!11705 fix_conv3d_bn3d
From: @jiangzg001 Reviewed-by: @liangchenghui,@wuxuejian Signed-off-by: @liangchenghui
This commit is contained in:
Gitee
commit
d4abe53f34
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@ -93,18 +93,22 @@ rel_strs = {
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def _check_3d_int_or_tuple(arg_name, arg_value, prim_name, allow_five=False,
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ret_five=False, greater_zero=True):
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ret_five=False, greater_zero=True, third_one=False):
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"""
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Checks whether an argument is a positive int or tuple with 3 or 5(when allow_five is True) positive int elements.
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"""
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def _raise_message():
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def _raise_message(third_one=False):
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if third_one:
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raise ValueError(f"For '{prim_name}' attr '{arg_name[-3]}' should be 1, but got {arg_value}")
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raise ValueError(f"For '{prim_name}' attr '{arg_name}' should be an positive int number or a tuple of three "
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f"{'or five ' if allow_five else ''}positive int numbers, but got {arg_value}")
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def _get_return_value():
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if isinstance(arg_value, int):
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ret = (1, 1, arg_value, arg_value, arg_value) if ret_five else (arg_value, arg_value, arg_value)
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if third_one:
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ret = (1, 1, 1, arg_value, arg_value) if ret_five else (1, arg_value, arg_value)
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elif len(arg_value) == 3:
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ret = (1, 1, arg_value[0], arg_value[1], arg_value[2]) if ret_five else arg_value
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elif len(arg_value) == 5:
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@ -123,7 +127,10 @@ def _check_3d_int_or_tuple(arg_name, arg_value, prim_name, allow_five=False,
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continue
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if not greater_zero and item >= 0:
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continue
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_raise_message()
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if third_one:
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if ret_value[-3] != 1:
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_raise_message(third_one)
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return tuple(ret_value)
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@ -418,6 +418,12 @@ class BatchNorm2d(_BatchNorm):
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pass
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@constexpr
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def _check_3d_shape(input_shape):
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if len(input_shape) != 5:
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raise ValueError("For BatchNorm3d, input data must be 5-dimensional.")
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class BatchNorm3d(Cell):
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r"""
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Batch normalization layer over a 5D input.
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@ -443,17 +449,13 @@ class BatchNorm3d(Cell):
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running_mean and running_var computation. Default: 0.9.
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affine (bool): A bool value. When set to True, gamma and beta can be learned. Default: True.
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gamma_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the gamma weight.
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The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform',
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'he_uniform', etc. Default: 'ones'.
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The values of str refer to the function `initializer` including 'zeros', 'ones', etc. Default: 'ones'.
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beta_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the beta weight.
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The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform',
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'he_uniform', etc. Default: 'zeros'.
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The values of str refer to the function `initializer` including 'zeros', 'ones', etc. Default: 'zeros'.
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moving_mean_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the moving mean.
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The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform',
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'he_uniform', etc. Default: 'zeros'.
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The values of str refer to the function `initializer` including 'zeros', 'ones', etc. Default: 'zeros'.
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moving_var_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the moving variance.
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The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform',
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'he_uniform', etc. Default: 'ones'.
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The values of str refer to the function `initializer` including 'zeros', 'ones', etc. Default: 'ones'.
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use_batch_statistics (bool): If true, use the mean value and variance value of current batch data. If false,
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use the mean value and variance value of specified value. If None, the training process will use the mean
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and variance of current batch data and track the running mean and variance, the evaluation process will use
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@ -491,6 +493,7 @@ class BatchNorm3d(Cell):
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data_format='NCDHW'):
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super(BatchNorm3d, self).__init__()
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self.format = validator.check_string(data_format, ['NCDHW'], 'format', self.cls_name)
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self.reshape = P.Reshape()
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self.bn2d = BatchNorm2d(num_features=num_features,
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eps=eps,
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momentum=momentum,
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@ -501,11 +504,10 @@ class BatchNorm3d(Cell):
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moving_var_init=moving_var_init,
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use_batch_statistics=use_batch_statistics,
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data_format="NCHW")
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self.shape = P.Shape()
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self.reshape = P.Reshape()
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def construct(self, input_x):
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x_shape = self.shape(input_x)
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x_shape = F.shape(input_x)
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_check_3d_shape(x_shape)
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input_x = self.reshape(input_x, (x_shape[0], x_shape[1], x_shape[2]*x_shape[3], x_shape[4]))
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bn2d_out = self.bn2d(input_x)
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bn3d_out = self.reshape(bn2d_out, x_shape)
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@ -98,12 +98,11 @@ def get_bprop_conv3d_transpose(self):
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out_channel=self.in_channel, kernel_size=self.kernel_size, mode=self.mode, pad_mode="pad",
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pad=self.pad, stride=self.stride, dilation=self.dilation, group=self.group, data_format=self.data_format
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)
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input_size = self.input_size
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def bprop(x, w, out, dout):
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dx = input_grad(dout, w)
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dw = filter_grad(dout, x, F.shape(w))
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return dx, dw, zeros_like(input_size)
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return dx, dw, zeros_like(out)
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return bprop
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@ -7385,8 +7385,17 @@ class Conv3D(PrimitiveWithInfer):
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3D convolution layer.
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Applies a 3D convolution over an input tensor which is typically of shape
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:math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`, where :math:`N` is batch size and :math:`C_{in}` is channel number.
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For each batch of shape :math:`(C_{in}, D_{in}, H_{in}, W_{in})`.
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For input shape :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})` and output shape
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:math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`. where :math:`N` is batch size. :math:`C` is channel number.
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the formula is defined as:
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.. math::
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\operatorname{out}\left(N_{i}, C_{\text {out}_j}\right)=\operatorname{bias}\left(C_{\text {out}_j}\right)+
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\sum_{k=0}^{C_{in}-1} ccor(\text {weight}\left(C_{\text {out}_j}, k\right),
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\operatorname{input}\left(N_{i}, k\right))
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where :math:`ccor` is the cross-correlation operator.
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If the 'pad_mode' is set to be "valid", the output height and width will be
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:math:`\left \lfloor{1 + \frac{D_{in} + 2 \times \text{padding} - \text{ks_d} -
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@ -7402,7 +7411,7 @@ class Conv3D(PrimitiveWithInfer):
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mode (int): Modes for different convolutions. Not currently used.
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pad_mode (str): Modes to fill padding. It could be "valid", "same", or "pad". Default: "valid".
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pad (Union(int, tuple[int])): The pad value to be filled. Default: 0. If `pad` is an integer, the paddings of
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head, tail, top, bottom, left and right are the same, equal to pad. If `pad` is a tuple of four
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head, tail, top, bottom, left and right are the same, equal to pad. If `pad` is a tuple of six
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integers, the padding of head, tail, top, bottom, left and right equal to pad[0], pad[1], pad[2],
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pad[3], pad[4] and pad[5] correspondingly.
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stride (Union(int, tuple[int])): The stride to be applied to the convolution filter. Default: 1.
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@ -7414,6 +7423,7 @@ class Conv3D(PrimitiveWithInfer):
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- **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`.
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- **weight** (Tensor) - Set size of kernel is :math:`(D_in, K_h, K_w)`, then the shape is
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:math:`(C_{out}, C_{in}, D_{in}, K_h, K_w)`.
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- **bias** (Tensor) - Tensor of shape :math:`C_{in}`. Currently, only support none or zero.
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Outputs:
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Tensor, the value that applied 3D convolution. The shape is :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`.
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@ -7422,8 +7432,8 @@ class Conv3D(PrimitiveWithInfer):
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``Ascend``
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Examples:
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>>> input = Tensor(np.ones([16, 3, 10, 32, 32]), mindspore.float32)
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>>> weight = Tensor(np.ones([32, 3, 4, 3, 3]), mindspore.float32)
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>>> input = Tensor(np.ones([16, 3, 10, 32, 32]), mindspore.float16)
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>>> weight = Tensor(np.ones([32, 3, 4, 3, 3]), mindspore.float16)
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>>> conv3d = P.Conv3D(out_channel=32, kernel_size=(4, 3, 3))
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>>> output = conv3d(input, weight)
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>>> print(output.shape)
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@ -7446,7 +7456,8 @@ class Conv3D(PrimitiveWithInfer):
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self.kernel_size = _check_3d_int_or_tuple('kernel_size', kernel_size, self.name)
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self.stride = _check_3d_int_or_tuple('stride', stride, self.name, allow_five=True, ret_five=True)
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self.add_prim_attr('strides', self.stride)
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self.dilation = _check_3d_int_or_tuple('dilation', dilation, self.name, allow_five=True, ret_five=True)
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self.dilation = _check_3d_int_or_tuple('dilation', dilation, self.name, allow_five=True,
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ret_five=True, third_one=True)
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self.add_prim_attr('dilations', self.dilation)
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validator.check_value_type('pad', pad, (int, tuple), self.name)
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if isinstance(pad, int):
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@ -7454,17 +7465,17 @@ class Conv3D(PrimitiveWithInfer):
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validator.check_equal_int(len(pad), 6, 'pad size', self.name)
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self.add_prim_attr("pad", pad)
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self.padding = pad
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validator.check_int_range(self.padding[0], 0, kernel_size[0], Rel.INC_LEFT,
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validator.check_int_range(self.padding[0], 0, self.kernel_size[0], Rel.INC_LEFT,
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'pad_d belonging [0, kernel_size_d)', self.name)
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validator.check_int_range(self.padding[1], 0, kernel_size[0], Rel.INC_LEFT,
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validator.check_int_range(self.padding[1], 0, self.kernel_size[0], Rel.INC_LEFT,
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'pad_d belonging [0, kernel_size_d)', self.name)
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validator.check_int_range(self.padding[2], 0, kernel_size[1], Rel.INC_LEFT,
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validator.check_int_range(self.padding[2], 0, self.kernel_size[1], Rel.INC_LEFT,
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'pad_h belonging [0, kernel_size_h)', self.name)
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validator.check_int_range(self.padding[3], 0, kernel_size[1], Rel.INC_LEFT,
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validator.check_int_range(self.padding[3], 0, self.kernel_size[1], Rel.INC_LEFT,
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'pad_h belonging [0, kernel_size_h)', self.name)
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validator.check_int_range(self.padding[4], 0, kernel_size[2], Rel.INC_LEFT,
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validator.check_int_range(self.padding[4], 0, self.kernel_size[2], Rel.INC_LEFT,
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'pad_w belonging [0, kernel_size_w)', self.name)
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validator.check_int_range(self.padding[5], 0, kernel_size[2], Rel.INC_LEFT,
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validator.check_int_range(self.padding[5], 0, self.kernel_size[2], Rel.INC_LEFT,
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'pad_w belonging [0, kernel_size_w)', self.name)
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self.pad_mode = validator.check_string(pad_mode.lower(), ['valid', 'same', 'pad'], 'pad_mode', self.name)
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self.add_prim_attr('pad_mode', self.pad_mode)
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@ -7588,8 +7599,8 @@ class Conv3DBackpropInput(PrimitiveWithInfer):
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``Ascend``
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Examples:
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>>> dout = Tensor(np.ones([16, 32, 10, 32, 32]), mindspore.float32)
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>>> weight = Tensor(np.ones([32, 32, 4, 6, 2]), mindspore.float32)
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>>> dout = Tensor(np.ones([16, 32, 10, 32, 32]), mindspore.float16)
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>>> weight = Tensor(np.ones([32, 32, 4, 6, 2]), mindspore.float16)
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>>> x = Tensor(np.ones([16, 32, 13, 37, 33]))
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>>> conv3d_backprop_input = P.Conv3DBackpropInput(out_channel=4, kernel_size=(4, 6, 2))
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>>> output = conv3d_backprop_input(dout, weight, F.shape(x))
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@ -7640,12 +7651,15 @@ class Conv3DBackpropInput(PrimitiveWithInfer):
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self.add_prim_attr('io_format', self.format)
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def __infer__(self, w, doutput, x_size):
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validator.check_equal_int(len(w['shape']), 5, 'The dimension of weight ', self.name)
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validator.check_equal_int(len(doutput['shape']), 5, 'The dimension of dout', self.name)
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validator.check_equal_int(len(x_size['shape']), 5, 'The dimension of input_size', self.name)
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x_size_v = x_size['value']
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validator.check_value_type('x_size', x_size_v, [tuple], self.name)
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for i, dim_len in enumerate(x_size_v):
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validator.check_value_type("x_size[%d]" % i, dim_len, [int], self.name)
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args = {'doutput': doutput['dtype'], 'w': w['dtype']}
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valid_dtypes = [mstype.float16, mstype.float32]
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valid_dtypes = [mstype.float16]
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validator.check_tensors_dtypes_same_and_valid(args, valid_dtypes, self.name)
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validator.check("filter's batch", w['shape'][0], "dout's channel", doutput['shape'][1], Rel.EQ, self.name)
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validator.check("filter's channel", w['shape'][1], "input_size's channel", x_size_v[1], Rel.EQ, self.name)
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@ -7690,15 +7704,30 @@ class Conv3DBackpropInput(PrimitiveWithInfer):
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class Conv3DTranspose(PrimitiveWithInfer):
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"""
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Computes the gradients of convolution 3D with respect to the input.
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Compute a 3D transposed convolution, which is also known as a deconvolution
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(although it is not an actual deconvolution).
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Input is typically of shape :math:`(N, C, D, H, W)`, where :math:`N` is batch size and :math:`C` is channel number.
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If the 'pad_mode' is set to be "pad", the height and width of output are defined as:
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.. math::
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D_{out} = (D_{in} - 1) \times \text{stride_d} - 2 \times \text{padding_d} + \text{dilation_d} \times
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(\text{kernel_size_d} - 1) + \text{output_padding_d} + 1
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H_{out} = (H_{in} - 1) \times \text{stride_h} - 2 \times \text{padding_h} + \text{dilation_h} \times
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(\text{kernel_size_h} - 1) + \text{output_padding_h} + 1
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W_{out} = (W_{in} - 1) \times \text{stride_w} - 2 \times \text{padding_w} + \text{dilation_w} \times
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(\text{kernel_size_w} - 1) + 1
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Args:
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in_channel (int): The channel of the input x.
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out_channel (int): The channel of the weight x.
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kernel_size (Union[int, tuple[int]]): The kernel size of the 3D convolution.
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mode (int): Modes for different convolutions. Not currently used.
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mode (int): Modes for different convolutions. Default is 1. Not currently used.
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pad (Union(int, tuple[int])): The pad value to be filled. Default: 0. If `pad` is an integer, the paddings of
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head, tail, top, bottom, left and right are the same, equal to pad. If `pad` is a tuple of four integers,
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head, tail, top, bottom, left and right are the same, equal to pad. If `pad` is a tuple of six integers,
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the padding of head, tail, top, bottom, left and right equal to pad[0], pad[1], pad[2], pad[3], pad[4]
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and pad[5] correspondingly.
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stride (Union(int, tuple[int])): The stride to be applied to the convolution filter. Default: 1.
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@ -7706,14 +7735,15 @@ class Conv3DTranspose(PrimitiveWithInfer):
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group (int): Splits input into groups. Default: 1.
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output_padding (Union(int, tuple[int])): Add extra size to each dimension of the output. Default: 0.
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data_format (str): The optional value for data format. Currently only support 'NCDHW'.
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input_size (tuple[int]): A tuple describes the shape of the input which conforms to the format
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:math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`. Not currently used.
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Inputs:
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- **dout** (Tensor) - the gradients w.r.t the output of the convolution. The shape conforms to the default
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data_format :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`.
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data_format :math:`(N, C_{in}, D_{out}, H_{out}, W_{out})`.
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- **weight** (Tensor) - Set size of kernel is :math:`(D_in, K_h, K_w)`, then the shape is
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:math:`(C_{out}, C_{in}, D_{in}, K_h, K_w)`.
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- **input_size** (Tensor) - A tuple describes the shape of the input which conforms to the format
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:math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`.
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:math:`(C_{in}//groups, C_{out}, D_{in}, K_h, K_w)`.
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- **bias** (Tensor) - Tensor of shape :math:`C_{out}`. Currently, only support none or zero.
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Outputs:
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Tensor, the gradients w.r.t the input of convolution 3D. It has the same shape as the input.
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@ -7722,8 +7752,8 @@ class Conv3DTranspose(PrimitiveWithInfer):
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``Ascend``
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Examples:
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>>> input_x = Tensor(np.ones([32, 16, 10, 32, 32]), mindspore.float32)
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>>> weight = Tensor(np.ones([16, 3, 4, 6, 2]), mindspore.float32)
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>>> input_x = Tensor(np.ones([32, 16, 10, 32, 32]), mindspore.float16)
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>>> weight = Tensor(np.ones([16, 3, 4, 6, 2]), mindspore.float16)
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>>> conv3d_transpose = P.Conv3DTranspose(in_channel=16, out_channel=3, kernel_size=(4, 6, 2))
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>>> output = conv3d_transpose(input_x, weight)
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>>> print(output.shape)
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@ -7751,7 +7781,8 @@ class Conv3DTranspose(PrimitiveWithInfer):
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self.kernel_size = _check_3d_int_or_tuple('kernel_size', kernel_size, self.name)
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self.stride = _check_3d_int_or_tuple('stride', stride, self.name, allow_five=True, ret_five=True)
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self.add_prim_attr('strides', self.stride)
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self.dilation = _check_3d_int_or_tuple('dilation', dilation, self.name, allow_five=True, ret_five=True)
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self.dilation = _check_3d_int_or_tuple('dilation', dilation, self.name,
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allow_five=True, ret_five=True, third_one=True)
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self.add_prim_attr('dilations', self.dilation)
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validator.check_value_type('pad', pad, (int, tuple), self.name)
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if isinstance(pad, int):
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@ -7760,17 +7791,17 @@ class Conv3DTranspose(PrimitiveWithInfer):
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self.pad_list = pad
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for item in self.pad_list:
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validator.check_non_negative_int(item, 'pad item', self.name)
|
||||
validator.check_int_range(self.pad_list[0], 0, kernel_size[0], Rel.INC_LEFT,
|
||||
validator.check_int_range(self.pad_list[0], 0, self.kernel_size[0], Rel.INC_LEFT,
|
||||
'pad_d belonging [0, kernel_size_d)', self.name)
|
||||
validator.check_int_range(self.pad_list[1], 0, kernel_size[0], Rel.INC_LEFT,
|
||||
validator.check_int_range(self.pad_list[1], 0, self.kernel_size[0], Rel.INC_LEFT,
|
||||
'pad_d belonging [0, kernel_size_d)', self.name)
|
||||
validator.check_int_range(self.pad_list[2], 0, kernel_size[1], Rel.INC_LEFT,
|
||||
validator.check_int_range(self.pad_list[2], 0, self.kernel_size[1], Rel.INC_LEFT,
|
||||
'pad_h belonging [0, kernel_size_h)', self.name)
|
||||
validator.check_int_range(self.pad_list[3], 0, kernel_size[1], Rel.INC_LEFT,
|
||||
validator.check_int_range(self.pad_list[3], 0, self.kernel_size[1], Rel.INC_LEFT,
|
||||
'pad_h belonging [0, kernel_size_h)', self.name)
|
||||
validator.check_int_range(self.pad_list[4], 0, kernel_size[2], Rel.INC_LEFT,
|
||||
validator.check_int_range(self.pad_list[4], 0, self.kernel_size[2], Rel.INC_LEFT,
|
||||
'pad_w belonging [0, kernel_size_w)', self.name)
|
||||
validator.check_int_range(self.pad_list[5], 0, kernel_size[2], Rel.INC_LEFT,
|
||||
validator.check_int_range(self.pad_list[5], 0, self.kernel_size[2], Rel.INC_LEFT,
|
||||
'pad_w belonging [0, kernel_size_w)', self.name)
|
||||
self.mode = validator.check_equal_int(mode, 1, 'mode', self.name)
|
||||
self.add_prim_attr('mode', self.mode)
|
||||
|
|
@ -7796,7 +7827,8 @@ class Conv3DTranspose(PrimitiveWithInfer):
|
|||
raise ValueError("Bias currently only support None.")
|
||||
valid_dtypes = [mstype.float16, mstype.float32]
|
||||
validator.check_tensors_dtypes_same_and_valid(args, valid_dtypes, self.name)
|
||||
validator.check("filter's batch", w['shape'][0], "input x's channel", x['shape'][1], Rel.EQ, self.name)
|
||||
validator.check("filter's batch", w['shape'][0], "input x's channel",
|
||||
x['shape'][1], Rel.EQ, self.name)
|
||||
# infer shape
|
||||
x_shape = x['shape']
|
||||
w_shape = w['shape']
|
||||
|
|
@ -7808,7 +7840,7 @@ class Conv3DTranspose(PrimitiveWithInfer):
|
|||
(self.kernel_size[1] - 1) + self.output_padding[3] + 1
|
||||
w_out = (x_shape[4] - 1) * self.stride[4] - (pad_left + pad_right) + self.dilation[4] * \
|
||||
(self.kernel_size[2] - 1) + self.output_padding[4] + 1
|
||||
output_shape = (x_shape[0], w_shape[1], d_out, h_out, w_out)
|
||||
output_shape = (x_shape[0], w_shape[1]*self.group, d_out, h_out, w_out)
|
||||
self.add_prim_attr('input_size', output_shape)
|
||||
out = {
|
||||
'value': None,
|
||||
|
|
|
|||
|
|
@ -116,7 +116,7 @@ run_distribute_train_s16_r1.sh
|
|||
run_distribute_train_s8_r1.sh
|
||||
```
|
||||
|
||||
3. Train s8 with voctrain dataset, finetuning from model in pervious step, training script is:
|
||||
3. Train s8 with voctrain dataset, finetuning from model in previous step, training script is:
|
||||
|
||||
```shell
|
||||
run_distribute_train_s8_r2.sh
|
||||
|
|
@ -302,7 +302,7 @@ do
|
|||
done
|
||||
```
|
||||
|
||||
3. Train s8 with voctrain dataset, finetuning from model in pervious step, training script is as follows:
|
||||
3. Train s8 with voctrain dataset, finetuning from model in previous step, training script is as follows:
|
||||
|
||||
```shell
|
||||
# run_distribute_train_s8_r2.sh
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@ def parse_args():
|
|||
if __name__ == '__main__':
|
||||
args = parse_args()
|
||||
|
||||
datas = []
|
||||
data = []
|
||||
with open(args.data_lst) as f:
|
||||
lines = f.readlines()
|
||||
if args.shuffle:
|
||||
|
|
@ -59,14 +59,14 @@ if __name__ == '__main__':
|
|||
sample_['data'] = f.read()
|
||||
with open(os.path.join(args.data_root, label_path), 'rb') as f:
|
||||
sample_['label'] = f.read()
|
||||
datas.append(sample_)
|
||||
data.append(sample_)
|
||||
cnt += 1
|
||||
if cnt % 1000 == 0:
|
||||
writer.write_raw_data(datas)
|
||||
writer.write_raw_data(data)
|
||||
print('number of samples written:', cnt)
|
||||
datas = []
|
||||
data = []
|
||||
|
||||
if datas:
|
||||
writer.write_raw_data(datas)
|
||||
if data:
|
||||
writer.write_raw_data(data)
|
||||
writer.commit()
|
||||
print('number of samples written:', cnt)
|
||||
|
|
|
|||
|
|
@ -112,7 +112,7 @@ def create_voc_train_aug_lst_txt():
|
|||
if id_ in voc_train_data_lst + voc_val_data_lst:
|
||||
continue
|
||||
id_ = id_.strip()
|
||||
img_ = os.path.join(SBD_ANNO_DIR, id_ + '.jpg')
|
||||
img_ = os.path.join(SBD_IMG_DIR, id_ + '.jpg')
|
||||
anno_ = os.path.join(SBD_ANNO_GRAY_DIR, id_ + '.png')
|
||||
f.write(img_ + ' ' + anno_ + '\n')
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue