diff --git a/mmdet/core/__init__.py b/mmdet/core/__init__.py
index 1eb03f76acdfaff65c96e5316e3b4898b7a7af6a..e1495428889df9ffb57331b0bbe17b2f9de24185 100644
--- a/mmdet/core/__init__.py
+++ b/mmdet/core/__init__.py
@@ -1,6 +1,7 @@
from .rpn_ops import *
from .bbox_ops import *
from .mask_ops import *
+from .targets import *
from .losses import *
from .eval import *
from .parallel import *
diff --git a/mmdet/core/targets/__init__.py b/mmdet/core/targets/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f32c296c48744edef287a62bafa88d6180caa48a
--- /dev/null
+++ b/mmdet/core/targets/__init__.py
@@ -0,0 +1 @@
+from .retina_target import retina_target
diff --git a/mmdet/core/targets/retina_target.py b/mmdet/core/targets/retina_target.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b42736e2f61979ddb932f3b31d8c4c53fcffa60
--- /dev/null
+++ b/mmdet/core/targets/retina_target.py
@@ -0,0 +1,168 @@
+import torch
+
+from ..bbox_ops import bbox_assign, bbox2delta
+from ..utils import multi_apply
+
+
+def retina_target(anchor_list, valid_flag_list, gt_bboxes_list, gt_labels_list,
+ img_metas, target_means, target_stds, cls_out_channels, cfg):
+ """Compute regression and classification targets for anchors.
+
+ Args:
+ anchor_list (list[list]): Multi level anchors of each image.
+ valid_flag_list (list[list]): Multi level valid flags of each image.
+ gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
+ img_metas (list[dict]): Meta info of each image.
+ target_means (Iterable): Mean value of regression targets.
+ target_stds (Iterable): Std value of regression targets.
+ cfg (dict): RPN train configs.
+
+ Returns:
+ tuple
+ """
+ num_imgs = len(img_metas)
+ assert len(anchor_list) == len(valid_flag_list) == num_imgs
+
+ # anchor number of multi levels
+ num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
+ # concat all level anchors and flags to a single tensor
+ for i in range(num_imgs):
+ assert len(anchor_list[i]) == len(valid_flag_list[i])
+ anchor_list[i] = torch.cat(anchor_list[i])
+ valid_flag_list[i] = torch.cat(valid_flag_list[i])
+
+ # compute targets for each image
+ (all_labels, all_label_weights, all_bbox_targets, all_bbox_weights,
+ pos_inds_list, neg_inds_list) = multi_apply(
+ retina_target_single,
+ anchor_list,
+ valid_flag_list,
+ gt_bboxes_list,
+ gt_labels_list,
+ img_metas,
+ target_means=target_means,
+ target_stds=target_stds,
+ cls_out_channels=cls_out_channels,
+ cfg=cfg)
+ # no valid anchors
+ if any([labels is None for labels in all_labels]):
+ return None
+ # sampled anchors of all images
+ num_pos_samples = sum([
+ max(pos_inds.numel(), 1)
+ for pos_inds, neg_inds in zip(pos_inds_list, neg_inds_list)
+ ])
+ # split targets to a list w.r.t. multiple levels
+ labels_list = images_to_levels(all_labels, num_level_anchors)
+ label_weights_list = images_to_levels(all_label_weights, num_level_anchors)
+ bbox_targets_list = images_to_levels(all_bbox_targets, num_level_anchors)
+ bbox_weights_list = images_to_levels(all_bbox_weights, num_level_anchors)
+ return (labels_list, label_weights_list, bbox_targets_list,
+ bbox_weights_list, num_pos_samples)
+
+
+def images_to_levels(target, num_level_anchors):
+ """Convert targets by image to targets by feature level.
+
+ [target_img0, target_img1] -> [target_level0, target_level1, ...]
+ """
+ target = torch.stack(target, 0)
+ level_targets = []
+ start = 0
+ for n in num_level_anchors:
+ end = start + n
+ level_targets.append(target[:, start:end].squeeze(0))
+ start = end
+ return level_targets
+
+
+def retina_target_single(flat_anchors, valid_flags, gt_bboxes, gt_labels,
+ img_meta, target_means, target_stds, cls_out_channels,
+ cfg):
+ inside_flags = anchor_inside_flags(flat_anchors, valid_flags,
+ img_meta['img_shape'][:2],
+ cfg.allowed_border)
+ if not inside_flags.any():
+ return (None, ) * 6
+ # assign gt and sample anchors
+ anchors = flat_anchors[inside_flags, :]
+ assigned_gt_inds, argmax_overlaps, max_overlaps = bbox_assign(
+ anchors,
+ gt_bboxes,
+ pos_iou_thr=cfg.pos_iou_thr,
+ neg_iou_thr=cfg.neg_iou_thr,
+ min_pos_iou=cfg.min_pos_iou)
+ pos_inds = torch.nonzero(assigned_gt_inds > 0)
+ neg_inds = torch.nonzero(assigned_gt_inds == 0)
+
+ bbox_targets = torch.zeros_like(anchors)
+ bbox_weights = torch.zeros_like(anchors)
+ labels = torch.zeros_like(assigned_gt_inds)
+ label_weights = torch.zeros_like(assigned_gt_inds, dtype=anchors.dtype)
+
+ if len(pos_inds) > 0:
+ pos_inds = pos_inds.squeeze(1).unique()
+ pos_anchors = anchors[pos_inds, :]
+ pos_gt_bbox = gt_bboxes[assigned_gt_inds[pos_inds] - 1, :]
+ pos_bbox_targets = bbox2delta(pos_anchors, pos_gt_bbox, target_means,
+ target_stds)
+ bbox_targets[pos_inds, :] = pos_bbox_targets
+ bbox_weights[pos_inds, :] = 1.0
+ labels[pos_inds] = gt_labels[assigned_gt_inds[pos_inds] - 1]
+ if cfg.pos_weight <= 0:
+ label_weights[pos_inds] = 1.0
+ else:
+ label_weights[pos_inds] = cfg.pos_weight
+ if len(neg_inds) > 0:
+ neg_inds = neg_inds.squeeze(1).unique()
+ label_weights[neg_inds] = 1.0
+
+ # map up to original set of anchors
+ num_total_anchors = flat_anchors.size(0)
+ labels = unmap(labels, num_total_anchors, inside_flags)
+ label_weights = unmap(label_weights, num_total_anchors, inside_flags)
+ labels, label_weights = expand_binary_labels(labels, label_weights,
+ cls_out_channels)
+ bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags)
+ bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags)
+
+ return (labels, label_weights, bbox_targets, bbox_weights, pos_inds,
+ neg_inds)
+
+
+def expand_binary_labels(labels, label_weights, cls_out_channels):
+ bin_labels = labels.new_full(
+ (labels.size(0), cls_out_channels), 0, dtype=torch.float32)
+ inds = torch.nonzero(labels >= 1).squeeze()
+ if inds.numel() > 0:
+ bin_labels[inds, labels[inds] - 1] = 1
+ bin_label_weights = label_weights.view(-1, 1).expand(
+ label_weights.size(0), cls_out_channels)
+ return bin_labels, bin_label_weights
+
+
+def anchor_inside_flags(flat_anchors, valid_flags, img_shape,
+ allowed_border=0):
+ img_h, img_w = img_shape[:2]
+ if allowed_border >= 0:
+ inside_flags = valid_flags & \
+ (flat_anchors[:, 0] >= -allowed_border) & \
+ (flat_anchors[:, 1] >= -allowed_border) & \
+ (flat_anchors[:, 2] < img_w + allowed_border) & \
+ (flat_anchors[:, 3] < img_h + allowed_border)
+ else:
+ inside_flags = valid_flags
+ return inside_flags
+
+
+def unmap(data, count, inds, fill=0):
+ """ Unmap a subset of item (data) back to the original set of items (of
+ size count) """
+ if data.dim() == 1:
+ ret = data.new_full((count, ), fill)
+ ret[inds] = data
+ else:
+ new_size = (count, ) + data.size()[1:]
+ ret = data.new_full(new_size, fill)
+ ret[inds, :] = data
+ return ret
diff --git a/mmdet/models/builder.py b/mmdet/models/builder.py
index bdf0ac3d16f9aadb194f944b3f7c4dd1a741e8cd..340a2a33a1e4e96e6faf004a15368de6b5e7f08c 100644
--- a/mmdet/models/builder.py
+++ b/mmdet/models/builder.py
@@ -2,11 +2,12 @@ from mmcv.runner import obj_from_dict
from torch import nn
from . import (backbones, necks, roi_extractors, rpn_heads, bbox_heads,
- mask_heads, detectors)
+ mask_heads, single_stage_heads, detectors)
__all__ = [
'build_backbone', 'build_neck', 'build_rpn_head', 'build_roi_extractor',
- 'build_bbox_head', 'build_mask_head', 'build_detector'
+ 'build_bbox_head', 'build_mask_head', 'build_single_stage_head',
+ 'build_detector'
]
@@ -47,5 +48,9 @@ def build_mask_head(cfg):
return build(cfg, mask_heads)
+def build_single_stage_head(cfg):
+ return build(cfg, single_stage_heads)
+
+
def build_detector(cfg, train_cfg=None, test_cfg=None):
return build(cfg, detectors, dict(train_cfg=train_cfg, test_cfg=test_cfg))
diff --git a/mmdet/models/detectors/__init__.py b/mmdet/models/detectors/__init__.py
index b8914c1e5d3c834a1373b2a2e8360183a41de4da..d08bf99a223726f1bbb5f872a50e74f8e9d5ae1a 100644
--- a/mmdet/models/detectors/__init__.py
+++ b/mmdet/models/detectors/__init__.py
@@ -2,5 +2,6 @@ from .base import BaseDetector
from .rpn import RPN
from .faster_rcnn import FasterRCNN
from .mask_rcnn import MaskRCNN
+from .retina_net import RetinaNet
-__all__ = ['BaseDetector', 'RPN', 'FasterRCNN', 'MaskRCNN']
+__all__ = ['BaseDetector', 'RPN', 'FasterRCNN', 'MaskRCNN', 'RetinaNet']
diff --git a/mmdet/models/detectors/retina_net.py b/mmdet/models/detectors/retina_net.py
new file mode 100644
index 0000000000000000000000000000000000000000..da7ede8682836496b84423f983e3da6c79209d92
--- /dev/null
+++ b/mmdet/models/detectors/retina_net.py
@@ -0,0 +1,14 @@
+from .single_stage import SingleStageDetector
+
+
+class RetinaNet(SingleStageDetector):
+
+ def __init__(self,
+ backbone,
+ neck,
+ bbox_head,
+ train_cfg=None,
+ test_cfg=None,
+ pretrained=None):
+ super(RetinaNet, self).__init__(backbone, neck, bbox_head, train_cfg,
+ test_cfg, pretrained)
diff --git a/mmdet/models/detectors/single_stage.py b/mmdet/models/detectors/single_stage.py
new file mode 100644
index 0000000000000000000000000000000000000000..95075ed2fffccc449d0a7ecdcbe88cc167871a8d
--- /dev/null
+++ b/mmdet/models/detectors/single_stage.py
@@ -0,0 +1,62 @@
+import torch.nn as nn
+
+from .base import BaseDetector
+from .. import builder
+from mmdet.core import bbox2result
+
+
+class SingleStageDetector(BaseDetector):
+
+ def __init__(self,
+ backbone,
+ neck=None,
+ bbox_head=None,
+ train_cfg=None,
+ test_cfg=None,
+ pretrained=None):
+ super(SingleStageDetector, self).__init__()
+ self.backbone = builder.build_backbone(backbone)
+ if neck is not None:
+ self.neck = builder.build_neck(neck)
+ self.bbox_head = builder.build_single_stage_head(bbox_head)
+ self.train_cfg = train_cfg
+ self.test_cfg = test_cfg
+ self.init_weights(pretrained=pretrained)
+
+ def init_weights(self, pretrained=None):
+ super(SingleStageDetector, self).init_weights(pretrained)
+ self.backbone.init_weights(pretrained=pretrained)
+ if self.with_neck:
+ if isinstance(self.neck, nn.Sequential):
+ for m in self.neck:
+ m.init_weights()
+ else:
+ self.neck.init_weights()
+ self.bbox_head.init_weights()
+
+ def extract_feat(self, img):
+ x = self.backbone(img)
+ if self.with_neck:
+ x = self.neck(x)
+ return x
+
+ def forward_train(self, img, img_metas, gt_bboxes, gt_labels):
+ 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)
+ return losses
+
+ def 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_det_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
diff --git a/mmdet/models/single_stage_heads/__init__.py b/mmdet/models/single_stage_heads/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..265b0626bf1f4362426d71454870af0bb7395729
--- /dev/null
+++ b/mmdet/models/single_stage_heads/__init__.py
@@ -0,0 +1,3 @@
+from .retina_head import RetinaHead
+
+__all__ = ['RetinaHead']
diff --git a/mmdet/models/single_stage_heads/retina_head.py b/mmdet/models/single_stage_heads/retina_head.py
new file mode 100644
index 0000000000000000000000000000000000000000..55e5e94d85cf2551576953754cb2e419f672b00e
--- /dev/null
+++ b/mmdet/models/single_stage_heads/retina_head.py
@@ -0,0 +1,264 @@
+from __future__ import division
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+from mmdet.core import (AnchorGenerator, multi_apply, delta2bbox,
+ weighted_smoothl1, weighted_sigmoid_focal_loss,
+ multiclass_nms, retina_target)
+from ..utils import normal_init, bias_init_with_prob
+
+
+class RetinaHead(nn.Module):
+ """Head of RetinaNet.
+
+ / cls_convs - retina_cls (3x3 conv)
+ input -
+ \ reg_convs - retina_reg (3x3 conv)
+
+ Args:
+ in_channels (int): Number of channels in the input feature map.
+ num_classes (int): Class number (including background).
+ stacked_convs (int): Number of convolutional layers added for cls and
+ reg branch.
+ feat_channels (int): Number of channels for the RPN feature map.
+ """
+
+ def __init__(self,
+ in_channels,
+ num_classes,
+ stacked_convs=4,
+ feat_channels=256,
+ scales_per_octave=3,
+ anchor_scale=4,
+ anchor_ratios=[1.0, 2.0, 0.5],
+ anchor_strides=[8, 16, 32, 64, 128],
+ target_means=[.0, .0, .0, .0],
+ target_stds=[1.0, 1.0, 1.0, 1.0]):
+ super(RetinaHead, self).__init__()
+ self.in_channels = in_channels
+ self.num_classes = num_classes
+ self.scales_per_octave = scales_per_octave
+ self.anchor_scale = anchor_scale
+ self.anchor_strides = anchor_strides
+ self.anchor_ratios = anchor_ratios
+ self.target_means = target_means
+ self.target_stds = target_stds
+ self.anchor_generators = []
+ for anchor_stride in self.anchor_strides:
+ octave_scales = np.array([
+ 2**(octave / float(scales_per_octave))
+ for octave in range(scales_per_octave)
+ ])
+ octave_scales = octave_scales * anchor_scale
+ self.anchor_generators.append(
+ AnchorGenerator(anchor_stride, octave_scales, anchor_ratios))
+ self.relu = nn.ReLU(inplace=True)
+ self.num_anchors = int(
+ len(self.anchor_ratios) * self.scales_per_octave)
+ self.cls_out_channels = self.num_classes - 1
+ self.bbox_pred_dim = 4
+
+ self.stacked_convs = stacked_convs
+ self.cls_convs = nn.ModuleList()
+ self.reg_convs = nn.ModuleList()
+ for i in range(self.stacked_convs):
+ chn = in_channels if i == 0 else feat_channels
+ self.cls_convs.append(
+ nn.Conv2d(chn, feat_channels, 3, stride=1, padding=1))
+ self.reg_convs.append(
+ nn.Conv2d(chn, feat_channels, 3, stride=1, padding=1))
+ self.retina_cls = nn.Conv2d(
+ feat_channels,
+ self.num_anchors * self.cls_out_channels,
+ 3,
+ stride=1,
+ padding=1)
+ self.retina_reg = nn.Conv2d(
+ feat_channels,
+ self.num_anchors * self.bbox_pred_dim,
+ 3,
+ stride=1,
+ padding=1)
+ self.debug_imgs = None
+
+ def init_weights(self):
+ for m in self.cls_convs:
+ normal_init(m, std=0.01)
+ for m in self.reg_convs:
+ normal_init(m, 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 = x
+ reg_feat = x
+ for cls_conv in self.cls_convs:
+ cls_feat = self.relu(cls_conv(cls_feat))
+ for reg_conv in self.reg_convs:
+ reg_feat = self.relu(reg_conv(reg_feat))
+ cls_score = self.retina_cls(cls_feat)
+ bbox_pred = self.retina_reg(reg_feat)
+ return cls_score, bbox_pred
+
+ def forward(self, feats):
+ return multi_apply(self.forward_single, feats)
+
+ def get_anchors(self, featmap_sizes, img_metas):
+ """Get anchors according to feature map sizes.
+
+ Args:
+ featmap_sizes (list[tuple]): Multi-level feature map sizes.
+ img_metas (list[dict]): Image meta info.
+
+ Returns:
+ tuple: anchors of each image, valid flags of each image
+ """
+ num_imgs = len(img_metas)
+ num_levels = len(featmap_sizes)
+
+ # since feature map sizes of all images are the same, we only compute
+ # anchors for one time
+ multi_level_anchors = []
+ for i in range(num_levels):
+ anchors = self.anchor_generators[i].grid_anchors(
+ featmap_sizes[i], self.anchor_strides[i])
+ multi_level_anchors.append(anchors)
+ anchor_list = [multi_level_anchors for _ in range(num_imgs)]
+
+ # for each image, we compute valid flags of multi level anchors
+ valid_flag_list = []
+ for img_id, img_meta in enumerate(img_metas):
+ multi_level_flags = []
+ for i in range(num_levels):
+ anchor_stride = self.anchor_strides[i]
+ feat_h, feat_w = featmap_sizes[i]
+ h, w, _ = img_meta['pad_shape']
+ valid_feat_h = min(int(np.ceil(h / anchor_stride)), feat_h)
+ valid_feat_w = min(int(np.ceil(w / anchor_stride)), feat_w)
+ flags = self.anchor_generators[i].valid_flags(
+ (feat_h, feat_w), (valid_feat_h, valid_feat_w))
+ multi_level_flags.append(flags)
+ valid_flag_list.append(multi_level_flags)
+
+ return anchor_list, valid_flag_list
+
+ def loss_single(self, cls_score, bbox_pred, labels, label_weights,
+ bbox_targets, bbox_weights, num_pos_samples, cfg):
+ # classification loss
+ labels = labels.contiguous().view(-1, self.cls_out_channels)
+ label_weights = label_weights.contiguous().view(
+ -1, self.cls_out_channels)
+ cls_score = cls_score.permute(0, 2, 3, 1).contiguous().view(
+ -1, self.cls_out_channels)
+ loss_cls = weighted_sigmoid_focal_loss(
+ cls_score,
+ labels,
+ label_weights,
+ cfg.gamma,
+ cfg.alpha,
+ avg_factor=num_pos_samples)
+ # regression loss
+ bbox_targets = bbox_targets.contiguous().view(-1, 4)
+ bbox_weights = bbox_weights.contiguous().view(-1, 4)
+ bbox_pred = bbox_pred.permute(0, 2, 3, 1).contiguous().view(-1, 4)
+ loss_reg = weighted_smoothl1(
+ bbox_pred,
+ bbox_targets,
+ bbox_weights,
+ beta=cfg.smoothl1_beta,
+ avg_factor=num_pos_samples)
+ return loss_cls, loss_reg
+
+ def loss(self, cls_scores, bbox_preds, gt_bboxes, gt_labels, img_shapes,
+ cfg):
+ featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+ assert len(featmap_sizes) == len(self.anchor_generators)
+
+ anchor_list, valid_flag_list = self.get_anchors(
+ featmap_sizes, img_shapes)
+ cls_reg_targets = retina_target(
+ anchor_list, valid_flag_list, gt_bboxes, gt_labels, img_shapes,
+ self.target_means, self.target_stds, self.cls_out_channels, cfg)
+ if cls_reg_targets is None:
+ return None
+ (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
+ num_pos_samples) = cls_reg_targets
+
+ losses_cls, losses_reg = multi_apply(
+ self.loss_single,
+ cls_scores,
+ bbox_preds,
+ labels_list,
+ label_weights_list,
+ bbox_targets_list,
+ bbox_weights_list,
+ num_pos_samples=num_pos_samples,
+ cfg=cfg)
+ return dict(loss_cls=losses_cls, loss_reg=losses_reg)
+
+ def get_det_bboxes(self,
+ cls_scores,
+ bbox_preds,
+ img_metas,
+ cfg,
+ rescale=False):
+ assert len(cls_scores) == len(bbox_preds)
+ num_levels = len(cls_scores)
+
+ mlvl_anchors = [
+ self.anchor_generators[i].grid_anchors(cls_scores[i].size()[-2:],
+ self.anchor_strides[i])
+ for i in range(num_levels)
+ ]
+
+ result_list = []
+ for img_id in range(len(img_metas)):
+ cls_score_list = [
+ cls_scores[i][img_id].detach() for i in range(num_levels)
+ ]
+ bbox_pred_list = [
+ bbox_preds[i][img_id].detach() for i in range(num_levels)
+ ]
+ img_shape = img_metas[img_id]['img_shape']
+ scale_factor = img_metas[img_id]['scale_factor']
+ results = self._get_det_bboxes_single(
+ cls_score_list, bbox_pred_list, mlvl_anchors, img_shape,
+ scale_factor, cfg, rescale)
+ result_list.append(results)
+ return result_list
+
+ def _get_det_bboxes_single(self,
+ cls_scores,
+ bbox_preds,
+ mlvl_anchors,
+ img_shape,
+ scale_factor,
+ cfg,
+ rescale=False):
+ assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors)
+ mlvl_proposals = []
+ mlvl_scores = []
+ for cls_score, bbox_pred, anchors in zip(cls_scores, bbox_preds,
+ mlvl_anchors):
+ assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
+ cls_score = cls_score.permute(1, 2, 0).contiguous().view(
+ -1, self.cls_out_channels)
+ scores = cls_score.sigmoid()
+ bbox_pred = bbox_pred.permute(1, 2, 0).contiguous().view(-1, 4)
+ proposals = delta2bbox(anchors, bbox_pred, self.target_means,
+ self.target_stds, img_shape)
+ mlvl_proposals.append(proposals)
+ mlvl_scores.append(scores)
+ mlvl_proposals = torch.cat(mlvl_proposals)
+ if rescale:
+ mlvl_proposals /= scale_factor
+ mlvl_scores = torch.cat(mlvl_scores)
+ padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
+ mlvl_scores = torch.cat([padding, mlvl_scores], dim=1)
+ det_bboxes, det_labels = multiclass_nms(mlvl_proposals, mlvl_scores,
+ cfg.score_thr, cfg.nms_thr,
+ cfg.max_per_img)
+ return det_bboxes, det_labels
diff --git a/mmdet/models/utils/weight_init.py b/mmdet/models/utils/weight_init.py
index 2e9b13b4fbc17d6d1986da876108c1a813190c2d..17d49880fd867e9a8727776ef5a9fcbd6b01599f 100644
--- a/mmdet/models/utils/weight_init.py
+++ b/mmdet/models/utils/weight_init.py
@@ -1,3 +1,4 @@
+import numpy as np
import torch.nn as nn
@@ -37,3 +38,9 @@ def kaiming_init(module,
module.weight, mode=mode, nonlinearity=nonlinearity)
if hasattr(module, 'bias'):
nn.init.constant_(module.bias, bias)
+
+
+def bias_init_with_prob(prior_prob):
+ """ initialize conv/fc bias value according to giving probablity"""
+ bias_init = float(-np.log((1 - prior_prob) / prior_prob))
+ return bias_init
diff --git a/tools/configs/r50_retinanet_1x.py b/tools/configs/r50_retinanet_1x.py
new file mode 100644
index 0000000000000000000000000000000000000000..39abcaa34dcde69dbcf23fe88b2da21330bd4fb6
--- /dev/null
+++ b/tools/configs/r50_retinanet_1x.py
@@ -0,0 +1,117 @@
+# model settings
+model = dict(
+ type='RetinaNet',
+ pretrained='modelzoo://resnet50',
+ backbone=dict(
+ type='resnet',
+ depth=50,
+ num_stages=4,
+ out_indices=(0, 1, 2, 3),
+ frozen_stages=1,
+ style='pytorch'),
+ neck=dict(
+ type='FPN',
+ in_channels=[256, 512, 1024, 2048],
+ out_channels=256,
+ start_level=1,
+ add_extra_convs=True,
+ num_outs=5),
+ bbox_head=dict(
+ type='RetinaHead',
+ num_classes=81,
+ in_channels=256,
+ stacked_convs=4,
+ feat_channels=256,
+ scales_per_octave=3,
+ anchor_scale=4,
+ anchor_ratios=[1.0, 2.0, 0.5],
+ anchor_strides=[8, 16, 32, 64, 128],
+ target_means=[.0, .0, .0, .0],
+ target_stds=[1.0, 1.0, 1.0, 1.0]))
+# training and testing settings
+train_cfg = dict(
+ pos_iou_thr=0.5,
+ neg_iou_thr=0.4,
+ min_pos_iou=0.4,
+ smoothl1_beta=0.11,
+ gamma=2.0,
+ alpha=0.25,
+ allowed_border=-1,
+ pos_weight=-1,
+ debug=False)
+test_cfg = dict(
+ nms_pre=1000,
+ nms_thr=0.5,
+ min_bbox_size=0,
+ score_thr=0.05,
+ max_per_img=100)
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = '../data/coco/'
+img_norm_cfg = dict(
+ mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+data = dict(
+ imgs_per_gpu=2,
+ workers_per_gpu=2,
+ train=dict(
+ type=dataset_type,
+ ann_file=data_root + 'annotations/instances_train2017.json',
+ img_prefix=data_root + 'train2017/',
+ img_scale=(1333, 800),
+ img_norm_cfg=img_norm_cfg,
+ size_divisor=32,
+ flip_ratio=0.5,
+ with_mask=False,
+ with_crowd=False,
+ with_label=True),
+ val=dict(
+ type=dataset_type,
+ ann_file=data_root + 'annotations/instances_val2017.json',
+ img_prefix=data_root + 'val2017/',
+ img_scale=(1333, 800),
+ img_norm_cfg=img_norm_cfg,
+ size_divisor=32,
+ flip_ratio=0,
+ with_mask=False,
+ with_crowd=False,
+ with_label=True),
+ test=dict(
+ type=dataset_type,
+ ann_file=data_root + 'annotations/instances_val2017.json',
+ img_prefix=data_root + 'val2017/',
+ img_scale=(1333, 800),
+ img_norm_cfg=img_norm_cfg,
+ size_divisor=32,
+ flip_ratio=0,
+ with_mask=False,
+ with_crowd=False,
+ with_label=False,
+ test_mode=True))
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
+# learning policy
+lr_config = dict(
+ policy='step',
+ warmup='linear',
+ warmup_iters=500,
+ warmup_ratio=1.0 / 3,
+ step=[8, 11])
+checkpoint_config = dict(interval=1)
+# yapf:disable
+log_config = dict(
+ interval=20,
+ hooks=[
+ dict(type='TextLoggerHook'),
+ # dict(type='TensorboardLoggerHook', log_dir=work_dir + '/log')
+ ])
+# yapf:enable
+# runtime settings
+total_epochs = 12
+device_ids = range(8)
+dist_params = dict(backend='gloo')
+log_level = 'INFO'
+work_dir = './work_dirs/fpn_retinanet_r50_1x'
+load_from = None
+resume_from = None
+workflow = [('train', 1)]