def dense_block(name, input_, out_size, relu, dropout):
with tf.variable_scope(name):
in_size = input_.get_shape().as_list()[1]
w = tf.get_variable(
name="w",
shape=[in_size, out_size],
# initializer=tf.truncated_normal(, stddev=0.1, dtype=tf.float32),
dtype=tf.float32
)
b = tf.get_variable(
name="b",
shape=[out_size],
# initializer=tf.constant(0.0, shape=[out_size], dtype=tf.float32), dtype=tf.float32
)
variable_summaries(w, w.name)
variable_summaries(b, b.name)
data = tf.matmul(input_, w) + b
if relu:
data = tf.nn.relu(data)
if dropout:
data = tf.layers.dropout(data, training=self.is_training, rate=self.conf["dropout_rate"])
print_layer_info(name, w, b, data)
return data
def cnn_block(name, input, out_channels, stride, bn, pool, dropout):
with tf.variable_scope(name):
in_channels = input.get_shape().as_list()[3]
w = tf.get_variable(
name="w",
shape=[3, 3, in_channels, out_channels],
dtype=tf.float32)
b = tf.get_variable("b", initializer=tf.zeros([out_channels], dtype=tf.float32), dtype=tf.float32)
variable_summaries(w, w.name)
variable_summaries(b, b.name)
data = tf.nn.conv2d(input, w, [1, stride, stride, 1], padding='VALID') + b
print_layer_info(name + "_conv_output", w, b, data)
if bn:
data = tf.contrib.layers.batch_norm(data, center=False, scale=False, is_training=self.is_training, scope=name, decay=0.9)
data = tf.nn.relu(data)
if pool:
data = tf.nn.max_pool(data, [1, 2, 2, 1], [1, 2, 2, 1], 'VALID')
print_layer_info(name + "_pool_output", w, b, data)
if dropout:
data = tf.layers.dropout(data, training=self.is_training, rate=self.conf["dropout_rate"])
print_layer_info(name, w, b, data)
return data
def init_graph(self):
tf.reset_default_graph()
batch_size = None
# Input
self.tf_x = tf.placeholder(tf.float32, shape=(batch_size, cfg.IMG_SIZE, cfg.IMG_SIZE, cfg.NUM_CHANNELS), name="tf_x")
self.tf_label = tf.placeholder(tf.float32, shape=(batch_size, cfg.NUM_LABELS), name="tf_label")
self.is_training = tf.placeholder(tf.bool, shape=None, name="is_training")
def cnn_block(name, input, out_channels, stride, bn, pool, dropout):
with tf.variable_scope(name):
in_channels = input.get_shape().as_list()[3]
w = tf.get_variable(
name="w",
shape=[3, 3, in_channels, out_channels],
dtype=tf.float32)
b = tf.get_variable("b", initializer=tf.zeros([out_channels], dtype=tf.float32), dtype=tf.float32)
variable_summaries(w, w.name)
variable_summaries(b, b.name)
data = tf.nn.conv2d(input, w, [1, stride, stride, 1], padding='VALID') + b
print_layer_info(name + "_conv_output", w, b, data)
if bn:
data = tf.contrib.layers.batch_norm(data, center=False, scale=False, is_training=self.is_training, scope=name, decay=0.9)
data = tf.nn.relu(data)
if pool:
data = tf.nn.max_pool(data, [1, 2, 2, 1], [1, 2, 2, 1], 'VALID')
print_layer_info(name + "_pool_output", w, b, data)
if dropout:
data = tf.layers.dropout(data, training=self.is_training, rate=self.conf["dropout_rate"])
print_layer_info(name, w, b, data)
return data
def dense_block(name, input_, out_size, relu, dropout):
with tf.variable_scope(name):
in_size = input_.get_shape().as_list()[1]
w = tf.get_variable(
name="w",
shape=[in_size, out_size],
# initializer=tf.truncated_normal(, stddev=0.1, dtype=tf.float32),
dtype=tf.float32
)
b = tf.get_variable(
name="b",
shape=[out_size],
# initializer=tf.constant(0.0, shape=[out_size], dtype=tf.float32), dtype=tf.float32
)
variable_summaries(w, w.name)
variable_summaries(b, b.name)
data = tf.matmul(input_, w) + b
if relu:
data = tf.nn.relu(data)
if dropout:
data = tf.layers.dropout(data, training=self.is_training, rate=self.conf["dropout_rate"])
print_layer_info(name, w, b, data)
return data
def print_layer_info(name, w, b, data):
w_shape = w.get_shape().as_list()
b_shape = b.get_shape().as_list()
data_shape = data.get_shape().as_list()[1:]
print("{}: {} params, w:{} + b:{}".format(
name,
np.prod(w_shape) + np.prod(b_shape),
"*".join([str(val) for val in w_shape]),
"*".join([str(val) for val in b_shape])))
print("{}: {} activations, {} per example".format(
name,
np.prod(data_shape),
"*".join([str(val) for val in data_shape])))
# Net
activations = cnn_block("cnn_1", self.tf_x, self.conf["cnn_1_out_channels"], stride=1, bn=False, pool=True, dropout=self.conf["cnn_1_dropout"])
activations = cnn_block("cnn_2", activations, self.conf["cnn_2_out_channels"], stride=1, bn=False, pool=False, dropout=self.conf["cnn_2_dropout"])
activations = cnn_block("cnn_3", activations, self.conf["cnn_3_out_channels"], stride=1, bn=False, pool=True, dropout=self.conf["cnn_3_dropout"])
activations = cnn_block("cnn_4", activations, self.conf["cnn_4_out_channels"], stride=1, bn=False, pool=False, dropout=self.conf["cnn_4_dropout"])
activations = cnn_block("cnn_5", activations, self.conf["cnn_5_out_channels"], stride=1, bn=False, pool=True, dropout=self.conf["cnn_5_dropout"])
activations = cnn_block("cnn_6", activations, self.conf["cnn_6_out_channels"], stride=1, bn=False, pool=False, dropout=self.conf["cnn_6_dropout"])
with tf.variable_scope("reshape"):
shape = activations.get_shape().as_list()
activations_size = shape[1] * shape[2] * shape[3]
activations = tf.reshape(activations, [-1, activations_size]) # Unroll
variable_summaries(activations, "after_cnn")
activations = dense_block("fcn_1", activations, self.conf["fcn_1_out_size"], dropout=self.conf["fcn_1_dropout"], relu=True)
activations = dense_block("fcn_2", activations, self.conf["fcn_2_out_size"], dropout=self.conf["fcn_2_dropout"], relu=True)
logits = dense_block("fcn_3", activations, cfg.NUM_LABELS, dropout=False, relu=False)
variable_summaries(logits, "logits")
with tf.variable_scope("probability"):
self.p = tf.nn.sigmoid(logits)
self.p = smooth_p(self.p)
# Loss
with tf.variable_scope("loss"):
self.loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=self.tf_label, logits=logits))
tf.summary.scalar("loss", self.loss)
# Optimizer
with tf.variable_scope("optimizer"):
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
trainables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
op = tf.train.AdamOptimizer(self.conf["learning_rate"])
grads_and_vars = op.compute_gradients(self.loss, trainables)
for grad, var in grads_and_vars:
tf.summary.histogram(grad.name, grad)
variable_summaries(grads_and_vars[0][0], "grad_" + grads_and_vars[0][1].name)
self.optimizer = op.apply_gradients(grads_and_vars)
# Saver
self.saver = tf.train.Saver()
self.merged_summary_op = tf.summary.merge_all()
def init_graph(self):
tf.reset_default_graph()
batch_size = None
# Input
self.tf_x = tf.placeholder(tf.float32, shape=(batch_size, cfg.IMG_SIZE, cfg.IMG_SIZE, cfg.NUM_CHANNELS), name="tf_x")
self.tf_label = tf.placeholder(tf.float32, shape=(batch_size, cfg.NUM_LABELS), name="tf_label")
self.is_training = tf.placeholder(tf.bool, shape=None, name="is_training")
def dense_block(name, input_, out_size, bias, relu, dropout):
with tf.variable_scope(name):
in_size = input_.get_shape().as_list()[1]
w = tf.get_variable(
name="w",
shape=[in_size, out_size],
# initializer=tf.truncated_normal(, stddev=0.1, dtype=tf.float32),
dtype=tf.float32
)
variable_summaries(w, w.name)
data = tf.matmul(input_, w)
if bias:
b = tf.get_variable(
name="b",
shape=[out_size],
# initializer=tf.constant(0.0, shape=[out_size], dtype=tf.float32), dtype=tf.float32
)
data += b
variable_summaries(b, b.name)
if relu:
data = tf.nn.relu(data)
if dropout:
data = tf.layers.dropout(data, training=self.is_training, rate=self.conf["dropout_rate"])
# print_layer_info(name, w, b, data)
return data
def print_layer_info(name, w, b, data):
w_shape = w.get_shape().as_list()
b_shape = b.get_shape().as_list()
data_shape = data.get_shape().as_list()[1:]
print("{}: {} params, w:{} + b:{}".format(
name,
np.prod(w_shape) + np.prod(b_shape),
"*".join([str(val) for val in w_shape]),
"*".join([str(val) for val in b_shape])))
print("{}: {} activations, {} per example".format(
name,
np.prod(data_shape),
"*".join([str(val) for val in data_shape])))
# Net
activations, endpoints = inception_v4(
self.tf_x,
num_classes=None,
is_training=self.is_training,
dropout_keep_prob=1., # Applies only if num_classes > 0
reuse=None,
scope='InceptionV4',
create_aux_logits=True)
if self.conf["inception_dropout"]:
activations = tf.layers.dropout(activations, training=self.is_training, rate=self.conf["dropout_rate"])
with tf.variable_scope("reshape"):
shape = activations.get_shape().as_list()
activations_size = shape[1] * shape[2] * shape[3]
activations = tf.reshape(activations, [-1, activations_size]) # Unroll
variable_summaries(activations, "after_cnn")
if self.conf["fcn_1"]:
activations = dense_block("fcn_1", activations, self.conf["fcn_1_out_size"], bias=True, dropout=self.conf["fcn_1_dropout"], relu=True)
# activations = dense_block("fcn_2", activations, self.conf["fcn_2_out_size"], dropout=self.conf["fcn_2_dropout"], relu=True)
logits = dense_block("fcn_3", activations, cfg.NUM_LABELS, bias=False, dropout=False, relu=False)
variable_summaries(logits, "logits")
with tf.variable_scope("probability"):
self.p = tf.nn.sigmoid(logits)
self.p = smooth_p(self.p)
# Loss
with tf.variable_scope("loss"):
self.loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=self.tf_label, logits=logits))
tf.summary.scalar("loss", self.loss)
# Optimizer
with tf.variable_scope("optimizer"):
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
trainables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
op = tf.train.AdamOptimizer(self.conf["learning_rate"])
grads_and_vars = op.compute_gradients(self.loss, trainables)
for grad, var in grads_and_vars:
tf.summary.histogram(grad.name, grad)
variable_summaries(grads_and_vars[0][0], "grad_" + grads_and_vars[0][1].name)
self.optimizer = op.apply_gradients(grads_and_vars)
# Saver
self.saver = tf.train.Saver()
self.merged_summary_op = tf.summary.merge_all()