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_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'
else:
raise Exception('Invalid Mode.')
pretrain_path_full = os.path.join(_get_base_path(), pretrain_path)
if sess_for_load is not None:
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 == '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 build(self, features):
n_heatmaps = 17
paf_nfields = 18
paf_nvectors = 2
paf_nscales = 0
if self.backbone == 'mobilenet_v1':
logits, end_points = mobilenet_v1(
features,
num_classes=False,
is_training=self.is_training,
depth_multiplier=self.depth_multiplier)
backbone_end = end_points['Conv2d_13_pointwise'] #1, 36, 46, 54
nets = self.headnet('paf', backbone_end, n_heatmaps, paf_nfields,
paf_nvectors, paf_nscales)
end_points['PAF'] = nets
end_points['outputs'] = [nets]
elif self.backbone == 'mobilenet_v2':
with tf.contrib.slim.arg_scope(
training_scope(is_training=self.is_training)):
logits, end_points = mobilenet(
features,
num_classes=False,
depth_multiplier=self.depth_multiplier)
backbone_end = end_points['layer_19']
nets = self.headnet('paf', backbone_end, n_heatmaps, paf_nfields,
paf_nvectors, paf_nscales)
end_points['PAF'] = nets
end_points['outputs'] = [nets]
elif self.backbone == 'shufflenet_v2':
basenet = ShuffleNetV2(depth_multiplier=self.depth_multiplier,
is_training=self.is_training)
end_points = basenet.build(features)
backbone_end = end_points['base_net/out']
nets = self.headnet('paf', backbone_end, n_heatmaps, paf_nfields,
paf_nvectors, paf_nscales)
end_points['PAF'] = nets
end_points['outputs'] = [nets]
elif self.backbone == 'mobilenet_thin':
out = MobilenetNetworkThin({'image': features},
conv_width=0.75,
conv_width2=0.50,
trainable=self.is_training)
end_points = out.get_layer()
thin_hm = end_points['MConv_Stage6_L2_5']
hm_ch1 = tf.layers.conv2d(thin_hm,
128,
kernel_size=[1, 1],
name='hm_channel1')
ps1 = self.PixelShuffle(hm_ch1, 2, scope='PixelShuffle1')
hm_out = tf.layers.conv2d(ps1,
17,
kernel_size=[1, 1],
name='hm_channel2')
hm = tf.transpose(hm_out, [0, 3, 1, 2], name='hm_out')
thin_paf = end_points['MConv_Stage6_L1_5']
paf_ch1 = tf.layers.conv2d(thin_paf,
256,
kernel_size=[1, 1],
name='paf_channel1')
ps2 = self.PixelShuffle(paf_ch1, 2, scope='PixelShuffle2')
paf_out = tf.layers.conv2d(ps2,
36,
kernel_size=[1, 1],
name='paf_channel2')
paf = tf.transpose(paf_out, [0, 3, 1, 2], name='paf_out')
end_points['heat_map'] = hm
end_points['PAF'] = paf
elif self.backbone == 'mobilenet_thin_s2d1':
out = MobilenetNetworkThin({'image': features},
conv_width=0.75,
conv_width2=0.50,
trainable=self.is_training)
end_points = out.get_layer()
###HEATMAP
thin_hm = end_points['MConv_Stage6_L2_5']
s2d_hm = tf.space_to_depth(thin_hm,
block_size=int(2),
data_format='NHWC',
name='space_to_depth_hm')
hm_duc = self.DUC(s2d_hm,
filters=512,
upscale_factor=2,
is_training=self.is_training,
scope='DUC_hm')
hm_ch1 = tf.layers.conv2d(hm_duc,
128,
kernel_size=[1, 1],
name='hm_channel1')
ps1 = self.PixelShuffle(hm_ch1, 2, scope='PixelShuffle1')
hm_out = tf.layers.conv2d(ps1,
17,
kernel_size=[1, 1],
name='hm_conv')
hm = tf.transpose(hm_out, [0, 3, 1, 2], name='hm_out')
###PAF
thin_paf = end_points['MConv_Stage6_L1_5']
s2d_paf = tf.space_to_depth(thin_paf,
block_size=int(2),
data_format='NHWC',
name='space_to_depth_paf')
paf_duc = self.DUC(s2d_paf,
filters=512,
upscale_factor=2,
is_training=self.is_training,
scope='DUC_paf')
paf_ch1 = tf.layers.conv2d(paf_duc,
256,
kernel_size=[1, 1],
name='paf_channel1')
ps2 = self.PixelShuffle(paf_ch1, 2, scope='PixelShuffle2')
paf_out = tf.layers.conv2d(ps2,
36,
kernel_size=[1, 1],
name='paf_conv')
paf = tf.transpose(paf_out, [0, 3, 1, 2], name='paf_out')
end_points['heat_map'] = hm
end_points['PAF'] = paf
elif self.backbone == 'mobilenet_thin_FPN':
out = MobilenetNetworkThin({'image': features},
conv_width=0.75,
conv_width2=0.50,
trainable=self.is_training)
end_points = out.get_layer()
###HEATMAP
thin_hm = end_points['MConv_Stage6_L2_5']
classes_hm1 = tf.layers.conv2d(thin_hm,
128,
3,
strides=2,
name='cls1')
classes_hm2 = tf.layers.conv2d(classes_hm1,
256,
3,
strides=2,
name='cls2')
con1_hm2 = tf.layers.conv2d(classes_hm2, 256, 1, name='1con2')
duc_hm2 = self.DUC(con1_hm2,
filters=512,
upscale_factor=2,
is_training=self.is_training,
scope='DUC_hm2')
pad_hm2 = tf.pad(duc_hm2, [[0, 0], [1, 1], [1, 1], [0, 0]],
name='pad_hm2')
con1_hm1 = tf.layers.conv2d(classes_hm1, 512, 1, name='1con1')
concat_feat = tf.concat(values=[con1_hm1, pad_hm2],
axis=3,
name='concat_feat_p1')
duc_hm1 = self.DUC(concat_feat,
filters=256,
upscale_factor=2,
is_training=self.is_training,
scope='DUC_hm1')
pad_hm1 = tf.pad(duc_hm1, [[0, 0], [1, 1], [1, 1], [0, 0]],
name='pad_hm1')
hm_duc = tf.concat(values=[pad_hm1, thin_hm],
axis=3,
name='concat_feat_p2')
hm_ch1 = tf.layers.conv2d(hm_duc,
128,
kernel_size=[1, 1],
name='hm_channel1')
ps1 = self.PixelShuffle(hm_ch1, 2, scope='PixelShuffle1')
hm_out = tf.layers.conv2d(ps1,
17,
kernel_size=[1, 1],
name='hm_conv')
hm = tf.transpose(hm_out, [0, 3, 1, 2], name='hm_out')
###PAF
thin_paf = end_points['MConv_Stage6_L1_5']
classes_paf1 = tf.layers.conv2d(thin_paf,
128,
3,
strides=2,
name='cls1_paf')
classes_paf2 = tf.layers.conv2d(classes_paf1,
256,
3,
strides=2,
name='cls2_paf')
con1_paf2 = tf.layers.conv2d(classes_paf2,
256,
1,
name='1con2_paf')
duc_paf2 = self.DUC(con1_paf2,
filters=512,
upscale_factor=2,
is_training=self.is_training,
scope='DUC_paf2')
pad_paf2 = tf.pad(duc_paf2, [[0, 0], [1, 1], [1, 1], [0, 0]],
name='pad_paf2')
con1_paf1 = tf.layers.conv2d(classes_paf1,
512,
1,
name='1con1_paf')
concat_feat_paf = tf.concat(values=[con1_paf1, pad_paf2],
axis=3,
name='concat_feat_p1_paf')
duc_paf1 = self.DUC(concat_feat_paf,
filters=256,
upscale_factor=2,
is_training=self.is_training,
scope='DUC_paf1')
pad_paf1 = tf.pad(duc_paf1, [[0, 0], [1, 1], [1, 1], [0, 0]],
name='pad_paf1')
paf_duc = tf.concat(values=[pad_paf1, thin_paf],
axis=3,
name='concat_feat_p2_paf')
paf_ch1 = tf.layers.conv2d(paf_duc,
256,
kernel_size=[1, 1],
name='paf_channel1')
ps2 = self.PixelShuffle(paf_ch1, 2, scope='PixelShuffle2')
paf_out = tf.layers.conv2d(ps2,
36,
kernel_size=[1, 1],
name='paf_conv')
paf = tf.transpose(paf_out, [0, 3, 1, 2], name='paf_out')
end_points['heat_map'] = hm
end_points['PAF'] = paf
elif self.backbone == 'hrnet':
end_points = dict()
out = HRNet(features)
backbone_end = out
s2d_1 = tf.space_to_depth(backbone_end,
block_size=int(4),
data_format='NHWC',
name='space_to_depth_1')
paf_cov1 = tf.layers.conv2d(
s2d_1,
64, #38
kernel_size=[1, 1],
name='paf_cov1')
s2d_2 = tf.space_to_depth(paf_cov1,
block_size=int(2),
data_format='NHWC',
name='space_to_depth_2')
paf = tf.layers.conv2d(
s2d_2,
36, #38
kernel_size=[1, 1],
name='paf_conv')
concat_feat = tf.concat(values=[s2d_1, paf_cov1],
axis=3,
name='concat_feat')
ps1 = self.PixelShuffle(concat_feat, 2, scope='PixelShuffle1')
hm_duc1 = self.DUC(ps1,
filters=512,
upscale_factor=2,
is_training=self.is_training,
scope='DUC1')
hm_duc2 = self.DUC(hm_duc1,
filters=256,
upscale_factor=2,
is_training=self.is_training,
scope='DUC2')
s2d_3 = tf.space_to_depth(paf_cov1,
block_size=int(2),
data_format='NHWC',
name='space_to_depth_3')
hm = tf.layers.conv2d(
s2d_2,
17, #38
kernel_size=[1, 1],
name='hm_conv')
hm_out = tf.transpose(hm, [0, 3, 1, 2], name='hm_out')
paf_out = tf.transpose(paf, [0, 3, 1, 2], name='paf_out')
end_points['heat_map'] = hm_out
end_points['PAF'] = paf_out
elif self.backbone == 'hrnet_tiny':
end_points = dict()
out = HRNet(features)
backbone_end = out
conv_paf1 = tf.layers.conv2d(backbone_end,
128,
3,
strides=2,
name='paf_conv1')
conv_paf2 = tf.layers.conv2d(conv_paf1,
128,
3,
strides=2,
name='paf_conv2')
conv_paf3 = tf.layers.conv2d(conv_paf2,
128,
3,
strides=1,
name='paf_conv3')
conv_paf4 = tf.layers.conv2d(conv_paf3,
128,
3,
strides=2,
name='paf_conv4')
pad_paf = tf.pad(conv_paf4, [[0, 0], [1, 1], [1, 1], [0, 0]],
name='paf_pad')
paf_ch1 = tf.layers.conv2d(pad_paf,
256,
kernel_size=[1, 1],
name='paf_channel1')
ps2 = self.PixelShuffle(paf_ch1, 2, scope='PixelShuffle2')
paf = tf.layers.conv2d(ps2,
36,
kernel_size=[1, 1],
name='paf_conv')
conv_hm1 = tf.layers.conv2d(backbone_end,
128,
3,
strides=2,
name='hm_conv1')
conv_hm2 = tf.layers.conv2d(conv_hm1,
128,
3,
strides=2,
name='hm_conv2')
conv_hm3 = tf.layers.conv2d(conv_hm2,
128,
3,
strides=1,
name='hm_conv3')
conv_hm4 = tf.layers.conv2d(conv_hm3,
128,
3,
strides=2,
name='hm_conv4')
pad_hm = tf.pad(conv_hm4, [[0, 0], [1, 1], [1, 1], [0, 0]],
name='hm_pad')
hm_ch1 = tf.layers.conv2d(pad_hm,
128,
kernel_size=[1, 1],
name='hm_channel1')
ps1 = self.PixelShuffle(hm_ch1, 2, scope='PixelShuffle1')
hm = tf.layers.conv2d(ps1, 17, kernel_size=[1, 1], name='hm_conv')
hm_out = tf.transpose(hm, [0, 3, 1, 2], name='hm_out')
paf_out = tf.transpose(paf, [0, 3, 1, 2], name='paf_out')
end_points['heat_map'] = hm_out
end_points['PAF'] = paf_out
elif self.backbone == 'higher_hrnet':
is_training = True
end_points = dict()
backbone_end = HRNet(features)
#Downsampling
downsample1 = tf.layers.conv2d(backbone_end,
64,
1,
strides=2,
name='downsample_1')
bn_downsample1 = tf.layers.batch_normalization(
downsample1, name='downsample_1_bn', training=is_training)
downsample1 = tf.nn.relu(bn_downsample1)
downsample2 = tf.layers.conv2d(downsample1,
64,
1,
strides=2,
name='downsample_2')
bn_downsample2 = tf.layers.batch_normalization(
downsample2, name='downsample2_bn', training=is_training)
downsample2 = tf.keras.activations.relu(
bn_downsample2) #1/4 input size (1, 92, 108, 128)
conv_paf3 = tf.layers.conv2d(downsample2,
64,
1,
strides=2,
name='paf_conv3')
bn_downsample3 = tf.layers.batch_normalization(
conv_paf3, name='downsample3_bn', training=is_training)
downsample3 = tf.keras.activations.relu(
bn_downsample3) #(1, 46, 54, 128)
#paf layer
paf_final_conv1 = tf.layers.conv2d(downsample3,
192,
1,
strides=1,
name='final_conv1_paf')
paf_final_conv2 = tf.layers.conv2d(paf_final_conv1,
192,
1,
strides=1,
name='final_conv2_paf')
paf_output = tf.concat(values=[paf_final_conv2, downsample3],
axis=3,
name='ouput_paf')
paf_adjust = tf.layers.conv2d(paf_output,
36,
1,
strides=1,
name='adjust_paf')
#FinalLayer
final_conv1 = tf.layers.conv2d(downsample2,
192,
1,
strides=1,
name='final_conv1')
final_conv2 = tf.layers.conv2d(final_conv1,
192,
1,
strides=1,
name='final_conv2')
conc_final_conv2 = tf.concat(values=[final_conv2, downsample2],
axis=3,
name='concat_finalconv2_downsam2')
#Deconv block
ps1 = self.DUC(conc_final_conv2,
filters=32,
upscale_factor=2,
is_training=self.is_training,
scope='DUC1')
ps2 = self.DUC(ps1,
filters=32,
upscale_factor=2,
is_training=self.is_training,
scope='DUC2')
s2d_1 = tf.space_to_depth(ps2,
block_size=int(4),
data_format='NHWC',
name='space_to_depth_1')
s2d_2 = tf.space_to_depth(s2d_1,
block_size=int(2),
data_format='NHWC',
name='space_to_depth_2')
#BasicLayer
basic1 = self.HR_BasicBlock(s2d_2,
filters=32,
is_training=self.is_training,
scope='basic_block1')
basic2 = self.HR_BasicBlock(basic1,
filters=32,
is_training=self.is_training,
scope='basic_block2')
basic3 = self.HR_BasicBlock(basic2,
filters=32,
is_training=self.is_training,
scope='basic_block3')
basic4 = self.HR_BasicBlock(basic3,
filters=32,
is_training=self.is_training,
scope='basic_block4')
basic4 = tf.nn.relu(basic4)
pad_basic4 = tf.pad(basic4, [[0, 0], [1, 1], [1, 1], [0, 0]],
name='basic4_padding')
adjust = tf.layers.conv2d(pad_basic4,
17,
3,
strides=1,
name='adjust')
hm_out = tf.transpose(adjust, [0, 3, 1, 2], name='hm_out')
paf_out = tf.transpose(paf_adjust, [0, 3, 1, 2], name='paf_out')
end_points['heat_map'] = hm_out
end_points['PAF'] = paf_out
elif self.backbone == 'pre_hrnet':
######
end_points = dict()
hrnet = preHRnet(cfgfile='/cfgs/w30_s4.cfg')
backbone_end = hrnet.forward_train(features)
print(backbone_end)
return end_points
def build(self, features):
n_heatmaps = 17
paf_nfields = 18
paf_nvectors = 2
paf_nscales = 0
if self.backbone == 'mobilenet_v1':
logits, end_points = mobilenet_v1(
features,
num_classes=False,
is_training=self.is_training,
depth_multiplier=self.depth_multiplier)
backbone_end = end_points['Conv2d_13_pointwise'] #1, 36, 46, 54
print(backbone_end)
elif self.backbone == 'mobilenet_v2':
with tf.contrib.slim.arg_scope(
training_scope(is_training=self.is_training)):
logits, end_points = mobilenet(
features,
num_classes=False,
depth_multiplier=self.depth_multiplier)
backbone_end = end_points['layer_19']
elif self.backbone == 'shufflenet_v2':
basenet = ShuffleNetV2(depth_multiplier=self.depth_multiplier,
is_training=self.is_training)
end_points = basenet.build(features)
backbone_end = end_points['base_net/out']
elif self.backbone == 'mobilenet_thin':
out = MobilenetNetworkThin({'image': features},
conv_width=0.75,
conv_width2=0.50,
trainable=self.is_training)
end_points = out.get_layer()
thin_hm = end_points['MConv_Stage6_L2_5']
hm_out = tf.layers.conv2d(thin_hm,
17,
kernel_size=[1, 1],
name='hm_conv')
hm = tf.transpose(hm_out, [0, 3, 1, 2], name='hm_out')
thin_paf = end_points['MConv_Stage6_L1_5']
paf_out = tf.layers.conv2d(thin_paf,
36,
kernel_size=[1, 1],
name='paf_conv')
paf = tf.transpose(paf_out, [0, 3, 1, 2], name='paf_out')
elif self.backbone == 'hrnet':
end_points = dict()
out = HRNet(features)
backbone_end = out
s2d_1 = tf.space_to_depth(backbone_end,
block_size=int(4),
data_format='NHWC',
name='space_to_depth_1')
paf_cov1 = tf.layers.conv2d(
s2d_1,
64, #38
kernel_size=[1, 1],
name='paf_cov1')
s2d_2 = tf.space_to_depth(paf_cov1,
block_size=int(2),
data_format='NHWC',
name='space_to_depth_2')
paf = tf.layers.conv2d(
s2d_2,
36, #38
kernel_size=[1, 1],
name='paf_conv')
concat_feat = tf.concat(values=[s2d_1, paf_cov1],
axis=3,
name='concat_feat')
ps1 = self.PixelShuffle(concat_feat, 2, scope='PixelShuffle1')
hm_duc1 = self.DUC(ps1,
filters=512,
upscale_factor=2,
is_training=self.is_training,
scope='DUC1')
hm_duc2 = self.DUC(hm_duc1,
filters=256,
upscale_factor=2,
is_training=self.is_training,
scope='DUC2')
s2d_3 = tf.space_to_depth(paf_cov1,
block_size=int(2),
data_format='NHWC',
name='space_to_depth_3')
hm = tf.layers.conv2d(
s2d_2,
17, #38
kernel_size=[1, 1],
name='hm_conv')
hm_out = tf.transpose(hm, [0, 3, 1, 2], name='hm_out')
paf_out = tf.transpose(paf, [0, 3, 1, 2], name='paf_out')
end_points['heat_map'] = hm_out
end_points['PAF'] = paf_out
if self.backbone == 'mobilenet_thin':
end_points['heat_map'] = hm
end_points['PAF'] = paf
if self.backbone == 'pafmodel':
with tf.contrib.slim.arg_scope(
training_scope(is_training=self.is_training)):
logits, end_points = mobilenet(
features,
num_classes=False,
depth_multiplier=self.depth_multiplier)
backbone_end = end_points['layer_19']
ps1 = self.PixelShuffle(backbone_end, 2, scope='PixelShuffle1')
paf_duc1 = self.DUC(ps1,
filters=512,
upscale_factor=2,
is_training=self.is_training,
scope='PAF_DUC1')
paf_duc2 = self.DUC(paf_duc1,
filters=256,
upscale_factor=2,
is_training=self.is_training,
scope='PAF_DUC2')
paf_conv_feature1 = tf.space_to_depth(paf_duc2,
block_size=int(2),
data_format='NHWC',
name='space_to_depth_1')
paf_conv_out1 = tf.layers.conv2d(
paf_conv_feature1,
20, #38
kernel_size=[3, 3],
name='PAF_output')
paf_duc2_pad = tf.pad(paf_duc2, [[0, 0], [1, 1], [1, 1], [0, 0]],
name='duc2_padding')
paf_conv_out = tf.layers.conv2d(
paf_duc2_pad,
20, #38
kernel_size=[3, 3],
name='PAF_conv')
paf_conv_feature = tf.space_to_depth(paf_conv_out,
block_size=int(4),
data_format='NHWC',
name='space_to_depth_2')
concat_feat = tf.concat(values=[ps1, paf_conv_feature],
axis=3,
name='concat_feat')
duc1 = self.DUC(concat_feat,
filters=512,
upscale_factor=2,
is_training=self.is_training,
scope='DUC1')
duc2 = self.DUC(duc1,
filters=256,
upscale_factor=2,
is_training=self.is_training,
scope='DUC2')
hm_feature = tf.space_to_depth(duc2,
block_size=int(2),
data_format='NHWC',
name='space_to_depth_3')
hm_out = tf.layers.conv2d(
hm_feature,
self.number_keypoints, #38
kernel_size=[3, 3],
name='output')
conv_out = tf.transpose(hm_out, [0, 3, 1, 2], name='hm_out')
paf_conv_out = tf.transpose(paf_conv_out1, [0, 3, 1, 2],
name='paf_out')
end_points['heat_map'] = conv_out
end_points['PAF'] = paf_conv_out
return end_points
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