BDF/query_methods.py at main · RansML/BDF · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
import argparse
import json
import os
import pandas as pd
import time
import torch
import numpy as np

from bhmtorch_cpu import BHM_VELOCITY_PYTORCH
from utils_filereader import read_frame_velocity
from utils_metrics import calc_scores_velocity


def load_mdl(args, path):
    """
    @param path (str): path relative to the mdl folder to load from
    @returns: model: BHM Module that is loaded
    """
    filename = './mdls/{}'.format(path)
    print(' Loading the trained model from ' + filename)
    model_params = torch.load(filename)

    if args.likelihood_type == "gamma":
        model = BHM_VELOCITY_PYTORCH(
            gamma=args.gamma,
            grid=model_params['grid'],
            w_hatx=model_params["w_hatx"],
            w_haty=model_params["w_haty"],
            w_hatz=model_params["w_hatz"],
            kernel_type=args.kernel_type,
            likelihood_type=model_params["likelihood_type"]
        )
    elif args.likelihood_type == "gaussian":
        model = BHM_VELOCITY_PYTORCH(
            alpha=model_params['alpha'],
            beta=model_params['beta'],
            gamma=args.gamma,
            grid=model_params['grid'],
            kernel_type=args.kernel_type,
            likelihood_type=model_params["likelihood_type"]
        )
        model.updateMuSig(model_params['mu_x'], model_params['sig_x'],
                          model_params['mu_y'], model_params['sig_y'],
                          model_params['mu_z'], model_params['sig_z'])
    else:
        raise ValueError("Unsupported likelihood type: \"{}\"".format(args.likelihood_type))

    return model, model_params['train_time']


def save_query_data(data, path):
    """
    @param data (tuple of elements): datapoints from regression/occupancy query to save
    @param path (str): path relative to query_data folder to save data
    """

    ###===###
    complete_dir = './query_data/{}'.format(path).split("/")
    complete_dir = "/".join(complete_dir[:-1])

    if not os.path.isdir(complete_dir):
        os.makedirs(complete_dir)
    ###///###

    filename = './query_data/{}'.format(path)
    torch.save(data, filename)
    print( ' Saving queried output as ' + filename)

def query_velocity(args, X, y_vx, y_vy, y_vz, partitions, cell_resolution, cell_max_min, framei):
    bhm_velocity_mdl, train_time = load_mdl(args, 'velocity/{}_f{}'.format(args.save_model_path, framei))

    option = ''
    if args.eval_path != '' and args.eval:
        #if eval is True, test the query
        print(" Query data from the test dataset")
        Xq_mv, y_vx_true, y_vy_true, y_vz_true, _ = read_frame_velocity(args, framei, args.eval_path, cell_max_min)
        option = args.eval_path
    elif args.query_dist[0] <= 0 and args.query_dist[1] <= 0 and args.query_dist[2] <= 0:
        #if all q_res are non-positive, then query input = X
        print(" Query data is the same as input data")
        Xq_mv = X
        option = 'Train data'
    elif args.query_dist[0] <= 0 or args.query_dist[1] <= 0 or args.query_dist[2] <= 0:
        #if at least one q_res is non-positive, then
        if args.query_dist[0] <= 0: #x-slice
            print(" Query data is x={} slice ".format(args.query_dist[3]))
            xx, yy, zz = torch.meshgrid(
                torch.arange(
                    args.query_dist[3],
                    args.query_dist[3] + 0.1,
                    1
                ),
                torch.arange(
                    cell_max_min[2],
                    cell_max_min[3] + args.query_dist[1],
                    args.query_dist[1]
                ),
                torch.arange(
                    cell_max_min[4],
                    cell_max_min[5] + args.query_dist[2],
                    args.query_dist[2]
                )
            )
            Xq_mv = torch.stack([xx.flatten(), yy.flatten(), zz.flatten()], dim=1)
            option = 'X slice at '.format(args.query_dist[3])
        elif args.query_dist[1] <= 0: #y-slice
            print(" Query data is y={} slice ".format(args.query_dist[3]))
            xx, yy, zz = torch.meshgrid(
                torch.arange(
                    cell_max_min[0],
                    cell_max_min[1] + args.query_dist[0],
                    args.query_dist[0]
                ),
                torch.arange(
                    args.query_dist[3],
                    args.query_dist[3] + 0.1,
                    1
                ),
                torch.arange(
                    cell_max_min[4],
                    cell_max_min[5] + args.query_dist[2],
                    args.query_dist[2]
                )
            )
            Xq_mv = torch.stack([xx.flatten(), yy.flatten(), zz.flatten()], dim=1)
            option = 'Y slice at '.format(args.query_dist[3])
        else: #z-slice
            print(" Query data is z={} slice ".format(args.query_dist[3]))
            xx, yy, zz = torch.meshgrid(
                torch.arange(
                    cell_max_min[0],
                    cell_max_min[1] + args.query_dist[0],
                    args.query_dist[0]
                ),
                torch.arange(
                    cell_max_min[2],
                    cell_max_min[3] + args.query_dist[1],
                    args.query_dist[1]
                ),
                torch.arange(
                    args.query_dist[3],
                    args.query_dist[3] + 0.1,
                    1
                )
            )
            Xq_mv = torch.stack([xx.flatten(), yy.flatten(), zz.flatten()], dim=1)
            option = 'Z slice at '.format(args.query_dist[3])
    else:
        #if not use the grid
        print(" Query data is a 3D grid.")
        xx, yy, zz = torch.meshgrid(
            torch.arange(
                cell_max_min[0],
                cell_max_min[1]+args.query_dist[0],
                args.query_dist[0]
            ),
            torch.arange(
                cell_max_min[2],
                cell_max_min[3]+args.query_dist[1],
                args.query_dist[1]
            ),
            torch.arange(
                cell_max_min[4],
                cell_max_min[5]+args.query_dist[2],
                args.query_dist[2]
            )
        )
        Xq_mv = torch.stack([xx.flatten(), yy.flatten(), zz.flatten()], dim=1)
        option = '3D grid'

    time1 = time.time()

    if args.likelihood_type == "gamma":
        mean_x, mean_y, mean_z = bhm_velocity_mdl.predict(Xq_mv)
    elif args.likelihood_type == "gaussian":
        mean_x, var_x, mean_y, var_y, mean_z, var_z = bhm_velocity_mdl.predict(Xq_mv, args.query_blocks, args.variance_only)
    else:
        raise ValueError("Unsupported likelihood type: \"{}\"".format(args.likelihood_type))

    query_time = time.time() - time1

    print(' Total querying time={} s'.format(round(query_time, 2)))
    save_query_data((X, y_vx, y_vy, y_vz, Xq_mv, mean_x, var_x, mean_y, var_y, mean_z, var_z, framei), \
                    'velocity/{}_f{}'.format(args.save_query_data_path, framei))

    if args.eval:
        if hasattr(args, 'report_notes'):
            notes = args.report_notes
        else:
            notes = ''
        axes = [('x', y_vx_true, mean_x, var_x), ('y', y_vy_true, mean_y, var_y), ('z', y_vz_true, mean_z, var_z)]
        for axis, Xqi, mean, var in axes:
            mdl_name = 'reports/' + args.plot_title + '_' + axis
            calc_scores_velocity(mdl_name, option, Xqi.numpy(), mean.numpy().ravel(), predicted_var=\
                np.diagonal(var.numpy()), train_time=train_time, query_time=query_time, save_report=True, notes=notes)