fix effnet.py

mindspore/lite/examples/export_models/models/effnet.py

@@ -25,24 +25,20 @@ def weight_variable():
     return TruncatedNormal(0.02)
 
 
-def _make_divisible(v, divisor, min_value=None):
+def _make_value_divisible(value, factor, min_value=None):
     """
-    This function is taken from the original tf repo.
     It ensures that all layers have a channel number that is divisible by 8
-    It can be seen here:
-    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
-    :param v:
-    :param divisor:
-    iparam min_value:
-    :return:
+    :param v: value to process
+    :param factor: divisor
+    :param min_value: new value always greater than the min_value
+    :return: new value
     """
     if min_value is None:
-        min_value = divisor
-    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
-    # Make sure that round down does not go down by more than 10%.
-    if new_v < 0.9 * v:
-        new_v += divisor
-    return new_v
+        min_value = factor
+    new_value = max(int(value + factor / 2) // factor * factor, min_value)
+    if new_value < value * 0.9:
+        new_value += factor
+    return new_value
 
 class Swish(nn.Cell):
     def __init__(self):
@@ -69,7 +65,7 @@ class SELayer(nn.Cell):
     """SELayer"""
     def __init__(self, channel, reduction=4):
         super().__init__()
-        reduced_chs = _make_divisible(channel/reduction, 1)
+        reduced_chs = _make_value_divisible(channel/reduction, 1)
         self.avg_pool = AdaptiveAvgPool(output_size=(1, 1))
         weight = weight_variable()
         self.conv_reduce = nn.Conv2d(in_channels=channel, out_channels=reduced_chs, kernel_size=1, has_bias=True,
@@ -152,7 +148,7 @@ class InvertedResidual(nn.Cell):
     def __init__(self, in_chs, out_chs, kernel_size, stride, padding, expansion, se_ratio):
         super().__init__()
         assert stride in [1, 2]
-        mid_chs: int = _make_divisible(in_chs * expansion, 1)
+        mid_chs: int = _make_value_divisible(in_chs * expansion, 1)
         self.has_residual = (in_chs == out_chs and stride == 1)
         self.drop_connect_rate = 0