#! /usr/bin/python
# -*- coding: utf-8 -*-
"""MobileNet for ImageNet."""
import os
import tensorflow as tf
from tensorlayer import logging
from tensorlayer.layers import Layer
from tensorlayer.layers import BatchNormLayer
from tensorlayer.layers import Conv2d
from tensorlayer.layers import DepthwiseConv2d
from tensorlayer.layers import FlattenLayer
from tensorlayer.layers import GlobalMeanPool2d
from tensorlayer.layers import InputLayer
from tensorlayer.layers import ReshapeLayer
from tensorlayer.files import maybe_download_and_extract, assign_params, load_npz
__all__ = [
'MobileNetV1',
]
[docs]class MobileNetV1(Layer):
"""Pre-trained MobileNetV1 model.
Parameters
------------
x : placeholder
shape [None, 224, 224, 3], value range [0, 1].
end_with : str
The end point of the model [conv, depth1, depth2 ... depth13, globalmeanpool, out]. Default ``out`` i.e. the whole model.
is_train : boolean
Whether the model is used for training i.e. enable dropout.
reuse : boolean
Whether to reuse the model.
Examples
---------
Classify ImageNet classes, see `tutorial_models_mobilenetv1.py <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_models_mobilenetv1.py>`__
>>> x = tf.placeholder(tf.float32, [None, 224, 224, 3])
>>> # get the whole model
>>> net = tl.models.MobileNetV1(x)
>>> # restore pre-trained parameters
>>> sess = tf.InteractiveSession()
>>> net.restore_params(sess)
>>> # use for inferencing
>>> probs = tf.nn.softmax(net.outputs)
Extract features and Train a classifier with 100 classes
>>> x = tf.placeholder(tf.float32, [None, 224, 224, 3])
>>> # get model without the last layer
>>> cnn = tl.models.MobileNetV1(x, end_with='reshape')
>>> # add one more layer
>>> net = Conv2d(cnn, 100, (1, 1), (1, 1), name='out')
>>> net = FlattenLayer(net, name='flatten')
>>> # initialize all parameters
>>> sess = tf.InteractiveSession()
>>> tl.layers.initialize_global_variables(sess)
>>> # restore pre-trained parameters
>>> cnn.restore_params(sess)
>>> # train your own classifier (only update the last layer)
>>> train_params = tl.layers.get_variables_with_name('out')
Reuse model
>>> x1 = tf.placeholder(tf.float32, [None, 224, 224, 3])
>>> x2 = tf.placeholder(tf.float32, [None, 224, 224, 3])
>>> # get model without the last layer
>>> net1 = tl.models.MobileNetV1(x1, end_with='reshape')
>>> # reuse the parameters with different input
>>> net2 = tl.models.MobileNetV1(x2, end_with='reshape', reuse=True)
>>> # restore pre-trained parameters (as they share parameters, we don’t need to restore net2)
>>> sess = tf.InteractiveSession()
>>> net1.restore_params(sess)
"""
def __init__(self, x, end_with='out', is_train=False, reuse=None):
self.net = self.mobilenetv1(x, end_with, is_train, reuse)
self.outputs = self.net.outputs
self.all_params = list(self.net.all_params)
self.all_layers = list(self.net.all_layers)
self.all_drop = dict(self.net.all_drop)
self.print_layers = self.net.print_layers
self.print_params = self.net.print_params
# @classmethod
def mobilenetv1(self, x, end_with='out', is_train=False, reuse=None):
with tf.variable_scope("mobilenetv1", reuse=reuse):
n = InputLayer(x)
n = self.conv_block(n, 32, strides=(2, 2), is_train=is_train, name="conv")
if end_with in n.outputs.name:
return n
n = self.depthwise_conv_block(n, 64, is_train=is_train, name="depth1")
if end_with in n.outputs.name:
return n
n = self.depthwise_conv_block(n, 128, strides=(2, 2), is_train=is_train, name="depth2")
if end_with in n.outputs.name:
return n
n = self.depthwise_conv_block(n, 128, is_train=is_train, name="depth3")
if end_with in n.outputs.name:
return n
n = self.depthwise_conv_block(n, 256, strides=(2, 2), is_train=is_train, name="depth4")
if end_with in n.outputs.name:
return n
n = self.depthwise_conv_block(n, 256, is_train=is_train, name="depth5")
if end_with in n.outputs.name:
return n
n = self.depthwise_conv_block(n, 512, strides=(2, 2), is_train=is_train, name="depth6")
if end_with in n.outputs.name:
return n
n = self.depthwise_conv_block(n, 512, is_train=is_train, name="depth7")
if end_with in n.outputs.name:
return n
n = self.depthwise_conv_block(n, 512, is_train=is_train, name="depth8")
if end_with in n.outputs.name:
return n
n = self.depthwise_conv_block(n, 512, is_train=is_train, name="depth9")
if end_with in n.outputs.name:
return n
n = self.depthwise_conv_block(n, 512, is_train=is_train, name="depth10")
if end_with in n.outputs.name:
return n
n = self.depthwise_conv_block(n, 512, is_train=is_train, name="depth11")
if end_with in n.outputs.name:
return n
n = self.depthwise_conv_block(n, 1024, strides=(2, 2), is_train=is_train, name="depth12")
if end_with in n.outputs.name:
return n
n = self.depthwise_conv_block(n, 1024, is_train=is_train, name="depth13")
if end_with in n.outputs.name:
return n
n = GlobalMeanPool2d(n, name='globalmeanpool')
if end_with in n.outputs.name:
return n
# n = DropoutLayer(n, 1-1e-3, True, is_train, name='drop')
# n = DenseLayer(n, 1000, name='output') # equal
n = ReshapeLayer(n, [-1, 1, 1, 1024], name='reshape')
if end_with in n.outputs.name:
return n
n = Conv2d(n, 1000, (1, 1), (1, 1), name='out')
n = FlattenLayer(n, name='flatten')
if end_with == 'out':
return n
raise Exception("end_with : conv, depth1, depth2 ... depth13, globalmeanpool, out")
@classmethod
def conv_block(cls, n, n_filter, filter_size=(3, 3), strides=(1, 1), is_train=False, name='conv_block'):
# ref: https://github.com/keras-team/keras/blob/master/keras/applications/mobilenet.py
with tf.variable_scope(name):
n = Conv2d(n, n_filter, filter_size, strides, b_init=None, name='conv')
n = BatchNormLayer(n, act=tf.nn.relu6, is_train=is_train, name='batchnorm')
return n
@classmethod
def depthwise_conv_block(cls, n, n_filter, strides=(1, 1), is_train=False, name="depth_block"):
with tf.variable_scope(name):
n = DepthwiseConv2d(n, (3, 3), strides, b_init=None, name='depthwise')
n = BatchNormLayer(n, act=tf.nn.relu6, is_train=is_train, name='batchnorm1')
n = Conv2d(n, n_filter, (1, 1), (1, 1), b_init=None, name='conv')
n = BatchNormLayer(n, act=tf.nn.relu6, is_train=is_train, name='batchnorm2')
return n
def restore_params(self, sess, path='models'):
logging.info("Restore pre-trained parameters")
maybe_download_and_extract(
'mobilenet.npz', path, 'https://github.com/tensorlayer/pretrained-models/raw/master/models/',
expected_bytes=25600116
) # ls -al
params = load_npz(name=os.path.join(path, 'mobilenet.npz'))
assign_params(sess, params[:len(self.net.all_params)], self.net)
del params