def _tiny_res_block(inputs,in_channels,data_format):
net = _conv2d_fixed_padding(inputs,in_channels,kernel_size=3)
route = net
#_,split=tf.split(net,num_or_size_splits=2,axis=1 if data_format =="NCHW" else 3)
split = net[:, in_channels//2:, :, :]if data_format=="NCHW" else net[:, :, :, in_channels//2:]
net = _conv2d_fixed_padding(split,in_channels//2,kernel_size=3)
route1 = net
net = _conv2d_fixed_padding(net,in_channels//2,kernel_size=3)
net = tf.concat([net, route1], axis=1 if data_format == 'NCHW' else 3)
net = _conv2d_fixed_padding(net,in_channels,kernel_size=1)
feat = net
net = tf.concat([route, net], axis=1 if data_format == 'NCHW' else 3)
net = slim.max_pool2d(
net, [2, 2], scope='pool2')
return net,feat
def yolo_v4_tiny(inputs,
num_classes,
is_training=False,
data_format='NCHW',
reuse=False):
"""
Creates YOLO v4 tiny model.
:param inputs: a 4-D tensor of size [batch_size, height, width, channels].
Dimension batch_size may be undefined. The channel order is RGB.
:param num_classes: number of predicted classes.
:param is_training: whether is training or not.
:param data_format: data format NCHW or NHWC.
:param reuse: whether or not the network and its variables should be reused.
:return:
"""
# it will be needed later on
img_size = inputs.get_shape().as_list()[1:3]
# transpose the inputs to NCHW
if data_format == 'NCHW':
inputs = tf.transpose(inputs, [0, 3, 1, 2])
# normalize values to range [0..1]
inputs = inputs / 255
# set batch norm params
batch_norm_params = {
'decay': _BATCH_NORM_DECAY,
'epsilon': _BATCH_NORM_EPSILON,
'scale': True,
'is_training': is_training,
'fused': None, # Use fused batch norm if possible.
}
# Set activation_fn and parameters for conv2d, batch_norm.
with slim.arg_scope(
[slim.conv2d, slim.batch_norm, _fixed_padding, slim.max_pool2d],
data_format=data_format):
with slim.arg_scope([slim.conv2d, slim.batch_norm, _fixed_padding],
reuse=reuse):
with slim.arg_scope([slim.conv2d],
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params,
biases_initializer=None,
activation_fn=lambda x: tf.nn.leaky_relu(
x, alpha=_LEAKY_RELU)):
with tf.variable_scope('yolo-v4-tiny'):
#CSPDARKENT BEGIN
net = _conv2d_fixed_padding(inputs,
32,
kernel_size=3,
strides=2)
net = _conv2d_fixed_padding(net,
64,
kernel_size=3,
strides=2)
net, _ = _tiny_res_block(net, 64, data_format)
net, _ = _tiny_res_block(net, 128, data_format)
net, feat = _tiny_res_block(net, 256, data_format)
net = _conv2d_fixed_padding(net, 512, kernel_size=3)
feat2 = net
#CSPDARKNET END
net = _conv2d_fixed_padding(feat2, 256, kernel_size=1)
route = net
net = _conv2d_fixed_padding(route, 512, kernel_size=3)
detect_1 = _detection_layer(net, num_classes,
_ANCHORS[3:6], img_size,
data_format)
detect_1 = tf.identity(detect_1, name='detect_1')
net = _conv2d_fixed_padding(route, 128, kernel_size=1)
upsample_size = feat.get_shape().as_list()
net = _upsample(net, upsample_size, data_format)
net = tf.concat([net, feat],
axis=1 if data_format == 'NCHW' else 3)
net = _conv2d_fixed_padding(net, 256, kernel_size=3)
detect_2 = _detection_layer(net, num_classes,
_ANCHORS[1:4], img_size,
data_format)
detect_2 = tf.identity(detect_2, name='detect_2')
detections = tf.concat([detect_1, detect_2], axis=1)
detections = tf.identity(detections, name='detections')
return detections