def bottleneck_layer(_x, num_channels, hgid):
# 4 residual blocks with 512 channels in the middle
pow_str = 'center.' * 5
_x = residual(_x, num_channels, name='kps.%d.%s0' % (hgid, pow_str))
_x = residual(_x, num_channels, name='kps.%d.%s1' % (hgid, pow_str))
_x = residual(_x, num_channels, name='kps.%d.%s2' % (hgid, pow_str))
_x = residual(_x, num_channels, name='kps.%d.%s3' % (hgid, pow_str))
return _x
def connect_left_right(left, right, num_channels, num_channels_next, name):
# left: 2 residual modules
left = residual(left, num_channels_next, name=name + 'skip.0')
left = residual(left, num_channels_next, name=name + 'skip.1')
# up: 2 times residual & nearest neighbour
out = residual(right, num_channels, name=name + 'out.0')
out = residual(out, num_channels_next, name=name + 'out.1')
out = UpSampling2D(name=name + 'out.upsampleNN')(out)
out = Add(name=name + 'out.add')([left, out])
return out
def left_features(bottom, hgid, dims):
# create left half blocks for hourglass module
# f1, f2, f4 , f8, f16, f32 : 1, 1/2, 1/4 1/8, 1/16, 1/32 resolution
# 5 times reduce/increase: (256, 384, 384, 384, 512)
features = [bottom]
for kk, nh in enumerate(dims):
pow_str = ''
for _ in range(kk):
pow_str += '.center'
_x = residual(features[-1],
nh,
name='kps.%d%s.down.0' % (hgid, pow_str),
stride=2)
_x = residual(_x, nh, name='kps.%d%s.down.1' % (hgid, pow_str))
features.append(_x)
return features
def HourglassNetwork(heads, num_stacks, cnv_dim=256, inres=(512, 512), weights='ctdet_coco',
dims=[256, 384, 384, 384, 512]):
"""Instantiates the Hourglass architecture.
Optionally loads weights pre-trained on COCO.
Note that the data format convention used by the model is
the one specified in your Keras config at `~/.keras/keras.json`.
# Arguments
num_stacks: number of hourglass modules.
cnv_dim: number of filters after the resolution is decreased.
inres: network input shape, should be a multiple of 128.
weights: one of `None` (random initialization),
'ctdet_coco' (pre-training on COCO for 2D object detection),
'hpdet_coco' (pre-training on COCO for human pose detection),
or the path to the weights file to be loaded.
dims: numbers of channels in the hourglass blocks.
# Returns
A Keras model instance.
# Raises
ValueError: in case of invalid argument for `weights`,
or invalid input shape.
"""
if not (weights in {'ctdet_coco', 'hpdet_coco', None} or os.path.exists(weights)):
raise ValueError('The `weights` argument should be either '
'`None` (random initialization), `ctdet_coco` '
'(pre-trained on COCO), `hpdet_coco` (pre-trained on COCO) '
'or the path to the weights file to be loaded.')
input_layer = Input(shape=(inres[0], inres[1], 3), name='HGInput')
inter = pre(input_layer, cnv_dim)
prev_inter = None
outputs = []
for i in range(num_stacks):
prev_inter = inter
_heads, inter = hourglass_module(heads, inter, cnv_dim, i, dims)
outputs.extend(_heads)
if i < num_stacks - 1:
inter_ = Conv2D(cnv_dim, 1, use_bias=False, name='inter_.%d.0' % i)(prev_inter)
inter_ = BatchNormalization(epsilon=1e-5, name='inter_.%d.1' % i)(inter_)
cnv_ = Conv2D(cnv_dim, 1, use_bias=False, name='cnv_.%d.0' % i)(inter)
cnv_ = BatchNormalization(epsilon=1e-5, name='cnv_.%d.1' % i)(cnv_)
inter = Add(name='inters.%d.inters.add' % i)([inter_, cnv_])
inter = Activation('relu', name='inters.%d.inters.relu' % i)(inter)
inter = residual(inter, cnv_dim, 'inters.%d' % i)
model = Model(inputs=input_layer, outputs=outputs)
if weights == 'ctdet_coco':
weights_path = get_file(
'%s_hg.hdf5' % weights,
CTDET_COCO_WEIGHTS_PATH,
cache_subdir='models',
file_hash='ce01e92f75b533e3ff8e396c76d55d97ff3ec27e99b1bdac1d7b0d6dcf5d90eb')
model.load_weights(weights_path)
elif weights == 'hpdet_coco':
weights_path = get_file(
'%s_hg.hdf5' % weights,
HPDET_COCO_WEIGHTS_PATH,
cache_subdir='models',
file_hash='5c562ee22dc383080629dae975f269d62de3a41da6fd0c821085fbee183d555d')
model.load_weights(weights_path)
elif weights is not None:
model.load_weights(weights)
return model
def pre(_x, num_channels): # front module, input to 1/4 resolution _x = convolution(_x, 7, 128, name='pre.0', stride=2) _x = residual(_x, num_channels, name='pre.1', stride=2) return _x