Pre-release
This release is a pre-release and may not be stable for production use.
Description
This repository contains code to make datasets stored on th corpora network drive of the chair compatible with the tensorflow dataset api .
Currently available Datasets
| Dataset | Status | Url |
|---|---|---|
| ckplus | ✅ | http://www.iainm.com/publications/Lucey2010-The-Extended/paper.pdf |
| affectnet | ✅ | http://mohammadmahoor.com/affectnet/ |
| faces | ✅ | https://faces.mpdl.mpg.de/imeji/ |
| nova_dynamic | ✅ | https://github.com/hcmlab/nova |
| audioset | ❌ | https://research.google.com/audioset/ |
| is2021_ess | ❌ | - |
| librispeech | ❌ | https://www.openslr.org/12 |
Example Usage
import os
import tensorflow as tf
import tensorflow_datasets as tfds
import hcai_datasets
from matplotlib import pyplot as plt
# Preprocessing function
def preprocess(x, y):
img = x.numpy()
return img, y
# Creating a dataset
ds, ds_info = tfds.load(
'hcai_example_dataset',
split='train',
with_info=True,
as_supervised=True,
builder_kwargs={'dataset_dir': os.path.join('path', 'to', 'directory')}
)
# Input output mapping
ds = ds.map(lambda x, y: (tf.py_function(func=preprocess, inp=[x, y], Tout=[tf.float32, tf.int64])))
# Manually iterate over dataset
img, label = next(ds.as_numpy_iterator())
# Visualize
plt.imshow(img / 255.)
plt.show()
Example Usage Nova Dynamic Data
import os
import hcai_datasets
import tensorflow_datasets as tfds
from sklearn.svm import LinearSVC
import numpy as np
from sklearn.calibration import CalibratedClassifierCV
import warnings
warnings.simplefilter("ignore")
## Load Data
ds, ds_info = tfds.load(
'hcai_nova_dynamic',
split='dynamic_split',
with_info=True,
as_supervised=True,
data_dir='.',
read_config=tfds.ReadConfig(
shuffle_seed=1337
),
builder_kwargs={
# Database Config
'db_config_path': 'nova_db.cfg',
'db_config_dict': None,
# Dataset Config
'dataset': '<dataset_name>',
'nova_data_dir': os.path.join('C:', 'Nova', 'Data'),
'sessions': ['<session_name>'],
'roles': ['<role_one>', '<role_two>'],
'schemes': ['<label_scheme_one'],
'annotator': '<annotator_id>',
'data_streams': ['<stream_name>'],
# Sample Config
'frame_step': 1,
'left_context': 0,
'right_context': 0,
'start': None,
'end': None,
'flatten_samples': False,
'supervised_keys': ['<role_one>.<stream_name>', '<scheme_two>'],
# Additional Config
'clear_cache' : True
}
)
data_it = ds.as_numpy_iterator()
data_list = list(data_it)
data_list.sort(key=lambda x: int(x['frame'].decode('utf-8').split('_')[0]))
x = [v['<stream_name>'] for v in data_list]
y = [v['<scheme_two'] for v in data_list]
x_np = np.ma.concatenate( x, axis=0 )
y_np = np.array( y )
linear_svc = LinearSVC()
model = CalibratedClassifierCV(linear_svc,
method='sigmoid',
cv=3)
print('train_x shape: {} | train_x[0] shape: {}'.format(x_np.shape, x_np[0].shape))
model.fit(x_np, y_np)
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