提交 4bafbf41 编写于 作者: Y Yibing Liu

Enable groups for conv3d transpose op

上级 adbf97b4
......@@ -50,7 +50,7 @@ void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
"dimension should be the same.");
PADDLE_ENFORCE_EQ(in_dims[1], filter_dims[0],
"In ConvTransposeOp, The number of input channels should "
"be equal to the number of filter' channels.");
"be equal to the number of filter's channels.");
std::vector<int64_t> output_shape({in_dims[0], filter_dims[1] * groups});
for (size_t i = 0; i < strides.size(); ++i) {
......@@ -208,6 +208,10 @@ void Conv3DTransposeOpMaker::Make() {
"(vector<int> default:{0, 0, 0}), paddings(d_pad, "
"h_pad, w_pad) of convolution transpose operator.")
.SetDefault({0, 0, 0});
AddAttr<int>("groups",
"(int default:1), the groups number of the convolution3d "
"transpose operator. ")
.SetDefault(1);
AddAttr<bool>(
"use_cudnn",
"(bool, default false) Only used in cudnn kernel, need install cudnn")
......
......@@ -21,8 +21,11 @@ from op_test import OpTest
def conv3dtranspose_forward_naive(input_, filter_, attrs):
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, f_out_c, f_d, f_h, f_w = filter_.shape
groups = attrs['groups']
assert in_c == f_c
out_c = f_out_c * groups
sub_in_c = in_c / groups
stride, pad, dilations = attrs['strides'], attrs['paddings'], attrs[
'dilations']
......@@ -39,18 +42,23 @@ def conv3dtranspose_forward_naive(input_, filter_, attrs):
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))
for g in range(groups):
input_masked = input_[n, g * sub_in_c:(g + 1
) * sub_in_c, d,
i, j] # (c)
input_masked = np.reshape(input_masked,
(sub_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, :, :, :],
for k in range(f_out_c):
tmp_out = np.sum(input_masked * filter_[
g * sub_in_c:(g + 1) * sub_in_c, k, :, :, :],
axis=0)
d1, d2 = d * stride[0], d * stride[0] + d_bolck_d
i1, i2 = i * stride[1], i * stride[1] + d_bolck_h
j1, j2 = j * stride[2], j * stride[2] + d_bolck_w
out[n, k, d1:d2:dilations[0], i1:i2:dilations[1], j1:j2:
dilations[2]] += tmp_out
out[n, g * f_out_c + 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 -
pad[2]]
......@@ -72,6 +80,7 @@ class TestConv3dTransposeOp(OpTest):
'strides': self.stride,
'paddings': self.pad,
'dilations': self.dilations,
'groups': self.groups,
'use_cudnn': self.use_cudnn,
'data_format': 'AnyLayout' # TODO(dzhwinter) : should be fix latter
}
......@@ -134,6 +143,7 @@ class TestConv3dTransposeOp(OpTest):
self.pad = [0, 0, 0]
self.stride = [1, 1, 1]
self.dilations = [1, 1, 1]
self.groups = 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]
......@@ -147,16 +157,29 @@ class TestWithPad(TestConv3dTransposeOp):
self.pad = [1, 1, 1]
self.stride = [1, 1, 1]
self.dilations = [1, 1, 1]
self.groups = 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]
class TestWithGroups(TestConv3dTransposeOp):
def init_test_case(self):
self.pad = [1, 1, 1]
self.stride = [1, 1, 1]
self.dilations = [1, 1, 1]
self.groups = 2
self.input_size = [2, 4, 5, 5, 5] # NCHW
f_c = self.input_size[1]
self.filter_size = [f_c, 3, 3, 3, 3]
class TestWithStride(TestConv3dTransposeOp):
def init_test_case(self):
self.pad = [1, 1, 1]
self.stride = [2, 2, 2]
self.dilations = [1, 1, 1]
self.groups = 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]
......@@ -167,6 +190,7 @@ class TestWithDilation(TestConv3dTransposeOp):
self.pad = [1, 1, 1]
self.stride = [1, 1, 1]
self.dilations = [2, 2, 2]
self.groups = 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]
......@@ -184,6 +208,7 @@ class TestCUDNNWithPad(TestWithPad):
self.pad = [1, 1, 1]
self.stride = [1, 1, 1]
self.dilations = [1, 1, 1]
self.groups = 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]
......@@ -198,6 +223,7 @@ class TestCUDNNWithStride(TestWithStride):
self.pad = [1, 1, 1]
self.stride = [2, 2, 2]
self.dilations = [1, 1, 1]
self.groups = 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]
......@@ -207,6 +233,21 @@ class TestCUDNNWithStride(TestWithStride):
self.op_type = "conv3d_transpose"
class TestCUDNNWithGroups(TestWithGroups):
def init_test_case(self):
self.pad = [1, 1, 1]
self.stride = [1, 1, 1]
self.dilations = [1, 1, 1]
self.groups = 2
self.input_size = [2, 4, 5, 5, 5] # NCHW
f_c = self.input_size[1]
self.filter_size = [f_c, 3, 3, 3, 3]
def init_op_type(self):
self.use_cudnn = True
self.op_type = "conv3d_transpose"
# Please Don't remove the following code.
# Currently, CI use cudnn V5.0 which not support dilation conv.
# class TestCUDNNWithDilation(TestWithDilation):
......
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