add single stage detector

mmdet/core/__init__.py

 from .rpn_ops import *
 from .bbox_ops import *
 from .mask_ops import *
+from .targets import *
 from .losses import *
 from .eval import *
 from .parallel import *

mmdet/core/targets/__init__.py

+from .retina_target import retina_target

mmdet/core/targets/retina_target.py

+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

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))

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']

mmdet/models/detectors/retina_net.py

+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)

mmdet/models/detectors/single_stage.py

+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

mmdet/models/single_stage_heads/__init__.py

+from .retina_head import RetinaHead
+
+__all__ = ['RetinaHead']

mmdet/models/single_stage_heads/retina_head.py

+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

mmdet/models/utils/weight_init.py

+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

tools/configs/r50_retinanet_1x.py

+# 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)]