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127 lines
3.8 KiB
127 lines
3.8 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 bilinear_interp_np(input, out_h, out_w, out_size):
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if out_size is not None:
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out_h = out_size[0]
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out_w = out_size[1]
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batch_size, channel, in_h, in_w = input.shape
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if out_h > 1:
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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 = 0.0
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if out_w > 1:
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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 = 0.0
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out = np.zeros((batch_size, channel, out_h, out_w))
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for i in range(out_h):
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h = int(ratio_h * i)
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hid = 1 if h < in_h - 1 else 0
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h1lambda = ratio_h * i - h
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h2lambda = 1.0 - h1lambda
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for j in range(out_w):
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w = int(ratio_w * j)
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wid = 1 if w < in_w - 1 else 0
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w1lambda = ratio_w * j - w
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w2lambda = 1.0 - w1lambda
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out[:, :, i, j] = h2lambda*(w2lambda*input[:, :, h, w] +
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w1lambda*input[:, :, h, w+wid]) + \
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h1lambda*(w2lambda*input[:, :, h+hid, w] +
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w1lambda*input[:, :, h+hid, w+wid])
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return out.astype("float32")
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class TestBilinearInterpOp(OpTest):
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def setUp(self):
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self.out_size = None
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self.init_test_case()
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self.op_type = "bilinear_interp"
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input_np = np.random.random(self.input_shape).astype("float32")
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output_np = bilinear_interp_np(input_np, self.out_h, self.out_w,
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self.out_size)
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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 = {'out_h': self.out_h, 'out_w': self.out_w}
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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.input_shape = [2, 3, 4, 4]
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self.out_h = 2
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self.out_w = 2
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self.out_size = np.array([3, 3]).astype("int32")
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class TestCase1(TestBilinearInterpOp):
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def init_test_case(self):
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self.input_shape = [4, 1, 7, 8]
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self.out_h = 1
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self.out_w = 1
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class TestCase2(TestBilinearInterpOp):
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def init_test_case(self):
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self.input_shape = [3, 3, 9, 6]
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self.out_h = 12
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self.out_w = 12
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class TestCase3(TestBilinearInterpOp):
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def init_test_case(self):
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self.input_shape = [1, 1, 128, 64]
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self.out_h = 64
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self.out_w = 128
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class TestCase4(TestBilinearInterpOp):
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def init_test_case(self):
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self.input_shape = [4, 1, 7, 8]
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self.out_h = 1
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self.out_w = 1
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self.out_size = np.array([2, 2]).astype("int32")
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class TestCase5(TestBilinearInterpOp):
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def init_test_case(self):
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self.input_shape = [3, 3, 9, 6]
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self.out_h = 12
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self.out_w = 12
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self.out_size = np.array([11, 11]).astype("int32")
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class TestCase6(TestBilinearInterpOp):
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def init_test_case(self):
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self.input_shape = [1, 1, 128, 64]
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self.out_h = 64
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self.out_w = 128
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self.out_size = np.array([65, 129]).astype("int32")
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if __name__ == "__main__":
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unittest.main()
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