Ejemplos de get_seq_preds en Python

Ejemplo n.º 1

Archivo: compute_cv_correction_bdd.py Proyecto: Senwang98/MOF

features['Final_x'] = features['Future_x'].apply(lambda x: x[-1])
features['Final_y'] = features['Future_y'].apply(lambda y: y[-1])

##################### Compute velocity and make predictions #####################
print('Computing velocity...')

features['Velocity_x'] = features['Past_x'].apply(lambda x: utils.mean_velocity(x, VELOCITY_FRAMES))
features['Velocity_y'] = features['Past_y'].apply(lambda y: utils.mean_velocity(y, VELOCITY_FRAMES))

features['Predicted_x'] = features['Mid_x'] + (MIN_LENGTH_FUTURE * features['Velocity_x'])
features['Predicted_y'] = features['Mid_y'] + (MIN_LENGTH_FUTURE * features['Velocity_y'])

print('Getting predictions...')

features['Predicted_x_seq'] = features.apply(
    lambda x: utils.get_seq_preds(x['Mid_x'], x['Velocity_x'], MIN_LENGTH_FUTURE), axis=1)
features['Predicted_y_seq'] = features.apply(
    lambda x: utils.get_seq_preds(x['Mid_y'], x['Velocity_y'], MIN_LENGTH_FUTURE), axis=1)

features['E_x'] = features.apply(lambda x: (x['Future_x'] - x['Predicted_x_seq']), axis=1)
features['E_y'] = features.apply(lambda y: (y['Future_y'] - y['Predicted_y_seq']), axis=1)

##################### Get errors #####################

print('Computing errors...')
features['MSE_15'] = features.apply(
    lambda x: utils.calc_mse(x['Predicted_x_seq'], x['Future_x'], x['Predicted_y_seq'], x['Future_y'], 15), axis=1)
features['MSE_10'] = features.apply(
    lambda x: utils.calc_mse(x['Predicted_x_seq'], x['Future_x'], x['Predicted_y_seq'], x['Future_y'], 10), axis=1)
features['MSE_5'] = features.apply(
    lambda x: utils.calc_mse(x['Predicted_x_seq'], x['Future_x'], x['Predicted_y_seq'], x['Future_y'], 5), axis=1)

Ejemplo n.º 2

Archivo: compute_cv_correction_jaad.py Proyecto: Senwang98/MOF

# VELOCITY_FRAMES ~ len(x)之间的平均速度，该条目为单一值
features['Velocity_x'] = features['Past_x'].apply(
    lambda x: utils.mean_velocity(x, VELOCITY_FRAMES))
features['Velocity_y'] = features['Past_y'].apply(
    lambda y: utils.mean_velocity(y, VELOCITY_FRAMES))

# 计算 MIN_LENGTH_FUTURE 之后匀速运动的位置，该条目为单一值
# 该值将会作为ResNet的初始值，模型初始权重（“赢在起跑线”）
features['Predicted_x'] = features['Mid_x'] + (MIN_LENGTH_FUTURE *
                                               features['Velocity_x'])
features['Predicted_y'] = features['Mid_y'] + (MIN_LENGTH_FUTURE *
                                               features['Velocity_y'])

# 产生 MIN_LENGTH_FUTURE 长度的未来位置序列，该值为序列
features['Predicted_x_seq'] = features.apply(lambda x: utils.get_seq_preds(
    x['Mid_x'], x['Velocity_x'], MIN_LENGTH_FUTURE),
                                             axis=1)
features['Predicted_y_seq'] = features.apply(lambda x: utils.get_seq_preds(
    x['Mid_y'], x['Velocity_y'], MIN_LENGTH_FUTURE),
                                             axis=1)

# 当前帧之后的15帧的gt与CV之间的差值，该值为序列
features['E_x'] = features.apply(lambda x:
                                 (x['Future_x'] - x['Predicted_x_seq']),
                                 axis=1)
features['E_y'] = features.apply(lambda y:
                                 (y['Future_y'] - y['Predicted_y_seq']),
                                 axis=1)

##################### Get errors #####################
features['MSE'] = features.apply(

Ejemplo n.º 3

features['Velocity_x'] = features['Past_x'].apply(
    lambda x: utils.mean_velocity(x,
                                  len(x) - VELOCITY_FRAMES))
features['Velocity_y'] = features['Past_y'].apply(
    lambda y: utils.mean_velocity(y,
                                  len(y) - VELOCITY_FRAMES))
# 计算匀速运动终点
# features['Predicted_x'] = features['Mid_x'] + \
#     (MIN_LENGTH_FUTURE * features['Velocity_x'])
# features['Predicted_y'] = features['Mid_y'] + \
#     (MIN_LENGTH_FUTURE * features['Velocity_y'])

print('Getting predictions...')
# 计算匀速运动坐标序列
features['Predicted_x_seq'] = features.apply(
    lambda x: utils.get_seq_preds(x['Mid_x'], x['Velocity_x'], 60), axis=1)
features['Predicted_y_seq'] = features.apply(
    lambda x: utils.get_seq_preds(x['Mid_y'], x['Velocity_y'], 60), axis=1)

# 作为训练的ground truth，其代表匀速运动与真实值的误差序列
# features['E_x'] = features.apply(lambda x: (
#     x['Future_x'] - x['Predicted_x_seq']), axis=1)
# features['E_y'] = features.apply(lambda y: (
#     y['Future_y'] - y['Predicted_y_seq']), axis=1)

##################### Get errors #####################
print('Computing errors...')
# features['MSE_15'] = features.apply(
#     lambda x: utils.calc_mse(x['Predicted_x_seq'], x['Future_x'], x['Predicted_y_seq'], x['Future_y'], 15), axis=1)
# features['MSE_10'] = features.apply(
#     lambda x: utils.calc_mse(x['Predicted_x_seq'], x['Future_x'], x['Predicted_y_seq'], x['Future_y'], 10), axis=1)