stanford-futuredata/blazeit

Its BlazeIt because it's blazing fast

blazeit's Issues

Error on reproduce the aggregation experiments (step 3 of Reproducing experiments section)

Hi,

I am trying to reproducing the experiments with the blazeit system, the environment setup and data preprocessing went well, but when I tried to reproduce the aggregation experiments, it throws out some error. I tried to fix it many times but I can not solve it. Could you please help me somehow? Here is the step by step how did I run the experiment

Reproduce problem:

• Environment: prepared as requirements
• step 1: prepared jackson-town-square video and structure folders as the instruction. (succeed)
• step 2: Extract the videos into npy files by run command: python gen_small_vid.py --base_name jackson-town-square --date 2017-12-14 (succeed)
• step 3: run the aggregation experiments and it has failed and gave the errors as bellow:
  python blazeit/aggregation/run_counter.py --base_name jackson-town-square --train_date 2017-12-14 --thresh_date 2017-12-16 --test_date 2017-12-17 --objects car --no-load_video --out_csv csvs/jackson-town-square-2017-12-17.csv

Error:

Traceback (most recent call last):

File "run_counter.py", line 54, in
main()
File "run_counter.py", line 46, in main
load_video=args.load_video
File "/root/blazeit/blazeit/aggregation/counter.py", line 129, in train_and_test
spec.load_data(selection='balanced', nb_train=150000)
File "/root/blazeit/blazeit/specializers/base_specializer.py", line 155, in load_data
t1, t2 = self.get_train_val(**kwargs)
File "/root/blazeit/blazeit/specializers/base_specializer.py", line 149, in get_train_val
X_train, X_val = split(X, train_inds, val_inds)
File "/root/blazeit/blazeit/specializers/base_specializer.py", line 137, in split
return Z[train_inds], Z[val_inds]
File "/opt/conda/lib/python3.6/site-packages/numpy/core/memmap.py", line 331, in getitem
res = super(memmap, self).getitem(index)
IndexError: index 973488 is out of bounds for axis 0 with size 973488

After re-run step 3 a few time, the error above passed itself, then another error appears:

Traceback (most recent call last): | 0/8915 [00:00<?, ?it/s] File "blazeit/aggregation/run_counter.py", line 54, in
main()
File "blazeit/aggregation/run_counter.py", line 46, in main
load_video=args.load_video
File "/root/blazeit/blazeit/aggregation/counter.py", line 133, in train_and_test
spec.train(silent=True)
File "/root/blazeit/blazeit/aggregation/counter.py", line 18, in train
super().train(**kwargs, epochs=[1, 0], lrs=[0.001, 0.001])
File "/root/blazeit/blazeit/specializers/specializers.py", line 203, in train
self._train(criterion, metric='topk', **kwargs)
File "/root/blazeit/blazeit/specializers/base_specializer.py", line 206, in _train
run_epoch(lrs[0], epochs[0])
File "/root/blazeit/blazeit/specializers/base_specializer.py", line 198, in run_epoch
silent=silent)
File "/root/blazeit/blazeit/specializers/pytorch_utils.py", line 163, in trainer
train_epoch(train_loader, model, criterion, optimizer, epoch, metric, silent=silent)
File "/root/blazeit/blazeit/specializers/pytorch_utils.py", line 100, in train_epoch
output = model(input_var)
File "/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py", line 545, in call
result = self.forward(*input, **kwargs)
File "/root/blazeit/blazeit/specializers/resnet_simple.py", line 117, in forward
x = self.conv1(x)
File "/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py", line 545, in call
result = self.forward(*input, **kwargs)
File "/opt/conda/lib/python3.6/site-packages/torch/nn/modules/conv.py", line 345, in forward
return self.conv2d_forward(input, self.weight)
File "/opt/conda/lib/python3.6/site-packages/torch/nn/modules/conv.py", line 342, in conv2d_forward
self.padding, self.dilation, self.groups)
RuntimeError: Given groups=1, weight of size 16 3 3 3, expected input[16, 65, 3, 65] to have 3 channels, but got 65 channels instead

Then continue re-run a few time more, the first error appears again

base_specializer.py - can not find the file named "collections" and the definition of "SingleVideoDecoder"

hi, when I read from collections import defaultdict in /blazeit/blazeit/specializers/base_specializer.py, I can not find any file named "collections". Can you share it?

I also can not find the definition of SingleVideoDecoder in base_specializer.py.

 def getX(self, dump=True, data_path='/lfs/1/ddkang/blazeit/data/'):
        if self.video_fname[-4:] == '.npy':
            X = np.load(self.video_fname, mmap_mode='r')
        elif self.video_fname[-4:] == '.mp4' or self.video_fname[-5:] == '.webm':
            # TODO: make swag parallel decode
            raise NotImplementedError
            _, json_fname = tempfile.mkstemp()
            self.vid_data.serialize(json_fname)
            bd = SingleVideoDecoder(json_fname, self.video_fname)
            X = bd.read()
           ....

Thank you!

Error on run EBS sampling (step 3 of Reproducing experiments section)

Hi,

I am trying to reproducing the experiments with the blazeit system, after faced the error from issue #6 and I can not fix it, I skip it and move to run the EBS sampling, then it gave another error, could you please help me with this too? Here is the step by step how did I run the experiment:

Reproduce problem:

• Environment: prepared as requirements
• Step 1: prepared jackson-town-square video and structure folders as the instruction. (succeed)
• Step 2: Extract the videos into npy files by run command: python gen_small_vid.py --base_name jackson-town-square --date 2017-12-14 (succeed)
• Step 3: run EBS sampling by run the command: python blazeit/aggregation/run_ebs_sampling.py --obj_name car --err_tol 0.01 --base_name jackson-town-square --test_date 2017-12-17 --train_date 2017-12-14 then it throw error as bellow:

Error:

Traceback (most recent call last):
File "blazeit/aggregation/run_ebs_sampling.py", line 96, in
main()
File "blazeit/aggregation/run_ebs_sampling.py", line 71, in main
Y_pred, Y_true = get_data(base_name, test_date, obj_name, data_path)
File "blazeit/aggregation/run_ebs_sampling.py", line 40, in get_data
trues = np.zeros(np.max(true_idx.index) + 1)
File "<array_function internals>", line 6, in amax
File "/opt/conda/lib/python3.6/site-packages/numpy/core/fromnumeric.py", line 2621, in amax
keepdims=keepdims, initial=initial, where=where)
File "/opt/conda/lib/python3.6/site-packages/numpy/core/fromnumeric.py", line 88, in _wrapreduction
return reduction(axis=axis, out=out, **passkwargs)
TypeError: max() got an unexpected keyword argument 'out'

README is outdated. Please update.

Hi,
I tried to follow the instruction to reproducing experiments, but I found out that the structure of the code did not match the instruction paths. I tried to run the script with the same name in another folder, and it gave out some bugs.

at step 3: the script run_counter.py is now in folder aggregation, not in folder counting anymore, and when I run it, it gave out bug:

File "/root/blazeit/blazeit/specializers/specializers.py", line 192, in getY
    Y[frame] = min(self.max_count, len(frame_to_rows[frame]))
IndexError: index 973488 is out of bounds for axis 0 with size 973488

Issue with Missing 'detectron.tools.standalone'

Hi,
I am currently working on reproducing the experiment, and I have encountered an issue related to a missing module, 'detectron.tools.standalone', which is referenced in the code. However, there is no such file or module within the Detectron package from the mentioned https://github.com/facebookresearch/Detectron repository.

Detectron.tools.standalone is mentioned in the blazeit/labelers/detectron_labeler.py file

Is 'detectron.tools.standalone' a custom modification or an external module? If it is a custom modification, I would appreciate guidance on where I can obtain the necessary code.

Trackid from the entity resolution method

Thanks for sharing code and dataset.
According to your paper, trackid is generated from the entity resolution method, but I couldn't fully understand how it works.
Could you let me know the detailed algorithm for getting trackid or where can i find the code for generating trackid?