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1403119 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 | import numpy as np
import argparse
import os
import time
import torch
import torch.backends.cudnn as cudnn
import torch.nn.init as init
import torch.optim as optim
import torch.utils.data
from glob import glob
torch.autograd.set_detect_anomaly(True)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(device)
all_folders = glob("...") # Collects all previous trainings (the folder names start with the training number)
all_folder_nums = np.array([int(x.split("/")[-1].split("_")[0]) for x in all_folders]) # Contains the train ids
parser = argparse.ArgumentParser()
parser.add_argument('--id', help='ID of the train run', required=True)
parser.add_argument('--test', help='number of the test dataset', required=True)
args = parser.parse_args()
folder_id = int(args.id)
test_num = int(args.test)
# If the folder for the defined train id doesn't exist, then exit
if folder_id not in all_folder_nums:
print("Folder ID not finished yet")
exit()
folder = all_folders[np.where(all_folder_nums == folder_id)[0][0]] # pick the correct folder out of the folder ids
# Read in the parameters specified in the parameter file of the current train id
f = open(folder + "/parameters.txt", "r")
lines = f.read()
train_num = int(lines.split("\n")[1].split(" ")[-1])
BETA = 50
time_size = int(lines.split("\n")[6].split(" ")[-1])
rnn_size = int(lines.split("\n")[7].split(" ")[-1])
backrot = int(lines.split("\n")[8].split(" ")[-1])
mode = int(lines.split("\n")[9].split(" ")[-1])
net_name = lines.split("\n")[10].split(" ")[-1]
stacks = int(lines.split("\n")[11].split(" ")[-1])
f.close()
print(folder)
# Collect all weight files and use the last one
weights = glob(folder + "/weights/*")
startweight = weights[-1]
loss_name = "rmse"
if not os.path.exists(folder + "/error/"):
os.makedirs(folder + "/error/")
# Choose and create the network specified in the arguments
if net_name == "lstm":
from lstm import *
model = LSTM_stack(14, rnn_size, device, stacks)
model.train()
model = model.to(device)
elif net_name == "cfn":
from cfn import *
model = CFN_stack(14, rnn_size, device, stacks)
model.train()
model.to(device)
elif net_name == "gru":
from gru import *
model = GRU_stack(14, rnn_size, device, stacks)
model.train()
model.to(device)
elif net_name == "mgu":
from mgu import *
model = MGU_stack(14, rnn_size, device, stacks)
model.train()
model.to(device)
elif net_name == "qrnn":
from qrnn import *
model = fakeQRNN_stack(14, rnn_size, device, stacks)
model.train()
model.to(device)
elif net_name == "ran":
from ran import *
model = RAN_stack(14, rnn_size, device, stacks)
model.train()
model.to(device)
elif net_name == "sru":
from sru import *
model = SRU_stack(14, rnn_size, device, stacks)
model.train()
model.to(device)
elif net_name == "trnn":
from trnn import *
model = TRNN_stack(14, rnn_size, device, stacks)
model.train()
model.to(device)
import utilities_comb_notqdm as utilities
# The following deals with paths and folders for the data. It has to be adjusted to your setup. -------------------- #
# In the end 2 file paths are defined: test_dataset_abs/test_dataset_rel
# Folders with the test data for the different methods
# Each is a dictionary with an entry for every train number
abs_datasets_sfm = {1: ..., 2: ...}
abs_datasets = {1: ..., 2: ...}
rel_datasets = {1: ..., 2: ...}
abs_synt_post_datasets = {1: ..., 2: ...}
rel_synt_post_datasets = {1: ..., 2: ...}
# Depending on the mode, the absolute and relative data is combined from different methods
# !! Use the test num to differentiate between the different tests
if mode == 1:
train_base_abs = abs_datasets[train_num]
train_base_rel = rel_datasets[train_num]
test_dataset_abs = train_base_abs + "..."
test_dataset_rel = train_base_rel + "..."
elif mode == 2:
train_base_abs = abs_synt_post_datasets[train_num]
train_base_rel = rel_synt_post_datasets[train_num]
test_dataset_abs = train_base_abs + "..."
test_dataset_rel = train_base_rel + "..."
elif mode == 3:
train_base_abs = abs_datasets_sfm[train_num]
train_base_rel = rel_datasets[train_num]
test_dataset_abs = train_base_abs + "..."
test_dataset_rel = train_base_rel + "..."
elif mode == 4:
train_base_abs = abs_datasets[train_num]
train_base_rel = rel_synt_post_datasets[train_num]
test_dataset_abs = train_base_abs + "..."
test_dataset_rel = train_base_rel + "..."
elif mode == 5:
train_base_abs = abs_synt_post_datasets[train_num]
train_base_rel = rel_datasets[train_num]
test_dataset_abs = train_base_abs + "..."
test_dataset_rel = train_base_rel + "..."
elif mode == 6:
train_base_abs = abs_datasets_sfm[train_num]
train_base_rel = rel_synt_post_datasets[train_num]
test_dataset_abs = train_base_abs + "..."
test_dataset_rel = train_base_rel + "..."
# ------------------------------------------------------------------------------------------------------------------- #
# Create files to save the results in
tests_error = open(folder + "/error/" + folder.split("/")[-1] + "_result_" + test_dataset_abs.split("/")[-1].split(".")[0] + ".txt", "w")
tests_error_pred = open(folder + "/error/" + folder.split("/")[-1] + "_prediction_" + test_dataset_abs.split("/")[-1].split(".")[0] + ".txt", "w")
def getError(posx, posq, actualx, actualq):
""" Calculates the position and rotation error """
q1 = actualq / np.linalg.norm(actualq)
q2 = posq / np.linalg.norm(posq)
d = abs(np.sum(np.multiply(q1, q2)))
theta = 2 * np.arccos(d) * 180 / np.pi
errx = np.linalg.norm(actualx - posx)
return errx, theta
def rmse_p(y_true, y_pred):
return torch.sqrt(torch.sum(torch.square(y_pred-y_true), axis=-1))
def rmse_q(y_true, y_pred):
return BETA * torch.sqrt(torch.sum(torch.square(y_true-y_pred), axis=-1))
class rmse_wrap(torch.nn.Module):
""" Wrapper for dealing with the position loss and rotation loss. The rotation loss is multiplied with BETA. """
def __init__(self):
super(rmse_wrap, self).__init__()
def __call__(self, output, target):
loss1 = rmse_p(output[:, :3], target[:, :3])
loss2 = rmse_q(output[:, 3:], target[:, 3:])
return loss1 + loss2
# Loads the test data
pred_abs_test, pred_rel_test, gt_abs_test, gt_rel_test = utilities.get_training_data(test_dataset_abs, test_dataset_rel, backrot)
optimizer = optim.Adam(model.parameters())
criterion = rmse_wrap().to(device)
model.load_state_dict(torch.load(startweight)) # Load the trained weights
model.eval()
# create a generator for the test data (Batchsize = 1)
datagen = utilities.limited_gen_data(pred_abs_test, pred_rel_test, gt_abs_test, gt_rel_test, time_size)
results = []
results2d = []
i = 0
for ims,xs,qs in datagen: # Iterate over the test data and evaluate it
inputs = np.zeros((1, time_size, 14)).astype("float32")
inputs[0, :] = ims
inputs = inputs.transpose((1, 0, 2))
inputs = torch.tensor(inputs).to(device)
out = model(inputs).float()
pred_x = out[0][:3].detach().cpu().numpy()
pred_q = out[0][3:].detach().cpu().numpy()
label_x = xs
label_q = qs
_, theta = getError(pred_x, pred_q, label_x, label_q)
label_x = np.squeeze(label_x)
label_q = np.squeeze(label_q)
pred_x = np.squeeze(pred_x)
pred_q = np.squeeze(pred_q)
# Calculate the error in 3d and 2d
error_x = np.linalg.norm(label_x - pred_x)
error_x_2d = np.linalg.norm(label_x - [pred_x[0], pred_x[1], label_x[2]])
results.append([error_x, theta])
results2d.append([error_x_2d])
# Write the error and the predicted values in the above defined files
tests_error.write("\t Image %d: Error xyz: %fm, Error wpqr: %f degrees \n" % (int(i), error_x, theta))
tests_error.flush()
tests_error_pred.write("Timestamp: " + " 1" + ", Label: x: " + str(label_x[0]) + ", y: " + str(label_x[1]) + ", z: " + str(label_x[2]) + ", w0: " + str(label_q[0]) + ", w1: " + str(label_q[1]) + ", w2: " + str(label_q[2]) + ", w3: " + str(label_q[3]) + " Prediction: x: " + str(pred_x[0]) + ", y: " + str(pred_x[1]) + ", z: " + str(pred_x[2]) + ", w0: " + str(pred_q[0]) + ", w1: " + str(pred_q[1]) + ", w2: " + str(pred_q[2]) + ", w3: " + str(pred_q[3]) + "\n")
tests_error_pred.flush()
i = i + 1
# Caluclate the mean/median and percentiles of the errors
mean_result = np.nanmean(results, axis=0)
median_result = np.nanmedian(results, axis=0)
percentile50_result_2d = np.nanpercentile(results2d, axis=0, q=50)
percentile95_result_2d = np.nanpercentile(results2d, axis=0, q=95)
percentile50_result = np.nanpercentile(results, axis=0, q=50)
percentile95_result = np.nanpercentile(results, axis=0, q=95)
# Print and save the overview of the results
print("\n Results: \n \t MAE 2D: %f m \n\t Median 2D: %f m \n\t CEP: %f m \n\t CE95: %f m \n\t MAE 2D: %f deg \n\t Median 2D: %f deg \n\t CE95: %f deg \n" % (mean_result[0], median_result[0], percentile50_result_2d[0], percentile95_result_2d[0], mean_result[1], median_result[1], percentile95_result[1]))
tests_error.write("\n Results: \n \t MAE 2D: %f m \n\t Median 2D: %f m \n\t CEP: %f m \n\t CE95: %f m \n\t MAE 2D: %f deg \n\t Median 2D: %f deg \n\t CE95: %f deg \n" % (mean_result[0], median_result[0], percentile50_result_2d[0], percentile95_result_2d[0], mean_result[1], median_result[1], percentile95_result[1]))
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