Delete 'model/attention/ShuffleAttention.py'

model/attention/ShuffleAttention.py

@@ -1,58 +0,0 @@
-"""
-MindSpore implementation of 'ShuffleAttention'
-Refer to "SA-NET: SHUFFLE ATTENTION FOR DEEP CONVOLUTIONAL NEURAL NETWORKS"
-"""
-import mindspore as ms
-from mindspore import nn
-
-
-class ShuffleAttention(nn.Cell):
-    """ ShuffleAttention """
-    def __init__(self, channels=512, G=8):
-        super().__init__()
-        self.G = G
-        self.channels = channels
-        self.avg_pool = nn.AdaptiveAvgPool2d(1)
-        self.gn = nn.GroupNorm(channels // (2 * G), channels // (2 * G))
-        self.cweight = ms.Parameter(ms.ops.zeros((1, channels // (2 * G), 1, 1)))
-        self.cbias = ms.Parameter(ms.ops.ones((1, channels // (2 * G), 1, 1)))
-        self.sweight = ms.Parameter(ms.ops.zeros((1, channels // (2 * G), 1, 1)))
-        self.sbias = ms.Parameter(ms.ops.ones((1, channels // (2 * G), 1, 1)))
-        self.sigmoid = nn.Sigmoid()
-
-    @staticmethod
-    def channel_shuffle(x, groups):
-        """ channel shuffle """
-        B, _, H, W = x.shape
-        x = x.reshape(B, groups, -1, H, W)
-        x = x.permute(0, 2, 1, 3, 4)
-        x = x.reshape(B, -1, H, W)
-        return x
-
-    def construct(self, x):
-        B, _, H, W = x.shape
-        x = x.view(B * self.G, -1, H, W)
-
-        x_0, x_1 = x.chunk(2, axis=1)
-
-        # channel attention
-        x_channel = self.avg_pool(x_0)
-        x_channel = self.cweight * x_channel + self.cbias
-        x_channel = x_0 * self.sigmoid(x_channel)
-
-        # spatial attention
-        x_spatial = self.gn(x_1)
-        x_spatial = self.sweight * x_spatial + self.sbias
-        x_spatial = x_1 * self.sigmoid(x_spatial)
-
-        out = ms.ops.cat((x_channel, x_spatial), axis=1)
-        out = out.view(B, -1, H, W)
-        out = self.channel_shuffle(out, 2)
-        return out
-
-
-if __name__ == '__main__':
-    dummy_input = ms.ops.randn(50, 512, 7, 7)
-    se = ShuffleAttention(channels=512, G=8)
-    output = se(dummy_input)
-    print(output.shape)