Hello, I would like to download the features of the 50 salada dataset. I want to train this model for academic research. I have submitted the request but have not received feedback. Can these trained features still be downloaded? Thank you for your reply.
Can you tell if the accelerometer features from 50 salads mid-level dataset(which I saw is a 30x1 vector at a timestep) are raw acceleration values or have they been processed?
Assuming the 30x1 vector has 3 subsequent values as acc_x,acc_y ,acc_z from each of the IMUs (which I believe were 8), are there 10 sensors or some values are expected to be empty?
I just want to make sure if I can use this for IMU data I have for action detection.
id@ipadress:# python3 code/TCN_main.py
Using TensorFlow backend.
2020-06-22 02:48:46.974129: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
/workspace/code/utils.py:124: NumbaWarning:
Compilation is falling back to object mode WITH looplifting enabled because Function "partition_latent_labels" failed type inference due to: Untyped global name 'segment_intervals': cannot determine Numba type of <class 'function'>
File "code/utils.py", line 130:
def partition_latent_labels(Yi, n_latent):
Zi = Yi.copy()
intervals = segment_intervals(Yi)
^
@jit("int64[:](int64[:], int64)")
/workspace/code/utils.py:124: NumbaWarning:
Compilation is falling back to object mode WITHOUT looplifting enabled because Function "partition_latent_labels" failed type inference due to: Untyped global name 'segment_intervals': cannot determine Numba type of <class 'function'>
File "code/utils.py", line 130:
def partition_latent_labels(Yi, n_latent):
Zi = Yi.copy()
intervals = segment_intervals(Yi)
^
@jit("int64[:](int64[:], int64)")
/usr/local/lib/python3.6/dist-packages/numba/core/object_mode_passes.py:178: NumbaWarning: Function "partition_latent_labels" was compiled in object mode without forceobj=True, but has lifted loops.
File "code/utils.py", line 126:
def partition_latent_labels(Yi, n_latent):
if n_latent == 1:
^
state.func_ir.loc))
/usr/local/lib/python3.6/dist-packages/numba/core/object_mode_passes.py:188: NumbaDeprecationWarning:
Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.
File "code/utils.py", line 126:
def partition_latent_labels(Yi, n_latent):
if n_latent == 1:
^
state.func_ir.loc))
/workspace/code/metrics.py:143: NumbaWarning:
Compilation is falling back to object mode WITH looplifting enabled because Function "levenstein_" failed type inference due to: No implementation of function Function() found for signature:
zeros(list(int64), Function(<class 'float'>))
There are 2 candidate implementations:
Of which 2 did not match due to:
Overload in function 'zeros': File: : Line <N/A>.
With argument(s): '(list(int64), Function(<class 'float'>))':
No match.
During: resolving callee type: Function()
During: typing of call at /workspace/code/metrics.py (147)
File "code/metrics.py", line 147:
def levenstein_(p,y, norm=False):
n_col = len(y)
D = np.zeros([m_row+1, n_col+1], np.float)
^
@jit("float64(int64[:], int64[:], boolean)")
/workspace/code/metrics.py:143: NumbaWarning:
Compilation is falling back to object mode WITHOUT looplifting enabled because Function "levenstein_" failed type inference due to: cannot determine Numba type of <class 'numba.core.dispatcher.LiftedLoop'>
File "code/metrics.py", line 148:
def levenstein_(p,y, norm=False):
D = np.zeros([m_row+1, n_col+1], np.float)
for i in range(m_row+1):
^
@jit("float64(int64[:], int64[:], boolean)")
/usr/local/lib/python3.6/dist-packages/numba/core/object_mode_passes.py:178: NumbaWarning: Function "levenstein_" was compiled in object mode without forceobj=True, but has lifted loops.
state.func_ir.loc))
/usr/local/lib/python3.6/dist-packages/numba/core/object_mode_passes.py:188: NumbaDeprecationWarning:
Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.
mid_video_SVM
/usr/local/lib/python3.6/dist-packages/numba/core/ir_utils.py:2031: NumbaPendingDeprecationWarning:
Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'p' of function 'levenstein_'.
File "code/metrics.py", line 153:
def levenstein_(p,y, norm=False):
for j in range(1, n_col+1):
^
warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))
/usr/local/lib/python3.6/dist-packages/numba/core/ir_utils.py:2031: NumbaPendingDeprecationWarning:
Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'y' of function 'levenstein_'.
/usr/local/lib/python3.6/dist-packages/numpy/core/asarray.py:136: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
return array(a, dtype, copy=False, order=order, subok=True)
code/TCN_main.py:229: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
P_test = np.array(P_test)/float(n_classes-1)
code/TCN_main.py:230: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
y_test_ = np.array(y_test)/float(n_classes-1)
/usr/local/lib/python3.6/dist-packages/numpy/core/asarray.py:136: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
return array(a, dtype, copy=False, order=order, subok=True)
code/TCN_main.py:229: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
P_test = np.array(P_test)/float(n_classes-1)
code/TCN_main.py:230: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
y_test_ = np.array(y_test)/float(n_classes-1)
/usr/local/lib/python3.6/dist-packages/numpy/core/asarray.py:136: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
return array(a, dtype, copy=False, order=order, subok=True)
code/TCN_main.py:229: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
P_test = np.array(P_test)/float(n_classes-1)
code/TCN_main.py:230: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
y_test_ = np.array(y_test)/float(n_classes-1)
/usr/local/lib/python3.6/dist-packages/numpy/core/asarray.py:136: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
return array(a, dtype, copy=False, order=order, subok=True)
code/TCN_main.py:229: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
P_test = np.array(P_test)/float(n_classes-1)
code/TCN_main.py:230: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
y_test_ = np.array(y_test)/float(n_classes-1)
/usr/local/lib/python3.6/dist-packages/numpy/core/asarray.py:136: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
return array(a, dtype, copy=False, order=order, subok=True)
code/TCN_main.py:229: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
P_test = np.array(P_test)/float(n_classes-1)
code/TCN_main.py:230: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
y_test_ = np.array(y_test)/float(n_classes-1)
Hi Colin, thanks a lot for sharing this code, really helpful. Can I ask question about the implementation of you ED-TCN. Based on your paper(CVPR17), figure 1 shows there are two conv layers in the encoder and the decoder. However, your paper says "(e.g., 3 in the encoder)", and I can only see one conv layer in the encoder, "Convolution1D" line 81, tf_models.py. I might miss something there, I wonder which one is correct? Thanks.
Can anybody tell me, why is there an error?
While I run the TCN_main.py, an error arises.
Traceback (most recent call last):
File "TCN_main.py", line 40, in
import tf_models, datasets, utils, metrics
File "/home/yangchihyuan/Research/TCN/code/metrics.py", line 8, in
from LCTM.IoU_metrics import *
ImportError: No module named 'LCTM.IoU_metrics'
The LCTM module was downloaded from the GitHub and I installed it using sudo -H python3 setup.py install
I would like to thank you for sharing the code, it is a really great job. I would appreciate if you can tell me what are L and B values that you used for Dilated TCN. Also, I would like to know if you have the trained models.
Thank you!
I run the file TCN_main.py using iPython and got an error message
/TCN/code/metrics.py in ()
3 from numba import jit, int64, boolean
4
----> 5 from LCTM import utils
6 import sklearn.metrics as sm
7
ImportError: No module named LCTM
I can't find the LCTM module in the utils.py file.
Why does it happen?
I appreciate any suggestion.