thanks for your work. but i found an error when i try to test the code in VOC `Checkpoint <E:\Context-Aware-Consistency-master\pretrained\voc_1over8_datalist0_deeplabv3+_resnet101.pth> (epoch 63) was loaded

EVALUATION

0%| | 0/724 [00:17<?, ?it/s] Traceback (most recent call last): File "D:\ProgramData\Anaconda3\envs\cv\lib\runpy.py", line 193, in _run_module_as_main "main", mod_spec) File "D:\ProgramData\Anaconda3\envs\cv\lib\runpy.py", line 85, in run_code exec(code, run_globals) File "c:\Users\Administrator.vscode\extensions\ms-python.python-2020.7.96456\pythonFiles\lib\python\debugpy_main.py", line 45, in cli.main() File "c:\Users\Administrator.vscode\extensions\ms-python.python-2020.7.96456\pythonFiles\lib\python\debugpy/..\debugpy\server\cli.py", line 430, in main run() File "c:\Users\Administrator.vscode\extensions\ms-python.python-2020.7.96456\pythonFiles\lib\python\debugpy/..\debugpy\server\cli.py", line 267, in run_file runpy.run_path(options.target, run_name=compat.force_str("main")) File "D:\ProgramData\Anaconda3\envs\cv\lib\runpy.py", line 263, in run_path pkg_name=pkg_name, script_name=fname) File "D:\ProgramData\Anaconda3\envs\cv\lib\runpy.py", line 96, in _run_module_code mod_name, mod_spec, pkg_name, script_name) File "D:\ProgramData\Anaconda3\envs\cv\lib\runpy.py", line 85, in _run_code exec(code, run_globals) File "e:\Context-Aware-Consistency-master\train.py", line 128, in main(config['n_gpu'], config['n_gpu'], config, args.resume, args.test) File "e:\Context-Aware-Consistency-master\train.py", line 99, in main trainer.train() File "e:\Context-Aware-Consistency-master\base\base_trainer.py", line 105, in train results = self._valid_epoch(0) File "e:\Context-Aware-Consistency-master\trainer.py", line 145, in _valid_epoch for batch_idx, (data, target) in enumerate(tbar): File "D:\ProgramData\Anaconda3\envs\cv\lib\site-packages\tqdm\std.py", line 1185, in iter for obj in iterable: File "D:\ProgramData\Anaconda3\envs\cv\lib\site-packages\torch\utils\data\dataloader.py", line 435, in next data = self._next_data() File "D:\ProgramData\Anaconda3\envs\cv\lib\site-packages\torch\utils\data\dataloader.py", line 475, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "D:\ProgramData\Anaconda3\envs\cv\lib\site-packages\torch\utils\data_utils\fetch.py", line 47, in fetch return self.collate_fn(data) File "D:\ProgramData\Anaconda3\envs\cv\lib\site-packages\torch\utils\data_utils\collate.py", line 83, in default_collate return [default_collate(samples) for samples in transposed] File "D:\ProgramData\Anaconda3\envs\cv\lib\site-packages\torch\utils\data_utils\collate.py", line 83, in return [default_collate(samples) for samples in transposed] File "D:\ProgramData\Anaconda3\envs\cv\lib\site-packages\torch\utils\data_utils\collate.py", line 55, in default_collate return torch.stack(batch, 0, out=out) RuntimeError: stack expects each tensor to be equal size, but got [3, 375, 500] at entry 0 and [3, 396, 500] at entry 1`

I try to train the model by only one GPU. But the process is ended by the following error :

Traceback (most recent call last): File "train.py", line 127, in mp.spawn(main, nprocs=config['n_gpu'], args=(config['n_gpu'], config, args.resume, args.test)) File "/home/ders/anaconda3/envs/sdp/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 230, in spawn return start_processes(fn, args, nprocs, join, daemon, start_method='spawn') File "/home/ders/anaconda3/envs/sdp/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 188, in start_processes while not context.join(): File "/home/ders/anaconda3/envs/sdp/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 150, in join raise ProcessRaisedException(msg, error_index, failed_process.pid) torch.multiprocessing.spawn.ProcessRaisedException:

-- Process 0 terminated with the following error: Traceback (most recent call last): File "/home/ders/anaconda3/envs/sdp/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 59, in _wrap fn(i, *args) File "/media/ders/sundingpeng/paper_code/Context-Aware-Consistency-master-2/train.py", line 98, in main trainer.train() File "/media/ders/sundingpeng/paper_code/Context-Aware-Consistency-master-2/base/base_trainer.py", line 115, in train results = self._valid_epoch(epoch) File "/media/ders/sundingpeng/paper_code/Context-Aware-Consistency-master-2/trainer.py", line 145, in _valid_epoch for batch_idx, (data, target) in enumerate(tbar): File "/home/ders/anaconda3/envs/sdp/lib/python3.6/site-packages/tqdm/std.py", line 1178, in iter for obj in iterable: File "/home/ders/anaconda3/envs/sdp/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 517, in next data = self._next_data() File "/home/ders/anaconda3/envs/sdp/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 1199, in _next_data return self._process_data(data) File "/home/ders/anaconda3/envs/sdp/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 1225, in _process_data data.reraise() File "/home/ders/anaconda3/envs/sdp/lib/python3.6/site-packages/torch/_utils.py", line 429, in reraise raise self.exc_type(msg) RuntimeError: Caught RuntimeError in DataLoader worker process 0. Original Traceback (most recent call last): File "/home/ders/anaconda3/envs/sdp/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py", line 202, in _worker_loop data = fetcher.fetch(index) File "/home/ders/anaconda3/envs/sdp/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 47, in fetch return self.collate_fn(data) File "/home/ders/anaconda3/envs/sdp/lib/python3.6/site-packages/torch/utils/data/_utils/collate.py", line 83, in default_collate return [default_collate(samples) for samples in transposed] File "/home/ders/anaconda3/envs/sdp/lib/python3.6/site-packages/torch/utils/data/_utils/collate.py", line 83, in return [default_collate(samples) for samples in transposed] File "/home/ders/anaconda3/envs/sdp/lib/python3.6/site-packages/torch/utils/data/_utils/collate.py", line 55, in default_collate return torch.stack(batch, 0, out=out)

RuntimeError: stack expects each tensor to be equal size, but got [3, 366, 500] at entry 0 and [3, 335, 500] at entry 1

Hi X-Lai,

Thank you very much for sharing your well-organized code and I'm trying to use your Directional Contrastive Loss in my work. I have a question on the calculation of Directional Contrastive Loss. Equation 1 in your conference paper has two items in the denominator, while the calculation in your code is as following: logits1 = torch.exp(pos1 - neg_max1).squeeze(-1) / (logits1_down + eps) # in model.py Line 231. Should it be 'torch.exp(pos1 - neg_max1).squeeze(-1) / (torch.exp(pos1 - eg_max1).squeeze(-1)+logits1_down + eps)'? Or 'torch.exp(pos1 - neg_max1).squeeze(-1)' is already added up to logits1_down.

Ps: Is 'neg_max1' for normalization?

I'm looking forward to your reply!

if self.mode == 'supervised': # dataloader = iter(self.supervised_loader) # tbar = tqdm(range(len(self.supervised_loader)), ncols=135) dataloader = iter(cycle(self.supervised_loader)) tbar = tqdm(range(self.iter_per_epoch), ncols=135) else: dataloader = iter(zip(cycle(self.supervised_loader), cycle(self.unsupervised_loader))) tbar = tqdm(range(self.iter_per_epoch), ncols=135)

The comment part is your original code. In the semi-supervised method, 'cycle' was used to expand the number of iterations of labeled images. Obviously the number of iterations in the fully-supervised is much less. I think this comparison may be unfair. What is your opinion or modification plan? Looking forward to your answer, thanks!

Hello, I was training your model using:

python3 train.py --config configs/voc_cac_deeplabv3+_resnet50_1over8_datalist0.json

The training starts but till 60 iterations of the 1st epoch the RAM explodes and the system crashes.

GPU: P100 16GB CPU RAM: 25gb Batch SIze: 2

Hello, I use the config file you provide to reproduce on Pascal Voc dataset. But I got somehow slightly lower results in multiple dataset split setting. My reproduce result are as following. And config file used in my experiment is as following.

{
    "name": "CAC",
    "experim_name": "cac_datalist0_1of8_3",
    "dataset": "voc",
    "data_dir": ###,
    "datalist": 3,
    "n_gpu": 4,
    "n_labeled_examples": 10582,
    "diff_lrs": true,
    "ramp_up": 0.1,
    "unsupervised_w": 30,
    "ignore_index": 255,
    "lr_scheduler": "Poly",
    "use_weak_lables":false,
    "weakly_loss_w": 0.4,
    "pretrained": true,
    "random_seed": 42,

    "model":{
        "supervised": false,
        "semi": true,
        "supervised_w": 1,

        "sup_loss": "CE",

        "layers": 101,
        "downsample": true,
        "proj_final_dim": 128,
        "out_dim": 256,
        "backbone": "deeplab_v3+",
        "pos_thresh_value": 0.75,
        "weight_unsup": 0.1,
        "epoch_start_unsup": 5,
        "selected_num": 3200,
        "temp": 0.1,
        "step_save": 2,
        "stride": 8
    },


    "optimizer": {
        "type": "SGD",
        "args":{
            "lr": 0.01,
            "weight_decay": 1e-4,
            "momentum": 0.9
        }
    },

    "train_supervised": {
        "batch_size": 8,
        "crop_size": 320,
        "shuffle": true,
        "base_size": 400,
        "scale": true,
        "augment": true,
        "flip": true,
        "rotate": false,
        "blur": false,
        "split": "train_supervised",
        "num_workers": 8
    },

    "train_unsupervised": {
        "batch_size": 8,
        "crop_size": 320,
        "shuffle": true,
        "base_size": 400,
        "scale": true,
        "augment": true,
        "flip": true,
        "rotate": false,
        "blur": false,
        "split": "train_unsupervised",
        "num_workers": 8,
        "iou_bound": [0.1, 1.0],
        "stride": 8
    },

    "val_loader": {
        "batch_size": 4,
        "val": true,
        "split": "val",
        "shuffle": false,
        "num_workers": 4
    },

    "trainer": {
        "epochs": 80,
        "save_dir": "saved/",
        "save_period": 1,
  
        "monitor": "max Mean_IoU",
        "early_stop": 100,
        
        "tensorboardX": true,
        "log_dir": "saved/",
        "log_per_iter": 20,

        "val": true,
        "val_per_epochs": 1
    }
}

Could you give me some advice about how to correctly reproduce your results? Thanks a lot.

I get ValueError when I try to reproduce your paper results on Pascal Voc dataset. Concretely, it raises "ValueError: Cannot take a larger sample than population when 'replace=False'" when use default selected_num setting with value of 6400. And error aforementioned occurs at following line. https://github.com/dvlab-research/Context-Aware-Consistency/blob/4fdec7af8ad22eaabbf852727f2d824b66069999/models/model.py#L174 I don't know how to solve this bug. Hope for your solution.

Thank you for your amazing job.

I have one question about your ablation results. As in Table 3, you applied Proj+Context+L2 regularization in Exp.1. However, the performance is lower than SupOnly.

Could you please give me some more explanation about this phenomenon?

Hey @X-Lai ,

Thank you for sharing your work!

I was able to set up the repository and run the experiments in accordance with the steps provided. However , I am finding it difficult to understand some part of the code related to unsupervised data loader. Please find my queries below:

1. Why have we chosen 320x320 for VOC dataset and 720x720 for Cityscapes? Just wanted to understand the rational behind it.
2. In code, in file dataloaders/voc.py:

        overlap1_ul = [max(0, y2-y1), max(0, x2-x1)]
        overlap1_br = [min(self.crop_size, self.crop_size+y2-y1, h//self.stride * self.stride), min(self.crop_size, self.crop_size+x2-x1, w//self.stride * self.stride)]
        overlap2_ul = [max(0, y1-y2), max(0, x1-x2)]
        overlap2_br = [min(self.crop_size, self.crop_size+y1-y2, h//self.stride * self.stride), min(self.crop_size, self.crop_size+x1-x2, w//self.stride * self.stride)]

I am not quite able to understand the utility of self.stride, why is this necessary and what exactly does overlap1_ul and overlap1_br represent

Regards Nitin

May the learning rate used not 'poly'?

def get_lr(self): T = self.last_epoch * self.iters_per_epoch + self.cur_iter factor = pow((1 - 1.0 * T / self.N), 0.9) if self.warmup_iters > 0 and T < self.warmup_iters: factor = 1.0 * T / self.warmup_iters self.cur_iter %= self.iters_per_epoch self.cur_iter += 1 assert factor >= 0, 'error in lr_scheduler' return [base_lr * factor for base_lr in self.base_lrs]

However, the lr updated as follows, self.lr_scheduler.step(epoch=epoch-1)

Is that a bug or a specific design? https://github.com/pytorch/pytorch/blob/41054f2ab5bb39d28a3eb8497f1a65b42385a996/torch/optim/lr_scheduler.py#L155

Hi, thanks for your nice work!

You have listed some results of other methods (such as CCT), have you just re-implement the method under your settings?

As we know, the splits of dataset differ in those semi-sup papers.

Thanks.

This error will be reported at 20% of the first round of training. Can you tell me the reason? How to solve it? My configuration is two pieces of 2080ti and 64g memory

This adds hubconf.py so that, for example, the following can work when "Ivan1248" is replaced with "dvlab-research":

model = torch.hub.load('Ivan1248/Context-Aware-Consistency', 'DeepLabV3Plus', backbone='resnet50',
                       pretrained=True, num_classes=21)

when use "python3 train.py --config configs/voc_cac_deeplabv3+_resnet50_1over8_datalist0.json" with two GPUS to run , i find that one GPUS imput image size is (1,3,335,500) while another is (1,3,366,500) . in this case ,i can run to end. but ,when i run with only one GPU,the problem of diffrent size is occr. how is go ???