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
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 in ['mobilenet_v2_w1.4_r1.0', 'mobilenet_v2_large', 'mobilenet_v2_large_quantize']: # m_v2_large
net = Mobilenetv2Network({'image': placeholder_input}, conv_width=1.4, conv_width2=1.0, trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v2_1.4_224/mobilenet_v2_1.4_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'mobilenet_v2_w1.4_r0.5':
net = Mobilenetv2Network({'image': placeholder_input}, conv_width=1.4, conv_width2=0.5, trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v2_1.4_224/mobilenet_v2_1.4_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'mobilenet_v2_w1.0_r1.0':
net = Mobilenetv2Network({'image': placeholder_input}, conv_width=1.0, conv_width2=1.0, trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'mobilenet_v2_w1.0_r0.75':
net = Mobilenetv2Network({'image': placeholder_input}, conv_width=1.0, conv_width2=0.75, trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'mobilenet_v2_w1.0_r0.5':
net = Mobilenetv2Network({'image': placeholder_input}, conv_width=1.0, conv_width2=0.5, trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'mobilenet_v2_w0.75_r0.75':
net = Mobilenetv2Network({'image': placeholder_input}, conv_width=0.75, conv_width2=0.75, trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v2_0.75_224/mobilenet_v2_0.75_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'mobilenet_v2_w0.5_r0.5' or type == 'mobilenet_v2_small': # m_v2_fast
net = Mobilenetv2Network({'image': placeholder_input}, conv_width=0.5, conv_width2=0.5, trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v2_0.5_224/mobilenet_v2_0.5_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'mobilenet_v2_1.4':
net = Mobilenetv2Network({'image': placeholder_input}, conv_width=1.4, trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v2_1.4_224/mobilenet_v2_1.4_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'mobilenet_v2_1.0':
net = Mobilenetv2Network({'image': placeholder_input}, conv_width=1.0, trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'mobilenet_v2_0.75':
net = Mobilenetv2Network({'image': placeholder_input}, conv_width=0.75, trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v2_0.75_224/mobilenet_v2_0.75_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type == 'mobilenet_v2_0.5':
net = Mobilenetv2Network({'image': placeholder_input}, conv_width=0.5, trainable=trainable)
pretrain_path = 'pretrained/mobilenet_v2_0.5_224/mobilenet_v2_0.5_224.ckpt'
last_layer = 'MConv_Stage6_L{aux}_5'
elif type in ['cmu', 'openpose']:
net = CmuNetwork({'image': placeholder_input}, trainable=trainable)
pretrain_path = 'numpy/openpose_coco.npy'
last_layer = 'Mconv7_stage6_L{aux}'
elif type in ['cmu_quantize', 'openpose_quantize']:
net = CmuNetwork({'image': placeholder_input}, trainable=trainable)
pretrain_path = 'train/cmu/bs8_lr0.0001_q_e80/model_latest-18000'
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 Model Name.')
pretrain_path_full = os.path.join(_get_base_path(), pretrain_path)
if sess_for_load is not None:
if type in ['cmu', 'vgg', 'openpose']:
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:
try:
s = '%dx%d' % (placeholder_input.shape[2], placeholder_input.shape[1])
except:
s = ''
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',
'mobilenet_v2_w1.4_r0.5': 'trained/mobilenet_v2_w1.4_r0.5/model_latest-380401',
'mobilenet_v2_large': 'trained/mobilenet_v2_w1.4_r1.0/model-570000',
'mobilenet_v2_small': 'trained/mobilenet_v2_w0.5_r0.5/model_latest-380401',
}
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
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 in [
"mobilenet_v2_w1.4_r1.0",
"mobilenet_v2_large",
"mobilenet_v2_large_quantize",
]: # m_v2_large
net = Mobilenetv2Network(
{"image": placeholder_input},
conv_width=1.4,
conv_width2=1.0,
trainable=trainable,
)
pretrain_path = "pretrained/mobilenet_v2_1.4_224/mobilenet_v2_1.4_224.ckpt"
last_layer = "MConv_Stage6_L{aux}_5"
elif type == "mobilenet_v2_w1.4_r0.5":
net = Mobilenetv2Network(
{"image": placeholder_input},
conv_width=1.4,
conv_width2=0.5,
trainable=trainable,
)
pretrain_path = "pretrained/mobilenet_v2_1.4_224/mobilenet_v2_1.4_224.ckpt"
last_layer = "MConv_Stage6_L{aux}_5"
elif type == "mobilenet_v2_w1.0_r1.0":
net = Mobilenetv2Network(
{"image": placeholder_input},
conv_width=1.0,
conv_width2=1.0,
trainable=trainable,
)
pretrain_path = "pretrained/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224.ckpt"
last_layer = "MConv_Stage6_L{aux}_5"
elif type == "mobilenet_v2_w1.0_r0.75":
net = Mobilenetv2Network(
{"image": placeholder_input},
conv_width=1.0,
conv_width2=0.75,
trainable=trainable,
)
pretrain_path = "pretrained/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224.ckpt"
last_layer = "MConv_Stage6_L{aux}_5"
elif type == "mobilenet_v2_w1.0_r0.5":
net = Mobilenetv2Network(
{"image": placeholder_input},
conv_width=1.0,
conv_width2=0.5,
trainable=trainable,
)
pretrain_path = "pretrained/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224.ckpt"
last_layer = "MConv_Stage6_L{aux}_5"
elif type == "mobilenet_v2_w0.75_r0.75":
net = Mobilenetv2Network(
{"image": placeholder_input},
conv_width=0.75,
conv_width2=0.75,
trainable=trainable,
)
pretrain_path = "pretrained/mobilenet_v2_0.75_224/mobilenet_v2_0.75_224.ckpt"
last_layer = "MConv_Stage6_L{aux}_5"
elif type == "mobilenet_v2_w0.5_r0.5" or type == "mobilenet_v2_small": # m_v2_fast
net = Mobilenetv2Network(
{"image": placeholder_input},
conv_width=0.5,
conv_width2=0.5,
trainable=trainable,
)
pretrain_path = "pretrained/mobilenet_v2_0.5_224/mobilenet_v2_0.5_224.ckpt"
last_layer = "MConv_Stage6_L{aux}_5"
elif type == "mobilenet_v2_1.4":
net = Mobilenetv2Network({"image": placeholder_input},
conv_width=1.4,
trainable=trainable)
pretrain_path = "pretrained/mobilenet_v2_1.4_224/mobilenet_v2_1.4_224.ckpt"
last_layer = "MConv_Stage6_L{aux}_5"
elif type == "mobilenet_v2_1.0":
net = Mobilenetv2Network({"image": placeholder_input},
conv_width=1.0,
trainable=trainable)
pretrain_path = "pretrained/mobilenet_v2_1.0_224/mobilenet_v2_1.0_224.ckpt"
last_layer = "MConv_Stage6_L{aux}_5"
elif type == "mobilenet_v2_0.75":
net = Mobilenetv2Network({"image": placeholder_input},
conv_width=0.75,
trainable=trainable)
pretrain_path = "pretrained/mobilenet_v2_0.75_224/mobilenet_v2_0.75_224.ckpt"
last_layer = "MConv_Stage6_L{aux}_5"
elif type == "mobilenet_v2_0.5":
net = Mobilenetv2Network({"image": placeholder_input},
conv_width=0.5,
trainable=trainable)
pretrain_path = "pretrained/mobilenet_v2_0.5_224/mobilenet_v2_0.5_224.ckpt"
last_layer = "MConv_Stage6_L{aux}_5"
elif type in ["cmu", "openpose"]:
net = CmuNetwork({"image": placeholder_input}, trainable=trainable)
pretrain_path = "numpy/openpose_coco.npy"
last_layer = "Mconv7_stage6_L{aux}"
elif type in ["cmu_quantize", "openpose_quantize"]:
net = CmuNetwork({"image": placeholder_input}, trainable=trainable)
pretrain_path = "train/cmu/bs8_lr0.0001_q_e80/model_latest-18000"
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 Model Name.")
pretrain_path_full = os.path.join(_get_base_path(), pretrain_path)
if sess_for_load is not None:
if type in ["cmu", "vgg", "openpose"]:
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:
try:
s = "%dx%d" % (placeholder_input.shape[2],
placeholder_input.shape[1])
except:
s = ""
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",
"mobilenet_v2_w1.4_r0.5":
"trained/mobilenet_v2_w1.4_r0.5/model_latest-380401",
"mobilenet_v2_large":
"trained/mobilenet_v2_w1.4_r1.0/model-570000",
"mobilenet_v2_small":
"trained/mobilenet_v2_w0.5_r0.5/model_latest-380401",
}
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