def _data(self):
file_train = '/home/dcs41/Documents/tf-Faster-RCNN/Data/data_clutter/clutter_mnist_train.tfrecords'
self.x, self.gt_boxes, self.im_dims = Data.batch_inputs(self.read_and_decode, file_train,
batch_size=self.flags['batch_size'])
file_valid = '/home/dcs41/Documents/tf-Faster-RCNN/Data/data_clutter/clutter_mnist_valid.tfrecords'
self.x_valid, self.gt_boxes_valid, self.im_dims_valid = Data.batch_inputs(self.read_and_decode, file_valid,
mode="eval", batch_size=1, num_threads=1, num_readers=1)
self.num_train_images = 55000
self.num_valid_images = 5000
self.num_test_images = 10000
def _data(self):
file_train = '/home/dcs41/Documents/tf-Faster-RCNN/Data/data_clutter/clutter_mnist_train.tfrecords'
self.x, self.gt_boxes, self.im_dims = Data.batch_inputs(
self.read_and_decode,
file_train,
batch_size=self.flags['batch_size'])
file_valid = '/home/dcs41/Documents/tf-Faster-RCNN/Data/data_clutter/clutter_mnist_valid.tfrecords'
self.x_valid, self.gt_boxes_valid, self.im_dims_valid = Data.batch_inputs(
self.read_and_decode,
file_valid,
mode="eval",
batch_size=1,
num_threads=1,
num_readers=1)
self.num_train_images = 55000
self.num_valid_images = 5000
self.num_test_images = 10000
def _data(self):
# Initialize placeholder dicts
self.x = {}
self.gt_boxes = {}
self.im_dims = {}
# Train data
file_train = flags['data_directory'] + 'trans_mnist_train.tfrecords'
self.x['TRAIN'], self.gt_boxes['TRAIN'], self.im_dims['TRAIN'] = Data.batch_inputs(self.read_and_decode,
file_train, batch_size=self.flags['batch_size'])
# Validation data; ground truth boxes used for evaluation/visualization only
file_valid = flags['data_directory'] + 'trans_mnist_valid.tfrecords'
self.x['VALID'], self.gt_boxes['VALID'], self.im_dims['VALID'] = Data.batch_inputs(self.read_and_decode,
file_valid, mode="eval",
batch_size=self.flags['batch_size'],
num_threads=1, num_readers=1)
# Test data. No GT Boxes.
self.x['TEST'] = tf.placeholder(tf.float32, [None, 128, 128, 1])
self.im_dims['TEST'] = tf.placeholder(tf.int32, [None, 2])
self.num_images = {'TRAIN': 55000, 'VALID': 5000, 'TEST': 10000}
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 _data(self):
# Initialize placeholder dicts
self.x = {}
self.gt_boxes = {}
self.im_dims = {}
# Train data
file_train = flags['data_directory'] + 'clutter_mnist_train.tfrecords'
self.x['TRAIN'], self.gt_boxes['TRAIN'], self.im_dims['TRAIN'] = Data.batch_inputs(self.read_and_decode,
file_train, batch_size=
self.flags['batch_size'])
# Validation data. No GT Boxes necessary.
file_valid = flags['data_directory'] + 'clutter_mnist_valid.tfrecords'
self.x['VALID'], _, self.im_dims['VALID'] = Data.batch_inputs(self.read_and_decode,
file_valid, mode="eval",
batch_size=
self.flags['batch_size'],
num_threads=1, num_readers=1)
# Test data. No GT Boxes.
self.x['TEST'] = tf.placeholder(tf.float32, [None, 128, 128, 1])
self.im_dims['TEST'] = tf.placeholder(tf.int32, [None, 2])
self.num_images = {'TRAIN': 55000, 'VALID': 5000, 'TEST': 10000}
def main():
file_train = flags['data_directory'] + 'trans_mnist_train.tfrecords'
x, _, _ = Data.batch_inputs(read_and_decode,
file_train,
batch_size=flags['batch_size'])
x = tf.stack([x, x, x], 3)
with slim.arg_scope(resnet_arg_scope()):
_ = resnet50(x)
variables_to_restore = slim.get_model_variables()
saver = tf_saver.Saver(variables_to_restore)
with tf.Session() as sess:
saver.restore(
sess, "/home/kd/Documents/tf-Faster-RCNN/Models/resnet_v1_50.ckpt")
a = input('Now finished restoring...')
def _data(self):
# Initialize placeholder dicts
self.x = {}
self.gt_boxes = {}
self.im_dims = {}
# Train data
file_train = flags['data_directory'] + 'clutter_mnist_train.tfrecords'
self.x['TRAIN'], self.gt_boxes['TRAIN'], self.im_dims[
'TRAIN'] = Data.batch_inputs(self.read_and_decode,
file_train,
batch_size=self.flags['batch_size'])
# Validation data. No GT Boxes.
self.x['EVAL'] = tf.placeholder(tf.float32, [None, 128, 128, 1])
self.im_dims['EVAL'] = tf.placeholder(tf.int32, [None, 2])
self.num_images = {'TRAIN': 55000, 'VALID': 5000, 'TEST': 10000}
def _data(self):
file = '/home/dcs41/Documents/tf-Faster-RCNN/Data/data_clutter/clutter_mnist_train.tfrecords'
self.x, self.gt_boxes, self.im_dims = Data.batch_inputs(
self.read_and_decode, file, batch_size=self.flags['batch_size'])
self.num_train_images = 55000
