def build_network(self, input, trainable):
is_trainable(trainable)
intermediate_heatmap_layers = []
tower = layers.Conv2D(out_channel_ratio(64),
kernel_size=[3, 3],
strides=(2, 2),
activation=None,
padding='same')(input)
tower = layers.BatchNormalization()(tower)
tower = layers.ReLU()(tower)
tower = inverted_bottleneck(tower, 1, out_channel_ratio(16), 1, 3)
tower = max_pool(tower, 2, 2, 2, 2, name='max_pool_0')
net_h_w = int(tower.shape[1])
# build network recursively
hg_out = self.hourglass_module(tower, STAGE_NUM,
intermediate_heatmap_layers)
for index, l2 in enumerate(intermediate_heatmap_layers):
l2_w_h = int(l2.shape[1])
if l2_w_h == net_h_w:
continue
scale = net_h_w // l2_w_h
intermediate_heatmap_layers[index] = upsample(
l2, scale, name="upsample_for_loss_%d" % index)
merged_layer = tf.keras.layers.Average()(intermediate_heatmap_layers)
return hg_out, merged_layer
def build_network(input, trainable):
is_trainable(trainable)
net = convb(input, 3, 3, out_channel_ratio(16), 2, name="Conv2d_0")
# 128, 112
net = slim.stack(net,
inverted_bottleneck, [(1, out_channel_ratio(16), 0, 3),
(1, out_channel_ratio(16), 0, 3)],
scope="Conv2d_1")
# 64, 56
net = slim.stack(net,
inverted_bottleneck, [
(up_channel_ratio(6), out_channel_ratio(24), 1, 3),
(up_channel_ratio(6), out_channel_ratio(24), 0, 3),
(up_channel_ratio(6), out_channel_ratio(24), 0, 3),
(up_channel_ratio(6), out_channel_ratio(24), 0, 3),
(up_channel_ratio(6), out_channel_ratio(24), 0, 3),
],
scope="Conv2d_2")
net_h_w = int(net.shape[1])
# build network recursively
hg_out = hourglass_module(net, STAGE_NUM)
for index, l2 in enumerate(l2s):
l2_w_h = int(l2.shape[1])
if l2_w_h == net_h_w:
continue
scale = net_h_w // l2_w_h
l2s[index] = upsample(l2, scale, name="upsample_for_loss_%d" % index)
return hg_out, l2s
def hourglass_module(self, inp, stage_nums, intermediate_heatmap_layers):
if stage_nums > 0:
down_sample = max_pool(inp,
2,
2,
2,
2,
name="hourglass_downsample_%d" % stage_nums)
tower = inverted_bottleneck(down_sample, up_channel_ratio(6),
out_channel_ratio(24), 0, 3)
tower = inverted_bottleneck(tower, up_channel_ratio(6),
out_channel_ratio(24), 0, 3)
tower = inverted_bottleneck(tower, up_channel_ratio(6),
out_channel_ratio(24), 0, 3)
tower = inverted_bottleneck(tower, up_channel_ratio(6),
out_channel_ratio(24), 0, 3)
block_front = inverted_bottleneck(tower, up_channel_ratio(6),
out_channel_ratio(24), 0, 3)
stage_nums -= 1
block_mid = self.hourglass_module(block_front, stage_nums,
intermediate_heatmap_layers)
block_back = inverted_bottleneck(block_mid,
up_channel_ratio(6),
N_KPOINTS,
0,
3,
scope="hourglass_back_%d" %
stage_nums)
up_sample = upsample(block_back, 2,
"hourglass_upsample_%d" % stage_nums)
# jump layer
tower = inverted_bottleneck(inp, up_channel_ratio(6),
out_channel_ratio(24), 0, 3)
tower = inverted_bottleneck(tower, up_channel_ratio(6),
out_channel_ratio(24), 0, 3)
tower = inverted_bottleneck(tower, up_channel_ratio(6),
out_channel_ratio(24), 0, 3)
tower = inverted_bottleneck(tower, up_channel_ratio(6),
out_channel_ratio(24), 0, 3)
branch_jump = inverted_bottleneck(tower, up_channel_ratio(6),
N_KPOINTS, 0, 3)
curr_hg_out = layers.Add()([up_sample, branch_jump])
# mid supervise
intermediate_heatmap_layers.append(curr_hg_out)
return curr_hg_out
else:
return inverted_bottleneck(inp,
up_channel_ratio(6),
out_channel_ratio(24),
0,
3,
scope="hourglass_mid_%d" % stage_nums)
def hourglass_module(inp, stage_nums):
if stage_nums > 0:
down_sample = max_pool(inp,
2,
2,
2,
2,
name="hourglass_downsample_%d" % stage_nums)
block_front = slim.stack(
down_sample,
inverted_bottleneck, [
(up_channel_ratio(6), out_channel_ratio(24), 0, 3),
(up_channel_ratio(6), out_channel_ratio(24), 0, 3),
(up_channel_ratio(6), out_channel_ratio(24), 0, 3),
(up_channel_ratio(6), out_channel_ratio(24), 0, 3),
(up_channel_ratio(6), out_channel_ratio(24), 0, 3),
],
scope="hourglass_front_%d" % stage_nums)
stage_nums -= 1
block_mid = hourglass_module(block_front, stage_nums)
block_back = inverted_bottleneck(block_mid,
up_channel_ratio(6),
N_KPOINTS,
0,
3,
scope="hourglass_back_%d" %
stage_nums)
up_sample = upsample(block_back, 2,
"hourglass_upsample_%d" % stage_nums)
# jump layer
branch_jump = slim.stack(
inp,
inverted_bottleneck, [
(up_channel_ratio(6), out_channel_ratio(24), 0, 3),
(up_channel_ratio(6), out_channel_ratio(24), 0, 3),
(up_channel_ratio(6), out_channel_ratio(24), 0, 3),
(up_channel_ratio(6), out_channel_ratio(24), 0, 3),
(up_channel_ratio(6), N_KPOINTS, 0, 3),
],
scope="hourglass_branch_jump_%d" % stage_nums)
curr_hg_out = tf.add(up_sample,
branch_jump,
name="hourglass_out_%d" % stage_nums)
# mid supervise
l2s.append(curr_hg_out)
return curr_hg_out
_ = inverted_bottleneck(inp,
up_channel_ratio(6),
out_channel_ratio(24),
0,
3,
scope="hourglass_mid_%d" % stage_nums)
return _
def build_network(self, input, trainable):
is_trainable(trainable)
intermediate_heatmap_layers = []
tower = convb(input, 3, 3, out_channel_ratio(16), 2, name="Conv2d_0")
# 128, 112
tower = inverted_bottleneck(tower, 1, out_channel_ratio(16), 0, 3)
tower = inverted_bottleneck(tower, 1, out_channel_ratio(16), 0, 3)
# 64, 56
tower = inverted_bottleneck(tower, up_channel_ratio(6),
out_channel_ratio(24), 1, 3)
tower = inverted_bottleneck(tower, up_channel_ratio(6),
out_channel_ratio(24), 0, 3)
tower = inverted_bottleneck(tower, up_channel_ratio(6),
out_channel_ratio(24), 0, 3)
tower = inverted_bottleneck(tower, up_channel_ratio(6),
out_channel_ratio(24), 0, 3)
tower = inverted_bottleneck(tower, up_channel_ratio(6),
out_channel_ratio(24), 0, 3)
net_h_w = int(tower.shape[1])
# build network recursively
hg_out = self.hourglass_module(tower, STAGE_NUM,
intermediate_heatmap_layers)
for index, l2 in enumerate(intermediate_heatmap_layers):
l2_w_h = int(l2.shape[1])
if l2_w_h == net_h_w:
continue
scale = net_h_w // l2_w_h
intermediate_heatmap_layers[index] = upsample(
l2, scale, name="upsample_for_loss_%d" % index)
merged_layer = tf.keras.layers.Average()(intermediate_heatmap_layers)
return hg_out, merged_layer
def build_network(input, trainable):
is_trainable(trainable)
net = convb(input, 3, 3, out_channel_ratio(32), 2, name="Conv2d_0")
with tf.variable_scope('MobilenetV2'):
# 128, 112
mv2_branch_0 = slim.stack(net,
inverted_bottleneck,
[(1, out_channel_ratio(16), 0, 3),
(1, out_channel_ratio(16), 0, 3)],
scope="MobilenetV2_part_0")
# 64, 56
mv2_branch_1 = slim.stack(
mv2_branch_0,
inverted_bottleneck, [
(up_channel_ratio(6), out_channel_ratio(24), 1, 3),
(up_channel_ratio(6), out_channel_ratio(24), 0, 3),
(up_channel_ratio(6), out_channel_ratio(24), 0, 3),
(up_channel_ratio(6), out_channel_ratio(24), 0, 3),
(up_channel_ratio(6), out_channel_ratio(24), 0, 3),
],
scope="MobilenetV2_part_1")
# 32, 28
mv2_branch_2 = slim.stack(
mv2_branch_1,
inverted_bottleneck, [
(up_channel_ratio(6), out_channel_ratio(32), 1, 3),
(up_channel_ratio(6), out_channel_ratio(32), 0, 3),
(up_channel_ratio(6), out_channel_ratio(32), 0, 3),
(up_channel_ratio(6), out_channel_ratio(32), 0, 3),
(up_channel_ratio(6), out_channel_ratio(32), 0, 3),
],
scope="MobilenetV2_part_2")
# 16, 14
mv2_branch_3 = slim.stack(
mv2_branch_2,
inverted_bottleneck, [
(up_channel_ratio(6), out_channel_ratio(64), 1, 3),
(up_channel_ratio(6), out_channel_ratio(64), 0, 3),
(up_channel_ratio(6), out_channel_ratio(64), 0, 3),
(up_channel_ratio(6), out_channel_ratio(64), 0, 3),
(up_channel_ratio(6), out_channel_ratio(64), 0, 3),
],
scope="MobilenetV2_part_3")
# 8, 7
mv2_branch_4 = slim.stack(
mv2_branch_3,
inverted_bottleneck,
[(up_channel_ratio(6), out_channel_ratio(96), 1, 3),
(up_channel_ratio(6), out_channel_ratio(96), 0, 3),
(up_channel_ratio(6), out_channel_ratio(96), 0, 3),
(up_channel_ratio(6), out_channel_ratio(96), 0, 3),
(up_channel_ratio(6), out_channel_ratio(96), 0, 3)],
scope="MobilenetV2_part_4")
cancat_mv2 = tf.concat([
max_pool(mv2_branch_0, 4, 4, 4, 4, name="mv2_0_max_pool"),
max_pool(mv2_branch_1, 2, 2, 2, 2, name="mv2_1_max_pool"),
mv2_branch_2,
upsample(mv2_branch_3, 2, name="mv2_3_upsample"),
upsample(mv2_branch_4, 4, name="mv2_4_upsample")
],
axis=3)
with tf.variable_scope("Convolutional_Pose_Machine"):
l2s = []
prev = None
for stage_number in range(STAGE_NUM):
if prev is not None:
inputs = tf.concat([cancat_mv2, prev], axis=3)
else:
inputs = cancat_mv2
kernel_size = 7
lastest_channel_size = 128
if stage_number == 0:
kernel_size = 3
lastest_channel_size = 512
_ = slim.stack(
inputs,
inverted_bottleneck, [
(2, out_channel_cpm(32), 0, kernel_size),
(up_channel_ratio(4), out_channel_cpm(32), 0, kernel_size),
(up_channel_ratio(4), out_channel_cpm(32), 0, kernel_size),
],
scope="stage_%d_mv2" % stage_number)
_ = slim.stack(_,
separable_conv,
[(out_channel_ratio(lastest_channel_size), 1, 1),
(N_KPOINTS, 1, 1)],
scope="stage_%d_mv1" % stage_number)
prev = _
cpm_out = upsample(_, 4, "stage_%d_out" % stage_number)
l2s.append(cpm_out)
return cpm_out, l2s
