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model.py
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model.py
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#coding=utf-8
import tensorflow as tf
from tensorflow.contrib import slim
from config.cfg import config_instace as cfg
import utils
from data_set.data_loader import q
import cv2
import numpy as np
from nets import inception_v2
import visual
from loss import losses
import glob
import time
#tf.enable_eager_execution()
def model(img):
with slim.arg_scope(inception_v2.inception_v2_arg_scope()):
with slim.arg_scope([slim.batch_norm],is_training=True):
with slim.arg_scope([slim.conv2d], trainable=True):
logits, end_point = inception_v2.inception_v2_base(img)
c1 = end_point['Mixed_3c']
c2 = end_point['Mixed_4e']
c3 = end_point['Mixed_5c']
vbs = slim.get_variables_to_restore()
c3 = slim.conv2d(c3, 256, 1, 1, activation_fn=None)
c2 = slim.conv2d(c2, 256, 1, 1, activation_fn=None) + tf.image.resize_bilinear(c3, size=tf.shape(c2)[1:3])
c1 = slim.conv2d(c1, 256, 1, 1, activation_fn=None) + tf.image.resize_bilinear(c2, size=tf.shape(c1)[1:3])
return c1,c2,c3,vbs
def rpn_graph(feature_map,anchors_per_location=9):
shared = slim.conv2d(feature_map,512,3,activation_fn=slim.nn.relu)
x = slim.conv2d(shared,2 * anchors_per_location,kernel_size=1,padding='VALID',activation_fn=None)
rpn_class_logits = tf.reshape(x,shape=[tf.shape(x)[0],-1,2])
rpn_probs = slim.nn.softmax(rpn_class_logits)
x = slim.conv2d(shared, 4 * anchors_per_location, kernel_size=1, padding='VALID', activation_fn=None)
rpn_bbox =tf.reshape(x,shape=[tf.shape(x)[0],-1,4])
return [rpn_class_logits, rpn_probs, rpn_bbox]
def propsal(rpn_probs, rpn_bbox):
scores = rpn_probs[:, :, 1]
deltas = rpn_bbox
deltas = deltas * np.reshape(cfg.RPN_BBOX_STD_DEV, [1, 1, 4])
anchors = cfg.norm_anchors
pre_nms_limit = tf.minimum(6000, cfg.total_anchors)
ix = tf.nn.top_k(scores, pre_nms_limit, sorted=True,
name="top_anchors").indices
window = np.array([0, 0, 1, 1], dtype=np.float32)
result = []
for b in range(cfg.batch_size):
scores_tp = tf.gather(scores[b,:],ix[b,:])
deltas_tp = tf.gather(deltas[b, :], ix[b, :])
pre_nms_anchors_tp = tf.gather(anchors, ix[b, :])
boxes_tp = utils.apply_box_deltas_graph(pre_nms_anchors_tp,deltas_tp)
boxes_tp = utils.clip_boxes_graph(boxes_tp,window)
props = utils.nms(boxes_tp,scores_tp,cfg)
result.append(props)
return tf.stack(result,axis=0)
def detection_target(input_proposals, input_gt_class_ids, input_gt_boxes):
roiss = []
roi_gt_class_idss = []
deltass = []
for b in range(cfg.batch_size):
proposals = input_proposals[b,:,:]
gt_class_ids = input_gt_class_ids[b,:]
gt_boxes = input_gt_boxes[b,:,:]
proposals, _ = utils.trim_zeros_graph(proposals, name="trim_proposals")
gt_boxes, non_zeros = utils.trim_zeros(gt_boxes, name="trim_gt_boxes")
gt_class_ids = tf.boolean_mask(gt_class_ids, non_zeros,
name="trim_gt_class_ids")
crowd_ix = tf.where(gt_class_ids < 0)[:, 0]
non_crowd_ix = tf.where(gt_class_ids > 0)[:, 0]
crowd_boxes = tf.gather(gt_boxes, crowd_ix)
gt_class_ids = tf.gather(gt_class_ids, non_crowd_ix)
gt_boxes = tf.gather(gt_boxes, non_crowd_ix)
overlaps = utils.overlaps_graph(proposals, gt_boxes)
crowd_overlaps = utils.overlaps_graph(proposals, crowd_boxes)
crowd_iou_max = tf.reduce_max(crowd_overlaps, axis=1)
no_crowd_bool = (crowd_iou_max < 0.001)
roi_iou_max = tf.reduce_max(overlaps, axis=1)
positive_roi_bool = (roi_iou_max >= 0.5)
positive_indices = tf.where(positive_roi_bool)[:, 0]
negative_indices = tf.where(tf.logical_and(roi_iou_max < 0.5, no_crowd_bool))[:, 0]
positive_count = int(cfg.TRAIN_ROIS_PER_IMAGE *
cfg.ROI_POSITIVE_RATIO)
positive_indices = tf.random_shuffle(positive_indices)[:positive_count]
positive_count = tf.shape(positive_indices)[0]
r = 1.0 / cfg.ROI_POSITIVE_RATIO
negative_count = tf.cast(r * tf.cast(positive_count, tf.float32), tf.int32) - positive_count
negative_indices = tf.random_shuffle(negative_indices)[:negative_count]
positive_rois = tf.gather(proposals, positive_indices)
negative_rois = tf.gather(proposals, negative_indices)
# Assign positive ROIs to GT boxes.
positive_overlaps = tf.gather(overlaps, positive_indices)
roi_gt_box_assignment = tf.argmax(positive_overlaps, axis=1)
roi_gt_boxes = tf.gather(gt_boxes, roi_gt_box_assignment)
roi_gt_class_ids = tf.gather(gt_class_ids, roi_gt_box_assignment)
deltas = utils.box_refinement_graph(positive_rois, roi_gt_boxes)
deltas /= cfg.BBOX_STD_DEV
rois = tf.concat([positive_rois, negative_rois], axis=0)
N = tf.shape(negative_rois)[0]
P = tf.maximum(cfg.TRAIN_ROIS_PER_IMAGE - tf.shape(rois)[0], 0)
rois = tf.pad(rois, [(0, P), (0, 0)])
roi_gt_boxes = tf.pad(roi_gt_boxes, [(0, N + P), (0, 0)])
roi_gt_class_ids = tf.pad(roi_gt_class_ids, [(0, N + P)])
deltas = tf.pad(deltas, [(0, N + P), (0, 0)])
roiss.append(rois)
roi_gt_class_idss.append(roi_gt_class_ids)
deltass.append(deltas)
return tf.stack(roiss,axis=0),tf.stack(roi_gt_class_idss,axis=0),tf.stack(deltass,axis=0)
def fpn_classifier_graph(rois, feature_maps):
x = utils.roi_align(rois, feature_maps, cfg)
x = slim.conv2d(x,512,kernel_size=cfg.pool_shape,padding='VALID')
x = slim.conv2d(x, 512, kernel_size=1)
x1 = slim.dropout(x,keep_prob=1.0)
mrcnn_class_logits = slim.fully_connected(x1,cfg.num_class)
mrcnn_probs = slim.softmax(mrcnn_class_logits)
x = slim.fully_connected(x,cfg.num_class*4)
mrcnn_bbox = tf.reshape(x,shape=(-1,cfg.num_class,4))
return mrcnn_class_logits, mrcnn_probs, mrcnn_bbox
def predict(images,window):
c1, c2, c3, vbs = model(images)
fp = [c1, c2, c3]
rpn_c_l = []
r_p = []
r_b = []
for f in fp:
rpn_class_logits, rpn_probs, rpn_bbox = rpn_graph(f)
rpn_c_l.append(rpn_class_logits)
r_p.append(rpn_probs)
r_b.append(rpn_bbox)
rpn_class_logits = tf.concat(rpn_c_l, axis=1)
rpn_probs = tf.concat(r_p, axis=1)
rpn_bbox = tf.concat(r_b, axis=1)
rpn_rois = propsal(rpn_probs, rpn_bbox)
mrcnn_class_logits, mrcnn_class, mrcnn_bbox = fpn_classifier_graph(rpn_rois, fp)
detections = utils.refine_detections_graph(rpn_rois,mrcnn_class, mrcnn_bbox,window,cfg)
return detections
def loss(gt_boxs, images, input_rpn_bbox, input_rpn_match, label):
c1, c2, c3, vbs= model(images)
fp = [c1, c2, c3]
rpn_c_l = []
r_p = []
r_b = []
for f in fp:
rpn_class_logits, rpn_probs, rpn_bbox = rpn_graph(f)
rpn_c_l.append(rpn_class_logits)
r_p.append(rpn_probs)
r_b.append(rpn_bbox)
rpn_class_logits = tf.concat(rpn_c_l, axis=1)
rpn_probs = tf.concat(r_p, axis=1)
rpn_bbox = tf.concat(r_b, axis=1)
rpn_rois = propsal(rpn_probs, rpn_bbox)
rois, target_class_ids, target_bbox = detection_target(rpn_rois, label, gt_boxs)
mrcnn_class_logits, mrcnn_class, mrcnn_bbox = fpn_classifier_graph(rois, fp)
mrcnn_class_logits = tf.squeeze(mrcnn_class_logits, axis=[1, 2])
rpn_class_loss = losses.rpn_class_loss_graph(input_rpn_match, rpn_class_logits)
rpn_bbox_loss = losses.rpn_bbox_loss_graph(input_rpn_bbox, input_rpn_match, rpn_bbox, cfg)
class_loss = losses.mrcnn_class_loss_graph(target_class_ids, mrcnn_class_logits)
bbox_loss = losses.mrcnn_bbox_loss_graph(target_bbox, target_class_ids, mrcnn_bbox)
tf.losses.add_loss(rpn_class_loss)
tf.losses.add_loss(rpn_bbox_loss)
tf.losses.add_loss(class_loss)
tf.losses.add_loss(bbox_loss)
total_loss = tf.losses.get_losses()
tf.summary.scalar(name='rpn_class_loss', tensor=rpn_class_loss)
tf.summary.scalar(name='rpn_bbox_loss', tensor=rpn_bbox_loss)
tf.summary.scalar(name='class_loss', tensor=class_loss)
tf.summary.scalar(name='bbox_loss', tensor=bbox_loss)
sum_op = tf.summary.merge_all()
train_tensors = tf.identity(total_loss, 'ss')
return train_tensors, sum_op, vbs
def detect():
ig = tf.placeholder(shape=(1, 512, 512, 3), dtype=tf.float32)
wind = tf.placeholder(shape=(4,1),dtype=tf.float32)
detections = predict(images=ig,window=wind)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver.restore(sess, '/home/dsl/all_check/face_detect/nn_faster_rcnn_sec/model.ckpt-32467')
for ip in glob.glob('/media/dsl/20d6b919-92e1-4489-b2be-a092290668e4/VOCdevkit/VOCdevkit/VOC2012/JPEGImages/*.jpg'):
print(ip)
img = cv2.imread(ip)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
org, window, scale, padding, crop = utils.resize_image(img, min_dim=512, max_dim=512)
window = np.asarray(window)/cfg.image_size[0]*1.0
window = np.reshape(window,[4,1])
img = (org/ 255.0-0.5)*2
img = np.expand_dims(img, axis=0)
t = time.time()
detects = sess.run([detections],feed_dict={ig:img,wind:window})
print(time.time()-t)
arr = detects[0]
ix = np.where(np.sum(arr,axis=1)>0)
box = arr[ix]
boxes = box[:,0:4]
label = box[:,4]
score = box[:,5]
visual.display_instances_title(org, np.asarray(boxes) * 512, class_ids=label,
class_names=cfg.VOC_CLASSES, scores=score)
def run():
pl_images = tf.placeholder(shape=[cfg.batch_size, cfg.image_size[0], cfg.image_size[1], 3], dtype=tf.float32)
pl_gt_boxs = tf.placeholder(shape=[cfg.batch_size, 50, 4], dtype=tf.float32)
pl_label = tf.placeholder(shape=[cfg.batch_size, 50], dtype=tf.int32)
pl_input_rpn_match = tf.placeholder(shape=[cfg.batch_size, cfg.total_anchors, 1], dtype=tf.int32)
pl_input_rpn_bbox = tf.placeholder(shape=[cfg.batch_size, cfg.RPN_TRAIN_ANCHORS_PER_IMAGE, 4], dtype=tf.float32)
train_tensors, sum_op, vbs = loss(pl_gt_boxs, pl_images, pl_input_rpn_bbox, pl_input_rpn_match, pl_label)
optimizer = tf.train.MomentumOptimizer(learning_rate=0.001, momentum=0.9)
train_op = slim.learning.create_train_op(train_tensors, optimizer)
saver = tf.train.Saver(vbs)
def restore(sess):
saver.restore(sess, '/home/dsl/all_check/face_detect/nn_faster_rcnn/model.ckpt-86737')
sv = tf.train.Supervisor(logdir='/home/dsl/all_check/face_detect/nn_faster_rcnn_sec', summary_op=None, init_fn=restore)
with sv.managed_session() as sess:
for step in range(1000000000):
images, boxs, label, input_rpn_match, input_rpn_bbox = q.get()
gt_boxs = utils.norm_boxes(boxs,shape=cfg.image_size)
feed_dict = {pl_images: images, pl_gt_boxs: gt_boxs,
pl_label: label,pl_input_rpn_bbox:input_rpn_bbox,
pl_input_rpn_match:input_rpn_match}
t = time.time()
ls = sess.run(train_op, feed_dict=feed_dict)
if step % 10 == 0:
print(time.time()-t)
summaries = sess.run(sum_op, feed_dict=feed_dict)
sv.summary_computed(sess, summaries)
print(ls)
def eager_run():
tf.enable_eager_execution()
for s in range(10):
images, boxs, label, input_rpn_match, input_rpn_bbox = q.get()
print(input_rpn_bbox.shape)
gt_boxs = utils.norm_boxes(boxes=boxs,shape=cfg.image_size)
c1,c2,c3,v = model(images)
fp = [c1,c2,c3]
rpn_c_l = []
r_p = []
r_b = []
for f in fp:
rpn_class_logits, rpn_probs, rpn_bbox = rpn_graph(f)
rpn_c_l.append(rpn_class_logits)
r_p.append(rpn_probs)
r_b.append(rpn_bbox)
rpn_class_logits = tf.concat(rpn_c_l,axis=1)
rpn_probs = tf.concat(r_p, axis=1)
rpn_bbox = tf.concat(r_b, axis=1)
rpn_rois = propsal(rpn_probs,rpn_bbox)
rois, target_class_ids, target_bbox = detection_target(rpn_rois,label,gt_boxs)
mrcnn_class_logits, mrcnn_class, mrcnn_bbox = fpn_classifier_graph(rois,fp)
mrcnn_class_logits = tf.squeeze(mrcnn_class_logits,axis=[1,2])
rpn_class_loss = losses.rpn_class_loss_graph(input_rpn_match,rpn_class_logits)
rpn_bbox_loss = losses.rpn_bbox_loss_graph(input_rpn_bbox,input_rpn_match,rpn_bbox,cfg)
class_loss = losses.mrcnn_class_loss_graph(target_class_ids,mrcnn_class_logits)
bbox_loss = losses.mrcnn_bbox_loss_graph(target_bbox,target_class_ids,mrcnn_bbox)
def eager_val():
tf.enable_eager_execution()
for s in range(10):
images, boxs, label, input_rpn_match, input_rpn_bbox = q.get()
predict(images)
if __name__ == '__main__':
detect()