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 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