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