!5916 unify Conv2d and DepthwiseConv2d

Merge pull request !5916 from caozhou/unify_conv2d_depthwise2d

mindspore/nn/layer/conv.py

@@ -16,6 +16,7 @@
 
 import numpy as np
 from mindspore import log as logger
+from mindspore import context
 from mindspore.ops import operations as P
 from mindspore.ops.primitive import constexpr
 from mindspore.common.parameter import Parameter
@@ -27,7 +28,7 @@ from mindspore._checkparam import check_bool, twice, check_int_positive
 from mindspore._extends import cell_attr_register
 from ..cell import Cell
 
-__all__ = ['Conv2d', 'Conv2dTranspose', 'DepthwiseConv2d', 'Conv1d', 'Conv1dTranspose']
+__all__ = ['Conv2d', 'Conv2dTranspose', 'Conv1d', 'Conv1dTranspose']
 
 
 class _Conv(Cell):
@@ -171,7 +172,8 @@ class Conv2d(_Conv):
                                       be greater or equal to 1 and bounded by the height and width of the
                                       input. Default: 1.
         group (int): Split filter into groups, `in_ channels` and `out_channels` should be
-            divisible by the number of groups. Default: 1.
+            divisible by the number of groups. If the group is equal to `in_channels` and `out_channels`,
+            this 2D convolution layer also can be called 2D depthwise convolution layer. Default: 1.
         has_bias (bool): Specifies whether the layer uses a bias vector. Default: False.
         weight_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the convolution kernel.
             It can be a Tensor, a string, an Initializer or a number. When a string is specified,
@@ -211,6 +213,7 @@ class Conv2d(_Conv):
                  bias_init='zeros'):
         kernel_size = twice(kernel_size)
         stride = twice(stride)
+        self._dilation = dilation
         dilation = twice(dilation)
         super(Conv2d, self).__init__(
             in_channels,
@@ -232,10 +235,23 @@ class Conv2d(_Conv):
                                stride=self.stride,
                                dilation=self.dilation,
                                group=self.group)
+        self._init_depthwise_conv2d()
         self.bias_add = P.BiasAdd()
-        if pad_mode not in ('valid', 'same', 'pad'):
+
-            raise ValueError('Attr \'pad_mode\' of \'Conv2d\' Op passed '
+    def _init_depthwise_conv2d(self):
-                             + str(pad_mode) + ', should be one of values in \'valid\', \'same\', \'pad\'.')
+        """Init depthwise conv2d op"""
+        if context.get_context("device_target") == "Ascend" and self.group > 1:
+            self.dilation = self._dilation
+            validator.check_integer('group', self.group, self.in_channels, Rel.EQ)
+            validator.check_integer('group', self.group, self.out_channels, Rel.EQ)
+            self.conv2d = P.DepthwiseConv2dNative(channel_multiplier=1,
+                                                  kernel_size=self.kernel_size,
+                                                  pad_mode=self.pad_mode,
+                                                  pad=self.padding,
+                                                  stride=self.stride,
+                                                  dilation=self.dilation)
+            weight_shape = [1, self.in_channels, *self.kernel_size]
+            self.weight = Parameter(initializer(self.weight_init, weight_shape), name='weight')
 
     def construct(self, x):
         output = self.conv2d(x, self.weight)
@@ -798,161 +814,3 @@ class Conv1dTranspose(_Conv):
                                                   self.weight_init,
                                                   self.bias_init)
         return s
-
-
-class DepthwiseConv2d(Cell):
-    r"""
-    2D depthwise convolution layer.
-
-    Applies a 2D depthwise convolution over an input tensor which is typically of shape:
-    math:`(N, C_{in}, H_{in}, W_{in})`, where :math:`N` is batch size and :math:`C_{in}` is channel number.
-    For each batch of shape:math:`(C_{in}, H_{in}, W_{in})`, the formula is defined as:
-
-    .. math::
-
-        out_j = \sum_{i=0}^{C_{in} - 1} ccor(W_{ij}, X_i) + b_j,
-
-    where :math:`ccor` is the cross correlation operator, :math:`C_{in}` is the input channel number, :math:`j` ranges
-    from :math:`0` to :math:`C_{out} - 1`, :math:`W_{ij}` corresponds to the :math:`i`-th channel of the :math:`j`-th
-    filter and :math:`out_{j}` corresponds to the :math:`j`-th channel of the output. :math:`W_{ij}` is a slice
-    of kernel and it has shape :math:`(\text{ks_h}, \text{ks_w})`, where :math:`\text{ks_h}` and
-    :math:`\text{ks_w}` are the height and width of the convolution kernel. The full kernel has shape
-    :math:`(C_{out}, C_{in} // \text{group}, \text{ks_h}, \text{ks_w})`, where group is the group number
-    to split the input in the channel dimension.
-
-    If the 'pad_mode' is set to be "valid", the output height and width will be
-    :math:`\left \lfloor{1 + \frac{H_{in} + 2 \times \text{padding} - \text{ks_h} -
-    (\text{ks_h} - 1) \times (\text{dilation} - 1) }{\text{stride}}} \right \rfloor` and
-    :math:`\left \lfloor{1 + \frac{W_{in} + 2 \times \text{padding} - \text{ks_w} -
-    (\text{ks_w} - 1) \times (\text{dilation} - 1) }{\text{stride}}} \right \rfloor` respectively.
-
-    The first introduction can be found in paper `Gradient Based Learning Applied to Document Recognition
-    <http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf>`_.
-
-    Args:
-        in_channels (int): The number of input channel :math:`C_{in}`.
-        out_channels (int): The number of output channel :math:`C_{out}`.
-        kernel_size (Union[int, tuple[int]]): The data type is int or a tuple of 2 integers. Specifies the height
-            and width of the 2D convolution window. Single int means the value is for both the height and the width of
-            the kernel. A tuple of 2 ints means the first value is for the height and the other is for the
-            width of the kernel.
-        stride (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents
-            the height and width of movement are both strides, or a tuple of two int numbers that
-            represent height and width of movement respectively. Default: 1.
-        pad_mode (str): Specifies padding mode. The optional values are
-            "same", "valid", "pad". Default: "same".
-
-            - same: Adopts the way of completion. The height and width of the output will be the same as
-              the input. The total number of padding will be calculated in horizontal and vertical
-              directions and evenly distributed to top and bottom, left and right if possible. Otherwise, the
-              last extra padding will be done from the bottom and the right side. If this mode is set, `padding`
-              must be 0.
-
-            - valid: Adopts the way of discarding. The possible largest height and width of output will be returned
-              without padding. Extra pixels will be discarded. If this mode is set, `padding`
-              must be 0.
-
-            - pad: Implicit paddings on both sides of the input. The number of `padding` will be padded to the input
-              Tensor borders. `padding` should be greater than or equal to 0.
-
-        padding (Union[int, tuple[int]]): Implicit paddings on both sides of the input. If `padding` is one integer,
-            the paddings of top, bottom, left and right are the same, equal to padding. If `padding` is a tuple
-            with four integers, the paddings of top, bottom, left and right will be equal to padding[0],
-            padding[1], padding[2], and padding[3] accordingly. Default: 0.
-        dilation (Union[int, tuple[int]]): The data type is int or a tuple of 2 integers. Specifies the dilation rate
-                                      to use for dilated convolution. If set to be :math:`k > 1`, there will
-                                      be :math:`k - 1` pixels skipped for each sampling location. Its value should
-                                      be greater than or equal to 1 and bounded by the height and width of the
-                                      input. Default: 1.
-        group (int): Split filter into groups, `in_ channels` and `out_channels` should be
-            divisible by the number of groups. If 'group' is None, it will be set as the value of 'in_channels'
-        has_bias (bool): Specifies whether the layer uses a bias vector. Default: False.
-        weight_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the convolution kernel.
-            It can be a Tensor, a string, an Initializer or a number. When a string is specified,
-            values from 'TruncatedNormal', 'Normal', 'Uniform', 'HeUniform' and 'XavierUniform' distributions as well
-            as constant 'One' and 'Zero' distributions are possible. Alias 'xavier_uniform', 'he_uniform', 'ones'
-            and 'zeros' are acceptable. Uppercase and lowercase are both acceptable. Refer to the values of
-            Initializer for more details. Default: 'normal'.
-        bias_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the bias vector. Possible
-            Initializer and string are the same as 'weight_init'. Refer to the values of
-            Initializer for more details. Default: 'zeros'.
-
-    Inputs:
-        - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
-
-    Outputs:
-        Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
-
-    Examples:
-        >>> net = nn.DepthwiseConv2d(240, 240, 4, group=None, has_bias=False, weight_init='normal')
-        >>> input = Tensor(np.ones([1, 240, 1024, 640]), mindspore.float32)
-        >>> net(input).shape
-        (1, 240, 1024, 640)
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 group,
-                 stride=1,
-                 pad_mode='same',
-                 padding=0,
-                 dilation=1,
-                 has_bias=False,
-                 weight_init='normal',
-                 bias_init='zeros'):
-        super(DepthwiseConv2d, self).__init__()
-        self.kernel_size = twice(kernel_size)
-        self.stride = twice(stride)
-        self.dilation = twice(dilation)
-        self.in_channels = check_int_positive(in_channels)
-        self.out_channels = check_int_positive(out_channels)
-        if group is None:
-            group = in_channels
-        validator.check_integer('group', group, in_channels, Rel.EQ)
-        validator.check_integer('group', group, out_channels, Rel.EQ)
-        validator.check_integer('group', group, 1, Rel.GE)
-        self.pad_mode = pad_mode
-        self.dilation = dilation
-        self.group = group
-        self.has_bias = has_bias
-        self.weight_init = weight_init
-        self.bias_init = bias_init
-        Validator.check_value_type('padding', padding, (int, tuple), self.cls_name)
-        if isinstance(padding, tuple):
-            Validator.check_integer('padding size', len(padding), 4, Rel.EQ, self.cls_name)
-        self.padding = padding
-        self.conv = P.DepthwiseConv2dNative(channel_multiplier=1,
-                                            kernel_size=self.kernel_size,
-                                            pad_mode=self.pad_mode,
-                                            pad=self.padding,
-                                            stride=self.stride,
-                                            dilation=self.dilation)
-        self.bias_add = P.BiasAdd()
-        weight_shape = [1, in_channels, *self.kernel_size]
-        self.weight = Parameter(initializer(weight_init, weight_shape), name='weight')
-        if check_bool(has_bias):
-            self.bias = Parameter(initializer(bias_init, [out_channels]), name='bias')
-        else:
-            if bias_init != 'zeros':
-                logger.warning("value of `has_bias` is False, value of `bias_init` will be ignore.")
-            self.bias = None
-
-    def construct(self, x):
-        out = self.conv(x, self.weight)
-        if self.has_bias:
-            out = self.bias_add(out, self.bias)
-        return out
-
-    def extend_repr(self):
-        s = 'input_channels={}, output_channels={}, kernel_size={}, stride={}, ' \
-            'pad_mode={}, padding={}, dilation={}, group={}, ' \
-            'has_bias={}, weight_init={}, bias_init={}'.format(
-                self.in_channels, self.out_channels, self.kernel_size, self.stride,
-                self.pad_mode, self.padding, self.dilation, self.group,
-                self.has_bias, self.weight_init, self.bias_init)
-
-        if self.has_bias:
-            s += ', bias={}'.format(self.bias)
-        return s

mindspore/nn/layer/quant.py

@@ -122,19 +122,6 @@ class Conv2dBnAct(Cell):
                  after_fake=True):
         super(Conv2dBnAct, self).__init__()
 
-        if context.get_context('device_target') == "Ascend" and group > 1:
-            self.conv = conv.DepthwiseConv2d(in_channels,
-                                             out_channels,
-                                             kernel_size=kernel_size,
-                                             stride=stride,
-                                             pad_mode=pad_mode,
-                                             padding=padding,
-                                             dilation=dilation,
-                                             group=group,
-                                             has_bias=has_bias,
-                                             weight_init=weight_init,
-                                             bias_init=bias_init)
-        else:
         self.conv = conv.Conv2d(in_channels,
                                 out_channels,
                                 kernel_size=kernel_size,
@@ -146,7 +133,6 @@ class Conv2dBnAct(Cell):
                                 has_bias=has_bias,
                                 weight_init=weight_init,
                                 bias_init=bias_init)
-
         self.has_bn = validator.check_bool("has_bn", has_bn)
         self.has_act = activation is not None
         self.after_fake = after_fake

model_zoo/official/cv/mobilenetv2/src/mobilenetV2.py

@@ -17,8 +17,7 @@ import numpy as np
 import mindspore.nn as nn
 from mindspore.ops import operations as P
 from mindspore.ops.operations import TensorAdd
-from mindspore import Parameter, Tensor
+from mindspore import Tensor
-from mindspore.common.initializer import initializer
 
 __all__ = ['MobileNetV2', 'MobileNetV2Backbone', 'MobileNetV2Head', 'mobilenet_v2']
 
@@ -55,52 +54,6 @@ class GlobalAvgPooling(nn.Cell):
         return x
 
 
-class DepthwiseConv(nn.Cell):
-    """
-    Depthwise Convolution warpper definition.
-
-    Args:
-        in_planes (int): Input channel.
-        kernel_size (int): Input kernel size.
-        stride (int): Stride size.
-        pad_mode (str): pad mode in (pad, same, valid)
-        channel_multiplier (int): Output channel multiplier
-        has_bias (bool): has bias or not
-
-    Returns:
-        Tensor, output tensor.
-
-    Examples:
-        >>> DepthwiseConv(16, 3, 1, 'pad', 1, channel_multiplier=1)
-    """
-
-    def __init__(self, in_planes, kernel_size, stride, pad_mode, pad, channel_multiplier=1, has_bias=False):
-        super(DepthwiseConv, self).__init__()
-        self.has_bias = has_bias
-        self.in_channels = in_planes
-        self.channel_multiplier = channel_multiplier
-        self.out_channels = in_planes * channel_multiplier
-        self.kernel_size = (kernel_size, kernel_size)
-        self.depthwise_conv = P.DepthwiseConv2dNative(channel_multiplier=channel_multiplier,
-                                                      kernel_size=self.kernel_size,
-                                                      stride=stride, pad_mode=pad_mode, pad=pad)
-        self.bias_add = P.BiasAdd()
-        weight_shape = [channel_multiplier, in_planes, *self.kernel_size]
-        self.weight = Parameter(initializer('ones', weight_shape), name='weight')
-
-        if has_bias:
-            bias_shape = [channel_multiplier * in_planes]
-            self.bias = Parameter(initializer('zeros', bias_shape), name='bias')
-        else:
-            self.bias = None
-
-    def construct(self, x):
-        output = self.depthwise_conv(x, self.weight)
-        if self.has_bias:
-            output = self.bias_add(output, self.bias)
-        return output
-
-
 class ConvBNReLU(nn.Cell):
     """
     Convolution/Depthwise fused with Batchnorm and ReLU block definition.
@@ -122,16 +75,14 @@ class ConvBNReLU(nn.Cell):
     def __init__(self, platform, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
         super(ConvBNReLU, self).__init__()
         padding = (kernel_size - 1) // 2
+        in_channels = in_planes
+        out_channels = out_planes
         if groups == 1:
-            conv = nn.Conv2d(in_planes, out_planes, kernel_size, stride, pad_mode='pad', padding=padding)
+            conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, pad_mode='pad', padding=padding)
         else:
-            if platform in ("CPU", "GPU"):
+            out_channels = in_planes
-                conv = nn.Conv2d(in_planes, out_planes, kernel_size, stride, group=in_planes, pad_mode='pad', \
+            conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, pad_mode='pad',
-                    padding=padding)
+                             padding=padding, group=in_channels)
-            elif platform == "Ascend":
-                conv = DepthwiseConv(in_planes, kernel_size, stride, pad_mode='pad', pad=padding)
-            else:
-                raise ValueError("Unsupported Device, only support CPU, GPU and Ascend.")
 
         layers = [conv, nn.BatchNorm2d(out_planes), nn.ReLU6()]
         self.features = nn.SequentialCell(layers)
@@ -188,6 +139,7 @@ class InvertedResidual(nn.Cell):
             return self.add(identity, x)
         return x
 
+
 class MobileNetV2Backbone(nn.Cell):
     """
     MobileNetV2 architecture.
@@ -258,7 +210,7 @@ class MobileNetV2Backbone(nn.Cell):
         """
         self.init_parameters_data()
         for _, m in self.cells_and_names():
-            if isinstance(m, (nn.Conv2d, DepthwiseConv)):
+            if isinstance(m, nn.Conv2d):
                 n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                 m.weight.set_data(Tensor(np.random.normal(0, np.sqrt(2. / n),
                                                           m.weight.data.shape).astype("float32")))
@@ -275,6 +227,7 @@ class MobileNetV2Backbone(nn.Cell):
     def get_features(self):
         return self.features
 
+
 class MobileNetV2Head(nn.Cell):
     """
     MobileNetV2 architecture.
@@ -325,6 +278,7 @@ class MobileNetV2Head(nn.Cell):
     def get_head(self):
         return self.head
 
+
 class MobileNetV2(nn.Cell):
     """
     MobileNetV2 architecture.
@@ -353,6 +307,7 @@ class MobileNetV2(nn.Cell):
         x = self.head(x)
         return x
 
+
 class MobileNetV2Combine(nn.Cell):
     """
     MobileNetV2 architecture.
@@ -380,5 +335,6 @@ class MobileNetV2Combine(nn.Cell):
         x = self.head(x)
         return x
 
+
 def mobilenet_v2(backbone, head):
     return MobileNetV2Combine(backbone, head)

model_zoo/official/cv/ssd/src/ssd.py

@@ -18,14 +18,13 @@
 import mindspore.common.dtype as mstype
 import mindspore as ms
 import mindspore.nn as nn
-from mindspore import Parameter, context, Tensor
+from mindspore import context, Tensor
 from mindspore.context import ParallelMode
 from mindspore.parallel._auto_parallel_context import auto_parallel_context
 from mindspore.communication.management import get_group_size
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore.ops import composite as C
-from mindspore.common.initializer import initializer
 
 
 def _make_divisible(v, divisor, min_value=None):
@@ -50,7 +49,10 @@ def _bn(channel):
 
 
 def _last_conv2d(in_channel, out_channel, kernel_size=3, stride=1, pad_mod='same', pad=0):
-    depthwise_conv = DepthwiseConv(in_channel, kernel_size, stride, pad_mode='same', pad=pad)
+    in_channels = in_channel
+    out_channels = in_channel
+    depthwise_conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, pad_mode='same',
+                               padding=pad, group=in_channels)
     conv = _conv2d(in_channel, out_channel, kernel_size=1)
     return nn.SequentialCell([depthwise_conv, _bn(in_channel), nn.ReLU6(), conv])
 
@@ -75,11 +77,14 @@ class ConvBNReLU(nn.Cell):
     def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
         super(ConvBNReLU, self).__init__()
         padding = 0
+        in_channels = in_planes
+        out_channels = out_planes
         if groups == 1:
-            conv = nn.Conv2d(in_planes, out_planes, kernel_size, stride, pad_mode='same',
+            conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, pad_mode='same', padding=padding)
-                             padding=padding)
         else:
-            conv = DepthwiseConv(in_planes, kernel_size, stride, pad_mode='same', pad=padding)
+            out_channels = in_planes
+            conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, pad_mode='same',
+                             padding=padding, group=in_channels)
         layers = [conv, _bn(out_planes), nn.ReLU6()]
         self.features = nn.SequentialCell(layers)
 
@@ -88,52 +93,6 @@ class ConvBNReLU(nn.Cell):
         return output
 
 
-class DepthwiseConv(nn.Cell):
-    """
-    Depthwise Convolution warpper definition.
-
-    Args:
-        in_planes (int): Input channel.
-        kernel_size (int): Input kernel size.
-        stride (int): Stride size.
-        pad_mode (str): pad mode in (pad, same, valid)
-        channel_multiplier (int): Output channel multiplier
-        has_bias (bool): has bias or not
-
-    Returns:
-        Tensor, output tensor.
-
-    Examples:
-        >>> DepthwiseConv(16, 3, 1, 'pad', 1, channel_multiplier=1)
-    """
-
-    def __init__(self, in_planes, kernel_size, stride, pad_mode, pad, channel_multiplier=1, has_bias=False):
-        super(DepthwiseConv, self).__init__()
-        self.has_bias = has_bias
-        self.in_channels = in_planes
-        self.channel_multiplier = channel_multiplier
-        self.out_channels = in_planes * channel_multiplier
-        self.kernel_size = (kernel_size, kernel_size)
-        self.depthwise_conv = P.DepthwiseConv2dNative(channel_multiplier=channel_multiplier,
-                                                      kernel_size=self.kernel_size,
-                                                      stride=stride, pad_mode=pad_mode, pad=pad)
-        self.bias_add = P.BiasAdd()
-        weight_shape = [channel_multiplier, in_planes, *self.kernel_size]
-        self.weight = Parameter(initializer('ones', weight_shape), name='weight')
-
-        if has_bias:
-            bias_shape = [channel_multiplier * in_planes]
-            self.bias = Parameter(initializer('zeros', bias_shape), name='bias')
-        else:
-            self.bias = None
-
-    def construct(self, x):
-        output = self.depthwise_conv(x, self.weight)
-        if self.has_bias:
-            output = self.bias_add(output, self.bias)
-        return output
-
-
 class InvertedResidual(nn.Cell):
     """
     Residual block definition.