upgrade pylint version for python3 and fix lints

tfjs-converter/python/requirements-dev.txt

@@ -1,3 +1,3 @@
 -r requirements.txt
 pylint==1.9.4; python_version < '3.0'
-pylint==2.3.0; python_version > '3.0'
+pylint==2.5.0; python_version > '3.0'

tfjs-converter/python/tensorflowjs/converters/fold_batch_norms_test.py

@@ -225,7 +225,7 @@ def _generate_fused_batchnorm(data_format, conv2d_func, count=1,
       np.array(inputs),
       shape=[1, 1, 6, 2] if data_format == "NHWC" else [1, 2, 1, 6],
       dtype=dtypes.float32)
-  if conv2d_func == nn_ops.conv2d:
+  if conv2d_func == nn_ops.conv2d: # pylint: disable=W0143
     weights = [1, 2, 3, 4, 0.1, 0.2, 0.3, 0.4]
     weights_op = constant_op.constant(
         np.array(weights), shape=[1, 2, 2, 2], dtype=dtypes.float32)

tfjs-converter/python/tensorflowjs/converters/keras_h5_conversion.py

@@ -66,7 +66,8 @@ def _convert_h5_group(group):
   group_out = []
   if 'weight_names' in group.attrs:
     # This is a leaf node in namespace (e.g., 'Dense' in 'foo/bar/Dense').
-    names = [name for name in group.attrs['weight_names']]
+    names = group.attrs['weight_names'].tolist()
+
     if not names:
       return group_out
 

tfjs-converter/python/tensorflowjs/quantization.py

@@ -68,7 +68,7 @@ def map_layers_to_quantization_dtype(names, quantization_dtype_map):
             'exactly one is supported')
       fallthrough = dtype_name
       continue
-    elif isinstance(patterns, str):
+    if isinstance(patterns, str):
       patterns = list([patterns])
 
     # Record matched weights for dtype