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92 lines
2.4 KiB
92 lines
2.4 KiB
import unittest
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import numpy as np
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from op_test import OpTest
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def crop(data, offsets, crop_shape):
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def indexOf(shape, index):
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result = []
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for dim in reversed(shape):
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result.append(index % dim)
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index = index / dim
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return result[::-1]
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result = []
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for i, value in enumerate(data.flatten()):
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index = indexOf(data.shape, i)
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selected = True
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if len(index) == len(offsets):
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for j, offset in enumerate(offsets):
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selected = selected and index[j] >= offset and index[
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j] < crop_shape[j] + offset
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if selected:
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result.append(value)
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return np.array(result).reshape(crop_shape)
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class TestCropOp(OpTest):
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def setUp(self):
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self.op_type = "crop"
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self.crop_by_input = False
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self.attrs = {}
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self.initTestCase()
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self.attrs['offsets'] = self.offsets
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if self.crop_by_input:
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self.inputs = {
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'X': np.random.random(self.x_shape).astype("float32"),
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'Y': np.random.random(self.crop_shape).astype("float32")
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}
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else:
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self.attrs['shape'] = self.crop_shape
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self.inputs = {
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'X': np.random.random(self.x_shape).astype("float32"),
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}
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self.outputs = {
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'Out': crop(self.inputs['X'], self.offsets, self.crop_shape)
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}
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def initTestCase(self):
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self.x_shape = (8, 8)
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self.crop_shape = (2, 2)
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self.offsets = [1, 2]
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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(['X'], 'Out', max_relative_error=0.006)
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class TestCase1(TestCropOp):
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def initTestCase(self):
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self.x_shape = (16, 8, 32)
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self.crop_shape = [2, 2, 3]
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self.offsets = [1, 5, 3]
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class TestCase2(TestCropOp):
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def initTestCase(self):
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self.x_shape = (4, 8)
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self.crop_shape = [4, 8]
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self.offsets = [0, 0]
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class TestCase3(TestCropOp):
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def initTestCase(self):
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self.x_shape = (4, 8, 16)
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self.crop_shape = [2, 2, 3]
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self.offsets = [1, 5, 3]
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self.crop_by_input = True
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class TestCase4(TestCropOp):
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def initTestCase(self):
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self.x_shape = (4, 4)
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self.crop_shape = [4, 4]
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self.offsets = [0, 0]
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self.crop_by_input = True
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if __name__ == '__main__':
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unittest.main()
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