lint with flake8-comprehensions

.github/workflows/workflow.yml

@@ -17,7 +17,7 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install flake8
+          pip install flake8 flake8-comprehensions flake8-bugbear
           flake8 --version
       - name: Lint
         run: |

scripts/dump-model-params.py

@@ -108,8 +108,8 @@ def guess_inputs(input_dir):
         if len(set(var_to_dump)) != len(var_to_dump):
             logger.warn("TRAINABLE and MODEL variables have duplication!")
         var_to_dump = list(set(var_to_dump))
-        globvarname = set([k.name for k in tf.global_variables()])
-        var_to_dump = set([k.name for k in var_to_dump if k.name in globvarname])
+        globvarname = {k.name for k in tf.global_variables()}
+        var_to_dump = {k.name for k in var_to_dump if k.name in globvarname}
 
         for name in var_to_dump:
             assert name in dic, "Variable {} not found in the model!".format(name)

tensorpack/callbacks/monitor.py

@@ -425,7 +425,7 @@ def __init__(self, enable_step=False, enable_epoch=True,
         def compile_regex(rs):
             if rs is None:
                 return None
-            rs = set([re.compile(r) for r in rs])
+            rs = {re.compile(r) for r in rs}
             return rs
 
         self._whitelist = compile_regex(whitelist)

tensorpack/contrib/keras.py

@@ -49,20 +49,20 @@ def __call__(self, *input_tensors):
         """
         reuse = tf.get_variable_scope().reuse
 
-        old_trainable_names = set([x.name for x in tf.trainable_variables()])
+        old_trainable_names = {x.name for x in tf.trainable_variables()}
         trainable_backup = backup_collection([tf.GraphKeys.TRAINABLE_VARIABLES])
         update_ops_backup = backup_collection([tf.GraphKeys.UPDATE_OPS])
 
         def post_process_model(model):
-            added_trainable_names = set([x.name for x in tf.trainable_variables()])
+            added_trainable_names = {x.name for x in tf.trainable_variables()}
             restore_collection(trainable_backup)
 
             for v in model.weights:
                 # In Keras, the collection is not respected and could contain non-trainable vars.
                 # We put M.weights into the collection instead.
                 if v.name not in old_trainable_names and v.name in added_trainable_names:
                     tf.add_to_collection(tf.GraphKeys.TRAINABLE_VARIABLES, v)
-            new_trainable_names = set([x.name for x in tf.trainable_variables()])
+            new_trainable_names = {x.name for x in tf.trainable_variables()}
 
             for n in added_trainable_names:
                 if n not in new_trainable_names:

tensorpack/dataflow/common.py

@@ -431,7 +431,7 @@ def __init__(self, df_lists):
         """
         super(RandomChooseData, self).__init__()
         if isinstance(df_lists[0], (tuple, list)):
-            assert sum([v[1] for v in df_lists]) == 1.0
+            assert sum(v[1] for v in df_lists) == 1.0
             self.df_lists = df_lists
         else:
             prob = 1.0 / len(df_lists)
@@ -512,7 +512,7 @@ def reset_state(self):
             d.reset_state()
 
     def __len__(self):
-        return sum([len(x) for x in self.df_lists])
+        return sum(len(x) for x in self.df_lists)
 
     def __iter__(self):
         for d in self.df_lists:
@@ -565,7 +565,7 @@ def __len__(self):
         """
         Return the minimum size among all.
         """
-        return min([len(k) for k in self.df_lists])
+        return min(len(k) for k in self.df_lists)
 
     def __iter__(self):
         itrs = [k.__iter__() for k in self.df_lists]

tensorpack/dataflow/format.py

@@ -45,7 +45,7 @@ def __init__(self, filename, data_paths, shuffle=True):
         logger.info("Loading {} to memory...".format(filename))
         self.dps = [self.f[k].value for k in data_paths]
         lens = [len(k) for k in self.dps]
-        assert all([k == lens[0] for k in lens])
+        assert all(k == lens[0] for k in lens)
         self._size = lens[0]
         self.shuffle = shuffle
 

tensorpack/graph_builder/distributed.py

@@ -230,7 +230,7 @@ def _shadow_model_variables(shadow_vars):
             list of (shadow_model_var, local_model_var) used for syncing.
         """
         G = tf.get_default_graph()
-        curr_shadow_vars = set([v.name for v in shadow_vars])
+        curr_shadow_vars = {v.name for v in shadow_vars}
         model_vars = tf.model_variables()
         shadow_model_vars = []
         for v in model_vars:
@@ -346,7 +346,7 @@ def strip_port(s):
                 return s[:-2]
             return s
         local_vars = tf.local_variables()
-        local_var_by_name = dict([(strip_port(v.name), v) for v in local_vars])
+        local_var_by_name = {strip_port(v.name): v for v in local_vars}
         ops = []
         nr_shadow_vars = len(self._shadow_vars)
         for v in self._shadow_vars:

tensorpack/graph_builder/training.py

@@ -63,7 +63,7 @@ def basename(x):
             return re.sub('tower[0-9]+/', '', x.op.name)
 
         if len(set(nvars)) != 1:
-            names_per_gpu = [set([basename(k[1]) for k in grad_and_vars]) for grad_and_vars in grad_list]
+            names_per_gpu = [{basename(k[1]) for k in grad_and_vars} for grad_and_vars in grad_list]
             inters = copy.copy(names_per_gpu[0])
             for s in names_per_gpu:
                 inters &= s
@@ -247,11 +247,11 @@ def build(self, grad_list, get_opt_fn):
 
         DataParallelBuilder._check_grad_list(grad_list)
 
-        dtypes = set([x[0].dtype.base_dtype for x in grad_list[0]])
+        dtypes = {x[0].dtype.base_dtype for x in grad_list[0]}
         dtypes_nccl_supported = [tf.float32, tf.float64]
         if get_tf_version_tuple() >= (1, 8):
             dtypes_nccl_supported.append(tf.float16)
-        valid_for_nccl = all([k in dtypes_nccl_supported for k in dtypes])
+        valid_for_nccl = all(k in dtypes_nccl_supported for k in dtypes)
         if self._mode == 'nccl' and not valid_for_nccl:
             logger.warn("Cannot use mode='nccl' because some gradients have unsupported types. Fallback to mode='cpu'")
             self._mode = 'cpu'
@@ -314,8 +314,8 @@ def get_post_init_ops():
         """
         # literally all variables, because it's better to sync optimizer-internal variables as well
         all_vars = tf.global_variables() + tf.local_variables()
-        var_by_name = dict([(v.name, v) for v in all_vars])
-        trainable_names = set([x.name for x in tf.trainable_variables()])
+        var_by_name = {v.name: v for v in all_vars}
+        trainable_names = {x.name for x in tf.trainable_variables()}
         post_init_ops = []
 
         def log_failure(name, reason):

tensorpack/graph_builder/utils.py

@@ -25,7 +25,7 @@ def _replace_global_by_local(kwargs):
     if 'collections' in kwargs:
         collections = kwargs['collections']
     if not collections:
-        collections = set([tf.GraphKeys.GLOBAL_VARIABLES])
+        collections = set(tf.GraphKeys.GLOBAL_VARIABLES)
     else:
         collections = set(collections.copy())
     collections.remove(tf.GraphKeys.GLOBAL_VARIABLES)
@@ -343,7 +343,7 @@ def compute_strategy(self, grads):
             logger.info("Skip GradientPacker due to too few gradients.")
             return False
         # should have the same dtype
-        dtypes = set([g.dtype for g in grads])
+        dtypes = {g.dtype for g in grads}
         if len(dtypes) != 1:
             logger.info("Skip GradientPacker due to inconsistent gradient types.")
             return False

tensorpack/input_source/input_source.py

@@ -471,7 +471,7 @@ def _setup(self, input_signature):
         self._spec = input_signature
         if self._dataset is not None:
             types = self._dataset.output_types
-            spec_types = tuple([k.dtype for k in input_signature])
+            spec_types = tuple(k.dtype for k in input_signature)
             assert len(types) == len(spec_types), \
                 "Dataset and input signature have different length! {} != {}".format(
                     len(types), len(spec_types))

tensorpack/models/conv2d.py

@@ -100,7 +100,7 @@ def Conv2D(
         filter_shape = kernel_shape + [in_channel / split, out_channel]
         stride = shape4d(strides, data_format=data_format)
 
-        kwargs = dict(data_format=data_format)
+        kwargs = {"data_format": data_format}
         if get_tf_version_tuple() >= (1, 5):
             kwargs['dilations'] = shape4d(dilation_rate, data_format=data_format)
 

tensorpack/tfutils/model_utils.py

@@ -42,14 +42,14 @@ def describe_trainable_vars():
         data.append([get_op_tensor_name(v.name)[0], shape, ele, v.device, v.dtype.base_dtype.name])
     headers = ['name', 'shape', '#elements', 'device', 'dtype']
 
-    dtypes = list(set([x[4] for x in data]))
+    dtypes = list({x[4] for x in data})
     if len(dtypes) == 1 and dtypes[0] == "float32":
         # don't log the dtype if all vars are float32 (default dtype)
         for x in data:
             del x[4]
         del headers[4]
 
-    devices = set([x[3] for x in data])
+    devices = {x[3] for x in data}
     if len(devices) == 1:
         # don't log the device if all vars on the same device
         for x in data:

tensorpack/tfutils/varmanip.py

@@ -150,7 +150,7 @@ def dump_session_params(path):
     var.extend(tf.get_collection(tf.GraphKeys.MODEL_VARIABLES))
     # TODO dedup
     assert len(set(var)) == len(var), "TRAINABLE and MODEL variables have duplication!"
-    gvars = set([k.name for k in tf.global_variables()])
+    gvars = {k.name for k in tf.global_variables()}
     var = [v for v in var if v.name in gvars]
     result = {}
     for v in var:
@@ -167,7 +167,7 @@ def save_chkpt_vars(dic, path):
         path: save as npz if the name ends with '.npz', otherwise save as a checkpoint.
     """
     logger.info("Variables to save to {}:".format(path))
-    keys = sorted(list(dic.keys()))
+    keys = sorted(dic.keys())
     logger.info(pprint.pformat(keys))
 
     assert not path.endswith('.npy')

tensorpack/utils/viz.py

@@ -91,8 +91,8 @@ def _pad_channel(plist):
 
     plist = _pad_channel(plist)
     shapes = [x.shape for x in plist]
-    ph = max([s[0] for s in shapes])
-    pw = max([s[1] for s in shapes])
+    ph = max(s[0] for s in shapes)
+    pw = max(s[1] for s in shapes)
 
     ret = np.zeros((len(plist), ph, pw, 3), dtype=plist[0].dtype)
     ret[:, :, :] = bgcolor

tests/benchmark-serializer.py

@@ -39,11 +39,11 @@ def benchmark_serializer(dumps, loads, data, num):
 
 def display_results(name, results):
     logger.info("Encoding benchmark for {}:".format(name))
-    data = sorted([(x, y[0]) for x, y in results], key=operator.itemgetter(1))
+    data = sorted(((x, y[0]) for x, y in results), key=operator.itemgetter(1))
     print(tabulate(data, floatfmt='.5f'))
 
     logger.info("Decoding benchmark for {}:".format(name))
-    data = sorted([(x, y[1]) for x, y in results], key=operator.itemgetter(1))
+    data = sorted(((x, y[1]) for x, y in results), key=operator.itemgetter(1))
     print(tabulate(data, floatfmt='.5f'))
 
 
@@ -64,8 +64,8 @@ def fake_json_data():
             pellentesque quis sollicitudin id, adipiscing.
             """ * 100,
         'list': list(range(100)) * 500,
-        'dict': dict((str(i), 'a') for i in range(50000)),
-        'dict2': dict((i, 'a') for i in range(50000)),
+        'dict': {str(i): 'a' for i in range(50000)},
+        'dict2': {i: 'a' for i in range(50000)},
         'int': 3000,
         'float': 100.123456
     }

tox.ini

@@ -1,7 +1,7 @@
 [flake8]
 max-line-length = 120
 # See https://pep8.readthedocs.io/en/latest/intro.html#error-codes
-ignore = E265,E741,E742,E743,W504,W605
+ignore = E265,E741,E742,E743,W504,W605,C408
 exclude = .git,
           __init__.py,
           setup.py,