def __init__(self, w1, w2):
super(Conv2dBpropInputInplace, self).__init__()
self.conv2d_1 = P.Conv2DBackpropInput(out_channel=256, kernel_size=1)
self.w1 = Parameter(initializer(w1, w1.shape), name='w1')
self.conv2d_2 = P.Conv2DBackpropInput(out_channel=256, kernel_size=1)
self.w2 = Parameter(initializer(w2, w2.shape), name='w2')
self.add = P.TensorAdd()
self.maxpool = P.MaxPool(kernel_size=3, strides=2, pad_mode='SAME')
self.maxpool_grad = G.MaxPoolGrad(kernel_size=3, strides=2, pad_mode='SAME')
self.shape = (32, 64, 56, 56)
def __init__(self):
super(Net5, self).__init__()
out_channel = 4
kernel_size = 1
self.conv_input = P.Conv2DBackpropInput(out_channel,
kernel_size,
pad_mode="valid",
pad=0,
mode=1,
stride=1,
dilation=1,
group=1)
self.w = Parameter(initializer(
Tensor(
np.array([[[[1, 0, -1], [1, 0, -1],
[1, 0, -1]]]]).astype(np.float32)), [1, 1, 3, 3]),
name='w')
self.x = Parameter(initializer(
Tensor(
np.array([[[[3, 0, 1, 2, 7, 4], [1, 5, 8, 9, 3, 1],
[2, 7, 2, 5, 1, 3], [0, 1, 3, 1, 7, 8],
[4, 2, 1, 6, 2, 8], [2, 4, 5, 2, 3,
9]]]]).astype(np.float32)),
[1, 1, 6, 6]),
name='x')
self.out = Parameter(initializer(
Tensor(
np.array([[[[-5, -4, 0, 8], [-10, -2, 2, 3], [0, -2, -4, -7],
[-3, -2, -3, -16]]]]).astype(np.float32)),
[1, 1, 4, 4]),
name='y')
self.get_shape = P.Shape()
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
pad_mode='same',
padding=0,
dilation=1,
group=1,
has_bias=False,
weight_init='normal',
bias_init='zeros'):
kernel_size = twice(kernel_size)
stride = twice(stride)
dilation = twice(dilation)
# out_channels and in_channels swap.
# cause Conv2DBackpropInput's out_channel refers to Conv2D's out_channel,
# then Conv2dTranspose's out_channel refers to Conv2DBackpropInput's in_channel.
super(Conv2dTranspose, self).__init__(in_channels,
out_channels,
kernel_size,
stride,
pad_mode,
padding,
dilation,
group,
has_bias,
weight_init,
bias_init,
transposed=True)
self.in_channels = in_channels
self.out_channels = out_channels
self.shape = P.Shape()
if pad_mode not in ('valid', 'same', 'pad'):
raise ValueError(
'Attr \'pad_mode\' of \'Conv2dTranspose\' Op passed ' +
str(pad_mode) +
', should be one of values in \'valid\', \'same\', \'pad\'.')
self.is_valid = self.pad_mode == 'valid'
self.is_same = self.pad_mode == 'same'
self.is_pad = self.pad_mode == 'pad'
if check_bool(has_bias):
self.bias = Parameter(initializer(bias_init, [out_channels]),
name='bias')
# cause Conv2DBackpropInput's out_channel refers to Conv2D's out_channel.
self.conv2d_transpose = P.Conv2DBackpropInput(out_channel=in_channels,
kernel_size=kernel_size,
mode=1,
pad_mode=pad_mode,
pad=padding,
stride=stride,
dilation=dilation,
group=group)
self.bias_add = P.BiasAdd()
def __init__(self):
super(Conv2dInput, self).__init__()
out_channel = 1
kernel_size = 3
self.conv_input = P.Conv2DBackpropInput(out_channel,
kernel_size,
pad_mode="valid",
pad=0,
mode=1,
stride=1,
dilation=1,
group=1)
self.get_shape = P.Shape()
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
pad_mode='same',
padding=0,
dilation=1,
group=1,
has_bias=False,
weight_init='normal',
bias_init='zeros'):
Validator.check_value_type("kernel_size", kernel_size, [int], self.cls_name)
Validator.check_value_type("stride", stride, [int], self.cls_name)
Validator.check_value_type("padding", padding, [int], self.cls_name)
Validator.check_value_type("dilation", dilation, [int], self.cls_name)
Validator.check_integer('kernel_size', kernel_size, 1, Rel.GE, self.cls_name)
Validator.check_integer('stride', stride, 1, Rel.GE, self.cls_name)
Validator.check_integer('padding', padding, 0, Rel.GE, self.cls_name)
Validator.check_integer('dilation', dilation, 1, Rel.GE, self.cls_name)
kernel_size = (1, kernel_size)
stride = (1, stride)
dilation = (1, dilation)
get_shape = P.Shape()
get_dtype = P.DType()
if isinstance(weight_init, Tensor):
weight_init_shape = get_shape(weight_init)
Validator.check_integer('weight_init_shape', len(weight_init_shape), 3, Rel.EQ, self.cls_name)
weight_init_dtype = get_dtype(weight_init)
weight_init_value = weight_init.asnumpy()
weight_init_value = np.expand_dims(weight_init_value, 2)
weight_init = Tensor(weight_init_value, weight_init_dtype)
# out_channels and in_channels swap.
# cause Conv2DBackpropInput's out_channel refers to Conv2D's out_channel,
# then Conv1dTranspose's out_channel refers to Conv2DBackpropInput's in_channel.
super(Conv1dTranspose, self).__init__(
in_channels,
out_channels,
kernel_size,
stride,
pad_mode,
padding,
dilation,
group,
has_bias,
weight_init,
bias_init,
transposed=True)
self.padding = (0, 0, padding, padding)
self.in_channels = in_channels
self.out_channels = out_channels
self.shape = P.Shape()
if pad_mode not in ('valid', 'same', 'pad'):
raise ValueError('Attr \'pad_mode\' of \'Conv1dTranspose\' Op passed '
+ str(pad_mode) + ', should be one of values in \'valid\', \'same\', \'pad\'.')
self.is_valid = self.pad_mode == 'valid'
self.is_same = self.pad_mode == 'same'
self.is_pad = self.pad_mode == 'pad'
if check_bool(has_bias):
self.bias = Parameter(initializer(bias_init, [out_channels]), name='bias')
# cause Conv2DBackpropInput's out_channel refers to Conv2D's out_channel.
self.conv2d_transpose = P.Conv2DBackpropInput(out_channel=in_channels,
kernel_size=kernel_size,
mode=1,
pad_mode=pad_mode,
pad=self.padding,
stride=stride,
dilation=dilation,
group=group)
self.bias_add = P.BiasAdd()
self.expand_dims = P.ExpandDims()
self.squeeze = P.Squeeze(2)
'skip': ['backward']
}),
# rank of x is not 4
('AvgPool2', {
'block': (P.AvgPool(ksize=50, strides=1), {
'exception': ValueError,
'error_keywords': ['AvgPool']
}),
'desc_inputs': [Tensor(np.ones([1, 1, 32, 32]).astype(np.float32))],
'skip': ['backward']
}),
# input is scalar
('Conv2DBackpropInput0', {
'block':
(Conv2DBackpropInputNet(P.Conv2DBackpropInput(2, (5, 5)), (2, 3)), {
'exception': TypeError,
'error_keywords': ['Conv2DBackpropInput']
}),
'desc_inputs': [5.0, 5.0],
'skip': ['backward']
}),
# input is Tensor(bool)
('Conv2DBackpropInput1', {
'block':
(Conv2DBackpropInputNet(P.Conv2DBackpropInput(2, (5, 5)), (2, 3)), {
'exception': TypeError,
'error_keywords': ['Conv2DBackpropInput']
}),
'desc_inputs': [
Tensor(np.ones([5]).astype(np.bool_)),
