Fix assertion bug (#4772)

* fix assertion bug

* fix assertion bug

ppdet/modeling/backbones/lite_hrnet.py

@@ -48,10 +48,8 @@ class ConvNormLayer(nn.Layer):
         self.act = act
         norm_lr = 0. if freeze_norm else 1.
         if norm_type is not None:
-            assert (
-                norm_type in ['bn', 'sync_bn', 'gn'],
-                "norm_type should be one of ['bn', 'sync_bn', 'gn'], but got {}".
-                format(norm_type))
+            assert norm_type in ['bn', 'sync_bn', 'gn'], \
+                "norm_type should be one of ['bn', 'sync_bn', 'gn'], but got {}".format(norm_type)
             param_attr = ParamAttr(
                 initializer=Constant(1.0),
                 learning_rate=norm_lr,
@@ -277,10 +275,8 @@ class ShuffleUnit(nn.Layer):
         branch_channel = out_channel // 2
         self.stride = stride
         if self.stride == 1:
-            assert (
-                in_channel == branch_channel * 2,
-                "when stride=1, in_channel {} should equal to branch_channel*2 {}"
-                .format(in_channel, branch_channel * 2))
+            assert in_channel == branch_channel * 2, \
+                "when stride=1, in_channel {} should equal to branch_channel*2 {}".format(in_channel, branch_channel * 2)
         if stride > 1:
             self.branch1 = nn.Sequential(
                 ConvNormLayer(
@@ -500,11 +496,11 @@ class LiteHRNetModule(nn.Layer):
                  freeze_norm=False,
                  norm_decay=0.):
         super(LiteHRNetModule, self).__init__()
-        assert (num_branches == len(in_channels),
-                "num_branches {} should equal to num_in_channels {}"
-                .format(num_branches, len(in_channels)))
-        assert (module_type in ['LITE', 'NAIVE'],
-                "module_type should be one of ['LITE', 'NAIVE']")
+        assert num_branches == len(in_channels),\
+            "num_branches {} should equal to num_in_channels {}".format(num_branches, len(in_channels))
+        assert module_type in [
+            'LITE', 'NAIVE'
+        ], "module_type should be one of ['LITE', 'NAIVE']"
         self.num_branches = num_branches
         self.in_channels = in_channels
         self.multiscale_output = multiscale_output
@@ -699,10 +695,8 @@ class LiteHRNet(nn.Layer):
         super(LiteHRNet, self).__init__()
         if isinstance(return_idx, Integral):
             return_idx = [return_idx]
-        assert (
-            network_type in ["lite_18", "lite_30", "naive", "wider_naive"],
+        assert network_type in ["lite_18", "lite_30", "naive", "wider_naive"], \
             "the network_type should be one of [lite_18, lite_30, naive, wider_naive]"
-        )
         assert len(return_idx) > 0, "need one or more return index"
         self.freeze_at = freeze_at
         self.freeze_norm = freeze_norm

ppdet/modeling/ops.py

@@ -1592,8 +1592,7 @@ def smooth_l1(input, label, inside_weight=None, outside_weight=None,
 
 def channel_shuffle(x, groups):
     batch_size, num_channels, height, width = x.shape[0:4]
-    assert (num_channels % groups == 0,
-            'num_channels should be divisible by groups')
+    assert num_channels % groups == 0, 'num_channels should be divisible by groups'
     channels_per_group = num_channels // groups
     x = paddle.reshape(
         x=x, shape=[batch_size, groups, channels_per_group, height, width])