Datasets:

FelixOtt
/

industry_datasets

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]))

	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]))