 6, 8]
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self.out_d = 12
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self.out_h = 12
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self.out_w = 12
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self.scale = 0.
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self.align_corners = True
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self.align_mode = 1
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class TestTrilinearInterpCase3(TestTrilinearInterpOp):
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def init_test_case(self):
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self.interp_method = 'trilinear'
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self.input_shape = [3, 2, 16, 8, 4]
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self.out_d = 32
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self.out_h = 16
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self.out_w = 8
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self.scale = 0.
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self.align_corners = True
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self.align_mode = 1
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class TestTrilinearInterpCase4(TestTrilinearInterpOp):
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def init_test_case(self):
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self.interp_method = 'trilinear'
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self.input_shape = [4, 1, 7, 8, 9]
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self.out_d = 1
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self.out_h = 1
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self.out_w = 1
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self.scale = 0.
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self.out_size = np.array([2, 2, 2]).astype("int32")
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self.align_corners = True
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self.align_mode = 1
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class TestTrilinearInterpCase5(TestTrilinearInterpOp):
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def init_test_case(self):
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self.interp_method = 'trilinear'
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self.input_shape = [3, 3, 9, 6, 8]
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self.out_d = 12
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self.out_h = 12
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self.out_w = 12
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self.scale = 0.
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self.out_size = np.array([11, 11, 11]).astype("int32")
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self.align_corners = True
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self.align_mode = 1
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class TestTrilinearInterpCase6(TestTrilinearInterpOp):
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def init_test_case(self):
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self.interp_method = 'trilinear'
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self.input_shape = [1, 1, 16, 8, 4]
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self.out_d = 8
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self.out_h = 32
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self.out_w = 16
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self.scale = 0.
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self.out_size = np.array([17, 9, 5]).astype("int32")
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self.align_corners = True
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self.align_mode = 1
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class TestTrilinearInterpSame(TestTrilinearInterpOp):
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def init_test_case(self):
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self.interp_method = 'trilinear'
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self.input_shape = [1, 1, 16, 8, 4]
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self.out_d = 16
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self.out_h = 8
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self.out_w = 4
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self.scale = 0.
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self.align_corners = True
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self.align_mode = 1
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class TestTrilinearInterpSameHW(TestTrilinearInterpOp):
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def init_test_case(self):
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self.interp_method = 'trilinear'
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self.input_shape = [1, 1, 16, 8, 4]
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self.out_d = 8
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self.out_h = 8
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self.out_w = 4
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self.scale = 0.
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self.align_corners = True
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self.align_mode = 1
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class TestTrilinearInterpActualShape(TestTrilinearInterpOp):
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def init_test_case(self):
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self.interp_method = 'trilinear'
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self.input_shape = [3, 2, 16, 8, 4]
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self.out_d = 64
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self.out_h = 32
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self.out_w = 16
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self.scale = 0.
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self.out_size = np.array([33, 19, 7]).astype("int32")
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self.align_corners = True
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self.align_mode = 1
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class TestTrilinearInterpDatalayout(TestTrilinearInterpOp):
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def init_test_case(self):
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self.interp_method = 'trilinear'
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self.input_shape = [2, 4, 4, 4, 3]
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self.out_d = 2
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self.out_h = 2
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self.out_w = 2
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self.scale = 0.
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self.out_size = np.array([3, 3, 3]).astype("int32")
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self.align_corners = True
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self.align_mode = 1
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self.data_layout = "NDHWC"
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class TestTrilinearInterpOpUint8(OpTest):
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def setUp(self):
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self.out_size = None
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self.actual_shape = None
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self.init_test_case()
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self.op_type = "trilinear_interp"
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input_np = np.random.randint(
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low=0, high=256, size=self.input_shape).astype("uint8")
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if self.scale > 0:
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out_d = int(self.input_shape[2] * self.scale)
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out_h = int(self.input_shape[3] * self.scale)
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out_w = int(self.input_shape[4] * self.scale)
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else:
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out_d = self.out_d
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out_h = self.out_h
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out_w = self.out_w
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output_np = trilinear_interp_np(input_np, out_d, out_h, out_w,
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self.out_size, self.actual_shape,
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self.align_corners, self.align_mode)
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self.inputs = {'X': input_np}
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if self.out_size is not None:
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self.inputs['OutSize'] = self.out_size
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self.attrs = {
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'out_d': self.out_d,
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'out_h': self.out_h,
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'out_w': self.out_w,
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'scale': self.scale,
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'interp_method': self.interp_method,
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'align_corners': self.align_corners,
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'align_mode': self.align_mode
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}
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self.outputs = {'Out': output_np}
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def test_check_output(self):
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self.check_output_with_place(place=core.CPUPlace(), atol=1)
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def init_test_case(self):
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self.interp_method = 'trilinear'
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self.input_shape = [1, 3, 9, 6, 8]
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self.out_d = 13
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self.out_h = 10
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self.out_w = 9
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self.scale = 0.
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self.align_corners = True
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self.align_mode = 1
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class TestTrilinearInterpCase1Uint8(TestTrilinearInterpOpUint8):
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def init_test_case(self):
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self.interp_method = 'trilinear'
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self.input_shape = [2, 3, 16, 8, 4]
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self.out_d = 13
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self.out_h = 7
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self.out_w = 2
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self.scale = 0.
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self.align_corners = True
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self.align_mode = 1
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class TestTrilinearInterpCase2Uint8(TestTrilinearInterpOpUint8):
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def init_test_case(self):
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self.interp_method = 'trilinear'
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self.input_shape = [4, 1, 7, 8, 9]
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self.out_d = 3
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self.out_h = 5
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self.out_w = 13
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self.scale = 0.
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self.out_size = np.array([6, 15, 21]).astype("int32")
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self.align_corners = True
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self.align_mode = 1
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class TestTrilinearInterpOtherMethod1(TestTrilinearInterpOp):
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def set_align_mode(self):
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self.align_corners = False
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self.align_mode = 1
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class TestTrilinearInterpWithMethod2(TestTrilinearInterpOp):
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def set_align_mode(self):
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self.align_corners = False
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self.align_mode = 0
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class TestTrilinearInterpWithMethod3(TestTrilinearInterpOp):
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def set_align_mode(self):
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self.align_corners = True
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self.align_mode = 0
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class TestTrilinearInterpScale1(TestTrilinearInterpOp):
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def init_test_case(self):
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self.interp_method = 'trilinear'
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self.input_shape = [2, 3, 5, 7, 9]
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self.out_d = 82
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self.out_h = 60
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self.out_w = 25
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self.scale = 2.
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self.align_corners = True
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self.align_mode = 1
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class TestTrilinearInterpScale2(TestTrilinearInterpOp):
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def init_test_case(self):
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self.interp_method = 'trilinear'
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self.input_shape = [2, 3, 5, 7, 9]
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self.out_d = 82
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self.out_h = 60
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self.out_w = 25
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self.scale = 1.
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self.align_corners = True
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self.align_mode = 1
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class TestTrilinearInterpScale3(TestTrilinearInterpOp):
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def init_test_case(self):
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self.interp_method = 'trilinear'
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self.input_shape = [2, 3, 5, 7, 9]
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self.out_d = 82
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self.out_h = 60
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self.out_w = 25
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self.scale = 1.5
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self.align_corners = True
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self.align_mode = 1
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class TestTrilinearInterpZero(TestTrilinearInterpOp):
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def init_test_case(self):
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self.interp_method = 'trilinear'
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self.input_shape = [2, 3, 5, 7, 11]
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self.out_d = 82
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self.out_h = 60
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self.out_w = 25
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self.scale = 0.2
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self.align_corners = False
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self.align_mode = 0
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class TestTrilinearInterpOp_attr_tensor(OpTest):
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def setUp(self):
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self.out_size = None
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self.actual_shape = None
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self.init_test_case()
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self.op_type = "trilinear_interp"
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self.shape_by_1Dtensor = False
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self.scale_by_1Dtensor = False
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self.attrs = {
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'interp_method': self.interp_method,
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'align_corners': self.align_corners,
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'align_mode': self.align_mode
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}
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input_np = np.random.random(self.input_shape).astype("float32")
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self.inputs = {'X': input_np}
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if self.scale_by_1Dtensor:
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self.inputs['Scale'] = np.array([self.scale]).astype("float32")
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elif self.scale > 0:
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out_d = int(self.input_shape[2] * self.scale)
|
|
out_h = int(self.input_shape[3] * self.scale)
|
|
out_w = int(self.input_shape[4] * self.scale)
|
|
self.attrs['scale'] = self.scale
|
|
else:
|
|
out_d = self.out_d
|
|
out_h = self.out_h
|
|
out_w = self.out_w
|
|
|
|
if self.shape_by_1Dtensor:
|
|
self.inputs['OutSize'] = self.out_size
|
|
elif self.out_size is not None:
|
|
size_tensor = []
|
|
for index, ele in enumerate(self.out_size):
|
|
size_tensor.append(("x" + str(index), np.ones(
|
|
(1)).astype('int32') * ele))
|
|
self.inputs['SizeTensor'] = size_tensor
|
|
|
|
self.attrs['out_d'] = self.out_d
|
|
self.attrs['out_h'] = self.out_h
|
|
self.attrs['out_w'] = self.out_w
|
|
output_np = trilinear_interp_np(input_np, out_d, out_h, out_w,
|
|
self.out_size, self.actual_shape,
|
|
self.align_corners, self.align_mode)
|
|
self.outputs = {'Out': output_np}
|
|
|
|
def test_check_output(self):
|
|
self.check_output()
|
|
|
|
def test_check_grad(self):
|
|
self.check_grad(['X'], 'Out', in_place=True)
|
|
|
|
def init_test_case(self):
|
|
self.interp_method = 'trilinear'
|
|
self.input_shape = [2, 3, 4, 4, 4]
|
|
self.out_d = 2
|
|
self.out_h = 3
|
|
self.out_w = 3
|
|
self.scale = 0.
|
|
self.out_size = [2, 3, 3]
|
|
self.align_corners = True
|
|
self.align_mode = 1
|
|
|
|
|
|
# out_size is a 1-D tensor
|
|
class TestTrilinearInterp_attr_tensor_Case1(TestTrilinearInterpOp_attr_tensor):
|
|
def init_test_case(self):
|
|
self.interp_method = 'trilinear'
|
|
self.input_shape = [3, 2, 9, 6, 8]
|
|
self.out_d = 32
|
|
self.out_h = 16
|
|
self.out_w = 8
|
|
self.scale = 0.3
|
|
self.out_size = [12, 4, 4]
|
|
self.align_corners = True
|
|
self.align_mode = 1
|
|
|
|
|
|
# scale is a 1-D tensor
|
|
class TestTrilinearInterp_attr_tensor_Case2(TestTrilinearInterpOp_attr_tensor):
|
|
def init_test_case(self):
|
|
self.interp_method = 'trilinear'
|
|
self.input_shape = [2, 3, 8, 8, 4]
|
|
self.out_d = 16
|
|
self.out_h = 12
|
|
self.out_w = 4
|
|
self.scale = 0.
|
|
self.out_size = [16, 4, 10]
|
|
self.align_corners = True
|
|
self.align_mode = 1
|
|
self.shape_by_1Dtensor = True
|
|
|
|
|
|
# scale is a 1-D tensor
|
|
class TestTrilinearInterp_attr_tensor_Case3(TestTrilinearInterpOp_attr_tensor):
|
|
def init_test_case(self):
|
|
self.interp_method = 'trilinear'
|
|
self.input_shape = [2, 3, 8, 8, 4]
|
|
self.out_d = 16
|
|
self.out_h = 16
|
|
self.out_w = 8
|
|
self.scale = 2.0
|
|
self.out_size = None
|
|
self.align_corners = True
|
|
self.align_mode = 1
|
|
self.scale_by_1Dtensor = True
|
|
|
|
|
|
class TestTrilinearInterpAPI(OpTest):
|
|
def test_case(self):
|
|
x = fluid.layers.data(name="x", shape=[3, 6, 9, 4], dtype="float32")
|
|
y = fluid.layers.data(name="y", shape=[6, 9, 4, 3], dtype="float32")
|
|
|
|
dim = fluid.layers.data(name="dim", shape=[1], dtype="int32")
|
|
shape_tensor = fluid.layers.data(
|
|
name="shape_tensor",
|
|
shape=[3],
|
|
dtype="int32",
|
|
append_batch_size=False)
|
|
actual_size = fluid.layers.data(
|
|
name="actual_size",
|
|
shape=[3],
|
|
dtype="int32",
|
|
append_batch_size=False)
|
|
scale_tensor = fluid.layers.data(
|
|
name="scale_tensor",
|
|
shape=[1],
|
|
dtype="float32",
|
|
append_batch_size=False)
|
|
|
|
out1 = fluid.layers.resize_trilinear(
|
|
y, out_shape=[12, 18, 8], data_format='NDHWC')
|
|
out2 = fluid.layers.resize_trilinear(x, out_shape=[12, dim, 8])
|
|
out3 = fluid.layers.resize_trilinear(x, out_shape=shape_tensor)
|
|
out4 = fluid.layers.resize_trilinear(
|
|
x, out_shape=[4, 4, 8], actual_shape=actual_size)
|
|
out5 = fluid.layers.resize_trilinear(x, scale=scale_tensor)
|
|
|
|
x_data = np.random.random((1, 3, 6, 9, 4)).astype("float32")
|
|
dim_data = np.array([18]).astype("int32")
|
|
shape_data = np.array([12, 18, 8]).astype("int32")
|
|
actual_size_data = np.array([12, 18, 8]).astype("int32")
|
|
scale_data = np.array([2.0]).astype("float32")
|
|
|
|
place = core.CPUPlace()
|
|
exe = fluid.Executor(place)
|
|
results = exe.run(fluid.default_main_program(),
|
|
feed={
|
|
"x": x_data,
|
|
"y": np.transpose(x_data, (0, 2, 3, 4, 1)),
|
|
"dim": dim_data,
|
|
"shape_tensor": shape_data,
|
|
"actual_size": actual_size_data,
|
|
"scale_tensor": scale_data
|
|
},
|
|
fetch_list=[out1, out2, out3, out4, out5],
|
|
return_numpy=True)
|
|
|
|
expect_res = trilinear_interp_np(
|
|
x_data, out_d=12, out_h=18, out_w=8, align_mode=1)
|
|
self.assertTrue(
|
|
np.allclose(results[0], np.transpose(expect_res, (0, 2, 3, 4, 1))))
|
|
for i in range(len(results) - 1):
|
|
self.assertTrue(np.allclose(results[i + 1], expect_res))
|
|
|
|
def test_exception(self):
|
|
input = fluid.layers.data(
|
|
name="input", shape=[3, 6, 9, 4], dtype="float32")
|
|
try:
|
|
# for 5-D input, data_format only can be NCDHW or NDHWC
|
|
out = fluid.layers.resize_trilinear(
|
|
input, out_shape=[4, 8, 4], data_format='NHWC')
|
|
except:
|
|
pass
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|