add ade20k and a bit small fixes

.gitignore

@@ -111,6 +111,8 @@ adj.md
 tensorrt/build/*
 datasets/coco/train.txt
 datasets/coco/val.txt
+datasets/ade20k/train.txt
+datasets/ade20k/val.txt
 pretrained/*
 run.sh
 openvino/build/*

README.md

@@ -15,6 +15,12 @@ mIOUs on cocostuff val2017 set:
 | bisenetv1 | 31.49 | 31.42 | 32.46 | 32.55 | [download](https://github.com/CoinCheung/BiSeNet/releases/download/0.0.0/model_final_v1_coco_new.pth) |
 | bisenetv2 | 30.49 | 30.55 | 31.81 | 31.73 | [download](https://github.com/CoinCheung/BiSeNet/releases/download/0.0.0/model_final_v2_coco.pth) |
 
+mIOUs on ade20k val set:
+| none | ss | ssc | msf | mscf | link |
+|------|:--:|:---:|:---:|:----:|:----:|
+| bisenetv1 | 36.15 | 36.04 | 37.27 | 36.58 | [download](https://github.com/CoinCheung/BiSeNet/releases/download/0.0.0/model_final_v1_ade20k.pth) |
+| bisenetv2 | 32.53 | 32.43 | 33.23 | 31.72 | [download](https://github.com/CoinCheung/BiSeNet/releases/download/0.0.0/model_final_v2_ade20k.pth) |
+
 Tips: 
 
 1. **ss** means single scale evaluation, **ssc** means single scale crop evaluation, **msf** means multi-scale evaluation with flip augment, and **mscf** means multi-scale crop evaluation with flip evaluation. The eval scales and crop size of multi-scales evaluation can be found in [configs](./configs/).
@@ -23,7 +29,9 @@ Tips:
 
 3. The authors of bisenetv2 used cocostuff-10k, while I used cocostuff-123k(do not know how to say, just same 118k train and 5k val images as object detection). Thus the results maybe different from paper. 
 
-4. The model has a big variance, which means that the results of training for many times would vary within a relatively big margin. For example, if you train bisenetv2 for many times, you will observe that the result of **ss** evaluation of bisenetv2 varies between 73.1-75.1. 
+4. The authors did not do experiments on ade20k, thus there is no official training settings, here I simply provide a "make it work" result. Maybe the results on ade20k can be boosted with better settings.
+
+5. The model has a big variance, which means that the results of training for many times would vary within a relatively big margin. For example, if you train bisenetv2 on cityscapes for many times, you will observe that the result of **ss** evaluation of bisenetv2 varies between 73.1-75.1. 
 
 
 ## deploy trained models
@@ -85,7 +93,7 @@ $ unzip gtFine_trainvaltest.zip
 
 2.cocostuff   
 
-Download `train2017.zip`, `val2017.zip` and `stuffthingmaps_trainval2017.zip` split from official [website](https://cocodataset.org/#download). Then do as following:
+Download `train2017.zip`, `val2017.zip` and `stuffthingmaps_trainval2017.zip` split from official [website](https://cocodataset.org/#download). Then do as following:  
 ```
 $ unzip train2017.zip
 $ unzip val2017.zip
@@ -97,10 +105,21 @@ $ mv train2017/ /path/to/BiSeNet/datasets/coco/labels
 $ mv val2017/ /path/to/BiSeNet/datasets/coco/labels
 
 $ cd /path/to/BiSeNet
-$ python tools/gen_coco_annos.py
+$ python tools/gen_dataset_annos.py --dataset coco
 ```
 
-3.custom dataset  
+3. ade20k
+
+Download `ADEChallengeData2016.zip` and unzip it. Then we can move the uncompressed folders to `datasets/ade20k`, and generate the txt files with the script I prepared for you:  
+```
+$ unzip ADEChallengeData2016.zip
+$ mv ADEChallengeData2016/images /path/to/BiSeNet/datasets/ade20k/
+$ mv ADEChallengeData2016/annotations /path/to/BiSeNet/datasets/ade20k/
+$ python tools/gen_dataset_annos.py --ade20k
+```
+
+
+4. custom dataset  
 
 If you want to train on your own dataset, you should generate annotation files first with the format like this: 
 ```

configs/bisenetv1_ade20k.py

@@ -0,0 +1,24 @@
+
+cfg = dict(
+    model_type='bisenetv1',
+    n_cats=150,
+    num_aux_heads=2,
+    lr_start=4e-2,
+    weight_decay=1e-4,
+    warmup_iters=1000,
+    max_iter=40000,
+    dataset='ADE20k',
+    im_root='./datasets/ade20k',
+    train_im_anns='./datasets/ade20k/train.txt',
+    val_im_anns='./datasets/ade20k/val.txt',
+    scales=[0.5, 2.],
+    cropsize=[512, 512],
+    eval_crop=[512, 512],
+    eval_scales=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
+    eval_start_shortside=512,
+    ims_per_gpu=8,
+    eval_ims_per_gpu=1,
+    use_fp16=True,
+    use_sync_bn=True,
+    respth='./res',
+)

configs/bisenetv2_ade20k.py

@@ -0,0 +1,25 @@
+
+## bisenetv2
+cfg = dict(
+    model_type='bisenetv2',
+    n_cats=150,
+    num_aux_heads=4,
+    lr_start=5e-3,
+    weight_decay=1e-4,
+    warmup_iters=1000,
+    max_iter=160000,
+    dataset='ADE20k',
+    im_root='./datasets/ade20k',
+    train_im_anns='./datasets/ade20k/train.txt',
+    val_im_anns='./datasets/ade20k/val.txt',
+    scales=[0.5, 2.],
+    cropsize=[640, 640],
+    eval_crop=[640, 640],
+    eval_start_shortside=640,
+    eval_scales=[0.5, 0.75, 1, 1.25, 1.5, 1.75],
+    ims_per_gpu=2,
+    eval_ims_per_gpu=1,
+    use_fp16=True,
+    use_sync_bn=True,
+    respth='./res',
+)

datasets/ade20k/annotations

@@ -0,0 +1 @@
+/data/zzy/.datasets/ADEChallengeData2016/annotations/

datasets/ade20k/images

@@ -0,0 +1 @@
+/data/zzy/.datasets/ADEChallengeData2016/images/

lib/data/ade20k.py

@@ -0,0 +1,40 @@
+#!/usr/bin/python
+# -*- encoding: utf-8 -*-
+
+import os
+import os.path as osp
+import json
+
+import torch
+from torch.utils.data import Dataset, DataLoader
+import torch.distributed as dist
+import cv2
+import numpy as np
+
+import lib.data.transform_cv2 as T
+from lib.data.base_dataset import BaseDataset
+
+'''
+proportion of each class label pixels:
+    [0.1692778570779725, 0.11564757275917185, 0.0952101638485813, 0.06663867349694136, 0.05213595836428788, 0.04856869977177328, 0.04285300460652723, 0.024667459730413076, 0.021459432596108052, 0.01951911788079975, 0.019458422169334556, 0.017972951662770457, 0.017102797922112795, 0.016127154995430226, 0.012743318904507446, 0.011871312183986243, 0.01169223174996906, 0.010873715499098895, 0.01119535711707017, 0.01106824347921356, 0.010700814956159628, 0.00792769980935508, 0.007320940186670243, 0.007101978087028939, 0.006652130884336369, 0.0065129268341813954, 0.005905601374046595, 0.005655465856321791, 0.00485152244584825, 0.004812313401121428, 0.004808430157907591, 0.004852065319115992, 0.0035166264746248105, 0.0034049293812196796, 0.0031501695661207163, 0.003200865983720736, 0.0027563053654176255, 0.0026019635559833536, 0.002535207367187799, 0.0024709898687369503, 0.002511264681160722, 0.002349575022340693, 0.0022952289072600395, 0.0021756144527500325, 0.0020667410351909894,
+    0.002019785482875027, 0.001971430263652598, 0.0019830032929254865, 0.0019170129596070547, 0.0019400873699042965, 0.0019177214046286212, 0.001992758707175458, 0.0019064211898405371, 0.001794991169874655, 0.0017086228805355563, 0.001816450049952539, 0.0018115561530790863, 0.0017526224833158293, 0.0016693853602227783, 0.001690968246884664, 0.001672815290479542, 0.0016435338913693607, 0.0015994805524026869, 0.001415586825791652, 0.0015309535955159497, 0.0015066783881302896, 0.0015584265652761034, 0.0014294452504793305, 0.0014381224963739522, 0.0013854752714941247, 0.001299217899155161, 0.0012526667460881378, 0.0013178209535318454, 0.0012941402888239277, 0.0010893388225083507, 0.0011300189527483507, 0.0010488809855522653, 0.0009206912461167046, 0.0009957668988478528, 0.0009413381127111981, 0.0009365154048026355, 0.0009059601825045681, 0.0008541199189880419, 0.0008971791385063005, 0.0008428502465623139, 0.0008056902958152122, 0.0008098830962054097, 0.0007822564960661871, 0.0007982742428082544, 0.0007502832355158758, 0.0007779780392762995, 0.0007712568824233966, 0.0007453305503359334, 0.0006837047894907241, 0.0007144561259049724, 0.0006892632697976981,
+    0.0006652429648347085, 0.0006708271650257716, 0.0006737982709217282, 0.0006266153732017621, 0.0006591083131957701, 0.0006729084088606035, 0.0006615025588342957, 0.0005978453864296776, 0.0005662905332794616, 0.0005832571600309656, 0.000558171776296493, 0.0005270943484946844, 0.0005918616094679417, 0.0005653340750898915, 0.0005626451989934503, 0.0005906185582842337, 0.0005217418569022469, 0.0005282586325333688, 0.0005198277923139954, 0.0004861910064034809, 0.0005218504774841597, 0.0005172358250665335, 0.0005247616468645153, 0.0005357304885031275, 0.0004276964118043196, 0.0004607179872730913, 0.00041193838996318965, 0.00042133234798497776, 0.000374820234027733, 0.00041071531761801536, 0.0003664373889492048, 0.00043033958917813777, 0.00037797413481418125, 0.0004129435322190717, 0.00037504252731164754, 0.0003633328611545351, 0.00039741354470741193, 0.0003815260048785467, 0.00037395769934345317, 0.00037914990094397704, 0.000360210650939554, 0.0003641708241638368, 0.0003354311501122861, 0.0003386525655944687, 0.0003593692433029189, 0.00034422115014162057, 0.00032131529694189243, 0.00031263024322531515, 0.0003252564098949305, 0.00034751306566322646, 0.0002711341955909471, 0.00022987904222809388, 0.000242549759411221, 0.0002045743505533957]
+'''
+
+
+
+class ADE20k(BaseDataset):
+
+    def __init__(self, dataroot, annpath, trans_func=None, mode='train'):
+        super(ADE20k, self).__init__(
+                dataroot, annpath, trans_func, mode)
+        self.n_cats = 150
+        self.lb_ignore = 255
+        self.lb_map = np.arange(200) - 1 # label range from 1 to 149, 0 is ignored
+        self.lb_map[0] = 255
+
+        self.to_tensor = T.ToTensor(
+            mean=(0.49343230, 0.46819794,  0.43106043), # ade20k, rgb
+            std=(0.25680755, 0.25506608, 0.27422913),
+        )
+

lib/data/get_dataloader.py

@@ -8,6 +8,7 @@
 
 from lib.data.cityscapes_cv2 import CityScapes
 from lib.data.coco import CocoStuff
+from lib.data.ade20k import ADE20k
 from lib.data.customer_dataset import CustomerDataset
 
 

openvino/README.md

@@ -4,7 +4,10 @@
 
 Openvino is used to deploy model on intel cpus or "gpu inside cpu".  
 
-My cpu is Intel(R) Xeon(R) Gold 6240 CPU @ 2.60GHz.
+My platform:  
+* Ubuntu 18.04
+* Intel(R) Xeon(R) Gold 6240 CPU @ 2.60GHz
+* openvino_2021.4.689
 
 
 ### preparation

tools/check_dataset_info.py

@@ -62,15 +62,12 @@
         max_lb_val = max(max_lb_val, np.max(lb))
         min_lb_val = min(min_lb_val, np.min(lb))
 
-min_lb_val = 0
-max_lb_val = 181
-lb_minlength = 182
 ## label info
 lb_minlength = max_lb_val+1-min_lb_val
 lb_hist = np.zeros(lb_minlength)
 for lbpth in tqdm(lbpaths):
     lb = cv2.imread(lbpth, 0)
-    lb = lb[lb != lb_ignore] + min_lb_val
+    lb = lb[lb != lb_ignore] - min_lb_val
     lb_hist += np.bincount(lb, minlength=lb_minlength)
 
 lb_missing_vals = [ind + min_lb_val
@@ -113,7 +110,7 @@
 print(f'we ignore label value of {args.lb_ignore} in label images')
 print(f'label values are within range of [{min_lb_val}, {max_lb_val}]')
 print(f'label values that are missing: {lb_missing_vals}')
-print('ratios of each label value: ')
+print('ratios of each label value(from small to big, without ignored): ')
 print('\t', lb_ratios)
 print('\n')
 

tools/evaluate.py

@@ -31,9 +31,10 @@ def get_round_size(size, divisor=32):
 
 class SizePreprocessor(object):
 
-    def __init__(self, shape=None, shortside=None):
+    def __init__(self, shape=None, shortside=None, longside=None):
         self.shape = shape
         self.shortside = shortside
+        self.longside = longside
 
     def __call__(self, imgs):
         new_size = None
@@ -45,6 +46,13 @@ def __call__(self, imgs):
             if h < w: h, w = ss, int(ss / h * w)
             else: h, w = int(ss / w * h), ss
             new_size = h, w
+        elif not self.longside is None: # long size limit
+            h, w = imgs.size()[2:]
+            if max(h, w) > self.longside:
+                ls = self.longside
+                if h < w: h, w = int(ls / w * h), ls
+                else: h, w = ls, int(ls / h * w)
+                new_size = h, w
 
         if not new_size is None:
             imgs = F.interpolate(imgs, size=new_size,
@@ -125,6 +133,7 @@ def __init__(self, n_classes, scales=(0.5, ), flip=False, lb_ignore=255, size_pr
         self.sp = size_processor
         self.metric_observer = Metrics(n_classes, lb_ignore)
 
+    @torch.no_grad()
     def __call__(self, net, dl):
         ## evaluate
         n_classes = self.n_classes
@@ -305,7 +314,9 @@ def eval_model(cfg, net):
 
     size_processor = SizePreprocessor(
             cfg.get('eval_start_shape'),
-            cfg.get('eval_start_shortside'))
+            cfg.get('eval_start_shortside'),
+            cfg.get('eval_start_longside'),
+            )
 
     single_scale = MscEvalV0(
             n_classes=cfg.n_cats,
@@ -411,7 +422,9 @@ def evaluate(cfg, weight_pth):
 
     ## evaluator
     iou_heads, iou_content, f1_heads, f1_content = eval_model(cfg, net)
+    logger.info('\neval results of f1 score metric:')
     logger.info('\n' + tabulate(f1_content, headers=f1_heads, tablefmt='orgtbl'))
+    logger.info('\neval results of miou metric:')
     logger.info('\n' + tabulate(iou_content, headers=iou_heads, tablefmt='orgtbl'))
 
 
@@ -432,11 +445,9 @@ def main():
         torch.cuda.set_device(local_rank)
         dist.init_process_group(backend='nccl')
     if not osp.exists(cfg.respth): os.makedirs(cfg.respth)
-    setup_logger('{}-eval'.format(cfg.model_type), cfg.respth)
+    setup_logger(f'{cfg.model_type}-{cfg.dataset.lower()}-eval', cfg.respth)
     evaluate(cfg, args.weight_pth)
 
 
 if __name__ == "__main__":
     main()
-     # 0.70646 |            0.719953 |  0.715522 |       0.712184
-     # 0.70646  | 0.719953 | 0.715522 | 0.712184