def next_batch_train(self, initial_step):
"""
returns:
a tuple(image, depths) where:
image is a float tensor with shape [batch size] + input_size
depth is a float tensor with shape [batch size] + depth_size
"""
filenames = self.train_file
dataset = tf.contrib.data.TFRecordDataset(filenames)
dataset = dataset.map(
self._parse_function) # Parse the record into tensors.
dataset = dataset.shuffle(buffer_size=3000)
dataset = dataset.batch(self.config_dict["batch_size"])
dataset = dataset.repeat(
self.config_dict["num_epochs"]) # Repeat the input.
dataset = dataset.skip(initial_step)
iterator = dataset.make_one_shot_iterator()
images, depths = iterator.get_next()
#depths = tf.reshape(depths, [None] + self.output_size)
#images = tf.reshape(images, [None] + self.input_size)
images = simulator.applyTurbidity(images, depths, self.c, self.binf,
self.range_array)
tf.summary.image("depth", depths)
tf.summary.image("image", images)
return images, depths
def input_fn(filenames, train=True, batch_size=16, buffer_size=512):
# Args:
# filenames: Filenames for the TFRecords files.
# train: Boolean whether training (True) or testing (False).
# batch_size: Return batches of this size.
# buffer_size: Read buffers of this size. The random shuffling
# is done on the buffer, so it must be big enough.
# Create a TensorFlow Dataset-object which has functionality
# for reading and shuffling data from TFRecords files.
dataset = tf.data.TFRecordDataset(filenames=filenames)
# Parse the serialized data in the TFRecords files.
# This returns TensorFlow tensors for the image and labels.
dataset = dataset.map(parse)
if train:
# If training then read a buffer of the given size and
# randomly shuffle it.
dataset = dataset.shuffle(buffer_size=buffer_size)
# Allow infinite reading of the data.
num_repeat = None
else:
# If testing then don't shuffle the data.
num_repeat = 1
# Repeat the dataset the given number of times.
dataset = dataset.repeat(num_repeat)
# Get a batch of data with the given size.
dataset = dataset.batch(batch_size)
# Create an iterator for the dataset and the above modifications.
iterator = dataset.make_one_shot_iterator()
# Get the next batch of images and labels, may take dimensionality info later but for now we set to _
images_batch, labels_batch, \
xdim_batch, ydim_batch, channels_batch = iterator.get_next()
if train:
images_batch = distort_batch(images_batch)
# The input-function must return a dict wrapping the images.
x = {'x': images_batch}
y = labels_batch
return x, y
def next_batch_train(self, initial_step):
"""
args:
batch_size:
number of examples per returned batch
num_epochs:
number of time to read the input data
returns:
a tuple(image, transmissions) where:
image is a float tensor with shape [batch size] + patch_size
transmissions is a float tensor with shape [batch size]
"""
filenames = self.train_file
dataset = tf.contrib.data.TFRecordDataset(filenames)
dataset = dataset.map(
self._parse_function) # Parse the record into tensors.
dataset = dataset.shuffle(buffer_size=3000)
dataset = dataset.batch(self.config_dict["batch_size"])
dataset = dataset.repeat(
self.config_dict["num_epochs"]) # Repeat the input.
dataset = dataset.skip(initial_step)
iterator = dataset.make_one_shot_iterator()
images = iterator.get_next()
size_x = self.config_dict['patch_size'][0]
size_y = self.config_dict['patch_size'][1]
offset_x = random.randint(0, self.input_size[0] - size_x - 1)
offset_y = random.randint(0, self.input_size[0] - size_y - 1)
images = images[:, offset_x:offset_x + size_x,
offset_y:offset_y + size_y]
transmissions = self.random_transmissions(self.batch_size)
images = simulator.applyTurbidityTransmission(images, self.binf,
transmissions)
tf.summary.image("image", images)
return images, transmissions
def next_batch_train(self, initial_step, sess):
"""
returns:
a tuple(image, depths) where:
image is a float tensor with shape [batch size] + input_size
depth is a float tensor with shape [batch size] + depth_size
"""
filenames = self.train_file
dataset = tf.contrib.data.TFRecordDataset(filenames)
dataset = dataset.map(
self._parse_function) # Parse the record into tensors.
dataset = dataset.shuffle(buffer_size=3000)
dataset = dataset.batch(self.config_dict["batch_size"])
dataset = dataset.repeat(
self.config_dict["num_epochs"]) # Repeat the input.
dataset = dataset.skip(initial_step)
iterator = dataset.make_one_shot_iterator()
images_gt, depths = iterator.get_next()
#print (depths.eval(session=sess))
#depths = tf.reshape(depths, [None] + self.output_size)
#images = tf.reshape(images, [None] + self.input_size)
images = simulator.applyTurbidity(images_gt, depths, self.c, self.binf,
self.range_array)
tf.summary.image("image_gt", images_gt)
tf.summary.image("depth", depths)
tf.summary.image("image", images)
# sess = tf.Session()
# d, im = sess.run([depths[0], images_gt[0]])
# print (im.dtype)
# pic = Image.fromarray(img_as_ubyte(im))
# print("depths[0]", np.min(d), np.max(d), np.mean(d))
# pic.show()
return images, images_gt
dense_ssd = Densenet_SSD(4, 12, training_flag)
anchors = dense_ssd.anchors
gclasses, glocations, gscores = dense_ssd.bboxes_encode(
train_y, train_location, anchors)
predictions, locations = dense_ssd.densenet_ssd(train_x)
#predictions=tf.nn.softmax(predictions)
#tf.cast(predictions,tf.int32)
loss = dense_ssd.loss(predictions, locations, gclasses, glocations, gscores)
optimizer = tf.train.AdagradOptimizer(learning_rate=1e-4)
train = optimizer.minimize(loss)
dataset = dataset.get_dataset('./train.tfrecords')
dataset = dataset.shuffle(2)
dataset = dataset.batch(1)
dataset = dataset.repeat(2)
iterator = dataset.make_one_shot_iterator()
initializer = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(initializer)
for i in range(iteration):
data_x, data_y, data_location = iterator.get_next()
data_x = tf.decode_raw(data_x, tf.uint8)
data_x = tf.reshape(data_x, [-1, 4096, 2048, 3])
data_y = tf.reshape(data_y, [-1, 1])
data_location = tf.reshape(data_location, [-1, 4])
#print(data_x)
#dic={train_x:data_x,train_y:data_y,train_location:data_location}
data_x, data_y, data_location = sess.run(
[data_x, data_y, data_location])
sess.run(train,