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frontend.py
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from keras.models import Model
from keras.layers import Reshape, Activation, Conv2D, Input, MaxPooling2D, BatchNormalization, Flatten, Dense, Lambda
from keras.layers.advanced_activations import LeakyReLU
import tensorflow as tf
import numpy as np
import os
import cv2
from keras.applications.mobilenet import MobileNet
from keras.layers.merge import concatenate
from keras.optimizers import SGD, Adam, RMSprop
from preprocessing import BatchGenerator
from keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard
from utils import BoundBox
from backend import TinyYoloFeature, FullYoloFeature, MobileNetFeature, SqueezeNetFeature, Inception3Feature, VGG16Feature, ResNet50Feature
from keras.utils import multi_gpu_model
#from sklearn.metrics import roc_auc_score #lxx
from keras.callbacks import Callback
class Histories(Callback):
def __init__(self, test_data=None):
self.test_data = test_data
def on_train_begin(self, logs={}):
return
def on_train_end(self, logs={}):
return
def on_epoch_begin(self, epoch, logs={}):
return
def on_epoch_end(self, epoch, logs={}):
print(' \n ### loss: {}, acc: {}, val_loss: {}, val_acc: {} ### \n, '.format(logs.get('loss'),
logs.get('acc'), logs.get('val_loss'), logs.get('val_acc')))
if self.test_data is not None :
x, y = self.test_data
loss, acc = self.model.evaluate(x, y, verbose=0)
print('\nTesting loss: {}, acc: {}\n'.format(loss, acc))
return
def on_batch_begin(self, batch, logs={}):
return
def on_batch_end(self, batch, logs={}):
return
class YOLO(object):
def __init__(self, architecture,
input_size,
labels,
max_box_per_image,
anchors,
gpus = 1):
self.input_size = input_size
self.labels = list(labels)
self.nb_class = len(self.labels)
self.nb_box = 5
self.class_wt = np.ones(self.nb_class, dtype='float32')
self.anchors = anchors
self.gpus = gpus
self.max_box_per_image = max_box_per_image
##########################
# Make the model
##########################
with tf.device('/cpu:0'):
# make the feature extractor layers
input_image = Input(shape=(self.input_size, self.input_size, 3))
self.true_boxes = Input(shape=(1, 1, 1, max_box_per_image , 4))
if architecture == 'Inception3':
self.feature_extractor = Inception3Feature(self.input_size)
elif architecture == 'SqueezeNet':
self.feature_extractor = SqueezeNetFeature(self.input_size)
elif architecture == 'MobileNet':
self.feature_extractor = MobileNetFeature(self.input_size)
elif architecture == 'Full Yolo':
self.feature_extractor = FullYoloFeature(self.input_size)
elif architecture == 'Tiny Yolo':
self.feature_extractor = TinyYoloFeature(self.input_size)
elif architecture == 'VGG16':
self.feature_extractor = VGG16Feature(self.input_size)
elif architecture == 'ResNet50':
self.feature_extractor = VGG16Feature(self.input_size)
else:
raise Exception('Architecture not supported! Only support Full Yolo, \
Tiny Yolo, MobileNet, SqueezeNet, VGG16, ResNet50, and Inception3 at the moment!')
print (self.feature_extractor.get_output_shape() )
self.grid_h, self.grid_w = self.feature_extractor.get_output_shape()
features = self.feature_extractor.extract(input_image)
# make the object detection layer
output = Conv2D(self.nb_box * (4 + 1 + self.nb_class), (1,1),
strides=(1,1),
padding='same',
name='conv_23',
kernel_initializer='lecun_normal')(features)
output = Reshape((self.grid_h, self.grid_w, self.nb_box, 4 + 1 + self.nb_class))(output)
output = Lambda(lambda args: args[0])([output, self.true_boxes])
self.orgmodel = Model([input_image, self.true_boxes], output)
# initialize the weights of the detection layer
layer = self.orgmodel.layers[-4]
weights = layer.get_weights()
new_kernel = np.random.normal(size=weights[0].shape)/(self.grid_h*self.grid_w)
new_bias = np.random.normal(size=weights[1].shape)/(self.grid_h*self.grid_w)
layer.set_weights([new_kernel, new_bias])
# print a summary of the whole model
self.orgmodel.summary()
if (gpus >1) :
self.model = multi_gpu_model(self.orgmodel, gpus= self.gpus)
else:
self.model = self.orgmodel
def custom_loss(self, y_true, y_pred):
mask_shape = tf.shape(y_true)[:4]
cell_x = tf.to_float(tf.reshape(tf.tile(tf.range(self.grid_w), [self.grid_h]), (1, self.grid_h, self.grid_w, 1, 1)))
cell_y = tf.transpose(cell_x, (0,2,1,3,4))
cell_grid = tf.tile(tf.concat([cell_x,cell_y], -1), [self.batch_size, 1, 1, 5, 1])
coord_mask = tf.zeros(mask_shape)
conf_mask = tf.zeros(mask_shape)
class_mask = tf.zeros(mask_shape)
seen = tf.Variable(0.)
total_recall = tf.Variable(0.)
"""
Adjust prediction
"""
### adjust x and y
pred_box_xy = tf.sigmoid(y_pred[..., :2]) + cell_grid
### adjust w and h
pred_box_wh = tf.exp(y_pred[..., 2:4]) * np.reshape(self.anchors, [1,1,1,self.nb_box,2])
### adjust confidence
pred_box_conf = tf.sigmoid(y_pred[..., 4])
### adjust class probabilities
pred_box_class = y_pred[..., 5:]
"""
Adjust ground truth
"""
### adjust x and y
true_box_xy = y_true[..., 0:2] # relative position to the containing cell
### adjust w and h
true_box_wh = y_true[..., 2:4] # number of cells accross, horizontally and vertically
### adjust confidence
true_wh_half = true_box_wh / 2.
true_mins = true_box_xy - true_wh_half
true_maxes = true_box_xy + true_wh_half
pred_wh_half = pred_box_wh / 2.
pred_mins = pred_box_xy - pred_wh_half
pred_maxes = pred_box_xy + pred_wh_half
intersect_mins = tf.maximum(pred_mins, true_mins)
intersect_maxes = tf.minimum(pred_maxes, true_maxes)
intersect_wh = tf.maximum(intersect_maxes - intersect_mins, 0.)
intersect_areas = intersect_wh[..., 0] * intersect_wh[..., 1]
true_areas = true_box_wh[..., 0] * true_box_wh[..., 1]
pred_areas = pred_box_wh[..., 0] * pred_box_wh[..., 1]
union_areas = pred_areas + true_areas - intersect_areas
iou_scores = tf.truediv(intersect_areas, union_areas)
true_box_conf = iou_scores * y_true[..., 4]
### adjust class probabilities
true_box_class = tf.to_int32(y_true[..., 5])
"""
Determine the masks
"""
### coordinate mask: simply the position of the ground truth boxes (the predictors)
coord_mask = tf.expand_dims(y_true[..., 4], axis=-1) * self.coord_scale
### confidence mask: penelize predictors + penalize boxes with low IOU
# penalize the confidence of the boxes, which have IOU with some ground truth box < 0.6
true_xy = self.true_boxes[..., 0:2]
true_wh = self.true_boxes[..., 2:4]
true_wh_half = true_wh / 2.
true_mins = true_xy - true_wh_half
true_maxes = true_xy + true_wh_half
pred_xy = tf.expand_dims(pred_box_xy, 4)
pred_wh = tf.expand_dims(pred_box_wh, 4)
pred_wh_half = pred_wh / 2.
pred_mins = pred_xy - pred_wh_half
pred_maxes = pred_xy + pred_wh_half
intersect_mins = tf.maximum(pred_mins, true_mins)
intersect_maxes = tf.minimum(pred_maxes, true_maxes)
intersect_wh = tf.maximum(intersect_maxes - intersect_mins, 0.)
intersect_areas = intersect_wh[..., 0] * intersect_wh[..., 1]
true_areas = true_wh[..., 0] * true_wh[..., 1]
pred_areas = pred_wh[..., 0] * pred_wh[..., 1]
union_areas = pred_areas + true_areas - intersect_areas
iou_scores = tf.truediv(intersect_areas, union_areas)
best_ious = tf.reduce_max(iou_scores, axis=4)
conf_mask = conf_mask + tf.to_float(best_ious < 0.6) * (1 - y_true[..., 4]) * self.no_object_scale
# penalize the confidence of the boxes, which are reponsible for corresponding ground truth box
conf_mask = conf_mask + y_true[..., 4] * self.object_scale
### class mask: simply the position of the ground truth boxes (the predictors)
class_mask = y_true[..., 4] * tf.gather(self.class_wt, true_box_class) * self.class_scale
"""
Warm-up training
"""
no_boxes_mask = tf.to_float(coord_mask < self.coord_scale/2.)
seen = tf.assign_add(seen, 1.)
true_box_xy, true_box_wh, coord_mask = tf.cond(tf.less(seen, self.warmup_bs),
lambda: [true_box_xy + (0.5 + cell_grid) * no_boxes_mask,
true_box_wh + tf.ones_like(true_box_wh) * np.reshape(self.anchors, [1,1,1,self.nb_box,2]) * no_boxes_mask,
tf.ones_like(coord_mask)],
lambda: [true_box_xy,
true_box_wh,
coord_mask])
"""
Finalize the loss
"""
nb_coord_box = tf.reduce_sum(tf.to_float(coord_mask > 0.0))
nb_conf_box = tf.reduce_sum(tf.to_float(conf_mask > 0.0))
nb_class_box = tf.reduce_sum(tf.to_float(class_mask > 0.0))
loss_xy = tf.reduce_sum(tf.square(true_box_xy-pred_box_xy) * coord_mask) / (nb_coord_box + 1e-6) / 2.
loss_wh = tf.reduce_sum(tf.square(true_box_wh-pred_box_wh) * coord_mask) / (nb_coord_box + 1e-6) / 2.
loss_conf = tf.reduce_sum(tf.square(true_box_conf-pred_box_conf) * conf_mask) / (nb_conf_box + 1e-6) / 2.
loss_class = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=true_box_class, logits=pred_box_class)
loss_class = tf.reduce_sum(loss_class * class_mask) / (nb_class_box + 1e-6)
loss = loss_xy + loss_wh + loss_conf + loss_class
if self.debug:
nb_true_box = tf.reduce_sum(y_true[..., 4])
nb_pred_box = tf.reduce_sum(tf.to_float(true_box_conf > 0.5) * tf.to_float(pred_box_conf > 0.3))
current_recall = nb_pred_box/(nb_true_box + 1e-6)
total_recall = tf.assign_add(total_recall, current_recall)
loss = tf.Print(loss, [tf.zeros((1))], message='Dummy Line \t', summarize=1000)
loss = tf.Print(loss, [loss_xy], message='Loss XY \t', summarize=1000)
loss = tf.Print(loss, [loss_wh], message='Loss WH \t', summarize=1000)
loss = tf.Print(loss, [loss_conf], message='Loss Conf \t', summarize=1000)
loss = tf.Print(loss, [loss_class], message='Loss Class \t', summarize=1000)
loss = tf.Print(loss, [loss], message='Total Loss \t', summarize=1000)
loss = tf.Print(loss, [current_recall], message='Current Recall \t', summarize=1000)
loss = tf.Print(loss, [total_recall/seen], message='Average Recall \t', summarize=1000)
return loss
def load_weights(self, weight_path):
self.model.load_weights(weight_path)
def predict(self, image):
image = cv2.resize(image, (self.input_size, self.input_size))
image = self.feature_extractor.normalize(image)
input_image = image[:,:,::-1]
input_image = np.expand_dims(input_image, 0)
dummy_array = dummy_array = np.zeros((1,1,1,1,self.max_box_per_image,4))
netout = self.model.predict([input_image, dummy_array])[0]
boxes = self.decode_netout(netout)
return boxes
def bbox_iou(self, box1, box2):
x1_min = box1.x - box1.w/2
x1_max = box1.x + box1.w/2
y1_min = box1.y - box1.h/2
y1_max = box1.y + box1.h/2
x2_min = box2.x - box2.w/2
x2_max = box2.x + box2.w/2
y2_min = box2.y - box2.h/2
y2_max = box2.y + box2.h/2
intersect_w = self.interval_overlap([x1_min, x1_max], [x2_min, x2_max])
intersect_h = self.interval_overlap([y1_min, y1_max], [y2_min, y2_max])
intersect = intersect_w * intersect_h
union = box1.w * box1.h + box2.w * box2.h - intersect
return float(intersect) / union
def interval_overlap(self, interval_a, interval_b):
x1, x2 = interval_a
x3, x4 = interval_b
if x3 < x1:
if x4 < x1:
return 0
else:
return min(x2,x4) - x1
else:
if x2 < x3:
return 0
else:
return min(x2,x4) - x3
def decode_netout(self, netout, obj_threshold=0.3, nms_threshold=0.3):
grid_h, grid_w, nb_box = netout.shape[:3]
boxes = []
# decode the output by the network
netout[..., 4] = self.sigmoid(netout[..., 4])
netout[..., 5:] = netout[..., 4][..., np.newaxis] * self.softmax(netout[..., 5:])
netout[..., 5:] *= netout[..., 5:] > obj_threshold
for row in range(grid_h):
for col in range(grid_w):
for b in range(nb_box):
# from 4th element onwards are confidence and class classes
classes = netout[row,col,b,5:]
if np.sum(classes) > 0:
# first 4 elements are x, y, w, and h
x, y, w, h = netout[row,col,b,:4]
x = (col + self.sigmoid(x)) / grid_w # center position, unit: image width
y = (row + self.sigmoid(y)) / grid_h # center position, unit: image height
w = self.anchors[2 * b + 0] * np.exp(w) / grid_w # unit: image width
h = self.anchors[2 * b + 1] * np.exp(h) / grid_h # unit: image height
confidence = netout[row,col,b,4]
box = BoundBox(x, y, w, h, confidence, classes)
boxes.append(box)
# suppress non-maximal boxes
for c in range(self.nb_class):
sorted_indices = list(reversed(np.argsort([box.classes[c] for box in boxes])))
for i in range(len(sorted_indices)):
index_i = sorted_indices[i]
if boxes[index_i].classes[c] == 0:
continue
else:
for j in range(i+1, len(sorted_indices)):
index_j = sorted_indices[j]
if self.bbox_iou(boxes[index_i], boxes[index_j]) >= nms_threshold:
boxes[index_j].classes[c] = 0
# remove the boxes which are less likely than a obj_threshold
boxes = [box for box in boxes if box.get_score() > obj_threshold]
return boxes
def sigmoid(self, x):
return 1. / (1. + np.exp(-x))
def softmax(self, x, axis=-1, t=-100.):
x = x - np.max(x)
if np.min(x) < t:
x = x/np.min(x)*t
e_x = np.exp(x)
return e_x / e_x.sum(axis, keepdims=True)
def train(self, train_imgs, # the list of images to train the model
valid_imgs, # the list of images used to validate the model
train_times, # the number of time to repeat the training set, often used for small datasets
valid_times, # the number of times to repeat the validation set, often used for small datasets
nb_epoch, # number of epoches
learning_rate, # the learning rate
batch_size, # the size of the batch
warmup_bs, # number of initial batches to let the model familiarize with the new dataset
object_scale,
no_object_scale,
coord_scale,
class_scale,
saved_weights_name='best_weights.h5',
debug=False):
self.batch_size = batch_size
self.warmup_bs = warmup_bs
self.object_scale = object_scale
self.no_object_scale = no_object_scale
self.coord_scale = coord_scale
self.class_scale = class_scale
self.debug = debug
############################################
# Compile the model
############################################
optimizer = Adam(lr=learning_rate, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
self.model.compile(loss=self.custom_loss, optimizer=optimizer, metrics=['accuracy'])
############################################
# Make train and validation generators
############################################
generator_config = {
'IMAGE_H' : self.input_size,
'IMAGE_W' : self.input_size,
'GRID_H' : self.grid_h,
'GRID_W' : self.grid_w,
'BOX' : self.nb_box,
'LABELS' : self.labels,
'CLASS' : len(self.labels),
'ANCHORS' : self.anchors,
'BATCH_SIZE' : self.batch_size,
'TRUE_BOX_BUFFER' : self.max_box_per_image,
}
train_batch = BatchGenerator(train_imgs,
generator_config,
norm=self.feature_extractor.normalize)
valid_batch = BatchGenerator(valid_imgs,
generator_config,
norm=self.feature_extractor.normalize,
jitter=False)
############################################
# Make a few callbacks
############################################
early_stop = EarlyStopping(monitor='val_loss',
min_delta=0.001,
patience=3,
mode='min',
verbose=1)
"""
checkpoint = ModelCheckpoint(saved_weights_name,
monitor='val_loss',
verbose=1,
save_best_only=True,
mode='min',
period=1)
"""
# prepare loss and acc cauculation callback
histories = Histories()
logs_path = os.getcwd()+"/logs/"
if not os.path.exists(logs_path): logs_path = os.path.expanduser('~/logs/')
tb_counter = len([log for log in os.listdir(logs_path) if 'yolo' in log]) + 1
tensorboard = TensorBoard(log_dir=logs_path + 'yolo' + '_' + str(tb_counter),
histogram_freq=0,
#write_batch_performance=True,
write_graph=True,
write_images=False)
############################################
# Start the training process
############################################
#from IPython.core.debugger import Pdb; Pdb().set_trace()
history= self.model.fit_generator(generator = train_batch,
steps_per_epoch = len(train_batch) * train_times //self.gpus,
epochs = nb_epoch * self.gpus,
verbose = 1,
validation_data = valid_batch,
validation_steps = len(valid_batch) * valid_times // self.gpus,
callbacks = [early_stop, histories], #[early_stop, checkpoint, tensorboard],
workers = 3,
max_queue_size = 8 )
print('Loss: ', history)
self.orgmodel.save_weights(saved_weights_name)