python3 update

lib/fast_rcnn/config.py

@@ -262,9 +262,9 @@ def _merge_a_into_b(a, b):
     if type(a) is not edict:
         return
 
-    for k, v in a.iteritems():
+    for k, v in a.items():
         # a must specify keys that are in b
-        if not b.has_key(k):
+        if k not in b:
             raise KeyError('{} is not a valid config key'.format(k))
 
         # the types must match, too
@@ -282,7 +282,7 @@ def _merge_a_into_b(a, b):
             try:
                 _merge_a_into_b(a[k], b[k])
             except:
-                print('Error under config key: {}'.format(k))
+                print(('Error under config key: {}'.format(k)))
                 raise
         else:
             b[k] = v
@@ -303,10 +303,10 @@ def cfg_from_list(cfg_list):
         key_list = k.split('.')
         d = __C
         for subkey in key_list[:-1]:
-            assert d.has_key(subkey)
+            assert subkey in d
             d = d[subkey]
         subkey = key_list[-1]
-        assert d.has_key(subkey)
+        assert subkey in d
         try:
             value = literal_eval(v)
         except:

lib/fast_rcnn/config.py.bak

@@ -0,0 +1,318 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+"""Fast R-CNN config system.
+
+This file specifies default config options for Fast R-CNN. You should not
+change values in this file. Instead, you should write a config file (in yaml)
+and use cfg_from_file(yaml_file) to load it and override the default options.
+
+Most tools in $ROOT/tools take a --cfg option to specify an override file.
+    - See tools/{train,test}_net.py for example code that uses cfg_from_file()
+    - See experiments/cfgs/*.yml for example YAML config override files
+"""
+
+import os
+import os.path as osp
+import numpy as np
+from distutils import spawn
+# `pip install easydict` if you don't have it
+from easydict import EasyDict as edict
+
+__C = edict()
+# Consumers can get config by:
+#   from fast_rcnn_config import cfg
+cfg = __C
+
+#
+# Training options
+#
+
+__C.TRAIN = edict()
+#__C.NET_NAME = 'VGGnet'
+# learning rate
+__C.TRAIN.LEARNING_RATE = 0.001
+__C.TRAIN.MOMENTUM = 0.9
+__C.TRAIN.GAMMA = 0.1
+__C.TRAIN.STEPSIZE = 50000
+__C.TRAIN.DISPLAY = 10
+__C.IS_MULTISCALE = False
+
+# Scales to compute real features
+#__C.TRAIN.SCALES_BASE = (0.25, 0.5, 1.0, 2.0, 3.0)
+#__C.TRAIN.SCALES_BASE = (1.0,)
+
+# parameters for ROI generating
+#__C.TRAIN.SPATIAL_SCALE = 0.0625
+#__C.TRAIN.KERNEL_SIZE = 5
+
+# Aspect ratio to use during training
+#__C.TRAIN.ASPECTS = (1, 0.75, 0.5, 0.25)
+#__C.TRAIN.ASPECTS= (1,)
+
+
+# Scales to use during training (can list multiple scales)
+# Each scale is the pixel size of an image's shortest side
+__C.TRAIN.SCALES = (600,)
+
+# Max pixel size of the longest side of a scaled input image
+__C.TRAIN.MAX_SIZE = 1000
+
+# Images to use per minibatch
+__C.TRAIN.IMS_PER_BATCH = 2
+
+# Minibatch size (number of regions of interest [ROIs])
+__C.TRAIN.BATCH_SIZE = 128
+
+# Fraction of minibatch that is labeled foreground (i.e. class > 0)
+__C.TRAIN.FG_FRACTION = 0.25
+
+# Overlap threshold for a ROI to be considered foreground (if >= FG_THRESH)
+__C.TRAIN.FG_THRESH = 0.5
+
+# Overlap threshold for a ROI to be considered background (class = 0 if
+# overlap in [LO, HI))
+__C.TRAIN.BG_THRESH_HI = 0.5
+__C.TRAIN.BG_THRESH_LO = 0.1
+
+# Use horizontally-flipped images during training?
+__C.TRAIN.USE_FLIPPED = True
+
+# Train bounding-box regressors
+__C.TRAIN.BBOX_REG = True
+
+# Overlap required between a ROI and ground-truth box in order for that ROI to
+# be used as a bounding-box regression training example
+__C.TRAIN.BBOX_THRESH = 0.5
+
+# Iterations between snapshots
+__C.TRAIN.SNAPSHOT_ITERS = 5000
+
+# solver.prototxt specifies the snapshot path prefix, this adds an optional
+# infix to yield the path: <prefix>[_<infix>]_iters_XYZ.caffemodel
+__C.TRAIN.SNAPSHOT_PREFIX = 'VGGnet_fast_rcnn'
+__C.TRAIN.SNAPSHOT_INFIX = ''
+
+# Use a prefetch thread in roi_data_layer.layer
+# So far I haven't found this useful; likely more engineering work is required
+__C.TRAIN.USE_PREFETCH = False
+
+# Normalize the targets (subtract empirical mean, divide by empirical stddev)
+__C.TRAIN.BBOX_NORMALIZE_TARGETS = True
+# Deprecated (inside weights)
+__C.TRAIN.BBOX_INSIDE_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
+# Normalize the targets using "precomputed" (or made up) means and stdevs
+# (BBOX_NORMALIZE_TARGETS must also be True)
+__C.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED = False
+__C.TRAIN.BBOX_NORMALIZE_MEANS = (0.0, 0.0, 0.0, 0.0)
+__C.TRAIN.BBOX_NORMALIZE_STDS = (0.1, 0.1, 0.2, 0.2)
+
+# Train using these proposals
+__C.TRAIN.PROPOSAL_METHOD = 'selective_search'
+
+# Make minibatches from images that have similar aspect ratios (i.e. both
+# tall and thin or both short and wide) in order to avoid wasting computation
+# on zero-padding.
+__C.TRAIN.ASPECT_GROUPING = True
+
+# Use RPN to detect objects
+__C.TRAIN.HAS_RPN = False
+# IOU >= thresh: positive example
+__C.TRAIN.RPN_POSITIVE_OVERLAP = 0.7
+# IOU < thresh: negative example
+__C.TRAIN.RPN_NEGATIVE_OVERLAP = 0.3
+# If an anchor statisfied by positive and negative conditions set to negative
+__C.TRAIN.RPN_CLOBBER_POSITIVES = False
+# Max number of foreground examples
+__C.TRAIN.RPN_FG_FRACTION = 0.5
+# Total number of examples
+__C.TRAIN.RPN_BATCHSIZE = 256
+# NMS threshold used on RPN proposals
+__C.TRAIN.RPN_NMS_THRESH = 0.7
+# Number of top scoring boxes to keep before apply NMS to RPN proposals
+__C.TRAIN.RPN_PRE_NMS_TOP_N = 12000
+# Number of top scoring boxes to keep after applying NMS to RPN proposals
+__C.TRAIN.RPN_POST_NMS_TOP_N = 2000
+# Proposal height and width both need to be greater than RPN_MIN_SIZE (at orig image scale)
+__C.TRAIN.RPN_MIN_SIZE = 16
+# Deprecated (outside weights)
+__C.TRAIN.RPN_BBOX_INSIDE_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
+# Give the positive RPN examples weight of p * 1 / {num positives}
+# and give negatives a weight of (1 - p)
+# Set to -1.0 to use uniform example weighting
+__C.TRAIN.RPN_POSITIVE_WEIGHT = -1.0
+
+# Enable timeline generation
+__C.TRAIN.DEBUG_TIMELINE = False
+
+#
+# Testing options
+#
+
+__C.TEST = edict()
+
+# Scales to use during testing (can list multiple scales)
+# Each scale is the pixel size of an image's shortest side
+__C.TEST.SCALES = (600,)
+
+# Max pixel size of the longest side of a scaled input image
+__C.TEST.MAX_SIZE = 1000
+
+# Overlap threshold used for non-maximum suppression (suppress boxes with
+# IoU >= this threshold)
+__C.TEST.NMS = 0.3
+
+# Experimental: treat the (K+1) units in the cls_score layer as linear
+# predictors (trained, eg, with one-vs-rest SVMs).
+__C.TEST.SVM = False
+
+# Test using bounding-box regressors
+__C.TEST.BBOX_REG = True
+
+# Propose boxes
+__C.TEST.HAS_RPN = True
+
+# Test using these proposals
+__C.TEST.PROPOSAL_METHOD = 'selective_search'
+
+## NMS threshold used on RPN proposals
+__C.TEST.RPN_NMS_THRESH = 0.7
+## Number of top scoring boxes to keep before apply NMS to RPN proposals
+__C.TEST.RPN_PRE_NMS_TOP_N = 6000
+#__C.TEST.RPN_PRE_NMS_TOP_N = 12000
+## Number of top scoring boxes to keep after applying NMS to RPN proposals
+__C.TEST.RPN_POST_NMS_TOP_N = 300
+#__C.TEST.RPN_POST_NMS_TOP_N = 2000
+# Proposal height and width both need to be greater than RPN_MIN_SIZE (at orig image scale)
+__C.TEST.RPN_MIN_SIZE = 16
+
+# Enable timeline generation
+__C.TEST.DEBUG_TIMELINE = False
+
+#
+# MISC
+#
+
+# The mapping from image coordinates to feature map coordinates might cause
+# some boxes that are distinct in image space to become identical in feature
+# coordinates. If DEDUP_BOXES > 0, then DEDUP_BOXES is used as the scale factor
+# for identifying duplicate boxes.
+# 1/16 is correct for {Alex,Caffe}Net, VGG_CNN_M_1024, and VGG16
+__C.DEDUP_BOXES = 1./16.
+
+# Pixel mean values (BGR order) as a (1, 1, 3) array
+# We use the same pixel mean for all networks even though it's not exactly what
+# they were trained with
+__C.PIXEL_MEANS = np.array([[[102.9801, 115.9465, 122.7717]]])
+
+# For reproducibility
+__C.RNG_SEED = 3
+
+# A small number that's used many times
+__C.EPS = 1e-14
+
+# Root directory of project
+__C.ROOT_DIR = osp.abspath(osp.join(osp.dirname(__file__), '..', '..'))
+
+# Data directory
+__C.DATA_DIR = osp.abspath(osp.join(__C.ROOT_DIR, 'data'))
+
+# Model directory
+__C.MODELS_DIR = osp.abspath(osp.join(__C.ROOT_DIR, 'models', 'pascal_voc'))
+
+# Name (or path to) the matlab executable
+__C.MATLAB = 'matlab'
+
+# Place outputs under an experiments directory
+__C.EXP_DIR = 'default'
+
+
+if spawn.find_executable("nvcc"):
+    # Use GPU implementation of non-maximum suppression
+    __C.USE_GPU_NMS = True
+
+    # Default GPU device id
+    __C.GPU_ID = 0
+else:
+    __C.USE_GPU_NMS = False
+
+
+def get_output_dir(imdb, weights_filename):
+    """Return the directory where experimental artifacts are placed.
+    If the directory does not exist, it is created.
+
+    A canonical path is built using the name from an imdb and a network
+    (if not None).
+    """
+    outdir = osp.abspath(osp.join(__C.ROOT_DIR, 'output', __C.EXP_DIR, imdb.name))
+    if weights_filename is not None:
+        outdir = osp.join(outdir, weights_filename)
+    if not os.path.exists(outdir):
+        os.makedirs(outdir)
+    return outdir
+
+def _merge_a_into_b(a, b):
+    """Merge config dictionary a into config dictionary b, clobbering the
+    options in b whenever they are also specified in a.
+    """
+    if type(a) is not edict:
+        return
+
+    for k, v in a.iteritems():
+        # a must specify keys that are in b
+        if not b.has_key(k):
+            raise KeyError('{} is not a valid config key'.format(k))
+
+        # the types must match, too
+        old_type = type(b[k])
+        if old_type is not type(v):
+            if isinstance(b[k], np.ndarray):
+                v = np.array(v, dtype=b[k].dtype)
+            else:
+                raise ValueError(('Type mismatch ({} vs. {}) '
+                                'for config key: {}').format(type(b[k]),
+                                                            type(v), k))
+
+        # recursively merge dicts
+        if type(v) is edict:
+            try:
+                _merge_a_into_b(a[k], b[k])
+            except:
+                print('Error under config key: {}'.format(k))
+                raise
+        else:
+            b[k] = v
+
+def cfg_from_file(filename):
+    """Load a config file and merge it into the default options."""
+    import yaml
+    with open(filename, 'r') as f:
+        yaml_cfg = edict(yaml.load(f))
+
+    _merge_a_into_b(yaml_cfg, __C)
+
+def cfg_from_list(cfg_list):
+    """Set config keys via list (e.g., from command line)."""
+    from ast import literal_eval
+    assert len(cfg_list) % 2 == 0
+    for k, v in zip(cfg_list[0::2], cfg_list[1::2]):
+        key_list = k.split('.')
+        d = __C
+        for subkey in key_list[:-1]:
+            assert d.has_key(subkey)
+            d = d[subkey]
+        subkey = key_list[-1]
+        assert d.has_key(subkey)
+        try:
+            value = literal_eval(v)
+        except:
+            # handle the case when v is a string literal
+            value = v
+        assert type(value) == type(d[subkey]), \
+            'type {} does not match original type {}'.format(
+            type(value), type(d[subkey]))
+        d[subkey] = value