!1632 modify ssd script for merging backbone

Merge pull request !1632 from chengxb7532/master

mindspore/model_zoo/ssd.py

@@ -24,7 +24,8 @@ 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
-from .mobilenet import InvertedResidual, ConvBNReLU
+from mindspore.ops.operations import TensorAdd
+from mindspore import Parameter
 
 
 def _conv2d(in_channel, out_channel, kernel_size=3, stride=1, pad_mod='same'):
@@ -45,6 +46,129 @@ def _make_divisible(v, divisor, min_value=None):
     return new_v
 
 
+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.
+
+    Args:
+        in_planes (int): Input channel.
+        out_planes (int): Output channel.
+        kernel_size (int): Input kernel size.
+        stride (int): Stride size for the first convolutional layer. Default: 1.
+        groups (int): channel group. Convolution is 1 while Depthiwse is input channel. Default: 1.
+
+    Returns:
+        Tensor, output tensor.
+
+    Examples:
+        >>> ConvBNReLU(16, 256, kernel_size=1, stride=1, groups=1)
+    """
+    def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
+        super(ConvBNReLU, self).__init__()
+        padding = (kernel_size - 1) // 2
+        if groups == 1:
+            conv = nn.Conv2d(in_planes, out_planes, kernel_size, stride, pad_mode='pad',
+                             padding=padding)
+        else:
+            conv = DepthwiseConv(in_planes, kernel_size, stride, pad_mode='pad', pad=padding)
+        layers = [conv, nn.BatchNorm2d(out_planes), nn.ReLU6()]
+        self.features = nn.SequentialCell(layers)
+
+    def construct(self, x):
+        output = self.features(x)
+        return output
+
+
+class InvertedResidual(nn.Cell):
+    """
+    Mobilenetv2 residual block definition.
+
+    Args:
+        inp (int): Input channel.
+        oup (int): Output channel.
+        stride (int): Stride size for the first convolutional layer. Default: 1.
+        expand_ratio (int): expand ration of input channel
+
+    Returns:
+        Tensor, output tensor.
+
+    Examples:
+        >>> ResidualBlock(3, 256, 1, 1)
+    """
+    def __init__(self, inp, oup, stride, expand_ratio):
+        super(InvertedResidual, self).__init__()
+        assert stride in [1, 2]
+
+        hidden_dim = int(round(inp * expand_ratio))
+        self.use_res_connect = stride == 1 and inp == oup
+
+        layers = []
+        if expand_ratio != 1:
+            layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
+        layers.extend([
+            # dw
+            ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
+            # pw-linear
+            nn.Conv2d(hidden_dim, oup, kernel_size=1, stride=1, has_bias=False),
+            nn.BatchNorm2d(oup),
+        ])
+        self.conv = nn.SequentialCell(layers)
+        self.add = TensorAdd()
+        self.cast = P.Cast()
+
+    def construct(self, x):
+        identity = x
+        x = self.conv(x)
+        if self.use_res_connect:
+            return self.add(identity, x)
+        return x
+
+
 class FlattenConcat(nn.Cell):
     """
     Concatenate predictions into a single tensor.
@@ -57,20 +181,17 @@ class FlattenConcat(nn.Cell):
     """
     def __init__(self, config):
         super(FlattenConcat, self).__init__()
-        self.sizes = config.FEATURE_SIZE
+        self.num_ssd_boxes = config.NUM_SSD_BOXES
-        self.length = len(self.sizes)
+        self.concat = P.Concat(axis=1)
-        self.num_default = config.NUM_DEFAULT
-        self.concat = P.Concat(axis=-1)
         self.transpose = P.Transpose()
-    def construct(self, x):
+    def construct(self, inputs):
         output = ()
-        for i in range(self.length):
+        batch_size = F.shape(inputs[0])[0]
-            shape = F.shape(x[i])
+        for x in inputs:
-            mid_shape = (shape[0], -1, self.num_default[i], self.sizes[i], self.sizes[i])
+            x = self.transpose(x, (0, 2, 3, 1))
-            final_shape = (shape[0], -1, self.num_default[i] * self.sizes[i] * self.sizes[i])
+            output += (F.reshape(x, (batch_size, -1)),)
-            output += (F.reshape(F.reshape(x[i], mid_shape), final_shape),)
         res = self.concat(output)
-        return self.transpose(res, (0, 2, 1))
+        return F.reshape(res, (batch_size, self.num_ssd_boxes, -1))
 
 
 class MultiBox(nn.Cell):
@@ -145,7 +266,6 @@ class SSD300(nn.Cell):
         if not is_training:
             self.softmax = P.Softmax()
 
-
     def construct(self, x):
         layer_out_13, output = self.backbone(x)
         multi_feature = (layer_out_13, output)