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efficientnet_builder.py
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efficientnet_builder.py
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# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Model Builder for EfficientNet."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import re
import tensorflow as tf
import efficientnet_model
def efficientnet_params(model_name):
"""Get efficientnet params based on model name."""
params_dict = {
# (width_coefficient, depth_coefficient, resolution, dropout_rate)
'efficientnet-b0': (1.0, 1.0, 224, 0.2),
'efficientnet-b1': (1.0, 1.1, 240, 0.2),
'efficientnet-b2': (1.1, 1.2, 260, 0.3),
'efficientnet-b3': (1.2, 1.4, 300, 0.3),
'efficientnet-b4': (1.4, 1.8, 380, 0.4),
'efficientnet-b5': (1.6, 2.2, 456, 0.4),
'efficientnet-b6': (1.8, 2.6, 528, 0.5),
'efficientnet-b7': (2.0, 3.1, 600, 0.5),
}
return params_dict[model_name]
class BlockDecoder(object):
"""Block Decoder for readability."""
def _decode_block_string(self, block_string):
"""Gets a block through a string notation of arguments."""
assert isinstance(block_string, str)
ops = block_string.split('_')
options = {}
for op in ops:
splits = re.split(r'(\d.*)', op)
if len(splits) >= 2:
key, value = splits[:2]
options[key] = value
if 's' not in options or len(options['s']) != 2:
raise ValueError('Strides options should be a pair of integers.')
return efficientnet_model.BlockArgs(
kernel_size=int(options['k']),
num_repeat=int(options['r']),
input_filters=int(options['i']),
output_filters=int(options['o']),
expand_ratio=int(options['e']),
id_skip=('noskip' not in block_string),
se_ratio=float(options['se']) if 'se' in options else None,
strides=[int(options['s'][0]), int(options['s'][1])])
def _encode_block_string(self, block):
"""Encodes a block to a string."""
args = [
'r%d' % block.num_repeat,
'k%d' % block.kernel_size,
's%d%d' % (block.strides[0], block.strides[1]),
'e%s' % block.expand_ratio,
'i%d' % block.input_filters,
'o%d' % block.output_filters
]
if block.se_ratio > 0 and block.se_ratio <= 1:
args.append('se%s' % block.se_ratio)
if block.id_skip is False:
args.append('noskip')
return '_'.join(args)
def decode(self, string_list):
"""Decodes a list of string notations to specify blocks inside the network.
Args:
string_list: a list of strings, each string is a notation of block.
Returns:
A list of namedtuples to represent blocks arguments.
"""
assert isinstance(string_list, list)
blocks_args = []
for block_string in string_list:
blocks_args.append(self._decode_block_string(block_string))
return blocks_args
def encode(self, blocks_args):
"""Encodes a list of Blocks to a list of strings.
Args:
blocks_args: A list of namedtuples to represent blocks arguments.
Returns:
a list of strings, each string is a notation of block.
"""
block_strings = []
for block in blocks_args:
block_strings.append(self._encode_block_string(block))
return block_strings
def efficientnet(width_coefficient=None,
depth_coefficient=None,
dropout_rate=0.2,
drop_connect_rate=0.2):
"""Creates a efficientnet model."""
blocks_args = [
'r1_k3_s11_e1_i32_o16_se0.25', 'r2_k3_s22_e6_i16_o24_se0.25',
'r2_k5_s22_e6_i24_o40_se0.25', 'r3_k3_s22_e6_i40_o80_se0.25',
'r3_k5_s11_e6_i80_o112_se0.25', 'r4_k5_s22_e6_i112_o192_se0.25',
'r1_k3_s11_e6_i192_o320_se0.25',
]
global_params = efficientnet_model.GlobalParams(
batch_norm_momentum=0.99,
batch_norm_epsilon=1e-3,
dropout_rate=dropout_rate,
drop_connect_rate=drop_connect_rate,
data_format='channels_last',
num_classes=1000,
width_coefficient=width_coefficient,
depth_coefficient=depth_coefficient,
depth_divisor=8,
min_depth=None)
decoder = BlockDecoder()
return decoder.decode(blocks_args), global_params
def get_model_params(model_name, override_params):
"""Get the block args and global params for a given model."""
if model_name.startswith('efficientnet'):
width_coefficient, depth_coefficient, _, dropout_rate = (
efficientnet_params(model_name))
blocks_args, global_params = efficientnet(
width_coefficient, depth_coefficient, dropout_rate)
else:
raise NotImplementedError('model name is not pre-defined: %s' % model_name)
if override_params:
# ValueError will be raised here if override_params has fields not included
# in global_params.
global_params = global_params._replace(**override_params)
tf.logging.info('global_params= %s', global_params)
tf.logging.info('blocks_args= %s', blocks_args)
return blocks_args, global_params
def build_model(images,
model_name,
training,
override_params=None,
model_dir=None):
"""A helper functiion to creates a model and returns predicted logits.
Args:
images: input images tensor.
model_name: string, the predefined model name.
training: boolean, whether the model is constructed for training.
override_params: A dictionary of params for overriding. Fields must exist in
efficientnet_model.GlobalParams.
model_dir: string, optional model dir for saving configs.
Returns:
logits: the logits tensor of classes.
endpoints: the endpoints for each layer.
Raises:
When model_name specified an undefined model, raises NotImplementedError.
When override_params has invalid fields, raises ValueError.
"""
assert isinstance(images, tf.Tensor)
blocks_args, global_params = get_model_params(model_name, override_params)
if model_dir:
param_file = os.path.join(model_dir, 'model_params.txt')
if not tf.gfile.Exists(param_file):
with tf.gfile.GFile(param_file, 'w') as f:
tf.logging.info('writing to %s' % param_file)
f.write('model_name= %s\n\n' % model_name)
f.write('global_params= %s\n\n' % str(global_params))
f.write('blocks_args= %s\n\n' % str(blocks_args))
with tf.variable_scope(model_name):
model = efficientnet_model.Model(blocks_args, global_params)
logits = model(images, training=training)
logits = tf.identity(logits, 'logits')
return logits, model.endpoints
def build_model_base(images, model_name, training, override_params=None):
"""A helper functiion to create a base model and return global_pool.
Args:
images: input images tensor.
model_name: string, the model name of a pre-defined MnasNet.
training: boolean, whether the model is constructed for training.
override_params: A dictionary of params for overriding. Fields must exist in
mnasnet_model.GlobalParams.
Returns:
features: global pool features.
endpoints: the endpoints for each layer.
Raises:
When model_name specified an undefined model, raises NotImplementedError.
When override_params has invalid fields, raises ValueError.
"""
assert isinstance(images, tf.Tensor)
blocks_args, global_params = get_model_params(model_name, override_params)
with tf.variable_scope(model_name):
model = efficientnet_model.Model(blocks_args, global_params)
features = model(images, training=training, features_only=True)
features = tf.identity(features, 'global_pool')
return features, model.endpoints