def top_feature_net_r(input, anchors, inds_inside, num_bases):
"""
:param input:
:param anchors:
:param inds_inside:
:param num_bases:
:return:
top_features, top_scores, top_probs, top_deltas, proposals, proposal_scores
"""
stride=1.
#with tf.variable_scope('top-preprocess') as scope:
# input = input
batch_size, img_height, img_width, img_channel = input.get_shape().as_list()
with tf.variable_scope('feature-extract-resnet') as scope:
print('build_resnet')
block = ResnetBuilder.resnet_tiny(input)
# resnet_input = resnet.get_layer('input_1').input
# resnet_output = resnet.get_layer('add_7').output
# resnet_f = Model(inputs=resnet_input, outputs=resnet_output) # add_7
# # print(resnet_f.summary())
# block = resnet_f(input)
block = conv2d_bn_relu(block, num_kernels=128, kernel_size=(1, 1), stride=[1, 1, 1, 1], padding='SAME', name='2')
stride = 8
feature = block
with tf.variable_scope('predict') as scope:
# up = upsample2d(block, factor = 2, has_bias=True, trainable=True, name='1')
# up = block
kernel_size = config.cfg.TOP_CONV_KERNEL_SIZE
print('\ntop_predict kernal_size: {}\n'.format(kernel_size) )
block = conv2d_bn_relu(block, num_kernels=128, kernel_size=(kernel_size, kernel_size),
stride=[1, 1, 1, 1], padding='SAME', name='1')
block = conv2d_bn_relu(block, num_kernels=128, kernel_size=(kernel_size, kernel_size),
stride=[1, 1, 1, 1], padding='SAME', name='2')
scores = conv2d(block, num_kernels=2 * num_bases, kernel_size=(1, 1), stride=[1, 1, 1, 1], padding='SAME',name='score')
probs = tf.nn.softmax(tf.reshape(scores, [-1, 2]), name='prob')
deltas = conv2d(block, num_kernels=4 * num_bases, kernel_size=(1, 1), stride=[1, 1, 1, 1], padding='SAME',name='delta')
#<todo> flip to train and test mode nms (e.g. different nms_pre_topn values): use tf.cond
with tf.variable_scope('NMS') as scope: #non-max
img_scale = 1
rois, roi_scores = tf_rpn_nms( probs, deltas, anchors, inds_inside,
stride, img_width, img_height, img_scale,
nms_thresh=0.7, min_size=stride, nms_pre_topn=500, nms_post_topn=100,
name ='nms')
print ('top: scale=%f, stride=%d'%(1./stride, stride))
return feature, scores, probs, deltas, rois, roi_scores, stride
def rgb_feature_net_r(input):
#with tf.variable_scope('rgb-preprocess') as scope:
# input = input-128
batch_size, img_height, img_width, img_channel = input.get_shape().as_list()
with tf.variable_scope('resnet-block-1') as scope:
print('build_resnet')
block = ResnetBuilder.resnet_tiny(input)
block = conv2d_bn_relu(block, num_kernels=128, kernel_size=(1, 1), stride=[1, 1, 1, 1], padding='SAME', name='2')
stride = 8
#<todo> feature = upsample2d(block, factor = 4, ...)
feature = block
print ('rgb : scale=%f, stride=%d'%(1./stride, stride))
return feature, stride
def rgb_feature_net_r(input):
#with tf.variable_scope('rgb-preprocess') as scope:
# input = input-128
batch_size, img_height, img_width, img_channel = input.get_shape().as_list()
with tf.variable_scope('resnet-block-1') as scope:
print('build_resnet')
block = ResnetBuilder.resnet_tiny(input)
block = conv2d_bn_relu(block, num_kernels=128, kernel_size=(1, 1), stride=[1, 1, 1, 1], padding='SAME', name='2')
stride = 8
#<todo> feature = upsample2d(block, factor = 4, ...)
feature = block
print ('rgb : scale=%f, stride=%d'%(1./stride, stride))
return feature, stride
def top_feature_net_r(input, anchors, inds_inside, num_bases):
"""
:param input:
:param anchors:
:param inds_inside:
:param num_bases:
:return:
top_features, top_scores, top_probs, top_deltas, proposals, proposal_scores
"""
batch_size, img_height, img_width, img_channel = input.get_shape().as_list(
)
with tf.variable_scope('feature-extract-resnet') as scope:
print('build_resnet')
block = ResnetBuilder.resnet_tiny_smaller_kernel(input)
feature = block
top_feature_stride = 4
# resnet_input = resnet.get_layer('input_1').input
# resnet_output = resnet.get_layer('add_7').output
# resnet_f = Model(inputs=resnet_input, outputs=resnet_output) # add_7
# # print(resnet_f.summary())
# block = resnet_f(input)
block = upsample2d(block,
factor=2,
has_bias=True,
trainable=True,
name='upsampling')
with tf.variable_scope('predict') as scope:
# block = upsample2d(block, factor=4, has_bias=True, trainable=True, name='1')
# up = block
# kernel_size = config.cfg.TOP_CONV_KERNEL_SIZE
top_anchors_stride = 2
block = conv2d_bn_relu(block,
num_kernels=128,
kernel_size=(1, 1),
stride=[1, 1, 1, 1],
padding='SAME',
name='2')
scores = conv2d(block,
num_kernels=2 * num_bases,
kernel_size=(1, 1),
stride=[1, 1, 1, 1],
padding='SAME',
name='score')
probs = tf.nn.softmax(tf.reshape(scores, [-1, 2]), name='prob')
deltas = conv2d(block,
num_kernels=4 * num_bases,
kernel_size=(1, 1),
stride=[1, 1, 1, 1],
padding='SAME',
name='delta')
with tf.variable_scope('RPN-NMS') as scope:
train_rois, train_roi_scores = tf_rpn_nms( probs, deltas, anchors, inds_inside, \
top_anchors_stride, img_width, img_height, img_scale=1, \
nms_thresh=0.7, min_size=top_anchors_stride, \
nms_pre_topn=CFG.TRAIN.RPN_NMS_PRE_TOPN, nms_post_topn=CFG.TRAIN.RPN_NMS_POST_TOPN, \
name ='train-rpn-nms')
infer_rois, infer_roi_scores = tf_rpn_nms( probs, deltas, anchors, inds_inside, \
top_anchors_stride, img_width, img_height, img_scale=1, \
nms_thresh=0.1, min_size=top_anchors_stride, \
nms_pre_topn=CFG.TRAIN.RPN_NMS_PRE_TOPN, nms_post_topn=CFG.TEST.RPN_NMS_POST_TOPN, \
name ='infer-rpn-nms')
print('top: anchor stride=%d, feature_stride=%d' %
(top_anchors_stride, top_feature_stride))
return feature, scores, probs, deltas, train_rois, train_roi_scores, top_anchors_stride,top_feature_stride, \
infer_rois, infer_roi_scores
def top_feature_net_r(input,
anchors,
inds_inside,
num_bases,
top_last_states=None):
"""
:param input:
:param anchors:
:param inds_inside:
:param num_bases:
:return:
top_features, top_scores, top_probs, top_deltas, proposals, proposal_scores
"""
stride = 1.
#with tf.variable_scope('top-preprocess') as scope:
# input = input
batch_size, img_height, img_width, img_channel = input.get_shape().as_list(
)
with tf.variable_scope('feature-extract-resnet') as scope:
print('build_resnet')
block = ResnetBuilder.resnet_tiny(input)
# resnet_input = resnet.get_layer('input_1').input
# resnet_output = resnet.get_layer('add_7').output
# resnet_f = Model(inputs=resnet_input, outputs=resnet_output) # add_7
# # print(resnet_f.summary())
# block = resnet_f(input)
block = conv2d_bn_relu(block,
num_kernels=128,
kernel_size=(1, 1),
stride=[1, 1, 1, 1],
padding='SAME',
name='2')
stride = 8
with tf.variable_scope('memory') as scope:
with tf.variable_scope('get_last_channel') as scope:
lstm_input = block[:, :, :, 127]
ori_shape = lstm_input.get_shape().as_list()
# [batch_size, sequence_length_max, vector_size]
lstm_input = tf.reshape(lstm_input,
[-1, 1, np.prod(ori_shape[1:])])
with tf.variable_scope('lstm') as scope:
lstm_cell = rnn.BasicLSTMCell(np.prod(ori_shape[1:]))
outputs, top_states = tf.nn.dynamic_rnn(
lstm_cell,
lstm_input,
initial_state=top_last_states,
dtype=tf.float32)
outputs = tf.reshape(outputs, [-1, ori_shape[1], ori_shape[2]],
'reshape')
with tf.variable_scope('merge') as scope:
block = tf.concat([
block[:, :, :, 0:127],
tf.reshape(outputs, [-1, ori_shape[1], ori_shape[2], 1])
], 3)
with tf.variable_scope('predict') as scope:
# up = upsample2d(block, factor = 2, has_bias=True, trainable=True, name='1')
# up = block
up = conv2d_bn_relu(block,
num_kernels=128,
kernel_size=(3, 3),
stride=[1, 1, 1, 1],
padding='SAME',
name='2')
scores = conv2d(up,
num_kernels=2 * num_bases,
kernel_size=(1, 1),
stride=[1, 1, 1, 1],
padding='SAME',
name='score')
probs = tf.nn.softmax(tf.reshape(scores, [-1, 2]), name='prob')
deltas = conv2d(up,
num_kernels=4 * num_bases,
kernel_size=(1, 1),
stride=[1, 1, 1, 1],
padding='SAME',
name='delta')
#<todo> flip to train and test mode nms (e.g. different nms_pre_topn values): use tf.cond
with tf.variable_scope('NMS') as scope: #non-max
img_scale = 1
rois, roi_scores = tf_rpn_nms(probs,
deltas,
anchors,
inds_inside,
stride,
img_width,
img_height,
img_scale,
nms_thresh=0.7,
min_size=stride,
nms_pre_topn=500,
nms_post_topn=100,
name='nms')
#<todo> feature = upsample2d(block, factor = 4, ...)
feature = block
print('top: scale=%f, stride=%d' % (1. / stride, stride))
return feature, scores, probs, deltas, rois, roi_scores, stride, top_states
def top_feature_net_r(input, anchors, inds_inside, num_bases, top_last_states=None):
"""
:param input:
:param anchors:
:param inds_inside:
:param num_bases:
:return:
top_features, top_scores, top_probs, top_deltas, proposals, proposal_scores
"""
stride=1.
#with tf.variable_scope('top-preprocess') as scope:
# input = input
batch_size, img_height, img_width, img_channel = input.get_shape().as_list()
with tf.variable_scope('feature-extract-resnet') as scope:
print('build_resnet')
block = ResnetBuilder.resnet_tiny(input)
# resnet_input = resnet.get_layer('input_1').input
# resnet_output = resnet.get_layer('add_7').output
# resnet_f = Model(inputs=resnet_input, outputs=resnet_output) # add_7
# # print(resnet_f.summary())
# block = resnet_f(input)
block = conv2d_bn_relu(block, num_kernels=128, kernel_size=(1, 1), stride=[1, 1, 1, 1], padding='SAME', name='2')
stride = 8
with tf.variable_scope('memory') as scope:
with tf.variable_scope('get_last_channel') as scope:
lstm_input = block[:,:,:,127]
ori_shape = lstm_input.get_shape().as_list()
# [batch_size, sequence_length_max, vector_size]
lstm_input = tf.reshape(lstm_input, [-1, 1, np.prod(ori_shape[1:])])
with tf.variable_scope('lstm') as scope:
lstm_cell = rnn.BasicLSTMCell(np.prod(ori_shape[1:]) )
outputs, top_states = tf.nn.dynamic_rnn(lstm_cell, lstm_input, initial_state=top_last_states, dtype=tf.float32)
outputs = tf.reshape(outputs, [-1, ori_shape[1],ori_shape[2]], 'reshape')
with tf.variable_scope('merge') as scope:
block = tf.concat([block[:, :, :, 0:127],tf.reshape(outputs,[-1,ori_shape[1],ori_shape[2],1])], 3)
with tf.variable_scope('predict') as scope:
# up = upsample2d(block, factor = 2, has_bias=True, trainable=True, name='1')
# up = block
up = conv2d_bn_relu(block, num_kernels=128, kernel_size=(3, 3), stride=[1, 1, 1, 1], padding='SAME', name='2')
scores = conv2d(up, num_kernels=2 * num_bases, kernel_size=(1, 1), stride=[1, 1, 1, 1], padding='SAME',name='score')
probs = tf.nn.softmax(tf.reshape(scores, [-1, 2]), name='prob')
deltas = conv2d(up, num_kernels=4 * num_bases, kernel_size=(1, 1), stride=[1, 1, 1, 1], padding='SAME',name='delta')
#<todo> flip to train and test mode nms (e.g. different nms_pre_topn values): use tf.cond
with tf.variable_scope('NMS') as scope: #non-max
img_scale = 1
rois, roi_scores = tf_rpn_nms( probs, deltas, anchors, inds_inside,
stride, img_width, img_height, img_scale,
nms_thresh=0.7, min_size=stride, nms_pre_topn=500, nms_post_topn=100,
name ='nms')
#<todo> feature = upsample2d(block, factor = 4, ...)
feature = block
print ('top: scale=%f, stride=%d'%(1./stride, stride))
return feature, scores, probs, deltas, rois, roi_scores, stride, top_states
