提交 ed120ee7 编写于 作者: C chengduoZH

Add unit test

上级 56bbfd1a
...@@ -42,12 +42,12 @@ void Conv3DTransposeOp::InferShape(framework::InferShapeContext* ctx) const { ...@@ -42,12 +42,12 @@ void Conv3DTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
PADDLE_ENFORCE_EQ(in_dims[1], filter_dims[0], PADDLE_ENFORCE_EQ(in_dims[1], filter_dims[0],
"input and kernel input dimension should be equal."); "input and kernel input dimension should be equal.");
std::vector<int64_t> output_shape({in_dims[0], in_dims[1]}); std::vector<int64_t> output_shape({in_dims[0], filter_dims[1]});
for (size_t i = 0; i < filter_dims.size(); ++i) { for (size_t i = 0; i < paddings.size(); ++i) {
output_shape.push_back((in_dims[i + 2] - 1) * strides[i] + output_shape.push_back((in_dims[i + 2] - 1) * strides[i] +
filter_dims[i + 2]); filter_dims[i + 2]);
} }
ctx->SetOutputDim("Out", framework::make_ddim(output_shape)); ctx->SetOutputDim("Output", framework::make_ddim(output_shape));
} }
Conv3DTransposeOpMaker::Conv3DTransposeOpMaker( Conv3DTransposeOpMaker::Conv3DTransposeOpMaker(
......
...@@ -43,8 +43,8 @@ class TestConv2dTransposeOp(OpTest): ...@@ -43,8 +43,8 @@ class TestConv2dTransposeOp(OpTest):
conv2dtranspose_param = {'stride': self.stride, 'pad': self.pad} conv2dtranspose_param = {'stride': self.stride, 'pad': self.pad}
input_ = np.random.random(self.input_size).astype("float32") input_ = np.random.random(self.input_size).astype("float32")
filter_ = np.random.random(self.filter_size).astype("float32") filter_ = np.random.random(self.filter_size).astype("float32")
output = conv2dtranspose_forward_naive(input_, filter_, output = conv2dtranspose_forward_naive(
conv2dtranspose_param) input_, filter_, conv2dtranspose_param).astype("float32")
# print 'deconv output py', output, output.shape # print 'deconv output py', output, output.shape
self.inputs = {'Input': input_, 'Filter': filter_} self.inputs = {'Input': input_, 'Filter': filter_}
......
import unittest
import numpy as np
from op_test import OpTest
def conv3dtranspose_forward_naive(input_, filter_, conv3dtranspose_param):
# [2, 3, 5, 5, 5]
in_n, in_c, in_d, in_h, in_w = input_.shape
# [3, 6, 3, 3, 3]
f_c, out_c, f_d, f_h, f_w = filter_.shape
assert in_c == f_c
stride, pad = conv3dtranspose_param['stride'], conv3dtranspose_param['pad']
out_d = (in_d - 1) * stride[0] + f_d
out_h = (in_h - 1) * stride[1] + f_h
out_w = (in_w - 1) * stride[2] + f_w
out = np.zeros((in_n, out_c, out_d, out_h, out_w))
for n in range(in_n):
for d in range(in_d):
for i in range(in_h):
for j in range(in_w):
input_masked = input_[n, :, d, i, j] # (c)
input_masked = np.reshape(input_masked, (in_c, 1, 1, 1))
input_masked = np.tile(input_masked, (1, f_d, f_h, f_w))
for k in range(out_c):
tmp_out = np.sum(input_masked * filter_[:, k, :, :, :],
axis=0)
d1, d2 = d * stride[0], d * stride[0] + f_d
i1, i2 = i * stride[1], i * stride[1] + f_h
j1, j2 = j * stride[2], j * stride[2] + f_w
out[n, k, d1:d2, i1:i2, j1:j2] += tmp_out
return out
class TestConv3dTransposeOp(OpTest):
def setUp(self):
# init as conv transpose
self.init_op_type()
# [2, 3, 5, 5, 5] -> kernel [3, 6, 3, 3, 3] -> output [2, 6, 7, 7, 7]
self.init_test_case()
conv3dtranspose_param = {'stride': self.stride, 'pad': self.pad}
input_ = np.random.random(self.input_size).astype("float32")
filter_ = np.random.random(self.filter_size).astype("float32")
output = conv3dtranspose_forward_naive(
input_, filter_, conv3dtranspose_param).astype("float32")
# print 'deconv output py', output, output.shape
self.inputs = {'Input': input_, 'Filter': filter_}
self.attrs = {
'strides': self.stride,
'paddings': self.pad,
# 'dilations': self.dilations
}
self.outputs = {'Output': output}
def test_check_output(self):
print 'check output here'
self.check_output()
def test_check_grad(self):
self.check_grad(
set(['Input', 'Filter']), 'Output', max_relative_error=0.05)
def test_check_grad_no_filter(self):
self.check_grad(
['Input'],
'Output',
max_relative_error=0.05,
no_grad_set=set(['Filter']))
def test_check_grad_no_input(self):
self.check_grad(
['Filter'],
'Output',
max_relative_error=0.05,
no_grad_set=set(['Input']))
def init_test_case(self):
self.pad = [0, 0, 0]
self.stride = [1, 1, 1]
self.dilations = [1, 1, 1]
self.input_size = [2, 3, 5, 5, 5] # NCHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3, 3]
def init_op_type(self):
self.op_type = "conv3dtranspose"
if __name__ == '__main__':
unittest.main()
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