697 lines
28 KiB
Python
697 lines
28 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 test 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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from os import makedirs, listdir
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from os.path import exists, join
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import time
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import json
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from sklearn.neighbors import KDTree
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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 sklearn.metrics import confusion_matrix
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#from utils.visualizer import show_ModelNet_models
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# ----------------------------------------------------------------------------------------------------------------------
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#
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# Tester Class
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# \******************/
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#
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class ModelTester:
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# Initialization methods
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# ------------------------------------------------------------------------------------------------------------------
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def __init__(self, net, chkp_path=None, on_gpu=True):
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############
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# Parameters
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############
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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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checkpoint = torch.load(chkp_path)
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net.load_state_dict(checkpoint['model_state_dict'])
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self.epoch = checkpoint['epoch']
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net.eval()
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print("Model and training state restored.")
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return
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# Test main methods
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# ------------------------------------------------------------------------------------------------------------------
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def cloud_segmentation_test(self, net, test_loader, config, num_votes=100, debug=False):
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"""
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Test 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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# Choose test smoothing parameter (0 for no smothing, 0.99 for big smoothing)
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test_smooth = 0.95
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test_radius_ratio = 0.7
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softmax = torch.nn.Softmax(1)
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# Number of classes including ignored labels
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nc_tot = test_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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# Initiate global prediction over test clouds
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self.test_probs = [np.zeros((l.shape[0], nc_model)) for l in test_loader.dataset.input_labels]
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# Test saving path
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if config.saving:
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test_path = join('test', config.saving_path.split('/')[-1])
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if not exists(test_path):
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makedirs(test_path)
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if not exists(join(test_path, 'predictions')):
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makedirs(join(test_path, 'predictions'))
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if not exists(join(test_path, 'probs')):
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makedirs(join(test_path, 'probs'))
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if not exists(join(test_path, 'potentials')):
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makedirs(join(test_path, 'potentials'))
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else:
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test_path = None
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# If on validation directly compute score
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if test_loader.dataset.set == 'validation':
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val_proportions = np.zeros(nc_model, dtype=np.float32)
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i = 0
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for label_value in test_loader.dataset.label_values:
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if label_value not in test_loader.dataset.ignored_labels:
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val_proportions[i] = np.sum([np.sum(labels == label_value)
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for labels in test_loader.dataset.validation_labels])
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i += 1
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else:
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val_proportions = None
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#####################
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# Network predictions
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#####################
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test_epoch = 0
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last_min = -0.5
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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 test loop
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while True:
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print('Initialize workers')
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for i, batch in enumerate(test_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 i == 0:
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print('Done in {:.1f}s'.format(t[1] - t[0]))
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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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t += [time.time()]
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# Get probs and labels
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stacked_probs = softmax(outputs).cpu().detach().numpy()
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s_points = batch.points[0].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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points = s_points[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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if test_radius_ratio < 0.99:
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mask = np.sum(points ** 2, axis=1) < (test_radius_ratio * config.in_radius) ** 2
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inds = inds[mask]
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probs = probs[mask]
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# Update current probs in whole cloud
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self.test_probs[c_i][inds] = test_smooth * self.test_probs[c_i][inds] + (1 - test_smooth) * probs
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i0 += length
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# Average timing
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t += [time.time()]
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if i < 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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# 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 = 'e{:03d}-i{:04d} => {:.1f}% (timings : {:4.2f} {:4.2f} {:4.2f})'
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print(message.format(test_epoch, i,
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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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1000 * (mean_dt[2])))
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# Update minimum od potentials
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new_min = torch.min(test_loader.dataset.min_potentials)
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print('Test epoch {:d}, end. Min potential = {:.1f}'.format(test_epoch, new_min))
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#print([np.mean(pots) for pots in test_loader.dataset.potentials])
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# Save predicted cloud
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if last_min + 1 < new_min:
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# Update last_min
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last_min += 1
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# Show vote results (On subcloud so it is not the good values here)
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if test_loader.dataset.set == 'validation':
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print('\nConfusion on sub clouds')
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Confs = []
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for i, file_path in enumerate(test_loader.dataset.files):
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# Insert false columns for ignored labels
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probs = np.array(self.test_probs[i], copy=True)
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for l_ind, label_value in enumerate(test_loader.dataset.label_values):
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if label_value in test_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 = test_loader.dataset.label_values[np.argmax(probs, axis=1)].astype(np.int32)
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# Targets
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targets = test_loader.dataset.input_labels[i]
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# Confs
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Confs += [fast_confusion(targets, preds, test_loader.dataset.label_values)]
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# Regroup confusions
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C = np.sum(np.stack(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(test_loader.dataset.label_values))):
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if label_value in test_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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# Rescale with the right number of point per class
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C *= np.expand_dims(val_proportions / (np.sum(C, axis=1) + 1e-6), 1)
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# Compute IoUs
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IoUs = IoU_from_confusions(C)
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mIoU = np.mean(IoUs)
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s = '{:5.2f} | '.format(100 * mIoU)
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for IoU in IoUs:
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s += '{:5.2f} '.format(100 * IoU)
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print(s + '\n')
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# Save real IoU once in a while
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if int(np.ceil(new_min)) % 10 == 0:
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# Project predictions
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print('\nReproject Vote #{:d}'.format(int(np.floor(new_min))))
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t1 = time.time()
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proj_probs = []
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for i, file_path in enumerate(test_loader.dataset.files):
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print(i, file_path, test_loader.dataset.test_proj[i].shape, self.test_probs[i].shape)
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print(test_loader.dataset.test_proj[i].dtype, np.max(test_loader.dataset.test_proj[i]))
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print(test_loader.dataset.test_proj[i][:5])
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# Reproject probs on the evaluations points
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probs = self.test_probs[i][test_loader.dataset.test_proj[i], :]
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proj_probs += [probs]
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t2 = time.time()
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print('Done in {:.1f} s\n'.format(t2 - t1))
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# Show vote results
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if test_loader.dataset.set == 'validation':
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print('Confusion on full clouds')
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t1 = time.time()
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Confs = []
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for i, file_path in enumerate(test_loader.dataset.files):
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# Insert false columns for ignored labels
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for l_ind, label_value in enumerate(test_loader.dataset.label_values):
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if label_value in test_loader.dataset.ignored_labels:
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proj_probs[i] = np.insert(proj_probs[i], l_ind, 0, axis=1)
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# Get the predicted labels
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preds = test_loader.dataset.label_values[np.argmax(proj_probs[i], axis=1)].astype(np.int32)
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# Confusion
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targets = test_loader.dataset.validation_labels[i]
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Confs += [fast_confusion(targets, preds, test_loader.dataset.label_values)]
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t2 = time.time()
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print('Done in {:.1f} s\n'.format(t2 - t1))
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# Regroup confusions
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C = np.sum(np.stack(Confs), axis=0)
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# Remove ignored labels from confusions
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for l_ind, label_value in reversed(list(enumerate(test_loader.dataset.label_values))):
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if label_value in test_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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IoUs = IoU_from_confusions(C)
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mIoU = np.mean(IoUs)
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s = '{:5.2f} | '.format(100 * mIoU)
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for IoU in IoUs:
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s += '{:5.2f} '.format(100 * IoU)
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print('-' * len(s))
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print(s)
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print('-' * len(s) + '\n')
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# Save predictions
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print('Saving clouds')
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t1 = time.time()
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for i, file_path in enumerate(test_loader.dataset.files):
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# Get file
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points = test_loader.dataset.load_evaluation_points(file_path)
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# Get the predicted labels
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preds = test_loader.dataset.label_values[np.argmax(proj_probs[i], axis=1)].astype(np.int32)
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# Save plys
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cloud_name = file_path.split('/')[-1]
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test_name = join(test_path, 'predictions', cloud_name)
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write_ply(test_name,
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[points, preds],
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['x', 'y', 'z', 'preds'])
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test_name2 = join(test_path, 'probs', cloud_name)
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prob_names = ['_'.join(test_loader.dataset.label_to_names[label].split())
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for label in test_loader.dataset.label_values]
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write_ply(test_name2,
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[points, proj_probs[i]],
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['x', 'y', 'z'] + prob_names)
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# Save potentials
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pot_points = np.array(test_loader.dataset.pot_trees[i].data, copy=False)
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pot_name = join(test_path, 'potentials', cloud_name)
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pots = test_loader.dataset.potentials[i].numpy().astype(np.float32)
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write_ply(pot_name,
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[pot_points.astype(np.float32), pots],
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['x', 'y', 'z', 'pots'])
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# Save ascii preds
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if test_loader.dataset.set == 'test':
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if test_loader.dataset.name.startswith('Semantic3D'):
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ascii_name = join(test_path, 'predictions', test_loader.dataset.ascii_files[cloud_name])
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else:
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ascii_name = join(test_path, 'predictions', cloud_name[:-4] + '.txt')
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np.savetxt(ascii_name, preds, fmt='%d')
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t2 = time.time()
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print('Done in {:.1f} s\n'.format(t2 - t1))
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test_epoch += 1
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# Break when reaching number of desired votes
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if last_min > num_votes:
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break
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return
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def slam_segmentation_test(self, net, test_loader, config, num_votes=100, debug=False):
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"""
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Test method for slam segmentation models
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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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test_smooth = 0.5
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last_min = -0.5
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softmax = torch.nn.Softmax(1)
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# Number of classes including ignored labels
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nc_tot = test_loader.dataset.num_classes
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nc_model = net.C
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# Test saving path
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test_path = None
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report_path = None
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if config.saving:
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test_path = join('test', config.saving_path.split('/')[-1])
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if not exists(test_path):
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makedirs(test_path)
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report_path = join(test_path, 'reports')
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if not exists(report_path):
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makedirs(report_path)
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if test_loader.dataset.set == 'validation':
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for folder in ['val_predictions', 'val_probs']:
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if not exists(join(test_path, folder)):
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makedirs(join(test_path, folder))
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else:
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for folder in ['predictions', 'probs']:
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if not exists(join(test_path, folder)):
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makedirs(join(test_path, folder))
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# Init validation container
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all_f_preds = []
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all_f_labels = []
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if test_loader.dataset.set == 'validation':
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for i, seq_frames in enumerate(test_loader.dataset.frames):
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all_f_preds.append([np.zeros((0,), dtype=np.int32) for _ in seq_frames])
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all_f_labels.append([np.zeros((0,), dtype=np.int32) for _ in seq_frames])
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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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test_epoch = 0
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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 test loop
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while True:
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print('Initialize workers')
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for i, batch in enumerate(test_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 i == 0:
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print('Done in {:.1f}s'.format(t[1] - t[0]))
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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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stk_probs = softmax(outputs).cpu().detach().numpy()
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lengths = batch.lengths[0].cpu().numpy()
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f_inds = batch.frame_inds.cpu().numpy()
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r_inds_list = batch.reproj_inds
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r_mask_list = batch.reproj_masks
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labels_list = batch.val_labels
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torch.cuda.synchronize(self.device)
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t += [time.time()]
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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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probs = stk_probs[i0:i0 + length]
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proj_inds = r_inds_list[b_i]
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proj_mask = r_mask_list[b_i]
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frame_labels = labels_list[b_i]
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s_ind = f_inds[b_i, 0]
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f_ind = f_inds[b_i, 1]
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# Project predictions on the frame points
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proj_probs = probs[proj_inds]
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# Safe check if only one point:
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if proj_probs.ndim < 2:
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proj_probs = np.expand_dims(proj_probs, 0)
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# Save probs in a binary file (uint8 format for lighter weight)
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seq_name = test_loader.dataset.sequences[s_ind]
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if test_loader.dataset.set == 'validation':
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folder = 'val_probs'
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pred_folder = 'val_predictions'
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else:
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folder = 'probs'
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pred_folder = 'predictions'
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filename = '{:s}_{:07d}.npy'.format(seq_name, f_ind)
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filepath = join(test_path, folder, filename)
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if exists(filepath):
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frame_probs_uint8 = np.load(filepath)
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else:
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frame_probs_uint8 = np.zeros((proj_mask.shape[0], nc_model), dtype=np.uint8)
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frame_probs = frame_probs_uint8[proj_mask, :].astype(np.float32) / 255
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frame_probs = test_smooth * frame_probs + (1 - test_smooth) * proj_probs
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frame_probs_uint8[proj_mask, :] = (frame_probs * 255).astype(np.uint8)
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np.save(filepath, frame_probs_uint8)
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# Save some prediction in ply format for visual
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if test_loader.dataset.set == 'validation':
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# Insert false columns for ignored labels
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for l_ind, label_value in enumerate(test_loader.dataset.label_values):
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if label_value in test_loader.dataset.ignored_labels:
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frame_probs_uint8 = np.insert(frame_probs_uint8, l_ind, 0, axis=1)
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# Predicted labels
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frame_preds = test_loader.dataset.label_values[np.argmax(frame_probs_uint8,
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axis=1)].astype(np.int32)
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# Save some of the frame pots
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if f_ind % 20 == 0:
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seq_path = join(test_loader.dataset.path, 'sequences', test_loader.dataset.sequences[s_ind])
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velo_file = join(seq_path, 'velodyne', test_loader.dataset.frames[s_ind][f_ind] + '.bin')
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frame_points = np.fromfile(velo_file, dtype=np.float32)
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frame_points = frame_points.reshape((-1, 4))
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predpath = join(test_path, pred_folder, filename[:-4] + '.ply')
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#pots = test_loader.dataset.f_potentials[s_ind][f_ind]
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pots = np.zeros((0,))
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if pots.shape[0] > 0:
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write_ply(predpath,
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[frame_points[:, :3], frame_labels, frame_preds, pots],
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['x', 'y', 'z', 'gt', 'pre', 'pots'])
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else:
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write_ply(predpath,
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[frame_points[:, :3], frame_labels, frame_preds],
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['x', 'y', 'z', 'gt', 'pre'])
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# keep frame preds in memory
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all_f_preds[s_ind][f_ind] = frame_preds
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all_f_labels[s_ind][f_ind] = frame_labels
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else:
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# Save some of the frame preds
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if f_inds[b_i, 1] % 100 == 0:
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# Insert false columns for ignored labels
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for l_ind, label_value in enumerate(test_loader.dataset.label_values):
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if label_value in test_loader.dataset.ignored_labels:
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frame_probs_uint8 = np.insert(frame_probs_uint8, l_ind, 0, axis=1)
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# Predicted labels
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frame_preds = test_loader.dataset.label_values[np.argmax(frame_probs_uint8,
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axis=1)].astype(np.int32)
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# Load points
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seq_path = join(test_loader.dataset.path, 'sequences', test_loader.dataset.sequences[s_ind])
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velo_file = join(seq_path, 'velodyne', test_loader.dataset.frames[s_ind][f_ind] + '.bin')
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frame_points = np.fromfile(velo_file, dtype=np.float32)
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frame_points = frame_points.reshape((-1, 4))
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predpath = join(test_path, pred_folder, filename[:-4] + '.ply')
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#pots = test_loader.dataset.f_potentials[s_ind][f_ind]
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pots = np.zeros((0,))
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if pots.shape[0] > 0:
|
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write_ply(predpath,
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[frame_points[:, :3], frame_preds, pots],
|
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['x', 'y', 'z', 'pre', 'pots'])
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else:
|
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write_ply(predpath,
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[frame_points[:, :3], frame_preds],
|
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['x', 'y', 'z', 'pre'])
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# Stack all prediction for this epoch
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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 = 'e{:03d}-i{:04d} => {:.1f}% (timings : {:4.2f} {:4.2f} {:4.2f}) / pots {:d} => {:.1f}%'
|
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min_pot = int(torch.floor(torch.min(test_loader.dataset.potentials)))
|
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pot_num = torch.sum(test_loader.dataset.potentials > min_pot).type(torch.int32).item()
|
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current_num = pot_num + (i0 + 1 - config.validation_size) * config.val_batch_num
|
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print(message.format(test_epoch, i,
|
|
100 * i / config.validation_size,
|
|
1000 * (mean_dt[0]),
|
|
1000 * (mean_dt[1]),
|
|
1000 * (mean_dt[2]),
|
|
min_pot,
|
|
100.0 * current_num / len(test_loader.dataset.potentials)))
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|
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# Update minimum od potentials
|
|
new_min = torch.min(test_loader.dataset.potentials)
|
|
print('Test epoch {:d}, end. Min potential = {:.1f}'.format(test_epoch, new_min))
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|
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if last_min + 1 < new_min:
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|
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# Update last_min
|
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last_min += 1
|
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if test_loader.dataset.set == 'validation' and last_min % 1 == 0:
|
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#####################################
|
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# Results on the whole validation set
|
|
#####################################
|
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|
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# 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)):
|
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|
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# Confusions
|
|
Confs[i, :, :] = fast_confusion(truth, preds, test_loader.dataset.label_values).astype(np.int32)
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# Show vote results
|
|
print('\nCompute confusion')
|
|
|
|
val_preds = []
|
|
val_labels = []
|
|
t1 = time.time()
|
|
for i, seq_frames in enumerate(test_loader.dataset.frames):
|
|
val_preds += [np.hstack(all_f_preds[i])]
|
|
val_labels += [np.hstack(all_f_labels[i])]
|
|
val_preds = np.hstack(val_preds)
|
|
val_labels = np.hstack(val_labels)
|
|
t2 = time.time()
|
|
C_tot = fast_confusion(val_labels, val_preds, test_loader.dataset.label_values)
|
|
t3 = time.time()
|
|
print(' Stacking time : {:.1f}s'.format(t2 - t1))
|
|
print('Confusion time : {:.1f}s'.format(t3 - t2))
|
|
|
|
s1 = '\n'
|
|
for cc in C_tot:
|
|
for c in cc:
|
|
s1 += '{:7.0f} '.format(c)
|
|
s1 += '\n'
|
|
if debug:
|
|
print(s1)
|
|
|
|
# Remove ignored labels from confusions
|
|
for l_ind, label_value in reversed(list(enumerate(test_loader.dataset.label_values))):
|
|
if label_value in test_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)
|
|
|
|
# Compute IoUs
|
|
mIoU = np.mean(val_IoUs)
|
|
s2 = '{:5.2f} | '.format(100 * mIoU)
|
|
for IoU in val_IoUs:
|
|
s2 += '{:5.2f} '.format(100 * IoU)
|
|
print(s2 + '\n')
|
|
|
|
# Save a report
|
|
report_file = join(report_path, 'report_{:04d}.txt'.format(int(np.floor(last_min))))
|
|
str = 'Report of the confusion and metrics\n'
|
|
str += '***********************************\n\n\n'
|
|
str += 'Confusion matrix:\n\n'
|
|
str += s1
|
|
str += '\nIoU values:\n\n'
|
|
str += s2
|
|
str += '\n\n'
|
|
with open(report_file, 'w') as f:
|
|
f.write(str)
|
|
|
|
test_epoch += 1
|
|
|
|
# Break when reaching number of desired votes
|
|
if last_min > num_votes:
|
|
break
|
|
|
|
return
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