import tensorflow as tf
from tensorflow.python.framework.convert_to_constants import (
convert_variables_to_constants_v2_as_graph)
from .model_io import load_model
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def get_flops(model):
"""
Calculate FLOPS for a tf.keras.Model or tf.keras.Sequential model in inference mode.
It uses tf.compat.v1.profiler under the hood.
Args:
model (:obj:`keras.Model`): the model to evaluate
Returns:
:obj:`tf.compat.v1.profiler.GraphNodeProto`: object containing the FLOPS
"""
# Prepare a constant input to pass to the profiler
input_shape = model.inputs[0].shape.as_list()
input_shape[0] = 1
x = tf.constant(tf.fill(input_shape, 1))
if not isinstance(model, (tf.keras.models.Sequential, tf.keras.models.Model)):
raise ValueError("Calculating FLOPS is only supported for `tf.keras.Model`"
"and `tf.keras.Sequential` instances.")
# convert tf.keras model into frozen graph to count FLOPs about operations used at inference
real_model = tf.function(model).get_concrete_function(x)
frozen_func, _ = convert_variables_to_constants_v2_as_graph(real_model)
# Calculate FLOPs with tf.profiler
run_meta = tf.compat.v1.RunMetadata()
opts = (
tf.compat.v1.profiler.ProfileOptionBuilder(
tf.compat.v1.profiler.ProfileOptionBuilder().float_operation()
)
.with_empty_output()
.build()
)
flops = tf.compat.v1.profiler.profile(
graph=frozen_func.graph, run_meta=run_meta, cmd="scope", options=opts
)
tf.compat.v1.reset_default_graph()
return flops
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def display_macs(model_path, verbose=False):
"""Displays the MACs for a keras model
By default it displays only the total MACS.
Args:
model (:obj:`keras.Model`): the model to evaluate
verbose (bool): display MACS for each operation
"""
model = load_model(model_path)
flops = get_flops(model)
if verbose:
def display_children_macs(nodes):
for node in nodes:
print(f"{node.name}: {node.total_float_ops / 2:e} MACS")
display_children_macs(node.children)
# Recursively display MACS by node (i.e. operation)
display_children_macs(flops.children)
# We divide FLOPS by 2 to obtain an estimate of Multiply and Accumulate (MACS)
print(f"Total: {flops.total_float_ops / 2:e} MACS")