【目標檢測三】TensorFlow版本Faster R-CNN特徵圖可視化

參考文獻:

https://blog.csdn.net/zd_nupt/article/details/77508083

endernewton 的 Tensorflow 版 Faster RCNN 代碼:

https://github.com/endernewton/tf-faster-rcnn

網上一般使用tensorboard,但是slim封裝沒有保存中間操作,無法用,只能自己提取block的特徵可視化。

具體代碼:

 在下面

我用的resnetv1_101,也就是slim封裝好的模型。下面可視化RPN輸入的特徵圖。

單張顯示:(看出來已經對目標有感應了。)

集體顯示:

說得具體一點:

在faster-master-lib文件夾下,有生成網絡的network類代碼network.py主要修改這裏面。在RPN生成的時候,把輸入的特徵數據保存下來,然後再test-image的時候,加入上面的繪圖程序就可以了,如要要其他地方的特徵就需要想辦法把其他位置的特徵數據保存下來,然後繪圖。

具體修改後的Network代碼是這樣的。

# --------------------------------------------------------
# Tensorflow Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Xinlei Chen
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf
import tensorflow.contrib.slim as slim
from tensorflow.contrib.slim import losses
from tensorflow.contrib.slim import arg_scope

import numpy as np

from layer_utils.snippets import generate_anchors_pre, generate_anchors_pre_tf
from layer_utils.proposal_layer import proposal_layer, proposal_layer_tf
from layer_utils.proposal_top_layer import proposal_top_layer, proposal_top_layer_tf
from layer_utils.anchor_target_layer import anchor_target_layer
from layer_utils.proposal_target_layer import proposal_target_layer
from utils.visualization import draw_bounding_boxes

from model.config import cfg
from tensorflow.python.ops import variable_scope
import matplotlib.pyplot as plt

class Network(object):
  def __init__(self):
    self._predictions = {}
    self._losses = {}
    self._anchor_targets = {}
    self._proposal_targets = {}
    self._layers = {}
    self._gt_image = None
    self._act_summaries = []
    self._score_summaries = {}
    self._train_summaries = []
    self._event_summaries = {}
    self._variables_to_fix = {}
    self._bb = {}#添加這個

  def _add_gt_image(self):
    # add back mean
    image = self._image + cfg.PIXEL_MEANS
    # BGR to RGB (opencv uses BGR)
    resized = tf.image.resize_bilinear(image, tf.to_int32(self._im_info[:2] / self._im_info[2]))
    self._gt_image = tf.reverse(resized, axis=[-1])

  def _add_gt_image_summary(self):
    # use a customized visualization function to visualize the boxes
    if self._gt_image is None:
      self._add_gt_image()
    image = tf.py_func(draw_bounding_boxes, 
                      [self._gt_image, self._gt_boxes, self._im_info],
                      tf.float32, name="gt_boxes")
    
    return tf.summary.image('GROUND_TRUTH', image)

  def _add_act_summary(self, tensor):
    tf.summary.histogram('ACT/' + tensor.op.name + '/activations', tensor)
    tf.summary.scalar('ACT/' + tensor.op.name + '/zero_fraction',
                      tf.nn.zero_fraction(tensor))

  def _add_score_summary(self, key, tensor):
    tf.summary.histogram('SCORE/' + tensor.op.name + '/' + key + '/scores', tensor)

  def _add_train_summary(self, var):
    tf.summary.histogram('TRAIN/' + var.op.name, var)

  def _reshape_layer(self, bottom, num_dim, name):
    input_shape = tf.shape(bottom)
    with tf.variable_scope(name) as scope:
      # change the channel to the caffe format
      to_caffe = tf.transpose(bottom, [0, 3, 1, 2])
      # then force it to have channel 2
      reshaped = tf.reshape(to_caffe,
                            tf.concat(axis=0, values=[[1, num_dim, -1], [input_shape[2]]]))
      # then swap the channel back
      to_tf = tf.transpose(reshaped, [0, 2, 3, 1])
      return to_tf

  def _softmax_layer(self, bottom, name):
    if name.startswith('rpn_cls_prob_reshape'):
      input_shape = tf.shape(bottom)
      bottom_reshaped = tf.reshape(bottom, [-1, input_shape[-1]])
      reshaped_score = tf.nn.softmax(bottom_reshaped, name=name)
      return tf.reshape(reshaped_score, input_shape)
    return tf.nn.softmax(bottom, name=name)

  def _proposal_top_layer(self, rpn_cls_prob, rpn_bbox_pred, name):
    with tf.variable_scope(name) as scope:
      if cfg.USE_E2E_TF:
        rois, rpn_scores = proposal_top_layer_tf(
          rpn_cls_prob,
          rpn_bbox_pred,
          self._im_info,
          self._feat_stride,
          self._anchors,
          self._num_anchors
        )
      else:
        rois, rpn_scores = tf.py_func(proposal_top_layer,
                              [rpn_cls_prob, rpn_bbox_pred, self._im_info,
                               self._feat_stride, self._anchors, self._num_anchors],
                              [tf.float32, tf.float32], name="proposal_top")
        
      rois.set_shape([cfg.TEST.RPN_TOP_N, 5])
      rpn_scores.set_shape([cfg.TEST.RPN_TOP_N, 1])

    return rois, rpn_scores

  def _proposal_layer(self, rpn_cls_prob, rpn_bbox_pred, name):
    with tf.variable_scope(name) as scope:
      if cfg.USE_E2E_TF:
        rois, rpn_scores = proposal_layer_tf(
          rpn_cls_prob,
          rpn_bbox_pred,
          self._im_info,
          self._mode,
          self._feat_stride,
          self._anchors,
          self._num_anchors
        )
      else:
        rois, rpn_scores = tf.py_func(proposal_layer,
                              [rpn_cls_prob, rpn_bbox_pred, self._im_info, self._mode,
                               self._feat_stride, self._anchors, self._num_anchors],
                              [tf.float32, tf.float32], name="proposal")

      rois.set_shape([None, 5])
      rpn_scores.set_shape([None, 1])

    return rois, rpn_scores

  # Only use it if you have roi_pooling op written in tf.image
  def _roi_pool_layer(self, bootom, rois, name):
    with tf.variable_scope(name) as scope:
      return tf.image.roi_pooling(bootom, rois,
                                  pooled_height=cfg.POOLING_SIZE,
                                  pooled_width=cfg.POOLING_SIZE,
                                  spatial_scale=1. / 16.)[0]

  def _crop_pool_layer(self, bottom, rois, name):
    with tf.variable_scope(name) as scope:
      batch_ids = tf.squeeze(tf.slice(rois, [0, 0], [-1, 1], name="batch_id"), [1])
      # Get the normalized coordinates of bounding boxes
      bottom_shape = tf.shape(bottom)
      height = (tf.to_float(bottom_shape[1]) - 1.) * np.float32(self._feat_stride[0])
      width = (tf.to_float(bottom_shape[2]) - 1.) * np.float32(self._feat_stride[0])
      x1 = tf.slice(rois, [0, 1], [-1, 1], name="x1") / width
      y1 = tf.slice(rois, [0, 2], [-1, 1], name="y1") / height
      x2 = tf.slice(rois, [0, 3], [-1, 1], name="x2") / width
      y2 = tf.slice(rois, [0, 4], [-1, 1], name="y2") / height
      # Won't be back-propagated to rois anyway, but to save time
      bboxes = tf.stop_gradient(tf.concat([y1, x1, y2, x2], axis=1))
      pre_pool_size = cfg.POOLING_SIZE * 2
      crops = tf.image.crop_and_resize(bottom, bboxes, tf.to_int32(batch_ids), [pre_pool_size, pre_pool_size], name="crops")

    return slim.max_pool2d(crops, [2, 2], padding='SAME')

  def _dropout_layer(self, bottom, name, ratio=0.5):
    return tf.nn.dropout(bottom, ratio, name=name)

  def _anchor_target_layer(self, rpn_cls_score, name):
    with tf.variable_scope(name) as scope:
      rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights = tf.py_func(
        anchor_target_layer,
        [rpn_cls_score, self._gt_boxes, self._im_info, self._feat_stride, self._anchors, self._num_anchors],
        [tf.float32, tf.float32, tf.float32, tf.float32],
        name="anchor_target")

      rpn_labels.set_shape([1, 1, None, None])
      rpn_bbox_targets.set_shape([1, None, None, self._num_anchors * 4])
      rpn_bbox_inside_weights.set_shape([1, None, None, self._num_anchors * 4])
      rpn_bbox_outside_weights.set_shape([1, None, None, self._num_anchors * 4])

      rpn_labels = tf.to_int32(rpn_labels, name="to_int32")
      self._anchor_targets['rpn_labels'] = rpn_labels
      self._anchor_targets['rpn_bbox_targets'] = rpn_bbox_targets
      self._anchor_targets['rpn_bbox_inside_weights'] = rpn_bbox_inside_weights
      self._anchor_targets['rpn_bbox_outside_weights'] = rpn_bbox_outside_weights

      self._score_summaries.update(self._anchor_targets)

    return rpn_labels

  def _proposal_target_layer(self, rois, roi_scores, name):
    with tf.variable_scope(name) as scope:
      rois, roi_scores, labels, bbox_targets, bbox_inside_weights, bbox_outside_weights = tf.py_func(
        proposal_target_layer,
        [rois, roi_scores, self._gt_boxes, self._num_classes],
        [tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32],
        name="proposal_target")

      rois.set_shape([cfg.TRAIN.BATCH_SIZE, 5])
      roi_scores.set_shape([cfg.TRAIN.BATCH_SIZE])
      labels.set_shape([cfg.TRAIN.BATCH_SIZE, 1])
      bbox_targets.set_shape([cfg.TRAIN.BATCH_SIZE, self._num_classes * 4])
      bbox_inside_weights.set_shape([cfg.TRAIN.BATCH_SIZE, self._num_classes * 4])
      bbox_outside_weights.set_shape([cfg.TRAIN.BATCH_SIZE, self._num_classes * 4])

      self._proposal_targets['rois'] = rois
      self._proposal_targets['labels'] = tf.to_int32(labels, name="to_int32")
      self._proposal_targets['bbox_targets'] = bbox_targets
      self._proposal_targets['bbox_inside_weights'] = bbox_inside_weights
      self._proposal_targets['bbox_outside_weights'] = bbox_outside_weights

      self._score_summaries.update(self._proposal_targets)

      return rois, roi_scores

  def _anchor_component(self):
    with tf.variable_scope('ANCHOR_' + self._tag) as scope:
      # just to get the shape right
      height = tf.to_int32(tf.ceil(self._im_info[0] / np.float32(self._feat_stride[0])))
      width = tf.to_int32(tf.ceil(self._im_info[1] / np.float32(self._feat_stride[0])))
      if cfg.USE_E2E_TF:
        anchors, anchor_length = generate_anchors_pre_tf(
          height,
          width,
          self._feat_stride,
          self._anchor_scales,
          self._anchor_ratios
        )
      else:
        anchors, anchor_length = tf.py_func(generate_anchors_pre,
                                            [height, width,
                                             self._feat_stride, self._anchor_scales, self._anchor_ratios],
                                            [tf.float32, tf.int32], name="generate_anchors")
      anchors.set_shape([None, 4])
      anchor_length.set_shape([])
      self._anchors = anchors
      self._anchor_length = anchor_length

  def _build_network(self, is_training=True):
    # select initializers
    if cfg.TRAIN.TRUNCATED:
      initializer = tf.truncated_normal_initializer(mean=0.0, stddev=0.01)
      initializer_bbox = tf.truncated_normal_initializer(mean=0.0, stddev=0.001)
    else:
      initializer = tf.random_normal_initializer(mean=0.0, stddev=0.01)
      initializer_bbox = tf.random_normal_initializer(mean=0.0, stddev=0.001)

    net_conv = self._image_to_head(is_training)
    with tf.variable_scope(self._scope, self._scope):
      # build the anchors for the image
      self._anchor_component()
      # region proposal network
      rois = self._region_proposal(net_conv, is_training, initializer)
      # region of interest pooling
      if cfg.POOLING_MODE == 'crop':
        pool5 = self._crop_pool_layer(net_conv, rois, "pool5")
      else:
        raise NotImplementedError

    fc7 = self._head_to_tail(pool5, is_training)
    with tf.variable_scope(self._scope, self._scope):
      # region classification
      cls_prob, bbox_pred = self._region_classification(fc7, is_training, 
                                                        initializer, initializer_bbox)

    self._score_summaries.update(self._predictions)

    return rois, cls_prob, bbox_pred

  def _smooth_l1_loss(self, bbox_pred, bbox_targets, bbox_inside_weights, bbox_outside_weights, sigma=1.0, dim=[1]):
    sigma_2 = sigma ** 2
    box_diff = bbox_pred - bbox_targets
    in_box_diff = bbox_inside_weights * box_diff
    abs_in_box_diff = tf.abs(in_box_diff)
    smoothL1_sign = tf.stop_gradient(tf.to_float(tf.less(abs_in_box_diff, 1. / sigma_2)))
    in_loss_box = tf.pow(in_box_diff, 2) * (sigma_2 / 2.) * smoothL1_sign \
                  + (abs_in_box_diff - (0.5 / sigma_2)) * (1. - smoothL1_sign)
    out_loss_box = bbox_outside_weights * in_loss_box
    loss_box = tf.reduce_mean(tf.reduce_sum(
      out_loss_box,
      axis=dim
    ))
    return loss_box

  def _add_losses(self, sigma_rpn=3.0):
    with tf.variable_scope('LOSS_' + self._tag) as scope:
      # RPN, class loss
      rpn_cls_score = tf.reshape(self._predictions['rpn_cls_score_reshape'], [-1, 2])
      rpn_label = tf.reshape(self._anchor_targets['rpn_labels'], [-1])
      rpn_select = tf.where(tf.not_equal(rpn_label, -1))
      rpn_cls_score = tf.reshape(tf.gather(rpn_cls_score, rpn_select), [-1, 2])
      rpn_label = tf.reshape(tf.gather(rpn_label, rpn_select), [-1])
      rpn_cross_entropy = tf.reduce_mean(
        tf.nn.sparse_softmax_cross_entropy_with_logits(logits=rpn_cls_score, labels=rpn_label))

      # RPN, bbox loss
      rpn_bbox_pred = self._predictions['rpn_bbox_pred']
      rpn_bbox_targets = self._anchor_targets['rpn_bbox_targets']
      rpn_bbox_inside_weights = self._anchor_targets['rpn_bbox_inside_weights']
      rpn_bbox_outside_weights = self._anchor_targets['rpn_bbox_outside_weights']
      rpn_loss_box = self._smooth_l1_loss(rpn_bbox_pred, rpn_bbox_targets, rpn_bbox_inside_weights,
                                          rpn_bbox_outside_weights, sigma=sigma_rpn, dim=[1, 2, 3])

      # RCNN, class loss
      cls_score = self._predictions["cls_score"]
      label = tf.reshape(self._proposal_targets["labels"], [-1])
      cross_entropy = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=cls_score, labels=label))

      # RCNN, bbox loss
      bbox_pred = self._predictions['bbox_pred']
      bbox_targets = self._proposal_targets['bbox_targets']
      bbox_inside_weights = self._proposal_targets['bbox_inside_weights']
      bbox_outside_weights = self._proposal_targets['bbox_outside_weights']
      loss_box = self._smooth_l1_loss(bbox_pred, bbox_targets, bbox_inside_weights, bbox_outside_weights)

      self._losses['cross_entropy'] = cross_entropy
      self._losses['loss_box'] = loss_box
      self._losses['rpn_cross_entropy'] = rpn_cross_entropy
      self._losses['rpn_loss_box'] = rpn_loss_box

      loss = cross_entropy + loss_box + rpn_cross_entropy + rpn_loss_box
      regularization_loss = tf.add_n(tf.losses.get_regularization_losses(), 'regu')
      self._losses['total_loss'] = loss + regularization_loss

      self._event_summaries.update(self._losses)

    return loss

  def _region_proposal(self, net_conv, is_training, initializer):
    rpn = slim.conv2d(net_conv, cfg.RPN_CHANNELS, [3, 3], trainable=is_training, weights_initializer=initializer,
                        scope="rpn_conv/3x3")
    self._bb=rpn#把tensor的數值提取出來
    self._act_summaries.append(rpn)
    rpn_cls_score = slim.conv2d(rpn, self._num_anchors * 2, [1, 1], trainable=is_training,
                                weights_initializer=initializer,
                                padding='VALID', activation_fn=None, scope='rpn_cls_score')
    # change it so that the score has 2 as its channel size
    rpn_cls_score_reshape = self._reshape_layer(rpn_cls_score, 2, 'rpn_cls_score_reshape')
    rpn_cls_prob_reshape = self._softmax_layer(rpn_cls_score_reshape, "rpn_cls_prob_reshape")
    rpn_cls_pred = tf.argmax(tf.reshape(rpn_cls_score_reshape, [-1, 2]), axis=1, name="rpn_cls_pred")
    rpn_cls_prob = self._reshape_layer(rpn_cls_prob_reshape, self._num_anchors * 2, "rpn_cls_prob")
    rpn_bbox_pred = slim.conv2d(rpn, self._num_anchors * 4, [1, 1], trainable=is_training,
                                weights_initializer=initializer,
                                padding='VALID', activation_fn=None, scope='rpn_bbox_pred')
    if is_training:
      rois, roi_scores = self._proposal_layer(rpn_cls_prob, rpn_bbox_pred, "rois")
      rpn_labels = self._anchor_target_layer(rpn_cls_score, "anchor")
      # Try to have a deterministic order for the computing graph, for reproducibility
      with tf.control_dependencies([rpn_labels]):
        rois, _ = self._proposal_target_layer(rois, roi_scores, "rpn_rois")
    else:
      if cfg.TEST.MODE == 'nms':
        rois, _ = self._proposal_layer(rpn_cls_prob, rpn_bbox_pred, "rois")
      elif cfg.TEST.MODE == 'top':
        rois, _ = self._proposal_top_layer(rpn_cls_prob, rpn_bbox_pred, "rois")
      else:
        raise NotImplementedError

    self._predictions["rpn_cls_score"] = rpn_cls_score
    self._predictions["rpn_cls_score_reshape"] = rpn_cls_score_reshape
    self._predictions["rpn_cls_prob"] = rpn_cls_prob
    self._predictions["rpn_cls_pred"] = rpn_cls_pred
    self._predictions["rpn_bbox_pred"] = rpn_bbox_pred
    self._predictions["rois"] = rois

    return rois

  def _region_classification(self, fc7, is_training, initializer, initializer_bbox):
    cls_score = slim.fully_connected(fc7, self._num_classes, 
                                       weights_initializer=initializer,
                                       trainable=is_training,
                                       activation_fn=None, scope='cls_score')
    cls_prob = self._softmax_layer(cls_score, "cls_prob")
    cls_pred = tf.argmax(cls_score, axis=1, name="cls_pred")
    bbox_pred = slim.fully_connected(fc7, self._num_classes * 4, 
                                     weights_initializer=initializer_bbox,
                                     trainable=is_training,
                                     activation_fn=None, scope='bbox_pred')

    self._predictions["cls_score"] = cls_score
    self._predictions["cls_pred"] = cls_pred
    self._predictions["cls_prob"] = cls_prob
    self._predictions["bbox_pred"] = bbox_pred

    return cls_prob, bbox_pred

  def _image_to_head(self, is_training, reuse=None):
    raise NotImplementedError

  def _head_to_tail(self, pool5, is_training, reuse=None):
    raise NotImplementedError

  def create_architecture(self, mode, num_classes, tag=None,
                          anchor_scales=(8, 16, 32), anchor_ratios=(0.5, 1, 2)):
    self._image = tf.placeholder(tf.float32, shape=[1, None, None, 3])
    self._im_info = tf.placeholder(tf.float32, shape=[3])
    self._gt_boxes = tf.placeholder(tf.float32, shape=[None, 5])
    self._tag = tag

    self._num_classes = num_classes
    self._mode = mode
    self._anchor_scales = anchor_scales
    self._num_scales = len(anchor_scales)

    self._anchor_ratios = anchor_ratios
    self._num_ratios = len(anchor_ratios)

    self._num_anchors = self._num_scales * self._num_ratios

    training = mode == 'TRAIN'
    testing = mode == 'TEST'

    assert tag != None

    # handle most of the regularizers here
    weights_regularizer = tf.contrib.layers.l2_regularizer(cfg.TRAIN.WEIGHT_DECAY)
    if cfg.TRAIN.BIAS_DECAY:
      biases_regularizer = weights_regularizer
    else:
      biases_regularizer = tf.no_regularizer

    # list as many types of layers as possible, even if they are not used now
    with arg_scope([slim.conv2d, slim.conv2d_in_plane, \
                    slim.conv2d_transpose, slim.separable_conv2d, slim.fully_connected], 
                    weights_regularizer=weights_regularizer,
                    biases_regularizer=biases_regularizer, 
                    biases_initializer=tf.constant_initializer(0.0)): 
      rois, cls_prob, bbox_pred = self._build_network(training)

    layers_to_output = {'rois': rois}

    for var in tf.trainable_variables():
      self._train_summaries.append(var)

    if testing:
      stds = np.tile(np.array(cfg.TRAIN.BBOX_NORMALIZE_STDS), (self._num_classes))
      means = np.tile(np.array(cfg.TRAIN.BBOX_NORMALIZE_MEANS), (self._num_classes))
      self._predictions["bbox_pred"] *= stds
      self._predictions["bbox_pred"] += means
    else:
      self._add_losses()
      layers_to_output.update(self._losses)

      val_summaries = []
      with tf.device("/cpu:0"):
        val_summaries.append(self._add_gt_image_summary())
        for key, var in self._event_summaries.items():
          val_summaries.append(tf.summary.scalar(key, var))
        for key, var in self._score_summaries.items():
          self._add_score_summary(key, var)
        for var in self._act_summaries:
          self._add_act_summary(var)
        for var in self._train_summaries:
          self._add_train_summary(var)

      self._summary_op = tf.summary.merge_all()
      self._summary_op_val = tf.summary.merge(val_summaries)

    layers_to_output.update(self._predictions)

    return layers_to_output

  def get_variables_to_restore(self, variables, var_keep_dic):
    raise NotImplementedError

  def fix_variables(self, sess, pretrained_model):
    raise NotImplementedError

  # Extract the head feature maps, for example for vgg16 it is conv5_3
  # only useful during testing mode
  def extract_head(self, sess, image):
    feed_dict = {self._image: image}
    feat = sess.run(self._layers["head"], feed_dict=feed_dict)
    return feat


  def test_image(self, sess, image, im_info):
    feed_dict = {self._image: image,
                 self._im_info: im_info}

#    resultss = sess.run("rpn_conv/3x3", feed_dict=feed_dict) 
 #   print(resultss)
 #   for i in range(64): 
 #    show_img = resultss[:, :, :, i] # print type(show_img) 
 #    plt.subplot(4, 8, i + 1) 
 #    plt.imshow(show_img, cmap='gray') 
 #    plt.axis('off') 
 #   plt.show()


    cls_score, cls_prob, bbox_pred, rois = sess.run([self._predictions["cls_score"],
                                                     self._predictions['cls_prob'],
                                                     self._predictions['bbox_pred'],
                                                     self._predictions['rois']],
                                                    feed_dict=feed_dict)
    ###下面就是提取變量加上繪圖了。
    asd = sess.run(self._bb, feed_dict=feed_dict)
    print(asd.shape[0])
    print(asd.shape[1])
    print(asd.shape[2])
    print(asd.shape[3])
    for i in range(10):
        show_img = asd[:, :, :, i]
        # print type(show_img)
        show_img.shape = [asd.shape[1], asd.shape[2]]
        plt.imshow(show_img, cmap='jet')
        plt.axis('off')
        plt.show()
##

#    print(aa)
##
    return cls_score, cls_prob, bbox_pred, rois

  def get_summary(self, sess, blobs):
    feed_dict = {self._image: blobs['data'], self._im_info: blobs['im_info'],
                 self._gt_boxes: blobs['gt_boxes']}
    summary = sess.run(self._summary_op_val, feed_dict=feed_dict)

    return summary

  def train_step(self, sess, blobs, train_op):
    feed_dict = {self._image: blobs['data'], self._im_info: blobs['im_info'],
                 self._gt_boxes: blobs['gt_boxes']}
    rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, loss, _ = sess.run([self._losses["rpn_cross_entropy"],
                                                                        self._losses['rpn_loss_box'],
                                                                        self._losses['cross_entropy'],
                                                                        self._losses['loss_box'],
                                                                        self._losses['total_loss'],
                                                                        train_op],
                                                                       feed_dict=feed_dict)
    return rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, loss

  def train_step_with_summary(self, sess, blobs, train_op):
    feed_dict = {self._image: blobs['data'], self._im_info: blobs['im_info'],
                 self._gt_boxes: blobs['gt_boxes']}
    rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, loss, summary, _ = sess.run([self._losses["rpn_cross_entropy"],
                                                                                 self._losses['rpn_loss_box'],
                                                                                 self._losses['cross_entropy'],
                                                                                 self._losses['loss_box'],
                                                                                 self._losses['total_loss'],
                                                                                 self._summary_op,
                                                                                 train_op],
                                                                                feed_dict=feed_dict)
    return rpn_loss_cls, rpn_loss_box, loss_cls, loss_box, loss, summary

  def train_step_no_return(self, sess, blobs, train_op):
    feed_dict = {self._image: blobs['data'], self._im_info: blobs['im_info'],
                 self._gt_boxes: blobs['gt_boxes']}
    sess.run([train_op], feed_dict=feed_dict)

如果你也是用這個版本的faster代碼可以直接複製使用,在測試圖像的時候就會自己蹦出特徵圖了。

如果不是,你可以參考如下的繪圖代碼:

  def test_image(self, sess, image, im_info):
    feed_dict = {self._image: image,
                 self._im_info: im_info}

#    resultss = sess.run("rpn_conv/3x3", feed_dict=feed_dict) 
 #   print(resultss)
 #   for i in range(64): 
 #    show_img = resultss[:, :, :, i] # print type(show_img) 
 #    plt.subplot(4, 8, i + 1) 
 #    plt.imshow(show_img, cmap='gray') 
 #    plt.axis('off') 
 #   plt.show()


    cls_score, cls_prob, bbox_pred, rois = sess.run([self._predictions["cls_score"],
                                                     self._predictions['cls_prob'],
                                                     self._predictions['bbox_pred'],
                                                     self._predictions['rois']],
                                                    feed_dict=feed_dict)
    ###下面就是提取變量加上繪圖了。
    asd = sess.run(self._bb, feed_dict=feed_dict)
    print(asd.shape[0])
    print(asd.shape[1])
    print(asd.shape[2])
    print(asd.shape[3])
    for i in range(10):
        show_img = asd[:, :, :, i]
        # print type(show_img)
        show_img.shape = [asd.shape[1], asd.shape[2]]
        plt.imshow(show_img, cmap='jet')
        plt.axis('off')
        plt.show()
##

#    print(aa)
##
    return cls_score, cls_prob, bbox_pred, rois

tensorflow裏面要用在會話裏面run一下,tensor纔有取值,所以我首先是把RPN的輸入那一層的卷積操作保存了下來,命名爲self._bb,然後run出來是asd,隨便命名的,不太好。然後數據流到這個位置就會得到asd的取值了。上面繪圖程序的print[0]到[3]是我打印出來前幾張特徵圖看了一下,後面寫了個for循環,將前10個特徵圖放到同一個畫布上顯示,實際上rpn輸入的特徵圖數量如果是resnet101的話應有有512張對吧,隨便大家改。

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