import unittest import numpy as np from op_test import OpTest class TestLRNOp(OpTest): def get_input(self): ''' TODO(gongweibao): why it's grad diff is so large? x = np.ndarray( shape=(self.N, self.C, self.H, self.W), dtype=float, order='C') for m in range(0, self.N): for i in range(0, self.C): for h in range(0, self.H): for w in range(0, self.W): x[m][i][h][w] = m * self.C * self.H * self.W + \ i * self.H * self.W + \ h * self.W + w + 1 ''' x = np.random.rand(self.N, self.C, self.H, self.W).astype("float32") return x + 1 def get_out(self): start = -(self.n - 1) / 2 end = start + self.n mid = np.empty((self.N, self.C, self.H, self.W)).astype("float32") mid.fill(self.k) for m in range(0, self.N): for i in range(0, self.C): for c in range(start, end + 1): ch = i + c if ch < 0 or ch >= self.C: continue s = mid[m][i][:][:] r = self.x[m][ch][:][:] s += np.square(r) * self.alpha mid2 = np.power(mid, -self.beta) return np.multiply(self.x, mid2), mid def get_attrs(self): attrs = { 'n': self.n, 'k': self.k, 'alpha': self.alpha, 'beta': self.beta } return attrs def setUp(self): self.op_type = "lrn" self.N = 2 self.C = 3 self.H = 5 self.W = 5 self.n = 5 self.k = 2.0 self.alpha = 0.0001 self.beta = 0.75 self.x = self.get_input() self.out, self.mid_out = self.get_out() self.inputs = {'X': self.x} self.outputs = {'Out': self.out, 'MidOut': self.mid_out} self.attrs = self.get_attrs() def test_check_output(self): self.check_output() def test_check_grad_normal(self): self.check_grad(['X'], 'Out', max_relative_error=0.01) if __name__ == "__main__": unittest.main()