def main(_argv):
"""Main functions. Runs all anaylses."""
# pylint: disable=W0212
tfprof_logger._merge_default_with_oplog = merge_default_with_oplog
FLAGS.model_dir = os.path.abspath(os.path.expanduser(FLAGS.model_dir))
output_dir = os.path.join(FLAGS.model_dir, "profile")
gfile.MakeDirs(output_dir)
run_meta, graph, op_log = load_metadata(FLAGS.model_dir)
param_arguments = [
param_analysis_options(output_dir),
micro_anaylsis_options(output_dir),
flops_analysis_options(output_dir),
device_analysis_options(output_dir),
]
for tfprof_cmd, params in param_arguments:
model_analyzer.print_model_analysis(
graph=graph,
run_meta=run_meta,
op_log=op_log,
tfprof_cmd=tfprof_cmd,
tfprof_options=params)
if params["dump_to_file"] != "":
print("Wrote {}".format(params["dump_to_file"]))
def main(_argv):
"""Main functions. Runs all anaylses."""
# pylint: disable=W0212
tfprof_logger._merge_default_with_oplog = merge_default_with_oplog
FLAGS.model_dir = os.path.abspath(os.path.expanduser(FLAGS.model_dir))
output_dir = os.path.join(FLAGS.model_dir, "profile")
gfile.MakeDirs(output_dir)
run_meta, graph, op_log = load_metadata(FLAGS.model_dir)
param_arguments = [
param_analysis_options(output_dir),
micro_anaylsis_options(output_dir),
flops_analysis_options(output_dir),
device_analysis_options(output_dir),
]
for tfprof_cmd, params in param_arguments:
model_analyzer.print_model_analysis(
graph=graph,
run_meta=run_meta,
op_log=op_log,
tfprof_cmd=tfprof_cmd,
tfprof_options=params)
if params["dump_to_file"] != "":
print("Wrote {}".format(params["dump_to_file"]))
def test_model_size_less_then1_gb(self):
# NOTE: Actual amount of memory occupied my TF during training will be at
# least 4X times bigger because of space need to store original weights,
# updates, gradients and variances. It also depends on the type of used
# optimizer.
ocr_model = self.create_model()
ocr_model.create_base(images=self.fake_images, labels_one_hot=None)
with self.test_session() as sess:
tfprof_root = model_analyzer.print_model_analysis(
sess.graph,
tfprof_options=model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS)
model_size_bytes = 4 * tfprof_root.total_parameters
self.assertLess(model_size_bytes, 1 * 2**30)
def test_model_size_less_then1_gb(self):
# NOTE: Actual amount of memory occupied my TF during training will be at
# least 4X times bigger because of space need to store original weights,
# updates, gradients and variances. It also depends on the type of used
# optimizer.
ocr_model = self.create_model()
ocr_model.create_base(images=self.fake_images, labels_one_hot=None)
with self.test_session() as sess:
tfprof_root = model_analyzer.print_model_analysis(
sess.graph,
tfprof_options=model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS)
model_size_bytes = 4 * tfprof_root.total_parameters
self.assertLess(model_size_bytes, 1 * 2**30)
def calculate_graph_metrics():
param_stats = model_analyzer.print_model_analysis(
tf.get_default_graph(),
tfprof_options=model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS)
return param_stats.total_parameters
def cnn_model(features, labels, mode):
feat_size = 150
batch_size = 100
input_layer = tf.reshape(features,
[-1, feat_size, feat_size, feat_size, 1])
labels = tf.reshape(labels, [-1, 1])
print("shape cnn")
print(labels.shape)
print(input_layer.shape)
# inputs = [ batch_size , 150 , 150 , 150 , 1 ]
conv1 = tf.layers.conv3d(inputs=input_layer,
filters=4,
kernel_size=[5, 5, 5],
padding="same",
activation=tf.nn.sigmoid)
pool1 = tf.layers.max_pooling3d(inputs=conv1,
pool_size=[10, 10, 10],
strides=10)
print("shape layer1")
print(conv1.shape)
print(pool1.shape)
# pool1 = [batch_size , 30, 30, 30 , 32 ]
"""
conv2 = tf.layers.conv3d(
inputs = pool1 ,
filters = 16 ,
kernel_size = [ 5 , 5, 5 ] ,
padding = "same" ,
activation = tf.nn.sigmoid
)
#conv2 = [ batch_size , 30, 30 , 30 , 64]
pool2 = tf.layers.max_pooling3d(
inputs = conv2 ,
pool_size = [ 5 , 5 , 5 ] ,
strides = 5
)
# pool = [ batch_size , 6 , 6 , 6 , 64]
#size capa final = 5*5*5*128
#size = 15*15*15*2 # cambiar 5 por 2
print("shapes second layer")
print( conv2.shape )
print(pool2.shape)
"""
#size = 15*15*15*8
size = 15 * 15 * 15 * 4
pool_flat = tf.reshape(pool1, [-1, size])
# 43 MB
print("shape pool_flat")
print(pool_flat.shape)
dropout = tf.layers.dropout(
inputs=pool_flat,
rate=0.4,
training=mode == model_fn_lib.ModeKeys.TRAIN)
result = tf.layers.dense(inputs=dropout,
units=1,
activation=tf.nn.sigmoid)
# activation = tf.nn.relu
"""
result = tf.layers.dense(
inputs = dense1,
units = 1 ,
activation = tf.nn.relu
)
"""
#
#
print("shape result")
print(result.shape)
loss = None
train_op = None
param_stats = model_analyzer.print_model_analysis(
tf.get_default_graph(),
tfprof_options=tf.contrib.tfprof.model_analyzer.
TRAINABLE_VARS_PARAMS_STAT_OPTIONS)
sys.stdout.write('total_params: %d\n' % param_stats.total_parameters)
tf.contrib.tfprof.model_analyzer.print_model_analysis(
tf.get_default_graph(),
tfprof_options=tf.contrib.tfprof.model_analyzer.FLOAT_OPS_OPTIONS)
print("STAAAAAP")
#infer and test mode
if mode != model_fn_lib.ModeKeys.INFER:
#onehot_labels = tf.one_hot( indices = tf.cast(labels, tf.int32 ) , depth = 1 )
loss = tf.losses.log_loss(predictions=result, labels=labels)
print(loss.shape)
if mode == model_fn_lib.ModeKeys.TRAIN:
print("TRAININIGNIGNI")
print(input_layer.shape)
"""
train_op = tf.contrib.layers.optimize_loss(
loss = loss,
global_step = tf.contrib.framework.get_global_step(),
learning_rate = learning_rate ,
optimizer = "Adam"
)
"""
global_step = tf.contrib.framework.get_global_step()
#optimizer = tf.train.GradientDescentOptimizer( learning_rate )
optimizer = tf.train.AdamOptimizer(learning_rate, epsilon=0.0001)
train_op = optimizer.minimize(loss, global_step=global_step)
tf.summary.scalar("log_loss", loss)
else:
train_op = None
print("Training not build ")
predictions = {
"probabilities": tf.nn.softmax(result, name="softmax_tensor"),
"loss": loss
}
return predictions, loss, train_op
def calculate_graph_metrics():
param_state = model_analyzer.print_model_analysis(
tf.get_default_graph(),
tfprof_options=model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS)
return param_state.total_params
