def get_detector_mask(self, boxes, anchors):
'''
Precompute detectors_mask and matching_true_boxes for training.
Detectors mask is 1 for each spatial position in the final conv layer and
anchor that should be active for the given boxes and 0 otherwise.
Matching true boxes gives the regression targets for the ground truth box
that caused a detector to be active or 0 otherwise.
Copied from YAD2K retrain_yolo.py
'''
detector_save_path = os.path.join(self.output_path, "KITTI-masks.npz")
if os.path.exists(detector_save_path):
self.__print("Loading detector masks from file...")
data = np.load(detector_save_path)
detectors_mask = data['detectors_mask']
matching_true_boxes = data['matching_true_boxes']
else:
self.__print("Computing detector masks...")
detectors_mask = [0 for i in range(len(boxes))]
matching_true_boxes = [0 for i in range(len(boxes))]
for i, box in enumerate(boxes):
detectors_mask[i], matching_true_boxes[i] = \
preprocess_true_boxes(box, anchors, [self.image_data_size[1], self.image_data_size[0]])
detectors_mask = np.array(detectors_mask)
matching_true_boxes = np.array(matching_true_boxes)
np.savez(detector_save_path, detectors_mask=detectors_mask, matching_true_boxes=matching_true_boxes)
return np.array(detectors_mask, dtype=np.bool), np.array(matching_true_boxes)
def get_detector_mask(boxes, anchors):
detectors_mask = [0 for i in range(len(boxes))]
matching_true_boxes = [0 for i in range(len(boxes))]
for i, box in enumerate(boxes):
box = box.reshape((-1, 5))
detectors_mask[i], matching_true_boxes[i] = preprocess_true_boxes(
box, anchors, [416, 416])
return np.array(detectors_mask), np.array(matching_true_boxes)
def get_all_detector_masks(data, anchors):
#data is full dict from process_MOT_dataset
for video in data:
video['detector_mask'] = {}
for frame_id in video['frame']:
detectors_mask, matching_true_box = preprocess_true_boxes(
video['frame'][frame_id], anchors, [608, 608])
video['detector_mask'][frame_id] = [
detectors_mask, matching_true_box
]
return data
def get_detector_mask(boxes, anchors):
'''
Precompute detectors_mask and matching_true_boxes for training.
Detectors mask is 1 for each spatial position in the final conv layer and
anchor that should be active for the given boxes and 0 otherwise.
Matching true boxes gives the regression targets for the ground truth box
that caused a detector to be active or 0 otherwise.
'''
detectors_mask = [0 for i in range(len(boxes))]
matching_true_boxes = [0 for i in range(len(boxes))]
for i, box in enumerate(boxes):
detectors_mask[i], matching_true_boxes[i] = preprocess_true_boxes(box, anchors, [224, 320])
return np.array(detectors_mask), np.array(matching_true_boxes)
def get_detector_mask(boxes, anchors):
'''
Precompute detectors_mask and matching_true_boxes for training.
Detectors mask is 1 for each spatial position in the final conv layer and
anchor that should be active for the given boxes and 0 otherwise.
Matching true boxes gives the regression targets for the ground truth box
that caused a detector to be active or 0 otherwise.
'''
detectors_mask = [0 for i in range(len(boxes))]
matching_true_boxes = [0 for i in range(len(boxes))]
for i, box in enumerate(boxes):
detectors_mask[i], matching_true_boxes[i] = preprocess_true_boxes(box, anchors, [416, 416])
return np.array(detectors_mask), np.array(matching_true_boxes)
def get_detector_mask(boxes, anchors):
"""
Precompute detectors_mask and matching_true_boxes methods for training.
(Detectors mask is 1 for each spatial position in the final conv layer and anchor that should be active for the
given boxes and 0 otherwise. Matching true boxes gives the regression targets for the ground truth box that
caused a detector to be active or 0 otherwise).
:param boxes: list of bounding box information from the annotation
:param anchors: list of anchors from the annotation
:return:
"""
detectors_mask = [0 for i in range(len(boxes))]
matching_true_boxes = [0 for i in range(len(boxes))]
for i, box in enumerate(boxes):
detectors_mask[i], matching_true_boxes[i] = preprocess_true_boxes(
box, anchors, [416, 416])
return np.array(detectors_mask), np.array(matching_true_boxes)
def get_detector_mask(boxes, anchors):
'''
Precompute detectors_mask and matching_true_boxes for training.
Detectors mask is 1 for each spatial position in the final conv layer and
anchor that should be active for the given boxes and 0 otherwise.
Matching true boxes gives the regression targets for the ground truth box
that caused a detector to be active or 0 otherwise.
Remember: matching true boxes is Corresponding ground truth boxes for positive detector positions
with shape [batch, num_true_boxes, 5] containing box x_center, y_center, width, height, and class.
'''
detectors_mask = [0 for i in range(len(boxes))]
matching_true_boxes = [0 for i in range(len(boxes))]
for i, box in enumerate(
boxes): #enumerate returns index and its corresponding value
detectors_mask[i], matching_true_boxes[i] = preprocess_true_boxes(
box, anchors, [416, 416])
return np.array(detectors_mask), np.array(matching_true_boxes)
def _main():
voc_path = os.path.expanduser(
'/Users/ss/Data/VOCdevkit/pascal_voc_07_07.hdf5')
classes_path = os.path.expanduser('model_data/pascal_classes.txt')
with open(classes_path) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]
voc = h5py.File(voc_path, 'r')
image = PIL.Image.open(io.BytesIO(voc['train/images'][28]))
orig_size = np.array([image.width, image.height])
orig_size = np.expand_dims(orig_size, axis=0)
# Image preprocessing.
image = image.resize((416, 416), PIL.Image.BICUBIC)
image_data = np.array(image, dtype=np.float)
image_data /= 255.
# Box preprocessing.
# Original boxes stored as 1D list of class, x_min, y_min, x_max, y_max.
boxes = voc['train/boxes'][28]
boxes = boxes.reshape((-1, 5))
# Get extents as y_min, x_min, y_max, x_max, class for comparision with
# model output.
boxes_extents = boxes[:, [2, 1, 4, 3, 0]]
# Get box parameters as x_center, y_center, box_width, box_height, class.
boxes_xy = 0.5 * (boxes[:, 3:5] + boxes[:, 1:3])
boxes_wh = boxes[:, 3:5] - boxes[:, 1:3]
boxes_xy = boxes_xy / orig_size
boxes_wh = boxes_wh / orig_size
boxes = np.concatenate((boxes_xy, boxes_wh, boxes[:, 0:1]), axis=1)
# Precompute detectors_mask and matching_true_boxes for training.
# Detectors mask is 1 for each spatial position in the final conv layer and
# anchor that should be active for the given boxes and 0 otherwise.
# Matching true boxes gives the regression targets for the ground truth box
# that caused a detector to be active or 0 otherwise.
anchors = COCO_ANCHORS
detectors_mask_shape = (13, 13, 5, 1)
matching_boxes_shape = (13, 13, 5, 5)
detectors_mask, matching_true_boxes = preprocess_true_boxes(
boxes, anchors, [416, 416])
# Create model input layers.
image_input = Input(shape=(416, 416, 3))
boxes_input = Input(shape=(None, 5))
detectors_mask_input = Input(shape=detectors_mask_shape)
matching_boxes_input = Input(shape=matching_boxes_shape)
print(boxes)
print(boxes_extents)
print(np.where(detectors_mask == 1)[:-1])
print(matching_true_boxes[np.where(detectors_mask == 1)[:-1]])
# Create model body.
model_body = yolo_body(image_input, len(anchors), len(class_names))
model_body = Model(image_input, model_body.output)
# Place model loss on CPU to reduce GPU memory usage.
with tf.device('/cpu:0'):
# TODO: Replace Lambda with custom Keras layer for loss.
model_loss = Lambda(yolo_loss,
output_shape=(1, ),
name='yolo_loss',
arguments={
'anchors': anchors,
'num_classes': len(class_names)
})([
model_body.output, boxes_input,
detectors_mask_input, matching_boxes_input
])
model = Model(
[image_input, boxes_input, detectors_mask_input, matching_boxes_input],
model_loss)
model.compile(
optimizer='adam', loss={
'yolo_loss': lambda y_true, y_pred: y_pred
}) # This is a hack to use the custom loss function in the last layer.
# Add batch dimension for training.
image_data = np.expand_dims(image_data, axis=0)
boxes = np.expand_dims(boxes, axis=0)
detectors_mask = np.expand_dims(detectors_mask, axis=0)
matching_true_boxes = np.expand_dims(matching_true_boxes, axis=0)
num_steps = 1000
# TODO: For full training, put preprocessing inside training loop.
# for i in range(num_steps):
# loss = model.train_on_batch(
# [image_data, boxes, detectors_mask, matching_true_boxes],
# np.zeros(len(image_data)))
model.fit([image_data, boxes, detectors_mask, matching_true_boxes],
np.zeros(len(image_data)),
batch_size=1,
epochs=num_steps)
model.save_weights('overfit_weights.h5')
# Create output variables for prediction.
yolo_outputs = yolo_head(model_body.output, anchors, len(class_names))
input_image_shape = K.placeholder(shape=(2, ))
boxes, scores, classes = yolo_eval(yolo_outputs,
input_image_shape,
score_threshold=.3,
iou_threshold=.9)
# Run prediction on overfit image.
sess = K.get_session() # TODO: Remove dependence on Tensorflow session.
out_boxes, out_scores, out_classes = sess.run(
[boxes, scores, classes],
feed_dict={
model_body.input: image_data,
input_image_shape: [image.size[1], image.size[0]],
K.learning_phase(): 0
})
print('Found {} boxes for image.'.format(len(out_boxes)))
print(out_boxes)
# Plot image with predicted boxes.
image_with_boxes = draw_boxes(image_data[0], out_boxes, out_classes,
class_names, out_scores)
plt.imshow(image_with_boxes, interpolation='nearest')
plt.show()
def _main(args):
#訓練データセットに対する情報の読み込みfrom voc_to_hdf5
voc_path = os.path.expanduser(args.data_path)
#ラベル情報.txtへのpath
classes_path = os.path.expanduser(args.classes_path)
#アスペクト比の格納？
anchors_path = os.path.expanduser(args.anchors_path)
#クラス名のリストを作成
with open(classes_path) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]
if os.path.isfile(anchors_path):
with open(anchors_path) as f:
anchors = f.readline()
anchors = [float(x) for x in anchors.split(',')]
anchors = np.array(anchors).reshape(-1, 2)
else:
anchors = YOLO_ANCHORS
voc = h5py.File(voc_path, 'r')
image = PIL.Image.open(io.BytesIO(voc['test/images'][0]))
orig_size = np.array([image.width, image.height])
orig_size = np.expand_dims(orig_size, axis=0)
# Image preprocessing.
image = image.resize((416, 416), PIL.Image.BICUBIC)
image_data = np.array(image, dtype=np.float)
image_data /= 255.
# Box preprocessing.
# Original boxes stored as 1D list of class, x_min, y_min, x_max, y_max.
boxes = voc['test/boxes'][0]
boxes = boxes.reshape((-1, 5))
# Get extents as y_min, x_min, y_max, x_max, class for comparision with
# model output.
boxes_extents = boxes[:, [2, 1, 4, 3, 0]]
# Get box parameters as x_center, y_center, box_width, box_height, class.
boxes_xy = 0.5 * (boxes[:, 3:5] + boxes[:, 1:3])
boxes_wh = boxes[:, 3:5] - boxes[:, 1:3]
boxes_xy = boxes_xy / orig_size
boxes_wh = boxes_wh / orig_size
#annotation部分のxy座標、幅
boxes = np.concatenate((boxes_xy, boxes_wh, boxes[:, 0:1]), axis=1)
# Precompute detectors_mask and matching_true_boxes for training.
# Detectors mask is 1 for each spatial position in the final conv layer and
# anchor that should be active for the given boxes and 0 otherwise.
# Matching true boxes gives the regression targets for the ground truth box
# that caused a detector to be active or 0 otherwise.
detectors_mask_shape = (13, 13, 5, 1)
matching_boxes_shape = (13, 13, 5, 5)
detectors_mask, matching_true_boxes = preprocess_true_boxes(
boxes, anchors, [416, 416])
# Create model input layers.
image_input = Input(shape=(416, 416, 3))
boxes_input = Input(shape=(None, 5))
detectors_mask_input = Input(shape=detectors_mask_shape)
matching_boxes_input = Input(shape=matching_boxes_shape)
print('Boxes:')
print(boxes)
print('Box corners:')
print(boxes_extents)
print('Active detectors:')
print(np.where(detectors_mask == 1)[:-1])
print('Matching boxes for active detectors:')
print(matching_true_boxes[np.where(detectors_mask == 1)[:-1]])
# Create model body.
model_body = yolo_body(image_input, len(anchors), len(class_names))
model_body = Model(image_input, model_body.output)
# Place model loss on CPU to reduce GPU memory usage.
with tf.device('/cpu:0'):
# TODO: Replace Lambda with custom Keras layer for loss.
model_loss = Lambda(yolo_loss,
output_shape=(1, ),
name='yolo_loss',
arguments={
'anchors': anchors,
'num_classes': len(class_names)
})([
model_body.output, boxes_input,
detectors_mask_input, matching_boxes_input
])
model = Model(
[image_input, boxes_input, detectors_mask_input, matching_boxes_input],
model_loss)
model.compile(
optimizer='adam', loss={
'yolo_loss': lambda y_true, y_pred: y_pred
}) # This is a hack to use the custom loss function in the last layer.
# Add batch dimension for training.
image_data = np.expand_dims(image_data, axis=0)
boxes = np.expand_dims(boxes, axis=0)
detectors_mask = np.expand_dims(detectors_mask, axis=0)
matching_true_boxes = np.expand_dims(matching_true_boxes, axis=0)
num_steps = 50
#checkpoint = ModelCheckpoint('model1.h5', monitor='val_loss', verbose=1,
#save_best_only=True, save_weights_only=False, mode='auto', period=1)
# TODO: For full training, put preprocessing inside training loop.
# for i in range(num_steps):
# loss = model.train_on_batch(
# [image_data, boxes, detectors_mask, matching_true_boxes],
# np.zeros(len(image_data)))
model.fit(
[image_data, boxes, detectors_mask, matching_true_boxes],
np.zeros(len(image_data)),
batch_size=1,
epochs=num_steps,
#callbacks=[checkpoint]
)
model.save('model.h5')
model.save_weights('overfit_weights.h5')
#model_json_str = model.to_json()
#open('mlp_model.json', 'w').write(model_json_str)
#model.save_weights('mlp_weights.h5');
# Create output variables for prediction.
yolo_outputs = yolo_head(model_body.output, anchors, len(class_names))
input_image_shape = K.placeholder(shape=(2, ))
boxes, scores, classes = yolo_eval(yolo_outputs,
input_image_shape,
score_threshold=.3,
iou_threshold=.9)
# Run prediction on overfit image.
sess = K.get_session() # TODO: Remove dependence on Tensorflow session.
out_boxes, out_scores, out_classes = sess.run(
[boxes, scores, classes],
feed_dict={
model_body.input: image_data,
input_image_shape: [image.size[1], image.size[0]],
K.learning_phase(): 0
})
print('Found {} boxes for image.'.format(len(out_boxes)))
print(out_boxes)
# Plot image with predicted boxes.
image_with_boxes = draw_boxes(image_data[0], out_boxes, out_classes,
class_names, out_scores)
plt.imshow(image_with_boxes, interpolation='nearest')
plt.show()
def train_yolo_model(numb_image=10,
learning_rate=0.00005,
num_epochs=100,
minibatch_size=5,
print_cost=True,
model_path="./model_data/yolo_trained_model.h5"):
""" Create and train yolo model
Parameters:
-----------
numb_image : int
number of images used for training
learning_rate : float
learning rate of the optimization
num_epochs : int
number of epochs of the optimization loop
minibatch_size : int
size of a minibatch
print_cost : boolean
True to print the cost every 5 epochs
model_path : string
location to save trained model
Returns:
--------
costs : list
list of cost values
"""
costs = [] # To keep track of the cost
# Load data
image_size = (608, 608)
dataset = load_data(numb_image, "model_data/true_boxes.h5")
# Create model
X = Input(batch_shape=[None, 608, 608, 3], dtype=tf.float32)
Y1 = tf.placeholder(shape=[None, 10, 5], dtype=tf.float32) # true boxes
Y2 = tf.placeholder(shape=[None, 19, 19, 5, 1],
dtype=tf.float32) # detector mask
Y3 = tf.placeholder(shape=[None, 19, 19, 5, 5],
dtype=tf.float32) # matching true boxes
yolo_m = yolo_body(inputs=X,
num_anchors=len(anchors),
num_classes=len(class_names))
args = [yolo_m.output, Y1, Y2, Y3]
cost = yolo_loss(args, anchors, len(class_names))
# Backpropagation: Define the tensorflow optimizer. Use an AdamOptimizer that minimizes the cost.
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)
# Initialize all the variables globally
init = tf.global_variables_initializer()
# Run session to train network
with tf.Session() as sess:
# Run initialization
sess.run(init)
# # Run training loop
for epoch in range(num_epochs):
minibatch_cost = 0.
num_minibatches = int(
numb_image / minibatch_size
) # number of minibatches of size minibatch_size in the train set
minibatches = random_mini_batches(dataset['X'], dataset['Y'],
minibatch_size)
for minibatch in minibatches:
# Select a minibatch
(minibatch_X, minibatch_Y) = minibatch
# Preprocess true boxes
detectors_mask = []
matching_true_boxes = []
for i in range(0, minibatch_Y.shape[0]):
detectors_mask_tmp, matching_true_boxes_tmp = preprocess_true_boxes(
minibatch_Y[i, :, :], anchors, image_size)
if len(detectors_mask) == 0:
detectors_mask = np.expand_dims(detectors_mask_tmp,
axis=0)
else:
detectors_mask = np.append(detectors_mask,
np.expand_dims(
detectors_mask_tmp,
axis=0),
axis=0)
if len(matching_true_boxes) == 0:
matching_true_boxes = np.expand_dims(
matching_true_boxes_tmp, axis=0)
else:
matching_true_boxes = np.append(
matching_true_boxes,
np.expand_dims(matching_true_boxes_tmp, axis=0),
axis=0)
# Run the session to execute the optimizer and the cost
_, temp_cost = sess.run(
[optimizer, cost],
feed_dict={
X: minibatch_X,
Y1: minibatch_Y,
Y2: detectors_mask,
Y3: matching_true_boxes
})
minibatch_cost += temp_cost / num_minibatches
# Print the cost every epoch
if print_cost == True and epoch % 5 == 0:
print("Cost after epoch %i: %f" % (epoch, minibatch_cost))
if print_cost == True and epoch % 1 == 0:
costs.append(minibatch_cost)
yolo_m.save_weights(model_path)
return costs
def _main(args):
voc_path = os.path.expanduser(args.data_path)
classes_path = os.path.expanduser(args.classes_path)
anchors_path = os.path.expanduser(args.anchors_path)
with open(classes_path) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]
if os.path.isfile(anchors_path):
with open(anchors_path) as f:
anchors = f.readline()
anchors = [float(x) for x in anchors.split(',')]
anchors = np.array(anchors).reshape(-1, 2)
else:
anchors = YOLO_ANCHORS
voc = h5py.File(voc_path, 'r')
image = PIL.Image.open(io.BytesIO(voc['train/images'][28]))
orig_size = np.array([image.width, image.height])
orig_size = np.expand_dims(orig_size, axis=0)
# Image preprocessing.
image = image.resize((416, 416), PIL.Image.BICUBIC)
image_data = np.array(image, dtype=np.float)
image_data /= 255.
# Box preprocessing.
# Original boxes stored as 1D list of class, x_min, y_min, x_max, y_max.
boxes = voc['train/boxes'][28]
boxes = boxes.reshape((-1, 5))
# Get extents as y_min, x_min, y_max, x_max, class for comparision with
# model output.
boxes_extents = boxes[:, [2, 1, 4, 3, 0]]
# Get box parameters as x_center, y_center, box_width, box_height, class.
boxes_xy = 0.5 * (boxes[:, 3:5] + boxes[:, 1:3])
boxes_wh = boxes[:, 3:5] - boxes[:, 1:3]
boxes_xy = boxes_xy / orig_size
boxes_wh = boxes_wh / orig_size
boxes = np.concatenate((boxes_xy, boxes_wh, boxes[:, 0:1]), axis=1)
# Precompute detectors_mask and matching_true_boxes for training.
# Detectors mask is 1 for each spatial position in the final conv layer and
# anchor that should be active for the given boxes and 0 otherwise.
# Matching true boxes gives the regression targets for the ground truth box
# that caused a detector to be active or 0 otherwise.
detectors_mask_shape = (13, 13, 5, 1)
matching_boxes_shape = (13, 13, 5, 5)
detectors_mask, matching_true_boxes = preprocess_true_boxes(boxes, anchors,
[416, 416])
# Create model input layers.
image_input = Input(shape=(416, 416, 3))
boxes_input = Input(shape=(None, 5))
detectors_mask_input = Input(shape=detectors_mask_shape)
matching_boxes_input = Input(shape=matching_boxes_shape)
print('Boxes:')
print(boxes)
print('Box corners:')
print(boxes_extents)
print('Active detectors:')
print(np.where(detectors_mask == 1)[:-1])
print('Matching boxes for active detectors:')
print(matching_true_boxes[np.where(detectors_mask == 1)[:-1]])
# Create model body.
model_body = yolo_body(image_input, len(anchors), len(class_names))
model_body = Model(image_input, model_body.output)
# Place model loss on CPU to reduce GPU memory usage.
with tf.device('/cpu:0'):
# TODO: Replace Lambda with custom Keras layer for loss.
model_loss = Lambda(
yolo_loss,
output_shape=(1, ),
name='yolo_loss',
arguments={'anchors': anchors,
'num_classes': len(class_names)})([
model_body.output, boxes_input,
detectors_mask_input, matching_boxes_input
])
model = Model(
[image_input, boxes_input, detectors_mask_input,
matching_boxes_input], model_loss)
model.compile(
optimizer='adam', loss={
'yolo_loss': lambda y_true, y_pred: y_pred
}) # This is a hack to use the custom loss function in the last layer.
# Add batch dimension for training.
image_data = np.expand_dims(image_data, axis=0)
boxes = np.expand_dims(boxes, axis=0)
detectors_mask = np.expand_dims(detectors_mask, axis=0)
matching_true_boxes = np.expand_dims(matching_true_boxes, axis=0)
num_steps = 1000
# TODO: For full training, put preprocessing inside training loop.
# for i in range(num_steps):
# loss = model.train_on_batch(
# [image_data, boxes, detectors_mask, matching_true_boxes],
# np.zeros(len(image_data)))
model.fit([image_data, boxes, detectors_mask, matching_true_boxes],
np.zeros(len(image_data)),
batch_size=1,
epochs=num_steps)
model.save_weights('overfit_weights.h5')
# Create output variables for prediction.
yolo_outputs = yolo_head(model_body.output, anchors, len(class_names))
input_image_shape = K.placeholder(shape=(2, ))
boxes, scores, classes = yolo_eval(
yolo_outputs, input_image_shape, score_threshold=.3, iou_threshold=.9)
# Run prediction on overfit image.
sess = K.get_session() # TODO: Remove dependence on Tensorflow session.
out_boxes, out_scores, out_classes = sess.run(
[boxes, scores, classes],
feed_dict={
model_body.input: image_data,
input_image_shape: [image.size[1], image.size[0]],
K.learning_phase(): 0
})
print('Found {} boxes for image.'.format(len(out_boxes)))
print(out_boxes)
# Plot image with predicted boxes.
image_with_boxes = draw_boxes(image_data[0], out_boxes, out_classes,
class_names, out_scores)
plt.imshow(image_with_boxes, interpolation='nearest')
plt.show()
true_boxes = np.array(
[[0.16, 0.51355422, 0.296, 0.68975904, 14.],
[0.428, 0.47590361, 0.276, 0.60240964, 14.],
[0.664, 0.5813253, 0.432, 0.8373494, 14.],
[0.863, 0.64457831, 0.274, 0.7108433, 14]])
image_size = (416, 416)
anchors = np.array([
[0.738768, 0.874946],
[2.42204, 2.65704],
[4.30971, 7.04493],
[10.246, 4.59428],
[12.6868, 11.8741]
])
actual_dm, actual_mtb = preprocess_true_boxes(true_boxes, anchors, image_size)
def numpy_ptb():
height, width = image_size
num_anchors = len(anchors)
# Downsampling factor of 5x 2-stride max_pools == 32.
assert height % 32 == 0, 'Image sizes in YOLO_v2 must be multiples of 32.'
assert width % 32 == 0, 'Image sizes in YOLO_v2 must be multiples of 32.'
conv_height = height // 32
conv_width = width // 32
detectors_mask = np.zeros((conv_height, conv_width, num_anchors, 1))
matching_true_boxes = np.zeros((conv_height, conv_width, num_anchors, 5))
boxes_i = np.floor(true_boxes[:,1] * conv_height).astype('int')
boxes_j = np.floor(true_boxes[:,0] * conv_width).astype('int')
