def trainModel(model=None,
epochs=1,
batch_size=32,
stepsPerEpochFull=True,
stepsPerEpoch=0):
# Trains a model
# model = optional parameter; creates new if not passed; otherwise keeps training
# epochs - number of max epochs to train (subject to early stopping)
# Returns:
# model: trained Keras model
#crop_range = 32 # number of pixels to crop image (if size is 235, crops are 0-223, 1-224, ... 11-234)
target_size = 150
datasrc = "ilsvrc14_DET"
#datasrc = "ilsvrc14"
# "presumed mean" of X, subtract from all input
#subtractMean=0.5
# Load pre-calculated RGB mean, PCA (Principal Component Analysis) eigenvectors and eigenvalues
#subtractMean=np.array ( [ 0.4493, 0.4542, 0.3901 ] )
#subtractMean = np.load("..\\rgb_mean.npy")
#pca_eigenvectors = np.load("..\\eigenvectors.npy")
#pca_eigenvalues = np.load("..\\eigenvalues.npy")
#dataGen = dg_v1.prepDataGen(target_size = target_size, batch_size = 64 )
dataGen = as_det_v7.AugSequence ( target_size=target_size, \
#crop_range=crop_range, allow_hor_flip=True,
batch_size=batch_size,
#subtractMean=subtractMean, pca_eigenvectors=pca_eigenvectors, pca_eigenvalues=pca_eigenvalues, \
preprocess="div255",
subdiv=19,
datasrc=datasrc, test=False, debug=True )
# Keep training the model is passed as param
if model is None:
model = m_det_v20.prepModel(input_shape=(target_size, target_size, 3),
subdiv=19)
#prepare a validation data generator, used for early stopping
#vldDataGen = dg_v1.prepDataGen( target_size=target_size, test=True, batch_size=128, datasrc=datasrc )
vldDataGen = as_det_v7.AugSequence ( target_size=target_size,\
#crop_range=1,
batch_size=batch_size, \
#subtractMean=subtractMean,
preprocess="div255",
subdiv=19,
datasrc=datasrc, test=True, debug=True )
#callback_earlystop = EarlyStopping ( monitor='val_acc', min_delta=0.001, patience=20, verbose=1, mode='max', restore_best_weights=True )
if stepsPerEpochFull:
stepsPerEpochTrain = len(dataGen)
stepsPerEchoVal = len(vldDataGen)
else:
stepsPerEpochTrain = stepsPerEpoch
stepsPerEchoVal = stepsPerEpoch
# full epoch is 12x12 = 144 passes over data: 1 times for each subframe
model.fit_generator ( dataGen, steps_per_epoch=stepsPerEpochTrain \
, epochs=epochs, verbose=2 \
, validation_data=vldDataGen , validation_steps=stepsPerEchoVal \
#, callbacks=[callback_earlystop] \
)
#print ("Evaluation on train set (1 frame)")
#e_v2.eval(model, target_size=target_size, datasrc=datasrc)
#print ("Evaluation on validation set (1 frame)")
#e_v2.eval(model, target_size=target_size, datasrc=datasrc, preprocess="vgg", test=True)
#print ("Evaluation on validation set (5 frames)")
#e_v3.eval(model, target_size=target_size, datasrc=datasrc, preprocess="vgg", test=True)
#print ("Evaluation on validation set (10 frames)")
#e_v4.eval(model, target_size=target_size, datasrc=datasrc, preprocess="vgg", test=True)
return model
# Set target size of image
target_size = 150
# Probability of object in subdivision threshold
Pr_obj_threshold = 0.15
datasrc = "ilsvrc14_DET"
# Prepare data generator
try:
_ = dataGen is None
except:
dataGen = as_det_v7.AugSequence ( target_size=target_size, \
#crop_range=crop_range, allow_hor_flip=True,
batch_size=64,
#subtractMean=subtractMean, pca_eigenvectors=pca_eigenvectors, pca_eigenvalues=pca_eigenvalues, \
#preprocess="vgg"
subdiv=19,
datasrc=datasrc, test=False, debug=True )
def getRectCoord(subdiv_x, subdiv_y, bx_rel, by_rel, subdiv_width,
subdiv_height, bw_rel, bh_rel, img_width, img_height):
# Center (x, y), and Size (with, height) - absolute in whole image
bx_abs = (subdiv_x + bx_rel) * subdiv_width
by_abs = (subdiv_y + by_rel) * subdiv_height
#Temporary changed so that sigmoid works for image size
bw_abs = bw_rel * img_width
bh_abs = bh_rel * img_height
#bw_abs = bw_rel * subdiv_width