def __init__(self, flags_input, dictionary):
self.epoch = flags_input['file_epoch'] if flags_input['restore'] else 0
self.lr = flags['learning_rate']
super().__init__(flags_input, flags_input['run_num'], vram=cfg.VRAM, restore=flags_input['restore_num'], restore_slim=flags_input['restore_slim_file'])
self.print_log(dictionary)
self.print_log(flags_input)
self.threads, self.coord = Data.init_threads(self.sess)
def __init__(self, flags_input, dictionary):
self.epoch = flags_input['file_epoch'] if flags_input['restore'] else 0
self.lr = flags['learning_rate']
super().__init__(flags_input, flags_input['run_num'], vram=cfg.VRAM, restore=flags_input['restore_num'])
self.print_log(dictionary)
self.print_log(flags_input)
self.threads, self.coord = Data.init_threads(self.sess)
def test_print_image(self):
""" Read data through self.sess and plot out """
threads, coord = Data.init_threads(self.sess) # Begin Queues
print("Running 100 iterations of simple data transfer from queue to np.array")
for i in range(100):
x, gt_boxes = self.sess.run([self.x, self.gt_boxes])
print(i)
# Plot an example
faster_rcnn_tests.plot_img(x[0], gt_boxes[0])
Data.exit_threads(threads, coord) # Exit Queues
def test_print_image(self):
""" Read data through self.sess and plot out """
threads, coord = Data.init_threads(self.sess) # Begin Queues
print("Running 100 iterations of simple data transfer from queue to np.array")
for i in range(100):
x, gt_boxes = self.sess.run([self.x['TRAIN'], self.gt_boxes['TRAIN']])
print(i)
# Plot an example
faster_rcnn_tests.plot_img(x[0], gt_boxes[0])
Data.exit_threads(threads, coord) # Exit Queues
def print_test_image(self):
""" Takes in a .tfrecord file and plots the image batch with bounding box """
file = '/home/dcs41/Documents/tf-Faster-RCNN/Data/data_clutter/clutter_mnist_valid.tfrecords'
im_dims, gt_boxes, image = Data.batch_inputs(self.read_and_decode,
file,
batch_size=32)
self.sess.run(tf.local_variables_initializer())
self.sess.run(tf.global_variables_initializer())
threads, coord = Data.init_threads(self.sess)
_, gt_boxes, image_out = self.sess.run([im_dims, gt_boxes, image])
self.plot_img(image_out[0], gt_boxes[0])
Data.exit_threads(threads, coord)
def __init__(self, flags_input, dictionary):
if flags_input['restore'] is True:
self.epochs = flags_input['file_epoch']
else: # not restore
self.epochs = 0
self.lr = flags['learning_rate']
super().__init__(flags_input,
flags_input['run_num'],
vram=0.2,
restore=flags_input['restore_num'])
self.print_log(dictionary)
self.print_log(flags_input)
self.threads, self.coord = Data.init_threads(self.sess)
def train(self):
""" Run training function. Save model upon completion """
iterations = int(
np.ceil(self.num_train_images / self.flags['batch_size']) *
self.flags['num_epochs'])
threads, coord = Data.init_threads(self.sess) # Begin Queues
self.print_log('Training for %d iterations' % iterations)
for i in range(iterations):
if self.step % self.flags['display_step'] != 0:
summary = self._run_train_iter()
else:
summary = self._run_train_metrics_iter()
self._record_train_metrics()
self._record_training_step(summary)
print(self.step)
self._save_model(section=1)
Data.exit_threads(threads, coord) # Exit Queues
def __init__(self, flags_input):
super().__init__(flags_input, flags_input['run_num'], vram=0.2, restore=flags_input['restore_num'])
self.print_log("Seed: %d" % flags_input['seed'])
self.threads, self.coord = Data.init_threads(self.sess)
def __init__(self, flags_input, run_num, restore):
super().__init__(flags_input, run_num, vram=0.3, restore=restore)
self.print_log("Seed: %d" % flags['seed'])
self.threads, self.coord = Data.init_threads(self.sess)
def __init__(self, flags_input, run_num, restore):
super().__init__(flags_input, run_num, vram=0.3, restore=restore)
self.print_log("Seed: %d" % flags['seed'])
self.threads, self.coord = Data.init_threads(self.sess)
