def post(self):
"""Return predictions from the trained model."""
global _SESSION
model, encoder = _load_keras_model()
bins = json.loads(Path(_MODEL_DIR / 'bins.json').read_text())
bins_partial = json.loads(
Path(_MODEL_DIR / 'bins_partial.json').read_text())
instances = request.get_json(force=True)['instances']
data = []
for sample in instances:
gesture = sample['gesture']
data_clean = list(clean_data([process_motion_rotation(sample)]))
if data_clean:
_, feature_df_total = featurize(data_clean,
col_bins=bins[gesture])
_, feature_df_partial = featurize(
data_clean, col_bins=bins_partial[gesture])
data.append(np.vstack([feature_df_partial, feature_df_total]))
input_t: np.ndarray = np.array(data, dtype=np.float64)
if not np.any(input_t):
return "Error: Invalid model input.", HTTPStatus.BAD_REQUEST
tf.keras.backend.set_session(_SESSION)
scores: np.ndarray = model.predict(input_t,
max_queue_size=20,
use_multiprocessing=True,
workers=4)
probas: np.ndarray = np.exp(scores)
probas /= np.sum(probas, axis=1)[:, np.newaxis]
labels: np.ndarray = np.argmax(scores, axis=1)
candidates: list = encoder.inverse_transform(labels).tolist()
response = {
'payload': [{
'candidate':
candidates[i],
'candidate_score':
float(sample[labels[i]]),
'predictions':
dict(zip(encoder.classes_, sample.tolist())),
} for i, sample in enumerate(probas)],
'total':
len(candidates)
}
return response, HTTPStatus.OK
def post(self):
"""Return predictions from the trained model."""
global _SESSION
model, encoder = _load_keras_model()
instances = request.get_json(force=True)['instances']
data = [
demo.process_motion_rotation(sample, i)
for i, sample in enumerate(instances)
]
df = demo.create_dataframe(demo.clean_data(data), False)
input_t: np.ndarray = np.array(df, dtype=np.float64)
tf.keras.backend.set_session(_SESSION)
scores: np.ndarray = model.predict(input_t,
max_queue_size=20,
use_multiprocessing=True,
workers=4)
probas: np.ndarray = np.exp(scores)
probas /= np.sum(probas, axis=1)[:, np.newaxis]
labels: np.ndarray = np.argmax(scores, axis=1)
candidates: list = encoder.inverse_transform(labels).tolist()
response = {
'payload': [{
'candidate':
candidates[i],
'candidate_score':
float(sample[labels[i]]),
'predictions':
dict(zip(encoder.classes_, sample.tolist())),
} for i, sample in enumerate(probas)],
'total':
len(candidates)
}
return response, HTTPStatus.OK