Flake8 fixes (tensorflow#320)

Autopep8 + flake8

dual_net.py

@@ -21,7 +21,6 @@
 from absl import flags
 import argparse
 import functools
-import math
 import os.path
 import sys
 
@@ -214,7 +213,7 @@ def model_fn(features, labels, mode, params=None):
         tf.square(value_output - labels['value_tensor']))
 
     reg_vars = [v for v in tf.trainable_variables()
-                if not 'bias' in v.name and not 'beta' in v.name]
+                if 'bias' not in v.name and 'beta' not in v.name]
     l2_cost = FLAGS.l2_strength * \
         tf.add_n([tf.nn.l2_loss(v) for v in reg_vars])
 

example_buffer.py

@@ -6,7 +6,6 @@
 import multiprocessing as mp
 import os
 import random
-import re
 import subprocess
 import time
 from collections import deque
@@ -94,7 +93,7 @@ def update(self, new_games):
                 self.total_updates += num_new_games
             self.examples.extend(self.func(game))
         if first_new_game is None:
-            print ("No new games", file_timestamp(new_games[-1]), self.examples[-1][0])
+            print("No new games", file_timestamp(new_games[-1]), self.examples[-1][0])
 
     def flush(self, path):
         # random.shuffle on deque is O(n^2) convert to list for O(n)
@@ -122,6 +121,7 @@ def __str__(self):
 def files_for_model(model):
     return tf.gfile.Glob(os.path.join(LOCAL_DIR, model[1], '*.zz'))
 
+
 def smart_rsync(
         from_model_num=0,
         source_dir=None,
@@ -133,6 +133,7 @@ def smart_rsync(
         _rsync_dir(os.path.join(
             source_dir, model), os.path.join(dest_dir, model))
 
+
 def time_rsync(from_date,
                source_dir=None,
                dest_dir=LOCAL_DIR):
@@ -178,6 +179,7 @@ def _determine_chunk_to_make(write_dir):
 
     return chunk_to_make, False
 
+
 def get_window_size(chunk_num):
     """ Adjust the window size by how far we are through a run.
     At the start of the run, there's a benefit to 'expiring' the completely
@@ -186,10 +188,11 @@ def get_window_size(chunk_num):
     """
     return min(500000, (chunk_num + 5) * (AVG_GAMES_PER_MODEL // 2))
 
+
 def fill_and_wait_time(bufsize=dual_net.EXAMPLES_PER_GENERATION,
-                  write_dir=None,
-                  threads=32,
-                  start_from=None):
+                       write_dir=None,
+                       threads=32,
+                       start_from=None):
     start_from = start_from or dt.datetime.utcnow()
     write_dir = write_dir or fsdb.golden_chunk_dir()
     buf = ExampleBuffer(bufsize)
@@ -202,8 +205,7 @@ def fill_and_wait_time(bufsize=dual_net.EXAMPLES_PER_GENERATION,
 
     hours = fsdb.get_hour_dirs()
     files = (tf.gfile.Glob(os.path.join(LOCAL_DIR, d, "*.zz"))
-                 for d in reversed(hours)
-             if tf.gfile.Exists(os.path.join(LOCAL_DIR, d)))
+             for d in reversed(hours) if tf.gfile.Exists(os.path.join(LOCAL_DIR, d)))
     files = itertools.islice(files, get_window_size(chunk_to_make))
 
     models = fsdb.get_models()
@@ -216,32 +218,32 @@ def fill_and_wait_time(bufsize=dual_net.EXAMPLES_PER_GENERATION,
         start_from = dt.datetime.utcnow()
         hours = sorted(fsdb.get_hour_dirs(LOCAL_DIR))
         new_files = list(map(lambda d: tf.gfile.Glob(
-                os.path.join(LOCAL_DIR, d, '*.zz')), hours[-2:]))
+            os.path.join(LOCAL_DIR, d, '*.zz')), hours[-2:]))
         buf.update(list(itertools.chain.from_iterable(new_files)))
         if fast_write:
             break
         time.sleep(30)
         if fsdb.get_latest_model() != models[-1]:
-            print ("New model!  Waiting for games. Got", buf.total_updates, "new games so far")
+            print("New model!  Waiting for games. Got", buf.total_updates, "new games so far")
 
     latest = fsdb.get_latest_model()
     print("New model!", latest[1], "!=", models[-1][1])
     print(buf)
     buf.flush(chunk_to_make)
 
+
 def fill_and_wait_models(bufsize=dual_net.EXAMPLES_PER_GENERATION,
-                  write_dir=None,
-                  threads=8,
-                  model_window=100,
-                  skip_first_rsync=False):
+                         write_dir=None,
+                         threads=8,
+                         model_window=100,
+                         skip_first_rsync=False):
     """ Fills a ringbuffer with positions from the most recent games, then
     continually rsync's and updates the buffer until a new model is promoted.
     Once it detects a new model, iit then dumps its contents for training to
     immediately begin on the next model.
     """
     write_dir = write_dir or fsdb.golden_chunk_dir()
     buf = ExampleBuffer(bufsize)
-    chunk_to_make = _determine_chunk_to_make(write_dir)
     models = fsdb.get_models()[-model_window:]
     if not skip_first_rsync:
         with timer("Rsync"):

fsdb.py

@@ -41,12 +41,14 @@
 
 FLAGS = flags.FLAGS
 
+
 def _with_base(*args):
     def inner():
         base_dir = FLAGS.base_dir or 'gs://{}'.format(FLAGS.bucket_name)
         return os.path.join(base_dir, *args)
     return inner
 
+
 # Functions to compute various important directories, based on FLAGS input.
 models_dir = _with_base('models')
 selfplay_dir = _with_base('data', 'selfplay')
@@ -84,6 +86,7 @@ def get_model(model_num):
     model_names_by_num = dict(get_models())
     return model_names_by_num[model_num]
 
+
 def get_hour_dirs(root=None):
     """Gets the directories under selfplay_dir that match YYYY-MM-DD-HH."""
     root = root or selfplay_dir()
@@ -96,4 +99,3 @@ def game_counts(n_back=20):
     for _, model_name in get_models[-n_back:]:
         games = gfile.Glob(os.path.join(selfplay_dir(), model_name, '*.zz'))
         print(model_name, len(games))
-

go.py

@@ -96,7 +96,7 @@ def find_reached(board, c):
         current = frontier.pop()
         chain.add(current)
         for n in NEIGHBORS[current]:
-            if board[n] == color and not n in chain:
+            if board[n] == color and n not in chain:
                 frontier.append(n)
             elif board[n] != color:
                 reached.add(n)
@@ -108,7 +108,7 @@ def is_koish(board, c):
     if board[c] != EMPTY:
         return None
     neighbors = {board[n] for n in NEIGHBORS[c]}
-    if len(neighbors) == 1 and not EMPTY in neighbors:
+    if len(neighbors) == 1 and EMPTY not in neighbors:
         return list(neighbors)[0]
     else:
         return None
@@ -303,7 +303,8 @@ def __init__(self, board=None, n=0, komi=7.5, caps=(0, 0),
         '''
         assert type(recent) is tuple
         self.board = board if board is not None else np.copy(EMPTY_BOARD)
-        self.n = n  # With a full history, self.n == len(self.recent) == num moves played
+        # With a full history, self.n == len(self.recent) == num moves played
+        self.n = n
         self.komi = komi
         self.caps = caps
         self.lib_tracker = lib_tracker or LibertyTracker.from_board(self.board)
@@ -401,7 +402,7 @@ def all_legal_moves(self):
         legal_moves[self.board != EMPTY] = 0
         # calculate which spots have 4 stones next to them
         # padding is because the edge always counts as a lost liberty.
-        adjacent = np.ones([N+2, N+2], dtype=np.int8)
+        adjacent = np.ones([N + 2, N + 2], dtype=np.int8)
         adjacent[1:-1, 1:-1] = np.abs(self.board)
         num_adjacent_stones = (adjacent[:-2, 1:-1] + adjacent[1:-1, :-2] +
                                adjacent[2:, 1:-1] + adjacent[1:-1, 2:])

gtp.py

@@ -39,6 +39,7 @@
 
 FLAGS = flags.FLAGS
 
+
 def make_gtp_instance(load_file, cgos_mode=False, kgs_mode=False, verbosity=1,
                       num_readouts=None):
     '''Takes a path to model files and set up a GTP engine instance.'''
@@ -65,6 +66,7 @@ def make_gtp_instance(load_file, cgos_mode=False, kgs_mode=False, verbosity=1,
 
     return engine
 
+
 def main(argv):
     '''Run Minigo in GTP mode.'''
     del argv
@@ -77,5 +79,6 @@ def main(argv):
         if not engine.handle_msg(msg.strip()):
             break
 
+
 if __name__ == '__main__':
     app.run(main)

gtp_cmd_handlers.py

@@ -49,8 +49,8 @@ def cmd_boardsize(self, n: int):
 
     def cmd_clear_board(self):
         position = self._player.get_position()
-        if (self._player.get_result_string()
-                and position and len(position.recent) > 1):
+        if (self._player.get_result_string() and
+                position and len(position.recent) > 1):
             try:
                 sgf = self._player.to_sgf()
                 with open(datetime.now().strftime("%Y-%m-%d-%H:%M.sgf"), 'w') as f:

inference_worker.py

@@ -25,10 +25,7 @@
 import sys
 import time
 import tensorflow as tf
-from tensorflow.python.framework import dtypes
 from tensorflow.python.training import saver
-from tensorflow.contrib.proto.python.ops import decode_proto_op
-from tensorflow.contrib.proto.python.ops import encode_proto_op
 import threading
 import numpy as np
 from absl import flags
@@ -131,7 +128,7 @@ def const_model_inference_fn(features):
     def custom_getter(getter, name, *args, **kwargs):
         with tf.control_dependencies(None):
             return tf.guarantee_const(
-                getter(name, *args, **kwargs), name=name+"/GuaranteeConst")
+                getter(name, *args, **kwargs), name=name + "/GuaranteeConst")
     with tf.variable_scope("", custom_getter=custom_getter):
         return dual_net.model_inference_fn(features, False)
 
@@ -312,7 +309,6 @@ def _prepare_features(self, raw_features):
         features = []
         for i in range(self._parallel_tpus):
             begin = i * num_features
-            end = begin + num_features
             x = np.frombuffer(
                 raw_features, dtype=np.int8, count=num_features, offset=begin)
             x = x.reshape([self._batch_size, go.N, go.N,

local_rl_loop.py

@@ -20,17 +20,13 @@
 """
 
 import os
-import sys
 import tempfile
 
 from absl import flags
-import preprocessing
 import dual_net
-import go
 import main
 import selfplay
 import example_buffer as eb
-from tensorflow import gfile
 import subprocess
 
 
@@ -117,6 +113,7 @@ def rl_loop():
             sgf_dir=sgf_dir,
             holdout_pct=0)
 
+
 if __name__ == '__main__':
     # horrible horrible hack to pass flag validation.
     # Problems come from local_rl_loop calling into main() as library calls

main.py

@@ -15,21 +15,16 @@
 import argh
 import argparse
 import os.path
-import random
-import socket
 import sys
 import tempfile
-import time
 
 import dual_net
 import evaluation
-import preprocessing
 import utils
 
 import cloud_logging
 import tensorflow as tf
 from absl import flags
-from tqdm import tqdm
 from tensorflow import gfile
 
 # How many positions we should aggregate per 'chunk'.
@@ -67,7 +62,7 @@ def train_dir(
 
 
 def train(tf_records: 'list of files of tf_records to train on',
-        model_save_path: 'Where to export the completed generation.'):
+          model_save_path: 'Where to export the completed generation.'):
     print("Training on:", tf_records[0], "to", tf_records[-1])
     with utils.logged_timer("Training"):
         dual_net.train(*tf_records)
@@ -107,8 +102,6 @@ def evaluate(
             black_net, white_net, games, output_dir, verbose)
 
 
-
-
 def convert(load_file, dest_file):
     from tensorflow.python.framework import meta_graph
     features, labels = dual_net.get_inference_input()

mcts.py

@@ -94,9 +94,8 @@ def __repr__(self):
 
     @property
     def child_action_score(self):
-        return (self.child_Q * self.position.to_play
-            + self.child_U
-            - 1000 * self.illegal_moves)
+        return (self.child_Q * self.position.to_play +
+                self.child_U - 1000 * self.illegal_moves)
 
     @property
     def child_Q(self):
@@ -142,9 +141,9 @@ def select_leaf(self):
                 break
             # HACK: if last move was a pass, always investigate double-pass first
             # to avoid situations where we auto-lose by passing too early.
-            if (current.position.recent
-                and current.position.recent[-1].move is None
-                    and current.child_N[pass_move] == 0):
+            if (current.position.recent and
+                current.position.recent[-1].move is None and
+                    current.child_N[pass_move] == 0):
                 current = current.maybe_add_child(pass_move)
                 continue
 

minigui/serve.py

@@ -12,7 +12,6 @@
 import json
 import logging
 import subprocess
-from threading import Lock
 
 parser = argparse.ArgumentParser()
 
@@ -150,4 +149,4 @@ def index():
 
 
 if __name__ == "__main__":
-  socketio.run(app, port=args.port, host=args.host)
+    socketio.run(app, port=args.port, host=args.host)

oneoffs/compare_examples.py

@@ -53,7 +53,7 @@ def ReadExamples(path):
 
 
 def main(unused_argv):
-    with tf.Session() as sess:
+    with tf.Session() as _:
         examples_a = ReadExamples(FLAGS.a)
         examples_b = ReadExamples(FLAGS.b)
     print(len(examples_a), len(examples_b))

oneoffs/heatmap.py

@@ -9,10 +9,8 @@
 import sys
 sys.path.insert(0, '.')
 
-import itertools
 import os
 
-import numpy as np
 import tensorflow as tf
 from absl import app, flags
 from tqdm import tqdm

oneoffs/l2_cost_by_var.py

@@ -76,7 +76,7 @@ def dual_net_list(model):
     print("Dual Net will calculate L2 cost over these variables")
     with dual.sess.graph.as_default():
         var_names = [v.name for v in tf.trainable_variables()]
-        _ = reduce_and_print_vars(var_names)
+        reduce_and_print_vars(var_names)
     print()