sweetsky0901
7 years ago
parent
822f28343b
commit
90f664d0b0
@ -1,47 +0,0 @@
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import unittest
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import numpy as np
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from op_test import OpTest
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def maxout_forward_naive(input, groups):
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s0, s1, s2, s3 = input.shape
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return np.ndarray([s0, s1 / groups, groups, s2, s3], \
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buffer = input, dtype=input.dtype).max(axis=(2))
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class TestUnpool2dOp(OpTest):
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def setUp(self):
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self.op_type = "unpool2d"
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self.init_test_case()
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input = np.random.random(self.shape).astype("float32")
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output = self.MaxOut_forward_naive(input, self.groups).astype("float32")
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self.inputs = {'X': input}
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self.attrs = {
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'strides': self.strides,
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'paddings': self.paddings,
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'ksize': self.ksize,
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'unpooling_type': self.pool_type,
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}
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self.outputs = {'Out': output.astype('float32')}
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def init_pool_type(self):
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self.pool_type = "max"
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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def init_test_case(self):
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self.MaxOut_forward_naive = maxout_forward_naive
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self.shape = [100, 6, 2, 2]
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self.groups=2
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if __name__ == '__main__':
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unittest.main()
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@ -0,0 +1,74 @@
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import unittest
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import numpy as np
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from op_test import OpTest
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def unpool2dmax_forward_naive(input, indices, ksize, strides, paddings):
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s0, s1, s2, s3 = input.shape
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out_H=(s2 - 1) * strides[0] - 2 * paddings[0] + ksize[0]
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out_W=(s2 - 1) * strides[1] - 2 * paddings[1] + ksize[1]
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out = np.zeros((s0, s1, out_H, out_W))
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for nidx in xrange(s0):
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for cidx in xrange(s1):
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for h in xrange(s2):
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for w in xrange(s3):
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index = indices[nidx, cidx, h, w]
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hidx = (index - index % out_W) / out_W
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widx = index % out_W
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out[nidx, cidx, int(hidx), int(widx)] = input[nidx, cidx, h, w]
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return out
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class TestUnpoolOp(OpTest):
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def setUp(self):
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self.op_type = "unpool"
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self.init_test_case()
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pre_input = np.random.random(self.shape).astype("float32")
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N, C, H, W = pre_input.shape
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H_out = (H - self.ksize[0] + 2 * self.paddings[0]) / self.strides[0] + 1
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W_out = (W - self.ksize[1] + 2 * self.paddings[1]) / self.strides[1] + 1
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input = np.zeros((N, C, H_out, W_out))
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indices = np.zeros((N, C, H_out, W_out))
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for i in xrange(H_out):
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for j in xrange(W_out):
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r_start = np.max((i * self.strides[0] - self.paddings[0], 0))
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r_end = np.min((i * self.strides[0] + self.ksize[0] - self.paddings[0], H))
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c_start = np.max((j * self.strides[1] - self.paddings[1], 0))
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c_end = np.min((j * self.strides[1] + self.ksize[1] - self.paddings[1], W))
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for nidx in xrange(N):
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for cidx in xrange(C):
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x_masked = pre_input[nidx, cidx, r_start:r_end, c_start:c_end]
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input[nidx, cidx, i, j] = x_masked.max()
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arg = x_masked.argmax()
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indices[nidx, cidx, i, j] = (r_start + arg / self.ksize[1]) * W + c_start + arg % self.ksize[1]
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output = self.Unpool2d_forward_naive(input, indices, self.ksize, self.strides, self.paddings).astype("float32")
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self.inputs = {'X': input.astype('float32'),
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'Y': indices.astype('int16')}
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self.attrs = {
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'strides': self.strides,
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'paddings': self.paddings,
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'ksize': self.ksize,
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'unpoolingtype': self.unpoolingtype,
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}
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self.outputs = {'Out': output.astype('float32')}
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def test_check_output(self):
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print self.outputs['Out']
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out', max_relative_error=0.5)
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def init_test_case(self):
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self.Unpool2d_forward_naive = unpool2dmax_forward_naive
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self.unpoolingtype = "max"
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self.shape = [10, 2, 5, 5]
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self.ksize = [3, 3]
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self.strides = [2, 2]
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self.paddings = [0, 0]
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if __name__ == '__main__':
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unittest.main()
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