Prepare for python3 compatibility [1]

Summary:
This is a first step towards python3 compatibility. Specifically, it
tackles:

 - `cPickle`: uses six.moves
 - `Queue`: uses six.moves
 - `urllib2`: uses six.moves and changes `urllib2.urlopen` to `urllib.request.urlopen`

Also, fundamentally it changes the types of all config "byte" types to
"string" types. Those configurations aren't un-encoded byte streams but very
specifically ascii (or unicode encoded) strings which are specified and
consumed by human eyes.

Reviewed By: rbgirshick

Differential Revision: D9662024

fbshipit-source-id: b8372f685b57ec4260ae881a2f8bb7967f337b10

detectron/core/config.py

@@ -44,12 +44,13 @@ from __future__ import unicode_literals
 
 from ast import literal_eval
 from future.utils import iteritems
-from past.builtins import basestring
 import copy
+import io
 import logging
 import numpy as np
 import os
 import os.path as osp
+import six
 import yaml
 
 from detectron.utils.collections import AttrDict
@@ -72,7 +73,7 @@ cfg = __C
 __C.TRAIN = AttrDict()
 
 # Initialize network with weights from this .pkl file
-__C.TRAIN.WEIGHTS = b''
+__C.TRAIN.WEIGHTS = ''
 
 # Datasets to train on
 # Available dataset list: detectron.datasets.dataset_catalog.datasets()
@@ -219,7 +220,7 @@ __C.DATA_LOADER.BLOBS_QUEUE_CAPACITY = 8
 __C.TEST = AttrDict()
 
 # Initialize network with weights from this .pkl file
-__C.TEST.WEIGHTS = b''
+__C.TEST.WEIGHTS = ''
 
 # Datasets to test on
 # Available dataset list: detectron.datasets.dataset_catalog.datasets()
@@ -297,11 +298,11 @@ __C.TEST.BBOX_AUG.ENABLED = False
 
 # Heuristic used to combine predicted box scores
 #   Valid options: ('ID', 'AVG', 'UNION')
-__C.TEST.BBOX_AUG.SCORE_HEUR = b'UNION'
+__C.TEST.BBOX_AUG.SCORE_HEUR = 'UNION'
 
 # Heuristic used to combine predicted box coordinates
 #   Valid options: ('ID', 'AVG', 'UNION')
-__C.TEST.BBOX_AUG.COORD_HEUR = b'UNION'
+__C.TEST.BBOX_AUG.COORD_HEUR = 'UNION'
 
 # Horizontal flip at the original scale (id transform)
 __C.TEST.BBOX_AUG.H_FLIP = False
@@ -338,7 +339,7 @@ __C.TEST.MASK_AUG.ENABLED = False
 # Heuristic used to combine mask predictions
 # SOFT prefix indicates that the computation is performed on soft masks
 #   Valid options: ('SOFT_AVG', 'SOFT_MAX', 'LOGIT_AVG')
-__C.TEST.MASK_AUG.HEUR = b'SOFT_AVG'
+__C.TEST.MASK_AUG.HEUR = 'SOFT_AVG'
 
 # Horizontal flip at the original scale (id transform)
 __C.TEST.MASK_AUG.H_FLIP = False
@@ -373,7 +374,7 @@ __C.TEST.KPS_AUG.ENABLED = False
 
 # Heuristic used to combine keypoint predictions
 #   Valid options: ('HM_AVG', 'HM_MAX')
-__C.TEST.KPS_AUG.HEUR = b'HM_AVG'
+__C.TEST.KPS_AUG.HEUR = 'HM_AVG'
 
 # Horizontal flip at the original scale (id transform)
 __C.TEST.KPS_AUG.H_FLIP = False
@@ -405,7 +406,7 @@ __C.TEST.SOFT_NMS = AttrDict()
 # Use soft NMS instead of standard NMS if set to True
 __C.TEST.SOFT_NMS.ENABLED = False
 # See soft NMS paper for definition of these options
-__C.TEST.SOFT_NMS.METHOD = b'linear'
+__C.TEST.SOFT_NMS.METHOD = 'linear'
 __C.TEST.SOFT_NMS.SIGMA = 0.5
 # For the soft NMS overlap threshold, we simply use TEST.NMS
 
@@ -423,7 +424,7 @@ __C.TEST.BBOX_VOTE.VOTE_TH = 0.8
 
 # The method used to combine scores when doing bounding box voting
 # Valid options include ('ID', 'AVG', 'IOU_AVG', 'GENERALIZED_AVG', 'QUASI_SUM')
-__C.TEST.BBOX_VOTE.SCORING_METHOD = b'ID'
+__C.TEST.BBOX_VOTE.SCORING_METHOD = 'ID'
 
 # Hyperparameter used by the scoring method (it has different meanings for
 # different methods)
@@ -438,13 +439,13 @@ __C.MODEL = AttrDict()
 # The type of model to use
 # The string must match a function in the modeling.model_builder module
 # (e.g., 'generalized_rcnn', 'mask_rcnn', ...)
-__C.MODEL.TYPE = b''
+__C.MODEL.TYPE = ''
 
 # The backbone conv body to use
 # The string must match a function that is imported in modeling.model_builder
 # (e.g., 'FPN.add_fpn_ResNet101_conv5_body' to specify a ResNet-101-FPN
 # backbone)
-__C.MODEL.CONV_BODY = b''
+__C.MODEL.CONV_BODY = ''
 
 # Number of classes in the dataset; must be set
 # E.g., 81 for COCO (80 foreground + 1 background)
@@ -483,7 +484,7 @@ __C.MODEL.RPN_ONLY = False
 
 # Caffe2 net execution type
 # Use 'prof_dag' to get profiling statistics
-__C.MODEL.EXECUTION_TYPE = b'dag'
+__C.MODEL.EXECUTION_TYPE = 'dag'
 
 
 # ---------------------------------------------------------------------------- #
@@ -567,7 +568,7 @@ __C.SOLVER.BASE_LR = 0.001
 
 # Schedule type (see functions in utils.lr_policy for options)
 # E.g., 'step', 'steps_with_decay', ...
-__C.SOLVER.LR_POLICY = b'step'
+__C.SOLVER.LR_POLICY = 'step'
 
 # Some LR Policies (by example):
 # 'step'
@@ -638,7 +639,7 @@ __C.FAST_RCNN = AttrDict()
 # The type of RoI head to use for bounding box classification and regression
 # The string must match a function this is imported in modeling.model_builder
 # (e.g., 'head_builder.add_roi_2mlp_head' to specify a two hidden layer MLP)
-__C.FAST_RCNN.ROI_BOX_HEAD = b''
+__C.FAST_RCNN.ROI_BOX_HEAD = ''
 
 # Hidden layer dimension when using an MLP for the RoI box head
 __C.FAST_RCNN.MLP_HEAD_DIM = 1024
@@ -650,7 +651,7 @@ __C.FAST_RCNN.NUM_STACKED_CONVS = 4
 
 # RoI transformation function (e.g., RoIPool or RoIAlign)
 # (RoIPoolF is the same as RoIPool; ignore the trailing 'F')
-__C.FAST_RCNN.ROI_XFORM_METHOD = b'RoIPoolF'
+__C.FAST_RCNN.ROI_XFORM_METHOD = 'RoIPoolF'
 
 # Number of grid sampling points in RoIAlign (usually use 2)
 # Only applies to RoIAlign
@@ -740,13 +741,13 @@ __C.MRCNN = AttrDict()
 # The type of RoI head to use for instance mask prediction
 # The string must match a function this is imported in modeling.model_builder
 # (e.g., 'mask_rcnn_heads.ResNet_mask_rcnn_fcn_head_v1up4convs')
-__C.MRCNN.ROI_MASK_HEAD = b''
+__C.MRCNN.ROI_MASK_HEAD = ''
 
 # Resolution of mask predictions
 __C.MRCNN.RESOLUTION = 14
 
 # RoI transformation function and associated options
-__C.MRCNN.ROI_XFORM_METHOD = b'RoIAlign'
+__C.MRCNN.ROI_XFORM_METHOD = 'RoIAlign'
 
 # RoI transformation function (e.g., RoIPool or RoIAlign)
 __C.MRCNN.ROI_XFORM_RESOLUTION = 7
@@ -768,7 +769,7 @@ __C.MRCNN.UPSAMPLE_RATIO = 1
 __C.MRCNN.USE_FC_OUTPUT = False
 
 # Weight initialization method for the mask head and mask output layers
-__C.MRCNN.CONV_INIT = b'GaussianFill'
+__C.MRCNN.CONV_INIT = 'GaussianFill'
 
 # Use class specific mask predictions if True (otherwise use class agnostic mask
 # predictions)
@@ -789,7 +790,7 @@ __C.KRCNN = AttrDict()
 # The type of RoI head to use for instance keypoint prediction
 # The string must match a function this is imported in modeling.model_builder
 # (e.g., 'keypoint_rcnn_heads.add_roi_pose_head_v1convX')
-__C.KRCNN.ROI_KEYPOINTS_HEAD = b''
+__C.KRCNN.ROI_KEYPOINTS_HEAD = ''
 
 # Output size (and size loss is computed on), e.g., 56x56
 __C.KRCNN.HEATMAP_SIZE = -1
@@ -824,17 +825,17 @@ __C.KRCNN.CONV_HEAD_DIM = 256
 # Conv kernel size used in the keypoint head
 __C.KRCNN.CONV_HEAD_KERNEL = 3
 # Conv kernel weight filling function
-__C.KRCNN.CONV_INIT = b'GaussianFill'
+__C.KRCNN.CONV_INIT = 'GaussianFill'
 
 # Use NMS based on OKS if True
 __C.KRCNN.NMS_OKS = False
 
 # Source of keypoint confidence
 #   Valid options: ('bbox', 'logit', 'prob')
-__C.KRCNN.KEYPOINT_CONFIDENCE = b'bbox'
+__C.KRCNN.KEYPOINT_CONFIDENCE = 'bbox'
 
 # Standard ROI XFORM options (see FAST_RCNN or MRCNN options)
-__C.KRCNN.ROI_XFORM_METHOD = b'RoIAlign'
+__C.KRCNN.ROI_XFORM_METHOD = 'RoIAlign'
 __C.KRCNN.ROI_XFORM_RESOLUTION = 7
 __C.KRCNN.ROI_XFORM_SAMPLING_RATIO = 0
 
@@ -884,11 +885,11 @@ __C.RESNETS.WIDTH_PER_GROUP = 64
 __C.RESNETS.STRIDE_1X1 = True
 
 # Residual transformation function
-__C.RESNETS.TRANS_FUNC = b'bottleneck_transformation'
+__C.RESNETS.TRANS_FUNC = 'bottleneck_transformation'
 # ResNet's stem function (conv1 and pool1)
-__C.RESNETS.STEM_FUNC = b'basic_bn_stem'
+__C.RESNETS.STEM_FUNC = 'basic_bn_stem'
 # ResNet's shortcut function
-__C.RESNETS.SHORTCUT_FUNC = b'basic_bn_shortcut'
+__C.RESNETS.SHORTCUT_FUNC = 'basic_bn_shortcut'
 
 # Apply dilation in stage "res5"
 __C.RESNETS.RES5_DILATION = 1
@@ -946,10 +947,10 @@ __C.EPS = 1e-14
 __C.ROOT_DIR = os.getcwd()
 
 # Output basedir
-__C.OUTPUT_DIR = b'/tmp'
+__C.OUTPUT_DIR = '/tmp'
 
 # Name (or path to) the matlab executable
-__C.MATLAB = b'matlab'
+__C.MATLAB = 'matlab'
 
 # Reduce memory usage with memonger gradient blob sharing
 __C.MEMONGER = True
@@ -976,11 +977,11 @@ __C.EXPECTED_RESULTS_ATOL = 0.005
 # that the actual value is within mean +/- SIGMA_TOL * std
 __C.EXPECTED_RESULTS_SIGMA_TOL = 4
 # Set to send email in case of an EXPECTED_RESULTS failure
-__C.EXPECTED_RESULTS_EMAIL = b''
+__C.EXPECTED_RESULTS_EMAIL = ''
 
 # Models and proposals referred to by URL are downloaded to a local cache
 # specified by DOWNLOAD_CACHE
-__C.DOWNLOAD_CACHE = b'/tmp/detectron-download-cache'
+__C.DOWNLOAD_CACHE = '/tmp/detectron-download-cache'
 
 
 # ---------------------------------------------------------------------------- #
@@ -1100,9 +1101,9 @@ def cache_cfg_urls():
 
 def get_output_dir(datasets, training=True):
     """Get the output directory determined by the current global config."""
-    assert isinstance(datasets, (tuple, list, basestring)), \
+    assert isinstance(datasets, tuple([tuple, list] + list(six.string_types))), \
         'datasets argument must be of type tuple, list or string'
-    is_string = isinstance(datasets, basestring)
+    is_string = isinstance(datasets, six.string_types)
     dataset_name = datasets if is_string else ':'.join(datasets)
     tag = 'train' if training else 'test'
     # <output-dir>/<train|test>/<dataset-name>/<model-type>/
@@ -1114,11 +1115,12 @@ def get_output_dir(datasets, training=True):
 
 def load_cfg(cfg_to_load):
     """Wrapper around yaml.load used for maintaining backward compatibility"""
-    assert isinstance(cfg_to_load, (file, basestring)), \
-        'Expected {} or {} got {}'.format(file, basestring, type(cfg_to_load))
-    if isinstance(cfg_to_load, file):
+    file_types = [file, io.IOBase] if six.PY2 else [io.IOBase]  # noqa false positive
+    expected_types = tuple(file_types + list(six.string_types))
+    assert isinstance(cfg_to_load, expected_types), \
+        'Expected one of {}, got {}'.format(expected_types, type(cfg_to_load))
+    if isinstance(cfg_to_load, tuple(file_types)):
         cfg_to_load = ''.join(cfg_to_load.readlines())
-    if isinstance(cfg_to_load, basestring):
     for old_module, new_module in iteritems(_RENAMED_MODULES):
         # yaml object encoding: !!python/object/new:<module>.<object>
         old_module, new_module = 'new:' + old_module, 'new:' + new_module
@@ -1232,7 +1234,7 @@ def _decode_cfg_value(v):
     if isinstance(v, dict):
         return AttrDict(v)
     # All remaining processing is only applied to strings
-    if not isinstance(v, basestring):
+    if not isinstance(v, six.string_types):
         return v
     # Try to interpret `v` as a:
     #   string, number, tuple, list, dict, boolean, or None
@@ -1270,7 +1272,7 @@ def _check_and_coerce_cfg_value_type(value_a, value_b, key, full_key):
     # Exceptions: numpy arrays, strings, tuple<->list
     if isinstance(value_b, np.ndarray):
         value_a = np.array(value_a, dtype=value_b.dtype)
-    elif isinstance(value_b, basestring):
+    elif isinstance(value_b, six.string_types):
         value_a = str(value_a)
     elif isinstance(value_a, tuple) and isinstance(value_b, list):
         value_a = list(value_a)

detectron/datasets/json_dataset.py

@@ -26,11 +26,11 @@ from __future__ import print_function
 from __future__ import unicode_literals
 
 import copy
-import cPickle as pickle
 import logging
 import numpy as np
 import os
 import scipy.sparse
+from six.moves import cPickle as pickle
 
 # Must happen before importing COCO API (which imports matplotlib)
 import detectron.utils.env as envu

detectron/datasets/voc_eval.py

@@ -22,11 +22,11 @@
 
 """Python implementation of the PASCAL VOC devkit's AP evaluation code."""
 
-import cPickle
 import logging
 import numpy as np
 import os
 import xml.etree.ElementTree as ET
+from six.moves import cPickle
 
 logger = logging.getLogger(__name__)
 

detectron/roi_data/loader.py

@@ -44,11 +44,11 @@ from collections import deque
 from collections import OrderedDict
 import logging
 import numpy as np
-import Queue
 import signal
 import threading
 import time
 import uuid
+from six.moves import queue as Queue
 
 from caffe2.python import core, workspace
 

detectron/utils/blob.py

@@ -28,9 +28,9 @@ from __future__ import division
 from __future__ import print_function
 from __future__ import unicode_literals
 
-import cPickle as pickle
 import cv2
 import numpy as np
+from six.moves import cPickle as pickle
 
 from caffe2.proto import caffe2_pb2
 

detectron/utils/coordinator.py

@@ -22,9 +22,9 @@ from __future__ import unicode_literals
 
 import contextlib
 import logging
-import Queue
 import threading
 import traceback
+from six.moves import queue as Queue
 
 log = logging.getLogger(__name__)
 

detectron/utils/io.py

@@ -20,13 +20,13 @@ from __future__ import division
 from __future__ import print_function
 from __future__ import unicode_literals
 
-import cPickle as pickle
 import hashlib
 import logging
 import os
 import re
 import sys
-import urllib2
+from six.moves import cPickle as pickle
+from six.moves import urllib
 
 logger = logging.getLogger(__name__)
 
@@ -45,7 +45,9 @@ def cache_url(url_or_file, cache_dir):
     path to the cached file. If the argument is not a URL, simply return it as
     is.
     """
-    is_url = re.match(r'^(?:http)s?://', url_or_file, re.IGNORECASE) is not None
+    is_url = re.match(
+        r'^(?:http)s?://', url_or_file, re.IGNORECASE
+    ) is not None
 
     if not is_url:
         return url_or_file
@@ -111,7 +113,7 @@ def download_url(
     Credit:
     https://stackoverflow.com/questions/2028517/python-urllib2-progress-hook
     """
-    response = urllib2.urlopen(url)
+    response = urllib.request.urlopen(url)
     total_size = response.info().getheader('Content-Length').strip()
     total_size = int(total_size)
     bytes_so_far = 0
@@ -140,5 +142,5 @@ def _get_file_md5sum(file_name):
 def _get_reference_md5sum(url):
     """By convention the md5 hash for url is stored in url + '.md5sum'."""
     url_md5sum = url + '.md5sum'
-    md5sum = urllib2.urlopen(url_md5sum).read().strip()
+    md5sum = urllib.request.urlopen(url_md5sum).read().strip()
     return md5sum

detectron/utils/net.py

@@ -21,12 +21,12 @@ from __future__ import print_function
 from __future__ import unicode_literals
 
 from collections import OrderedDict
-import cPickle as pickle
 import logging
 import numpy as np
 import os
 import pprint
 import yaml
+from six.moves import cPickle as pickle
 
 from caffe2.python import core
 from caffe2.python import workspace

detectron/utils/subprocess.py

@@ -27,7 +27,7 @@ import os
 import yaml
 import numpy as np
 import subprocess
-import cPickle as pickle
+from six.moves import cPickle as pickle
 from six.moves import shlex_quote
 
 from detectron.core.config import cfg

requirements.txt

@@ -6,3 +6,4 @@ setuptools
 Cython
 mock
 scipy
+six

tools/convert_coco_model_to_cityscapes.py

@@ -9,10 +9,10 @@ from __future__ import print_function
 from __future__ import unicode_literals
 
 import argparse
-import cPickle as pickle
 import numpy as np
 import os
 import sys
+from six.moves import cPickle as pickle
 
 import detectron.datasets.coco_to_cityscapes_id as cs
 

tools/convert_selective_search.py

@@ -24,10 +24,10 @@ from __future__ import division
 from __future__ import print_function
 from __future__ import unicode_literals
 
-import cPickle as pickle
 import numpy as np
 import scipy.io as sio
 import sys
+from six.moves import cPickle as pickle
 
 from detectron.datasets.json_dataset import JsonDataset
 

tools/pickle_caffe_blobs.py

@@ -26,10 +26,10 @@ from __future__ import print_function
 from __future__ import unicode_literals
 
 import argparse
-import cPickle as pickle
 import numpy as np
 import os
 import sys
+from six.moves import cPickle as pickle
 
 from caffe.proto import caffe_pb2
 from caffe2.proto import caffe2_pb2

tools/reval.py

@@ -31,10 +31,10 @@ from __future__ import print_function
 from __future__ import unicode_literals
 
 import argparse
-import cPickle as pickle
 import os
 import sys
 import yaml
+from six.moves import cPickle as pickle
 
 from detectron.core.config import cfg
 from detectron.datasets import task_evaluation

tools/visualize_results.py

@@ -23,10 +23,10 @@ from __future__ import print_function
 from __future__ import unicode_literals
 
 import argparse
-import cPickle as pickle
 import cv2
 import os
 import sys
+from six.moves import cPickle as pickle
 
 from detectron.datasets.json_dataset import JsonDataset
 import detectron.utils.vis as vis_utils