第一部分給出Fater RCNN文件的代碼解析，主要是模型主體的執(zhí)行過程，在此文件
引入下述幾個(gè)文件的函數(shù)引用，對(duì)于backbones，necks和test_mixins文件來說，主要是用來構(gòu)建模型結(jié)構(gòu)，并不復(fù)雜，主要是按照tf語(yǔ)法搭積木就可以了，因此在第二到第四部分給出，并不多做解析，至于剩下的四個(gè)文件，后面的文章繼續(xù)解析。

from detection.models.backbones import resnet from detection.models.necks import fpn from detection.models.detectors.test_mixins import RPNTestMixin, BBoxTestMixin from detection.models.rpn_heads import rpn_head from detection.models.bbox_heads import bbox_head from detection.models.roi_extractors import roi_align from detection.core.bbox import bbox_target import tensorflow as tf

第一部分 針對(duì)Fater RCNN文件代碼解析

class FasterRCNN(tf.keras.Model, RPNTestMixin, BBoxTestMixin):def __init__(self, num_classes, **kwags):super(FasterRCNN, self).__init__(**kwags)self.NUM_CLASSES = num_classes# RPN configuration# Anchor attributesself.ANCHOR_SCALES = (32, 64, 128, 256, 512)self.ANCHOR_RATIOS = (0.5, 1, 2)self.ANCHOR_FEATURE_STRIDES = (4, 8, 16, 32, 64)# Bounding box refinement mean and standard deviationself.RPN_TARGET_MEANS = (0., 0., 0., 0.)self.RPN_TARGET_STDS = (0.1, 0.1, 0.2, 0.2)# RPN training configurationself.PRN_BATCH_SIZE = 256self.RPN_POS_FRAC = 0.5self.RPN_POS_IOU_THR = 0.7self.RPN_NEG_IOU_THR = 0.3# ROIs kept configurationself.PRN_PROPOSAL_COUNT = 2000self.PRN_NMS_THRESHOLD = 0.7# RCNN configuration# Bounding box refinement mean and standard deviationself.RCNN_TARGET_MEANS = (0., 0., 0., 0.)self.RCNN_TARGET_STDS = (0.1, 0.1, 0.2, 0.2)# ROI Feat Sizeself.POOL_SIZE = (7, 7)# RCNN training configurationself.RCNN_BATCH_SIZE = 256self.RCNN_POS_FRAC = 0.25self.RCNN_POS_IOU_THR = 0.5self.RCNN_NEG_IOU_THR = 0.5# Boxes kept configurationself.RCNN_MIN_CONFIDENCE = 0.7self.RCNN_NME_THRESHOLD = 0.3self.RCNN_MAX_INSTANCES = 100# Target Generator for the second stage.self.bbox_target = bbox_target.ProposalTarget(target_means=self.RCNN_TARGET_MEANS,target_stds=self.RPN_TARGET_STDS, num_rcnn_deltas=self.RCNN_BATCH_SIZE,positive_fraction=self.RCNN_POS_FRAC,pos_iou_thr=self.RCNN_POS_IOU_THR,neg_iou_thr=self.RCNN_NEG_IOU_THR)# Modulesself.backbone = resnet.ResNet(depth=101, name='res_net')self.neck = fpn.FPN(name='fpn')self.rpn_head = rpn_head.RPNHead(anchor_scales=self.ANCHOR_SCALES,anchor_ratios=self.ANCHOR_RATIOS,anchor_feature_strides=self.ANCHOR_FEATURE_STRIDES,proposal_count=self.PRN_PROPOSAL_COUNT,nms_threshold=self.PRN_NMS_THRESHOLD,target_means=self.RPN_TARGET_MEANS,target_stds=self.RPN_TARGET_STDS,num_rpn_deltas=self.PRN_BATCH_SIZE,positive_fraction=self.RPN_POS_FRAC,pos_iou_thr=self.RPN_POS_IOU_THR,neg_iou_thr=self.RPN_NEG_IOU_THR,name='rpn_head')self.roi_align = roi_align.PyramidROIAlign(pool_shape=self.POOL_SIZE,name='pyramid_roi_align')self.bbox_head = bbox_head.BBoxHead(num_classes=self.NUM_CLASSES,pool_size=self.POOL_SIZE,target_means=self.RCNN_TARGET_MEANS,target_stds=self.RCNN_TARGET_STDS,min_confidence=self.RCNN_MIN_CONFIDENCE,nms_threshold=self.RCNN_NME_THRESHOLD,max_instances=self.RCNN_MAX_INSTANCES,name='b_box_head')# training是訓(xùn)練模式，是訓(xùn)練模型得出相應(yīng)的參數(shù)；不加training是測(cè)試模式，讀取訓(xùn)練好的模型參數(shù)進(jìn)行模擬就可以def call(self, inputs, training=True):""":param inputs: [1, 1216, 1216, 3], [1, 11], [1, 14, 4], [1, 14]:param training::return:"""if training: # 訓(xùn)練模式imgs, img_metas, gt_boxes, gt_class_ids = inputselse: # 測(cè)試模式imgs, img_metas = inputs # 1. 生成feature map # 圖片輸入ResNet網(wǎng)絡(luò)，輸出5種feature map# [1, 304, 304, 256] => [1, 152, 152, 512]=>[1,76,76,1024]=>[1,38,38,2048]C2, C3, C4, C5 = self.backbone(imgs, training=training)# 經(jīng)過FPN網(wǎng)絡(luò)# [1, 304, 304, 256] <= [1, 152, 152, 256]<=[1,76,76,256]<=[1,38,38,256]=>[1,19,19,256]P2, P3, P4, P5, P6 = self.neck([C2, C3, C4, C5], training=training)rpn_feature_maps = [P2, P3, P4, P5, P6]rcnn_feature_maps = [P2, P3, P4, P5]# 2. 生成proposal區(qū)域 # 具體：將feature map上每個(gè)anchors上的三種anchor box輸入到RPN網(wǎng)絡(luò)，輸出2000個(gè)anchor box的坐標(biāo)，即2000個(gè)proposal的坐標(biāo) # 該坐標(biāo)是大小為1216*1216的填充圖片上的歸一化坐標(biāo)# 經(jīng)過RPN鑒別 輸出每個(gè)# [1, 369303, 2] [1, 369303, 2], [1, 369303, 4], includes all anchors on pyramid level of featuresrpn_class_logits, rpn_probs, rpn_deltas = self.rpn_head(rpn_feature_maps, training=training)# 篩選出6000個(gè)能用的box，其格式為左上和右下的坐標(biāo)# [369303, 4] => [215169, 4], valid => [6000, 4], performance =>[2000, 4], NMSproposals_list = self.rpn_head.get_proposals(rpn_probs, rpn_deltas, img_metas)# 3. 訓(xùn)練模式：計(jì)算全連接層的優(yōu)化目標(biāo) # 具體：if training:# get target value for these proposal target label and target deltarois_list, rcnn_target_matchs_list, rcnn_target_deltas_list = \self.bbox_target.build_targets(proposals_list, gt_boxes, gt_class_ids, img_metas)else:rois_list = proposals_list# 4.進(jìn)行Roi Pooling # 具體：將2000個(gè)proposal的坐標(biāo)輸入到Roi Pooling，輸出2000個(gè)7*7*256的特征向量# rois_list only contains coordinates, rcnn_feature_maps save the 5 features data=>[192,7,7,256]pooled_regions_list = self.roi_align((rois_list, rcnn_feature_maps, img_metas), training=training)# [192, 81], [192, 81], [192, 81, 4]# 5.分類和位置rcnn_class_logits_list, rcnn_probs_list, rcnn_deltas_list = \self.bbox_head(pooled_regions_list, training=training)# 6. 訓(xùn)練模式：計(jì)算RPN的分類和位置回歸誤差，計(jì)算全連接層的分類和位置回歸誤差 # 測(cè)試模式：在原始圖片上輸出框的位置和類別if training: rpn_class_loss, rpn_bbox_loss = self.rpn_head.loss(rpn_class_logits, rpn_deltas, gt_boxes, gt_class_ids, img_metas)rcnn_class_loss, rcnn_bbox_loss = self.bbox_head.loss(rcnn_class_logits_list, rcnn_deltas_list, rcnn_target_matchs_list, rcnn_target_deltas_list)return [rpn_class_loss, rpn_bbox_loss, rcnn_class_loss, rcnn_bbox_loss]else:detections_list = self.bbox_head.get_bboxes(rcnn_probs_list, rcnn_deltas_list, rois_list, img_metas)return detections_list

第二部分 針對(duì)backbones文件代碼解析

'''ResNet model for Keras.# Reference: - [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)''' import tensorflow as tf from tensorflow.keras import layers class _Bottleneck(tf.keras.Model):def __init__(self, filters, block, downsampling=False, stride=1, **kwargs):super(_Bottleneck, self).__init__(**kwargs)filters1, filters2, filters3 = filtersconv_name_base = 'res' + block + '_branch'bn_name_base = 'bn' + block + '_branch'self.downsampling = downsamplingself.stride = strideself.out_channel = filters3self.conv2a = layers.Conv2D(filters1, (1, 1), strides=(stride, stride),kernel_initializer='he_normal',name=conv_name_base + '2a')self.bn2a = layers.BatchNormalization(name=bn_name_base + '2a')self.conv2b = layers.Conv2D(filters2, (3, 3), padding='same',kernel_initializer='he_normal',name=conv_name_base + '2b')self.bn2b = layers.BatchNormalization(name=bn_name_base + '2b')self.conv2c = layers.Conv2D(filters3, (1, 1),kernel_initializer='he_normal',name=conv_name_base + '2c')self.bn2c = layers.BatchNormalization(name=bn_name_base + '2c')if self.downsampling:self.conv_shortcut = layers.Conv2D(filters3, (1, 1), strides=(stride, stride),kernel_initializer='he_normal',name=conv_name_base + '1')self.bn_shortcut = layers.BatchNormalization(name=bn_name_base + '1') def call(self, inputs, training=False):x = self.conv2a(inputs)x = self.bn2a(x, training=training)x = tf.nn.relu(x)x = self.conv2b(x)x = self.bn2b(x, training=training)x = tf.nn.relu(x)x = self.conv2c(x)x = self.bn2c(x, training=training)if self.downsampling:shortcut = self.conv_shortcut(inputs)shortcut = self.bn_shortcut(shortcut, training=training)else:shortcut = inputsx += shortcutx = tf.nn.relu(x)return xdef compute_output_shape(self, input_shape):shape = tf.TensorShape(input_shape).as_list()shape[1] = shape[1] // self.strideshape[2] = shape[2] // self.strideshape[-1] = self.out_channelreturn tf.TensorShape(shape) class ResNet(tf.keras.Model):def __init__(self, depth, **kwargs):super(ResNet, self).__init__(**kwargs)if depth not in [50, 101]:raise AssertionError('depth must be 50 or 101.')self.depth = depthself.padding = layers.ZeroPadding2D((3, 3))self.conv1 = layers.Conv2D(64, (7, 7),strides=(2, 2),kernel_initializer='he_normal',name='conv1')self.bn_conv1 = layers.BatchNormalization(name='bn_conv1')self.max_pool = layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same')self.res2a = _Bottleneck([64, 64, 256], block='2a',downsampling=True, stride=1)self.res2b = _Bottleneck([64, 64, 256], block='2b')self.res2c = _Bottleneck([64, 64, 256], block='2c')self.res3a = _Bottleneck([128, 128, 512], block='3a', downsampling=True, stride=2)self.res3b = _Bottleneck([128, 128, 512], block='3b')self.res3c = _Bottleneck([128, 128, 512], block='3c')self.res3d = _Bottleneck([128, 128, 512], block='3d')self.res4a = _Bottleneck([256, 256, 1024], block='4a', downsampling=True, stride=2)self.res4b = _Bottleneck([256, 256, 1024], block='4b')self.res4c = _Bottleneck([256, 256, 1024], block='4c')self.res4d = _Bottleneck([256, 256, 1024], block='4d')self.res4e = _Bottleneck([256, 256, 1024], block='4e')self.res4f = _Bottleneck([256, 256, 1024], block='4f')if self.depth == 101:self.res4g = _Bottleneck([256, 256, 1024], block='4g')self.res4h = _Bottleneck([256, 256, 1024], block='4h')self.res4i = _Bottleneck([256, 256, 1024], block='4i')self.res4j = _Bottleneck([256, 256, 1024], block='4j')self.res4k = _Bottleneck([256, 256, 1024], block='4k')self.res4l = _Bottleneck([256, 256, 1024], block='4l')self.res4m = _Bottleneck([256, 256, 1024], block='4m')self.res4n = _Bottleneck([256, 256, 1024], block='4n')self.res4o = _Bottleneck([256, 256, 1024], block='4o')self.res4p = _Bottleneck([256, 256, 1024], block='4p')self.res4q = _Bottleneck([256, 256, 1024], block='4q')self.res4r = _Bottleneck([256, 256, 1024], block='4r')self.res4s = _Bottleneck([256, 256, 1024], block='4s')self.res4t = _Bottleneck([256, 256, 1024], block='4t')self.res4u = _Bottleneck([256, 256, 1024], block='4u')self.res4v = _Bottleneck([256, 256, 1024], block='4v')self.res4w = _Bottleneck([256, 256, 1024], block='4w') self.res5a = _Bottleneck([512, 512, 2048], block='5a', downsampling=True, stride=2)self.res5b = _Bottleneck([512, 512, 2048], block='5b')self.res5c = _Bottleneck([512, 512, 2048], block='5c')self.out_channel = (256, 512, 1024, 2048)def call(self, inputs, training=True):x = self.padding(inputs)x = self.conv1(x)x = self.bn_conv1(x, training=training)x = tf.nn.relu(x)x = self.max_pool(x)x = self.res2a(x, training=training)x = self.res2b(x, training=training)C2 = x = self.res2c(x, training=training)x = self.res3a(x, training=training)x = self.res3b(x, training=training)x = self.res3c(x, training=training)C3 = x = self.res3d(x, training=training)x = self.res4a(x, training=training)x = self.res4b(x, training=training)x = self.res4c(x, training=training)x = self.res4d(x, training=training)x = self.res4e(x, training=training)x = self.res4f(x, training=training)if self.depth == 101:x = self.res4g(x, training=training)x = self.res4h(x, training=training)x = self.res4i(x, training=training)x = self.res4j(x, training=training)x = self.res4k(x, training=training)x = self.res4l(x, training=training)x = self.res4m(x, training=training)x = self.res4n(x, training=training)x = self.res4o(x, training=training)x = self.res4p(x, training=training)x = self.res4q(x, training=training)x = self.res4r(x, training=training)x = self.res4s(x, training=training)x = self.res4t(x, training=training)x = self.res4u(x, training=training)x = self.res4v(x, training=training)x = self.res4w(x, training=training) C4 = xx = self.res5a(x, training=training)x = self.res5b(x, training=training)C5 = x = self.res5c(x, training=training)return (C2, C3, C4, C5)def compute_output_shape(self, input_shape):shape = tf.TensorShape(input_shape).as_list()batch, H, W, C = shapeC2_shape = tf.TensorShape([batch, H // 4, W // 4, self.out_channel[0]])C3_shape = tf.TensorShape([batch, H // 8, W // 8, self.out_channel[1]])C4_shape = tf.TensorShape([batch, H // 16, W // 16, self.out_channel[2]])C5_shape = tf.TensorShape([batch, H // 32, W // 32, self.out_channel[3]])return (C2_shape, C3_shape, C4_shape, C5_shape)

第三部分 針對(duì)neck文件代碼解析

''' FRN model for Keras.# Reference: - [Feature Pyramid Networks for Object Detection](https://arxiv.org/abs/1612.03144)''' import tensorflow as tf from tensorflow.keras import layersclass FPN(tf.keras.Model):def __init__(self, out_channels=256, **kwargs):'''Feature Pyramid NetworksAttributes---out_channels: int. the channels of pyramid feature maps.'''super(FPN, self).__init__(**kwargs)self.out_channels = out_channelsself.fpn_c2p2 = layers.Conv2D(out_channels, (1, 1), kernel_initializer='he_normal', name='fpn_c2p2')self.fpn_c3p3 = layers.Conv2D(out_channels, (1, 1), kernel_initializer='he_normal', name='fpn_c3p3')self.fpn_c4p4 = layers.Conv2D(out_channels, (1, 1), kernel_initializer='he_normal', name='fpn_c4p4')self.fpn_c5p5 = layers.Conv2D(out_channels, (1, 1), kernel_initializer='he_normal', name='fpn_c5p5')self.fpn_p3upsampled = layers.UpSampling2D(size=(2, 2), name='fpn_p3upsampled')self.fpn_p4upsampled = layers.UpSampling2D(size=(2, 2), name='fpn_p4upsampled')self.fpn_p5upsampled = layers.UpSampling2D(size=(2, 2), name='fpn_p5upsampled')self.fpn_p2 = layers.Conv2D(out_channels, (3, 3), padding='SAME', kernel_initializer='he_normal', name='fpn_p2')self.fpn_p3 = layers.Conv2D(out_channels, (3, 3), padding='SAME', kernel_initializer='he_normal', name='fpn_p3')self.fpn_p4 = layers.Conv2D(out_channels, (3, 3), padding='SAME', kernel_initializer='he_normal', name='fpn_p4')self.fpn_p5 = layers.Conv2D(out_channels, (3, 3), padding='SAME', kernel_initializer='he_normal', name='fpn_p5')self.fpn_p6 = layers.MaxPooling2D(pool_size=(1, 1), strides=2, name='fpn_p6')def call(self, inputs, training=True):C2, C3, C4, C5 = inputsP5 = self.fpn_c5p5(C5)P4 = self.fpn_c4p4(C4) + self.fpn_p5upsampled(P5)P3 = self.fpn_c3p3(C3) + self.fpn_p4upsampled(P4)P2 = self.fpn_c2p2(C2) + self.fpn_p3upsampled(P3)# Attach 3x3 conv to all P layers to get the final feature maps.P2 = self.fpn_p2(P2)P3 = self.fpn_p3(P3)P4 = self.fpn_p4(P4)P5 = self.fpn_p5(P5)# subsampling from P5 with stride of 2.P6 = self.fpn_p6(P5)return [P2, P3, P4, P5, P6]def compute_output_shape(self, input_shape):C2_shape, C3_shape, C4_shape, C5_shape = input_shapeC2_shape, C3_shape, C4_shape, C5_shape = \C2_shape.as_list(), C3_shape.as_list(), C4_shape.as_list(), C5_shape.as_list()C6_shape = [C5_shape[0], (C5_shape[1] + 1) // 2, (C5_shape[2] + 1) // 2, self.out_channels]C2_shape[-1] = self.out_channelsC3_shape[-1] = self.out_channelsC4_shape[-1] = self.out_channelsC5_shape[-1] = self.out_channelsreturn [tf.TensorShape(C2_shape),tf.TensorShape(C3_shape),tf.TensorShape(C4_shape),tf.TensorShape(C5_shape),tf.TensorShape(C6_shape)]if __name__ == '__main__':C2 = tf.random.normal((2, 256, 256, 256))C3 = tf.random.normal((2, 128, 128, 512))C4 = tf.random.normal((2, 64, 64, 1024))C5 = tf.random.normal((2, 32, 32, 2048))fpn = FPN()P2, P3, P4, P5, P6 = fpn([C2, C3, C4, C5])print('P2 shape:', P2.shape.as_list())print('P3 shape:', P3.shape.as_list())print('P4 shape:', P4.shape.as_list())print('P5 shape:', P5.shape.as_list())print('P6 shape:', P6.shape.as_list())

第四部分 針對(duì)test_mixins文件代碼解析

import numpy as np import tensorflow as tffrom detection.core.bbox import transforms from detection.utils.misc import *class RPNTestMixin:def simple_test_rpn(self, img, img_meta):'''Args---imgs: np.ndarray. [height, width, channel]img_metas: np.ndarray. [11]'''imgs = tf.Variable(np.expand_dims(img, 0))img_metas = tf.Variable(np.expand_dims(img_meta, 0))x = self.backbone(imgs, training=False)x = self.neck(x, training=False)rpn_class_logits, rpn_probs, rpn_deltas = self.rpn_head(x, training=False)proposals_list = self.rpn_head.get_proposals(rpn_probs, rpn_deltas, img_metas, with_probs=False)return proposals_list[0]class BBoxTestMixin(object):def _unmold_detections(self, detections_list, img_metas):return [self._unmold_single_detection(detections_list[i], img_metas[i])for i in range(img_metas.shape[0])]def _unmold_single_detection(self, detections, img_meta):zero_ix = tf.where(tf.not_equal(detections[:, 4], 0))detections = tf.gather_nd(detections, zero_ix)# Extract boxes, class_ids, scores, and class-specific masksboxes = detections[:, :4]class_ids = tf.cast(detections[:, 4], tf.int32)scores = detections[:, 5]boxes = transforms.bbox_mapping_back(boxes, img_meta)return {'rois': boxes.numpy(),'class_ids': class_ids.numpy(),'scores': scores.numpy()}def simple_test_bboxes(self, img, img_meta, proposals):'''Args---imgs: np.ndarray. [height, width, channel]img_meta: np.ndarray. [11]'''imgs = tf.Variable(np.expand_dims(img, 0))img_metas = tf.Variable(np.expand_dims(img_meta, 0))rois_list = [tf.Variable(proposals)]x = self.backbone(imgs, training=False)P2, P3, P4, P5, _ = self.neck(x, training=False)rcnn_feature_maps = [P2, P3, P4, P5]pooled_regions_list = self.roi_align((rois_list, rcnn_feature_maps, img_metas), training=False)rcnn_class_logits_list, rcnn_probs_list, rcnn_deltas_list = \self.bbox_head(pooled_regions_list, training=False)detections_list = self.bbox_head.get_bboxes(rcnn_probs_list, rcnn_deltas_list, rois_list, img_metas)return self._unmold_detections(detections_list, img_metas)[0]

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