Use stricter linter in examples. Clean-up imports

examples/A3C-Gym/simulator.py

@@ -3,7 +3,6 @@
 # File: simulator.py
 # Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
-import tensorflow as tf
 import multiprocessing as mp
 import time
 import os
@@ -15,9 +14,6 @@ import six
 from six.moves import queue
 import zmq
 
-from tensorpack.callbacks import Callback
-from tensorpack.tfutils.varmanip import SessionUpdate
-from tensorpack.predict import OfflinePredictor
 from tensorpack.utils import logger
 from tensorpack.utils.serialize import loads, dumps
 from tensorpack.utils.concurrency import LoopThread, ensure_proc_terminate

examples/A3C-Gym/train-atari.py

@@ -5,13 +5,8 @@
 
 import numpy as np
 import os
-import sys
-import time
-import random
 import uuid
 import argparse
-import multiprocessing
-import threading
 
 import cv2
 import tensorflow as tf
@@ -20,18 +15,16 @@ from six.moves import queue
 
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
 from tensorpack import *
-from tensorpack.utils.concurrency import *
-from tensorpack.utils.serialize import *
-from tensorpack.utils.stats import *
+from tensorpack.utils.concurrency import ensure_proc_terminate, start_proc_mask_signal
+from tensorpack.utils.serialize import dumps
 from tensorpack.tfutils import symbolic_functions as symbf
 from tensorpack.tfutils.gradproc import MapGradient, SummaryGradient
 from tensorpack.utils.gpu import get_nr_gpu
 
 
 import gym
-from simulator import *
-from common import (Evaluator, eval_model_multithread,
-                    play_one_episode, play_n_episodes)
+from simulator import SimulatorProcess, SimulatorMaster, TransitionExperience
+from common import Evaluator, eval_model_multithread, play_n_episodes
 from atari_wrapper import MapState, FrameStack, FireResetEnv, LimitLength
 
 if six.PY3:

examples/CTC-TIMIT/create-lmdb.py

@@ -2,16 +2,16 @@
 # -*- coding: utf-8 -*-
 # File: create-lmdb.py
 # Author: Yuxin Wu <ppwwyyxxc@gmail.com>
-import sys
 import os
 import scipy.io.wavfile as wavfile
 import string
 import numpy as np
 import argparse
 
-from tensorpack import *
+from tensorpack.dataflow import dftools, DataFlow, LMDBDataPoint
 from tensorpack.utils.argtools import memoized
 from tensorpack.utils.stats import OnlineMoments
+from tensorpack.utils import serialize, fs, logger
 from tensorpack.utils.utils import get_tqdm
 import bob.ap
 
@@ -34,6 +34,7 @@ def read_timit_txt(f):
     line = line.replace('.', '').lower()
     line = filter(lambda c: c in CHARSET, line)
     f.close()
+    ret = []
     for c in line:
         ret.append(WORD_DIC[c])
     return np.asarray(ret)

examples/CTC-TIMIT/train-timit.py

@@ -3,20 +3,13 @@
 # File: train-timit.py
 # Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
-import numpy as np
 import os
-import sys
 import argparse
-from collections import Counter
-import operator
-import six
-from six.moves import map, range
+from six.moves import range
 
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
 from tensorpack import *
 from tensorpack.tfutils.gradproc import SummaryGradient, GlobalNormClip
-from tensorpack.utils.globvars import globalns as param
-import tensorpack.tfutils.symbolic_functions as symbf
 import tensorflow as tf
 
 from timitdata import TIMITBatch

examples/Char-RNN/char-rnn.py

@@ -10,7 +10,7 @@ import argparse
 from collections import Counter
 import operator
 import six
-from six.moves import map, range
+from six.moves import range
 
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
 from tensorpack import *
@@ -96,7 +96,7 @@ class Model(ModelDesc):
         # seqlen x (Bxrnnsize)
         output = tf.reshape(tf.concat(outputs, 1), [-1, param.rnn_size])  # (Bxseqlen) x rnnsize
         logits = FullyConnected('fc', output, param.vocab_size, nl=tf.identity)
-        prob = tf.nn.softmax(logits / param.softmax_temprature, name='prob')
+        tf.nn.softmax(logits / param.softmax_temprature, name='prob')
 
         xent_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
             logits=logits, labels=tf.reshape(nextinput, [-1]))

examples/ConvolutionalPoseMachines/load-cpm.py

@@ -33,7 +33,6 @@ def colorize(img, heatmap):
 
 @memoized
 def get_gaussian_map():
-    sigma = 21
     gaussian_map = np.zeros((368, 368), dtype='float32')
     for x_p in range(368):
         for y_p in range(368):
@@ -93,9 +92,8 @@ def CPM(image):
         out3 = add_stage(3, out2)
         out4 = add_stage(4, out3)
         out5 = add_stage(5, out4)
-        out6 = add_stage(6, out4)
-        resized_map = tf.image.resize_bilinear(out6,
-                                               [368, 368], name='resized_map')
+        out6 = add_stage(6, out5)
+        tf.image.resize_bilinear(out6, [368, 368], name='resized_map')
 
 
 def run_test(model_path, img_file):

examples/DeepQNetwork/DQN.py

@@ -3,24 +3,13 @@
 # File: DQN.py
 # Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
-import numpy as np
-
 import os
-import sys
-import re
-import time
-import random
 import argparse
-import subprocess
-import multiprocessing
-import threading
-from collections import deque
 import cv2
+import tensorflow as tf
 
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
 from tensorpack import *
-from tensorpack.utils.concurrency import *
-import tensorflow as tf
 
 from DQNModel import Model as DQNModel
 from common import Evaluator, eval_model_multithread, play_n_episodes

examples/DeepQNetwork/DQNModel.py

@@ -9,8 +9,7 @@ import tensorpack
 from tensorpack import ModelDesc, InputDesc
 from tensorpack.utils import logger
 from tensorpack.tfutils import (
-    collection, summary, get_current_tower_context, optimizer, gradproc)
-from tensorpack.tfutils import symbolic_functions as symbf
+    summary, get_current_tower_context, optimizer, gradproc)
 from tensorpack.tfutils.scope_utils import auto_reuse_variable_scope
 assert tensorpack.tfutils.common.get_tf_version_number() >= 1.2
 

examples/DeepQNetwork/README.md

@@ -2,7 +2,7 @@
 
 [video demo](https://youtu.be/o21mddZtE5Y)
 
-Reproduce the following reinforcement learning methods:
+Reproduce (performance of) the following reinforcement learning methods:
 
 + Nature-DQN in:
 [Human-level Control Through Deep Reinforcement Learning](http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html)

examples/DeepQNetwork/atari.py

@@ -13,7 +13,6 @@ from six.moves import range
 from tensorpack.utils import logger
 from tensorpack.utils.utils import get_rng, execute_only_once
 from tensorpack.utils.fs import get_dataset_path
-from tensorpack.utils.stats import StatCounter
 
 import gym
 from gym import spaces
@@ -157,7 +156,7 @@ class AtariPlayer(gym.Env):
                     (self.live_lost_as_eoe and newlives < oldlives):
                 break
 
-        trueIsOver = isOver = self.ale.game_over()
+        isOver = self.ale.game_over()
         if self.live_lost_as_eoe:
             isOver = isOver or newlives < oldlives
 

examples/DeepQNetwork/atari_wrapper.py

@@ -3,7 +3,6 @@
 # File: atari_wrapper.py
 
 import numpy as np
-import cv2
 from collections import deque
 
 import gym

examples/DeepQNetwork/expreplay.py

@@ -7,7 +7,6 @@ import numpy as np
 import copy
 from collections import deque, namedtuple
 import threading
-import six
 from six.moves import queue, range
 
 from tensorpack.dataflow import DataFlow

examples/DisturbLabel/mnist-disturb.py

@@ -3,9 +3,7 @@
 # File: mnist-disturb.py
 # Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
-import numpy as np
 import os
-import sys
 import argparse
 
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
@@ -43,7 +41,7 @@ class Model(mnist_example.Model):
                       .MaxPooling('pool1', 2)
                       .FullyConnected('fc0', 512, nl=tf.nn.relu)
                       .FullyConnected('fc1', out_dim=10, nl=tf.identity)())
-        prob = tf.nn.softmax(logits, name='prob')
+        tf.nn.softmax(logits, name='prob')
 
         wrong = symbolic_functions.prediction_incorrect(logits, label)
         add_moving_summary(tf.reduce_mean(wrong, name='train_error'))

examples/DisturbLabel/svhn-disturb.py

@@ -4,7 +4,6 @@
 # Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
 import argparse
-import numpy as np
 import os
 import imp
 

examples/DoReFa-Net/alexnet-dorefa.py

@@ -7,14 +7,13 @@ import cv2
 import tensorflow as tf
 import argparse
 import numpy as np
-import multiprocessing
 import os
 import sys
 
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
 from tensorpack import *
-from tensorpack.tfutils.symbolic_functions import *
-from tensorpack.tfutils.summary import *
+from tensorpack.tfutils.symbolic_functions import prediction_incorrect
+from tensorpack.tfutils.summary import add_moving_summary, add_param_summary
 from tensorpack.tfutils.varreplace import remap_variables
 from tensorpack.dataflow import dataset
 from tensorpack.utils.gpu import get_nr_gpu
@@ -86,8 +85,6 @@ class Model(ModelDesc):
 
         fw, fa, fg = get_dorefa(BITW, BITA, BITG)
 
-        old_get_variable = tf.get_variable
-
         # monkey-patch tf.get_variable to apply fw
         def new_get_variable(v):
             name = v.op.name
@@ -146,7 +143,7 @@ class Model(ModelDesc):
                       .apply(nonlin)
                       .FullyConnected('fct', 1000, use_bias=True)())
 
-        prob = tf.nn.softmax(logits, name='output')
+        tf.nn.softmax(logits, name='output')
 
         cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=label)
         cost = tf.reduce_mean(cost, name='cross_entropy_loss')

examples/DoReFa-Net/resnet-dorefa.py

@@ -7,12 +7,9 @@ import tensorflow as tf
 import argparse
 import numpy as np
 import os
-import sys
 
 from tensorpack import *
 from tensorpack.dataflow import dataset
-from tensorpack.tfutils.symbolic_functions import *
-from tensorpack.utils.stats import RatioCounter
 from tensorpack.tfutils.varreplace import remap_variables
 
 from imagenet_utils import ImageNetModel, eval_on_ILSVRC12, fbresnet_augmentor
@@ -44,7 +41,6 @@ class Model(ModelDesc):
         image = image / 256.0
 
         fw, fa, fg = get_dorefa(BITW, BITA, BITG)
-        old_get_variable = tf.get_variable
 
         def new_get_variable(v):
             name = v.op.name
@@ -111,7 +107,7 @@ class Model(ModelDesc):
                       .GlobalAvgPooling('gap')
                       .tf.multiply(49)  # this is due to a bug in our model design
                       .FullyConnected('fct', 1000)())
-        prob = tf.nn.softmax(logits, name='output')
+        tf.nn.softmax(logits, name='output')
         ImageNetModel.compute_loss_and_error(logits, label)
 
 

examples/DoReFa-Net/svhn-digit-dorefa.py

@@ -4,13 +4,12 @@
 # Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
 import argparse
-import numpy as np
 import os
 
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
 from tensorpack import *
-from tensorpack.tfutils.symbolic_functions import *
-from tensorpack.tfutils.summary import *
+from tensorpack.tfutils.symbolic_functions import prediction_incorrect
+from tensorpack.tfutils.summary import add_moving_summary, add_param_summary
 from tensorpack.dataflow import dataset
 from tensorpack.tfutils.varreplace import remap_variables
 import tensorflow as tf
@@ -56,8 +55,6 @@ class Model(ModelDesc):
 
         fw, fa, fg = get_dorefa(BITW, BITA, BITG)
 
-        old_get_variable = tf.get_variable
-
         # monkey-patch tf.get_variable to apply fw
         def binarize_weight(v):
             name = v.op.name
@@ -112,7 +109,7 @@ class Model(ModelDesc):
                       .apply(fg).BatchNorm('bn6')
                       .apply(cabs)
                       .FullyConnected('fc1', 10, nl=tf.identity)())
-        prob = tf.nn.softmax(logits, name='output')
+        tf.nn.softmax(logits, name='output')
 
         # compute the number of failed samples
         wrong = prediction_incorrect(logits, label)

examples/FasterRCNN/README.md

 # Faster-RCNN on COCO
-This example aims to provide a minimal (1.2k lines) multi-GPU implementation of ResNet50-Faster-RCNN on COCO.
+This example aims to provide a minimal (1.2k lines) multi-GPU implementation of ResNet-Faster-RCNN on COCO.
 
 ## Dependencies
 + TensorFlow >= 1.4.0

examples/FasterRCNN/basemodel.py

@@ -3,12 +3,9 @@
 # File: basemodel.py
 
 import tensorflow as tf
-from tensorflow.contrib.layers import variance_scaling_initializer
-import tensorpack.tfutils.symbolic_functions as symbf
-from tensorpack.tfutils.summary import add_moving_summary, add_activation_summary
 from tensorpack.tfutils.argscope import argscope, get_arg_scope
 from tensorpack.models import (
-    Conv2D, MaxPooling, BatchNorm, BNReLU, GlobalAvgPooling, FullyConnected)
+    Conv2D, MaxPooling, BatchNorm, BNReLU)
 
 
 def image_preprocess(image, bgr=True):

examples/FasterRCNN/coco.py

@@ -4,11 +4,9 @@
 
 import numpy as np
 import os
-import six
 from termcolor import colored
 from tabulate import tabulate
 
-from tensorpack.dataflow import DataFromList
 from tensorpack.utils import logger
 from tensorpack.utils.rect import FloatBox
 from tensorpack.utils.timer import timed_operation

examples/FasterRCNN/data.py

@@ -3,17 +3,13 @@
 # File: data.py
 
 import cv2
-import os
 import numpy as np
-import logging
 
-from tensorpack.utils import logger
 from tensorpack.utils.argtools import memoized, log_once
 from tensorpack.dataflow import (
     MapData, imgaug, TestDataSpeed,
     MapDataComponent, DataFromList, PrefetchDataZMQ)
-import tensorpack.utils.viz as tpviz
-from tensorpack.utils.viz import interactive_imshow
+# import tensorpack.utils.viz as tpviz
 
 from coco import COCODetection
 from utils.generate_anchors import generate_anchors
@@ -121,7 +117,6 @@ def get_anchor_labels(anchors, gt_boxes, crowd_boxes):
         anchor_labels[overlap_with_crowd] = -1
 
     # Filter fg labels: ignore some fg if fg is too many
-    old_num_fg = np.sum(anchor_labels == 1)
     target_num_fg = int(config.RPN_BATCH_PER_IM * config.RPN_FG_RATIO)
     fg_inds = filter_box_label(anchor_labels, 1, target_num_fg)
     # Note that fg could be fewer than the target ratio
@@ -133,7 +128,7 @@ def get_anchor_labels(anchors, gt_boxes, crowd_boxes):
         # This can happen if, e.g. the image has large crowd.
         raise MalformedData("No valid foreground/background for RPN!")
     target_num_bg = config.RPN_BATCH_PER_IM - len(fg_inds)
-    bg_inds = filter_box_label(anchor_labels, 0, target_num_bg)
+    filter_box_label(anchor_labels, 0, target_num_bg)   # ignore return values
 
     # Set anchor boxes: the best gt_box for each fg anchor
     anchor_boxes = np.zeros((NA, 4), dtype='float32')

examples/FasterRCNN/eval.py

@@ -2,23 +2,17 @@
 # -*- coding: utf-8 -*-
 # File: eval.py
 
-import numpy as np
 import tqdm
-import cv2
-import six
 import os
-from collections import namedtuple, defaultdict
+from collections import namedtuple
 
-import tensorflow as tf
-from tensorpack.dataflow import MapDataComponent, TestDataSpeed
-from tensorpack.tfutils import get_default_sess_config
 from tensorpack.utils.utils import get_tqdm_kwargs
 
 from pycocotools.coco import COCO
 from pycocotools.cocoeval import COCOeval
 
-from coco import COCODetection, COCOMeta
-from common import clip_boxes, DataFromListOfDict, CustomResize
+from coco import COCOMeta
+from common import clip_boxes, CustomResize
 import config
 
 DetectionResult = namedtuple(

examples/FasterRCNN/model.py

@@ -2,7 +2,6 @@
 # -*- coding: utf-8 -*-
 # File: model.py
 
-import numpy as np
 import tensorflow as tf
 from tensorpack.tfutils import get_current_tower_context
 from tensorpack.tfutils.summary import add_moving_summary
@@ -206,7 +205,6 @@ def generate_rpn_proposals(boxes, scores, img_shape):
         topk_valid_boxes,
         nms_indices, name='boxes')
     final_scores = tf.gather(topk_valid_scores, nms_indices, name='scores')
-    final_probs = tf.gather(topk_valid_scores, nms_indices, name='probs')
     return final_boxes, final_scores
 
 
@@ -436,7 +434,6 @@ def fastrcnn_predictions(boxes, probs):
     """
     assert boxes.shape[1] == config.NUM_CLASS - 1
     assert probs.shape[1] == config.NUM_CLASS
-    N = tf.shape(boxes)[0]
     boxes = tf.transpose(boxes, [1, 0, 2])  # #catxnx4
     probs = tf.transpose(probs[:, 1:], [1, 0])  # #catxn
 

examples/FasterRCNN/train.py

@@ -2,7 +2,7 @@
 # -*- coding: utf-8 -*-
 # File: train.py
 
-import sys, os
+import os
 import argparse
 import cv2
 import shutil
@@ -16,7 +16,7 @@ import tensorflow as tf
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
 from tensorpack import *
 from tensorpack.tfutils.summary import add_moving_summary
-from tensorpack.tfutils import optimizer, gradproc
+from tensorpack.tfutils import optimizer
 import tensorpack.utils.viz as tpviz
 from tensorpack.utils.gpu import get_nr_gpu
 
@@ -35,7 +35,7 @@ from data import (
 from viz import (
     draw_annotation, draw_proposal_recall,
     draw_predictions, draw_final_outputs)
-from common import clip_boxes, CustomResize, print_config
+from common import print_config
 from eval import (
     eval_on_dataflow, detect_one_image, print_evaluation_scores)
 import config
@@ -145,8 +145,8 @@ class Model(ModelDesc):
             # indices: Nx2. Each index into (#proposal, #category)
             pred_indices, final_probs = fastrcnn_predictions(decoded_boxes, label_probs)
             final_probs = tf.identity(final_probs, 'final_probs')
-            final_boxes = tf.gather_nd(decoded_boxes, pred_indices, name='final_boxes')
-            final_labels = tf.add(pred_indices[:, 1], 1, name='final_labels')
+            tf.gather_nd(decoded_boxes, pred_indices, name='final_boxes')
+            tf.add(pred_indices[:, 1], 1, name='final_labels')
 
     def _get_optimizer(self):
         lr = tf.get_variable('learning_rate', initializer=0.003, trainable=False)

examples/GAN/BEGAN.py

@@ -4,7 +4,6 @@
 # Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
 import os
-import argparse
 
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
 from tensorpack import *

examples/GAN/ConditionalGAN-mnist.py

@@ -6,13 +6,12 @@
 import numpy as np
 import tensorflow as tf
 import os
-import sys
 import cv2
 import argparse
 
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
 from tensorpack import *
-from tensorpack.utils.viz import *
+from tensorpack.utils.viz import interactive_imshow, stack_patches
 import tensorpack.tfutils.symbolic_functions as symbf
 from tensorpack.tfutils.scope_utils import auto_reuse_variable_scope
 from tensorpack.dataflow import dataset

examples/GAN/CycleGAN.py

@@ -3,16 +3,13 @@
 # File: CycleGAN.py
 # Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
-import os, sys
+import os
 import argparse
 import glob
-from six.moves import map, zip, range
-import numpy as np
+from six.moves import range
 
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
 from tensorpack import *
-from tensorpack.utils.viz import *
-import tensorpack.tfutils.symbolic_functions as symbf
 from tensorpack.tfutils.summary import add_moving_summary
 from tensorpack.tfutils.scope_utils import auto_reuse_variable_scope
 import tensorflow as tf

examples/GAN/DCGAN.py

@@ -5,13 +5,12 @@
 
 import glob
 import numpy as np
-import os, sys
+import os
 import argparse
 
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
 from tensorpack import *
-from tensorpack.utils.viz import *
-from tensorpack.tfutils.summary import add_moving_summary
+from tensorpack.utils.viz import stack_patches
 from tensorpack.tfutils.scope_utils import auto_reuse_variable_scope
 from tensorpack.utils.globvars import globalns as opt
 import tensorflow as tf
@@ -127,11 +126,11 @@ def sample(model, model_path, output_name='gen/gen'):
         output_names=[output_name, 'z'])
     pred = SimpleDatasetPredictor(pred, RandomZData((100, opt.Z_DIM)))
     for o in pred.get_result():
-        o, zs = o[0] + 1, o[1]
+        o = o[0] + 1
         o = o * 128.0
         o = np.clip(o, 0, 255)
         o = o[:, :, :, ::-1]
-        viz = stack_patches(o, nr_row=10, nr_col=10, viz=True)
+        stack_patches(o, nr_row=10, nr_col=10, viz=True)
 
 
 def get_args():

examples/GAN/DiscoGAN-CelebA.py

@@ -3,15 +3,13 @@
 # File: DiscoGAN-CelebA.py
 # Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
-import os, sys
+import os
 import argparse
 from six.moves import map, zip
 import numpy as np
 
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
 from tensorpack import *
-from tensorpack.utils.viz import *
-import tensorpack.tfutils.symbolic_functions as symbf
 from tensorpack.tfutils.summary import add_moving_summary
 from tensorpack.tfutils.scope_utils import auto_reuse_variable_scope
 import tensorflow as tf

examples/GAN/GAN.py

@@ -5,8 +5,7 @@
 
 import tensorflow as tf
 import numpy as np
-import time
-from tensorpack import (TowerTrainer, QueueInput,
+from tensorpack import (TowerTrainer,
                         ModelDescBase, DataFlow, StagingInput)
 from tensorpack.tfutils.tower import TowerContext, TowerFuncWrapper
 from tensorpack.graph_builder import DataParallelBuilder, LeastLoadedDeviceSetter

examples/GAN/Image2Image.py

@@ -7,17 +7,14 @@ import cv2
 import numpy as np
 import tensorflow as tf
 import glob
-import pickle
 import os
-import sys
 import argparse
 
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
 from tensorpack import *
-from tensorpack.utils.viz import *
+from tensorpack.utils.viz import stack_patches
 from tensorpack.tfutils.summary import add_moving_summary
 from tensorpack.tfutils.scope_utils import auto_reuse_variable_scope
-import tensorpack.tfutils.symbolic_functions as symbf
 from GAN import GANTrainer, GANModelDesc
 
 """

examples/GAN/Improved-WGAN.py

@@ -4,7 +4,6 @@
 # Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
 import os
-import argparse
 
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
 from tensorpack import *

examples/GAN/InfoGAN-mnist.py

@@ -7,7 +7,6 @@ import cv2
 import numpy as np
 import tensorflow as tf
 import os
-import sys
 import argparse
 
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
@@ -16,7 +15,6 @@ from tensorpack.utils import viz
 from tensorpack.tfutils.scope_utils import auto_reuse_variable_scope, under_name_scope
 from tensorpack.tfutils import optimizer, summary
 import tensorpack.tfutils.symbolic_functions as symbf
-from tensorpack.tfutils.gradproc import ScaleGradient
 from tensorpack.dataflow import dataset
 from GAN import GANTrainer, GANModelDesc
 
@@ -149,8 +147,6 @@ class Model(GANModelDesc):
         of P, and whose parameters are predicted by the discriminator network.
         """
         with tf.name_scope("mutual_information"):
-            batch_prior = tf.tile(tf.expand_dims(DIST_PRIOR_PARAM, 0), [BATCH, 1], name='batch_prior')
-
             with tf.name_scope('prior_entropy'):
                 cat, uni = get_distributions(DIST_PRIOR_PARAM[:NUM_CLASS], DIST_PRIOR_PARAM[NUM_CLASS:])
                 ents = [cat.entropy(name='cat_entropy'), tf.reduce_sum(uni.entropy(), name='uni_entropy')]

examples/GAN/WGAN.py

@@ -4,11 +4,9 @@
 # Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
 import os
-import argparse
 
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
 from tensorpack import *
-from tensorpack.tfutils import optimizer
 from tensorpack.tfutils.summary import add_moving_summary
 from tensorpack.utils.globvars import globalns as G
 import tensorflow as tf
@@ -41,6 +39,7 @@ class Model(DCGAN.Model):
 
         # An alternative way to implement the clipping:
         """
+        from tensorpack.tfutils import optimizer
         def clip(v):
             n = v.op.name
             if not n.startswith('discrim/'):

examples/HED/hed.py

@@ -6,17 +6,15 @@
 import cv2
 import tensorflow as tf
 import argparse
-import numpy as np
 from six.moves import zip
 import os
-import sys
 
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
 from tensorpack import *
 from tensorpack.dataflow import dataset
 from tensorpack.utils.gpu import get_nr_gpu
 from tensorpack.tfutils import optimizer
-from tensorpack.tfutils.summary import *
+from tensorpack.tfutils.summary import add_moving_summary, add_param_summary
 
 
 def class_balanced_sigmoid_cross_entropy(logits, label, name='cross_entropy_loss'):
@@ -56,7 +54,7 @@ class Model(ModelDesc):
         edgemap = tf.expand_dims(edgemap, 3, name='edgemap4d')
 
         def branch(name, l, up):
-            with tf.variable_scope(name) as scope:
+            with tf.variable_scope(name):
                 l = Conv2D('convfc', l, 1, kernel_shape=1, nl=tf.identity,
                            use_bias=True,
                            W_init=tf.constant_initializer(),

examples/Inception/inception-bn.py

@@ -3,16 +3,14 @@
 # File: inception-bn.py
 # Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
-import cv2
 import argparse
-import numpy as np
 import os
 import tensorflow as tf
 
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
 from tensorpack import *
-from tensorpack.tfutils.symbolic_functions import *
-from tensorpack.tfutils.summary import *
+from tensorpack.tfutils.symbolic_functions import prediction_incorrect
+from tensorpack.tfutils.summary import add_moving_summary
 from tensorpack.dataflow import dataset
 from tensorpack.utils.gpu import get_nr_gpu
 
@@ -42,7 +40,7 @@ class Model(ModelDesc):
 
         def inception(name, x, nr1x1, nr3x3r, nr3x3, nr233r, nr233, nrpool, pooltype):
             stride = 2 if nr1x1 == 0 else 1
-            with tf.variable_scope(name) as scope:
+            with tf.variable_scope(name):
                 outs = []
                 if nr1x1 != 0:
                     outs.append(Conv2D('conv1x1', x, nr1x1, 1))
@@ -101,7 +99,7 @@ class Model(ModelDesc):
             l = GlobalAvgPooling('gap', l)
 
             logits = FullyConnected('linear', l, out_dim=1000, nl=tf.identity)
-        prob = tf.nn.softmax(logits, name='output')
+        tf.nn.softmax(logits, name='output')
         loss3 = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=label)
         loss3 = tf.reduce_mean(loss3, name='loss3')
 
@@ -119,7 +117,6 @@ class Model(ModelDesc):
                                           80000, 0.7, True)
         wd_cost = tf.multiply(wd_w, regularize_cost('.*/W', tf.nn.l2_loss), name='l2_regularize_loss')
 
-        add_param_summary(('.*/W', ['histogram']))   # monitor W
         self.cost = tf.add_n([cost, wd_cost], name='cost')
         add_moving_summary(wd_cost, self.cost)
 

examples/Inception/inceptionv3.py

@@ -5,15 +5,14 @@
 
 import cv2
 import argparse
-import numpy as np
 import os
 import tensorflow as tf
 import multiprocessing
 
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
 from tensorpack import *
-from tensorpack.tfutils.symbolic_functions import *
-from tensorpack.tfutils.summary import *
+from tensorpack.tfutils.symbolic_functions import prediction_incorrect
+from tensorpack.tfutils.summary import add_moving_summary
 from tensorpack.dataflow import dataset
 
 """
@@ -150,7 +149,7 @@ class Model(ModelDesc):
                     MaxPooling('maxpool', l, 3, 2)
                 ], 3, name='concat')
             for x in ['a', 'b']:
-                with tf.variable_scope('incep-8-2048{}'.format(x)) as scope:
+                with tf.variable_scope('incep-8-2048{}'.format(x)):
                     br11 = Conv2D('conv11', l, 320, 1)
                     br33 = Conv2D('conv133r', l, 384, 1)
                     br33 = tf.concat([

examples/PennTreebank/PTB-LSTM.py

@@ -9,8 +9,7 @@ import argparse
 
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
 from tensorpack import *
-from tensorpack.tfutils.gradproc import *
-from tensorpack.tfutils import optimizer, summary
+from tensorpack.tfutils import optimizer, summary, gradproc
 from tensorpack.utils import logger
 from tensorpack.utils.fs import download, get_dataset_path
 from tensorpack.utils.argtools import memoized_ignoreargs

examples/ResNet/cifar10-resnet.py

@@ -3,14 +3,12 @@
 # File: cifar10-resnet.py
 # Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
-import numpy as np
 import argparse
 import os
 
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
 from tensorpack import *
-from tensorpack.tfutils.symbolic_functions import *
-from tensorpack.tfutils.summary import *
+from tensorpack.tfutils.summary import add_moving_summary, add_param_summary
 from tensorpack.utils.gpu import get_nr_gpu
 from tensorpack.dataflow import dataset
 
@@ -64,7 +62,7 @@ class Model(ModelDesc):
                 out_channel = in_channel
                 stride1 = 1
 
-            with tf.variable_scope(name) as scope:
+            with tf.variable_scope(name):
                 b1 = l if first else BNReLU(l)
                 c1 = Conv2D('conv1', b1, out_channel, stride=stride1, nl=BNReLU)
                 c2 = Conv2D('conv2', c1, out_channel)
@@ -97,7 +95,7 @@ class Model(ModelDesc):
             l = GlobalAvgPooling('gap', l)
 
         logits = FullyConnected('linear', l, out_dim=10, nl=tf.identity)
-        prob = tf.nn.softmax(logits, name='output')
+        tf.nn.softmax(logits, name='output')
 
         cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=label)
         cost = tf.reduce_mean(cost, name='cross_entropy_loss')

examples/ResNet/imagenet-resnet.py

@@ -2,20 +2,16 @@
 # -*- coding: UTF-8 -*-
 # File: imagenet-resnet.py
 
-import sys
 import argparse
-import numpy as np
 import os
 
-import tensorflow as tf
-
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
 from tensorpack import logger, QueueInput
 from tensorpack.models import *
 from tensorpack.callbacks import *
 from tensorpack.train import (
     TrainConfig, SyncMultiGPUTrainerParameterServer, launch_train_with_config)
-from tensorpack.dataflow import imgaug, FakeData
+from tensorpack.dataflow import FakeData
 from tensorpack.tfutils import argscope, get_model_loader
 from tensorpack.utils.gpu import get_nr_gpu
 

examples/ResNet/load-resnet.py

@@ -8,18 +8,14 @@ import cv2
 import functools
 import tensorflow as tf
 import argparse
-import os
 import re
 import numpy as np
 import six
-from six.moves import zip
 from tensorflow.contrib.layers import variance_scaling_initializer
 
 from tensorpack import *
 from tensorpack.utils import logger
-from tensorpack.utils.stats import RatioCounter
-from tensorpack.tfutils.summary import *
-from tensorpack.dataflow.dataset import ILSVRCMeta, ILSVRC12
+from tensorpack.dataflow.dataset import ILSVRCMeta
 
 from imagenet_utils import eval_on_ILSVRC12, get_imagenet_dataflow, ImageNetModel
 from resnet_model import resnet_group, resnet_bottleneck
@@ -60,7 +56,7 @@ class Model(ModelDesc):
                       .apply(resnet_group, 'group3', bottleneck, 512, blocks[3], 2)
                       .GlobalAvgPooling('gap')
                       .FullyConnected('linear', 1000, nl=tf.identity)())
-        prob = tf.nn.softmax(logits, name='prob')
+        tf.nn.softmax(logits, name='prob')
         ImageNetModel.compute_loss_and_error(logits, label)
 
 

examples/ResNet/resnet_model.py

@@ -8,7 +8,7 @@ from tensorflow.contrib.layers import variance_scaling_initializer
 
 from tensorpack.tfutils.argscope import argscope, get_arg_scope
 from tensorpack.models import (
-    Conv2D, MaxPooling, GlobalAvgPooling, BatchNorm, BNReLU, FullyConnected,
+    Conv2D, GlobalAvgPooling, BatchNorm, BNReLU, FullyConnected,
     LinearWrap)
 
 

examples/Saliency/saliency-maps.py

@@ -3,7 +3,6 @@
 
 import cv2
 import sys
-import os
 
 from contextlib import contextmanager
 import numpy as np

examples/ShuffleNet/shufflenet.py

@@ -2,7 +2,6 @@
 # -*- coding: UTF-8 -*-
 # File: shufflenet.py
 
-import sys
 import argparse
 import numpy as np
 import os
@@ -21,7 +20,7 @@ from tensorpack.tfutils.scope_utils import under_name_scope
 from tensorpack.utils.gpu import get_nr_gpu
 
 from imagenet_utils import (
-    fbresnet_augmentor, get_imagenet_dataflow,
+    get_imagenet_dataflow,
     ImageNetModel, GoogleNetResize, eval_on_ILSVRC12)
 
 TOTAL_BATCH_SIZE = 256

examples/SimilarityLearning/mnist-embeddings.py

@@ -11,7 +11,6 @@ import tensorflow.contrib.slim as slim
 
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
 from tensorpack import *
-import tensorpack.tfutils.symbolic_functions as symbf
 from tensorpack.tfutils.summary import add_moving_summary
 from tensorpack.utils.gpu import change_gpu
 
@@ -19,7 +18,6 @@ from embedding_data import get_test_data, MnistPairs, MnistTriplets
 
 MATPLOTLIB_AVAIBLABLE = False
 try:
-    import matplotlib
     from matplotlib import offsetbox
     import matplotlib.pyplot as plt
     MATPLOTLIB_AVAIBLABLE = True

examples/SpatialTransformer/mnist-addition.py

@@ -7,7 +7,6 @@ import cv2
 import numpy as np
 import tensorflow as tf
 import os
-import sys
 import argparse
 
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
@@ -75,7 +74,7 @@ class Model(ModelDesc):
                   .FullyConnected('fc1', out_dim=256, nl=tf.nn.relu)
                   .FullyConnected('fc2', out_dim=128, nl=tf.nn.relu)
                   .FullyConnected('fct', out_dim=19, nl=tf.identity)())
-        prob = tf.nn.softmax(logits, name='prob')
+        tf.nn.softmax(logits, name='prob')
 
         cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=label)
         cost = tf.reduce_mean(cost, name='cross_entropy_loss')

examples/cifar-convnet.py

@@ -4,12 +4,10 @@
 # Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 import tensorflow as tf
 import argparse
-import numpy as np
 import os
 
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
 from tensorpack import *
-import tensorpack.tfutils.symbolic_functions as symbf
 from tensorpack.tfutils.summary import *
 from tensorpack.dataflow import dataset
 

examples/load-alexnet.py

@@ -42,7 +42,7 @@ def tower_func(image):
         l = FullyConnected('fc6', l, 4096)
         l = FullyConnected('fc7', l, out_dim=4096)
     logits = FullyConnected('fc8', l, out_dim=1000, nl=tf.identity)
-    prob = tf.nn.softmax(logits, name='prob')
+    tf.nn.softmax(logits, name='prob')
 
 
 def run_test(path, input):

examples/load-vgg16.py

@@ -54,7 +54,7 @@ def tower_func(image):
                   .FullyConnected('fc7', 4096, nl=tf.nn.relu)
                   .Dropout('drop1', 0.5)
                   .FullyConnected('fc8', out_dim=1000, nl=tf.identity)())
-    prob = tf.nn.softmax(logits, name='prob')
+    tf.nn.softmax(logits, name='prob')
 
 
 def run_test(path, input):

examples/mnist-convnet.py

@@ -2,9 +2,7 @@
 # -*- coding: utf-8 -*-
 # File: mnist-convnet.py
 
-import numpy as np
 import os
-import sys
 import argparse
 import tensorflow as tf
 """
@@ -57,7 +55,7 @@ class Model(ModelDesc):
                       .Dropout('dropout', 0.5)
                       .FullyConnected('fc1', out_dim=10, nl=tf.identity)())
 
-        prob = tf.nn.softmax(logits, name='prob')   # a Bx10 with probabilities
+        tf.nn.softmax(logits, name='prob')   # a Bx10 with probabilities
 
         # a vector of length B with loss of each sample
         cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=label)

examples/mnist-keras-v2.py

@@ -10,9 +10,7 @@ from tensorflow import keras
 KL = keras.layers
 
 
-from tensorpack.train import SimpleTrainer
 from tensorpack.input_source import QueueInput
-from tensorpack.callbacks import ModelSaver, InferenceRunner, ScalarStats
 from tensorpack.dataflow import dataset, BatchData, MapData
 from tensorpack.utils import logger
 from tensorpack.contrib.keras import KerasModel

examples/mnist-keras.py

@@ -3,12 +3,8 @@
 # File: mnist-keras.py
 # Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
-import numpy as np
 import tensorflow as tf
-import tensorflow.contrib.slim as slim
 import os
-import sys
-import argparse
 
 from tensorflow import keras
 KL = keras.layers

examples/mnist-tfslim.py

@@ -2,9 +2,7 @@
 # -*- coding: utf-8 -*-
 # File: mnist-tfslim.py
 
-import numpy as np
 import os
-import sys
 import argparse
 """
 MNIST ConvNet example using TensorFlow-slim.
@@ -48,7 +46,7 @@ class Model(ModelDesc):
             l = slim.layers.dropout(l, is_training=is_training)
             logits = slim.layers.fully_connected(l, 10, activation_fn=None, scope='fc1')
 
-        prob = tf.nn.softmax(logits, name='prob')
+        tf.nn.softmax(logits, name='prob')
 
         cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=label)
         cost = tf.reduce_mean(cost, name='cross_entropy_loss')

examples/mnist-visualizations.py

@@ -2,9 +2,7 @@
 # -*- coding: utf-8 -*-
 # File: mnist-visualizations.py
 
-import numpy as np
 import os
-import sys
 import argparse
 
 """

examples/svhn-digit-convnet.py

@@ -4,7 +4,6 @@
 # Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
 import argparse
-import numpy as np
 import os
 
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
@@ -44,9 +43,9 @@ class Model(ModelDesc):
                       .FullyConnected('fc0', 512,
                                       b_init=tf.constant_initializer(0.1), nl=tf.nn.relu)
                       .FullyConnected('linear', out_dim=10, nl=tf.identity)())
-        prob = tf.nn.softmax(logits, name='output')
+        tf.nn.softmax(logits, name='output')
 
-        acc = tf.to_float(tf.nn.in_top_k(logits, label, 1))
+        accuracy = tf.to_float(tf.nn.in_top_k(logits, label, 1))
         add_moving_summary(tf.reduce_mean(accuracy, name='accuracy'))
 
         cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=label)

examples/tox.ini

 [flake8]
 max-line-length = 120
-ignore = F403,F401,F405,F841,E4,E741,E742,E743
+# See https://pep8.readthedocs.io/en/latest/intro.html#error-codes
+ignore = F403,F405,E402,E741,E742,E743
 exclude = private,
 				  FasterRCNN/utils