Turoad · Turoad · Sep 11, 2021 · Jun 8, 2021 · Jun 19, 2021 · Jun 19, 2021
diff --git a/README.md b/README.md
@@ -25,7 +25,8 @@ Supported detectors:
 - [x] [UFLD](configs/ufld)
 - [x] [RESA](configs/resa)
 - [x] [LaneATT](configs/laneatt)
-- [x] [CondLane] (in branch [condlane](https://github.com/Turoad/lanedet/tree/condlane), will be merged soon)
+- [x] [CondLane](configs/condlane)
+
 
 ## Installation
 <!--
@@ -181,6 +182,7 @@ This project is released under the [Apache 2.0 license](LICNESE).
 * [ZJULearning/resa](https://github.com/ZJULearning/resa)
 * [cfzd/Ultra-Fast-Lane-Detection](https://github.com/cfzd/Ultra-Fast-Lane-Detection)
 * [lucastabelini/LaneATT](https://github.com/lucastabelini/LaneATT)
+* [aliyun/conditional-lane-detection](https://github.com/aliyun/conditional-lane-detection)
 <!--te-->
 
 <!-- 

diff --git a/configs/condlane/README.md b/configs/condlane/README.md
@@ -0,0 +1,17 @@
+# CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution
+
+## Introduction
+
+```latex
+@article{liu2021condlanenet,
+  title={CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution},
+  author={Liu, Lizhe and Chen, Xiaohao and Zhu, Siyu and Tan, Ping},
+  journal={arXiv preprint arXiv:2105.05003},
+  year={2021}
+}
+```
+
+## Models
+| Architecture| Backbone |Dataset | Metric | Config| Checkpoints  |
+|-------------|----------|--------|--------|-------|--------------|
+| CondLane   | ResNet101 | CULane | F1: 79.47| [config](configs/condlane/resnet101_culane.py)  |[model](https://github.com/Turoad/lanedet/releases/download/1.0/condlane_r101_culane.pth.zip) |
diff --git a/configs/condlane/resnet101_culane.py b/configs/condlane/resnet101_culane.py
@@ -0,0 +1,211 @@
+net = dict(
+    type='Detector',
+)
+
+backbone = dict(
+    type='ResNetWrapper',
+    resnet='resnet101',
+    pretrained=True,
+    replace_stride_with_dilation=[False, False, False],
+    out_conv=False,
+    in_channels=[64, 128, 256, 512]
+)
+
+sample_y = range(590, 270, -8)
+
+batch_size = 8
+aggregator = dict(
+    type='TransConvEncoderModule',
+    in_dim=2048,
+    attn_in_dims=[2048, 256],
+    attn_out_dims=[256, 256],
+    strides=[1, 1],
+    ratios=[4, 4],
+    pos_shape=(batch_size, 10, 25),
+)
+
+neck=dict(
+    type='FPN',
+    in_channels=[256, 512, 1024, 256],
+    out_channels=64,
+    num_outs=4,
+    #trans_idx=-1,
+)
+
+loss_weights=dict(
+        hm_weight=1,
+        kps_weight=0.4,
+        row_weight=1.,
+        range_weight=1.,
+    )
+
+num_lane_classes=1
+heads=dict(
+    type='CondLaneHead',
+    heads=dict(hm=num_lane_classes),
+    in_channels=(64, ),
+    num_classes=num_lane_classes,
+    head_channels=64,
+    head_layers=1,
+    disable_coords=False,
+    branch_in_channels=64,
+    branch_channels=64,
+    branch_out_channels=64,
+    reg_branch_channels=64,
+    branch_num_conv=1,
+    hm_idx=2,
+    mask_idx=0,
+    compute_locations_pre=True,
+    location_configs=dict(size=(batch_size, 1, 80, 200), device='cuda:0')
+)
+
+optimizer = dict(type='AdamW', lr=3e-4, betas=(0.9, 0.999), eps=1e-8)
+
+epochs = 16
+total_iter = (88880 // batch_size) * epochs
+import math
+scheduler = dict(
+    type = 'MultiStepLR',
+    milestones=[8, 14],
+    gamma=0.1
+)
+
+seg_loss_weight = 1.0
+eval_ep = 1
+save_ep = 1 
+
+img_norm = dict(
+    mean=[75.3, 76.6, 77.6],
+    std=[50.5, 53.8, 54.3]
+)
+
+img_height = 320 
+img_width = 800
+cut_height = 0 
+ori_img_h = 590
+ori_img_w = 1640
+
+mask_down_scale = 4
+hm_down_scale = 16
+num_lane_classes = 1
+line_width = 3
+radius = 6
+nms_thr = 4
+img_scale = (800, 320)
+crop_bbox = [0, 270, 1640, 590]
+mask_size = (1, 80, 200)
+
+train_process = [
+    dict(type='Alaug',
+    transforms=[dict(type='Compose', params=dict(bboxes=False, keypoints=True, masks=False)),
+    dict(
+        type='Crop',
+        x_min=crop_bbox[0],
+        x_max=crop_bbox[2],
+        y_min=crop_bbox[1],
+        y_max=crop_bbox[3],
+        p=1),
+    dict(type='Resize', height=img_scale[1], width=img_scale[0], p=1),
+    dict(
+        type='OneOf',
+        transforms=[
+            dict(
+                type='RGBShift',
+                r_shift_limit=10,
+                g_shift_limit=10,
+                b_shift_limit=10,
+                p=1.0),
+            dict(
+                type='HueSaturationValue',
+                hue_shift_limit=(-10, 10),
+                sat_shift_limit=(-15, 15),
+                val_shift_limit=(-10, 10),
+                p=1.0),
+        ],
+        p=0.7),
+    dict(type='JpegCompression', quality_lower=85, quality_upper=95, p=0.2),
+    dict(
+        type='OneOf',
+        transforms=[
+            dict(type='Blur', blur_limit=3, p=1.0),
+            dict(type='MedianBlur', blur_limit=3, p=1.0)
+        ],
+        p=0.2),
+    dict(type='RandomBrightness', limit=0.2, p=0.6),
+    dict(
+        type='ShiftScaleRotate',
+        shift_limit=0.1,
+        scale_limit=(-0.2, 0.2),
+        rotate_limit=10,
+        border_mode=0,
+        p=0.6),
+    dict(
+        type='RandomResizedCrop',
+        height=img_scale[1],
+        width=img_scale[0],
+        scale=(0.8, 1.2),
+        ratio=(1.7, 2.7),
+        p=0.6),
+    dict(type='Resize', height=img_scale[1], width=img_scale[0], p=1),]
+
+    ),
+    dict(type='CollectLane',
+        down_scale=mask_down_scale,
+        hm_down_scale=hm_down_scale,
+        max_mask_sample=5,
+        line_width=line_width,
+        radius=radius,
+        keys=['img', 'gt_hm'],
+        meta_keys=[
+            'gt_masks', 'mask_shape', 'hm_shape',
+            'down_scale', 'hm_down_scale', 'gt_points'
+        ]
+    ),
+    #dict(type='Resize', size=(img_width, img_height)),
+    dict(type='Normalize', img_norm=img_norm),
+    dict(type='ToTensor', keys=['img', 'gt_hm']),
+]
+
+
+val_process = [
+    dict(type='Alaug',
+        transforms=[dict(type='Compose', params=dict(bboxes=False, keypoints=True, masks=False)),
+            dict(type='Crop',
+            x_min=crop_bbox[0],
+            x_max=crop_bbox[2],
+            y_min=crop_bbox[1],
+            y_max=crop_bbox[3],
+            p=1),
+        dict(type='Resize', height=img_scale[1], width=img_scale[0], p=1)]
+    ),
+    #dict(type='Resize', size=(img_width, img_height)),
+    dict(type='Normalize', img_norm=img_norm),
+    dict(type='ToTensor', keys=['img']),
+]
+
+dataset_path = './data/CULane'
+dataset = dict(
+    train=dict(
+        type='CULane',
+        data_root=dataset_path,
+        split='train',
+        processes=train_process,
+    ),
+    val=dict(
+        type='CULane',
+        data_root=dataset_path,
+        split='test',
+        processes=val_process,
+    ),
+    test=dict(
+        type='CULane',
+        data_root=dataset_path,
+        split='test',
+        processes=val_process,
+    )
+)
+
+
+workers = 12 
+log_interval = 1000
+lr_update_by_epoch=True