提交 d93bbf1b 编写于 作者: C chengduoZH

add conv_trans unit test

上级 aa770198
...@@ -3,14 +3,17 @@ import numpy as np ...@@ -3,14 +3,17 @@ import numpy as np
from op_test import OpTest from op_test import OpTest
def conv2dtranspose_forward_naive(input_, filter_, conv2dtranspose_param): def conv2dtranspose_forward_naive(input_, filter_, attrs):
in_n, in_c, in_h, in_w = input_.shape in_n, in_c, in_h, in_w = input_.shape
f_c, out_c, f_h, f_w = filter_.shape f_c, out_c, f_h, f_w = filter_.shape
assert in_c == f_c assert in_c == f_c
stride, pad = conv2dtranspose_param['stride'], conv2dtranspose_param['pad'] stride, pad, dilations = attrs['strides'], attrs['paddings'], attrs[
out_h = (in_h - 1) * stride[0] + f_h 'dilations']
out_w = (in_w - 1) * stride[1] + f_w d_bolck_h = dilations[0] * (f_h - 1) + 1
d_bolck_w = dilations[1] * (f_w - 1) + 1
out_h = (in_h - 1) * stride[0] + d_bolck_h
out_w = (in_w - 1) * stride[1] + d_bolck_w
out = np.zeros((in_n, out_c, out_h, out_w)) out = np.zeros((in_n, out_c, out_h, out_w))
...@@ -23,9 +26,9 @@ def conv2dtranspose_forward_naive(input_, filter_, conv2dtranspose_param): ...@@ -23,9 +26,9 @@ def conv2dtranspose_forward_naive(input_, filter_, conv2dtranspose_param):
for k in range(out_c): for k in range(out_c):
tmp_out = np.sum(input_masked * filter_[:, k, :, :], axis=0) tmp_out = np.sum(input_masked * filter_[:, k, :, :], axis=0)
i1, i2 = i * stride[0], i * stride[0] + f_h i1, i2 = i * stride[0], i * stride[0] + d_bolck_h
j1, j2 = j * stride[0], j * stride[0] + f_w j1, j2 = j * stride[0], j * stride[0] + d_bolck_h
out[n, k, i1:i2, j1:j2] += tmp_out out[n, k, i1:i2:dilations[0], j1:j2:dilations[1]] += tmp_out
out = out[:, :, pad[0]:out_h - pad[0], pad[1]:out_w - pad[1]] out = out[:, :, pad[0]:out_h - pad[0], pad[1]:out_w - pad[1]]
return out return out
...@@ -37,11 +40,8 @@ class TestConv2dTransposeOp(OpTest): ...@@ -37,11 +40,8 @@ class TestConv2dTransposeOp(OpTest):
self.init_op_type() self.init_op_type()
self.init_test_case() self.init_test_case()
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_, conv2dtranspose_param).astype('float32')
self.inputs = {'Input': input_, 'Filter': filter_} self.inputs = {'Input': input_, 'Filter': filter_}
self.attrs = { self.attrs = {
...@@ -49,6 +49,10 @@ class TestConv2dTransposeOp(OpTest): ...@@ -49,6 +49,10 @@ class TestConv2dTransposeOp(OpTest):
'paddings': self.pad, 'paddings': self.pad,
'dilations': self.dilations 'dilations': self.dilations
} }
output = conv2dtranspose_forward_naive(input_, filter_,
self.attrs).astype('float32')
self.outputs = {'Output': output} self.outputs = {'Output': output}
def test_check_output(self): def test_check_output(self):
...@@ -104,11 +108,60 @@ class TestWithStride(TestConv2dTransposeOp): ...@@ -104,11 +108,60 @@ class TestWithStride(TestConv2dTransposeOp):
self.filter_size = [f_c, 6, 3, 3] self.filter_size = [f_c, 6, 3, 3]
class TestWithDilation(TestConv2dTransposeOp):
def init_test_case(self):
self.pad = [1, 1]
self.stride = [1, 1]
self.dilations = [2, 2]
self.input_size = [2, 3, 5, 5] # NCHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3]
# ------------ test_cudnn ------------ # ------------ test_cudnn ------------
class TestCudnn(TestConv2dTransposeOp): class TestCudnn(TestConv2dTransposeOp):
def init_op_type(self): def init_op_type(self):
self.op_type = "conv2d_transpose_cudnn" self.op_type = "conv2d_transpose_cudnn"
class TestCudnnWithPad(TestWithPad):
def init_test_case(self):
self.pad = [1, 1]
self.stride = [1, 1]
self.dilations = [1, 1]
self.input_size = [2, 3, 5, 5] # NCHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3]
def init_op_type(self):
self.op_type = "conv2d_transpose_cudnn"
class TestCudnnWithStride(TestWithStride):
def init_test_case(self):
self.pad = [1, 1]
self.stride = [2, 2]
self.dilations = [1, 1]
self.input_size = [2, 3, 5, 5] # NCHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3]
def init_op_type(self):
self.op_type = "conv2d_transpose_cudnn"
# #cudnn v5 does not support dilation conv.
# class TestCudnnWithDilation(TestWithDilation):
# def init_test_case(self):
# self.pad = [1, 1]
# self.stride = [2, 2]
# self.dilations = [2, 2]
# self.input_size = [2, 3, 5, 5] # NCHW
# f_c = self.input_size[1]
# self.filter_size = [f_c, 6, 3, 3]
#
# def init_op_type(self):
# self.op_type = "conv2d_transpose_cudnn"
if __name__ == '__main__': if __name__ == '__main__':
unittest.main() unittest.main()
...@@ -3,15 +3,20 @@ import numpy as np ...@@ -3,15 +3,20 @@ import numpy as np
from op_test import OpTest from op_test import OpTest
def conv3dtranspose_forward_naive(input_, filter_, conv3dtranspose_param): def conv3dtranspose_forward_naive(input_, filter_, attrs):
in_n, in_c, in_d, in_h, in_w = input_.shape in_n, in_c, in_d, in_h, in_w = input_.shape
f_c, out_c, f_d, f_h, f_w = filter_.shape f_c, out_c, f_d, f_h, f_w = filter_.shape
assert in_c == f_c assert in_c == f_c
stride, pad = conv3dtranspose_param['stride'], conv3dtranspose_param['pad'] stride, pad, dilations = attrs['strides'], attrs['paddings'], attrs[
out_d = (in_d - 1) * stride[0] + f_d 'dilations']
out_h = (in_h - 1) * stride[1] + f_h
out_w = (in_w - 1) * stride[2] + f_w d_bolck_d = dilations[0] * (f_d - 1) + 1
d_bolck_h = dilations[1] * (f_h - 1) + 1
d_bolck_w = dilations[2] * (f_w - 1) + 1
out_d = (in_d - 1) * stride[0] + d_bolck_d
out_h = (in_h - 1) * stride[1] + d_bolck_h
out_w = (in_w - 1) * stride[2] + d_bolck_w
out = np.zeros((in_n, out_c, out_d, out_h, out_w)) out = np.zeros((in_n, out_c, out_d, out_h, out_w))
for n in range(in_n): for n in range(in_n):
...@@ -25,10 +30,11 @@ def conv3dtranspose_forward_naive(input_, filter_, conv3dtranspose_param): ...@@ -25,10 +30,11 @@ def conv3dtranspose_forward_naive(input_, filter_, conv3dtranspose_param):
for k in range(out_c): for k in range(out_c):
tmp_out = np.sum(input_masked * filter_[:, k, :, :, :], tmp_out = np.sum(input_masked * filter_[:, k, :, :, :],
axis=0) axis=0)
d1, d2 = d * stride[0], d * stride[0] + f_d d1, d2 = d * stride[0], d * stride[0] + d_bolck_d
i1, i2 = i * stride[1], i * stride[1] + f_h i1, i2 = i * stride[1], i * stride[1] + d_bolck_h
j1, j2 = j * stride[2], j * stride[2] + f_w j1, j2 = j * stride[2], j * stride[2] + d_bolck_w
out[n, k, d1:d2, i1:i2, j1:j2] += tmp_out out[n, k, d1:d2:dilations[0], i1:i2:dilations[1], j1:j2:
dilations[2]] += tmp_out
out = out[:, :, pad[0]:out_d - pad[0], pad[1]:out_h - pad[1], pad[2]:out_w - out = out[:, :, pad[0]:out_d - pad[0], pad[1]:out_h - pad[1], pad[2]:out_w -
pad[2]] pad[2]]
...@@ -41,18 +47,19 @@ class TestConv3dTransposeOp(OpTest): ...@@ -41,18 +47,19 @@ class TestConv3dTransposeOp(OpTest):
self.init_op_type() self.init_op_type()
self.init_test_case() self.init_test_case()
conv3dtranspose_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 = conv3dtranspose_forward_naive(
input_, filter_, conv3dtranspose_param).astype("float32")
self.inputs = {'Input': input_, 'Filter': filter_} self.inputs = {'Input': input_, 'Filter': filter_}
self.attrs = { self.attrs = {
'strides': self.stride, 'strides': self.stride,
'paddings': self.pad, 'paddings': self.pad,
# 'dilations': self.dilations 'dilations': self.dilations
} }
output = conv3dtranspose_forward_naive(input_, filter_,
self.attrs).astype("float32")
self.outputs = {'Output': output} self.outputs = {'Output': output}
def test_check_output(self): def test_check_output(self):
...@@ -108,11 +115,60 @@ class TestWithStride(TestConv3dTransposeOp): ...@@ -108,11 +115,60 @@ class TestWithStride(TestConv3dTransposeOp):
self.filter_size = [f_c, 6, 3, 3, 3] self.filter_size = [f_c, 6, 3, 3, 3]
class TestWithDilation(TestConv3dTransposeOp):
def init_test_case(self):
self.pad = [1, 1, 1]
self.stride = [1, 1, 1]
self.dilations = [2, 2, 2]
self.input_size = [2, 3, 5, 5, 5] # NCDHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3, 3]
# ------------ test_cudnn ------------ # ------------ test_cudnn ------------
class TestCudnn(TestConv3dTransposeOp): class TestCudnn(TestConv3dTransposeOp):
def init_op_type(self): def init_op_type(self):
self.op_type = "conv3d_transpose_cudnn" self.op_type = "conv3d_transpose_cudnn"
class TestCudnnWithPad(TestWithPad):
def init_test_case(self):
self.pad = [1, 1, 1]
self.stride = [1, 1, 1]
self.dilations = [1, 1, 1]
self.input_size = [2, 3, 5, 5, 5] # NCDHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3, 3]
def init_op_type(self):
self.op_type = "conv3d_transpose_cudnn"
class TestCudnnWithStride(TestWithStride):
def init_test_case(self):
self.pad = [1, 1, 1]
self.stride = [2, 2, 2]
self.dilations = [1, 1, 1]
self.input_size = [2, 3, 5, 5, 5] # NCDHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3, 3]
def init_op_type(self):
self.op_type = "conv3d_transpose_cudnn"
# #cudnn v5 does not support dilation conv.
# class TestCudnnWithDilation(TestWithDilation):
# def init_test_case(self):
# self.pad = [1, 1, 1]
# self.stride = [2, 2, 2]
# self.dilations = [2, 2, 2]
# self.input_size = [2, 3, 5, 5, 5] # NCDHW
# f_c = self.input_size[1]
# self.filter_size = [f_c, 6, 3, 3, 3]
#
# def init_op_type(self):
# self.op_type = "conv3d_transpose_cudnn"
if __name__ == '__main__': if __name__ == '__main__':
unittest.main() unittest.main()
Markdown is supported
0% .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑!
想要评论请 注册