From 2d6d8f10b6f4ac8e753f79311c91a042eb166185 Mon Sep 17 00:00:00 2001 From: coincheung <867153576@qq.com> Date: Fri, 25 Jan 2019 10:36:07 +0800 Subject: [PATCH] a better demo though not complete --- .gitignore | 6 +- README.md | 33 ++- adj.md | 367 +++++++++++++++++++++++++++++++- cityscapes.py | 153 ++++++++++++++ cityscapes_info.json | 492 +++++++++++++++++++++++++++++++++++++++++++ data | 1 + evaluate.py | 89 ++++++++ example.png | Bin 0 -> 2355053 bytes gt.png | Bin 0 -> 25755 bytes infer.py | 64 ++++++ license.txt | 23 -- logger.py | 19 ++ model.py | 242 ++++++++++++--------- resnet.py | 222 +++++++++++++++++++ train.py | 207 ++++++++++++++++++ transform.py | 93 ++++++++ xception.py | 240 --------------------- 17 files changed, 1882 insertions(+), 369 deletions(-) create mode 100644 cityscapes.py create mode 100644 cityscapes_info.json create mode 120000 data create mode 100644 evaluate.py create mode 100644 example.png create mode 100644 gt.png create mode 100644 infer.py delete mode 100644 license.txt create mode 100644 logger.py create mode 100644 resnet.py create mode 100644 train.py create mode 100644 transform.py delete mode 100644 xception.py diff --git a/.gitignore b/.gitignore index b38d235..3848be1 100644 --- a/.gitignore +++ b/.gitignore @@ -104,6 +104,8 @@ venv.bak/ ## Coin: -gtFine_trainvaltest.zip -gtFine/ +data/ +play.py +preprocess_data.py +res/ diff --git a/README.md b/README.md index 128eafd..f81ce40 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,32 @@ # BiSeNet -My implementation of [BiSeNet](https://arxiv.org/abs/1808.00897). Still under developing, if you are keen about the process, you could refer to the `dev` branch. - +My implementation of [BiSeNet](https://arxiv.org/abs/1808.00897). My environment is pytorch1.0 and python3, the code is not tested with other environments. + + +### get cityscapes dataset +Register and download the dataset from the official [website](https://www.cityscapes-dataset.com/). Then decompress them in the `data/` directory: +``` + $ mkdir -p data + $ mv /path/to/leftImg8bit_trainvaltest.zip data + $ mv /path/to/gtFine_trainvaltest.zip data + $ cd data + $ unzip leftImg8bit_trainvaltest.zip + $ unzip gtFine_trainvaltest.zip +``` + +### train and evaluation +Just run the train script: +``` + $ python train.py +``` +This would take almost one day on a single 1080ti gpu, and the mIOU will also be computed after training is done. + + + +### Notes: +The paper proposes two versions with different backbones: resnet18 and xception39. I only implement the resnet18 version. There are a lot of results reported in the paper. I only implement the final experiments in the ablation study. The target mIOU is 71.4 and the crop size is (640, 360). + +Since the paper does not mention the training iters, I simply used a 9k schema. After plenty of experiments, I got to find that: With merely `u-shape+sp+ffm`, the mIOU can get the value of 69.1, much higher than reported in the paper(guess this is because of the difference between resnet18 backbone and xception39 backbone). Notwithstanding, with additional `ARM` and `GP`, the mIOU would drops to the range of 67-69. + +I have tried various ways to use these two modules, include using `GP` as some attention vector to be multiplied to the feature (instead of directly addition) and adding `ARM` to the resnet residual path rather than to the residual block output as does with [SENET](https://arxiv.org/abs/1709.01507). These methods all failed, so I did not involve them in this repository. + +In a word, I am stuck, and I cannot make further improvement with `ARM` or `GP`. Please spare some light on me, if you have better understanding of the usage of these two modules. diff --git a/adj.md b/adj.md index 03393e6..1c617c1 100644 --- a/adj.md +++ b/adj.md @@ -1 +1,366 @@ -1. see if xception should end with bn or relu or conv +* 看data是否正确,为啥输出变在(360, 640) 了 +* 看是否是训练20个类,但是预测时只剩下19个了 + + +#=========== +baseline +#=========== +1. +momentum = 0.9 +weight_decay = 1e-4 +lr_start = 1e-3 +max_iter = 11000 +power = 0.9 +warmup_steps = 2000 +warmup_start_lr = 1e-6 + +mIOU = 34.56 + +2. paper里面的train 参数, +11000iter无warmup: 37.67 +21000iter无warmup: +11000iter无warmup(修正warmup step): 38.18 + +1000/11000: 38.09 + + +1. 使用auxiliary的loss +aux_loss加3x3conv,使用feat16和32: 35.01 +使用8和16的feat加aux_loss + +2. ffm的conv_bn_relu的stride改成2: 31.72 + +3. 最后输出的net,再加一个1x1的conv,stride=1,通道不变: 28.63 + +4. 训练时只使用其中的19个id,让训练和测试时使用相同的id: 41.97 + +5. 不加auxiliary: 47.9,看来加auxiliary是有技巧的。 + +6. 调整sp和cp的输出通道数,让两个均匀一点: +* sp变成64,256,512: 47.47 +* 把cp变成FPN这种: 47.46 + + +7. 训练次数 +baseline: +20类,FPN结构,avg先conv-bn-sig再乘到FPN上面,先interpolate再1x1conv,不加auxiliary +* 11000次: 48.21 +* 31000次: 44.12, 48.69, 51.10 +* 51000次: 45.48, 46.09, 49.83, 51.17, 52.27 +* 71000次: 45.54, 48.50, 48.88, 51.43, 51.53, 52.19, 53.07 +* 91000次: 44.53, 47.90, 48.20, 50.03, 51.35, 50.86, 51.74, 52.91, 53.42 +看来还是加长训练效果更好。 + +8. baseline, 51000次, 19类 +screen: 37.86, 41.61, 41.98, 44.29, 46.19 + +9. cp 给interpolate之后再加一个3x3conv +19类, 51000次: 35.86, 40.52, 42.16, 44.25, 46.31 - 加3x3conv有用 + +9. 都用kaiming_normal给初始化: +46.22, 48.98, 50.08, 51.59, 52.09 - kaiming_normal作用明显 + +10. FPN换成nearest +eval里面有interpolate: +47.37, 49.41, 50.67, 52.48, 53.29 - nearest 提高了近一个点 +去掉eval里面的interpolate: 左 + +==== +11. 3x3之前的inter变成nearest +eval带inter的: 49.17, 49.33, 53.53, 54.38, 55.27 - 又提高了不少 + +12. 模型变成20类,把255当成19 +eval去掉inter的: 50.48, 53.25, 55.78, 56.18, 57.91 - 看样子是对的 + +=== +13. 使用avg而不是avg_sig: +49.09, 48.90,, 53.72, 55.80, 56.92 - 看样子使用avg_sig比avg好 + +14. avg_sig加在feat32上,再跟feat16相加 +50.88, 53.48, 54.59, 56.45, 57.97 - 强一丢丢,差不多 + +15. avg加在feat32上,再跟feat16相加 +50.86, 51.83, 54.81, 56.16, 57.50 - 仍然是差不多,总体上,avg_sig就比avg好 + +==== model.py.bak + +16. feat32与avg相乘,再与feat16 concat起来 +64, 256, 512, nearest, 无3x3conv: 42.88, 51.15, 51.14, 53.82, 54.98 +64, 256, 512, nearest, 有3x3conv, 1280: 45.49, 51.30, 51.69, 53.56, 55.58 +64, 256, 512, nearest, 有3x3conv, 1024: 47.60, 51.41, 52.92, 54.22, 55.73 +64, 256, 512, nearest, 有1x1conv, 1024: 46.45, 50.23, 51.13, 53.65, 55.19 - 变成1x1不行 +64, 256, 1024, nearest, 有3x3conv, 1536: 46.82, 46.12, 50.22, 53.66, 55.42 - 差不多 +64, 256, 512, bilinear, 有3x3conv, 1024: 43.04, 48.96, 50.34, 51.71, 53.01 -bilinear不行 + +17. feat32与avg_sig相乘,再与feat16 concat起来 +64, 256, 512, nearest, 有3x3conv, 1024: 49.68, 51.53, 53.75, 55.56, 56.33 - avg_sig更好 + +18. 先把feat16和feat32做concat,再与avg-conv-bn-sig相乘 +放大都是nearest +后面不加3x3conv, 1280: 48.95, 51.24, 52.39, 54.53, 55.07 +后面不加3x3conv, 1792: 47.78, 48.28, 53.26, 53.94, 55.42 +后面加3x3conv, 1024: 45.89, 51.59, 52.57, 54.42, 55.27 + +以上说明concat最好的结果也不如FPN的连接方式 +==== +用回FPN方式 +baseline: +avg_sig与arm32相乘,再通过FPN与arm16结合,放大成feat8大小,加3x3conv,都用nearest,ffm=512(64,256,512)+512 +50.63, 52.60, 55.09, 57.09, 57.98 + +19. 把sp的conv核加大试试,比如像resnet那种,第一个核改用7x7的 +第一个conv改成7x7: +47.74, 53.10, 54.50, 56.04, 58.22 + +第一个conv改成3个3x3,第一个s=2, 前两个32,最后一个64: +50.11, 53.87, 54.48, 56.57, 58.10 + +第二个conv改成5x5的: +50.81, 52.50, 56.01, 56.12, 58.09 + +第三个conv改成1024, conv3x3: +50.84, 53.98, 54.62, 57.15, 58.19 + +第三个conv改成1024, conv5x5: +50.90, 51.01, 55.43, 56.09, 58.35 + +- 说明第三个改成1024,并且第一个使用大核效果会更好一些 +- 第一个7x7改成3个3x3效果不算好 + +20. 最后的1x1conv换成3x3的 +48.84, 52.52, 55.01, 56.55, 58.31 +都差不多,说明作用不大 + +21. sp使用第一个7x7conv,第二个5x5,第三个5x4 1024。最后的conv使用1x1, 64-256-512 +51.56, 54.15, 55.01, 56.34, 58.50 + +22. cp 换成unsample-8s的仍然是FPN连接 +无conv_out: 50.14, 53.07, 55.52, 56.40, 58.21 +有conv_out: 49.76, 52.14, 54.78, 56.71, 57.86 - 已经有了conv_outer,再加也没啥用。 +无conv_out, FFM的conv改成s=1: 52.41, 55.97, 57.95, 59.40, 60.70 - 作用大 +有conv_out, FFM的conv改成s=1: + + +23. 最后的feat8是deconv到原图大小 +16x16-s8: 51.72, 54.88, 56.85, 59.24, 60.39 +改成3个4x4-2,不加bn-relu: + +24. 把cp里面的interpolate换成deconv +inner_conv是FPN形式1x1的: +47.90, 52.89, 55.58, 57.89, 59.46 + +inner_conv是FCN形式3x3的,并且没有outer_conv: +51.39, 50.62, 55.98, 58.44, 60.50 - deconv的话,fcn比fpn结构要好 + +25. unsample-8s,只upsample就行了,不加上feat8_arm的特征: +使用FPN结构: screen: 18205 +49.09, 54.31, 56.62, 58.22, 59.51 + +使用FCN结构(kaiming初始化deconv): +49.84, 52.76, 55.23, 56.86, 59.50 (baseline) + +- 看来有必要加上feat8_arm的特征 + + +26. FFM不改变输入的channel数,FFM之后加上1x1conv调通道再upconv +screen: 18205 + +27. 去掉conv16_outer直接输出特征之和 +48.97, 53.31, 55.92, 57.77, 59.90 + + +27. avg当成fcn的一层,放大然后加上去 +去掉conv16_outer之后: +48.82, 53.12, 55.82, 57.51, 59.74 + + +25. 加上auxiliary loss +只加一个feat32的: +一次放大32倍 + + +22. feat32 deconv成feat8, feat16 deconv成feat8,然后element加成feat8那么大 + + +22. cp的最后改成avg_pool然后unsample,再加到feat32上去 + + +13. sp改成32,64,256,调整sp和cp的channel数比例 + +14. 最基本的原始inter + concat方法,放弃conv + +15. 看两张卡跟一张卡比较的结果 + + + +7. 使用90k个iter去训练 +* cp变成FPN(bilinear), sp 64, 256, 512: +54.20 - 提高个点 +* cp变成FPN(nearest), sp 64, 256, 512: 晚 +* cp先FPN再channel-atten相乘(avg做conv-bn-sigmoid再乘): 更晚 + +* 先interpolate到原图大小,再conv1x1 + + +* 对arm输出先conv-bn-relu再interpolate,再1x1conv再用auxliary_loss: + + +* interpolate之后再加上conv-bn-relu + + +7. 使用自己的训练sheduel,两个step这种 + + + +2. eval 时像论文那样使用1024x1024 + +2. 如果不收敛就先warmup一下再弄 + + + +=================== +1. 论文中的 baseline: +使用64-32-16的大核作为out + 不使用auxiliary +49.17, 52.17, 54.44, 56.53, 57.64 + +加上auxiliary loss: +都16和32deconv成8s,然后再放大到原图,主out没有加deconv,直接用了双线性。 +46.44, 51.08, 52.13, 54.90, 55.94 + +不加auxiliary, feat32先3x3conv到n_classes,再interpolate,最后1x1conv +52.32 + +2. 不加auxiliary, feat32先1x1conv到n_classes,再interpolate,最后3x3conv, 改成90k +23.81, 34.05, 37.19, 40.61, 42.23, 43.49, 45.99, 46.58, 48.06 + +3. 像psp那样,先conv3x3(512)-bn-relu,再1x1conv(n_classes),再interpolate +52.65, 53.59, 54.19, 55.32, 55.81, 57.03, 55.78, 58.02, 58.50 + + +===== +加u-shape +1. 两个都是1x1conv-bn-relu,再concat + + + + +============== +baseline: cropsize=1024x1024 +65.64 + ++ auxiliary: PSP那种,一个conv-bn-relu加一个1x1conv +2048x1024: 65.69 +1536x768: 62.71 + ++ context 输出改成256通道 +2048x1024: 66.59 + ++ auxiliary 加上dropout 0.1 +66.73 + ++ linear 改成 nearest +67.41 + ++ sp 改成48 +右 + ++ 加上global avg + ++ feat_out之前加上一个conv-bn-relu + ++ 大cropsize,bn + ++ cropsize改成713\*713,使用gn + + +====== +baseline: +feat32直接加out_head: 56.54 +feat32先inter到8,再加out_head: 61.13 +把conv-bn-relu-dropout(out_chan=256)当成fcnhead: 61.25 + +baseline的做法是: +* feat32先nearest, upsample到8,再加conv-bn-relu(512-256),然后再conv_out到n_classes +* 使用fcn的head放在feat16上当成auxiliary loss + + +加u-shape: +feat32先upsample到16,再与feat16concat起来,再加conv-bn-relu到512,upsample到8, +最后fcnhead,feat32和feat16都加上fcnhead做成auxiliary loss +68.08 + + +加sp +* 用sum: +64-128-512,再相加,然后正常fcn_head_out: 68.76 +64-256-512,再相加,然后正常fcn_head_out: 68.79 +32-128-512,再相加,然后正常fcn_head_out: 68.59 +32-512-512: 68.76 +64-512-512: 68.83 +128-512-512: 68.88 +128-256-512: 68.72 +512-512-512: (memory overflow) + +用sum应该比ushape提高0.8个点, + +* 用ffm +sp用的32-128-512 +512+512cat到1024之后,conv成1024: 69.25 +512+512cat到1024之后,conv成512: 69.13 + +conv成1024: +sp 用64-256-512: 69.10 +sp 用64-512-512: +sp 用128-512-512: +sp 用256-512-512: + + +用ffm应该比ushape提高1.4个点。 + +final: +sp: 32-128-512: 69.127 + + +加arm不加avg: +arm学习特征: +* arm加在aux loss之前(aux loss不考虑arm) +68.98 +* aux loss考虑arm +68.02 + +arm学习残差: +* arm参与aux loss +68.28 +使用自制resnet: +68.30 + +* arm不参与aux loss +68.91 + +arm直接合并到bottleneck里面,就像se-resnet一样 +67.84 + +arm 合到bottleneck里面, 不加bn: +67.62 + +arm合到bottleneck里面,改成se的结构conv-relu-con-sig +67.40 + +用arm应该比ffm提高1.3 + + +加avg不加arm: +加avg应该会比ffm提高1个点 +68.68 +让avg变成atten,乘到feat32上去: +68.81 + +让avg变成atten,加到feat32上去: +68.73 + +=== +arm和avg都不要: +69.06 + + + diff --git a/cityscapes.py b/cityscapes.py new file mode 100644 index 0000000..85bd98a --- /dev/null +++ b/cityscapes.py @@ -0,0 +1,153 @@ +#!/usr/bin/python +# -*- encoding: utf-8 -*- + + +import torch +from torch.utils.data import Dataset +import torchvision.transforms as transforms + +import os.path as osp +import os +from PIL import Image +import numpy as np +import json + +from transform import * + + + +class CityScapes(Dataset): + def __init__(self, rootpth, cropsize=(640, 480), mode='train', *args, **kwargs): + super(CityScapes, self).__init__(*args, **kwargs) + assert mode in ('train', 'val', 'test') + self.mode = mode + self.ignore_lb = 255 + + with open('./cityscapes_info.json', 'r') as fr: + labels_info = json.load(fr) + self.lb_map = {el['id']: el['trainId'] for el in labels_info} + + ## parse img directory + self.imgs = {} + imgnames = [] + impth = osp.join(rootpth, 'leftImg8bit', mode) + folders = os.listdir(impth) + for fd in folders: + fdpth = osp.join(impth, fd) + im_names = os.listdir(fdpth) + names = [el.replace('_leftImg8bit.png', '') for el in im_names] + impths = [osp.join(fdpth, el) for el in im_names] + imgnames.extend(names) + self.imgs.update(dict(zip(names, impths))) + + ## parse gt directory + self.labels = {} + gtnames = [] + gtpth = osp.join(rootpth, 'gtFine', mode) + folders = os.listdir(gtpth) + for fd in folders: + fdpth = osp.join(gtpth, fd) + lbnames = os.listdir(fdpth) + lbnames = [el for el in lbnames if 'labelIds' in el] + names = [el.replace('_gtFine_labelIds.png', '') for el in lbnames] + lbpths = [osp.join(fdpth, el) for el in lbnames] + gtnames.extend(names) + self.labels.update(dict(zip(names, lbpths))) + + self.imnames = imgnames + self.len = len(self.imnames) + assert set(imgnames) == set(gtnames) + assert set(self.imnames) == set(self.imgs.keys()) + assert set(self.imnames) == set(self.labels.keys()) + + ## pre-processing + self.to_tensor = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), + ]) + self.trans_train = Compose([ + HorizontalFlip(), + RandomScale((0.75, 1.0, 1.5, 1.75, 2.0)), + RandomCrop(cropsize) + ]) + self.trans_val = Compose([ + ScaleBySize((1536, 768)), + ]) + + + def __getitem__(self, idx): + fn = self.imnames[idx] + impth = self.imgs[fn] + lbpth = self.labels[fn] + img = Image.open(impth) + label = Image.open(lbpth) + if self.mode == 'train': + im_lb = dict(im = img, lb = label) + im_lb = self.trans_train(im_lb) + img, label = im_lb['im'], im_lb['lb'] + else: + im_lb = dict(im = img, lb = label) + # im_lb = self.trans_val(im_lb) + img, label = im_lb['im'], im_lb['lb'] + img = self.to_tensor(img) + label = np.array(label).astype(np.int64)[np.newaxis, :] + label = self.convert_labels(label) + return img, label + + + def __len__(self): + return self.len + + + def convert_labels(self, label): + for k, v in self.lb_map.items(): + label[label == k] = v + return label + + + +if __name__ == "__main__": + from tqdm import tqdm + ds = CityScapes('./data/', n_classes=19, mode='val') + # ds = CityScapes('./data/', n_classes=19, mode='train') + # im, label = ds[10] + # _ = input() + uni = [] + for im, lb in tqdm(ds): + lb_uni = np.unique(lb).tolist() + uni.extend(lb_uni) + print(uni) + print(set(uni)) + + # # print(type(im)) + # # print(im.size()) + # # print(label.size()) + # # print(type(label)) + # from torch.utils.data import DataLoader + # dl = DataLoader(ds, + # batch_size = 30, + # shuffle = False, + # num_workers = 6, + # drop_last = False) + # im, lb = next(iter(dl)) + # lb = lb.numpy() + # # print(lb) + # print(type(lb)) + # print(lb.shape) + # print(label.shape) + # # print(im) + # print(im.size()) + # print(np.max(lb)) + # + # from tqdm import tqdm + # + # diter = iter(dl) + # lmax, lmin = -1, 1000 + # for i, (im, lb) in enumerate(tqdm(diter)): + # lb = lb.numpy() + # lb[lb == 255] = 3 + # lmax = np.max(lb) if lmax < np.max(lb) else lmax + # lmin = np.min(lb) if lmin > np.min(lb) else lmin + # + # print(lmax, lmin) + # diff --git a/cityscapes_info.json b/cityscapes_info.json new file mode 100644 index 0000000..161b147 --- /dev/null +++ b/cityscapes_info.json @@ -0,0 +1,492 @@ +[ + { + "hasInstances": false, + "category": "void", + "catid": 0, + "name": "unlabeled", + "ignoreInEval": true, + "id": 0, + "color": [ + 0, + 0, + 0 + ], + "trainId": 255 + }, + { + "hasInstances": false, + "category": "void", + "catid": 0, + "name": "ego vehicle", + "ignoreInEval": true, + "id": 1, + "color": [ + 0, + 0, + 0 + ], + "trainId": 255 + }, + { + "hasInstances": false, + "category": "void", + "catid": 0, + "name": "rectification border", + "ignoreInEval": true, + "id": 2, + "color": [ + 0, + 0, + 0 + ], + "trainId": 255 + }, + { + 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+/home/zhangzy/learn-projects/cityscapes/ \ No newline at end of file diff --git a/evaluate.py b/evaluate.py new file mode 100644 index 0000000..ec20dd6 --- /dev/null +++ b/evaluate.py @@ -0,0 +1,89 @@ +#!/usr/bin/python +# -*- encoding: utf-8 -*- + +from logger import * +from model import BiSeNet +from cityscapes import CityScapes + +import torch +import torch.nn as nn +from torch.utils.data import DataLoader +import torch.nn.functional as F + +import os +import os.path as osp +import logging +import time +import numpy as np +from tqdm import tqdm +import numba + + +@numba.jit +def compute_iou(pred, lb, lb_ignore=255): + assert pred.shape == lb.shape + + mask = np.logical_not(lb == lb_ignore) + clses = set(np.unique(lb).tolist()) + if lb_ignore in clses: clses.remove(lb_ignore) + ious = [] + for cls in clses: + ## TODO: use p_val, l_val = pred[mask], lb[mask] instead of always slice + pmask = pred[mask] == cls + lmask = lb[mask] == cls + cross = np.logical_and(pmask, lmask) + union = np.logical_or(pmask, lmask) + iou = float(np.sum(cross)) / float(np.sum(union)) + ious.append(iou) + ious = sum(ious) / len(ious) + return ious + + +def eval_model(net): + ## dataloader + dsval = CityScapes('./data', mode='val') + + ## evaluate + ious = [] + for i, (im, lb) in enumerate(tqdm(dsval)): + im = im.cuda().unsqueeze(0) + lb = np.squeeze(lb, 0) + H, W = lb.shape + with torch.no_grad(): + out_p, out16 ,out32 = net(im) + probs = F.softmax(out_p, 1).squeeze(0) + probs = probs.detach().cpu().numpy() + pred = np.argmax(probs, axis=0) + + iou = compute_iou(pred, lb) + ious.append(iou) + mIOU = sum(ious) / len(ious) + return mIOU + + +def evaluate(): + respth = './res' + logger = logging.getLogger() + + ## model + logger.info('setup and restore model') + n_classes = 19 + net = BiSeNet(n_classes=n_classes) + net.cuda() + net.eval() + save_pth = osp.join(respth, 'model_final.pth') + net.load_state_dict(torch.load(save_pth)) + + ## dataset + logger.info('compute the mIOU') + mIOU = 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dataset color map + with open('cityscapes_info.json', 'r') as fr: + cs_info = json.load(fr) + colormap = {el['trainId']:el['color'] for el in cs_info} + colormap[19] = colormap[255] + + ## load model + n_classes = 19 + net = BiSeNet(n_classes=n_classes) + net.cuda() + net.eval() + save_pth = './res/model_final.pth' + net.load_state_dict(torch.load(save_pth)) + + ## image + impth = './example.png' + im = Image.open(impth) + W, H = im.size + to_tensor = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), + ]) + im = to_tensor(im) + im = im.unsqueeze(0).cuda() + + ## inference + with torch.no_grad(): + out, out16, out32 = net(im) + probs = F.softmax(out, 1) + probs = probs.detach().cpu().numpy() + pred = np.squeeze(np.argmax(probs, axis=1), 0) + H, W = pred.shape + + ## show infered picture + out = np.empty((H, W, 3), dtype = np.uint8) + clses = np.unique(pred).tolist() + for cls in clses: + out[pred == cls, :] = colormap[cls] + + cv2.imshow('pred', out) + cv2.waitKey(0) + + + + +if __name__ == "__main__": + infer() diff --git a/license.txt b/license.txt deleted file mode 100644 index 20f10ca..0000000 --- a/license.txt +++ /dev/null @@ -1,23 +0,0 @@ ----------------------- -The Cityscapes Dataset ----------------------- - - -License agreement ------------------ - -This dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use the data given that you agree: - -1. That the dataset comes "AS IS", without express or implied warranty. Although every effort has been made to ensure accuracy, we (Daimler AG, MPI Informatics, TU Darmstadt) do not accept any responsibility for errors or omissions. -2. That you include a reference to the Cityscapes Dataset in any work that makes use of the dataset. For research papers, cite our preferred publication as listed on our website; for other media cite our preferred publication as listed on our website or link to the Cityscapes website. -3. That you do not distribute this dataset or modified versions. It is permissible to distribute derivative works in as far as they are abstract representations of this dataset (such as models trained on it or additional annotations that do not directly include any of our data) and do not allow to recover the dataset or something similar in character. -4. That you may not use the dataset or any derivative work for commercial purposes as, for example, licensing or selling the data, or using the data with a purpose to procure a commercial gain. -5. That all rights not expressly granted to you are reserved by us (Daimler AG, MPI Informatics, TU Darmstadt). - - -Contact -------- - -Marius Cordts, Mohamed Omran -www.cityscapes-dataset.net -mail@cityscapes-dataset.net diff --git a/logger.py b/logger.py new file mode 100644 index 0000000..9e62be3 --- /dev/null +++ b/logger.py @@ -0,0 +1,19 @@ +#!/usr/bin/python +# -*- encoding: utf-8 -*- + + +import os.path as osp +import time +import sys +import logging + + +## TODO: here log name change to BiSeNet rather than deeplab +def setup_logger(logpth): + logfile = 'BiSeNet-{}.log'.format(time.strftime('%Y-%m-%d-%H-%M-%S')) + logfile = osp.join(logpth, logfile) + FORMAT = '%(levelname)s %(filename)s(%(lineno)d): %(message)s' + logging.basicConfig(level=logging.INFO, format=FORMAT, filename=logfile) + logging.root.addHandler(logging.StreamHandler()) + + diff --git a/model.py b/model.py index 96bcaf5..c2bed6e 100644 --- a/model.py +++ b/model.py @@ -4,23 +4,25 @@ import torch import torch.nn as nn -import torch.utils.model_zoo as modelzoo import torch.nn.functional as F import torchvision +from resnet import Resnet18, Resnet18Arm + +## TODO: refactoring or simply split the code to two source files -resnet18_url = 'https://download.pytorch.org/models/resnet18-5c106cde.pth' class ConvBNReLU(nn.Module): - def __init__(self, in_chan, out_chan, ks = 3, stride=1, *args, **kwargs): + def __init__(self, in_chan, out_chan, ks = 3, stride=1, padding=1, *args, **kwargs): super(ConvBNReLU, self).__init__() self.conv = nn.Conv2d(in_chan, out_chan, kernel_size = ks, - stride=stride, - padding = 1, - bias=True) + stride = stride, + padding = padding, + bias = False) self.bn = nn.BatchNorm2d(out_chan) + self.init_weight() def forward(self, x): x = self.conv(x) @@ -28,20 +30,33 @@ def forward(self, x): x = F.relu(x, inplace = True) return x + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) -class SpatialPath(nn.Module): - def __init__(self, *args, **kwargs): - super(SpatialPath, self).__init__() - self.conv1 = ConvBNReLU(3, 64, stride = 2) - self.conv2 = ConvBNReLU(64, 128, stride = 2) - self.conv3 = ConvBNReLU(128, 256, stride = 2) + +class FCNOutput(nn.Module): + def __init__(self, in_chan, n_classes, *args, **kwargs): + super(FCNOutput, self).__init__() + self.conv = ConvBNReLU(in_chan, 256, ks=3, stride=1, padding=1) + self.dropout = nn.Dropout(0.1) + self.conv_out = nn.Conv2d(256, n_classes, kernel_size=1, bias=False) + self.init_weight() def forward(self, x): - x = self.conv1(x) - x = self.conv2(x) - x = self.conv3(x) + x = self.conv(x) + x = self.dropout(x) + x = self.conv_out(x) return x + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + class AttentionRefinementModule(nn.Module): def __init__(self, in_chan, *args, **kwargs): @@ -50,9 +65,10 @@ def __init__(self, in_chan, *args, **kwargs): self.conv = nn.Conv2d(in_chan, in_chan, kernel_size = 1, - bias=True) + bias = False) self.bn = nn.BatchNorm2d(in_chan) self.sigmoid = nn.Sigmoid() + self.init_weight() def forward(self, x): assert self.in_chan == x.size()[1] @@ -62,138 +78,162 @@ def forward(self, x): x = self.bn(x) x = self.sigmoid(x) x = torch.mul(in_ten, x) + x = x + in_ten return x + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) class ContextPath(nn.Module): - def __init__(self, n_classes = 10, *args, **kwargs): + def __init__(self, *args, **kwargs): super(ContextPath, self).__init__() - resnet = torchvision.models.resnet18() - self.conv1 = resnet.conv1 - self.bn1 = resnet.bn1 - self.relu = resnet.relu - self.maxpool = resnet.maxpool - self.layer1 = resnet.layer1 - self.layer2 = resnet.layer2 - self.layer3 = resnet.layer3 - self.layer4 = resnet.layer4 - + self.resnet = Resnet18() + # self.resnet = Resnet18Arm() + self.conv_cat = ConvBNReLU(768, 512, ks=3, stride=1, padding=1) self.arm16 = AttentionRefinementModule(256) self.arm32 = AttentionRefinementModule(512) - self.conv_feat16 = nn.Conv2d(256, - n_classes, - kernel_size = 3, - bias=True) - self.conv_feat32 = nn.Conv2d(512, - n_classes, - kernel_size = 3, + self.conv_avg = nn.Conv2d(512, + 512, + kernel_size = 1, bias=True) + self.bn_avg = nn.BatchNorm2d(512) + self.sig_avg = nn.Sigmoid() + + self.init_weight() def forward(self, x): - x = self.conv1(x) - x = self.bn1(x) - x = self.relu(x) - x = self.maxpool(x) - x = self.layer1(x) - x = self.layer2(x) - - feat16 = self.layer3(x) - feat32 = self.layer4(feat16) - avg = F.avg_pool2d(feat32, feat32.size()[2:]) - feat16_arm = self.arm16(feat16) - feat32_arm = self.arm32(feat32) - - feat32_with_avg = torch.mul(feat32_arm, avg) - feat32_up = F.interpolate(feat32_with_avg, scale_factor = 4) - feat16_up = F.interpolate(feat16_arm, scale_factor = 2) - - feat_out = torch.cat((feat32_up, feat16_up), dim = 1) - feat_out16 = self.conv_feat16(feat16) - feat_out32 = self.conv_feat32(feat32) + H0, W0 = x.size()[2:] + feat8, feat16, feat32 = self.resnet(x) + H8, W8 = feat8.size()[2:] + H16, W16 = feat16.size()[2:] + H32, W32 = feat32.size()[2:] - return feat_out, feat_out16, feat_out32 + # avg = F.avg_pool2d(feat32, feat32.size()[2:]) + # avg_atten = self.conv_avg(avg) + # avg_atten = self.bn_avg(avg_atten) + # avg_atten = self.sig_avg(avg_atten) + # # feat32 = torch.mul(feat32, avg) + # feat32 = feat32 + avg + + # feat32_arm = self.arm32(feat32) + # feat16_arm = self.arm16(feat16) + + feat32_up = F.interpolate(feat32, (H16, W16), mode='nearest') + feat16_cat = torch.cat([feat32_up, feat16], dim=1) + feat16_cat = self.conv_cat(feat16_cat) + + feat_out8 = F.interpolate(feat16_cat, (H8, W8), mode='nearest') + + return feat_out8, feat16, feat32 + # return feat_out8, feat16_arm, feat32_arm def init_weight(self): - state_dict = modelzoo.load_url(resnet18_url) - self_state_dict = self.state_dict() + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) +class SpatialPath(nn.Module): + def __init__(self, *args, **kwargs): + super(SpatialPath, self).__init__() + self.conv1 = ConvBNReLU(3, 32, ks=3, stride=2, padding=1) + self.conv2 = ConvBNReLU(32, 128, ks=3, stride=2, padding=1) + self.conv3 = ConvBNReLU(128, 512, ks=3, stride=2, padding=1) + self.init_weight() + + def forward(self, x): + x = self.conv1(x) + x = self.conv2(x) + x = self.conv3(x) + return x + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + nn.init.constant_(ly.bias, 0) + class FeatureFusionModule(nn.Module): - def __init__(self, in_chan, n_classes, *args, **kwargs): + def __init__(self, in_chan, out_chan, *args, **kwargs): super(FeatureFusionModule, self).__init__() - self.convblk = ConvBNReLU(in_chan, n_classes, ks = 3) - self.conv1 = nn.Conv2d(n_classes, n_classes, 1) - self.conv2 = nn.Conv2d(n_classes, n_classes, 1) + self.convblk = ConvBNReLU(in_chan, out_chan, ks=3, stride=1) + self.conv1 = nn.Conv2d(out_chan, out_chan, 1, bias=False) + self.conv2 = nn.Conv2d(out_chan, out_chan, 1, bias=False) self.sigmoid = nn.Sigmoid() + self.init_weight() def forward(self, fsp, fcp): - fcat = torch.cat((fsp, fcp), dim = 1) + fcat = torch.cat([fsp, fcp], dim=1) feat = self.convblk(fcat) atten = F.avg_pool2d(feat, feat.size()[2:]) atten = self.conv1(atten) - atten = F.relu(atten, inplace = True) + atten = F.relu(atten, inplace=True) atten = self.conv2(atten) atten = self.sigmoid(atten) feat_atten = torch.mul(feat, atten) feat_out = feat_atten + feat return feat_out + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) class BiSeNet(nn.Module): def __init__(self, n_classes, *args, **kwargs): super(BiSeNet, self).__init__() + self.cp = ContextPath() self.sp = SpatialPath() - self.cp = ContextPath(n_classes) - self.ffm = FeatureFusionModule(1024, n_classes) - + self.ffm = FeatureFusionModule(1024, 1024) + self.conv_out = FCNOutput(1024, n_classes) + self.conv_out16 = FCNOutput(256, n_classes) + self.conv_out32 = FCNOutput(512, n_classes) + self.init_weight() def forward(self, x): + H, W = x.size()[2:] + feat_cp, feat16, feat32 = self.cp(x) # 512 feat_sp = self.sp(x) - feat_cp, feat16, feat32 = self.cp(x) - feat_out = self.ffm(feat_sp, feat_cp) - return feat_out, feat16, feat32 + feat_fuse = self.ffm(feat_sp, feat_cp) + feat_out = self.conv_out(feat_fuse) + # feat_sum = feat_cp + feat_sp + # feat_out = self.conv_out(feat_sum) + feat_out16 = self.conv_out16(feat16) + feat_out32 = self.conv_out32(feat32) + + feat_out = F.interpolate(feat_out, (H, W), mode='bilinear') + feat_out16 = F.interpolate(feat_out16, (H, W), mode='bilinear') + feat_out32 = F.interpolate(feat_out32, (H, W), mode='bilinear') + return feat_out, feat_out16, feat_out32 + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) if __name__ == "__main__": - net = BiSeNet(21) - in_ten = torch.randn(10, 3, 224, 224) + net = BiSeNet(19) + net.cuda() + net.eval() + in_ten = torch.randn(16, 3, 640, 480).cuda() out, out16, out32 = net(in_ten) + # with torch.no_grad(): + # in_ten = torch.randn(1, 3, 1024, 2048).cuda() + # out, out16, out32 = net(in_ten) print(out.shape) - print(out16.shape) - print(out32.shape) + # print(out16.shape) + # print(out32.shape) - convbnrelu = ConvBNReLU(3, 10) - print(convbnrelu(in_ten).shape) - sp = SpatialPath() - out = sp(in_ten) - print(out.shape) - cp = ContextPath(10) - out, out16, out32 = cp(in_ten) - print(out.shape) - print(out16.shape) - print(out32.shape) - # arm = AttentionRefinementModule(3, 10) - # out = arm(in_ten) - # print(out.shape) - # # out_x, out_aux = net(in_ten) - # # print(out_x.shape) - # # print(out_aux.shape) - # in_ten = torch.randn(1, 2, 3,3) - # print(in_ten) - # import numpy as np - # sig = np.arange(2).reshape(1,2,1,1).astype(np.float32) - # sig = torch.tensor(sig) - # print(torch.mul(in_ten, sig)) - - ffm = FeatureFusionModule(in_chan = 1024, n_classes = 21) - feat1 = torch.randn(10, 768, 32, 32) - feat2 = torch.randn(10, 256, 32, 32) - feat_out = ffm(feat1, feat2) - print(feat_out.shape) diff --git a/resnet.py b/resnet.py new file mode 100644 index 0000000..29ddd20 --- /dev/null +++ b/resnet.py @@ -0,0 +1,222 @@ +#!/usr/bin/python +# -*- encoding: utf-8 -*- + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.model_zoo as modelzoo + + +resnet18_url = 'https://download.pytorch.org/models/resnet18-5c106cde.pth' + + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + + +class AttentionRefinementModule(nn.Module): + def __init__(self, in_chan, *args, **kwargs): + super(AttentionRefinementModule, self).__init__() + self.in_chan = in_chan + mid_chan = int(in_chan / 16) + self.conv1 = nn.Conv2d(in_chan, + mid_chan, + kernel_size = 1, + bias = False) + self.conv2 = nn.Conv2d(mid_chan, + in_chan, + kernel_size = 1, + bias = False) + self.relu = nn.ReLU(inplace=True) + # self.bn = nn.BatchNorm2d(in_chan) + self.sigmoid = nn.Sigmoid() + self.init_weight() + + def forward(self, x): + assert self.in_chan == x.size()[1] + in_ten = x + x = F.avg_pool2d(x, x.size()[2:]) + x = self.conv1(x) + x = self.relu(x) + x = self.conv2(x) + # x = self.bn(x) + x = self.sigmoid(x) + x = torch.mul(in_ten, x) + return x + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + +class BasicBlockArm(nn.Module): + def __init__(self, in_chan, out_chan, stride=1): + super(BasicBlockArm, self).__init__() + self.conv1 = conv3x3(in_chan, out_chan, stride) + self.bn1 = nn.BatchNorm2d(out_chan) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(out_chan, out_chan) + self.bn2 = nn.BatchNorm2d(out_chan) + self.arm = AttentionRefinementModule(out_chan) + self.downsample = None + if in_chan != out_chan or stride != 1: + self.downsample = nn.Sequential( + nn.Conv2d(in_chan, out_chan, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(out_chan), + ) + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.arm(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class BasicBlock(nn.Module): + def __init__(self, in_chan, out_chan, stride=1): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(in_chan, out_chan, stride) + self.bn1 = nn.BatchNorm2d(out_chan) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(out_chan, out_chan) + self.bn2 = nn.BatchNorm2d(out_chan) + self.downsample = None + if in_chan != out_chan or stride != 1: + self.downsample = nn.Sequential( + nn.Conv2d(in_chan, out_chan, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(out_chan), + ) + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +def create_layer_basic(in_chan, out_chan, bnum, stride=1): + layers = [BasicBlock(in_chan, out_chan, stride=stride)] + for i in range(bnum-1): + layers.append(BasicBlock(out_chan, out_chan, stride=1)) + return nn.Sequential(*layers) + + +def create_layer_basic_arm(in_chan, out_chan, bnum, stride=1): + layers = [BasicBlock(in_chan, out_chan, stride=stride)] + for i in range(bnum-2): + layers.append(BasicBlock(out_chan, out_chan, stride=1)) + layers.append(BasicBlockArm(out_chan, out_chan, stride=1)) + return nn.Sequential(*layers) + + +class Resnet18(nn.Module): + def __init__(self): + super(Resnet18, self).__init__() + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1) + self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2) + self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2) + self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2) + self.init_weight() + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + feat8 = self.layer2(x) # 1/8 + feat16 = self.layer3(feat8) # 1/16 + feat32 = self.layer4(feat16) # 1/32 + return feat8, feat16, feat32 + + def init_weight(self): + state_dict = modelzoo.load_url(resnet18_url) + self_state_dict = self.state_dict() + for k, v in state_dict.items(): + if 'fc' in k: continue + self_state_dict.update({k: v}) + self.load_state_dict(self_state_dict) + + +class Resnet18Arm(nn.Module): + def __init__(self): + super(Resnet18Arm, self).__init__() + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1) + self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2) + self.layer3 = create_layer_basic_arm(128, 256, bnum=2, stride=2) + self.layer4 = create_layer_basic_arm(256, 512, bnum=2, stride=2) + self.init_weight() + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + feat8 = self.layer2(x) # 1/8 + feat16 = self.layer3(feat8) # 1/16 + feat32 = self.layer4(feat16) # 1/32 + return feat8, feat16, feat32 + + def init_weight(self): + state_dict = modelzoo.load_url(resnet18_url) + self_state_dict = self.state_dict() + for k, v in state_dict.items(): + if k in self_state_dict.keys(): + self_state_dict.update({k: v}) + self.load_state_dict(self_state_dict) + + +if __name__ == "__main__": + net = Resnet18Arm() + x = torch.randn(16, 3, 224, 224) + out = net(x) + print(out[0].size()) + print(out[1].size()) + print(out[2].size()) + + + diff --git a/train.py b/train.py new file mode 100644 index 0000000..b4eba89 --- /dev/null +++ b/train.py @@ -0,0 +1,207 @@ +#!/usr/bin/python +# -*- encoding: utf-8 -*- + + +from logger import * +from model import BiSeNet +from cityscapes import CityScapes +from evaluate import eval_model + +import torch +import torch.nn as nn +from torch.utils.data import DataLoader +import torch.nn.functional as F + +import os +import logging +import time +import datetime + + +respth = './res' +if not osp.exists(respth): os.makedirs(respth) +setup_logger(respth) +logger = logging.getLogger() + +class Optimizer(object): + def __init__(self, + params, + lr0, + momentum, + wd, + warmup_steps, + warmup_start_lr, + max_iter, + power): + self.warmup_steps = warmup_steps + self.warmup_start_lr = warmup_start_lr + self.lr0 = lr0 + self.lr = self.lr0 + self.max_iter = float(max_iter) + self.power = power + self.it = 0 + self.optim = torch.optim.SGD( + params, + lr = lr0, + momentum = momentum, + weight_decay = wd) + self.warmup_factor = (self.lr0 / self.warmup_start_lr) ** (1. / self.warmup_steps) + + def get_lr(self): + if self.it <= self.warmup_steps: + lr = self.warmup_start_lr * (self.warmup_factor ** self.it) + else: + factor = (1 - (self.it - self.warmup_steps) / (self.max_iter - self.warmup_steps)) ** self.power + lr = self.lr0 * factor + return lr + + def step(self): + self.lr = self.get_lr() + for pg in self.optim.param_groups: + pg['lr'] = self.lr + self.optim.defaults['lr'] = self.lr + self.it += 1 + self.optim.step() + if self.it == self.warmup_steps: + logger.info('==> warmup done, start to implement poly lr strategy') + + def zero_grad(self): + self.optim.zero_grad() + + +def train(): + ### TODO: use inplace-abn and larger crop size, + ### TODO: use gn and smaller batch size but larger crop size + logger.info('') + ## dataset + n_classes = 19 + batchsize = 16 + n_workers = 16 + cropsize = [640, 360] + # cropsize = [960, 480] + ds = CityScapes('./data', cropsize=cropsize, mode='train') + dl = DataLoader(ds, + batch_size = batchsize, + shuffle = True, + num_workers = n_workers, + drop_last = True) + + ## model + ignore_idx = 255 + net = BiSeNet(n_classes=n_classes) + net.cuda() + net.train() + # net = nn.DataParallel(net) + LossP = nn.CrossEntropyLoss(ignore_index=ignore_idx) + Loss2 = nn.CrossEntropyLoss(ignore_index=ignore_idx) + Loss3 = nn.CrossEntropyLoss(ignore_index=ignore_idx) + + ## optimizer + momentum = 0.9 + weight_decay = 1e-4 + lr_start = 2.5e-2 + max_iter = 91000 + power = 0.9 + warmup_steps = 1000 + warmup_start_lr = 1e-5 + optim = Optimizer( + net.parameters(), + lr_start, + momentum, + weight_decay, + warmup_steps, + warmup_start_lr, + max_iter, + power) + + ## train loop + msg_iter = 50 + eval_iter = 100000 + loss_avg = [] + st = glob_st = time.time() + diter = iter(dl) + for it in range(max_iter): + try: + im, lb = next(diter) + if not im.size()[0]==batchsize: continue + except StopIteration: + diter = iter(dl) + im, lb = next(diter) + im = im.cuda() + lb = lb.cuda() + H, W = im.size()[2:] + lb = torch.squeeze(lb, 1) + + optim.zero_grad() + out, out16, out32 = net(im) + lossp = LossP(out, lb) + loss2 = Loss2(out16, lb) + loss3 = Loss3(out32, lb) + loss = lossp + loss2 + loss3 + # loss = lossp + loss2 + # loss = lossp + loss.backward() + optim.step() + + loss_avg.append(loss.item()) + ## print training log message + if it%msg_iter==0 and not it==0: + loss_avg = sum(loss_avg) / len(loss_avg) + lr = optim.lr + ed = time.time() + t_intv, glob_t_intv = ed - st, ed - glob_st + eta = int((max_iter - it) * (glob_t_intv / it)) + eta = str(datetime.timedelta(seconds = eta)) + msg = ', '.join([ + 'it: {it}/{max_it}', + 'lr: {lr:4f}', + 'loss: {loss:.4f}', + 'eta: {eta}', + 'time: {time:.4f}', + ]).format( + it = it, + max_it = max_iter, + lr = lr, + loss = loss_avg, + time = t_intv, + eta = eta + ) + logger.info(msg) + loss_avg = [] + st = ed + + ## eval the model and save checkpoint + if it>warmup_steps and (it-warmup_steps)%eval_iter==0 and not it==warmup_steps: + logger.info('evaluating the model at iter: {}'.format(it)) + net.eval() + mIOU = eval_model(net) + net.train() + logger.info('mIOU is: {}'.format(mIOU)) + save_pth = osp.join(respth, 'model_final_{}.pth'.format(it)) + if hasattr(net, 'module'): + state = net.module.state_dict() + else: + state = net.state_dict() + torch.save(state, save_pth) + logger.info('checkpoint saved to: {}'.format(save_pth)) + + + ## dump the final model and evaluate the result + save_pth = osp.join(respth, 'model_final.pth') + net.cpu() + if hasattr(net, 'module'): + state = net.module.state_dict() + else: + state = net.state_dict() + torch.save(state, save_pth) + logger.info('training done, model saved to: {}'.format(save_pth)) + logger.info('evaluating the final model') + net.cuda() + net.eval() + mIOU = eval_model(net) + logger.info('mIOU is: {}'.format(mIOU)) + + + +if __name__ == "__main__": + train() diff --git a/transform.py b/transform.py new file mode 100644 index 0000000..d93da43 --- /dev/null +++ b/transform.py @@ -0,0 +1,93 @@ +#!/usr/bin/python +# -*- encoding: utf-8 -*- + + +from PIL import Image +import random + + +class RandomCrop(object): + def __init__(self, size, *args, **kwargs): + self.size = size + + def __call__(self, im_lb): + im = im_lb['im'] + lb = im_lb['lb'] + assert im.size == lb.size + W, H = self.size + w, h = im.size + + if (W, H) == (w, h): return dict(im = im, lb = lb) + if w < W or h < H: + scale = float(W) / w if w < h else float(H) / h + w, h = int(scale * w + 1), int(scale * h + 1) + im = im.resize((w, h), Image.BILINEAR) + lb = lb.resize((w, h), Image.NEAREST) + sw, sh = random.random() * (w - W), random.random() * (h - H) + crop = int(sw), int(sh), int(sw) + W, int(sh) + H + return dict( + im = im.crop(crop), + lb = lb.crop(crop) + ) + + +class HorizontalFlip(object): + def __init__(self, p=0.5, *args, **kwargs): + self.p = p + + def __call__(self, im_lb): + if random.random() > self.p: + return im_lb + else: + im = im_lb['im'] + lb = im_lb['lb'] + return dict(im = im.transpose(Image.FLIP_LEFT_RIGHT), + lb = lb.transpose(Image.FLIP_LEFT_RIGHT), + ) + + +class RandomScale(object): + def __init__(self, scales=(1, ), *args, **kwargs): + self.scales = scales + + def __call__(self, im_lb): + im = im_lb['im'] + lb = im_lb['lb'] + W, H = im.size + scale = random.choice(self.scales) + w, h = int(W * scale), int(H * scale) + return dict(im = im.resize((w, h), Image.BILINEAR), + lb = lb.resize((w, h), Image.NEAREST), + ) + + +class ScaleBySize(object): + def __init__(self, size=(1536, 768), *args, **kwargs): + self.size = size + + def __call__(self, im_lb): + im = im_lb['im'] + lb = im_lb['lb'] + W, H = self.size + return dict(im = im.resize((W, H), Image.BILINEAR), + lb = lb.resize((W, H), Image.NEAREST), + ) + + +class Compose(object): + def __init__(self, do_list): + self.do_list = do_list + + def __call__(self, im_lb): + for comp in self.do_list: + im_lb = comp(im_lb) + return im_lb + + + +if __name__ == '__main__': + flip = HorizontalFlip(p = 1) + crop = RandomCrop((321, 321)) + rscales = RandomScale((0.75, 1.0, 1.5, 1.75, 2.0)) + img = Image.open('data/img.jpg') + lb = Image.open('data/label.png') diff --git a/xception.py b/xception.py deleted file mode 100644 index ca56300..0000000 --- a/xception.py +++ /dev/null @@ -1,240 +0,0 @@ -""" -Coin: I must give thanks to [Cadene](https://github.com/Cadene) for his xception models. -""" -""" -Ported to pytorch thanks to [tstandley](https://github.com/tstandley/Xception-PyTorch) - -@author: tstandley -Adapted by cadene - -Creates an Xception Model as defined in: - -Francois Chollet -Xception: Deep Learning with Depthwise Separable Convolutions -https://arxiv.org/pdf/1610.02357.pdf - -This weights ported from the Keras implementation. Achieves the following performance on the validation set: - -Loss:0.9173 Prec@1:78.892 Prec@5:94.292 - -REMEMBER to set your image size to 3x299x299 for both test and validation - -normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5], - std=[0.5, 0.5, 0.5]) - -The resize parameter of the validation transform should be 333, and make sure to center crop at 299x299 -""" -# from __future__ import print_function, division, absolute_import -import math -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.utils.model_zoo as model_zoo -from torch.nn import init - -__all__ = ['xception'] - -pretrained_settings = { - 'xception': { - 'imagenet': { - 'url': 'http://data.lip6.fr/cadene/pretrainedmodels/xception-43020ad28.pth', - 'input_space': 'RGB', - 'input_size': [3, 299, 299], - 'input_range': [0, 1], - 'mean': [0.5, 0.5, 0.5], - 'std': [0.5, 0.5, 0.5], - 'num_classes': 1000, - 'scale': 0.8975 # The resize parameter of the validation transform should be 333, and make sure to center crop at 299x299 - } - } -} - - -class SeparableConv2d(nn.Module): - def __init__(self,in_channels,out_channels,kernel_size=1,stride=1,padding=0,dilation=1,bias=False): - super(SeparableConv2d,self).__init__() - - self.conv1 = nn.Conv2d(in_channels,in_channels,kernel_size,stride,padding,dilation,groups=in_channels,bias=bias) - self.pointwise = nn.Conv2d(in_channels,out_channels,1,1,0,1,1,bias=bias) - - def forward(self,x): - x = self.conv1(x) - x = self.pointwise(x) - return x - - -class Block(nn.Module): - def __init__(self,in_filters,out_filters,reps,strides=1,start_with_relu=True,grow_first=True): - super(Block, self).__init__() - - if out_filters != in_filters or strides!=1: - self.skip = nn.Conv2d(in_filters,out_filters,1,stride=strides, bias=False) - self.skipbn = nn.BatchNorm2d(out_filters) - else: - self.skip=None - - self.relu = nn.ReLU(inplace=True) - rep=[] - - filters=in_filters - if grow_first: - rep.append(self.relu) - rep.append(SeparableConv2d(in_filters,out_filters,3,stride=1,padding=1,bias=False)) - rep.append(nn.BatchNorm2d(out_filters)) - filters = out_filters - - for i in range(reps-1): - rep.append(self.relu) - rep.append(SeparableConv2d(filters,filters,3,stride=1,padding=1,bias=False)) - rep.append(nn.BatchNorm2d(filters)) - - if not grow_first: - rep.append(self.relu) - rep.append(SeparableConv2d(in_filters,out_filters,3,stride=1,padding=1,bias=False)) - rep.append(nn.BatchNorm2d(out_filters)) - - if not start_with_relu: - rep = rep[1:] - else: - rep[0] = nn.ReLU(inplace=False) - - if strides != 1: - rep.append(nn.MaxPool2d(3,strides,1)) - self.rep = nn.Sequential(*rep) - - def forward(self,inp): - x = self.rep(inp) - - if self.skip is not None: - skip = self.skip(inp) - skip = self.skipbn(skip) - else: - skip = inp - - x+=skip - return x - - -class Xception(nn.Module): - """ - Xception optimized for the ImageNet dataset, as specified in - https://arxiv.org/pdf/1610.02357.pdf - """ - def __init__(self, num_classes=1000): - """ Constructor - Args: - num_classes: number of classes - """ - super(Xception, self).__init__() - self.num_classes = num_classes - - self.conv1 = nn.Conv2d(3, 32, 3,2, 0, bias=False) - self.bn1 = nn.BatchNorm2d(32) - self.relu = nn.ReLU(inplace=True) - - self.conv2 = nn.Conv2d(32,64,3,bias=False) - self.bn2 = nn.BatchNorm2d(64) - #do relu here - - self.block1=Block(64,128,2,2,start_with_relu=False,grow_first=True) - self.block2=Block(128,256,2,2,start_with_relu=True,grow_first=True) - self.block3=Block(256,728,2,2,start_with_relu=True,grow_first=True) - - self.block4=Block(728,728,3,1,start_with_relu=True,grow_first=True) - self.block5=Block(728,728,3,1,start_with_relu=True,grow_first=True) - self.block6=Block(728,728,3,1,start_with_relu=True,grow_first=True) - self.block7=Block(728,728,3,1,start_with_relu=True,grow_first=True) - - self.block8=Block(728,728,3,1,start_with_relu=True,grow_first=True) - self.block9=Block(728,728,3,1,start_with_relu=True,grow_first=True) - self.block10=Block(728,728,3,1,start_with_relu=True,grow_first=True) - self.block11=Block(728,728,3,1,start_with_relu=True,grow_first=True) - - self.block12=Block(728,1024,2,2,start_with_relu=True,grow_first=False) - - self.conv3 = SeparableConv2d(1024,1536,3,1,1) - self.bn3 = nn.BatchNorm2d(1536) - - #do relu here - self.conv4 = SeparableConv2d(1536,2048,3,1,1) - self.bn4 = nn.BatchNorm2d(2048) - - # self.fc = nn.Linear(2048, num_classes) - - # #------- init weights -------- - # for m in self.modules(): - # if isinstance(m, nn.Conv2d): - # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels - # m.weight.data.normal_(0, math.sqrt(2. / n)) - # elif isinstance(m, nn.BatchNorm2d): - # m.weight.data.fill_(1) - # m.bias.data.zero_() - # #----------------------------- - - def features(self, input): - x = self.conv1(input) - x = self.bn1(x) - x = self.relu(x) - - x = self.conv2(x) - x = self.bn2(x) - x = self.relu(x) - - x = self.block1(x) - x = self.block2(x) - x = self.block3(x) - x = self.block4(x) - x = self.block5(x) - x = self.block6(x) - x = self.block7(x) - x = self.block8(x) - x = self.block9(x) - x = self.block10(x) - x = self.block11(x) - x = self.block12(x) - - x = self.conv3(x) - x = self.bn3(x) - x = self.relu(x) - - x = self.conv4(x) - x = self.bn4(x) - return x - - def logits(self, features): - ## TODO - x = self.relu(features) - - # x = F.adaptive_avg_pool2d(x, (1, 1)) - # x = x.view(x.size(0), -1) - # x = self.last_linear(x) - return x - - def forward(self, x): - x = self.features(x) - x = self.logits(x) - return x - - -def xception(num_classes=1000, pretrained='imagenet'): - model = Xception(num_classes=num_classes) - if pretrained: - settings = pretrained_settings['xception'][pretrained] - # assert num_classes == settings['num_classes'], \ - # "num_classes should be {}, but is {}".format(settings['num_classes'], num_classes) - - model = Xception(num_classes=num_classes) - state_dict = model_zoo.load_url(settings['url']) - new_state = {k:v for k,v in state_dict.items() if not 'fc' in k} - model.load_state_dict(new_state) - - model.input_space = settings['input_space'] - model.input_size = settings['input_size'] - model.input_range = settings['input_range'] - model.mean = settings['mean'] - model.std = settings['std'] - - # TODO: ugly - # model.last_linear = model.fc - # del model.fc - return model