933 lines
32 KiB
Python
933 lines
32 KiB
Python
#
|
|
#
|
|
# 0=================================0
|
|
# | Kernel Point Convolutions |
|
|
# 0=================================0
|
|
#
|
|
#
|
|
# ----------------------------------------------------------------------------------------------------------------------
|
|
#
|
|
# Class handling the training of any model
|
|
#
|
|
# ----------------------------------------------------------------------------------------------------------------------
|
|
#
|
|
# Hugues THOMAS - 11/06/2018
|
|
#
|
|
|
|
|
|
# ----------------------------------------------------------------------------------------------------------------------
|
|
#
|
|
# Imports and global variables
|
|
# \**********************************/
|
|
#
|
|
|
|
|
|
# Basic libs
|
|
import torch
|
|
import torch.nn as nn
|
|
import numpy as np
|
|
import pickle
|
|
import os
|
|
from os import makedirs, remove
|
|
from os.path import exists, join
|
|
import time
|
|
import sys
|
|
|
|
# PLY reader
|
|
from utils.ply import read_ply, write_ply
|
|
|
|
# Metrics
|
|
from utils.metrics import IoU_from_confusions, fast_confusion
|
|
from utils.config import Config
|
|
from sklearn.neighbors import KDTree
|
|
|
|
from models.blocks import KPConv
|
|
|
|
|
|
# ----------------------------------------------------------------------------------------------------------------------
|
|
#
|
|
# Trainer Class
|
|
# \*******************/
|
|
#
|
|
|
|
|
|
class ModelTrainer:
|
|
|
|
# Initialization methods
|
|
# ------------------------------------------------------------------------------------------------------------------
|
|
|
|
def __init__(self, net, config, chkp_path=None, finetune=False, on_gpu=True):
|
|
"""
|
|
Initialize training parameters and reload previous model for restore/finetune
|
|
:param net: network object
|
|
:param config: configuration object
|
|
:param chkp_path: path to the checkpoint that needs to be loaded (None for new training)
|
|
:param finetune: finetune from checkpoint (True) or restore training from checkpoint (False)
|
|
:param on_gpu: Train on GPU or CPU
|
|
"""
|
|
|
|
############
|
|
# Parameters
|
|
############
|
|
|
|
# Epoch index
|
|
self.epoch = 0
|
|
self.step = 0
|
|
|
|
# Optimizer with specific learning rate for deformable KPConv
|
|
deform_params = [v for k, v in net.named_parameters() if 'offset' in k]
|
|
other_params = [v for k, v in net.named_parameters() if 'offset' not in k]
|
|
deform_lr = config.learning_rate * config.deform_lr_factor
|
|
self.optimizer = torch.optim.SGD([{'params': other_params},
|
|
{'params': deform_params, 'lr': deform_lr}],
|
|
lr=config.learning_rate,
|
|
momentum=config.momentum,
|
|
weight_decay=config.weight_decay)
|
|
|
|
# Choose to train on CPU or GPU
|
|
if on_gpu and torch.cuda.is_available():
|
|
self.device = torch.device("cuda:0")
|
|
else:
|
|
self.device = torch.device("cpu")
|
|
net.to(self.device)
|
|
|
|
##########################
|
|
# Load previous checkpoint
|
|
##########################
|
|
|
|
if (chkp_path is not None):
|
|
if finetune:
|
|
checkpoint = torch.load(chkp_path)
|
|
net.load_state_dict(checkpoint['model_state_dict'])
|
|
net.train()
|
|
print("Model restored and ready for finetuning.")
|
|
else:
|
|
checkpoint = torch.load(chkp_path)
|
|
net.load_state_dict(checkpoint['model_state_dict'])
|
|
self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
|
|
self.epoch = checkpoint['epoch']
|
|
net.train()
|
|
print("Model and training state restored.")
|
|
|
|
# Path of the result folder
|
|
if config.saving:
|
|
if config.saving_path is None:
|
|
config.saving_path = time.strftime('results/Log_%Y-%m-%d_%H-%M-%S', time.gmtime())
|
|
if not exists(config.saving_path):
|
|
makedirs(config.saving_path)
|
|
config.save()
|
|
|
|
return
|
|
|
|
# Training main method
|
|
# ------------------------------------------------------------------------------------------------------------------
|
|
|
|
def train(self, net, training_loader, val_loader, config):
|
|
"""
|
|
Train the model on a particular dataset.
|
|
"""
|
|
|
|
################
|
|
# Initialization
|
|
################
|
|
|
|
if config.saving:
|
|
# Training log file
|
|
with open(join(config.saving_path, 'training.txt'), "w") as file:
|
|
file.write('epochs steps out_loss offset_loss train_accuracy time\n')
|
|
|
|
# Killing file (simply delete this file when you want to stop the training)
|
|
PID_file = join(config.saving_path, 'running_PID.txt')
|
|
if not exists(PID_file):
|
|
with open(PID_file, "w") as file:
|
|
file.write('Launched with PyCharm')
|
|
|
|
# Checkpoints directory
|
|
checkpoint_directory = join(config.saving_path, 'checkpoints')
|
|
if not exists(checkpoint_directory):
|
|
makedirs(checkpoint_directory)
|
|
else:
|
|
checkpoint_directory = None
|
|
PID_file = None
|
|
|
|
# Loop variables
|
|
t0 = time.time()
|
|
t = [time.time()]
|
|
last_display = time.time()
|
|
mean_dt = np.zeros(1)
|
|
|
|
# Start training loop
|
|
for epoch in range(config.max_epoch):
|
|
|
|
# Remove File for kill signal
|
|
if epoch == config.max_epoch - 1 and exists(PID_file):
|
|
remove(PID_file)
|
|
|
|
self.step = 0
|
|
for batch in training_loader:
|
|
|
|
# Check kill signal (running_PID.txt deleted)
|
|
if config.saving and not exists(PID_file):
|
|
continue
|
|
|
|
##################
|
|
# Processing batch
|
|
##################
|
|
|
|
# New time
|
|
t = t[-1:]
|
|
t += [time.time()]
|
|
|
|
if 'cuda' in self.device.type:
|
|
batch.to(self.device)
|
|
|
|
# zero the parameter gradients
|
|
self.optimizer.zero_grad()
|
|
|
|
# Forward pass
|
|
outputs = net(batch, config)
|
|
loss = net.loss(outputs, batch.labels)
|
|
acc = net.accuracy(outputs, batch.labels)
|
|
|
|
t += [time.time()]
|
|
|
|
# Backward + optimize
|
|
loss.backward()
|
|
|
|
if config.grad_clip_norm > 0:
|
|
#torch.nn.utils.clip_grad_norm_(net.parameters(), config.grad_clip_norm)
|
|
torch.nn.utils.clip_grad_value_(net.parameters(), config.grad_clip_norm)
|
|
self.optimizer.step()
|
|
torch.cuda.synchronize(self.device)
|
|
|
|
t += [time.time()]
|
|
|
|
# Average timing
|
|
if self.step < 2:
|
|
mean_dt = np.array(t[1:]) - np.array(t[:-1])
|
|
else:
|
|
mean_dt = 0.9 * mean_dt + 0.1 * (np.array(t[1:]) - np.array(t[:-1]))
|
|
|
|
# Console display (only one per second)
|
|
if (t[-1] - last_display) > 1.0:
|
|
last_display = t[-1]
|
|
message = 'e{:03d}-i{:04d} => L={:.3f} acc={:3.0f}% / t(ms): {:5.1f} {:5.1f} {:5.1f})'
|
|
print(message.format(self.epoch, self.step,
|
|
loss.item(),
|
|
100*acc,
|
|
1000 * mean_dt[0],
|
|
1000 * mean_dt[1],
|
|
1000 * mean_dt[2]))
|
|
|
|
# Log file
|
|
if config.saving:
|
|
with open(join(config.saving_path, 'training.txt'), "a") as file:
|
|
message = '{:d} {:d} {:.3f} {:.3f} {:.3f} {:.3f}\n'
|
|
file.write(message.format(self.epoch,
|
|
self.step,
|
|
net.output_loss,
|
|
net.reg_loss,
|
|
acc,
|
|
t[-1] - t0))
|
|
|
|
|
|
self.step += 1
|
|
|
|
##############
|
|
# End of epoch
|
|
##############
|
|
|
|
# Check kill signal (running_PID.txt deleted)
|
|
if config.saving and not exists(PID_file):
|
|
break
|
|
|
|
# Update learning rate
|
|
if self.epoch in config.lr_decays:
|
|
for param_group in self.optimizer.param_groups:
|
|
param_group['lr'] *= config.lr_decays[self.epoch]
|
|
|
|
# Update epoch
|
|
self.epoch += 1
|
|
|
|
# Saving
|
|
if config.saving:
|
|
# Get current state dict
|
|
save_dict = {'epoch': self.epoch,
|
|
'model_state_dict': net.state_dict(),
|
|
'optimizer_state_dict': self.optimizer.state_dict(),
|
|
'saving_path': config.saving_path}
|
|
|
|
# Save current state of the network (for restoring purposes)
|
|
checkpoint_path = join(checkpoint_directory, 'current_chkp.tar')
|
|
torch.save(save_dict, checkpoint_path)
|
|
|
|
# Save checkpoints occasionally
|
|
if (self.epoch + 1) % config.checkpoint_gap == 0:
|
|
checkpoint_path = join(checkpoint_directory, 'chkp_{:04d}.tar'.format(self.epoch + 1))
|
|
torch.save(save_dict, checkpoint_path)
|
|
|
|
# Validation
|
|
net.eval()
|
|
self.validation(net, val_loader, config)
|
|
net.train()
|
|
|
|
print('Finished Training')
|
|
return
|
|
|
|
# Validation methods
|
|
# ------------------------------------------------------------------------------------------------------------------
|
|
|
|
def validation(self, net, val_loader, config: Config):
|
|
|
|
if config.dataset_task == 'classification':
|
|
self.object_classification_validation(net, val_loader, config)
|
|
elif config.dataset_task == 'segmentation':
|
|
self.object_segmentation_validation(net, val_loader, config)
|
|
elif config.dataset_task == 'cloud_segmentation':
|
|
self.cloud_segmentation_validation(net, val_loader, config)
|
|
elif config.dataset_task == 'slam_segmentation':
|
|
self.slam_segmentation_validation(net, val_loader, config)
|
|
else:
|
|
raise ValueError('No validation method implemented for this network type')
|
|
|
|
def object_classification_validation(self, net, val_loader, config):
|
|
"""
|
|
Perform a round of validation and show/save results
|
|
:param net: network object
|
|
:param val_loader: data loader for validation set
|
|
:param config: configuration object
|
|
"""
|
|
|
|
############
|
|
# Initialize
|
|
############
|
|
|
|
# Choose validation smoothing parameter (0 for no smothing, 0.99 for big smoothing)
|
|
val_smooth = 0.95
|
|
|
|
# Number of classes predicted by the model
|
|
nc_model = config.num_classes
|
|
softmax = torch.nn.Softmax(1)
|
|
|
|
# Initialize global prediction over all models
|
|
if not hasattr(self, 'val_probs'):
|
|
self.val_probs = np.zeros((val_loader.dataset.num_models, nc_model))
|
|
|
|
#####################
|
|
# Network predictions
|
|
#####################
|
|
|
|
probs = []
|
|
targets = []
|
|
obj_inds = []
|
|
|
|
t = [time.time()]
|
|
last_display = time.time()
|
|
mean_dt = np.zeros(1)
|
|
|
|
# Start validation loop
|
|
for batch in val_loader:
|
|
|
|
# New time
|
|
t = t[-1:]
|
|
t += [time.time()]
|
|
|
|
if 'cuda' in self.device.type:
|
|
batch.to(self.device)
|
|
|
|
# Forward pass
|
|
outputs = net(batch, config)
|
|
|
|
# Get probs and labels
|
|
probs += [softmax(outputs).cpu().detach().numpy()]
|
|
targets += [batch.labels.cpu().numpy()]
|
|
obj_inds += [batch.model_inds.cpu().numpy()]
|
|
torch.cuda.synchronize(self.device)
|
|
|
|
# Average timing
|
|
t += [time.time()]
|
|
mean_dt = 0.95 * mean_dt + 0.05 * (np.array(t[1:]) - np.array(t[:-1]))
|
|
|
|
# Display
|
|
if (t[-1] - last_display) > 1.0:
|
|
last_display = t[-1]
|
|
message = 'Validation : {:.1f}% (timings : {:4.2f} {:4.2f})'
|
|
print(message.format(100 * len(obj_inds) / config.validation_size,
|
|
1000 * (mean_dt[0]),
|
|
1000 * (mean_dt[1])))
|
|
|
|
# Stack all validation predictions
|
|
probs = np.vstack(probs)
|
|
targets = np.hstack(targets)
|
|
obj_inds = np.hstack(obj_inds)
|
|
|
|
###################
|
|
# Voting validation
|
|
###################
|
|
|
|
self.val_probs[obj_inds] = val_smooth * self.val_probs[obj_inds] + (1-val_smooth) * probs
|
|
|
|
############
|
|
# Confusions
|
|
############
|
|
|
|
validation_labels = np.array(val_loader.dataset.label_values)
|
|
|
|
# Compute classification results
|
|
C1 = fast_confusion(targets,
|
|
np.argmax(probs, axis=1),
|
|
validation_labels)
|
|
|
|
# Compute votes confusion
|
|
C2 = fast_confusion(val_loader.dataset.input_labels,
|
|
np.argmax(self.val_probs, axis=1),
|
|
validation_labels)
|
|
|
|
|
|
# Saving (optionnal)
|
|
if config.saving:
|
|
print("Save confusions")
|
|
conf_list = [C1, C2]
|
|
file_list = ['val_confs.txt', 'vote_confs.txt']
|
|
for conf, conf_file in zip(conf_list, file_list):
|
|
test_file = join(config.saving_path, conf_file)
|
|
if exists(test_file):
|
|
with open(test_file, "a") as text_file:
|
|
for line in conf:
|
|
for value in line:
|
|
text_file.write('%d ' % value)
|
|
text_file.write('\n')
|
|
else:
|
|
with open(test_file, "w") as text_file:
|
|
for line in conf:
|
|
for value in line:
|
|
text_file.write('%d ' % value)
|
|
text_file.write('\n')
|
|
|
|
val_ACC = 100 * np.sum(np.diag(C1)) / (np.sum(C1) + 1e-6)
|
|
vote_ACC = 100 * np.sum(np.diag(C2)) / (np.sum(C2) + 1e-6)
|
|
print('Accuracies : val = {:.1f}% / vote = {:.1f}%'.format(val_ACC, vote_ACC))
|
|
|
|
return C1
|
|
|
|
def cloud_segmentation_validation(self, net, val_loader, config, debug=False):
|
|
"""
|
|
Validation method for cloud segmentation models
|
|
"""
|
|
|
|
############
|
|
# Initialize
|
|
############
|
|
|
|
t0 = time.time()
|
|
|
|
# Choose validation smoothing parameter (0 for no smothing, 0.99 for big smoothing)
|
|
val_smooth = 0.95
|
|
softmax = torch.nn.Softmax(1)
|
|
|
|
# Do not validate if dataset has no validation cloud
|
|
if val_loader.dataset.validation_split not in val_loader.dataset.all_splits:
|
|
return
|
|
|
|
# Number of classes including ignored labels
|
|
nc_tot = val_loader.dataset.num_classes
|
|
|
|
# Number of classes predicted by the model
|
|
nc_model = config.num_classes
|
|
|
|
#print(nc_tot)
|
|
#print(nc_model)
|
|
|
|
# Initiate global prediction over validation clouds
|
|
if not hasattr(self, 'validation_probs'):
|
|
self.validation_probs = [np.zeros((l.shape[0], nc_model))
|
|
for l in val_loader.dataset.input_labels]
|
|
self.val_proportions = np.zeros(nc_model, dtype=np.float32)
|
|
i = 0
|
|
for label_value in val_loader.dataset.label_values:
|
|
if label_value not in val_loader.dataset.ignored_labels:
|
|
self.val_proportions[i] = np.sum([np.sum(labels == label_value)
|
|
for labels in val_loader.dataset.validation_labels])
|
|
i += 1
|
|
|
|
#####################
|
|
# Network predictions
|
|
#####################
|
|
|
|
predictions = []
|
|
targets = []
|
|
|
|
t = [time.time()]
|
|
last_display = time.time()
|
|
mean_dt = np.zeros(1)
|
|
|
|
|
|
t1 = time.time()
|
|
|
|
# Start validation loop
|
|
for i, batch in enumerate(val_loader):
|
|
|
|
# New time
|
|
t = t[-1:]
|
|
t += [time.time()]
|
|
|
|
if 'cuda' in self.device.type:
|
|
batch.to(self.device)
|
|
|
|
# Forward pass
|
|
outputs = net(batch, config)
|
|
|
|
# Get probs and labels
|
|
stacked_probs = softmax(outputs).cpu().detach().numpy()
|
|
labels = batch.labels.cpu().numpy()
|
|
lengths = batch.lengths[0].cpu().numpy()
|
|
in_inds = batch.input_inds.cpu().numpy()
|
|
cloud_inds = batch.cloud_inds.cpu().numpy()
|
|
torch.cuda.synchronize(self.device)
|
|
|
|
# Get predictions and labels per instance
|
|
# ***************************************
|
|
|
|
i0 = 0
|
|
for b_i, length in enumerate(lengths):
|
|
|
|
# Get prediction
|
|
target = labels[i0:i0 + length]
|
|
probs = stacked_probs[i0:i0 + length]
|
|
inds = in_inds[i0:i0 + length]
|
|
c_i = cloud_inds[b_i]
|
|
|
|
# Update current probs in whole cloud
|
|
self.validation_probs[c_i][inds] = val_smooth * self.validation_probs[c_i][inds] \
|
|
+ (1 - val_smooth) * probs
|
|
|
|
# Stack all prediction for this epoch
|
|
predictions.append(probs)
|
|
targets.append(target)
|
|
i0 += length
|
|
|
|
# Average timing
|
|
t += [time.time()]
|
|
mean_dt = 0.95 * mean_dt + 0.05 * (np.array(t[1:]) - np.array(t[:-1]))
|
|
|
|
# Display
|
|
if (t[-1] - last_display) > 1.0:
|
|
last_display = t[-1]
|
|
message = 'Validation : {:.1f}% (timings : {:4.2f} {:4.2f})'
|
|
print(message.format(100 * i / config.validation_size,
|
|
1000 * (mean_dt[0]),
|
|
1000 * (mean_dt[1])))
|
|
|
|
t2 = time.time()
|
|
|
|
# Confusions for our subparts of validation set
|
|
Confs = np.zeros((len(predictions), nc_tot, nc_tot), dtype=np.int32)
|
|
for i, (probs, truth) in enumerate(zip(predictions, targets)):
|
|
|
|
# Insert false columns for ignored labels
|
|
for l_ind, label_value in enumerate(val_loader.dataset.label_values):
|
|
if label_value in val_loader.dataset.ignored_labels:
|
|
probs = np.insert(probs, l_ind, 0, axis=1)
|
|
|
|
# Predicted labels
|
|
preds = val_loader.dataset.label_values[np.argmax(probs, axis=1)]
|
|
|
|
# Confusions
|
|
Confs[i, :, :] = fast_confusion(truth, preds, val_loader.dataset.label_values).astype(np.int32)
|
|
|
|
|
|
t3 = time.time()
|
|
|
|
# Sum all confusions
|
|
C = np.sum(Confs, axis=0).astype(np.float32)
|
|
|
|
# Remove ignored labels from confusions
|
|
for l_ind, label_value in reversed(list(enumerate(val_loader.dataset.label_values))):
|
|
if label_value in val_loader.dataset.ignored_labels:
|
|
C = np.delete(C, l_ind, axis=0)
|
|
C = np.delete(C, l_ind, axis=1)
|
|
|
|
# Balance with real validation proportions
|
|
C *= np.expand_dims(self.val_proportions / (np.sum(C, axis=1) + 1e-6), 1)
|
|
|
|
|
|
t4 = time.time()
|
|
|
|
# Objects IoU
|
|
IoUs = IoU_from_confusions(C)
|
|
|
|
t5 = time.time()
|
|
|
|
# Saving (optionnal)
|
|
if config.saving:
|
|
|
|
# Name of saving file
|
|
test_file = join(config.saving_path, 'val_IoUs.txt')
|
|
|
|
# Line to write:
|
|
line = ''
|
|
for IoU in IoUs:
|
|
line += '{:.3f} '.format(IoU)
|
|
line = line + '\n'
|
|
|
|
# Write in file
|
|
if exists(test_file):
|
|
with open(test_file, "a") as text_file:
|
|
text_file.write(line)
|
|
else:
|
|
with open(test_file, "w") as text_file:
|
|
text_file.write(line)
|
|
|
|
# Save potentials
|
|
pot_path = join(config.saving_path, 'potentials')
|
|
if not exists(pot_path):
|
|
makedirs(pot_path)
|
|
files = val_loader.dataset.files
|
|
for i, file_path in enumerate(files):
|
|
pot_points = np.array(val_loader.dataset.pot_trees[i].data, copy=False)
|
|
cloud_name = file_path.split('/')[-1]
|
|
pot_name = join(pot_path, cloud_name)
|
|
pots = val_loader.dataset.potentials[i].numpy().astype(np.float32)
|
|
write_ply(pot_name,
|
|
[pot_points.astype(np.float32), pots],
|
|
['x', 'y', 'z', 'pots'])
|
|
|
|
t6 = time.time()
|
|
|
|
# Print instance mean
|
|
mIoU = 100 * np.mean(IoUs)
|
|
print('{:s} mean IoU = {:.1f}%'.format(config.dataset, mIoU))
|
|
|
|
# Save predicted cloud occasionally
|
|
if config.saving and (self.epoch + 1) % config.checkpoint_gap == 0:
|
|
val_path = join(config.saving_path, 'val_preds_{:d}'.format(self.epoch + 1))
|
|
if not exists(val_path):
|
|
makedirs(val_path)
|
|
files = val_loader.dataset.files
|
|
for i, file_path in enumerate(files):
|
|
|
|
# Get points
|
|
points = val_loader.dataset.load_evaluation_points(file_path)
|
|
|
|
# Get probs on our own ply points
|
|
sub_probs = self.validation_probs[i]
|
|
|
|
# Insert false columns for ignored labels
|
|
for l_ind, label_value in enumerate(val_loader.dataset.label_values):
|
|
if label_value in val_loader.dataset.ignored_labels:
|
|
sub_probs = np.insert(sub_probs, l_ind, 0, axis=1)
|
|
|
|
# Get the predicted labels
|
|
sub_preds = val_loader.dataset.label_values[np.argmax(sub_probs, axis=1).astype(np.int32)]
|
|
|
|
# Reproject preds on the evaluations points
|
|
preds = (sub_preds[val_loader.dataset.test_proj[i]]).astype(np.int32)
|
|
|
|
# Path of saved validation file
|
|
cloud_name = file_path.split('/')[-1]
|
|
val_name = join(val_path, cloud_name)
|
|
|
|
# Save file
|
|
labels = val_loader.dataset.validation_labels[i].astype(np.int32)
|
|
write_ply(val_name,
|
|
[points, preds, labels],
|
|
['x', 'y', 'z', 'preds', 'class'])
|
|
|
|
# Display timings
|
|
t7 = time.time()
|
|
if debug:
|
|
print('\n************************\n')
|
|
print('Validation timings:')
|
|
print('Init ...... {:.1f}s'.format(t1 - t0))
|
|
print('Loop ...... {:.1f}s'.format(t2 - t1))
|
|
print('Confs ..... {:.1f}s'.format(t3 - t2))
|
|
print('Confs bis . {:.1f}s'.format(t4 - t3))
|
|
print('IoU ....... {:.1f}s'.format(t5 - t4))
|
|
print('Save1 ..... {:.1f}s'.format(t6 - t5))
|
|
print('Save2 ..... {:.1f}s'.format(t7 - t6))
|
|
print('\n************************\n')
|
|
|
|
return
|
|
|
|
def slam_segmentation_validation(self, net, val_loader, config, debug=True):
|
|
"""
|
|
Validation method for slam segmentation models
|
|
"""
|
|
|
|
############
|
|
# Initialize
|
|
############
|
|
|
|
t0 = time.time()
|
|
|
|
# Do not validate if dataset has no validation cloud
|
|
if val_loader is None:
|
|
return
|
|
|
|
# Choose validation smoothing parameter (0 for no smothing, 0.99 for big smoothing)
|
|
val_smooth = 0.95
|
|
softmax = torch.nn.Softmax(1)
|
|
|
|
# Create folder for validation predictions
|
|
if not exists (join(config.saving_path, 'val_preds')):
|
|
makedirs(join(config.saving_path, 'val_preds'))
|
|
|
|
# initiate the dataset validation containers
|
|
val_loader.dataset.val_points = []
|
|
val_loader.dataset.val_labels = []
|
|
|
|
# Number of classes including ignored labels
|
|
nc_tot = val_loader.dataset.num_classes
|
|
|
|
#####################
|
|
# Network predictions
|
|
#####################
|
|
|
|
predictions = []
|
|
targets = []
|
|
inds = []
|
|
val_i = 0
|
|
|
|
t = [time.time()]
|
|
last_display = time.time()
|
|
mean_dt = np.zeros(1)
|
|
|
|
|
|
t1 = time.time()
|
|
|
|
# Start validation loop
|
|
for i, batch in enumerate(val_loader):
|
|
|
|
# New time
|
|
t = t[-1:]
|
|
t += [time.time()]
|
|
|
|
if 'cuda' in self.device.type:
|
|
batch.to(self.device)
|
|
|
|
# Forward pass
|
|
outputs = net(batch, config)
|
|
|
|
# Get probs and labels
|
|
stk_probs = softmax(outputs).cpu().detach().numpy()
|
|
lengths = batch.lengths[0].cpu().numpy()
|
|
f_inds = batch.frame_inds.cpu().numpy()
|
|
r_inds_list = batch.reproj_inds
|
|
r_mask_list = batch.reproj_masks
|
|
labels_list = batch.val_labels
|
|
torch.cuda.synchronize(self.device)
|
|
|
|
# Get predictions and labels per instance
|
|
# ***************************************
|
|
|
|
i0 = 0
|
|
for b_i, length in enumerate(lengths):
|
|
|
|
# Get prediction
|
|
probs = stk_probs[i0:i0 + length]
|
|
proj_inds = r_inds_list[b_i]
|
|
proj_mask = r_mask_list[b_i]
|
|
frame_labels = labels_list[b_i]
|
|
s_ind = f_inds[b_i, 0]
|
|
f_ind = f_inds[b_i, 1]
|
|
|
|
# Project predictions on the frame points
|
|
proj_probs = probs[proj_inds]
|
|
|
|
# Safe check if only one point:
|
|
if proj_probs.ndim < 2:
|
|
proj_probs = np.expand_dims(proj_probs, 0)
|
|
|
|
# Insert false columns for ignored labels
|
|
for l_ind, label_value in enumerate(val_loader.dataset.label_values):
|
|
if label_value in val_loader.dataset.ignored_labels:
|
|
proj_probs = np.insert(proj_probs, l_ind, 0, axis=1)
|
|
|
|
# Predicted labels
|
|
preds = val_loader.dataset.label_values[np.argmax(proj_probs, axis=1)]
|
|
|
|
# Save predictions in a binary file
|
|
filename = '{:s}_{:07d}.npy'.format(val_loader.dataset.sequences[s_ind], f_ind)
|
|
filepath = join(config.saving_path, 'val_preds', filename)
|
|
if exists(filepath):
|
|
frame_preds = np.load(filepath)
|
|
else:
|
|
frame_preds = np.zeros(frame_labels.shape, dtype=np.uint8)
|
|
frame_preds[proj_mask] = preds.astype(np.uint8)
|
|
np.save(filepath, frame_preds)
|
|
|
|
# Save some of the frame pots
|
|
if f_ind % 20 == 0:
|
|
seq_path = join(val_loader.dataset.path, 'sequences', val_loader.dataset.sequences[s_ind])
|
|
velo_file = join(seq_path, 'velodyne', val_loader.dataset.frames[s_ind][f_ind] + '.bin')
|
|
frame_points = np.fromfile(velo_file, dtype=np.float32)
|
|
frame_points = frame_points.reshape((-1, 4))
|
|
write_ply(filepath[:-4] + '_pots.ply',
|
|
[frame_points[:, :3], frame_labels, frame_preds],
|
|
['x', 'y', 'z', 'gt', 'pre'])
|
|
|
|
# Update validation confusions
|
|
frame_C = fast_confusion(frame_labels,
|
|
frame_preds.astype(np.int32),
|
|
val_loader.dataset.label_values)
|
|
val_loader.dataset.val_confs[s_ind][f_ind, :, :] = frame_C
|
|
|
|
# Stack all prediction for this epoch
|
|
predictions += [preds]
|
|
targets += [frame_labels[proj_mask]]
|
|
inds += [f_inds[b_i, :]]
|
|
val_i += 1
|
|
i0 += length
|
|
|
|
# Average timing
|
|
t += [time.time()]
|
|
mean_dt = 0.95 * mean_dt + 0.05 * (np.array(t[1:]) - np.array(t[:-1]))
|
|
|
|
# Display
|
|
if (t[-1] - last_display) > 1.0:
|
|
last_display = t[-1]
|
|
message = 'Validation : {:.1f}% (timings : {:4.2f} {:4.2f})'
|
|
print(message.format(100 * i / config.validation_size,
|
|
1000 * (mean_dt[0]),
|
|
1000 * (mean_dt[1])))
|
|
|
|
t2 = time.time()
|
|
|
|
# Confusions for our subparts of validation set
|
|
Confs = np.zeros((len(predictions), nc_tot, nc_tot), dtype=np.int32)
|
|
for i, (preds, truth) in enumerate(zip(predictions, targets)):
|
|
|
|
# Confusions
|
|
Confs[i, :, :] = fast_confusion(truth, preds, val_loader.dataset.label_values).astype(np.int32)
|
|
|
|
t3 = time.time()
|
|
|
|
#######################################
|
|
# Results on this subpart of validation
|
|
#######################################
|
|
|
|
# Sum all confusions
|
|
C = np.sum(Confs, axis=0).astype(np.float32)
|
|
|
|
# Balance with real validation proportions
|
|
C *= np.expand_dims(val_loader.dataset.class_proportions / (np.sum(C, axis=1) + 1e-6), 1)
|
|
|
|
# Remove ignored labels from confusions
|
|
for l_ind, label_value in reversed(list(enumerate(val_loader.dataset.label_values))):
|
|
if label_value in val_loader.dataset.ignored_labels:
|
|
C = np.delete(C, l_ind, axis=0)
|
|
C = np.delete(C, l_ind, axis=1)
|
|
|
|
# Objects IoU
|
|
IoUs = IoU_from_confusions(C)
|
|
|
|
#####################################
|
|
# Results on the whole validation set
|
|
#####################################
|
|
|
|
t4 = time.time()
|
|
|
|
# Sum all validation confusions
|
|
C_tot = [np.sum(seq_C, axis=0) for seq_C in val_loader.dataset.val_confs if len(seq_C) > 0]
|
|
C_tot = np.sum(np.stack(C_tot, axis=0), axis=0)
|
|
|
|
if debug:
|
|
s = '\n'
|
|
for cc in C_tot:
|
|
for c in cc:
|
|
s += '{:8.1f} '.format(c)
|
|
s += '\n'
|
|
print(s)
|
|
|
|
# Remove ignored labels from confusions
|
|
for l_ind, label_value in reversed(list(enumerate(val_loader.dataset.label_values))):
|
|
if label_value in val_loader.dataset.ignored_labels:
|
|
C_tot = np.delete(C_tot, l_ind, axis=0)
|
|
C_tot = np.delete(C_tot, l_ind, axis=1)
|
|
|
|
# Objects IoU
|
|
val_IoUs = IoU_from_confusions(C_tot)
|
|
|
|
t5 = time.time()
|
|
|
|
# Saving (optionnal)
|
|
if config.saving:
|
|
|
|
IoU_list = [IoUs, val_IoUs]
|
|
file_list = ['subpart_IoUs.txt', 'val_IoUs.txt']
|
|
for IoUs_to_save, IoU_file in zip(IoU_list, file_list):
|
|
|
|
# Name of saving file
|
|
test_file = join(config.saving_path, IoU_file)
|
|
|
|
# Line to write:
|
|
line = ''
|
|
for IoU in IoUs_to_save:
|
|
line += '{:.3f} '.format(IoU)
|
|
line = line + '\n'
|
|
|
|
# Write in file
|
|
if exists(test_file):
|
|
with open(test_file, "a") as text_file:
|
|
text_file.write(line)
|
|
else:
|
|
with open(test_file, "w") as text_file:
|
|
text_file.write(line)
|
|
|
|
# Print instance mean
|
|
mIoU = 100 * np.mean(IoUs)
|
|
print('{:s} : subpart mIoU = {:.1f} %'.format(config.dataset, mIoU))
|
|
mIoU = 100 * np.mean(val_IoUs)
|
|
print('{:s} : val mIoU = {:.1f} %'.format(config.dataset, mIoU))
|
|
|
|
t6 = time.time()
|
|
|
|
# Display timings
|
|
if debug:
|
|
print('\n************************\n')
|
|
print('Validation timings:')
|
|
print('Init ...... {:.1f}s'.format(t1 - t0))
|
|
print('Loop ...... {:.1f}s'.format(t2 - t1))
|
|
print('Confs ..... {:.1f}s'.format(t3 - t2))
|
|
print('IoU1 ...... {:.1f}s'.format(t4 - t3))
|
|
print('IoU2 ...... {:.1f}s'.format(t5 - t4))
|
|
print('Save ...... {:.1f}s'.format(t6 - t5))
|
|
print('\n************************\n')
|
|
|
|
return
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|