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119 lines
3.8 KiB
119 lines
3.8 KiB
import unittest
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import numpy as np
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from op_test import OpTest
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def conv3dtranspose_forward_naive(input_, filter_, conv3dtranspose_param):
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in_n, in_c, in_d, in_h, in_w = input_.shape
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f_c, out_c, f_d, f_h, f_w = filter_.shape
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assert in_c == f_c
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stride, pad = conv3dtranspose_param['stride'], conv3dtranspose_param['pad']
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out_d = (in_d - 1) * stride[0] + f_d
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out_h = (in_h - 1) * stride[1] + f_h
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out_w = (in_w - 1) * stride[2] + f_w
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out = np.zeros((in_n, out_c, out_d, out_h, out_w))
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for n in range(in_n):
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for d in range(in_d):
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for i in range(in_h):
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for j in range(in_w):
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input_masked = input_[n, :, d, i, j] # (c)
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input_masked = np.reshape(input_masked, (in_c, 1, 1, 1))
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input_masked = np.tile(input_masked, (1, f_d, f_h, f_w))
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for k in range(out_c):
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tmp_out = np.sum(input_masked * filter_[:, k, :, :, :],
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axis=0)
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d1, d2 = d * stride[0], d * stride[0] + f_d
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i1, i2 = i * stride[1], i * stride[1] + f_h
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j1, j2 = j * stride[2], j * stride[2] + f_w
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out[n, k, d1:d2, i1:i2, j1:j2] += tmp_out
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out = out[:, :, pad[0]:out_d - pad[0], pad[1]:out_h - pad[1], pad[2]:out_w -
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pad[2]]
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return out
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class TestConv3dTransposeOp(OpTest):
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def setUp(self):
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# init as conv transpose
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self.init_op_type()
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self.init_test_case()
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conv3dtranspose_param = {'stride': self.stride, 'pad': self.pad}
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input_ = np.random.random(self.input_size).astype("float32")
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filter_ = np.random.random(self.filter_size).astype("float32")
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output = conv3dtranspose_forward_naive(
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input_, filter_, conv3dtranspose_param).astype("float32")
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self.inputs = {'Input': input_, 'Filter': filter_}
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self.attrs = {
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'strides': self.stride,
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'paddings': self.pad,
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# 'dilations': self.dilations
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}
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self.outputs = {'Output': output}
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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(
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set(['Input', 'Filter']), 'Output', max_relative_error=0.02)
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def test_check_grad_no_filter(self):
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self.check_grad(
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['Input'],
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'Output',
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max_relative_error=0.02,
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no_grad_set=set(['Filter']))
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def test_check_grad_no_input(self):
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self.check_grad(
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['Filter'],
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'Output',
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max_relative_error=0.02,
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no_grad_set=set(['Input']))
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def init_test_case(self):
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self.pad = [0, 0, 0]
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self.stride = [1, 1, 1]
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self.dilations = [1, 1, 1]
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self.input_size = [2, 3, 5, 5, 5] # NCDHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3, 3]
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def init_op_type(self):
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self.op_type = "conv3d_transpose"
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class TestWithPad(TestConv3dTransposeOp):
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def init_test_case(self):
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self.pad = [1, 1, 1]
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self.stride = [1, 1, 1]
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self.dilations = [1, 1, 1]
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self.input_size = [2, 3, 5, 5, 5] # NCDHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3, 3]
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class TestWithStride(TestConv3dTransposeOp):
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def init_test_case(self):
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self.pad = [1, 1, 1]
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self.stride = [2, 2, 2]
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self.dilations = [1, 1, 1]
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self.input_size = [2, 3, 5, 5, 5] # NCDHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3, 3]
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# ------------ test_cudnn ------------
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class TestCudnn(TestConv3dTransposeOp):
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def init_op_type(self):
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self.op_type = "conv3d_transpose_cudnn"
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if __name__ == '__main__':
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unittest.main()
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