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modify conv2dtranspose

This commit is contained in:
jinyaohui 2020-04-29 10:33:28 +08:00
parent 697e8d30ca
commit 88e763a98f
2 changed files with 30 additions and 17 deletions
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18
mindspore/nn/layer/conv.py
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@ -37,7 +37,8 @@ class _Conv(Cell):
group, group,
has_bias, has_bias,
weight_init, weight_init,
bias_init): bias_init,
transposed=False):
super(_Conv, self).__init__() super(_Conv, self).__init__()
self.in_channels = check_int_positive(in_channels) self.in_channels = check_int_positive(in_channels)
self.out_channels = check_int_positive(out_channels) self.out_channels = check_int_positive(out_channels)
@ -65,9 +66,11 @@ class _Conv(Cell):
if out_channels % group != 0: if out_channels % group != 0:
raise ValueError("Attr 'out_channels' of 'Conv2D' Op must be divisible by " raise ValueError("Attr 'out_channels' of 'Conv2D' Op must be divisible by "
"attr 'group' of 'Conv2D' Op.") "attr 'group' of 'Conv2D' Op.")
if transposed:
self.weight = Parameter(initializer(weight_init, [out_channels, in_channels // group, *kernel_size]), shape = [in_channels, out_channels // group, *kernel_size]
name='weight') else:
shape = [out_channels, in_channels // group, *kernel_size]
self.weight = Parameter(initializer(weight_init, shape), name='weight')
if check_bool(has_bias): if check_bool(has_bias):
self.bias = Parameter(initializer(bias_init, [out_channels]), name='bias') self.bias = Parameter(initializer(bias_init, [out_channels]), name='bias')
@ -312,8 +315,8 @@ class Conv2dTranspose(_Conv):
# cause Conv2DBackpropInput's out_channel refers to Conv2D's out_channel, # cause Conv2DBackpropInput's out_channel refers to Conv2D's out_channel,
# then Conv2dTranspose's out_channel refers to Conv2DBackpropInput's in_channel. # then Conv2dTranspose's out_channel refers to Conv2DBackpropInput's in_channel.
super(Conv2dTranspose, self).__init__( super(Conv2dTranspose, self).__init__(
out_channels,
in_channels, in_channels,
out_channels,
kernel_size, kernel_size,
stride, stride,
pad_mode, pad_mode,
@ -322,10 +325,11 @@ class Conv2dTranspose(_Conv):
group, group,
has_bias, has_bias,
weight_init, weight_init,
bias_init) bias_init,
transposed=True)
self.out_channels = out_channels
self.in_channels = in_channels self.in_channels = in_channels
self.out_channels = out_channels
self.shape = P.Shape() self.shape = P.Shape()
if pad_mode not in ('valid', 'same', 'pad'): if pad_mode not in ('valid', 'same', 'pad'):
raise ValueError('Attr \'pad_mode\' of \'Conv2dTranspose\' Op passed ' raise ValueError('Attr \'pad_mode\' of \'Conv2dTranspose\' Op passed '

11
tests/ut/python/nn/test_conv.py
View File

@ -20,7 +20,6 @@ import mindspore.nn as nn
from mindspore import Tensor from mindspore import Tensor
from ..ut_filter import non_graph_engine from ..ut_filter import non_graph_engine
weight = Tensor(np.ones([2, 2])) weight = Tensor(np.ones([2, 2]))
in_channels = 3 in_channels = 3
out_channels = 64 out_channels = 64
@ -28,6 +27,7 @@ out_channels = 64
class Net(nn.Cell): class Net(nn.Cell):
""" Net definition """ """ Net definition """
def __init__(self, def __init__(self,
cin, cin,
cout, cout,
@ -93,12 +93,14 @@ def test_compile_pad_pad():
input_data = Tensor(np.ones([1, 3, 16, 50], dtype=np.float32)) input_data = Tensor(np.ones([1, 3, 16, 50], dtype=np.float32))
net(input_data) net(input_data)
def test_conv_group_error(): def test_conv_group_error():
with pytest.raises(ValueError): with pytest.raises(ValueError):
nn.Conv2d(6, 8, 3, group=3) nn.Conv2d(6, 8, 3, group=3)
with pytest.raises(ValueError): with pytest.raises(ValueError):
nn.Conv2d(6, 9, 3, group=2) nn.Conv2d(6, 9, 3, group=2)
def test_conv_check(): def test_conv_check():
""" test_conv_check """ """ test_conv_check """
with pytest.raises(ValueError): with pytest.raises(ValueError):
@ -165,6 +167,13 @@ def test_compile_transpose_bias():
net(input_data) net(input_data)
def test_compile_transpose_bias_init():
bias = Tensor(np.random.randn(64).astype(np.float32))
net = NetConv2dTranspose(3, 64, 4, has_bias=True, weight_init='normal', bias_init=bias)
input_data = Tensor(np.ones([1, 3, 16, 50], dtype=np.float32))
net(input_data)
def test_compile_transpose_valid(): def test_compile_transpose_valid():
net = NetConv2dTranspose(3, 64, 4, pad_mode='valid', weight_init='normal') net = NetConv2dTranspose(3, 64, 4, pad_mode='valid', weight_init='normal')
input_data = Tensor(np.ones([1, 3, 16, 50], dtype=np.float32)) input_data = Tensor(np.ones([1, 3, 16, 50], dtype=np.float32))