import unittest import numpy as np from op_test import OpTest from test_pool2d_op import max_pool2D_forward_naive from test_pool2d_op import avg_pool2D_forward_naive class TestSppOp(OpTest): def setUp(self): self.op_type = "spp" self.init_test_case() input = np.random.random(self.shape).astype("float32") nsize, csize, hsize, wsize = input.shape out_level_flatten = [] for i in xrange(self.pyramid_height): bins = np.power(2, i) kernel_size = [0, 0] padding = [0, 0] kernel_size[0] = np.ceil(hsize / bins.astype("double")).astype("int32") padding[0] = ( (kernel_size[0] * bins - hsize + 1) / 2).astype("int32") kernel_size[1] = np.ceil(wsize / bins.astype("double")).astype("int32") padding[1] = ( (kernel_size[1] * bins - wsize + 1) / 2).astype("int32") out_level = self.pool2D_forward_naive(input, kernel_size, kernel_size, padding) out_level_flatten.append( out_level.reshape(nsize, bins * bins * csize)) if i == 0: output = out_level_flatten[i] else: output = np.concatenate((output, out_level_flatten[i]), 1) # output = np.concatenate(out_level_flatten.tolist(), 0); self.inputs = {'X': input.astype('float32'), } self.attrs = { 'pyramid_height': self.pyramid_height, 'pooling_type': self.pool_type } self.outputs = {'Out': output.astype('float32')} def test_check_output(self): self.check_output() def test_check_grad(self): if self.pool_type != "avg": self.check_grad(['X'], 'Out', max_relative_error=0.05) def init_test_case(self): self.shape = [3, 2, 4, 4] self.pyramid_height = 3 self.pool2D_forward_naive = max_pool2D_forward_naive self.pool_type = "max" class TestCase2(TestSppOp): def init_test_case(self): self.shape = [3, 2, 4, 4] self.pyramid_height = 3 self.pool2D_forward_naive = avg_pool2D_forward_naive self.pool_type = "avg" if __name__ == '__main__': unittest.main()