1131 lines
40 KiB
Python
1131 lines
40 KiB
Python
#
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#
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# 0=================================0
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# | Kernel Point Convolutions |
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# 0=================================0
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#
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#
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# ----------------------------------------------------------------------------------------------------------------------
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#
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# Class handling the training of any model
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#
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# ----------------------------------------------------------------------------------------------------------------------
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#
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# Hugues THOMAS - 11/06/2018
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#
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# ----------------------------------------------------------------------------------------------------------------------
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#
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# Imports and global variables
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# \**********************************/
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#
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# Basic libs
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import torch
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import torch.nn as nn
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import numpy as np
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import pickle
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import os
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from os import makedirs, remove
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from os.path import exists, join
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import time
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import sys
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# PLY reader
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from utils.ply import read_ply, write_ply
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# Metrics
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from utils.metrics import IoU_from_confusions, fast_confusion
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from utils.config import Config
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from sklearn.neighbors import KDTree
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from models.blocks import KPConv
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# ----------------------------------------------------------------------------------------------------------------------
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#
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# Trainer Class
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# \*******************/
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#
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class ModelTrainer:
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# Initialization methods
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# ------------------------------------------------------------------------------------------------------------------
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def __init__(self, net, config, chkp_path=None, finetune=False, on_gpu=True):
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"""
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Initialize training parameters and reload previous model for restore/finetune
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:param net: network object
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:param config: configuration object
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:param chkp_path: path to the checkpoint that needs to be loaded (None for new training)
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:param finetune: finetune from checkpoint (True) or restore training from checkpoint (False)
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:param on_gpu: Train on GPU or CPU
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"""
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############
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# Parameters
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############
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# Epoch index
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self.epoch = 0
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self.step = 0
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# Optimizer
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self.optimizer = torch.optim.SGD(net.parameters(),
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lr=config.learning_rate,
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momentum=config.momentum,
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weight_decay=config.weight_decay)
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# Choose to train on CPU or GPU
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if on_gpu and torch.cuda.is_available():
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self.device = torch.device("cuda:0")
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else:
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self.device = torch.device("cpu")
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net.to(self.device)
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##########################
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# Load previous checkpoint
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##########################
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if (chkp_path is not None):
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if finetune:
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checkpoint = torch.load(chkp_path)
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net.load_state_dict(checkpoint['model_state_dict'])
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net.train()
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print("Model restored and ready for finetuning.")
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else:
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checkpoint = torch.load(chkp_path)
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net.load_state_dict(checkpoint['model_state_dict'])
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self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
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self.epoch = checkpoint['epoch']
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net.train()
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print("Model and training state restored.")
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# Path of the result folder
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if config.saving:
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if config.saving_path is None:
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config.saving_path = time.strftime('results/Log_%Y-%m-%d_%H-%M-%S', time.gmtime())
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if not exists(config.saving_path):
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makedirs(config.saving_path)
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config.save()
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return
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# Training main method
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# ------------------------------------------------------------------------------------------------------------------
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def train(self, net, training_loader, val_loader, config):
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"""
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Train the model on a particular dataset.
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"""
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################
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# Initialization
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################
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if config.saving:
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# Training log file
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with open(join(config.saving_path, 'training.txt'), "w") as file:
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file.write('epochs steps out_loss offset_loss train_accuracy time\n')
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# Killing file (simply delete this file when you want to stop the training)
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PID_file = join(config.saving_path, 'running_PID.txt')
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if not exists(PID_file):
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with open(PID_file, "w") as file:
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file.write('Launched with PyCharm')
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# Checkpoints directory
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checkpoint_directory = join(config.saving_path, 'checkpoints')
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if not exists(checkpoint_directory):
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makedirs(checkpoint_directory)
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else:
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checkpoint_directory = None
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PID_file = None
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# Loop variables
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t0 = time.time()
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t = [time.time()]
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last_display = time.time()
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mean_dt = np.zeros(1)
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# Start training loop
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for epoch in range(config.max_epoch):
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# Remove File for kill signal
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if epoch == config.max_epoch - 1 and exists(PID_file):
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remove(PID_file)
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self.step = 0
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for batch in training_loader:
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# Check kill signal (running_PID.txt deleted)
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if config.saving and not exists(PID_file):
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continue
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##################
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# Processing batch
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##################
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# New time
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t = t[-1:]
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t += [time.time()]
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if 'cuda' in self.device.type:
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batch.to(self.device)
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# zero the parameter gradients
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self.optimizer.zero_grad()
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# Forward pass
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outputs = net(batch, config)
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loss = net.loss(outputs, batch.labels)
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acc = net.accuracy(outputs, batch.labels)
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t += [time.time()]
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# Backward + optimize
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loss.backward()
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if config.grad_clip_norm > 0:
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#torch.nn.utils.clip_grad_norm_(net.parameters(), config.grad_clip_norm)
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torch.nn.utils.clip_grad_value_(net.parameters(), config.grad_clip_norm)
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self.optimizer.step()
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torch.cuda.synchronize(self.device)
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t += [time.time()]
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# Average timing
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if self.step < 2:
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mean_dt = np.array(t[1:]) - np.array(t[:-1])
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else:
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mean_dt = 0.9 * mean_dt + 0.1 * (np.array(t[1:]) - np.array(t[:-1]))
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# Console display (only one per second)
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if (t[-1] - last_display) > 1.0:
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last_display = t[-1]
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message = 'e{:03d}-i{:04d} => L={:.3f} acc={:3.0f}% / t(ms): {:5.1f} {:5.1f} {:5.1f})'
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print(message.format(self.epoch, self.step,
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loss.item(),
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100*acc,
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1000 * mean_dt[0],
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1000 * mean_dt[1],
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1000 * mean_dt[2]))
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# Log file
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if config.saving:
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with open(join(config.saving_path, 'training.txt'), "a") as file:
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message = '{:d} {:d} {:.3f} {:.3f} {:.3f} {:.3f}\n'
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file.write(message.format(self.epoch,
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self.step,
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net.output_loss,
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net.reg_loss,
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acc,
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t[-1] - t0))
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self.step += 1
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##############
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# End of epoch
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##############
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# Check kill signal (running_PID.txt deleted)
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if config.saving and not exists(PID_file):
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break
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# Update learning rate
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if self.epoch in config.lr_decays:
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for param_group in self.optimizer.param_groups:
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param_group['lr'] *= config.lr_decays[self.epoch]
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# Update epoch
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self.epoch += 1
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# Saving
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if config.saving:
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# Get current state dict
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save_dict = {'epoch': self.epoch,
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'model_state_dict': net.state_dict(),
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'optimizer_state_dict': self.optimizer.state_dict(),
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'saving_path': config.saving_path}
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# Save current state of the network (for restoring purposes)
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checkpoint_path = join(checkpoint_directory, 'current_chkp.tar')
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torch.save(save_dict, checkpoint_path)
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# Save checkpoints occasionally
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if (self.epoch + 1) % config.checkpoint_gap == 0:
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checkpoint_path = join(checkpoint_directory, 'chkp_{:04d}.tar'.format(self.epoch + 1))
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torch.save(save_dict, checkpoint_path)
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# Validation
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net.eval()
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self.validation(net, val_loader, config)
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net.train()
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print('Finished Training')
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return
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# Validation methods
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# ------------------------------------------------------------------------------------------------------------------
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def validation(self, net, val_loader, config: Config):
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if config.dataset_task == 'classification':
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self.object_classification_validation(net, val_loader, config)
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elif config.dataset_task == 'segmentation':
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self.object_segmentation_validation(net, val_loader, config)
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elif config.dataset_task == 'cloud_segmentation':
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self.cloud_segmentation_validation(net, val_loader, config)
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elif config.dataset_task == 'slam_segmentation':
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self.slam_segmentation_validation(net, val_loader, config)
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else:
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raise ValueError('No validation method implemented for this network type')
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def object_classification_validation(self, net, val_loader, config):
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"""
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Perform a round of validation and show/save results
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:param net: network object
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:param val_loader: data loader for validation set
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:param config: configuration object
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"""
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############
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# Initialize
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############
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# Choose validation smoothing parameter (0 for no smothing, 0.99 for big smoothing)
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val_smooth = 0.95
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# Number of classes predicted by the model
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nc_model = config.num_classes
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softmax = torch.nn.Softmax(1)
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# Initialize global prediction over all models
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if not hasattr(self, 'val_probs'):
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self.val_probs = np.zeros((val_loader.dataset.num_models, nc_model))
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#####################
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# Network predictions
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#####################
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probs = []
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targets = []
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obj_inds = []
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t = [time.time()]
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last_display = time.time()
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mean_dt = np.zeros(1)
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# Start validation loop
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for batch in val_loader:
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# New time
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t = t[-1:]
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t += [time.time()]
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if 'cuda' in self.device.type:
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batch.to(self.device)
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# Forward pass
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outputs = net(batch, config)
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# Get probs and labels
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probs += [softmax(outputs).cpu().detach().numpy()]
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targets += [batch.labels.cpu().numpy()]
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obj_inds += [batch.model_inds.cpu().numpy()]
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torch.cuda.synchronize(self.device)
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# Average timing
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t += [time.time()]
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mean_dt = 0.95 * mean_dt + 0.05 * (np.array(t[1:]) - np.array(t[:-1]))
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# Display
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if (t[-1] - last_display) > 1.0:
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last_display = t[-1]
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message = 'Validation : {:.1f}% (timings : {:4.2f} {:4.2f})'
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print(message.format(100 * len(obj_inds) / config.validation_size,
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1000 * (mean_dt[0]),
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1000 * (mean_dt[1])))
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# Stack all validation predictions
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probs = np.vstack(probs)
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targets = np.hstack(targets)
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obj_inds = np.hstack(obj_inds)
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###################
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# Voting validation
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###################
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self.val_probs[obj_inds] = val_smooth * self.val_probs[obj_inds] + (1-val_smooth) * probs
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############
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# Confusions
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############
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validation_labels = np.array(val_loader.dataset.label_values)
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# Compute classification results
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C1 = fast_confusion(targets,
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np.argmax(probs, axis=1),
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validation_labels)
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# Compute votes confusion
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C2 = fast_confusion(val_loader.dataset.input_labels,
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np.argmax(self.val_probs, axis=1),
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validation_labels)
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# Saving (optionnal)
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if config.saving:
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print("Save confusions")
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conf_list = [C1, C2]
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file_list = ['val_confs.txt', 'vote_confs.txt']
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for conf, conf_file in zip(conf_list, file_list):
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test_file = join(config.saving_path, conf_file)
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if exists(test_file):
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with open(test_file, "a") as text_file:
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for line in conf:
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for value in line:
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text_file.write('%d ' % value)
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text_file.write('\n')
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else:
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with open(test_file, "w") as text_file:
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for line in conf:
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for value in line:
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text_file.write('%d ' % value)
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text_file.write('\n')
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val_ACC = 100 * np.sum(np.diag(C1)) / (np.sum(C1) + 1e-6)
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vote_ACC = 100 * np.sum(np.diag(C2)) / (np.sum(C2) + 1e-6)
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print('Accuracies : val = {:.1f}% / vote = {:.1f}%'.format(val_ACC, vote_ACC))
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return C1
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def cloud_segmentation_validation(self, net, val_loader, config, debug=False):
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"""
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Validation method for cloud segmentation models
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"""
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############
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# Initialize
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############
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t0 = time.time()
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# Choose validation smoothing parameter (0 for no smothing, 0.99 for big smoothing)
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val_smooth = 0.95
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softmax = torch.nn.Softmax(1)
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# Do not validate if dataset has no validation cloud
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if val_loader.dataset.validation_split not in val_loader.dataset.all_splits:
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return
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# Number of classes including ignored labels
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nc_tot = val_loader.dataset.num_classes
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# Number of classes predicted by the model
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nc_model = config.num_classes
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#print(nc_tot)
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#print(nc_model)
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# Initiate global prediction over validation clouds
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if not hasattr(self, 'validation_probs'):
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self.validation_probs = [np.zeros((l.shape[0], nc_model))
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for l in val_loader.dataset.input_labels]
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self.val_proportions = np.zeros(nc_model, dtype=np.float32)
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i = 0
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for label_value in val_loader.dataset.label_values:
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if label_value not in val_loader.dataset.ignored_labels:
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self.val_proportions[i] = np.sum([np.sum(labels == label_value)
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for labels in val_loader.dataset.validation_labels])
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i += 1
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#####################
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# Network predictions
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#####################
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predictions = []
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targets = []
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t = [time.time()]
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last_display = time.time()
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mean_dt = np.zeros(1)
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t1 = time.time()
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# Start validation loop
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for i, batch in enumerate(val_loader):
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# New time
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t = t[-1:]
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t += [time.time()]
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if 'cuda' in self.device.type:
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batch.to(self.device)
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# Forward pass
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outputs = net(batch, config)
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# Get probs and labels
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stacked_probs = softmax(outputs).cpu().detach().numpy()
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labels = batch.labels.cpu().numpy()
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lengths = batch.lengths[0].cpu().numpy()
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in_inds = batch.input_inds.cpu().numpy()
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cloud_inds = batch.cloud_inds.cpu().numpy()
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torch.cuda.synchronize(self.device)
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# Get predictions and labels per instance
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# ***************************************
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i0 = 0
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for b_i, length in enumerate(lengths):
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# Get prediction
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target = labels[i0:i0 + length]
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probs = stacked_probs[i0:i0 + length]
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inds = in_inds[i0:i0 + length]
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c_i = cloud_inds[b_i]
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# Update current probs in whole cloud
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self.validation_probs[c_i][inds] = val_smooth * self.validation_probs[c_i][inds] \
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+ (1 - val_smooth) * probs
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# Stack all prediction for this epoch
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predictions.append(probs)
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targets.append(target)
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i0 += length
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# Average timing
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t += [time.time()]
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mean_dt = 0.95 * mean_dt + 0.05 * (np.array(t[1:]) - np.array(t[:-1]))
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# Display
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if (t[-1] - last_display) > 1.0:
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last_display = t[-1]
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message = 'Validation : {:.1f}% (timings : {:4.2f} {:4.2f})'
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print(message.format(100 * i / config.validation_size,
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1000 * (mean_dt[0]),
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1000 * (mean_dt[1])))
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t2 = time.time()
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# Confusions for our subparts of validation set
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Confs = np.zeros((len(predictions), nc_tot, nc_tot), dtype=np.int32)
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for i, (probs, truth) in enumerate(zip(predictions, targets)):
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# Insert false columns for ignored labels
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for l_ind, label_value in enumerate(val_loader.dataset.label_values):
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if label_value in val_loader.dataset.ignored_labels:
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probs = np.insert(probs, l_ind, 0, axis=1)
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# Predicted labels
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preds = val_loader.dataset.label_values[np.argmax(probs, axis=1)]
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# Confusions
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Confs[i, :, :] = fast_confusion(truth, preds, val_loader.dataset.label_values).astype(np.int32)
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t3 = time.time()
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# Sum all confusions
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C = np.sum(Confs, axis=0).astype(np.float32)
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# Remove ignored labels from confusions
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for l_ind, label_value in reversed(list(enumerate(val_loader.dataset.label_values))):
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if label_value in val_loader.dataset.ignored_labels:
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C = np.delete(C, l_ind, axis=0)
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C = np.delete(C, l_ind, axis=1)
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|
|
|
# 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
|
|
|
|
|
|
|
|
# Saving methods
|
|
# ------------------------------------------------------------------------------------------------------------------
|
|
|
|
def save_kernel_points(self, model, epoch):
|
|
"""
|
|
Method saving kernel point disposition and current model weights for later visualization
|
|
"""
|
|
|
|
if model.config.saving:
|
|
|
|
# Create a directory to save kernels of this epoch
|
|
kernels_dir = join(model.saving_path, 'kernel_points', 'epoch{:d}'.format(epoch))
|
|
if not exists(kernels_dir):
|
|
makedirs(kernels_dir)
|
|
|
|
# Get points
|
|
all_kernel_points_tf = [v for v in tf.global_variables() if 'kernel_points' in v.name
|
|
and v.name.startswith('KernelPoint')]
|
|
all_kernel_points = self.sess.run(all_kernel_points_tf)
|
|
|
|
# Get Extents
|
|
if False and 'gaussian' in model.config.convolution_mode:
|
|
all_kernel_params_tf = [v for v in tf.global_variables() if 'kernel_extents' in v.name
|
|
and v.name.startswith('KernelPoint')]
|
|
all_kernel_params = self.sess.run(all_kernel_params_tf)
|
|
else:
|
|
all_kernel_params = [None for p in all_kernel_points]
|
|
|
|
# Save in ply file
|
|
for kernel_points, kernel_extents, v in zip(all_kernel_points, all_kernel_params, all_kernel_points_tf):
|
|
|
|
# Name of saving file
|
|
ply_name = '_'.join(v.name[:-2].split('/')[1:-1]) + '.ply'
|
|
ply_file = join(kernels_dir, ply_name)
|
|
|
|
# Data to save
|
|
if kernel_points.ndim > 2:
|
|
kernel_points = kernel_points[:, 0, :]
|
|
if False and 'gaussian' in model.config.convolution_mode:
|
|
data = [kernel_points, kernel_extents]
|
|
keys = ['x', 'y', 'z', 'sigma']
|
|
else:
|
|
data = kernel_points
|
|
keys = ['x', 'y', 'z']
|
|
|
|
# Save
|
|
write_ply(ply_file, data, keys)
|
|
|
|
# Get Weights
|
|
all_kernel_weights_tf = [v for v in tf.global_variables() if 'weights' in v.name
|
|
and v.name.startswith('KernelPointNetwork')]
|
|
all_kernel_weights = self.sess.run(all_kernel_weights_tf)
|
|
|
|
# Save in numpy file
|
|
for kernel_weights, v in zip(all_kernel_weights, all_kernel_weights_tf):
|
|
np_name = '_'.join(v.name[:-2].split('/')[1:-1]) + '.npy'
|
|
np_file = join(kernels_dir, np_name)
|
|
np.save(np_file, kernel_weights)
|
|
|
|
# Debug methods
|
|
# ------------------------------------------------------------------------------------------------------------------
|
|
|
|
def show_memory_usage(self, batch_to_feed):
|
|
|
|
for l in range(self.config.num_layers):
|
|
neighb_size = list(batch_to_feed[self.in_neighbors_f32[l]].shape)
|
|
dist_size = neighb_size + [self.config.num_kernel_points, 3]
|
|
dist_memory = np.prod(dist_size) * 4 * 1e-9
|
|
in_feature_size = neighb_size + [self.config.first_features_dim * 2**l]
|
|
in_feature_memory = np.prod(in_feature_size) * 4 * 1e-9
|
|
out_feature_size = [neighb_size[0], self.config.num_kernel_points, self.config.first_features_dim * 2**(l+1)]
|
|
out_feature_memory = np.prod(out_feature_size) * 4 * 1e-9
|
|
|
|
print('Layer {:d} => {:.1f}GB {:.1f}GB {:.1f}GB'.format(l,
|
|
dist_memory,
|
|
in_feature_memory,
|
|
out_feature_memory))
|
|
print('************************************')
|
|
|
|
def debug_nan(self, model, inputs, logits):
|
|
"""
|
|
NaN happened, find where
|
|
"""
|
|
|
|
print('\n\n------------------------ NaN DEBUG ------------------------\n')
|
|
|
|
# First save everything to reproduce error
|
|
file1 = join(model.saving_path, 'all_debug_inputs.pkl')
|
|
with open(file1, 'wb') as f1:
|
|
pickle.dump(inputs, f1)
|
|
|
|
# First save all inputs
|
|
file1 = join(model.saving_path, 'all_debug_logits.pkl')
|
|
with open(file1, 'wb') as f1:
|
|
pickle.dump(logits, f1)
|
|
|
|
# Then print a list of the trainable variables and if they have nan
|
|
print('List of variables :')
|
|
print('*******************\n')
|
|
all_vars = self.sess.run(tf.global_variables())
|
|
for v, value in zip(tf.global_variables(), all_vars):
|
|
nan_percentage = 100 * np.sum(np.isnan(value)) / np.prod(value.shape)
|
|
print(v.name, ' => {:.1f}% of values are NaN'.format(nan_percentage))
|
|
|
|
|
|
print('Inputs :')
|
|
print('********')
|
|
|
|
#Print inputs
|
|
nl = model.config.num_layers
|
|
for layer in range(nl):
|
|
|
|
print('Layer : {:d}'.format(layer))
|
|
|
|
points = inputs[layer]
|
|
neighbors = inputs[nl + layer]
|
|
pools = inputs[2*nl + layer]
|
|
upsamples = inputs[3*nl + layer]
|
|
|
|
nan_percentage = 100 * np.sum(np.isnan(points)) / np.prod(points.shape)
|
|
print('Points =>', points.shape, '{:.1f}% NaN'.format(nan_percentage))
|
|
nan_percentage = 100 * np.sum(np.isnan(neighbors)) / np.prod(neighbors.shape)
|
|
print('neighbors =>', neighbors.shape, '{:.1f}% NaN'.format(nan_percentage))
|
|
nan_percentage = 100 * np.sum(np.isnan(pools)) / np.prod(pools.shape)
|
|
print('pools =>', pools.shape, '{:.1f}% NaN'.format(nan_percentage))
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nan_percentage = 100 * np.sum(np.isnan(upsamples)) / np.prod(upsamples.shape)
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print('upsamples =>', upsamples.shape, '{:.1f}% NaN'.format(nan_percentage))
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ind = 4 * nl
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features = inputs[ind]
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nan_percentage = 100 * np.sum(np.isnan(features)) / np.prod(features.shape)
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print('features =>', features.shape, '{:.1f}% NaN'.format(nan_percentage))
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ind += 1
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batch_weights = inputs[ind]
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ind += 1
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in_batches = inputs[ind]
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max_b = np.max(in_batches)
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print(in_batches.shape)
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in_b_sizes = np.sum(in_batches < max_b - 0.5, axis=-1)
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print('in_batch_sizes =>', in_b_sizes)
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ind += 1
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out_batches = inputs[ind]
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max_b = np.max(out_batches)
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print(out_batches.shape)
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out_b_sizes = np.sum(out_batches < max_b - 0.5, axis=-1)
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print('out_batch_sizes =>', out_b_sizes)
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ind += 1
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point_labels = inputs[ind]
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print('point labels, ', point_labels.shape, ', values : ', np.unique(point_labels))
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print(np.array([int(100 * np.sum(point_labels == l) / len(point_labels)) for l in np.unique(point_labels)]))
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ind += 1
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if model.config.dataset.startswith('ShapeNetPart_multi'):
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object_labels = inputs[ind]
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nan_percentage = 100 * np.sum(np.isnan(object_labels)) / np.prod(object_labels.shape)
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print('object_labels =>', object_labels.shape, '{:.1f}% NaN'.format(nan_percentage))
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ind += 1
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augment_scales = inputs[ind]
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ind += 1
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augment_rotations = inputs[ind]
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ind += 1
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print('\npoolings and upsamples nums :\n')
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#Print inputs
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for layer in range(nl):
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print('\nLayer : {:d}'.format(layer))
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neighbors = inputs[nl + layer]
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pools = inputs[2*nl + layer]
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upsamples = inputs[3*nl + layer]
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max_n = np.max(neighbors)
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nums = np.sum(neighbors < max_n - 0.5, axis=-1)
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print('min neighbors =>', np.min(nums))
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if np.prod(pools.shape) > 0:
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max_n = np.max(pools)
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nums = np.sum(pools < max_n - 0.5, axis=-1)
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print('min pools =>', np.min(nums))
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else:
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print('pools empty')
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if np.prod(upsamples.shape) > 0:
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max_n = np.max(upsamples)
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nums = np.sum(upsamples < max_n - 0.5, axis=-1)
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print('min upsamples =>', np.min(nums))
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else:
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print('upsamples empty')
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print('\nFinished\n\n')
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time.sleep(0.5)
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