From e553d5728d52f4dd2ebc11228053ed31da05a62c Mon Sep 17 00:00:00 2001 From: sweetsky0901 Date: Wed, 22 Nov 2017 15:59:02 +0800 Subject: [PATCH] format test code --- .../paddle/v2/fluid/tests/test_unpool_op.py | 27 ++++++++++++------- 1 file changed, 18 insertions(+), 9 deletions(-) diff --git a/python/paddle/v2/fluid/tests/test_unpool_op.py b/python/paddle/v2/fluid/tests/test_unpool_op.py index b1ddf95ac..106af9f5d 100644 --- a/python/paddle/v2/fluid/tests/test_unpool_op.py +++ b/python/paddle/v2/fluid/tests/test_unpool_op.py @@ -15,7 +15,8 @@ def unpool2dmax_forward_naive(input, indices, ksize, strides, paddings): index = indices[nidx, cidx, h, w] hidx = (index - index % out_W) / out_W widx = index % out_W - out[nidx, cidx, int(hidx), int(widx)] = input[nidx, cidx, h, w] + out[nidx, cidx, int(hidx), int(widx)] = \ + input[nidx, cidx, h, w] return out @@ -26,23 +27,31 @@ class TestUnpoolOp(OpTest): self.init_test_case() pre_input = np.random.random(self.shape).astype("float32") N, C, H, W = pre_input.shape - H_out = (H - self.ksize[0] + 2 * self.paddings[0]) / self.strides[0] + 1 - W_out = (W - self.ksize[1] + 2 * self.paddings[1]) / self.strides[1] + 1 + H_out = (H - self.ksize[0] + 2 * self.paddings[0]) / \ + self.strides[0] + 1 + W_out = (W - self.ksize[1] + 2 * self.paddings[1]) / \ + self.strides[1] + 1 input = np.zeros((N, C, H_out, W_out)) indices = np.zeros((N, C, H_out, W_out)) for i in xrange(H_out): for j in xrange(W_out): r_start = np.max((i * self.strides[0] - self.paddings[0], 0)) - r_end = np.min((i * self.strides[0] + self.ksize[0] - self.paddings[0], H)) + r_end = np.min((i * self.strides[0] + self.ksize[0] - \ + self.paddings[0], H)) c_start = np.max((j * self.strides[1] - self.paddings[1], 0)) - c_end = np.min((j * self.strides[1] + self.ksize[1] - self.paddings[1], W)) + c_end = np.min((j * self.strides[1] + self.ksize[1] - \ + self.paddings[1], W)) for nidx in xrange(N): for cidx in xrange(C): - x_masked = pre_input[nidx, cidx, r_start:r_end, c_start:c_end] + x_masked = pre_input[nidx, cidx, r_start:r_end, \ + c_start:c_end] input[nidx, cidx, i, j] = x_masked.max() arg = x_masked.argmax() - indices[nidx, cidx, i, j] = (r_start + arg / self.ksize[1]) * W + c_start + arg % self.ksize[1] - output = self.Unpool2d_forward_naive(input, indices, self.ksize, self.strides, self.paddings).astype("float32") + indices[nidx, cidx, i, j] = \ + (r_start + arg / self.ksize[1]) * W + \ + c_start + arg % self.ksize[1] + output = self.Unpool2d_forward_naive(input, indices, self.ksize, \ + self.strides, self.paddings).astype("float32") self.inputs = {'X': input.astype('float32'), 'Y': indices.astype('int16')} self.attrs = { @@ -57,7 +66,7 @@ class TestUnpoolOp(OpTest): self.check_output() def test_check_grad(self): - self.check_grad(['X'], 'Out', max_relative_error=0.5) + self.check_grad(['X'], 'Out') def init_test_case(self): self.Unpool2d_forward_naive = unpool2dmax_forward_naive -- GitLab