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PARAMETERS.py
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PARAMETERS.py
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from __future__ import print_function
from helpers import *
from imdb_data import imdb_data
import fastRCNN, time, datetime
from fastRCNN.pascal_voc import pascal_voc
print(datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S'))
############################
# Adjust these parameters
# to make scripts run
############################
rootDir = os.path.dirname(os.path.realpath(sys.argv[0]))
############################
# default parameters
############################
datasetName = "grocery"
#directories
imgDir = rootDir + "/data/" + datasetName + "/"
procDir = rootDir + "/proc/" + datasetName + "/"
resultsDir = rootDir + "/results/" + datasetName + "/"
roiDir = procDir + "rois/"
modelDir = procDir + "models/"
cntkFilesDir = procDir + "cntkFiles/"
trainedSvmDir = procDir + "trainedSvm/"
cntkResourcesDir = rootDir + "/resources/cntk/"
# ROI generation
roi_maxImgDim = 200 # image size used for ROI generation
roi_minDimRel = 0.01 # minimum relative width/height of a ROI
roi_maxDimRel = 1.0 # maximum relative width/height of a ROI
roi_minNrPixelsRel = 0 # minimum relative area covered by a ROI
roi_maxNrPixelsRel = 1.0 # maximum relative area covered by a ROI
roi_maxAspectRatio = 4.0 # maximum aspect Ratio of a ROI, both vertically and horizontally
ss_minSize = 20 # for a description of the selective search parameters see:
ss_kvals = (50, 500, 6) # http://dlib.net/dlib/image_transforms/segment_image_abstract.h.html#find_candidate_object_locations
ss_max_merging_iterations = 20 #
ss_nmsThreshold = 0.85 # non-maxima surpression threshold run after selective search
grid_nrScales = 7 # uniform grid ROIs: number of iterations from largest possible ROI to smaller ROIs
grid_aspectRatios = [1.0, 2.0, 0.5] # uniform grid ROIs: allowed aspect ratio of ROIs
grid_downscaleRatioPerIteration = 1.5 # uniform grid ROIs: relative ROI width/height reduction per iteration, starting from largest possible ROI
# cntk model
cntk_nrRois = 2000 # DNN input number of ROIs per image. Zero-padded/truncated if necessary
cntk_padWidth = 1000 # DNN input image width [pixels]
cntk_padHeight = 1000 # DNN input image height [pixels]
cntk_featureDimensions = {'svm': 4096} # DNN output, dimension of each ROI
# nn and svm training
classifier = 'svm' # Options: 'svm', 'nn'. Train either a Support Vector Machine, or directly the Neural Network
train_posOverlapThres = 0.5 # DNN and SVM threshold for marking ROIs with significant overlap with a GT object as positive
# nn training
cntk_max_epochs = 18 # number of training epochs (only relevant if 'lassifier' is set to: 'nn')
cntk_mb_size = 5 # minibatch size
cntk_l2_reg_weight = 0.0005 # l2 regularizer weight
cntk_lr_per_image = [0.01] * 10 + [0.001] * 5 + [0.0001] #learning rate per image
cntk_momentum_time_constant = 10 # momentum
# svm training
svm_C = 0.001 # regularization parameter of the soft-margin error term
svm_B = 10.0 # intercept scaling
svm_nrEpochs = 3 # number of training iterations
svm_retrainLimit = 2000 # number of new items to trigger SVM training
svm_evictThreshold = -1.1 # remove easy negatives with decision value below this threshold
svm_posWeight = "auto" # automatically balance training set to correct for the majority of ROIs being negative
svm_targetNorm = 20.0 # magic value from traditional R-CNN (helps with convergence)
svm_penality = 'l2' # penalty norm
svm_loss = 'l1' # loss norm
svm_rngSeed = 3 # seed for randomization
# postprocessing
nmsThreshold = 0.3 # Non-Maxima suppression threshold (in range [0,1])
# The lower the more ROIs will be combined. Used during evaluation and visualization (scripts 5_)
vis_decisionThresholds = {'svm' : 0.5, # Reject detections with low confidence, used only in 5_visualizeResults
'nn' : None}
# evaluation
evalVocOverlapThreshold = 0.5 # voc-style intersection-over-union threshold used to determine if object was found
############################
# project-specific
# parameters / overrides
############################
if datasetName.startswith("grocery"):
classes = ('__background__', # always have '__background__' be at index 0
"avocado", "orange", "butter", "champagne", "eggBox", "gerkin", "joghurt", "ketchup",
"orangeJuice", "onion", "pepper", "tomato", "water", "milk", "tabasco", "mustard")
# classes = ('__background__', # always have '__background__' be at index 0
# "avocado", "orange", "butter", "champagne", "cheese", "eggBox", "gerkin", "joghurt", "ketchup",
# "orangeJuice", "onion", "pepper", "sausage", "tomato", "water", "apple", "milk",
# "tabasco", "soySauce", "mustard", "beer")
# roi generation
cntk_nrRois = 200 #this number is too low to get good accuracy but allows for fast training and scoring (for demo purposes)
roi_minDimRel = 0.04
roi_maxDimRel = 0.4
roi_minNrPixelsRel = 2 * roi_minDimRel * roi_minDimRel
roi_maxNrPixelsRel = 0.33 * roi_maxDimRel * roi_maxDimRel
# postprocessing
nmsThreshold = 0.01
# database
imdbs = dict() # database provider of images and image annotations
for image_set in ["train", "test"]:
imdbs[image_set] = imdb_data(image_set, classes, cntk_nrRois, imgDir, roiDir, cntkFilesDir, boAddGroundTruthRois = (image_set!='test'))
elif datasetName.startswith("pascalVoc"):
classes = ('__background__',
'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable',
'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor')
lutImageSet = {"train": "trainval", "test": "test"}
# model training / scoring
classifier = 'nn'
# cntk model (Should train a model with mean-AP around 0.45)
# more than 99% of the test images have less than 4000 rois, but 50% more than 2000
cntk_mb_size = 2
cntk_nrRois = 4000
cntk_lr_per_image = [0.05] * 10 + [0.005] * 5 + [0.0005]
# database
imdbs = dict()
for image_set, year in zip(["train", "test"], ["2007", "2007"]):
imdbs[image_set] = fastRCNN.pascal_voc(lutImageSet[image_set], year, classes, cntk_nrRois, cacheDir = cntkFilesDir)
print("Number of {} images: {}".format(image_set, imdbs[image_set].num_images))
else:
ERROR
############################
# computed parameters
############################
nrClasses = len(classes)
cntk_featureDimensions['nn'] = nrClasses
lutClass2Id = dict(zip(classes, range(len(classes))))
print("PARAMETERS: datasetName = " + datasetName)
assert cntk_padWidth == cntk_padHeight, "ERROR: different width and height for padding not supported."
assert classifier.lower() in ['svm','nn'], "ERROR: only 'nn' or 'svm' classifier supported."
assert not (datasetName == 'pascalVoc' and classifier == 'svm'), "ERROR: 'svm' classifier for pascal VOC not supported."
assert(train_posOverlapThres >= 0 and train_posOverlapThres <= 1)