def _build_graph(self, inputs): is_training = get_current_tower_context().is_training image, label = inputs #tf.summary.image('viz', image, max_outputs=10) image = image_preprocess(image, bgr=False) #image = image * (1.0 / 255) image = tf.transpose(image, [0, 3, 1, 2]) depth = config.RESNET_DEPTH basicblock = preresnet_basicblock if config.RESNET_MODE == 'preact' else resnet_basicblock bottleneck = { 'resnet': resnet_bottleneck, 'preact': preresnet_bottleneck, 'se': se_resnet_bottleneck }[config.RESNET_MODE] num_blocks, block_func = { 18: ([2, 2, 2, 2], basicblock), 26: ([2, 2, 2, 2], bottleneck), 34: ([3, 4, 6, 3], basicblock), 50: ([3, 4, 6, 3], bottleneck), 101: ([3, 4, 23, 3], bottleneck), 152: ([3, 8, 36, 3], bottleneck) }[depth] logits = get_logit(image, num_blocks, block_func) if is_training: loss = cls_loss(logits, label) #wd_cost = regularize_cost( # '.*/W', # l2_regularizer(1e-4), name='wd_cost') self.cost = tf.add_n([loss], 'total_cost') #add_moving_summary(self.cost) else: final_probs = tf.nn.sigmoid(logits, name="final_probs")
def _build_graph(self, inputs): is_training = get_current_tower_context().is_training image, label = inputs tf.summary.image('viz', image, max_outputs=10) image = image_preprocess(image, bgr=True) image = tf.transpose(image, [0, 3, 1, 2]) conv4 = pretrained_resnet_conv4(image, config.RESNET_NUM_BLOCK[:3]) # B*c*h*w conv5 = resnet_conv5(conv4, config.RESNET_NUM_BLOCK[-1]) # train head only logits = cls_head('cls', conv5) if is_training: loss = cls_loss(logits, label) wd_cost = regularize_cost('(?:group1|group2|group3|cls)/.*W', l2_regularizer(1e-4), name='wd_cost') self.cost = tf.add_n([loss, wd_cost], 'total_cost') add_moving_summary(self.cost, wd_cost) else: final_prob = tf.nn.sigmoid(logits) tf.identity(final_prob, name='final_probs') #final_label = tf.where(final_prob > 0.5, tf.ones_like(final_prob), tf.zeros_like(final_prob)) final_label = tf.round(final_prob) tf.identity(final_label, name='final_labels')
def preprocess(self, image): ''' norm图片之后,返回(N,C,H,W),caffe格式 :param image: :return: ''' image = tf.expand_dims(image, 0) image = image_preprocess(image, bgr=True) return tf.transpose(image, [0, 3, 1, 2])
def preprocess(self, image): image = tf.expand_dims(image, 0) image = image_preprocess(image, bgr=True) return tf.transpose(image, [0, 3, 1, 2])
def _preprocess(self, image): image = tf.expand_dims(image, 0) image = image_preprocess(image, bgr=True) return tf.transpose(image, [0, 3, 1, 2])
def _build_graph(self, inputs): is_training = get_current_tower_context().is_training image, anchor_labels, anchor_boxes, gt_boxes, gt_labels = inputs image = tf.expand_dims(image, 0) # FSxFSxNAx4 (FS=MAX_SIZE//ANCHOR_STRIDE) with tf.name_scope('anchors'): all_anchors = tf.constant(get_all_anchors(), name='all_anchors', dtype=tf.float32) fm_anchors = tf.slice( all_anchors, [0, 0, 0, 0], tf.stack([ tf.shape(image)[1] // config.ANCHOR_STRIDE, tf.shape(image)[2] // config.ANCHOR_STRIDE, -1, -1 ]), name='fm_anchors') anchor_boxes_encoded = encode_bbox_target(anchor_boxes, fm_anchors) image = image_preprocess(image, bgr=True) image = tf.transpose(image, [0, 3, 1, 2]) # resnet50 featuremap = pretrained_resnet_conv4(image, [3, 4, 6]) rpn_label_logits, rpn_box_logits = rpn_head(featuremap) rpn_label_loss, rpn_box_loss = rpn_losses(anchor_labels, anchor_boxes_encoded, rpn_label_logits, rpn_box_logits) decoded_boxes = decode_bbox_target( rpn_box_logits, fm_anchors) # (fHxfWxNA)x4, floatbox proposal_boxes, proposal_scores = generate_rpn_proposals( decoded_boxes, tf.reshape(rpn_label_logits, [-1]), tf.shape(image)[2:]) if is_training: rcnn_sampled_boxes, rcnn_encoded_boxes, rcnn_labels = sample_fast_rcnn_targets( proposal_boxes, gt_boxes, gt_labels) boxes_on_featuremap = rcnn_sampled_boxes * (1.0 / config.ANCHOR_STRIDE) roi_resized = roi_align(featuremap, boxes_on_featuremap, 14) feature_fastrcnn = resnet_conv5(roi_resized) # nxc fastrcnn_label_logits, fastrcnn_box_logits = fastrcnn_head( feature_fastrcnn, config.NUM_CLASS) fastrcnn_label_loss, fastrcnn_box_loss = fastrcnn_losses( rcnn_labels, rcnn_encoded_boxes, fastrcnn_label_logits, fastrcnn_box_logits) wd_cost = regularize_cost( '(?:group1|group2|group3|rpn|fastrcnn)/.*W', l2_regularizer(1e-4), name='wd_cost') self.cost = tf.add_n([ rpn_label_loss, rpn_box_loss, fastrcnn_label_loss, fastrcnn_box_loss, wd_cost ], 'total_cost') for k in self.cost, wd_cost: add_moving_summary(k) else: roi_resized = roi_align( featuremap, proposal_boxes * (1.0 / config.ANCHOR_STRIDE), 14) feature_fastrcnn = resnet_conv5(roi_resized) # nxc label_logits, fastrcnn_box_logits = fastrcnn_head( feature_fastrcnn, config.NUM_CLASS) label_probs = tf.nn.softmax(label_logits, name='fastrcnn_all_probs') # NP, labels = tf.argmax(label_logits, axis=1) fg_ind, fg_box_logits = fastrcnn_predict_boxes( labels, fastrcnn_box_logits) fg_label_probs = tf.gather(label_probs, fg_ind, name='fastrcnn_fg_probs') fg_boxes = tf.gather(proposal_boxes, fg_ind) fg_box_logits = fg_box_logits / tf.constant( config.FASTRCNN_BBOX_REG_WEIGHTS) decoded_boxes = decode_bbox_target(fg_box_logits, fg_boxes) # Nfx4, floatbox decoded_boxes = tf.identity(decoded_boxes, name='fastrcnn_fg_boxes')
def _preprocess(self, image): image = tf.expand_dims(image, 0) image = image_preprocess(image, bgr=True) return image