def get_network(type, placeholder_input, sess_for_load=None, trainable=True):
if type == 'mobilenet':
net = MobilenetNetwork({'image': placeholder_input},
conv_width=0.75,
conv_width2=1.00,
trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v1_0.75_224_2017_06_14/mobilenet_v1_0.75_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'mobilenet_fast':
net = MobilenetNetwork({'image': placeholder_input},
conv_width=0.5,
conv_width2=0.5,
trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v1_0.75_224_2017_06_14/mobilenet_v1_0.75_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'mobilenet_accurate':
net = MobilenetNetwork({'image': placeholder_input},
conv_width=1.00,
conv_width2=1.00,
trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v1_0.75_224_2017_06_14/mobilenet_v1_0.75_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'mobilenet_thin':
net = MobilenetNetworkThin({'image': placeholder_input},
conv_width=0.75,
conv_width2=0.50,
trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v1_0.75_224_2017_06_14/mobilenet_v1_1.0_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'cmu':
net = CmuNetwork({'image': placeholder_input}, trainable=trainable)
pretrain_path = 'numpy/openpose_coco.npy'
last_layer = 'Mconv7_stage6_L{aux}'
elif type == 'vgg':
net = CmuNetwork({'image': placeholder_input}, trainable=trainable)
pretrain_path = 'numpy/openpose_vgg16.npy'
last_layer = 'Mconv7_stage6_L{aux}'
else:
raise Exception('Invalid Mode.')
if sess_for_load is not None:
if type == 'cmu' or type == 'vgg':
net.load(os.path.join(_get_base_path(), pretrain_path),
sess_for_load)
else:
s = '%dx%d' % (placeholder_input.shape[2],
placeholder_input.shape[1])
ckpts = {
'mobilenet': 'trained/mobilenet_%s/model-246038' % s,
'mobilenet_thin': 'trained/mobilenet_thin_%s/model-449003' % s,
'mobilenet_fast': 'trained/mobilenet_fast_%s/model-189000' % s,
'mobilenet_accurate': 'trained/mobilenet_accurate/model-170000'
}
loader = tf.train.Saver()
loader.restore(sess_for_load,
os.path.join(_get_base_path(), ckpts[type]))
return net, os.path.join(_get_base_path(), pretrain_path), last_layer
def get_network(type, placeholder_input, sess_for_load=None):
if type == 'mobilenet':
net = MobilenetNetwork({'image': placeholder_input}, conv_width=0.75, conv_width2=0.50)
pretrain_path = 'pretrained/mobilenet_v1_0.75_224_2017_06_14/mobilenet_v1_0.75_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'mobilenet_accurate':
net = MobilenetNetwork({'image': placeholder_input}, conv_width=1.00)
pretrain_path = 'pretrained/mobilenet_v1_1.0_224_2017_06_14/mobilenet_v1_1.0_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'mobilenet_fast':
net = MobilenetNetwork({'image': placeholder_input}, conv_width=0.50)
pretrain_path = 'pretrained/mobilenet_v1_0.50_224_2017_06_14/mobilenet_v1_0.50_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'cmu':
net = CmuNetwork({'image': placeholder_input})
pretrain_path = 'numpy/openpose_coco.npy'
last_layer = 'Mconv7_stage6_L{aux}'
else:
raise Exception('Invalid Mode.')
if sess_for_load is not None:
if type == 'cmu':
net.load('./models/numpy/openpose_coco.npy', sess_for_load)
else:
ckpts = {
'mobilenet': os.path.join(_get_base_path(), 'pretrained/mobilenet_v1_0.50_224_2017_06_14/mobilenet_v1_0.50_224.ckpt'),
'mobilenet_fast': os.path.join(_get_base_path(), 'pretrained/mobilenet_fast/-163000'),
'mobilenet_accurate': os.path.join(_get_base_path(), 'pretrained/mobilenet_accurate/model-170000')
}
# loader = tf.train.import_meta_graph(ckpts[type] + '.meta')
loader = tf.train.Saver()
loader.restore(sess_for_load, ckpts[type])
return net, os.path.join(_get_base_path(), pretrain_path), last_layer
def get_network(type, placeholder_input, sess_for_load=None, trainable=True):
if type == 'mobilenet':
net = MobilenetNetwork({'image': placeholder_input}, conv_width=0.75, conv_width2=1.00, trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v1_0.75_224_2017_06_14/mobilenet_v1_0.75_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'mobilenet_fast':
net = MobilenetNetwork({'image': placeholder_input}, conv_width=0.5, conv_width2=0.5, trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v1_0.75_224_2017_06_14/mobilenet_v1_0.75_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'mobilenet_accurate':
net = MobilenetNetwork({'image': placeholder_input}, conv_width=1.00, conv_width2=1.00, trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v1_0.75_224_2017_06_14/mobilenet_v1_0.75_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'mobilenet_thin':
net = MobilenetNetworkThin({'image': placeholder_input}, conv_width=0.75, conv_width2=0.50, trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v1_0.75_224_2017_06_14/mobilenet_v1_1.0_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'cmu':
net = CmuNetwork({'image': placeholder_input}, trainable=trainable)
pretrain_path = 'numpy/openpose_coco.npy'
last_layer = 'Mconv7_stage6_L{aux}'
elif type == 'vgg':
net = CmuNetwork({'image': placeholder_input}, trainable=trainable)
pretrain_path = 'numpy/openpose_vgg16.npy'
last_layer = 'Mconv7_stage6_L{aux}'
else:
raise Exception('Invalid Mode.')
if sess_for_load is not None:
if type == 'cmu' or type == 'vgg':
net.load(os.path.join(_get_base_path(), pretrain_path), sess_for_load)
else:
s = '%dx%d' % (placeholder_input.shape[2], placeholder_input.shape[1])
ckpts = {
'mobilenet': 'trained/mobilenet_%s/model-246038' % s,
'mobilenet_thin': 'trained/mobilenet_thin_%s/model-449003' % s,
'mobilenet_fast': 'trained/mobilenet_fast_%s/model-189000' % s,
'mobilenet_accurate': 'trained/mobilenet_accurate/model-170000'
}
loader = tf.train.Saver()
loader.restore(sess_for_load, os.path.join(_get_base_path(), ckpts[type]))
return net, os.path.join(_get_base_path(), pretrain_path), last_layer
def get_network(type, placeholder_input, sess_for_load=None, trainable=True):
if type == 'mobilenet':
net = MobilenetNetwork({'image': placeholder_input},
conv_width=0.75,
conv_width2=1.00,
trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v1_0.75_224_2017_06_14/mobilenet_v1_0.75_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'mobilenet_fast':
net = MobilenetNetwork({'image': placeholder_input},
conv_width=0.5,
conv_width2=0.5,
trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v1_0.75_224_2017_06_14/mobilenet_v1_0.75_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'mobilenet_accurate':
net = MobilenetNetwork({'image': placeholder_input},
conv_width=1.00,
conv_width2=1.00,
trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v1_1.0_224_2017_06_14/mobilenet_v1_1.0_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'mobilenet_thin':
net = MobilenetNetworkThin({'image': placeholder_input},
conv_width=0.75,
conv_width2=0.50,
trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v1_0.75_224_2017_06_14/mobilenet_v1_0.75_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'cmu':
net = CmuNetwork({'image': placeholder_input}, trainable=trainable)
pretrain_path = 'numpy/openpose_coco.npy'
last_layer = 'Mconv7_stage6_L{aux}'
elif type == 'vgg':
net = CmuNetwork({'image': placeholder_input}, trainable=trainable)
pretrain_path = 'numpy/openpose_vgg16.npy'
last_layer = 'Mconv7_stage6_L{aux}'
elif type == 'personlab_resnet101':
net = PersonLabNetwork({'image': placeholder_input},
trainable=trainable)
pretrain_path = 'pretrained/resnet_v2_101/resnet_v2_101.ckpt'
last_layer = 'Mconv7_stage6_L{aux}'
else:
raise Exception('Invalid Mode.')
pretrain_path_full = os.path.join(_get_base_path(), pretrain_path)
if sess_for_load is not None:
if type == 'cmu' or type == 'vgg':
if not os.path.isfile(pretrain_path_full):
raise Exception('Model file doesn\'t exist, path=%s' %
pretrain_path_full)
net.load(os.path.join(_get_base_path(), pretrain_path),
sess_for_load)
else:
s = '%dx%d' % (placeholder_input.shape[2],
placeholder_input.shape[1])
ckpts = {
'mobilenet': 'trained/mobilenet_%s/model-246038' % s,
'mobilenet_thin': 'trained/mobilenet_thin_%s/model-449003' % s,
'mobilenet_fast': 'trained/mobilenet_fast_%s/model-189000' % s,
'mobilenet_accurate': 'trained/mobilenet_accurate/model-170000'
}
ckpt_path = os.path.join(_get_base_path(), ckpts[type])
loader = tf.train.Saver()
try:
loader.restore(sess_for_load, ckpt_path)
except Exception as e:
raise Exception('Fail to load model files. \npath=%s\nerr=%s' %
(ckpt_path, str(e)))
return net, pretrain_path_full, last_layer
log_device_placement=False)
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
if args.checkpoint:
logging.info('Restore from checkpoint...')
# loader = tf.train.Saver(net.restorable_variables())
# loader.restore(sess, tf.train.latest_checkpoint(args.checkpoint))
saver.restore(sess, tf.train.latest_checkpoint(args.checkpoint))
logging.info('Restore from checkpoint...Done')
elif pretrain_path:
logging.info('Restore pretrained weights...')
if '.ckpt' in pretrain_path:
loader = tf.train.Saver(net.restorable_variables())
loader.restore(sess, pretrain_path)
elif '.npy' in pretrain_path:
net.load(pretrain_path, sess, False)
logging.info('Restore pretrained weights...Done')
logging.info('prepare file writer')
training_name = '{}_batch:{}_lr:{}_gpus:{}'.format(
args.model, args.batchsize, args.lr, args.gpus)
file_writer = tf.summary.FileWriter(args.logpath + training_name,
sess.graph)
logging.info('prepare coordinator')
coord = tf.train.Coordinator()
enqueuer.set_coordinator(coord)
enqueuer.start()
logging.info('examine timeline')
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
def get_network(type, placeholder_input, sess_for_load=None, trainable=True):
if type == 'mobilenet':
net = MobilenetNetwork({'image': placeholder_input},
conv_width=0.75,
conv_width2=1.00,
trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v1_0.75_224_2017_06_14/mobilenet_v1_0.75_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'mobilenet_fast':
net = MobilenetNetworkFast({'image': placeholder_input},
conv_width=0.5,
conv_width2=0.5,
trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v1_0.50_224_2017_06_14/mobilenet_v1_0.50_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'mobilenet_accurate':
net = MobilenetNetworkThin({'image': placeholder_input},
conv_width=1.00,
conv_width2=0.50,
trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v1_1.0_224_2017_06_14/mobilenet_v1_1.0_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'mobilenet_thin':
net = MobilenetNetworkThin({'image': placeholder_input},
conv_width=0.75,
conv_width2=0.750,
trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v1_0.75_224_2017_06_14/mobilenet_v1_0.75_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'mobilenet_original':
net = MobilenetNetworkOriginal({'image': placeholder_input},
conv_width=0.75,
conv_width2=0.50,
trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v1_0.75_224_2017_06_14/mobilenet_v1_0.75_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'mobilenet_v2':
net = MobilenetNetworkV2({'image': placeholder_input},
conv_width=1,
conv_width2=0.50,
trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v2/model.ckpt-1450000'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'cmu':
net = CmuNetwork({'image': placeholder_input}, trainable=trainable)
pretrain_path = 'numpy/openpose_coco.npy'
last_layer = 'Mconv7_stage6_L{aux}'
elif type == 'vgg':
net = CmuNetwork({'image': placeholder_input}, trainable=trainable)
pretrain_path = 'numpy/openpose_coco.npy'
last_layer = 'Mconv7_stage6_L{aux}'
elif type == 'resnet32':
net = Resnet32({'image': placeholder_input},
conv_width=0.75,
conv_width2=0.50,
trainable=trainable)
pretrain_path = 'numpy/resnet32.npy'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'vgg16x4':
net = VGG16x4Network({'image': placeholder_input},
conv_width=0.75,
conv_width2=0.75,
trainable=trainable)
pretrain_path = 'numpy/vgg16x4.npy'
last_layer = 'Mconv7_stage6_L{aux}'
else:
raise Exception('Invalid Mode.')
if sess_for_load is not None:
if type == 'cmu' or type == 'vgg':
net.load(os.path.join(_get_base_path(), pretrain_path),
sess_for_load)
else:
s = '%dx%d' % (placeholder_input.shape[2],
placeholder_input.shape[1])
ckpts = {
'mobilenet': 'models/trained/mobilenet_%s/model-53008' % s,
'mobilenet_thin':
'models/trained/mobilenet_thin_432x368/model-160001',
'mobilenet_original':
'models/trained/mobilenet_thin_benchmark/model-388003',
'mobilenet_v2': 'models/trained/mobilenet_v2/model-218000',
'vgg16x4': 'models/trained/vgg16x4_0.75/model-35000',
}
loader = tf.train.Saver()
loader.restore(sess_for_load, os.path.join(base_path, ckpts[type]))
return net, os.path.join(_get_base_path(), pretrain_path), last_layer