Comments (13)
It looks like it is complaining about i/o from the same videos across multiple parallel processes. I can look into it. But it is probably in any case easier to just try training/predicting from a pretrained 3-camera model. Here are some links to weights:
weights.rat.MAX.3cam: https://www.dropbox.com/s/2fama0q45sdzwfj/weights_multigpu.30-0.00002.hdf5?dl=0
weights.rat.AVG.3cam: https://www.dropbox.com/s/mb05fqmsbugvhf6/weights_multigpu.30-8.19468_singleGPU.hdf5?dl=0
I would try finetuning both -- you can make an AVG network starting from either weights -- and comparing performance. We have found recently that, for finetuning on mouse data, starting from pretrained MAX weights actually works better.
Please LMK how it goes!
from dannce.
Thank you for sharing the weights of the pretrained 3-camera models! While I'm still aggregating training frames, with my current ~150 training frames, finetuning both networks already work pretty well.
from dannce.
Cool, you're welcome. Please let me know if you have any other questions.
from dannce.
I substituted the weights with the ones linked above in this issue, and I was able to resolve the indexing problem. However, now I am getting the following error when I run DANNCE with three cameras.
ValueError: Error when checking input: expected input_3 to have shape (64, 64, 64, 18) but got array with shape (64, 64, 64, 9)
Since 18 is twice as large as 9, it seems that part of the program is still expecting six cameras and not three. Do you know of a way to address this issue? I am trying to run DANNCE with three cameras without retraining the program. Are there some additional weights or training files that I need so that DANNCE works effectively with three cameras?
from dannce.
Hi @verpeutlab. Likely you are either (1) omitting n_views
from you config files or as a command line arg (n_views
defaults to 6 if omitted) or (2) still have n_views: 6
in one of your configs.
The solution is to make sure you have n_views: 3
, in your io.yaml.
If that doesn't work, please send all of the terminal text printed out before the error message.
from dannce.
I still got the same error after I placed n_views: 3 in each .yaml file, and here is the text before the error message:
Initializing Network...
Loading model from .\DANNCE\train_results\AVG\weights.1200-12.77642.hdf5
max
25
Predicting on batch 0
Loading new video: videos\Camera1\0.mp4 for 0_Camera1
Loading new video: videos\Camera3\0.mp4 for 0_Camera3
Loading new video: videos\Camera2\0.mp4 for 0_Camera2
Traceback (most recent call last):
File "C:\Users\verpeutlab\anaconda3\envs\dannce\Scripts\dannce-predict-script.py", line 33, in
sys.exit(load_entry_point('dannce', 'console_scripts', 'dannce-predict')())
File "c:\users\verpeutlab\desktop\dannce\dannce\cli.py", line 46, in dannce_predict_cli
dannce_predict(params)
File "c:\users\verpeutlab\desktop\dannce\dannce\interface.py", line 1611, in dannce_predict
evaluate_ondemand(start_batch, max_eval_batch, valid_generator)
File "c:\users\verpeutlab\desktop\dannce\dannce\interface.py", line 1493, in evaluate_ondemand
pred = model.predict(ims[0])
File "C:\Users\verpeutlab\anaconda3\envs\dannce\lib\site-packages\tensorflow\python\keras\engine\training_v1.py", line 992, in predict
use_multiprocessing=use_multiprocessing)
File "C:\Users\verpeutlab\anaconda3\envs\dannce\lib\site-packages\tensorflow\python\keras\engine\training_arrays.py", line 707, in predict
x, check_steps=True, steps_name='steps', steps=steps)
File "C:\Users\verpeutlab\anaconda3\envs\dannce\lib\site-packages\tensorflow\python\keras\engine\training_v1.py", line 2334, in _standardize_user_data
batch_size=batch_size)
File "C:\Users\verpeutlab\anaconda3\envs\dannce\lib\site-packages\tensorflow\python\keras\engine\training_v1.py", line 2361, in _standardize_tensors
exception_prefix='input')
File "C:\Users\verpeutlab\anaconda3\envs\dannce\lib\site-packages\tensorflow\python\keras\engine\training_utils.py", line 582, in standardize_input_data
str(data_shape))
ValueError: Error when checking input: expected input_3 to have shape (64, 64, 64, 18) but got array with shape (64, 64, 64, 9)
I have also included the parameters that are listed on the command window, which are printed right after the program begins to run:
com_train_dir set to: .\COM\train_results
com_predict_dir set to: .\COM\predict_results
com_file set to: .\COM\predict_results\com3d.mat
n_views set to: 3
dannce_train_dir set to: .\DANNCE\train_results\AVG
dannce_predict_dir set to: .\DANNCE\predict_results
dannce_predict_model set to: .\DANNCE\train_results\AVG\weights.1200-12.77642.hdf5
exp set to: [{'label3d_file': './label3d_demo.mat'}, {'label3d_file': '../markerless_mouse_2/label3d_demo.mat'}]
io_config set to: io.yaml
new_n_channels_out set to: 22
batch_size set to: 4
epochs set to: 1200
net_type set to: AVG
train_mode set to: finetune
num_validation_per_exp set to: 4
vol_size set to: 120
nvox set to: 64
max_num_samples set to: max
dannce_finetune_weights set to: .\DANNCE\weights
base_config set to: ....\configs\dannce_mouse_config.yaml
viddir set to: videos
crop_height set to: None
crop_width set to: None
camnames set to: None
n_channels_out set to: 20
sigma set to: 10
verbose set to: 1
net set to: None
gpu_id set to: 0
immode set to: vid
mono set to: False
mirror set to: False
start_batch set to: 0
start_sample set to: None
com_fromlabels set to: False
medfilt_window set to: None
new_last_kernel_size set to: [3, 3, 3]
n_layers_locked set to: 2
vmin set to: None
vmax set to: None
interp set to: nearest
depth set to: False
comthresh set to: 0
weighted set to: False
com_method set to: median
cthresh set to: None
channel_combo set to: None
predict_mode set to: torch
expval set to: None
from_weights set to: None
loss set to: mask_nan_keep_loss
n_channels_in set to: None
extension set to: None
vid_dir_flag set to: None
chunks set to: None
lockfirst set to: None
load_valid set to: None
augment_hue set to: False
augment_brightness set to: False
augment_hue_val set to: 0.05
augment_bright_val set to: 0.05
augment_rotation_val set to: 5
drop_landmark set to: None
raw_im_h set to: None
raw_im_w set to: None
metric set to: ['euclidean_distance_3D']
lr set to: 0.001
rotate set to: True
augment_continuous_rotation set to: False
com_thresh set to: None
cam3_train set to: None
debug_volume_tifdir set to: None
downfac set to: None
dannce_predict_vol_tifdir set to: None
Using the following *dannce.mat files: .\label3d_dannce.mat
Setting vid_dir_flag to True.
Setting extension to .mp4.
Setting chunks to 3000.
Setting n_channels_in to 3.
Setting raw_im_h to 1024.
Setting raw_im_w to 1280.
Setting expval to True.
Setting net to finetune_AVG.
Setting crop_height to [0, 1024].
Setting crop_width to [0, 1280].
Setting maxbatch to max.
Setting start_batch to 0.
Setting vmin to -60.0.
Setting vmax to 60.0.
n views: 3
Using the following *dannce.mat files: .\label3d_dannce.mat
Using torch predict mode
Using camnames: ['Camera1', 'Camera2', 'Camera3']
Experiment 0 using com3d: .\COM\predict_results\com3d.mat
Below these paramaters, the following sentence was printed:
Removed 8900 samples from the dataset because they either had COM positions over cthresh, or did not have matching sampleIDs in the COM file
Do you think this has to do with the error I am getting, and what could potentially be causing it?
from dannce.
It looks like your dannce_predict_model
is not set to one of the 3 camera network weights (the file name does not match either of the ones I linked above.
from dannce.
After working with a copy of DANNCE that I had pulled from this GitHub repository and have been modifying for a while now, I decided to stash my changes and re-pull DANNCE from the master branch.
I was able to successfully run markerless_mouse_1 with six cameras with this fresh copy of DANNCE. However, when I set n_views equal to 3 in io.yaml and the other config file and substituted the weights file in dannce_predict_model with eitherof the two weights you linked above, I got an error when running markerless_mouse_1. My error is copied below.
Traceback (most recent call last):
File "C:\Users\verpeutlab\anaconda3\envs\dannce\Scripts\dannce-predict-script.py", line 33, in
sys.exit(load_entry_point('dannce', 'console_scripts', 'dannce-predict')())
File "c:\users\verpeutlab\desktop\danncegood\dannce\cli.py", line 53, in dannce_predict_cli
params = build_clarg_params(args, dannce_net=True, prediction=True)
File "c:\users\verpeutlab\desktop\danncegood\dannce\cli.py", line 87, in build_clarg_params
params = infer_params(params, dannce_net, prediction)
File "c:\users\verpeutlab\desktop\danncegood\dannce\engine\processing.py", line 119, in infer_params
video_files = os.listdir(camdir)
FileNotFoundError: [WinError 3] The system cannot find the path specified: 'videos\Camera4'
Could you tell me what is causing this error and how it can be fixed?
from dannce.
I also wanted to let you know that I deleted the folders named Camera4, Camera5, and Camera6 in markerless_mouse_1. Therefore, I only have three cameras available to pass through DANNCE. I also changed camnames to ['Camera1' 'Camera2' 'Camera3'] in the config file dannce_rig_com_config.yaml
from dannce.
I suppose that your *_dannce.mat file is not changed to 3 cams. If you open it in matlab and navigate to cams, you will see that there's still 6 camnames, 6 syncs and 6 params for each camera. Try delete the last 3 of them.
from dannce.
Hi,
I have the same error. I used the 6cam model and finetuned it. I use five camera setup so during the training I was prompted to duplicate one camera and I did so. However, when the same is done for the dannce-predict i get the following :
2021-08-24 08:09:17.477757: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll
io_config not found in io.yaml file, falling back to main config
n_views not found in io.yaml file, falling back to main config
n_channels_out not found in io.yaml file, falling back to main config
batch_size not found in io.yaml file, falling back to main config
epochs not found in io.yaml file, falling back to main config
net_type not found in io.yaml file, falling back to main config
train_mode not found in io.yaml file, falling back to main config
num_validation_per_exp not found in io.yaml file, falling back to main config
vol_size not found in io.yaml file, falling back to main config
nvox not found in io.yaml file, falling back to main config
max_num_samples not found in io.yaml file, falling back to main config
dannce_finetune_weights not found in io.yaml file, falling back to main config
mono not found in io.yaml file, falling back to main config
com_train_dir set to: .\COM\train_results
com_predict_dir set to: .\COM\predict_results
com_file set to: E:\DANNCE_test_210608\COM\predict_results\com3d.mat
dannce_train_dir set to: .\DANNCE\train_results\AVG
dannce_predict_dir set to: .\DANNCE\predict_results
exp set to: [{'label3d_file': 'E:/DANNCE_test_210608/20210610_091000_Label3D_dannce.mat', 'com_file': 'E:/DANNCE_test_210608/COM/predict_results/com3d.mat'}]
io_config set to: io.yaml
n_views set to: 6
n_channels_out set to: 22
batch_size set to: 4
epochs set to: 1200
net_type set to: AVG
train_mode set to: finetune
num_validation_per_exp set to: 4
vol_size set to: 100
nvox set to: 64
max_num_samples set to: max
dannce_finetune_weights set to: C:\Users\realtime\dannce\demo\markerless_mouse_1\DANNCE\train_results\AVG
mono set to: True
base_config set to: C:\Users\realtime\dannce\configs\dannce_mouse_config.yaml
viddir set to: videos
crop_height set to: None
crop_width set to: None
camnames set to: None
sigma set to: 10
verbose set to: 1
net set to: None
gpu_id set to: 0
immode set to: vid
mirror set to: False
start_batch set to: 0
start_sample set to: None
com_fromlabels set to: False
medfilt_window set to: None
new_last_kernel_size set to: [3, 3, 3]
new_n_channels_out set to: None
n_layers_locked set to: 2
vmin set to: None
vmax set to: None
interp set to: nearest
depth set to: False
comthresh set to: 0
weighted set to: False
com_method set to: median
cthresh set to: None
channel_combo set to: None
predict_mode set to: torch
dannce_predict_model set to: None
expval set to: None
from_weights set to: None
write_npy set to: None
loss set to: mask_nan_keep_loss
n_channels_in set to: None
extension set to: None
vid_dir_flag set to: None
num_train_per_exp set to: None
chunks set to: None
lockfirst set to: None
load_valid set to: None
augment_hue set to: False
augment_brightness set to: False
augment_hue_val set to: 0.05
augment_bright_val set to: 0.05
augment_rotation_val set to: 5
drop_landmark set to: None
raw_im_h set to: None
raw_im_w set to: None
n_instances set to: 1
use_npy set to: False
data_split_seed set to: None
valid_exp set to: None
metric set to: ['euclidean_distance_3D']
lr set to: 0.001
rotate set to: True
augment_continuous_rotation set to: False
com_thresh set to: None
cam3_train set to: None
debug_volume_tifdir set to: None
downfac set to: None
dannce_predict_vol_tifdir set to: None
n_rand_views set to: 0
rand_view_replace set to: True
multi_gpu_train set to: False
Using the following *dannce.mat files: .\20210610_091000_Label3D_dannce.mat
Setting vid_dir_flag to True.
Setting extension to .avi.
Setting chunks to {'Camera1': array([0]), 'Camera2': array([0]), 'Camera3': array([0]), 'Camera4': array([0]), 'Camera5': array([0])}.
Setting n_channels_in to 3.
Setting raw_im_h to 600.
Setting raw_im_w to 960.
Setting expval to True.
Setting net to finetune_AVG.
Setting crop_height to [0, 600].
Setting crop_width to [0, 960].
Setting maxbatch to max.
Setting start_batch to 0.
Setting vmin to -50.0.
Setting vmax to 50.0.
Using the following *dannce.mat files: .\20210610_091000_Label3D_dannce.mat
Using torch predict mode
Using camnames: ['Camera1', 'Camera2', 'Camera3', 'Camera4', 'Camera5']
The length of the camnames list must divide evenly into 6. Duplicate a subset of the views starting from the first camera (y/n)?y
Duping camnames. Changed from ['Camera1', 'Camera2', 'Camera3', 'Camera4', 'Camera5'] to ['Camera1', 'Camera2', 'Camera3', 'Camera4', 'Camera5', 'Camera1']
Experiment 0 using com3d: E:\DANNCE_test_210608\COM\predict_results\com3d.mat
Removed 0 samples from the dataset because they either had COM positions over cthresh, or did not have matching sampleIDs in the COM file
Saving 3D COM to .\DANNCE\predict_results\com3d_used.mat
None
2021-08-24 08:09:30.244549: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations: AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-08-24 08:09:30.265855: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x20fc559dd10 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2021-08-24 08:09:30.265962: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
2021-08-24 08:09:30.270627: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library nvcuda.dll
2021-08-24 08:09:30.321214: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0000:65:00.0 name: TITAN RTX computeCapability: 7.5
coreClock: 1.77GHz coreCount: 72 deviceMemorySize: 24.00GiB deviceMemoryBandwidth: 625.94GiB/s
2021-08-24 08:09:30.321488: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll
2021-08-24 08:09:30.322502: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cublas64_10.dll
2021-08-24 08:09:30.323052: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cufft64_10.dll
2021-08-24 08:09:30.323567: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library curand64_10.dll
2021-08-24 08:09:30.324076: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cusolver64_10.dll
2021-08-24 08:09:30.324680: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cusparse64_10.dll
2021-08-24 08:09:30.325197: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudnn64_7.dll
2021-08-24 08:09:30.325810: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2021-08-24 08:09:30.861918: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-08-24 08:09:30.862148: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263] 0
2021-08-24 08:09:30.863211: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0: N
2021-08-24 08:09:30.864124: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 11059 MB memory) -> physical GPU (device: 0, name: TITAN RTX, pci bus id: 0000:65:00.0, compute capability: 7.5)
2021-08-24 08:09:30.868395: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x210144022c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2021-08-24 08:09:30.868489: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): TITAN RTX, Compute Capability 7.5
Init took 11.387974739074707 sec.
Initializing Network...
Loading model from .\DANNCE\train_results\AVG\weights.1056-11.55619.hdf5
Predicting on batch 0
Loading new video: videos\Camera1\0.avi for 0_Camera1
Loading new video: videos\Camera1\0.avi for 0_Camera1
Loading new video: videos\Camera2\0.avi for 0_Camera2
Loading new video: videos\Camera3\0.avi for 0_Camera3
Loading new video: videos\Camera5\0.avi for 0_Camera5
Loading new video: videos\Camera4\0.avi for 0_Camera4
Traceback (most recent call last):
File "C:\Users\realtime\anaconda3\envs\dannce\Scripts\dannce-predict-script.py", line 33, in
sys.exit(load_entry_point('dannce', 'console_scripts', 'dannce-predict')())
File "c:\users\realtime\dannce\dannce\cli.py", line 54, in dannce_predict_cli
dannce_predict(params)
File "c:\users\realtime\dannce\dannce\interface.py", line 1577, in dannce_predict
n_chn,
File "c:\users\realtime\dannce\dannce\engine\inference.py", line 696, in infer_dannce
ims = generator.getitem(i)
File "c:\users\realtime\dannce\dannce\engine\generator.py", line 966, in getitem
X, y = self.__data_generation(list_IDs_temp)
File "c:\users\realtime\dannce\dannce\engine\generator.py", line 1258, in __data_generation
result = self.threadpool.starmap(self.project_grid, arglist)
File "C:\Users\realtime\anaconda3\envs\dannce\lib\multiprocessing\pool.py", line 276, in starmap
return self._map_async(func, iterable, starmapstar, chunksize).get()
File "C:\Users\realtime\anaconda3\envs\dannce\lib\multiprocessing\pool.py", line 657, in get
raise self._value
File "C:\Users\realtime\anaconda3\envs\dannce\lib\multiprocessing\pool.py", line 121, in worker
result = (True, func(*args, **kwds))
File "C:\Users\realtime\anaconda3\envs\dannce\lib\multiprocessing\pool.py", line 47, in starmapstar
return list(itertools.starmap(args[0], args[1]))
File "c:\users\realtime\dannce\dannce\engine\generator.py", line 1028, in project_grid
extension=self.extension,
File "c:\users\realtime\dannce\dannce\engine\video.py", line 231, in load_vid_frame
self.currvideo[camname].close() if self.predict_flag else
File "C:\Users\realtime\anaconda3\envs\dannce\lib\site-packages\imageio\core\format.py", line 259, in close
self._close()
File "C:\Users\realtime\anaconda3\envs\dannce\lib\site-packages\imageio\plugins\ffmpeg.py", line 343, in _close
self._read_gen.close()
ValueError: generator already executing
So, I suppose one fix could be to get the pretrained weights for 5cameras, or to fix this error in the processing. Let me know how best I can proceed. Looking forward to your response. When I say
n_views = 5
in the cofig file I of course get the dimension mismatch
[ValueError: Error when checking input: expected input_3 to have shape (64, 64, 64, 6) but got array with shape (64, 64, 64, 5)]
since it was trained on pseudo 6 camera data
Thanks and looking forward to your response!
from dannce.
Sorry, this seems to be a common issue -- seems to stem from when we transitioned to loading in videos across multiple threads.
@harshk95 if you want to try predicting on the model you've already trained, the workaround would be to manually duplicate the Camera1
videos, put them in a new Camera6
folder inside videos
, and update your label3d_dannce.mat
camnames to be {'Camera1','Camera2','Camera3','Camera4','Camera5','Camera6'}.
If you want to do a new finetune, here are some pretrained 5 cam mono weights:
pre-trained AVG: https://www.dropbox.com/s/c4o7nd9wy7191la/weights_multigpu-v9.11-11.99217_singleGPU.hdf5?dl=0
pre-trained MAX: https://www.dropbox.com/s/j5hgq2241247yo3/weights_multigpu.30-0.00003.hdf5?dl=0
Note that for some recent 5-camera mono experiments we have been running on mice, the best setting is:
- AVG finetune starting from pretrained MAX weights.
- Using
mask_nan_l1_loss
- Setting
augment_brightness: True
- Setting
n_rand_views: None
We are wrapping up a more complete grid search over parameters for this 5-camera mono case and will keep you posted!
from dannce.
Hi, if I simply duplicate the camera and cam_names I get the following
2021-08-27 08:22:15.577775: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll
io_config not found in io.yaml file, falling back to main config
n_views not found in io.yaml file, falling back to main config
n_channels_out not found in io.yaml file, falling back to main config
batch_size not found in io.yaml file, falling back to main config
epochs not found in io.yaml file, falling back to main config
net_type not found in io.yaml file, falling back to main config
train_mode not found in io.yaml file, falling back to main config
num_validation_per_exp not found in io.yaml file, falling back to main config
vol_size not found in io.yaml file, falling back to main config
nvox not found in io.yaml file, falling back to main config
max_num_samples not found in io.yaml file, falling back to main config
dannce_finetune_weights not found in io.yaml file, falling back to main config
mono not found in io.yaml file, falling back to main config
com_train_dir set to: .\COM\train_results
com_predict_dir set to: .\COM\predict_results
com_file set to: E:\DANNCE_test_210608\COM\predict_results\com3d.mat
dannce_train_dir set to: .\DANNCE\train_results\AVG
dannce_predict_dir set to: .\DANNCE\predict_results
exp set to: [{'label3d_file': 'E:/DANNCE_test_210608/6_cam_20210610_091000_Label3D_dannce.mat', 'com_file': 'E:/DANNCE_test_210608/COM/predict_results/com3d.mat'}]
io_config set to: io.yaml
n_views set to: 6
n_channels_out set to: 22
batch_size set to: 4
epochs set to: 1200
net_type set to: AVG
train_mode set to: finetune
num_validation_per_exp set to: 4
vol_size set to: 100
nvox set to: 64
max_num_samples set to: max
dannce_finetune_weights set to: C:\Users\realtime\dannce\demo\markerless_mouse_1\DANNCE\train_results\AVG
mono set to: True
base_config set to: C:\Users\realtime\dannce\configs\dannce_mouse_config.yaml
viddir set to: videos
crop_height set to: None
crop_width set to: None
camnames set to: None
sigma set to: 10
verbose set to: 1
net set to: None
gpu_id set to: 0
immode set to: vid
mirror set to: False
start_batch set to: 0
start_sample set to: None
com_fromlabels set to: False
medfilt_window set to: None
new_last_kernel_size set to: [3, 3, 3]
new_n_channels_out set to: None
n_layers_locked set to: 2
vmin set to: None
vmax set to: None
interp set to: nearest
depth set to: False
comthresh set to: 0
weighted set to: False
com_method set to: median
cthresh set to: None
channel_combo set to: None
predict_mode set to: torch
dannce_predict_model set to: None
expval set to: None
from_weights set to: None
write_npy set to: None
loss set to: mask_nan_keep_loss
n_channels_in set to: None
extension set to: None
vid_dir_flag set to: None
num_train_per_exp set to: None
chunks set to: None
lockfirst set to: None
load_valid set to: None
augment_hue set to: False
augment_brightness set to: False
augment_hue_val set to: 0.05
augment_bright_val set to: 0.05
augment_rotation_val set to: 5
drop_landmark set to: None
raw_im_h set to: None
raw_im_w set to: None
n_instances set to: 1
use_npy set to: False
data_split_seed set to: None
valid_exp set to: None
metric set to: ['euclidean_distance_3D']
lr set to: 0.001
rotate set to: True
augment_continuous_rotation set to: False
com_thresh set to: None
cam3_train set to: None
debug_volume_tifdir set to: None
downfac set to: None
dannce_predict_vol_tifdir set to: None
n_rand_views set to: 0
rand_view_replace set to: True
multi_gpu_train set to: False
Using the following *dannce.mat files: .\6_cam_20210610_091000_Label3D_dannce.mat
Setting vid_dir_flag to True.
Setting extension to .avi.
Setting chunks to {'Camera1': array([0]), 'Camera2': array([0]), 'Camera3': array([0]), 'Camera4': array([0]), 'Camera5': array([0]), 'Camera6': array([0])}.
Setting n_channels_in to 3.
Setting raw_im_h to 600.
Setting raw_im_w to 960.
Setting expval to True.
Setting net to finetune_AVG.
Setting crop_height to [0, 600].
Setting crop_width to [0, 960].
Setting maxbatch to max.
Setting start_batch to 0.
Setting vmin to -50.0.
Setting vmax to 50.0.
Using the following *dannce.mat files: .\6_cam_20210610_091000_Label3D_dannce.mat
Using torch predict mode
Using camnames: ['Camera1', 'Camera2', 'Camera3', 'Camera4', 'Camera5', 'Camera6']
Traceback (most recent call last):
File "C:\Users\realtime\anaconda3\envs\dannce\Scripts\dannce-predict-script.py", line 33, in
sys.exit(load_entry_point('dannce', 'console_scripts', 'dannce-predict')())
File "c:\users\realtime\dannce\dannce\cli.py", line 54, in dannce_predict_cli
dannce_predict(params)
File "c:\users\realtime\dannce\dannce\interface.py", line 1319, in dannce_predict
training=False,
File "c:\users\realtime\dannce\dannce\interface.py", line 1642, in do_COM_load
exp, prediction=False if training else True, nanflag=False
File "c:\users\realtime\dannce\dannce\engine\serve_data_DANNCE.py", line 48, in prepare_data
cameras = {name: params[i] for i, name in enumerate(CONFIG_PARAMS["camnames"])}
File "c:\users\realtime\dannce\dannce\engine\serve_data_DANNCE.py", line 48, in
cameras = {name: params[i] for i, name in enumerate(CONFIG_PARAMS["camnames"])}
IndexError: list index out of range
So I guess I have to duplicate all the parameters etc too. In any case, I will use the new weights that you have provided, but it was just so I could get an idea.
Thanks!
from dannce.
Related Issues (20)
- COM prediction values are NaN HOT 3
- Could not load weights for finetune (likely because you are finetuning a previously finetuned network). Attempting to finetune from a full finetune model file. HOT 18
- Zero training/validation errors but completely wrong in labeled images. HOT 2
- How to train DANNCE with more than 6 cameras? HOT 1
- COM deviate a lot from animal HOT 2
- When running dannce-predict demo script, GPU usage is at 0% HOT 1
- Integration of DANNCE and CAPTURE HOT 1
- Could not find enough inliers in imagePoints and worldPoints HOT 2
- n_views error HOT 1
- dannce-predict loss very small, but result same like normal but shift HOT 6
- Re-train network with new labeled frames HOT 1
- Multi animal COM HOT 2
- how to use rats16.mat skeleton for CAPTURE_demo analysis HOT 1
- calibration HOT 1
- OOM error HOT 2
- File "E:\anaconda\envs\tfnew_25\lib\site-packages\tensorflow\python\framework\ops.py", line 6649, in __init__ raise ValueError("name for name_scope must be a string.") HOT 1
- ValueError: name for name_scope must be a string when doing dannce-predict. HOT 1
- ValueError: bad marshal data (unknown type code) when dannce-predict HOT 1
- frames_with_good_tracking
- Fintune with more than 6 cams HOT 2
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from dannce.