mmdetection - anchor-based方法训练流程解析

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训练流程图

最终会创建一个runner，然后调用runner.run时，实际会根据workflow中是train还是val，调用runner.py下的train和val函数。
batch_processor

def batch_processor(model, data, train_mode):# 这里的train_mode实际没用到losses = model(**data)loss, log_vars = parse_losses(losses)outputs = dict(loss=loss, log_vars=log_vars, num_samples=len(data['img'].data))return outputs

mmcv/runner/runner.py
train

def train(self, data_loader, **kwargs):self.model.train()self.mode = 'train'self.data_loader = data_loaderself._max_iters = self._max_epochs * len(data_loader)self.call_hook('before_train_epoch')for i, data_batch in enumerate(data_loader):self._inner_iter = iself.call_hook('before_train_iter')outputs = self.batch_processor(self.model, data_batch, train_mode=True, **kwargs)if not isinstance(outputs, dict):raise TypeError('batch_processor() must return a dict')if 'log_vars' in outputs:self.log_buffer.update(outputs['log_vars'],outputs['num_samples'])self.outputs = outputsself.call_hook('after_train_iter')self._iter += 1self.call_hook('after_train_epoch')self._epoch += 1

val

def val(self, data_loader, **kwargs):self.model.eval()self.mode = 'val'self.data_loader = data_loaderself.call_hook('before_val_epoch')for i, data_batch in enumerate(data_loader):self._inner_iter = iself.call_hook('before_val_iter')with torch.no_grad():outputs = self.batch_processor(self.model, data_batch, train_mode=False, **kwargs)if not isinstance(outputs, dict):raise TypeError('batch_processor() must return a dict')if 'log_vars' in outputs:self.log_buffer.update(outputs['log_vars'],outputs['num_samples'])self.outputs = outputsself.call_hook('after_val_iter')self.call_hook('after_val_epoch')

validate目前只在_dist_train中有用到

训练时，实际调用：losses = model(**data)，验证时，实际调用hook，运行：

with torch.no_grad():result = runner.model(return_loss=False, rescale=True, **data_gpu)

其中，TwoStageDetector和SingleStageDetector都继承了BaseDetector，在BaseDetector中，forward函数定义如下：

@auto_fp16(apply_to=('img', ))
def forward(self, img, img_meta, return_loss=True, **kwargs):if return_loss:return self.forward_train(img, img_meta, **kwargs)else:return self.forward_test(img, img_meta, **kwargs)

对于forward_test，其代码如下：

def forward_test(self, imgs, img_metas, **kwargs):for var, name in [(imgs, 'imgs'), (img_metas, 'img_metas')]:if not isinstance(var, list):raise TypeError('{} must be a list, but got {}'.format(name, type(var)))num_augs = len(imgs)if num_augs != len(img_metas):raise ValueError('num of augmentations ({}) != num of image meta ({})'.format(len(imgs), len(img_metas)))# TODO: remove the restriction of imgs_per_gpu == 1 when preparedimgs_per_gpu = imgs[0].size(0)assert imgs_per_gpu == 1if num_augs == 1:return self.simple_test(imgs[0], img_metas[0], **kwargs)else:return self.aug_test(imgs, img_metas, **kwargs)

由上可以看出，子类需要写simple_test和aub_test函数。
对于一个检测模型（一阶或者二阶），在其class中，需要重写以下函数：

• forward_train
• simple_test
• aug_test # 非必须

下面以retinanet举个例子，在retinanet的config文件中，model的type是RetinaNet，在mmdet/models/detectors/retinanet.py中，定义了RetinaNet，它的父类是SingleStageDetector，定义在mmdet/models/detectors/single_stage.py中，三个重要函数的代码如下：

def forward_train(self,img,img_metas,gt_bboxes,gt_labels,gt_bboxes_ignore=None):x = self.extract_feat(img)outs = self.bbox_head(x)loss_inputs = outs + (gt_bboxes, gt_labels, img_metas, self.train_cfg)losses = self.bbox_head.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)return lossesdef simple_test(self, img, img_meta, rescale=False):x = self.extract_feat(img)outs = self.bbox_head(x)bbox_inputs = outs + (img_meta, self.test_cfg, rescale)bbox_list = self.bbox_head.get_bboxes(*bbox_inputs)bbox_results = [bbox2result(det_bboxes, det_labels, self.bbox_head.num_classes)for det_bboxes, det_labels in bbox_list]return bbox_results[0]def aug_test(self, imgs, img_metas, rescale=False):raise NotImplementedError

由上可知，计算loss的函数是在head中定义的，RetinaHead定义在mmdet/models/anchor_heads/retina_head.py中，RetinaHead三个关键函数的代码如下：

def _init_layers(self):self.relu = nn.ReLU(inplace=True)self.cls_convs = nn.ModuleList()self.reg_convs = nn.ModuleList()for i in range(self.stacked_convs):chn = self.in_channels if i == 0 else self.feat_channelsself.cls_convs.append(ConvModule(chn,self.feat_channels,3,stride=1,padding=1,conv_cfg=self.conv_cfg,norm_cfg=self.norm_cfg))self.reg_convs.append(ConvModule(chn,self.feat_channels,3,stride=1,padding=1,conv_cfg=self.conv_cfg,norm_cfg=self.norm_cfg))self.retina_cls = nn.Conv2d(self.feat_channels,self.num_anchors * self.cls_out_channels,3,padding=1)self.retina_reg = nn.Conv2d(self.feat_channels, self.num_anchors * 4, 3, padding=1)def init_weights(self):for m in self.cls_convs:normal_init(m.conv, std=0.01)for m in self.reg_convs:normal_init(m.conv, std=0.01)bias_cls = bias_init_with_prob(0.01)normal_init(self.retina_cls, std=0.01, bias=bias_cls)normal_init(self.retina_reg, std=0.01)def forward_single(self, x):cls_feat = xreg_feat = xfor cls_conv in self.cls_convs:cls_feat = cls_conv(cls_feat)for reg_conv in self.reg_convs:reg_feat = reg_conv(reg_feat)cls_score = self.retina_cls(cls_feat)bbox_pred = self.retina_reg(reg_feat)return cls_score, bbox_pred

其中，_init_layers创建head的结构，init_weights对conv的weight和bias做初始化，forward_single是经过head计算得到的分类和检测框预测结果。
forward
在具体的方法对应的head定义forward_single，最后由anchor_head.py中的forward函数进行组装。

from six.moves import map, zip
def multi_apply(func, *args, **kwargs):pfunc = partial(func, **kwargs) if kwargs else func # 将func的kwargs固定，返回该函数# 这里的*args=feats，调用forward_single对feats的元素依次跑前向map_results = map(pfunc, *args) # 得到[(stride1_cls,stride1_bbox,...), (stride2_cls,stride2_bbox, ...]return tuple(map(list, zip(*map_results)))# zip(*map_results) 得到 [(stride1_cls,stride2_cls,stride3_cls,...)，(stride1_bbox,stride2_bbox,stride3_bbox,...)]# map(list, zip(*map_results)) 将(stride1_cls,stride2_cls,stride3_cls,...)变为[stride1_cls,stride2_cls,stride3_cls,...]# tuple之后，最后得到([stride1_cls,stride2_cls,stride3_cls,...],[stride1_bbox,stride2_bbox,stride3_bbox,...])def forward(self, feats):# 输入feats是一个list，长度为stride个数，其中元素为nchwreturn multi_apply(self.forward_single, feats)def forward_single(self, x):# 这里的x为feats中的某一个元素cls_feat = xreg_feat = xfor cls_conv in self.cls_convs:cls_feat = cls_conv(cls_feat)for reg_conv in self.reg_convs:reg_feat = reg_conv(reg_feat)cls_score = self.retina_cls(cls_feat)bbox_pred = self.retina_reg(reg_feat)return cls_score, bbox_pred

loss

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