From bd97d2bd805ab836441677d4aca09fd6781ff96d Mon Sep 17 00:00:00 2001 From: BjoernMichele Date: Thu, 6 Apr 2023 17:37:39 +0200 Subject: [PATCH] Initial commit SALUDA --- .gitignore | 7 + LICENSE | 217 +++++++++++++ README.md | 103 +++++- config.py | 141 ++++++++ da_baseline_hyper.py | 162 ++++++++++ datasets/__init__.py | 4 + datasets/dataset.py | 55 ++++ datasets/nuscenes.py | 217 +++++++++++++ datasets/semantic_kitti.py | 228 +++++++++++++ datasets/synlidar.py | 263 +++++++++++++++ doc/architecture2.png | Bin 0 -> 858222 bytes networks/__init__.py | 1 + networks/backbone/__init__.py | 6 + networks/backbone/torchsparse_minkunet.py | 247 ++++++++++++++ networks/decoder/__init__.py | 1 + networks/decoder/interpolator.py | 246 ++++++++++++++ networks/network.py | 111 +++++++ utils/__init__.py | 0 utils/all_transforms/__init__.py | 12 + utils/all_transforms/create_inputs.py | 19 ++ .../create_non_manifold_points.py | 63 ++++ utils/all_transforms/createdirs.py | 25 ++ utils/all_transforms/dupplicate.py | 12 + utils/all_transforms/fixed_points.py | 50 +++ utils/all_transforms/random_flip.py | 17 + utils/all_transforms/random_rotate.py | 40 +++ utils/all_transforms/scaling.py | 12 + utils/all_transforms/sparse_quantize.py | 34 ++ utils/all_transforms/to_dict.py | 11 + utils/all_transforms/transpose.py | 18 ++ utils/all_transforms/use_as_features.py | 20 ++ utils/argparseFromFile.py | 75 +++++ utils/collate_function.py | 54 ++++ utils/logging_files_functions.py | 213 ++++++++++++ utils/metrics.py | 125 +++++++ utils/shared_funcs.py | 304 ++++++++++++++++++ utils/transforms.py | 113 +++++++ utils/utils.py | 210 ++++++++++++ 38 files changed, 3434 insertions(+), 2 deletions(-) create mode 100644 .gitignore create mode 100644 LICENSE create mode 100644 config.py create mode 100755 da_baseline_hyper.py create mode 100644 datasets/__init__.py create mode 100644 datasets/dataset.py create mode 100644 datasets/nuscenes.py create mode 100644 datasets/semantic_kitti.py create mode 100755 datasets/synlidar.py create mode 100644 doc/architecture2.png create mode 100644 networks/__init__.py create mode 100644 networks/backbone/__init__.py create mode 100644 networks/backbone/torchsparse_minkunet.py create mode 100644 networks/decoder/__init__.py create mode 100644 networks/decoder/interpolator.py create mode 100644 networks/network.py create mode 100644 utils/__init__.py create mode 100755 utils/all_transforms/__init__.py create mode 100755 utils/all_transforms/create_inputs.py create mode 100755 utils/all_transforms/create_non_manifold_points.py create mode 100755 utils/all_transforms/createdirs.py create mode 100755 utils/all_transforms/dupplicate.py create mode 100644 utils/all_transforms/fixed_points.py create mode 100755 utils/all_transforms/random_flip.py create mode 100755 utils/all_transforms/random_rotate.py create mode 100755 utils/all_transforms/scaling.py create mode 100755 utils/all_transforms/sparse_quantize.py create mode 100644 utils/all_transforms/to_dict.py create mode 100755 utils/all_transforms/transpose.py create mode 100755 utils/all_transforms/use_as_features.py create mode 100644 utils/argparseFromFile.py create mode 100644 utils/collate_function.py create mode 100755 utils/logging_files_functions.py create mode 100644 utils/metrics.py create mode 100755 utils/shared_funcs.py create mode 100644 utils/transforms.py create mode 100644 utils/utils.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..bd91703 --- /dev/null +++ b/.gitignore @@ -0,0 +1,7 @@ +__pycache__ +data/ +results_ckpt/ +results_val/ +my_runs/ +runs/ +_eval_debug.sh \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..a207d43 --- /dev/null +++ b/LICENSE @@ -0,0 +1,217 @@ + SALUDA + + Copyright 2023 Valeo + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + https://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. \ No newline at end of file diff --git a/README.md b/README.md index 8929487..bdb4112 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,101 @@ -# SALUDA -SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation + +
+ +# SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation + +[Björn Michele](https://github.com/BjoernMichele)1,3   +[Alexandre Boulch](https://boulch.eu/)1    +[Gilles Puy](https://sites.google.com/site/puygilles/)1    +[Tuan-Hung Vu](https://scholar.google.com/citations?user=xYDkHEsAAAAJ&hl=en&oi=ao)1    +[Renaud Marlet](http://imagine.enpc.fr/~marletr/)1,2   +[Nicolas Courty](https://people.irisa.fr/Nicolas.Courty/)3    + + +1 Valeo.ai, Paris, France  +2 LIGM, Ecole des Ponts, Univ Gustave Eiffel, CNRS, Marne-la-Vallée, France + +3 CNRS, IRISA, Univ. Bretagne Sud, Vannes, France + + +
+ +[![Arxiv](https://img.shields.io/badge/paper-arxiv-B31B1B.svg)](https://arxiv.org/abs/2212.05867) + + +![Overview](doc/architecture2.png) + +
+ +
+ + +## Abstract +Learning models on one labeled dataset that generalize well on another domain is a difficult task, as several shifts might happen between the data domains. This is notably the case for lidar data, for which models can exhibit large performance discrepancies due for instance to different lidar patterns or changes in acquisition conditions. This paper addresses the corresponding Unsupervised Domain Adaptation (UDA) task for semantic segmentation. To mitigate this problem, we introduce an unsupervised auxiliary task of learning an implicit underlying surface representation simultaneously on source and target data. As both domains share the same latent representation, the model is forced to accommodate discrepancies between the two sources of data. This novel strategy differs from classical minimization of statistical divergences or lidar-specific state-of-the-art domain adaptation techniques. Our experiments demonstrate that our method achieves a better performance than the current state of the art in synthetic-to-real and real-to-real scenarios. + +--- + +## Dependencies + +This code was implemented and tested with python 3.10, PyTorch 1.11.0 and CUDA 11.3. +The backbone is implemented with version 1.4.0 of [Torchsparse](https://github.com/mit-han-lab/torchsparse.)([Exact commit](https://github.com/mit-han-lab/torchsparse/commit/69c1034ddb285798619380537802ea0ff03aeba6)) +Additionally, [Sacred](https://github.com/IDSIA/sacred) 0.8.3 is used. + +--- + +## Training + +(not yet included, watch the repository to not miss the updates) + +--- + +## Evaluation + +Evaluation of a SALUDA model on nuScenes to SemanticKITTI: + +``` +python da_baseline_hyper.py --name='EVAL_SALUDA_ns_sk' with da_ns_sk network_decoder=InterpAllRadiusNoDirsNet network_decoder_k=1.0 save_dir=results_val/ ckpt_path_model=path/to/folder +``` + +Evaluation on SyntheticLiDAR to SemanticKITTI: + +``` +python da_baseline_hyper.py --name='EVAL_SALUDA_syn_sk' with da_syn_sk network_decoder=InterpAllRadiusNoDirsNet network_decoder_k=1.0 save_dir=results_val/ ckpt_path_model=path/to/folder +``` + + +Evaluation of a SALUDA model which was refined with CoSMix: + +``` +python da_baseline_hyper.py --name='EVAL_SALUDA_ns_sk' with da_ns_sk network_decoder=InterpAllRadiusNoDirsNet network_decoder_k=1.0 save_dir=results_val/ cosmix_backbone=True ckpt_path_model=path/to/folder +``` + +--- +## Model zoo + +DA Setting | Method | Backbone | Link | +---|---|---|---| +nuScenes to SemanticKITTI | SALUDA |TorchSparse-MinkUNet | CKPT | +nuScenes to SemanticKITTI | SALUDA + CoSMix |TorchSparse-MinkUNet | CKPT | +SyntheticLiDAR to SemanticKITTI | SALUDA |TorchSparse-MinkUNet | CKPT | +SyntheticLiDAR to SemanticKITTI | SALUDA + CoSMix |TorchSparse-MinkUNet | CKPT | + +The checkpoint should be placed in a folder. The link to the folder is given to the "ckpt_path_model" parameter. + +--- + +## Acknowledgments + +This project would not have been possible without many community resources and repositories. Among them: + +- [ALSO](https://github.com/valeoai/ALSO/) +- [POCO](https://github.com/valeoai/POCO) +- [CoSMix](https://github.com/saltoricristiano/cosmix-uda/issues) +- [Torchsparse](https://github.com/mit-han-lab/torchsparse) + +Please, consider acknowleding these projects. + +--- + +## License + +This work released under the terms of the [Apache 2.0 license](LICENSE). \ No newline at end of file diff --git a/config.py b/config.py new file mode 100644 index 0000000..0efd0cb --- /dev/null +++ b/config.py @@ -0,0 +1,141 @@ +from sacred import SETTINGS, Experiment +from sacred.observers import FileStorageObserver, MongoObserver +from sacred.utils import apply_backspaces_and_linefeeds + +SETTINGS.CAPTURE_MODE = 'sys' # for tqdm + +ex = Experiment("DA_experiments", save_git_info=False) +ex.observers.append(FileStorageObserver('my_runs')) +ex.captured_out_filter = apply_backspaces_and_linefeeds # for tqdm + +@ex.config +def config(): + fast_rep_flag=False + + # Dataset configs + dataset_name = 'UnknwonDataset' + dataset_root = 'UnknownDatasetPath' + source_dataset_name = 'UnknwonDataset' + source_dataset_root = 'UnknownDatasetPath' + target_dataset_name = 'UnknwonDataset' + target_dataset_root = 'UnknownDatasetPath' + save_dir = 'results_ckpt' + ns_dataset_version='v1.0-trainval' + + # splits + train_split = 'parametrizing' + val_split = 'verifying' + test_split = 'val' + nb_classes = 1 + + # Method parameters + input_intensities = False + input_dirs = False + input_normals = False + source_input_intensities = False + source_input_dirs = False + target_input_intensities = False + target_input_dirs = False + + manifold_points = 10000 + non_manifold_points = 2048 + + # Training parameters + da_flag = False + dual_seg_head = True + training_iter_nbr = 50000 + training_batch_size = 8 + test_batch_size = 1 + training_lr_start = 0.001 + training_lr_start_head = None + optimizer = "AdamW" + lr_scheduler = None + step_lr_step_size = 200000 #Step size for steplr scheduler + step_lr_gamma = 0.7 #Gamma for steplr scheduler + voxel_size = 0.1 + val_interval = 5 + resume = False + + # Network parameter + network_backbone = 'TorchSparseMinkUNet' + network_latent_size = 128 + network_decoder = 'InterpAllRadiusNet' + network_decoder_k = 2.0 + network_n_labels = 1 + use_no_dirs_rec_head_flag = False + rotation_head_flag = False + + # Technical parameter + device = 'cuda' + threads = 6 + interactive_log = True + logging = 'INFO' + use_amp = False + + # Data augmentation + randRotationZ = True + randFlip = True + no_augmentation = False + + # Ckpt path + ckpt_path_model = "UnknownPath" + + # Weighting parameter for loss + weight_rec_src = 1.0 + weight_rec_trg = 1.0 + weight_ss_src = 1.0 + weight_ss_trg = 1.0 + weight_inside_seg_src = 1.0 + + # Ignorance idx + ignore_idx = 0 + get_latent = False + + + # Test flag + test_flag_eval = False + target_training = True + source_training = True + + # Which ckpt to load from in eval + ckpt_number = -1 + cosmix_backbone = False + +@ex.named_config +def da_ns_sk(): + da_flag = True + dual_seg_head = True + source_dataset_name = 'NuScenes' + source_dataset_root = 'data/nuscenes' + source_input_intensities = False + source_input_dirs = False + nb_classes = 11 + target_dataset_name = 'SemanticKITTI' + target_dataset_root = 'data/SemanticKITTI' + target_input_intensities = False + target_input_dirs = False + lr_scheduler="cos_an_half_lr" + training_iter_nbr=300000 + train_split = 'train' + val_split = 'val' + training_batch_size = 4 + + +@ex.named_config +def da_syn_sk(): + da_flag = True + dual_seg_head = True + source_dataset_name = 'SynLidar' + source_dataset_root = 'data/synlidar' + source_input_intensities = False + source_input_dirs = False + nb_classes = 20 + target_dataset_name = 'SemanticKITTI' + target_dataset_root = 'data/SemanticKITTI' + target_input_intensities = False + target_input_dirs = False + lr_scheduler = "cos_an_half_lr" + train_split = 'train' + val_split = 'val' + training_iter_nbr = 300000 + training_batch_size = 4 \ No newline at end of file diff --git a/da_baseline_hyper.py b/da_baseline_hyper.py new file mode 100755 index 0000000..e5823ba --- /dev/null +++ b/da_baseline_hyper.py @@ -0,0 +1,162 @@ +import logging +import os +import pandas as pd +import torch + +from torch.utils.tensorboard import SummaryWriter + +import datasets +import networks + +from config import ex +from utils.logging_files_functions import val_log_data_da +from utils.shared_funcs import (count_parameters, da_get_dataloader, get_bbdir_root, + get_savedir_root, ignore_selection, logs_file, + save_config_file) +from utils.transforms import da_get_inputs +from utils.utils import get_bbdir_root, get_savedir_root, validation + +def load_student_model(checkpoint_path): + # reloads model + def clean_state_dict(state): + # clean state dict from names of PL + for k in list(ckpt.keys()): + if "student_model" in k: + if "dual_seg_head" in k: + ckpt[k.replace("student_model.", "")] = ckpt[k] + else: + ckpt[k.replace("student_model.", "net.")] = ckpt[k] + del ckpt[k] + return state + + ckpt = torch.load(checkpoint_path, map_location=torch.device('cpu'))["state_dict"] + print(ckpt.keys()) + ckpt = clean_state_dict(ckpt) + + + return ckpt + +def main_run(_config, _run): + + # experiment_name + experiment_name = _run.experiment_info["name"] + + # writer for tensorboard + writer = SummaryWriter('runs/{}'.format(experiment_name)) + + # convert config to dict + config = eval(str(_config)) + + # device + device = torch.device(config['device']) + if config["device"] == "cuda": + torch.backends.cudnn.benchmark = True + + # define the logging + logging.getLogger().setLevel(config["logging"]) + + # get the savedir + savedir_root = get_savedir_root(config, experiment_name) + bb_dir_root = get_bbdir_root(config) + # create the network + disable_log = not config["interactive_log"] + N_LABELS = 2 # For occupancy + latent_size = config["network_latent_size"] + backbone = config["network_backbone"] + decoder = {'name': config["network_decoder"], 'k': config['network_decoder_k']} + + in_channels_source, _, in_channels_target, _ = da_get_inputs(config) + logging.info("Creating the network") + + def network_function(): + return networks.Network(in_channels_source, latent_size, config["network_n_labels"], backbone, decoder, + voxel_size=config["voxel_size"], dual_seg_head=config["dual_seg_head"], + nb_classes=config["nb_classes"], da_flag=False, target_in_channels=in_channels_target, config=config) + + net = network_function() + net.to(device) + logging.info(f"Network -- Number of parameters {count_parameters(net)}") + datasets_prefix = "datasets." + source_DatasetClass = datasets.get_dataset(eval(datasets_prefix+config["source_dataset_name"])) + target_DatasetClass = datasets.get_dataset(eval(datasets_prefix+config["target_dataset_name"])) + if config['test_flag_eval']: + val_number = -1 + else: + val_number = 1 # 1: verifying split, 2 train split, else: test split + dataloader_dict = da_get_dataloader(source_DatasetClass, target_DatasetClass, config, net, val=val_number) + source_train_loader = dataloader_dict ["source_train_loader"] + source_test_loader = dataloader_dict ["source_test_loader"] + target_train_loader = dataloader_dict ["target_train_loader"] + target_test_loader = dataloader_dict ["target_test_loader"] + + os.makedirs(savedir_root, exist_ok=True) + save_config_file(eval(str(config)), os.path.join(savedir_root, "config.yaml")) + epoch_start = 0 + train_iter_count = 0 + + + ckpt_path = os.path.join(bb_dir_root, 'checkpoint.pth') + prefix_save_name = "last_ckpt_" + + logging.info(f"CKPT -- Load ckpt from {ckpt_path}") + + # Load the checkpoint for the backbone + + if "cosmix_backbone" in config and config["cosmix_backbone"]: + # For models refined with CoSMix + checkpoint = load_student_model(os.path.join(bb_dir_root, "checkpoint.pth")) + else: + checkpoint = torch.load(os.path.join(bb_dir_root, "checkpoint.pth"), map_location=device) + checkpoint = checkpoint["state_dict"] + + + + try: + net.load_state_dict(checkpoint) + except Exception as e: + logging.info(e) + logging.info(f"Loaded parameters do not match exactly net architecture, switching to load_state_dict strict=false") + net.load_state_dict(checkpoint, strict=False) + + epoch_start = 0 + os.makedirs(savedir_root, exist_ok=True) + save_config_file(eval(str(config)), os.path.join(savedir_root, "config.yaml")) + epoch_start = 0 + train_iter_count = 0 + + # create the loss layer + loss_layer = torch.nn.BCEWithLogitsLoss() + weights_ss = torch.ones(config["nb_classes"]) + list_ignore_classes = ignore_selection(config["ignore_idx"]) + for idx_ignore_class in list_ignore_classes: + weights_ss[idx_ignore_class] = 0 + logging.info(f"Ignored classes {list_ignore_classes}") + logging.info(f"Weights of the different classes {weights_ss}") + weights_ss=weights_ss.to(device) + ce_loss_layer = torch.nn.CrossEntropyLoss(weight=weights_ss) + epoch = epoch_start + + # Validation on SOURCE + return_data_val_source = \ + validation(net, config, source_test_loader, N_LABELS, epoch, disable_log, device, ce_loss_layer, loss_layer, target_flag=False, list_ignore_classes=list_ignore_classes) + logging.info("Source mIoU per class: {}".format(return_data_val_source["seg_iou_per_class"])) + + # Validation on TARGET + return_data_val_target = \ + validation(net, config, target_test_loader, N_LABELS, epoch, disable_log, device, ce_loss_layer, loss_layer, target_flag=False, list_ignore_classes=list_ignore_classes) + logging.info("Target per class mIoU: {}".format(return_data_val_target["seg_iou_per_class"])) + + # save the logs + val_log_data = val_log_data_da(val_data_src=return_data_val_source, val_data_trg=return_data_val_target, train_iter_count=train_iter_count, _run= _run, writer=writer, config=config, long_prefix="validation", short_prefix="val") + logs_file(os.path.join(savedir_root, prefix_save_name+"logs_val.csv"), train_iter_count, val_log_data) + df_cm_source = pd.DataFrame(data=return_data_val_source["cm_seg_head"].astype(float)) + df_cm_source.to_csv(os.path.join(savedir_root, prefix_save_name+"validation_cm_source.csv"), sep=' ', header=False, float_format='%.2f', index=False) + df_cm_target = pd.DataFrame(data=return_data_val_target["cm_seg_head"].astype(float)) + df_cm_target.to_csv(os.path.join(savedir_root, prefix_save_name+"validation_cm_target.csv"), sep=' ', header=False, float_format='%.2f', index=False) + + logging.info("Save the results with prefix: {}".format(prefix_save_name)) + +@ex.automain +def main(_config, _run): + # Run the validation for the last epoch (or the one selected) + main_run(_config, _run) \ No newline at end of file diff --git a/datasets/__init__.py b/datasets/__init__.py new file mode 100644 index 0000000..14649ac --- /dev/null +++ b/datasets/__init__.py @@ -0,0 +1,4 @@ +from .dataset import get_dataset +from .nuscenes import NuScenes +from .semantic_kitti import SemanticKITTI +from .synlidar import SynLidar diff --git a/datasets/dataset.py b/datasets/dataset.py new file mode 100644 index 0000000..b78009c --- /dev/null +++ b/datasets/dataset.py @@ -0,0 +1,55 @@ +import torch + +def get_dataset(base_class): + # create a dataset class that will inherit from base_class + class LCPDataset(base_class): + + def __init__(self, *args, **kwargs): + + if "network_function" in kwargs: + net_func = kwargs["network_function"] + del kwargs["network_function"] + else: + net_func = None + + super().__init__(*args, **kwargs) + + if net_func is not None: + self.net = net_func() + else: + self.net = None + + + def download(self): + super().download() + + def process(self): + super().process() + + def __getitem__(self, idx): + + data = super().__getitem__(idx) + + if (self.net is not None) and ("lcp_preprocess" in self.net.__dict__) and (self.net.__dict__["lcp_preprocess"]): + + with torch.no_grad(): + return_data = self.net(data, spatial_only=True) + + for key in return_data.keys(): + if return_data[key] is not None: + if isinstance(return_data[key], torch.Tensor): + data[key] = return_data[key].detach() + else: + data[key] = return_data[key] + + # remove None type keys + to_delete_keys = [] + for key in data: + if data[key] is None: + to_delete_keys.append(key) + + for key in to_delete_keys: + data.pop(key, None) + + return data + return LCPDataset \ No newline at end of file diff --git a/datasets/nuscenes.py b/datasets/nuscenes.py new file mode 100644 index 0000000..2e55e3e --- /dev/null +++ b/datasets/nuscenes.py @@ -0,0 +1,217 @@ + +import os + +import numpy as np +import torch +import torch.nn.functional as F +from torch_geometric.data import Data, Dataset + +import logging + +import numpy as np +from nuscenes import NuScenes as NuScenes_ +from nuscenes.utils.data_classes import LidarPointCloud +from nuscenes.utils.data_io import load_bin_file +from nuscenes.utils.splits import create_splits_scenes + + + +def class_mapping_ns(): + return {0: 'noise', + 1: 'animal', + 2: 'human.pedestrian.adult', + 3: 'human.pedestrian.child', + 4: 'human.pedestrian.construction_worker', + 5: 'human.pedestrian.personal_mobility', + 6: 'human.pedestrian.police_officer', + 7: 'human.pedestrian.stroller', + 8: 'human.pedestrian.wheelchair', + 9: 'movable_object.barrier', + 10: 'movable_object.debris', + 11: 'movable_object.pushable_pullable', + 12: 'movable_object.trafficcone', + 13: 'static_object.bicycle_rack', + 14: 'vehicle.bicycle', + 15: 'vehicle.bus.bendy', + 16: 'vehicle.bus.rigid', + 17: 'vehicle.car', + 18: 'vehicle.construction', + 19: 'vehicle.emergency.ambulance', + 20: 'vehicle.emergency.police', + 21: 'vehicle.motorcycle', + 22: 'vehicle.trailer', + 23: 'vehicle.truck', + 24: 'flat.driveable_surface', + 25: 'flat.other', + 26: 'flat.sidewalk', + 27: 'flat.terrain', + 28: 'static.manmade', + 29: 'static.other', + 30: 'static.vegetation', + 31: 'vehicle.ego' + } +def class_mapping_da(config): + #Adaption of mapping of classes in DA Case + if config["source_dataset_name"] == "SemanticKITTI" or config["target_dataset_name"] == "SemanticKITTI" \ + or (config["source_dataset_name"] == "NuScenes" and config["target_dataset_name"] == "NuScenes"): + # Official class mappings from Complet&Label (CVPR 2021) + return { + 0: 0, + 1: 0, + 2: 6, + 3: 6, + 4: 6, + 5: 6, + 6: 6, + 7: 0, + 8: 0, + 9: 0, + 10: 0, + 11: 0, + 12: 0, + 13: 0, + 14: 2, + 15: 5, + 16: 5, + 17: 1, + 18: 5, + 19: 5, + 20: 5, + 21: 3, + 22: 5, + 23: 4, + 24: 7, + 25: 0, + 26: 8, + 27: 9, + 28: 0, + 29: 0, + 30: 10, + 31: 0, + } + else: + raise ValueError("No mapping") + +class NuScenes(Dataset): + + N_LABELS=11 + + def __init__(self, root, split="training", transform=None, + da_flag = False, config=None, **kwargs): + + super().__init__(root, transform, None) + + self.config = config + #self.nusc = NuScenes_(version=self.config['ns_dataset_version'], dataroot=self.root, verbose=True) + self.nusc = NuScenes_(version='v1.0-mini', dataroot=self.root, verbose=True) + + self.da_flag = da_flag + logging.info("Nuscenes dataset - creating splits") + + # get the scenes + assert(split in ["train", "val", "test"]) + phase_scenes = create_splits_scenes()[split] + + # create a list of camera & lidar scans + self.list_keyframes = [] + for scene_idx in range(len(self.nusc.scene)): + scene = self.nusc.scene[scene_idx] + if scene["name"] in phase_scenes: + current_sample_token = scene["first_sample_token"] + + # Loop to get all successive keyframes + list_data = [] + while current_sample_token != "": + current_sample = self.nusc.get("sample", current_sample_token) + list_data.append(current_sample["data"]) + current_sample_token = current_sample["next"] + + # Add new scans in the list + self.list_keyframes.extend(list_data) + + + + print(f"Nuscnes dataset split {split} - {len(self.list_keyframes)} frames") + + self.label_to_name = class_mapping_ns() + + if self.da_flag: + self.label_to_reduced = class_mapping_da(self.config) + + self.label_to_reduced_np = np.zeros(32, dtype=np.int) + for i in range(32): + self.label_to_reduced_np[i] = self.label_to_reduced[i] + + + def get_weights(self): + weights = torch.ones(self.N_LABELS) + weights[0] = 0 + return weights + + @staticmethod + def get_mask_filter_valid_labels(y): + return (y>0) + + @staticmethod + def get_ignore_index(): + return 0 + + @property + def raw_file_names(self): + return [] + + def _download(self): # override _download to remove makedirs + pass + + def download(self): + pass + + def process(self): + pass + + def _process(self): + pass + + def len(self): + return len(self.list_keyframes) + + def get_category(self, f_id): + raise NotImplementedError + + def get_object_name(self, f_id): + raise NotImplementedError + + def get_class_name(self, f_id): + raise NotImplementedError + + def get_save_dir(self, f_id): + raise NotImplementedError + + + def get(self, idx): + """Get item.""" + + data = self.list_keyframes[idx] + + # get the lidar + lidar_token = data['LIDAR_TOP'] + lidar_rec = self.nusc.get('sample_data', lidar_token) + pc = LidarPointCloud.from_file(os.path.join(self.nusc.dataroot, lidar_rec['filename'])) + pc = pc.points.T + + pos = pc[:,:3] + intensities = pc[:,3:] / 255 # intensities are not used + + # get the labels + lidarseg_label_filename = os.path.join(self.nusc.dataroot, self.nusc.get('lidarseg', lidar_token)['filename']) + y_complete_labels = load_bin_file(lidarseg_label_filename) + + y = self.label_to_reduced_np[y_complete_labels] + + # convert to torch + pos = torch.tensor(pos, dtype=torch.float) + y = torch.tensor(y, dtype=torch.long) + intensities = torch.tensor(intensities, dtype=torch.float) + x = torch.ones((pos.shape[0],1), dtype=torch.float) + + return Data(x=x, intensities=intensities, pos=pos, y=y, shape_id=idx) \ No newline at end of file diff --git a/datasets/semantic_kitti.py b/datasets/semantic_kitti.py new file mode 100644 index 0000000..68245e4 --- /dev/null +++ b/datasets/semantic_kitti.py @@ -0,0 +1,228 @@ +import importlib +import os + +import torch +import torch.nn.functional as F +from torch._C import Value +from torch_geometric.data import Data, Dataset + +import logging +from pathlib import Path + +# Basic libs +import numpy as np +import yaml + + +class SemanticKITTI(Dataset): + def __init__(self, root, split="training", transform=None, dataset_size=None, + multiframe_range=None, da_flag =False, config=None, **kwargs): + + super().__init__(root, transform, None) + + self.split = split + self.n_frames = 1 + self.da_flag = da_flag + self.config = config + self.multiframe_range = multiframe_range + self.N_LABELS = self.config["nb_classes"] if self.config is not None else 11 + + + logging.info(f"SemanticKITTI - split {split}") + + # get the scenes + assert(split in ["train", "val", "test"]) + + if split == "train": + self.sequences = ['{:02d}'.format(i) for i in range(11) if i != 8] + elif split == "val": + self.sequences = ['{:02d}'.format(i) for i in range(11) if i == 8] + elif split == "test": + self.sequences = ['{:02d}'.format(i) for i in range(11, 22)] + else: + raise ValueError('Unknown set for SemanticKitti data: ', split) + + # get the filenames + self.all_files = [] + for sequence in self.sequences: + self.all_files += [path for path in Path(os.path.join(self.root, "dataset", "sequences", sequence, "velodyne")).rglob('*.bin')] + + # Sort for verifying and parametrizing + if split == "verifying" or split == "val" or split == "parametrizing": + self.all_files = sorted(self.all_files, key=lambda i:str(i).lower()) + + self.all_labels = [] + for fname in self.all_files: + fname = str(fname).replace("/velodyne/", "/labels/") + fname = str(fname).replace(".bin", ".label") + self.all_labels.append(fname) + + + # Read labels + if self.n_frames == 1: + config_file = os.path.join(self.root, 'semantic-kitti.yaml') + elif self.n_frames > 1: + config_file = os.path.join(self.root, 'semantic-kitti-all.yaml') + else: + raise ValueError('number of frames has to be >= 1') + + with open(config_file, 'r') as stream: + doc = yaml.safe_load(stream) + all_labels = doc['labels'] + + if self.da_flag: + #Changes of mapping in DA case + if self.config["source_dataset_name"] == "NuScenes" or self.config["target_dataset_name"] == "NuScenes": + # Original class mapping from Complete&Label paper + learning_map={ + 0 : 0, # "unlabeled" + 1 : 0, # "outlier" mapped to "unlabeled" --------------------------mapped + 10: 1, # "car" + 11: 2, # "bicycle" + 13: 5, # "bus" mapped to "other-vehicle" --------------------------mapped + 15: 3, # "motorcycle" + 16: 0, # "on-rails" mapped to "other-vehicle" ---------------------mapped + 18: 4, # "truck" + 20: 5, # "other-vehicle" + 30: 6, # "person" + 31: 0, # "bicyclist" + 32: 0, # "motorcyclist" + 40: 7, # "road" + 44: 7, # "parking" + 48: 8, # "sidewalk" + 49: 0, # "other-ground" + 50: 0, # "building" + 51: 0, # "fence" + 52: 0, # "other-structure" mapped to "unlabeled" ------------------mapped + 60: 7, # "lane-marking" to "road" ---------------------------------mapped + 70: 10, # "vegetation" + 71: 10, # "trunk" + 72: 9, # "terrain" + 80: 0, # "pole" + 81: 0, # "traffic-sign" + 99: 0, # "other-object" to "unlabeled" ----------------------------mapped + 252: 1, # "moving-car" to "car" ------------------------------------mapped + 253: 0, # "moving-bicyclist" to "bicyclist" ------------------------mapped + 254: 6, # "moving-person" to "person" ------------------------------mapped + 255: 0, # "moving-motorcyclist" to "motorcyclist" ------------------mapped + 256: 0, # "moving-on-rails" mapped to "other-vehicle" --------------mapped + 257: 5, # "moving-bus" mapped to "other-vehicle" -------------------mapped + 258: 4, # "moving-truck" to "truck" --------------------------------mapped + 259: 5 # "moving-other"-vehicle to "other-vehicle" ----------------mapped + } + learning_map_inv ={ # inverse of previous map + 0: 0, # "unlabeled", and others ignored + 1: 10, # "car" + 2: 11, # "bicycle" + 3: 15, # "motorcycle" + 4: 18, # "truck" + 5: 20, # "other-vehicle" + 6: 30, # "person" + #: 31, # "bicyclist" No differentitation to bicycle + #8: 32, # "motorcyclist" No differentiation to motorcycle + 7: 40, # "road" + #: 44, # "parking" No differentation to road + 8: 48, # "sidewalk" + #: 49, # "other-ground" Ignored + #: 50, # "building" + #: 51, # "fence" + 10: 70, # "vegetation" + #: 71, # "trunk"is in vegetation + 9: 72, # "terrain" + #: 80, # "pole" + #: 81, # "traffic-sign" + + } + elif self.config["source_dataset_name"] == "SynLidar" or self.config["target_dataset_name"] == "SynLidar": + # Mapping with SynLiDAR + learning_map = doc['learning_map'] + learning_map_inv = doc['learning_map_inv'] + + else: + learning_map = doc['learning_map'] + learning_map_inv = doc['learning_map_inv'] + + self.learning_map = np.zeros((np.max([k for k in learning_map.keys()]) + 1), dtype=np.int32) + + for k, v in learning_map.items(): + self.learning_map[k] = v + + self.learning_map_inv = np.zeros((np.max([k for k in learning_map_inv.keys()]) + 1), dtype=np.int32) + for k, v in learning_map_inv.items(): + self.learning_map_inv[k] = v + + def get_weights(self): + weights = torch.ones(self.N_LABELS) + weights[0] = 0 + return weights + + @staticmethod + def get_mask_filter_valid_labels(y): + return (y>0) + + @property + def raw_file_names(self): + return [] + + def _download(self): # override _download to remove makedirs + pass + + def download(self): + pass + + def process(self): + pass + + def _process(self): + pass + + def len(self): + return len(self.all_files) + + def get_category(self, f_id): + return str(self.all_files[f_id]).split("/")[-3] + + def get_object_name(self, f_id): + return str(self.all_files[f_id]).split("/")[-1] + + def get_class_name(self, f_id): + return "lidar" + + def get_save_dir(self, f_id): + return os.path.join(str(self.all_files[f_id]).split("/")[-3], str(self.all_files[f_id]).split("/")[-2]) + + def get_filename(self, idx): + return self.all_files[idx] + + def get(self, idx): + """Get item.""" + + fname_points = self.all_files[idx] + frame_points = np.fromfile(fname_points, dtype=np.float32) + pos = frame_points.reshape((-1, 4)) + intensities = pos[:,3:] + pos = pos[:,:3] + + if self.split in ["test", "testing"]: + # Fake labels + y = np.zeros((pos.shape[0],), dtype=np.int32) + else: + # Read labels + label_file = self.all_labels[idx] + frame_labels = np.fromfile(label_file, dtype=np.int32) + y = frame_labels & 0xFFFF # semantic label in lower half + y = self.learning_map[y] + + # points are annotated only until 50 m + mask = np.linalg.norm(pos, axis=1)<50 + pos = pos[mask] + y = y[mask] + intensities = intensities[mask] + + + pos = torch.tensor(pos, dtype=torch.float) + y = torch.tensor(y, dtype=torch.long) + intensities = torch.tensor(intensities, dtype=torch.float) + x = torch.ones((pos.shape[0],1), dtype=torch.float) + return Data(x=x, intensities=intensities, pos=pos, y=y, shape_id=idx) + diff --git a/datasets/synlidar.py b/datasets/synlidar.py new file mode 100755 index 0000000..80a22d3 --- /dev/null +++ b/datasets/synlidar.py @@ -0,0 +1,263 @@ +import os + +import numpy as np +import torch +from torch_geometric.data import Data, Dataset + +import logging +from pathlib import Path + +# Basic libs +import numpy as np +import yaml + + +class SynLidar(Dataset): + def __init__(self, + root, + split="training", + transform=None, + dataset_size=None, + skip_ratio=1, + da_flag =False, config=None, + **kwargs): + + super().__init__(root, transform, None) + + self.split = split + self.n_frames = 1 + self.da_flag = da_flag + self.config = config + + + logging.info(f"SynLidar - split {split}") + + # get the scenes + assert(split in ["train", "val", "test", "verifying", "parametrizing"]) + if split == "verifying": + self.sequences = ['{:02d}'.format(i) for i in range(13) if i == 7] + elif split == "parametrizing": + self.sequences = ['{:02d}'.format(i) for i in range(13) if (i != 8 and i != 7)] + elif split == "train": + self.sequences = ['{:02d}'.format(i) for i in range(13)] + elif split == "val": + self.sequences = ['{:02d}'.format(i) for i in range(13) if i == 8] # Pseudo-Validation set, as seen during training. + elif split == "test": + raise ValueError('Unknown set for SynLidar data: ', split) + else: + raise ValueError('Unknown set for SynLidar data: ', split) + + # get the filenames + self.all_files = [] + for sequence in self.sequences: + self.all_files += [path for path in Path(os.path.join(self.root, "SubDataset", "sequences", sequence, "velodyne")).rglob('*.bin')] + + # Sort for verifying and parametrizing + if split == "verifying" or split == "val" or split == "parametrizing": + self.all_files = sorted(self.all_files, key=lambda i:str(i).lower()) + + self.all_files = self.all_files[::skip_ratio] + + self.all_labels = [] + for fname in self.all_files: + fname = str(fname).replace("/velodyne/", "/labels/") + fname = str(fname).replace(".bin", ".label") + self.all_labels.append(fname) + + + # Read labels + if self.n_frames == 1: + config_file = os.path.join(self.root, 'semantic-kitti.yaml') + elif self.n_frames > 1: + config_file = os.path.join(self.root, 'semantic-kitti-all.yaml') + else: + raise ValueError('number of frames has to be >= 1') + + labels_names={0 : "unlabeled", + 1: "car", + 2: "pick-up", + 3: "truck", + 4: "bus", + 5: "bicycle", + 6: "motorcycle", + 7: "other-vehicle", + 8: "road", + 9: "sidewalk", + 10: "parking", + 11: "other-ground", + 12: "female", + 13: "male", + 14: "kid", + 15: "crowd", # multiple person that are very close + 16: "bicyclist", + 17: "motorcyclist", + 18: "building", + 19: "other-structure", + 20: "vegetation", + 21: "trunk", + 22: "terrain", + 23: "traffic-sign", + 24: "pole", + 25: "traffic-cone", + 26: "fence", + 27: "garbage-can", + 28: "electric-box", + 29: "table", + 30: "chair", + 31: "bench", + 32: "other-object"} + + + if self.da_flag: + #Changes of mapping in DA case + if self.config["source_dataset_name"] == "SemanticKITTI" or self.config["target_dataset_name"] == "SemanticKITTI": + learning_map={ + 0: 0, # "unlabeled" + 1: 1, # "car" + 2: 4, # "pick-up" + 3: 4, # "truck" + 4: 5, # "bus" + 5: 2, # "bicycle" + 6: 3, # "motorcycle" + 7: 5, # "other-vehicle" + 8: 9, # "road" + 9: 11, # "sidewalk" + 10: 10, # "parking" + 11: 12, # "other-ground" + 12: 6, # "female" + 13: 6, # "male" + 14: 6, # "kid" + 15: 6, # "crowd" + 16: 7, # "bicyclist" + 17: 8, # "motorcyclist" + 18: 13, # "building" + 19: 0, # "other-structure" + 20: 15, # "vegetation" + 21: 16, # "trunk" + 22: 17, # "terrain" + 23: 19, # "traffic-sign" + 24: 18, # "pole" + 25: 0, # "traffic-cone" + 26: 14, # "fence" + 27: 0, # "garbage-can" + 28: 0, # "electric-box" + 29: 0, # "table" + 30: 0, # "chair" + 31: 0, # "bench" + 32: 0 # "other-object" + } + learning_map_inv ={ # inverse of previous map + 0: 0, + 1: 1, + 2: 5, + 3: 6, + 4: 2, + 5: 7, + 6: 12, + 7: 16, + 8: 17, + 9: 8, + 10: 10, + 11: 9, + 12:11, + 13:18, + 14:26, + 15:20, + 16:21, + 17:22, + 18:24, + 19:23, + + } + + else: + raise ValueError("No mapping") + + + sequences=["00","01","02","03","04","05","06","07","08","09","10","11","12"] + else: + with open(config_file, 'r') as stream: + doc = yaml.safe_load(stream) + all_labels = doc['labels'] + learning_map = doc['learning_map'] + #learning_map_inv = doc['learning_map_inv'] + + self.learning_map = np.zeros((np.max([k for k in learning_map.keys()]) + 1), dtype=np.int32) + + for k, v in learning_map.items(): + self.learning_map[k] = v + + + def get_weights(self): + weights = torch.ones(self.config['nb_classes']) + weights[0] = 0 + return weights + + @staticmethod + def get_mask_filter_valid_labels(y): + return (y>0) + + @property + def raw_file_names(self): + return [] + + def _download(self): # override _download to remove makedirs + pass + + def download(self): + pass + + def process(self): + pass + + def _process(self): + pass + + def len(self): + return len(self.all_files) + + def get_category(self, f_id): + return str(self.all_files[f_id]).split("/")[-3] + + def get_object_name(self, f_id): + return str(self.all_files[f_id]).split("/")[-1] + + def get_class_name(self, f_id): + return "lidar" + + def get_save_dir(self, f_id): + return os.path.join(str(self.all_files[f_id]).split("/")[-3], str(self.all_files[f_id]).split("/")[-2]) + + + + def get(self, idx): + """Get item.""" + + fname_points = self.all_files[idx] + frame_points = np.fromfile(fname_points, dtype=np.float32) + pos = frame_points.reshape((-1, 4)) + intensities = pos[:,3:] + pos = pos[:,:3] + + if self.split in ["test", "testing"]: + # Fake labels + y = np.zeros((pos.shape[0],), dtype=np.int32) + else: + # Read labels + label_path = self.all_labels[idx] + label = np.fromfile(label_path, dtype=np.uint32) + label = label.reshape((-1)) + y = self.learning_map[label] + + # points are annotated only until 50 m in SK + mask = np.linalg.norm(pos, axis=1)<50 + pos = pos[mask] + y = y[mask] + intensities = intensities[mask] + + pos = torch.tensor(pos, dtype=torch.float) + y = torch.tensor(y, dtype=torch.long) + intensities = torch.tensor(intensities, dtype=torch.float) + x = torch.ones((pos.shape[0],1), dtype=torch.float) + + return Data(x=x, intensities=intensities, pos=pos, y=y, shape_id=idx,) \ No newline at end of file diff --git a/doc/architecture2.png b/doc/architecture2.png new file mode 100644 index 0000000000000000000000000000000000000000..2d791c1088183057ac14ad0d48733a6f87b6588a GIT binary patch literal 858222 zcmagGby!s0+crFafQSf)w15cG-5n|*h#*4`-5}jLq|%{;bTc5`T_e(+Lw7TDH}Cd+ zpXdI5&;9)I9S1NRd-m+T_FC7vuJbz26GA>K$lyF7djbN1aAZHc2ZKP5%|ReEt;ZO^ zkr&g#9>5Eh-3Lua5QvTD?>CAYOxy)HNa`e|;iPP9>f~zRU;=V=b!D@#v2-*turpz^ zbudfb7a;?IUV&uaORBh~?9RKosi3b5AHaKi zJ0j~F_@4*<>!kPCF(91(y!L;c6HK%*I)nY69|+EUg8PIiBqEgQ%bs)aPh~Yo0pI@I z_WTU;|DClW|3B}r;))zy{!=ODaX!6T-280BVU&w|fcjm~Iq>oScXRvPoc56ayv6_T 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b/networks/__init__.py new file mode 100644 index 0000000..8779e25 --- /dev/null +++ b/networks/__init__.py @@ -0,0 +1 @@ +from .network import Network \ No newline at end of file diff --git a/networks/backbone/__init__.py b/networks/backbone/__init__.py new file mode 100644 index 0000000..91a63d3 --- /dev/null +++ b/networks/backbone/__init__.py @@ -0,0 +1,6 @@ +def get_backbone(name): + if name == "TorchSparseMinkUNet": + from .torchsparse_minkunet import TorchSparseMinkUNet + return TorchSparseMinkUNet + else: + raise ValueError("Unknown backbone") diff --git a/networks/backbone/torchsparse_minkunet.py b/networks/backbone/torchsparse_minkunet.py new file mode 100644 index 0000000..d86ac65 --- /dev/null +++ b/networks/backbone/torchsparse_minkunet.py @@ -0,0 +1,247 @@ +import torch.nn as nn +import torchsparse +import torchsparse.nn as spnn + +__all__ = ['TorchSparseMinkUNet'] + + +class BasicConvolutionBlock(nn.Module): + + def __init__(self, inc, outc, ks=3, stride=1, dilation=1): + super().__init__() + self.net = nn.Sequential( + spnn.Conv3d(inc, + outc, + kernel_size=ks, + dilation=dilation, + stride=stride), + spnn.BatchNorm(outc), + spnn.ReLU(True), + ) + + def forward(self, x): + out = self.net(x) + return out + + +class BasicDeconvolutionBlock(nn.Module): + + def __init__(self, inc, outc, ks=3, stride=1): + super().__init__() + self.net = nn.Sequential( + spnn.Conv3d(inc, + outc, + kernel_size=ks, + stride=stride, + transposed=True), + spnn.BatchNorm(outc), + spnn.ReLU(True), + ) + + def forward(self, x): + return self.net(x) + + +class ResidualBlock(nn.Module): + + def __init__(self, inc, outc, ks=3, stride=1, dilation=1): + super().__init__() + self.net = nn.Sequential( + spnn.Conv3d(inc, + outc, + kernel_size=ks, + dilation=dilation, + stride=stride), + spnn.BatchNorm(outc), + spnn.ReLU(True), + spnn.Conv3d(outc, outc, kernel_size=ks, dilation=dilation, + stride=1), + spnn.BatchNorm(outc), + ) + + if inc == outc and stride == 1: + self.downsample = nn.Sequential() + else: + self.downsample = nn.Sequential( + spnn.Conv3d(inc, outc, kernel_size=1, dilation=1, + stride=stride), + spnn.BatchNorm(outc), + ) + + self.relu = spnn.ReLU(True) + + def forward(self, x): + out = self.relu(self.net(x) + self.downsample(x)) + return out + + +class MinkUNet(nn.Module): + + def __init__(self, **kwargs): + super().__init__() + + cr = kwargs.get('cr', 1.0) + cs = [32, 32, 64, 128, 256, 256, 128, 96, 96] + cs = [int(cr * x) for x in cs] + self.run_up = kwargs.get('run_up', True) + + in_channels = kwargs["in_channels"] + + self.stem = nn.Sequential( + spnn.Conv3d(in_channels, cs[0], kernel_size=3, stride=1), + spnn.BatchNorm(cs[0]), spnn.ReLU(True), + spnn.Conv3d(cs[0], cs[0], kernel_size=3, stride=1), + spnn.BatchNorm(cs[0]), spnn.ReLU(True)) + + self.stage1 = nn.Sequential( + BasicConvolutionBlock(cs[0], cs[0], ks=2, stride=2, dilation=1), + ResidualBlock(cs[0], cs[1], ks=3, stride=1, dilation=1), + ResidualBlock(cs[1], cs[1], ks=3, stride=1, dilation=1), + ) + + self.stage2 = nn.Sequential( + BasicConvolutionBlock(cs[1], cs[1], ks=2, stride=2, dilation=1), + ResidualBlock(cs[1], cs[2], ks=3, stride=1, dilation=1), + ResidualBlock(cs[2], cs[2], ks=3, stride=1, dilation=1)) + + self.stage3 = nn.Sequential( + BasicConvolutionBlock(cs[2], cs[2], ks=2, stride=2, dilation=1), + ResidualBlock(cs[2], cs[3], ks=3, stride=1, dilation=1), + ResidualBlock(cs[3], cs[3], ks=3, stride=1, dilation=1), + ) + + self.stage4 = nn.Sequential( + BasicConvolutionBlock(cs[3], cs[3], ks=2, stride=2, dilation=1), + ResidualBlock(cs[3], cs[4], ks=3, stride=1, dilation=1), + ResidualBlock(cs[4], cs[4], ks=3, stride=1, dilation=1), + ) + + self.up1 = nn.ModuleList([ + BasicDeconvolutionBlock(cs[4], cs[5], ks=2, stride=2), + nn.Sequential( + ResidualBlock(cs[5] + cs[3], cs[5], ks=3, stride=1, dilation=1), + ResidualBlock(cs[5], cs[5], ks=3, stride=1, dilation=1), + ) + ]) + + self.up2 = nn.ModuleList([ + BasicDeconvolutionBlock(cs[5], cs[6], ks=2, stride=2), + nn.Sequential( + ResidualBlock(cs[6] + cs[2], cs[6], ks=3, stride=1, dilation=1), + ResidualBlock(cs[6], cs[6], ks=3, stride=1, dilation=1), + ) + ]) + + self.up3 = nn.ModuleList([ + BasicDeconvolutionBlock(cs[6], cs[7], ks=2, stride=2), + nn.Sequential( + ResidualBlock(cs[7] + cs[1], cs[7], ks=3, stride=1, dilation=1), + ResidualBlock(cs[7], cs[7], ks=3, stride=1, dilation=1), + ) + ]) + + self.up4 = nn.ModuleList([ + BasicDeconvolutionBlock(cs[7], cs[8], ks=2, stride=2), + nn.Sequential( + ResidualBlock(cs[8] + cs[0], cs[8], ks=3, stride=1, dilation=1), + ResidualBlock(cs[8], cs[8], ks=3, stride=1, dilation=1), + ) + ]) + + self.classifier = nn.Sequential(nn.Linear(cs[8], kwargs['out_channels'])) + + self.point_transforms = nn.ModuleList([ + nn.Sequential( + nn.Linear(cs[0], cs[4]), + nn.BatchNorm1d(cs[4]), + nn.ReLU(True), + ), + nn.Sequential( + nn.Linear(cs[4], cs[6]), + nn.BatchNorm1d(cs[6]), + nn.ReLU(True), + ), + nn.Sequential( + nn.Linear(cs[6], cs[8]), + nn.BatchNorm1d(cs[8]), + nn.ReLU(True), + ) + ]) + + self.weight_initialization() + self.dropout = nn.Dropout(0.3, True) + + def weight_initialization(self): + for m in self.modules(): + if isinstance(m, nn.BatchNorm1d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + def forward(self, x): + x0 = self.stem(x) + x1 = self.stage1(x0) + x2 = self.stage2(x1) + x3 = self.stage3(x2) + x4 = self.stage4(x3) + + y1 = self.up1[0](x4) + y1 = torchsparse.cat([y1, x3]) + y1 = self.up1[1](y1) + + y2 = self.up2[0](y1) + y2 = torchsparse.cat([y2, x2]) + y2 = self.up2[1](y2) + + y3 = self.up3[0](y2) + y3 = torchsparse.cat([y3, x1]) + y3 = self.up3[1](y3) + + y4 = self.up4[0](y3) + y4 = torchsparse.cat([y4, x0]) + y4 = self.up4[1](y4) + + out = self.classifier(y4.F) + + return out + + +class TorchSparseMinkUNet(MinkUNet): + + def __init__(self, in_channels, out_channels, + voxel_size=1, + cylindrical_coordinates=False, + cr=1.0, + **kwargs + ): + + super().__init__( + in_channels = in_channels, + out_channels = out_channels, cr=cr) ####"num classes is here used, alt + + self.voxel_size = voxel_size + self.cylindrical_coordinates = cylindrical_coordinates + + + def get_stack_item_list(self): + return [] + + def get_cat_item_list(self): + return [] + + def forward(self, data): + + # forward in the network + outputs = super().forward(data["sparse_input"]) + + # interpolate the outputs + outputs = outputs[data["sparse_input_invmap"]] + + return outputs + + @staticmethod + def get_final_layer_name(): + return "classifier" + + @staticmethod + def get_linear_layer(in_channels, out_channels): + return nn.Conv1d(in_channels, out_channels, 1) diff --git a/networks/decoder/__init__.py b/networks/decoder/__init__.py new file mode 100644 index 0000000..7d79c73 --- /dev/null +++ b/networks/decoder/__init__.py @@ -0,0 +1 @@ +from .interpolator import * \ No newline at end of file diff --git a/networks/decoder/interpolator.py b/networks/decoder/interpolator.py new file mode 100644 index 0000000..36f9221 --- /dev/null +++ b/networks/decoder/interpolator.py @@ -0,0 +1,246 @@ +import logging + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch_geometric.nn import avg_pool_x, knn, radius + + +class InterpNetBase(torch.nn.Module): + + def __init__(self, latent_size, out_channels, K=1, spatial_prefix="", + contrast_loss=False, + intensity_loss=False, + patch_similarity_loss=False, + predict_translation=False, + all_prediction=False, + bow=False, + use_dirs=True, radius_search=True, config=None): + + super().__init__() + + logging.info(f"InterpNet - Mean - radius={K}") + + self.contrast_loss = contrast_loss + self.intensity_loss = intensity_loss + self.predict_translation = predict_translation + self.bow = bow + self.all_prediction = all_prediction + self.dirs = use_dirs + self.patch_similarity_loss = patch_similarity_loss + + self.out_channels = out_channels + if self.intensity_loss: + self.out_channels += 1 + + in_size = latent_size + if self.dirs: + in_size += 3 + if not self.predict_translation: + in_size += 3 + + self.fc_in = torch.nn.Linear(in_size, latent_size) + mlp_layers = [torch.nn.Linear(latent_size, latent_size) for _ in range(2)] + self.mlp_layers = nn.ModuleList(mlp_layers) + self.fc_out = torch.nn.Linear(latent_size, self.out_channels) + + self.activation = torch.nn.ReLU() + self.spatial_prefix = spatial_prefix + + if radius_search: + self.radius = K + self.K = None + else: + self.K = int(K) + self.radius = None + + def get_stack_item_list(self): + return [] + + def get_cat_item_list(self): + return [] + + def forward(self, data, inference_mode=False): + + # get the data + pos = data["pos"] + latents = data["latents"] + dirs = data["dirs"][:,1:] + pos_non_manifold = data["pos_non_manifold"] + + # create batch and pos + batch_source = pos[:, 0].long() + pos_source = pos[:, 1:] + batch_target = pos_non_manifold[:, 0].long() + pos_target = pos_non_manifold[:, 1:] + + # neighborhood search + if self.radius is not None: + row, col = radius(pos_source, pos_target, self.radius, batch_source, batch_target) + else: + row, col = knn(pos_source, pos_target, self.K, batch_source, batch_target) + + # Moved translation after nn computation + # --> collapse to xy plane + if self.predict_translation: + # operateTranslation + pos_source = pos_source - latents[:, :3] + latents = latents[:, 3:] + + # compute reltive position between query and input point cloud + pos_relative = pos_target[row] - pos_source[col] + + # get the corresponding latent vectors + latents = latents[col] + + # create the input of the decoder + if self.dirs: + x = torch.cat([latents, pos_relative, dirs[col]], dim=1) + else: + x = torch.cat([latents, pos_relative], dim=1) + + if (not inference_mode) and ("occupancies" in data): + occupancies = data["occupancies"][row] + + # MLP + x = self.fc_in(x.contiguous()) + for i, l in enumerate(self.mlp_layers): + x = l(self.activation(x)) + + # Final layer + x = self.fc_out(x) + + # inference mode + if inference_mode: + # average pooling over all predictions + x, _ = avg_pool_x(row, x, batch_source[col]) + + output_pos_count = 0 + predictions = torch.full((pos_target.shape[0],), fill_value=-1e7, dtype=torch.float, device=x.device) + + target_point_ids = torch.unique(row) + predictions[target_point_ids] = x[:, output_pos_count] + + return_data = {"predictions":predictions} + if ("occupancies" in data): + return_data["occupancies"] = data["occupancies"] + return return_data + + output_pos_count = 0 + return_data = {"predictions":x[:, output_pos_count], "occupancies": occupancies} + + # Reconstruction loss + recons_loss = F.binary_cross_entropy_with_logits(x[:,output_pos_count], occupancies.float()) + return_data["recons_loss"] = recons_loss + output_pos_count += 1 + + return return_data + + + def forward_inference(self, data): + + return self.forward(data, inference_mode=True) + + +# All neighbors in radius +class InterpAllRadiusNoDirsNet(InterpNetBase): + def __init__(self, latent_size, out_channels, K=1, spatial_prefix="", config=None): + super().__init__(latent_size, out_channels, K, spatial_prefix, all_prediction=True, use_dirs=False, config=config) + +# With directions +class InterpAllRadiusNet(InterpNetBase): + def __init__(self, latent_size, out_channels, K=1, spatial_prefix="", config=None): + super().__init__(latent_size, out_channels, K, spatial_prefix, all_prediction=True, config=config) + +# Original POCO head +class InterpAttentionKHeadsNet(torch.nn.Module): + + def __init__(self, latent_size, out_channels, K=16, spatial_prefix="", config=None): + super().__init__() + + self.latent_size = latent_size + self.config = config + logging.info(f"InterpNet - Simple - K={K}") + if "use_no_dirs_rec_head_flag" in self.config and self.config["use_no_dirs_rec_head_flag"]: + logging.info(f"No dirs used for reconstruction head used") + self.fc1 = torch.nn.Linear(latent_size+3, latent_size) + else: + self.fc1 = torch.nn.Linear(latent_size+6, latent_size) + self.fc2 = torch.nn.Linear(latent_size, latent_size) + self.fc3 = torch.nn.Linear(latent_size, latent_size) + + self.fc8 = torch.nn.Linear(latent_size, out_channels) + self.activation = torch.nn.ReLU() + self.spatial_prefix = spatial_prefix + self.fc_query = torch.nn.Linear(latent_size, 64) + self.fc_value = torch.nn.Linear(latent_size, latent_size) + + self.K = K + self.spatial_prefix = spatial_prefix + self.decoder_ratio = config["decoder_ratio"] + + if "rotation_head_flag" in self.config and self.config['rotation_head_flag']: + logging.info(f"Rotation of the reconstruction ball") + + def get_stack_item_list(self): + return [] + + def get_cat_item_list(self): + return [] + + def forward(self, data, no_occupancies=False, inference=False, return_feature=False, inference_mode=None): + + # get the data + pos = data["pos"] + latents = data["latents"] + + dirs = data["dirs"][:,1:] + pos_non_manifold = data["pos_non_manifold"] + pos_source = pos[:,1:] + pos_target = pos_non_manifold[:,1:] + + batch_source = pos[:,0].long() + batch_target = pos_non_manifold[:,0].long() + row, col = knn(pos_source, pos_target, self.K, batch_source, batch_target) + + pos = pos_target[row] - pos_source[col] + + if "use_no_dirs_rec_head_flag" in self.config and self.config["use_no_dirs_rec_head_flag"]: + x = torch.cat([latents[col], pos], dim=1) + else: + x = torch.cat([latents[col], pos, dirs[col]], dim=1) + + if not return_feature: + occupancies = data["occupancies"]#[row] + + x = self.activation(self.fc1(x)) + x = self.activation(self.fc2(x)) + x = self.activation(self.fc3(x)) + + query = self.fc_query(x) + value = self.fc_value(x) + + attention = torch.nn.functional.softmax(query, dim=-1).mean(dim=1) + + attention = attention.view(-1,1) + product = torch.mul(attention, value) + product = product.view(pos_non_manifold.shape[0], -1, self.latent_size) + + x = torch.sum(product, dim=1) + pred = self.fc8(x) + + + if return_feature: + return_data = {"predictions":pred, "feature":x} + else: + return_data = {"predictions":pred, + "occupancies": occupancies} + return return_data + + def forward_inference(self, data): + #inference mode attribute is here only used for + return self.forward(data, inference=True) + + def forward_inference_feature(self, data): + + return self.forward(data, inference=True, return_feature=True) \ No newline at end of file diff --git a/networks/network.py b/networks/network.py new file mode 100644 index 0000000..40e0055 --- /dev/null +++ b/networks/network.py @@ -0,0 +1,111 @@ +import logging + +import torch +import torch.nn as nn + +from .backbone import * +from .decoder import * + + +def count_parameters(model): + return sum(p.numel() for p in model.parameters() if p.requires_grad) + + +class Network(torch.nn.Module): + + def __init__(self, in_channels, latent_size, out_channels, backbone, decoder=None, + frozen_backbone=False, + no_head = False, voxel_size = 1, + dual_seg_head = False, nb_classes = 1, da_flag = False, + target_in_channels = None, config=None, no_decoder = False, + **kwargs): + super().__init__() + + self.backbone = backbone + self.dual_seg_head_flag = dual_seg_head + self.config = config + self.da_flag = da_flag + self.latent_size = latent_size + self.activation = nn.ReLU(inplace=True) + self.net = get_backbone(backbone)(in_channels, self.latent_size, + segmentation=True, dropout=0, spatial_prefix="encoder_", + voxel_size = voxel_size, nb_classes=self.config["nb_classes"], + da_flag= self.da_flag, target_in_channels=target_in_channels) + + logging.info(f"Network -- backbone -- {count_parameters(self.net)} parameters") + + self.no_decoder = no_decoder + if not no_decoder: + self.decoder_name = decoder["name"] + self.projection = eval(decoder["name"])(self.latent_size, out_channels, decoder["k"], spatial_prefix="projection_", config=config) + logging.info(f"Network -- Surface head -- {count_parameters(self.projection)} parameters") + + if self.dual_seg_head_flag: + #Head for semantic segmentation + logging.info("Network -- Latent size creation {} and nb classes creation {}, larger seg head: False ".format(self.latent_size, self.config["nb_classes"])) + self.dual_seg_head = self.net.get_linear_layer(self.latent_size, self.config["nb_classes"]) + logging.info(f"Network -- Semantic head -- {count_parameters(self.dual_seg_head)} parameters") + + if self.da_flag: + #Add the target backbone + self.target_backbone = get_backbone(backbone)(target_in_channels, self.latent_size, segmentation=True, dropout=0, spatial_prefix="encoder_") + logging.info(f"Network -- Target backbone -- {count_parameters(self.target_backbone)} parameters") + + + def train(self, mode=True): + r"""Sets the module in training mode.""" + self.training = mode + self.net.train(mode) + + if not self.no_decoder: + self.projection.train(mode) + elif not self.no_decoder: + self.final.train(mode) + return self + + + def get_stack_item_list(self): + item_list = self.net.get_stack_item_list() + if not self.no_decoder: + item_list += self.projection.get_stack_item_list() + return item_list + + + def get_cat_item_list(self): + item_list = self.net.get_cat_item_list() + if not self.no_decoder: + item_list += self.projection.get_cat_item_list() + return item_list + + + def forward_pretraining(self, data, return_latents=False, return_projection=True, get_latent=False, inference=False): + if self.dual_seg_head_flag: + #Head for semantic segmentation + data["latents"] = self.net(data) + outs_sem = self.dual_seg_head(data["latents"][:,:,None]) + + if get_latent: + return self.projection(data, inference_mode=inference), outs_sem, data + else: + return self.projection(data, inference_mode=inference), outs_sem + + + def from_latent(self, data,no_occupancies=False): + return self.projection(data,no_occupancies) + + + def get_latent(self, data, return_latents=False, return_projection=True): + data["latents"] = self.net(data) + return data + + + def forward_latents(self, data, return_latents=False, return_projection=True): + return {"latents": self.net(data)} + + + def forward_projection_inference(self, data): + return self.projection.forward_inference(data) + + + def forward_inference_feature(self, data): + return self.projection.forward_inference_feature(data) \ No newline at end of file diff --git a/utils/__init__.py b/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/utils/all_transforms/__init__.py b/utils/all_transforms/__init__.py new file mode 100755 index 0000000..ef223f6 --- /dev/null +++ b/utils/all_transforms/__init__.py @@ -0,0 +1,12 @@ +from .create_inputs import CreateInputs +from .create_non_manifold_points import CreateNonManifoldPoints +from .createdirs import CreateDirs, CreateDirs_non_manifold +from .dupplicate import Dupplicate +from .fixed_points import FixedPoints +from .random_flip import RandomFlip +from .random_rotate import RandomRotate +from .scaling import Scaling +from .sparse_quantize import TorchSparseQuantize +from .transpose import Transpose +from .to_dict import ToDict +from .use_as_features import UseAsFeatures diff --git a/utils/all_transforms/create_inputs.py b/utils/all_transforms/create_inputs.py new file mode 100755 index 0000000..5509bd5 --- /dev/null +++ b/utils/all_transforms/create_inputs.py @@ -0,0 +1,19 @@ +import logging + +import torch + + +class CreateInputs(object): + + def __init__(self, item_list): + self.item_list = item_list + logging.info(f"CreateInputs -- {item_list}") + + def __call__(self, data): + + features = [] + for key in self.item_list: + features.append(data[key]) + + data["x"] = torch.cat(features, dim=1) + return data \ No newline at end of file diff --git a/utils/all_transforms/create_non_manifold_points.py b/utils/all_transforms/create_non_manifold_points.py new file mode 100755 index 0000000..accbcec --- /dev/null +++ b/utils/all_transforms/create_non_manifold_points.py @@ -0,0 +1,63 @@ +import torch +import torch.nn.functional as F + + +class CreateNonManifoldPoints(object): + + def __call__(self, data): + + pos = data["pos"] + label = data["y"] + + # build the non manifold points + if "dirs" in data.keys: + dirs = data["dirs"] + else: + dirs = F.normalize(pos, dim=1) + + pos_in = pos + 0.1 * dirs * torch.rand((pos.shape[0],1)) + pos_out = pos - 0.1 * dirs * torch.rand((pos.shape[0],1)) + pos_out_far = (pos - 0.1 * dirs) * torch.rand((pos.shape[0],1)) + data["pos_non_manifold"] = torch.cat([pos_in, pos_out, pos_out_far], dim=0) + data["label_non_manifold"] = torch.cat([label, label, torch.zeros_like(label)], dim=0) + + occ_in = torch.ones(pos_in.shape[0], dtype=torch.long) + occ_out = torch.zeros(pos_out.shape[0], dtype=torch.long) + occ_out_far = torch.zeros(pos_out_far.shape[0], dtype=torch.long) + data["occupancies"] = torch.cat([occ_in, occ_out, occ_out_far], dim=0) + + if "intensities" in data: + intensities_in = data["intensities"] + intensities_out = data["intensities"] + intensities_out_far = torch.full_like(intensities_out, fill_value=-1) + data["intensities_non_manifold"] = torch.cat([intensities_in, intensities_out, intensities_out_far], dim=0) + + + # dealing with multi-frame + if "second_pos" in data.keys: + pos = data["second_pos"] + + # build the non manifold points + if "second_dirs" in data.keys: + dirs = data["second_dirs"] + else: + dirs = F.normalize(pos, dim=1) + + pos_in = pos + 0.1 * dirs * torch.rand((pos.shape[0],1)) + pos_out = pos - 0.1 * dirs * torch.rand((pos.shape[0],1)) + pos_out_far = (pos - 0.1 * dirs) * torch.rand((pos.shape[0],1)) + + data["pos_non_manifold"] = torch.cat([data["pos_non_manifold"], pos_in, pos_out, pos_out_far], dim=0) + + occ_in = torch.ones(pos_in.shape[0], dtype=torch.long) + occ_out = torch.zeros(pos_out.shape[0], dtype=torch.long) + occ_out_far = torch.zeros(pos_out_far.shape[0], dtype=torch.long) + data["occupancies"] = torch.cat([data["occupancies"], occ_in, occ_out, occ_out_far], dim=0) + + if "second_intensities" in data: + intensities_in = data["second_intensities"] + intensities_out = data["second_intensities"] + intensities_out_far = torch.full_like(intensities_out, fill_value=-1) + data["intensities_non_manifold"] = torch.cat([data["intensities_non_manifold"], intensities_in, intensities_out, intensities_out_far], dim=0) + + return data \ No newline at end of file diff --git a/utils/all_transforms/createdirs.py b/utils/all_transforms/createdirs.py new file mode 100755 index 0000000..e546eb7 --- /dev/null +++ b/utils/all_transforms/createdirs.py @@ -0,0 +1,25 @@ +import torch.nn.functional as F + + +class CreateDirs(object): + + def __call__(self, data): + + dirs = F.normalize(data["pos"], dim=1) + data["dirs"] = dirs + + + # if second frame --> decimation of the second frame + if "second_pos" in data.keys: + second_dirs = F.normalize(data["second_pos"], dim=1) + data["second_dirs"] = second_dirs + + return data + +class CreateDirs_non_manifold(object): + def __call__(self, data): + + dirs_non_manifold = F.normalize(data["pos_non_manifold"], dim=1) + data["dirs_non_manifold"] = dirs_non_manifold + + return data \ No newline at end of file diff --git a/utils/all_transforms/dupplicate.py b/utils/all_transforms/dupplicate.py new file mode 100755 index 0000000..8b45f3a --- /dev/null +++ b/utils/all_transforms/dupplicate.py @@ -0,0 +1,12 @@ +class Dupplicate(object): + + def __init__(self, item_list, prefix) -> None: + self.item_list = item_list + self.prefix = prefix + + def __call__(self, data): + + for item in self.item_list: + data[self.prefix+item] = data[item].clone() + + return data \ No newline at end of file diff --git a/utils/all_transforms/fixed_points.py b/utils/all_transforms/fixed_points.py new file mode 100644 index 0000000..2d4d9cd --- /dev/null +++ b/utils/all_transforms/fixed_points.py @@ -0,0 +1,50 @@ +import torch +import re +import numpy as np + +class FixedPoints(object): + # extension of the Fixed points from torch_geometric with a given list of item to sample + def __init__(self, num, replace=True, allow_duplicates=False, item_list=None): + self.num = num + self.replace = replace + self.allow_duplicates = allow_duplicates + self.item_list = item_list + + def __call__(self, data): + if self.item_list is None: + num_nodes = data.num_nodes + else: + num_nodes = data[self.item_list[0]].shape[0] + + # Sampling + if self.replace: + choice = np.random.choice(num_nodes, self.num, replace=True) + choice = torch.from_numpy(choice).to(torch.long) + elif not self.allow_duplicates: + choice = torch.randperm(num_nodes)[:self.num] + else: + choice = torch.cat([ + torch.randperm(num_nodes) + for _ in range(math.ceil(self.num / num_nodes)) + ], dim=0)[:self.num] + + # selecting elements + if self.item_list is None: + for key, item in data: + if bool(re.search('edge', key)): + continue + if (torch.is_tensor(item) and item.size(0) == num_nodes + and item.size(0) != 1): + data[key] = item[choice] + else: + for key, item in data: + if key in self.item_list: + if bool(re.search('edge', key)): + continue + if (torch.is_tensor(item) and item.size(0) != 1): + data[key] = item[choice] + return data + + def __repr__(self): + return '{}({}, replace={})'.format(self.__class__.__name__, self.num, + self.replace) \ No newline at end of file diff --git a/utils/all_transforms/random_flip.py b/utils/all_transforms/random_flip.py new file mode 100755 index 0000000..6725534 --- /dev/null +++ b/utils/all_transforms/random_flip.py @@ -0,0 +1,17 @@ +import torch + + +class RandomFlip(object): + + def __init__(self, item_list) -> None: + self.item_list = item_list + + def __call__(self, data): + + if torch.randint(0, 2, size=(1,)).item(): + for item in self.item_list: + data[item][:,0] = -data[item][:,0] + if ("second_" + item) in data.keys: + data["second_" + item][:,0] = -data["second_" + item][:,0] + + return data \ No newline at end of file diff --git a/utils/all_transforms/random_rotate.py b/utils/all_transforms/random_rotate.py new file mode 100755 index 0000000..c1d3571 --- /dev/null +++ b/utils/all_transforms/random_rotate.py @@ -0,0 +1,40 @@ +import math +import numbers +import random + +import torch + + +class RandomRotate(object): + def __init__(self, degrees, axis=0, item_list=["pos"]): + if isinstance(degrees, numbers.Number): + degrees = (-abs(degrees), abs(degrees)) + assert isinstance(degrees, (tuple, list)) and len(degrees) == 2 + self.degrees = degrees + self.axis = axis + self.item_list = item_list + + def __call__(self, data): + degree = math.pi * random.uniform(*self.degrees) / 180.0 + sin, cos = math.sin(degree), math.cos(degree) + + if data.pos.size(-1) == 2: + matrix = [[cos, sin], [-sin, cos]] + else: + if self.axis == 0: + matrix = [[1, 0, 0], [0, cos, sin], [0, -sin, cos]] + elif self.axis == 1: + matrix = [[cos, 0, -sin], [0, 1, 0], [sin, 0, cos]] + else: + matrix = [[cos, sin, 0], [-sin, cos, 0], [0, 0, 1]] + + matrix = torch.tensor(matrix) + + for key, item in data: + if key in self.item_list: + if torch.is_tensor(item): + data[key] = torch.matmul(item, matrix.to(item.dtype).to(item.device)) + if ("second_" + key) in data.keys: + data["second_" + key] = torch.matmul(data["second_" + key], matrix.to(item.dtype).to(item.device)) + + return data \ No newline at end of file diff --git a/utils/all_transforms/scaling.py b/utils/all_transforms/scaling.py new file mode 100755 index 0000000..12d31a0 --- /dev/null +++ b/utils/all_transforms/scaling.py @@ -0,0 +1,12 @@ +class Scaling(object): + + def __init__(self, scale, item_list=["pos"]): + self.scale = scale + self.item_list = item_list + + def __call__(self, data): + + for key in self.item_list: + data[key] = data[key] * self.scale + + return data \ No newline at end of file diff --git a/utils/all_transforms/sparse_quantize.py b/utils/all_transforms/sparse_quantize.py new file mode 100755 index 0000000..3a82283 --- /dev/null +++ b/utils/all_transforms/sparse_quantize.py @@ -0,0 +1,34 @@ +import importlib + +torchsparse_found = importlib.util.find_spec("torchsparse") is not None +import logging + +logging.info(f"Torchsparse found - {torchsparse_found}") +if torchsparse_found: + from torchsparse.utils.quantize import sparse_quantize + from torchsparse import SparseTensor + +import numpy as np +import torch + + +class TorchSparseQuantize(object): + + def __init__(self, voxel_size) -> None: + self.voxel_size = voxel_size + + def __call__(self, data): + + pc_ = np.round(data["pos"].numpy() / self.voxel_size).astype(np.int32) + pc_ -= pc_.min(0, keepdims=1) + coords, indices, inverse_map = sparse_quantize(pc_, + return_index=True, + return_inverse=True) + coords = torch.tensor(coords, dtype=torch.int) + indices = torch.tensor(indices) + feats = data["x"][indices] + inverse_map = torch.tensor(inverse_map, dtype=torch.long) + data["sparse_input"] = SparseTensor(coords=coords, feats=feats) + data["sparse_input_invmap"] = inverse_map + + return data \ No newline at end of file diff --git a/utils/all_transforms/to_dict.py b/utils/all_transforms/to_dict.py new file mode 100644 index 0000000..0b5aedb --- /dev/null +++ b/utils/all_transforms/to_dict.py @@ -0,0 +1,11 @@ +class ToDict(object): + + def __call__(self, data): + + d = {} + for key in data.keys: + d[key] = data[key] + return d + + def __repr__(self): + return '{}'.format(self.__class__.__name__) \ No newline at end of file diff --git a/utils/all_transforms/transpose.py b/utils/all_transforms/transpose.py new file mode 100755 index 0000000..0cf8486 --- /dev/null +++ b/utils/all_transforms/transpose.py @@ -0,0 +1,18 @@ +import torch + + +class Transpose(object): + + # extension of the Fixed points from torch_geometric with a given list of item to sample + + def __init__(self, item_list): + self.item_list = item_list + + def __call__(self, data): + + for key in self.item_list: + data[key] = data[key].transpose(0,1) + return data + + def __repr__(self): + return '{}({})'.format(self.__class__.__name__, self.item_list) \ No newline at end of file diff --git a/utils/all_transforms/use_as_features.py b/utils/all_transforms/use_as_features.py new file mode 100755 index 0000000..af27c17 --- /dev/null +++ b/utils/all_transforms/use_as_features.py @@ -0,0 +1,20 @@ +import torch + + +class UseAsFeatures(object): + def __init__(self, item_list): + self.item_list = item_list + + def __call__(self, data): + + features = [] + for key in self.item_list: + value = data[key] + if len(value.shape)==1: + value = value.unsqueeze(1) + features.append(value) + + features = torch.cat(features, dim=1) + data["x"] = features + + return data \ No newline at end of file diff --git a/utils/argparseFromFile.py b/utils/argparseFromFile.py new file mode 100644 index 0000000..1df8743 --- /dev/null +++ b/utils/argparseFromFile.py @@ -0,0 +1,75 @@ +import yaml +from argparse import ArgumentParser, HelpFormatter + +class ArgumentParserFromFile(ArgumentParser): + + def __init__(self, + prog=None, + usage=None, + description=None, + epilog=None, + parents=[], + formatter_class=HelpFormatter, + prefix_chars='-', + fromfile_prefix_chars=None, + argument_default=None, + conflict_handler='error', + add_help=True, + allow_abbrev=True): + super().__init__(prog=prog, usage=usage, description=description, epilog=epilog, parents=parents, formatter_class=formatter_class, + prefix_chars=prefix_chars, fromfile_prefix_chars=fromfile_prefix_chars, argument_default=argument_default, + conflict_handler=conflict_handler, add_help=add_help, allow_abbrev=allow_abbrev) + + self.file_arg_names=None + + def update_file_arg_names(self, file_arg_names): + + self.file_arg_names=file_arg_names + + def parse(self, use_unknown=False): + + # get the arguments + if use_unknown: + args, unknown = self.parse_known_args() + else: + args = self.parse_args() + unknown = None + + config = {} + + # parse the filenames + if self.file_arg_names is not None: + for file_arg_name in self.file_arg_names: + filename = getattr(args, file_arg_name) + config_ = yaml.load(open(filename, 'r'), Loader=yaml.FullLoader) + for key, val in config_.items(): + config[key] = val + + # iterate over the args + for arg in vars(args): + config[arg] = getattr(args, arg) + + # use the unknow + if use_unknown: + pos = 0 + while(pos < len(unknown)): + arg = unknown[pos] + if "--" in arg: + key = str(arg[2:]) + pos += 1 + if pos < len(unknown): + + if "--" not in unknown[pos]: + try: + value = eval(unknown[pos]) + except: + value = unknown[pos] + print(key, value) + config[key] = value + else: + value = True + print(key, value) + config[key] = value + pos+=1 + + return config \ No newline at end of file diff --git a/utils/collate_function.py b/utils/collate_function.py new file mode 100644 index 0000000..ccc4227 --- /dev/null +++ b/utils/collate_function.py @@ -0,0 +1,54 @@ +import importlib +import logging + +import torch + +torchsparse_found = importlib.util.find_spec("torchsparse") is not None +logging.info(f"Torchsparse found - {torchsparse_found}") +if torchsparse_found: + from torchsparse.utils.collate import sparse_collate + + + +def collate_function(list_data, + cat_item_list, + stack_item_list, + sparse_item_list, + ): + + data = {} + + for key in stack_item_list: + tmp = [d[key] for d in list_data] + tmp = torch.stack(tmp, dim=0) + data[key] = tmp + + for key in cat_item_list: + + if "pos" in key or "dirs" in key or "dirs_non_manifold" in key: # denote a position --> add the batch as a first dim + tmp = [] + for b_id, d in enumerate(list_data): + pos = d[key] + batch = torch.full((pos.shape[0], 1), fill_value=b_id) + pos = torch.cat([batch, pos], dim=1) + tmp.append(pos) + else: + tmp = [d[key] for d in list_data] + tmp = torch.cat(tmp, dim=0) + data[key] = tmp + + # sparse + for key in sparse_item_list: + if key in list_data[0]: + key_inv = key+"_invmap" + pos = 0 + tmp = [] + tmp_inv = [] + for d in list_data: + tmp.append(d[key]) + tmp_inv.append(d[key_inv] + pos) + pos += d[key].C.shape[0] + data[key] = sparse_collate(tmp) + data[key_inv] = torch.cat(tmp_inv, dim=0) + + return data \ No newline at end of file diff --git a/utils/logging_files_functions.py b/utils/logging_files_functions.py new file mode 100755 index 0000000..866ffb5 --- /dev/null +++ b/utils/logging_files_functions.py @@ -0,0 +1,213 @@ +import os + +import numpy as np + + +def default_logger_basic(prefix, prefix2, logger_func, metrics, metrics_target, train_iter_count, trg_mIoU=False): + logger_func(f'{prefix}.OA', metrics[f"{prefix2}_oa"], train_iter_count) + logger_func(f"{prefix}.AA", metrics[f"{prefix2}_aa"], train_iter_count) + logger_func(f"{prefix}.IoU", metrics[f"{prefix2}_iou"], train_iter_count) + logger_func(f"{prefix}.loss", metrics[f"{prefix2}_aloss"], train_iter_count) + logger_func(f"{prefix}.lossRecons", metrics[f"{prefix2}_aloss_recons"], train_iter_count) + logger_func(f"{prefix}.lossAdditional", metrics[f"{prefix2}_aloss_additional"], train_iter_count) + logger_func(f"{prefix}.seg_MAA", metrics[f"{prefix2}_seg_head_maa"], train_iter_count) + logger_func(f"{prefix}.seg_mIoU", metrics[f"{prefix2}_seg_head_miou"], train_iter_count) + logger_func(f"{prefix}.seg_loss", metrics[f"{prefix2}_seg_head_loss"], train_iter_count) + logger_func(f"{prefix}.entropy_mean_avg", metrics[f"entropy_mean_avg"], train_iter_count) + logger_func(f"{prefix}.information_maximization_avg", metrics[f"information_maximization_avg"], train_iter_count) + logger_func(f"{prefix}.bnm_value_avg", metrics[f"bnm_value_avg"], train_iter_count) + if not (metrics_target is None): + logger_func(f"trg_{prefix}.OA", metrics_target[f"{prefix2}_oa"], train_iter_count) + logger_func(f"trg_{prefix}.AA", metrics_target[f"{prefix2}_aa"], train_iter_count) + logger_func(f"trg_{prefix}.IoU", metrics_target[f"{prefix2}_iou"], train_iter_count) + logger_func(f"trg_{prefix}.loss", metrics_target[f"{prefix2}_aloss"], train_iter_count) + logger_func(f"trg_{prefix}.lossRecons", metrics_target[f"{prefix2}_aloss_recons"], train_iter_count) + logger_func(f"trg_{prefix}.lossAdditional", metrics_target[f"{prefix2}_aloss_additional"], train_iter_count) + if trg_mIoU: + logger_func(f"trg_{prefix}.seg_MAA", metrics_target[f"{prefix2}_seg_head_maa"], train_iter_count) + logger_func(f"trg_{prefix}.seg_mIoU", metrics_target[f"{prefix2}_seg_head_miou"], train_iter_count) + logger_func(f"trg_{prefix}.seg_loss", metrics_target[f"{prefix2}_seg_head_loss"], train_iter_count) + + + +def writer_logger_inside_seg(prefix, writer, metrics, metrics_target, train_iter_count): + writer.add_scalar(f'{prefix}.inside_seg_iou', metrics["seg_inside_iou"], train_iter_count) + writer.add_scalar(f'{prefix}.inside_seg_acc', metrics["seg_inside_acc"], train_iter_count) + writer.add_scalar(f'trg_{prefix}.inside_seg_iou', metrics_target["seg_inside_iou"], train_iter_count) + writer.add_scalar(f'trg_{prefix}.inside_seg_acc', metrics_target["seg_inside_acc"], train_iter_count) + + + +def default_logger_per_class(prefix, logger_func, val_data, train_iter_count, config): + if config["source_dataset_name"] == "SynLidar" and config['target_dataset_name'] == "SemanticKITTI": + names_list = ["ignore","car","bicycle","motorcycle","truck","other_vehicle","pedestrian","bicyclist",\ + "motorcyclist","road","parking","sidewalk","other_ground","building","fence","vegetation","trunk","terrain","pole","traffic_sign"] + + for idx, class_name in enumerate(names_list): + logger_func(f"{prefix}_{class_name}", val_data["seg_iou_per_class"][idx],train_iter_count) + logger_func(f"{prefix}_acc_{class_name}", val_data["accuracy_per_class"][idx],train_iter_count) + + else: + names_list=["ignore", "car", "bicycle", "motorcycle", "truck", "other_vehicle", "pedestrian", "driveable_surface", "sidewalk", "terrain", "vegetation"] + for idx, class_name in enumerate(names_list): + logger_func(f"{prefix}_{class_name}", val_data["seg_iou_per_class"][idx],train_iter_count) + logger_func(f"{prefix}_acc_{class_name}", val_data["accuracy_per_class"][idx],train_iter_count) + + +def run_logger_per_class(prefix, _run, val_data, train_iter_count, config): + if config["source_dataset_name"] == "SynLidar": + names_list = ["ignore", "car", "bicycle", "motorcycle","truck", "other_vehicle", "pedestrian", "bicyclist", "motorcyclist",\ + "road", "parking", "sidewalk", "other_ground", "building", "fence", "vegetation", "trunk", "terrain", "pole", "traffic_sign"] + for idx, class_name in enumerate(names_list): + _run.log_scalar(f"{prefix}_{class_name}", val_data["seg_iou_per_class"][idx],train_iter_count) + _run.log_scalar(f"{prefix}_acc_{class_name}", val_data["accuracy_per_class"][idx],train_iter_count) + else: + names_list = ["ignore", "car", "bicycle", "motorcycle", "truck", "other_vehicle", "pedestrian",\ + "driveable_surface", "sidewalk", "terrain", "vegetation"] + for idx, class_name in enumerate(names_list): + _run.log_scalar(f"{prefix}_{class_name}", val_data["seg_iou_per_class"][idx],train_iter_count) + _run.log_scalar(f"{prefix}_acc_{class_name}", val_data["accuracy_per_class"][idx],train_iter_count) + + +def synlidar_configs(val_log_data, val_data, prefix="", short_prefix='val'): + names_list = ["ignore", "car", "bicycle", "motorcycle","truck", "other_vehicle", "pedestrian", "bicyclist", "motorcyclist",\ + "road", "parking", "sidewalk", "other_ground", "building", "fence", "vegetation", "trunk", "terrain", "pole", "traffic_sign"] + for idx, class_name in enumerate(names_list): + val_log_data[prefix+f"{short_prefix}_{class_name}"]= val_data["seg_iou_per_class"][idx] + val_log_data[prefix+f"{short_prefix}_acc_{class_name}"]=val_data["accuracy_per_class"][idx] + return val_log_data + +def sk_ns_da_configs(val_log_data, val_data, prefix="", short_prefix='val'): + names_list=["ignore", "car", "bicycle", "motorcycle", "truck", "other_vehicle", "pedestrian", "driveable_surface", "sidewalk", "terrain", "vegetation"] + for idx, class_name in enumerate(names_list): + val_log_data[prefix+f"{short_prefix}_{class_name}"]= val_data["seg_iou_per_class"][idx] + val_log_data[prefix+f"{short_prefix}_acc_{class_name}"]=val_data["accuracy_per_class"][idx] + return val_log_data + + +def val_log_data_da(val_data_src, val_data_trg, train_iter_count, _run, writer, config, long_prefix="validation", short_prefix="val"): + #For the DA case with 2 datasets (source and target) + val_log_data = { + f"{long_prefix}.OA": val_data_src["test_oa"], + f"{long_prefix}.AA": val_data_src["test_aa"], + f"{long_prefix}.IoU": val_data_src["test_iou"], + f"{long_prefix}.loss": val_data_src["test_aloss"], + f"{long_prefix}.lossRecons": val_data_src["test_aloss_recons"], + f"{long_prefix}.lossAdditional": val_data_src["test_aloss_additional"], + f"{long_prefix}.seg_MAA":val_data_src["test_seg_head_maa"], + f"{long_prefix}.seg_mIoU":val_data_src["test_seg_head_miou"], + f"{long_prefix}.seg_loss":val_data_src["test_seg_head_loss"], + f"{long_prefix}.entropy_mean_avg":val_data_src["entropy_mean_avg"], + f"{long_prefix}.information_maximization_avg":val_data_src["information_maximization_avg"], + f"{long_prefix}.bnm_value_avg":val_data_src["bnm_value_avg"], + f"trg_{long_prefix}.OA": val_data_trg["test_oa"], + f"trg_{long_prefix}.AA": val_data_trg["test_aa"], + f"trg_{long_prefix}.IoU": val_data_trg["test_iou"], + f"trg_{long_prefix}.loss": val_data_trg["test_aloss"], + f"trg_{long_prefix}.lossRecons": val_data_trg["test_aloss_recons"], + f"trg_{long_prefix}.lossAdditional": val_data_trg["test_aloss_additional"], + f"trg_{long_prefix}.seg_MAA":val_data_trg["test_seg_head_maa"], + f"trg_{long_prefix}.seg_mIoU":val_data_trg["test_seg_head_miou"], + f"trg_{long_prefix}.seg_loss":val_data_trg["test_seg_head_loss"], + f"trg_{long_prefix}.entropy_mean_avg":val_data_trg["entropy_mean_avg"], + f"trg_{long_prefix}.information_maximization_avg":val_data_trg["information_maximization_avg"], + f"trg_{long_prefix}.bnm_value_avg":val_data_trg["bnm_value_avg"] + } + + if config["source_dataset_name"] == "SynLidar" and config['target_dataset_name'] == "SemanticKITTI": + val_log_data=synlidar_configs(val_log_data, val_data_src, short_prefix=short_prefix) + val_log_data=synlidar_configs(val_log_data, val_data_trg, prefix="trg_", short_prefix=short_prefix) + else: + val_log_data=sk_ns_da_configs(val_log_data, val_data_src, prefix="", short_prefix=short_prefix) + val_log_data=sk_ns_da_configs(val_log_data, val_data_trg, prefix="trg_", short_prefix=short_prefix) + + default_logger_basic(long_prefix, "test", _run.log_scalar, val_data_src, val_data_trg, train_iter_count) + default_logger_per_class(short_prefix, _run.log_scalar, val_data_src, train_iter_count, config=config) + default_logger_per_class(f"trg_{short_prefix}", _run.log_scalar, val_data_trg, train_iter_count, config=config) + + #Tensorboard logger + default_logger_basic(long_prefix, "test", writer.add_scalar, val_data_src, val_data_trg, train_iter_count, trg_mIoU=True) + default_logger_per_class(short_prefix, writer.add_scalar, val_data_src, train_iter_count, config=config) + default_logger_per_class(f"trg_{short_prefix}", writer.add_scalar, val_data_trg, train_iter_count, config=config) + return val_log_data + + +def train_log_data_da(metrics, metrics_target, train_iter_count, _run, writer, config): + + if metrics is None: + train_log_data = {} + else: + train_log_data = { + "training.OA": metrics["train_oa"], + "training.AA": metrics["train_aa"], + "training.IoU": metrics["train_iou"], + "training.loss": metrics["train_aloss"], + "training.lossRecons": metrics["train_aloss_recons"], + "training.lossAdditional": metrics["train_aloss_additional"], + + } + + if metrics_target is None: + pass + else: + #Performance on target + train_log_data["trg_training.OA"]= metrics_target["train_oa"] + train_log_data["trg_training.AA"]= metrics_target["train_aa"] + train_log_data["trg_training.IoU"]= metrics_target["train_iou"] + train_log_data["trg_training.loss"]= metrics_target["train_aloss"] + train_log_data["trg_training.lossRecons"]= metrics_target["train_aloss_recons"] + train_log_data["trg_training.lossAdditional"]= metrics_target["train_aloss_additional"] + if metrics is None: + ###Make it more flexbile + if not ("train_seg_head_maa" in metrics): + #Add NaN + metrics["train_seg_head_maa"] = np.nan + + train_log_data["training.seg_MAA"] = metrics["train_seg_head_maa"] + + if not ("train_seg_head_miou" in metrics): + metrics["train_seg_head_miou"] = np.nan + + train_log_data["training.seg_mIoU"] = metrics["train_seg_head_miou"] + + if not ("train_seg_head_loss" in metrics): + metrics["train_seg_head_loss"]=np.nan + + train_log_data["training.seg_loss"] =metrics["train_seg_head_loss"] + + if config["source_dataset_name"] == "SynLidar" and config["source_dataset_name"] =="SemanticKITTI": + train_log_data=synlidar_configs(train_log_data, metrics) + else: + train_log_data=sk_ns_da_configs(train_log_data, metrics, prefix="") + + #Write the basic information for sacred and tensorboard + default_logger_basic("training", "train", _run.log_scalar, metrics, metrics_target, train_iter_count) + default_logger_basic("training", "train", writer.add_scalar, metrics, metrics_target, train_iter_count) + + return train_log_data + + +def logs_sacred(_run, epoch, log_data): + for key,value in log_data.items(): + _run.log_scalar(key, value, epoch) + +def logs_file(filepath, epoch, log_data): + + + if not os.path.exists(filepath): + log_str = f"epoch" + for key, value in log_data.items(): + log_str += f", {key}" + log_str += "\n" + with open(filepath, "a+") as logs: + logs.write(log_str) + logs.flush() + + # write the logs + log_str = f"{epoch}" + for key, value in log_data.items(): + log_str += f", {value}" + log_str += "\n" + with open(filepath, "a+") as logs: + logs.write(log_str) + logs.flush() \ No newline at end of file diff --git a/utils/metrics.py b/utils/metrics.py new file mode 100644 index 0000000..c7a5758 --- /dev/null +++ b/utils/metrics.py @@ -0,0 +1,125 @@ +import numpy as np + + +def ignore_cm_adaption(cm, ignore_list): + for idx in ignore_list: + #set the row and the column of the idx to 0 + cm[idx,:] = 0 + cm[:,idx] = 0 + return cm + +def stats_overall_accuracy(cm, ignore_list=[]): + """Computes the overall accuracy. + + # Arguments: + cm: 2-D numpy array. + Confusion matrix. + """ + if len(ignore_list) > 0: + #Set to 0 the rows and columns of the ignored class + cm = ignore_cm_adaption(cm, ignore_list) + return np.trace(cm) / cm.sum() + + +def stats_pfa_per_class(cm,ignore_list=[]): + """Computes the probability of false alarms. + + # Arguments: + cm: 2-D numpy array. + Confusion matrix. + """ + if len(ignore_list) > 0: + #Set to 0 the rows and columns of the ignored class + cm = ignore_cm_adaption(cm, ignore_list) + sums = np.sum(cm, axis=0) + mask = sums > 0 + sums[sums == 0] = 1 + pfa_per_class = (cm.sum(axis=0) - np.diag(cm)) / sums + pfa_per_class[np.logical_not(mask)] = -1 + average_pfa = pfa_per_class[mask].mean() + return average_pfa, pfa_per_class + + +def stats_accuracy_per_class(cm, ignore_list=[]): + """Computes the accuracy per class and average accuracy. + + # Arguments: + cm: 2-D numpy array. + Confusion matrix. + ignore_list: list of classes that are ignored in calculation + + # Returns + average_accuracy: float. + The average accuracy. + accuracy_per_class: 1-D numpy array. + The accuracy per class. + """ + if len(ignore_list) > 0: + #Set to 0 the rows and columns of the ignored class + cm = ignore_cm_adaption(cm, ignore_list) + sums = np.sum(cm, axis=1) + mask = sums > 0 + sums[sums == 0] = 1 + accuracy_per_class = np.diag(cm) / sums # sum over lines + accuracy_per_class[np.logical_not(mask)] = -1 + average_accuracy = accuracy_per_class[mask].mean() + return average_accuracy, accuracy_per_class + + +def stats_iou_per_class(cm, ignore_list=[] ): + """Computes the IoU per class and average IoU. + + # Arguments: + cm: 2-D numpy array. + Confusion matrix. + + # Returns + average_accuracy: float. + The average IoU. + accuracy_per_class: 1-D numpy array. + The IoU per class. + """ + if len(ignore_list) > 0: + #Set to 0 the rows and columns of the ignored class + cm = ignore_cm_adaption(cm, ignore_list) + + # compute TP, FN et FP + TP = np.diagonal(cm, axis1=-2, axis2=-1) + TP_plus_FN = np.sum(cm, axis=-1) + TP_plus_FP = np.sum(cm, axis=-2) + + # compute IoU + mask = TP_plus_FN == 0 + IoU = TP / (TP_plus_FN + TP_plus_FP - TP + mask) + + # replace IoU with 0 by the average IoU + aIoU = IoU[np.logical_not(mask)].mean(axis=-1, keepdims=True) + IoU += mask * aIoU + + return IoU.mean(axis=-1), IoU + + +def stats_f1score_per_class(cm, ignore_list=[]): + """Computes the F1 per class and average F1. + + # Arguments: + cm: 2-D numpy array. + Confusion matrix. + + # Returns + average_accuracy: float. + The average F1. + accuracy_per_class: 1-D numpy array. + The F1 per class. + """ + if len(ignore_list) > 0: + #Set to 0 the rows and columns of the ignored class + cm = ignore_cm_adaption(cm, ignore_list) + # defined as 2 * recall * prec / recall + prec + sums = np.sum(cm, axis=1) + np.sum(cm, axis=0) + mask = sums > 0 + sums[sums == 0] = 1 + f1score_per_class = 2 * np.diag(cm) / sums + f1score_per_class[np.logical_not(mask)] = -1 + average_f1_score = f1score_per_class[mask].mean() + return average_f1_score, f1score_per_class diff --git a/utils/shared_funcs.py b/utils/shared_funcs.py new file mode 100755 index 0000000..23d3c2f --- /dev/null +++ b/utils/shared_funcs.py @@ -0,0 +1,304 @@ +#Shared functions between the different networks +import logging +import os +from functools import partial + + +import numpy as np +import torch +import yaml + +import networks +from utils.collate_function import collate_function +from utils.logging_files_functions import (logs_file, train_log_data_da, + val_log_data_da) +from utils.transforms import da_get_inputs, da_get_transforms +from utils.utils import validation + + +def da_get_dataloader(source_DatasetClass, target_DatasetClass, config, net, val=1, train_shuffle=True, cat_list_changed=[]): + source_train_transforms = da_get_transforms(config, train=True, source_flag=True) + source_test_transforms = da_get_transforms(config, train=False, source_flag=True) + target_train_transforms = da_get_transforms(config, train=True, source_flag=False) + target_test_transforms = da_get_transforms(config, train=False, source_flag=False) + + # build the Train datasets for target and source + source_train_dataset=source_DatasetClass(config["source_dataset_root"], + split=config["train_split"], transform=source_train_transforms, + da_flag=True, config=config) + target_train_dataset=target_DatasetClass(config["target_dataset_root"], + split=config["train_split"], transform=target_train_transforms, + da_flag=True, config=config) + + # build the test datasets for target and source + if val == 1: + target_test_dataset = target_DatasetClass(config["target_dataset_root"], + split=config["val_split"], transform=target_test_transforms, + da_flag = True,config=config) + + source_test_dataset = source_DatasetClass(config["source_dataset_root"], + split=config["val_split"], transform=source_test_transforms, + da_flag = True, config=config) + elif val == 2: + target_test_dataset = target_DatasetClass(config["target_dataset_root"], + split=config["train_split"], transform=target_test_transforms, + da_flag = True,config=config) + + source_test_dataset = source_DatasetClass(config["source_dataset_root"], + split=config["train_split"], + transform=source_test_transforms, + da_flag=True, config=config) + else: + # Loading the test datasets + print(" !!!!!!!!!!!!!!!!!Taking the test split !!!!!!!!!!!!!!!!!!!!!!!") + source_test_dataset=source_DatasetClass(config["source_dataset_root"], + split=config["test_split"], transform=source_test_transforms, + da_flag=True, config=config) + target_test_dataset=target_DatasetClass(config["target_dataset_root"], + split=config["test_split"], transform=target_test_transforms, + da_flag=True, config=config) + + # create the collate function + if len(cat_list_changed) > 0: + cat_item_list = cat_list_changed + net.get_cat_item_list() + else: + cat_item_list = ["pos", "x", "y", "dirs", "pos_non_manifold", "occupancies"] + net.get_cat_item_list() + + stack_item_list = [] + net.get_stack_item_list() + sparse_item_list = ["sparse_input"] + + collate_fn = partial(collate_function, + cat_item_list=cat_item_list, + stack_item_list=stack_item_list, + sparse_item_list=sparse_item_list + ) + + # build the data loaders for SOURCE + source_train_loader = torch.utils.data.DataLoader( + source_train_dataset, batch_size=config["training_batch_size"], + shuffle=train_shuffle, pin_memory=True, + num_workers=config["threads"], collate_fn=collate_fn, + ) + + source_test_loader = torch.utils.data.DataLoader( + source_test_dataset, batch_size=config["test_batch_size"], + shuffle=False, num_workers=config["threads"], + pin_memory=True, collate_fn=collate_fn, + ) + + # build the data loaders for TARGET + target_train_loader = torch.utils.data.DataLoader( + target_train_dataset, + batch_size=config["training_batch_size"], + shuffle=train_shuffle, + pin_memory=True, + num_workers=config["threads"], + collate_fn=collate_fn, + ) + + target_test_loader = torch.utils.data.DataLoader( + target_test_dataset, + batch_size=config["test_batch_size"], + shuffle=False, + pin_memory=True, + num_workers=config["threads"], + collate_fn=collate_fn, + ) + return {"source_train_loader": source_train_loader, "source_test_loader": source_test_loader, "target_train_loader": target_train_loader, "target_test_loader": target_test_loader} + + +def optimizer_selection(logging, config, net, net_parameters=None): + if net_parameters is None: + # Train the complete network, otherwise only the parameters in net_parameters are trained + net_parameters = net.parameters() + if config["optimizer"] == "AdamW": + logging.info(f"Selected optimizer: {config['optimizer']}, LR(start): {config['training_lr_start']}") + optimizer = torch.optim.AdamW(net_parameters,config["training_lr_start"]) + elif config["optimizer"] == "Adam": + betas = (0.9, 0.99) + logging.info(f"Selected optimizer: {config['optimizer']}, LR(start): {config['training_lr_start']}, betas: {betas}") + optimizer = torch.optim.Adam(net_parameters, lr=config["training_lr_start"], betas=betas) + else: + raise NotImplementedError + + return optimizer + + +def learning_rate_scheduler_selection(logging, config, optimizer): + if config['lr_scheduler'] == "cos_an_half_lr": + # Cosine annealing + T_max = config['training_iter_nbr'] + eta_min = 0 + logging.info(f"Cosine annealing LR rate scheduling with only half period T_max: {T_max}, eta_min: {eta_min}") + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=T_max, eta_min=eta_min) + else: + raise NotImplementedError + + return scheduler + + +def validation_process_training(net, config, source_test_loader, target_test_loader, N_LABELS, epoch, disable_log, device, ce_loss_layer, loss_layer, list_ignore_classes, logging): + # Validation on SOURCE + val_data_src = \ + validation(net, config, source_test_loader, N_LABELS, epoch, disable_log, device, ce_loss_layer, loss_layer, target_flag=False, list_ignore_classes=list_ignore_classes) + logging.info("Source mIoU per class: {}".format(val_data_src["seg_iou_per_class"])) + if config["in_seg_loss"]: + logging.info("Source inside mIoU per class: {}".format(val_data_src["seg_inside_iou_per_class"])) + # Validation on TARGET + val_data_trg = \ + validation(net, config, target_test_loader, N_LABELS, epoch, disable_log, device, ce_loss_layer, loss_layer, target_flag=False, list_ignore_classes=list_ignore_classes) + logging.info("Target per class mIoU: {}".format(val_data_trg["seg_iou_per_class"])) + if config["in_seg_loss"]: + logging.info("Target inside mIoU per class: {}".format(val_data_trg["seg_inside_iou_per_class"])) + return val_data_src, val_data_trg +def construct_network(config, logging): + latent_size = config["network_latent_size"] + backbone = config["network_backbone"] + decoder = {'name':config["network_decoder"], 'k': config['network_decoder_k']} + logging.info("Backbone {}".format(backbone)) + logging.info("Decoder {}".format(decoder)) + in_channels_source, _, in_channels_target, _ = da_get_inputs(config) + logging.info("In channels source: {}".format(in_channels_source)) + logging.info("in channels target: {}".format(in_channels_target)) + logging.info("Creating the network:") + def network_function(): + return networks.Network(in_channels_source, latent_size, config["network_n_labels"], backbone, decoder, + voxel_size=config["voxel_size"], + dual_seg_head=config["dual_seg_head"], + nb_classes=config["nb_classes"], + da_flag=False, target_in_channels=in_channels_target, config=config) + net = network_function() + return net, network_function + + +def get_savedir_root(config, experiment_name): + savedir = f"{experiment_name}_{config['network_backbone']}_{config['network_decoder']}_{config['network_decoder_k']}" + savedir += f"_{config['train_split']}Split" + if ("desc" in config) and config["desc"]: + savedir += f"_{config['desc']}" + + savedir_root = os.path.join(config["save_dir"], savedir) + + return savedir_root + + +def get_bbdir_root(config): + # Gives the backbone dir + savedir_root = config["ckpt_path_model"] + return savedir_root + + +def count_parameters(model): + return sum(p.numel() for p in model.parameters() if p.requires_grad) + + +def save_config_file(config, filename): + with open(filename, 'w') as outfile: + yaml.dump(config, outfile, default_flow_style=False) + + +def ignore_selection(idx=-1): + if idx == -1: + return [] + elif idx == 0: + return [0] + elif idx == 1: + # SK original classes (20), mapped to noise of DA + return [0, 7, 8, 12, 13, 14, 18, 19] + elif idx == 2: + # NS original classes (17), mapped to noise of NS + return [0, 1, 8, 12, 15] + else: + return [] + + +class metrics_holder(): + def __init__(self, N_LABELS, config, target_flag): + self.cm = np.zeros((N_LABELS, N_LABELS)) # Used for occupancy + self.cm_occ_seg = np.zeros((config["nb_classes"], config["nb_classes"])) + self.error = 0 + self.error_seg_head = 0 + self.error_recons = 0 + self.error_additional = 0 + self.cm_seg_head = np.zeros((config["nb_classes"], config["nb_classes"])) + self.config = config + self.target = target_flag + self.counter = 0 + self.entropy_average_sum = 0 + self.information_maximation_sum = 0 + + + +def resume_model(net, savedir_root, device, optimizer, scheduler, source_train_loader): + checkpoint = torch.load(os.path.join(savedir_root, "checkpoint.pth"), map_location=device) + net.load_state_dict(checkpoint["state_dict"]) + optimizer.load_state_dict(checkpoint["optimizer"]) + scheduler.load_state_dict(checkpoint["scheduler"]) + epoch_start = checkpoint["epoch"] + train_iter_count = len(source_train_loader) * epoch_start + logging.info(f"Resume on epoch {epoch_start} with iteration count {train_iter_count}") + current_lr = optimizer.param_groups[0]["lr"] + logging.info(f"LR is at {current_lr} ") + try: + best_checkpoint = torch.load(os.path.join(savedir_root, "best_checkpoint.pth")) + except: + logging.info(f"!! No best checkpoint found, go on without it") + best_checkpoint=None + return net, optimizer, scheduler, epoch_start, train_iter_count, current_lr, best_checkpoint + + +def save_val_model(config, data_saver): + metrics = data_saver["metrics"] + metrics_target = data_saver["metrics_target"] + train_iter_count = data_saver["train_iter_count"] + _run = data_saver["_run"] + writer = data_saver["writer"] + epoch = data_saver["epoch"] + net = data_saver["net"] + source_test_loader =data_saver["source_test_loader"] + target_test_loader =data_saver["target_test_loader"] + N_LABELS = data_saver["N_LABELS"] + disable_log =data_saver["disable_log"] + disable_log =data_saver["disable_log"] + ce_loss_layer =data_saver["ce_loss_layer"] + loss_layer =data_saver["loss_layer"] + list_ignore_classes =data_saver["list_ignore_classes"] + list_ignore_classes = data_saver["list_ignore_classes"] + device = data_saver["device"] + optimizer = data_saver["optimizer"] + scheduler = data_saver["scheduler"] + savedir_root = data_saver["savedir_root"] + best_ckpt_mioU_target = data_saver["best_ckpt_mioU_target"] + best_ckpt_epoch = data_saver["best_ckpt_epoch"] + ###################################### + # save the training logs + if not config["fast_rep_flag"]: + train_log_data = train_log_data_da(metrics, metrics_target, train_iter_count, _run, writer, config) + logs_file(os.path.join(savedir_root, "logs_train.csv"), train_iter_count, train_log_data) + + + ###################################### Validation during Training #################################################### + if not config["fast_rep_flag"] or (epoch+1)%5==0 or (epoch+1)%config["val_interval"]==0: + val_data_src, val_data_trg= validation_process_training(net, config, source_test_loader,\ + target_test_loader, N_LABELS, epoch, disable_log, device, ce_loss_layer, loss_layer, list_ignore_classes, logging) + + ###################################### + # save the validation logs + val_log_data = val_log_data_da(val_data_src, val_data_trg, train_iter_count, _run, writer, config=config) + logs_file(os.path.join(savedir_root, "logs_val.csv"), train_iter_count, val_log_data) + + # Save only the ckpts in the val generation duration + if (epoch+1)%config["val_interval"]==0 or (train_iter_count >= config["training_iter_nbr"]): + torch.save({"epoch": epoch + 1, "state_dict": net.state_dict(), "optimizer": optimizer.state_dict(),"scheduler":scheduler.state_dict(),}, + os.path.join(savedir_root, f"checkpoint_{epoch}_epoch.pth"),) + + if val_log_data["trg_validation.seg_mIoU"] > best_ckpt_mioU_target: + best_ckpt_mioU_target = val_log_data["trg_validation.seg_mIoU"] + best_ckpt_epoch = epoch + logging.info("New best target mIou: {}".format(best_ckpt_mioU_target)) + torch.save({"epoch": epoch + 1, "state_dict": net.state_dict(),"optimizer": optimizer.state_dict(),"scheduler":scheduler.state_dict(), "best_mIoU":best_ckpt_mioU_target}, + os.path.join(savedir_root, f"best_checkpoint.pth"),) + else: + logging.info("No new best target mIou, best remains: {} from epoch {}".format(best_ckpt_mioU_target, best_ckpt_epoch)) + + return best_ckpt_mioU_target, best_ckpt_epoch \ No newline at end of file diff --git a/utils/transforms.py b/utils/transforms.py new file mode 100644 index 0000000..8ed3bcc --- /dev/null +++ b/utils/transforms.py @@ -0,0 +1,113 @@ +import importlib +import logging +import math +import numbers +import random +import re +import warnings + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch_geometric.transforms as T +import torch_sparse + + +from .all_transforms import * + +torchsparse_found = importlib.util.find_spec("torchsparse") is not None +logging.info(f"Torchsparse found - {torchsparse_found}") +if torchsparse_found: + from torchsparse import SparseTensor + from torchsparse.utils.quantize import sparse_quantize + +me_found = importlib.util.find_spec("MinkowskiEngine") is not None +logging.info(f"TorchSparse found - {torchsparse_found}") +warnings.filterwarnings("ignore", message="__floordiv__ is deprecated, and its behavior will change in a future version of pytorch") +logging.info("Filtering in TorchSparseQuantize the warning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch") + + + +def da_get_inputs(config): + # compute the input size: + in_channels_source = 0 + inputs_source = [] + in_channels_target = 0 + inputs_target = [] + if config["source_input_intensities"]: + inputs_source.append("intensities") + in_channels_source += 1 + if config["source_input_dirs"]: + in_channels_source += 3 + inputs_source.append("dirs") + if in_channels_source == 0: + in_channels_source += 1 + inputs_source.append("x") + if config["target_input_intensities"]: + inputs_target.append("intensities") + in_channels_target += 1 + if config["target_input_dirs"]: + in_channels_target += 3 + inputs_target.append("dirs") + if in_channels_target == 0: + in_channels_target += 1 + inputs_target.append("x") + + return in_channels_source, inputs_source, in_channels_target, inputs_target + +def da_get_transforms(config, train=True, downstream=False, keep_orignal_data=False, source_flag=True): + logging.info(f"Transforms - Train {train} - Downstream {downstream}") + _, inputs_source, _, inputs_target = da_get_inputs(config) + if source_flag: + # If it is SOURCE + inputs = inputs_source + else: + # If it is TARGET + inputs = inputs_target + + transforms = [] + + if keep_orignal_data: + logging.info("Transforms - Dupplicate - pos, y") + transforms.append(Dupplicate(["pos", "y"], "original_")) + + logging.info(f"Transforms - No voxel decimation") + + # create directions to the sensors + logging.info("Transforms - CreateDirs") + transforms.append(CreateDirs()) + + if train and not config['no_augmentation']: + if ("randRotationZ" in config) and config["randRotationZ"]: + item_list = ["pos", "dirs"] + if "mf_flag" in config and config["mf_flag"]: + item_list = ["pos", "dirs", "p_merged"] + logging.info(f"Transforms - Rotation on: {item_list}") + transforms.append(RandomRotate(180, axis=2, item_list=item_list)) + + if ("randFlip" in config) and config["randFlip"]: + item_list = ["pos", "dirs"] + if "mf_flag" in config and config["mf_flag"]: + item_list = ["pos", "dirs", "p_merged"] + logging.info("Transforms - Flip on {}".format(item_list)) + transforms.append(RandomFlip(item_list)) + + logging.info("Transforms - CreateInputs") + transforms.append(CreateInputs(inputs)) + + logging.info("Transforms - Non manifold points") + transforms += [CreateNonManifoldPoints(), + FixedPoints(config["non_manifold_points"], item_list=["pos_non_manifold", "occupancies", "intensities_non_manifold", "label_non_manifold"]), + ] + + logging.info("Take all points as inputs") + + if "TorchSparse" in config["network_backbone"]: + logging.info("Transforms - TorchSparseQuantize") + transforms.append(TorchSparseQuantize(config["voxel_size"])) + + transforms.append(ToDict()) + transforms = T.Compose(transforms) + + return transforms \ No newline at end of file diff --git a/utils/utils.py b/utils/utils.py new file mode 100644 index 0000000..d72b913 --- /dev/null +++ b/utils/utils.py @@ -0,0 +1,210 @@ +import os + +import numpy as np +import scipy +import torch +from scipy.stats import entropy +from sklearn.metrics import confusion_matrix +from tqdm import tqdm + +import utils.metrics as metrics +#from lightconvpoint.utils.misc import dict_to_device +from torchsparse import SparseTensor + +class bcolors: + HEADER = '\033[95m' + OKBLUE = '\033[94m' + OKGREEN = '\033[92m' + WARNING = '\033[93m' + FAIL = '\033[91m' + ENDC = '\033[0m' + BOLD = '\033[1m' + UNDERLINE = '\033[4m' + + +# wrap blue / green +def wblue(str): + return bcolors.OKBLUE+str+bcolors.ENDC + + +def wgreen(str): + return bcolors.OKGREEN+str+bcolors.ENDC + + +def wred(str): + return bcolors.FAIL+str+bcolors.ENDC + +def dict_to_device(data, device): + for key, value in data.items(): + if torch.is_tensor(value): + data[key] = value.to(device) + elif isinstance(value, list): + data[key] = list_to_device(value, device) + elif isinstance(value, dict): + data[key] = dict_to_device(value, device) + elif isinstance(value, SparseTensor): + data[key] = data[key].to(device) + return data + + +def get_savedir_root(config, experiment_name): + savedir = f"{experiment_name}_{config['network_decoder']}_{config['network_decoder_k']}" + savedir += f"_{config['train_split']}Split" + if ("desc" in config) and config["desc"]: + savedir += f"_{config['desc']}" + print(savedir) + savedir_root = os.path.join(config["save_dir"], savedir) + + return savedir_root + + +def get_bbdir_root(config): + # Gives the backbone dir + savedir_root = config["ckpt_path_model"] + return savedir_root + + +def validation(net, config, test_loader, N_LABELS, epoch, disable_log, device, ce_loss_layer, loss_layer, target_flag=False, list_ignore_classes=[]): + net.eval() + error = 0 + error_recons = 0 + error_additional = 0 + error_seg_head = 0 + bnm_value_sum = torch.zeros((1)) + entropy_mean_sum = 0 + information_maximization_sum = 0 + torch_softmax = torch.nn.Softmax(dim=1) + cm = np.zeros((N_LABELS, N_LABELS)) + cm_seg_head = np.zeros((config["nb_classes"],config["nb_classes"])) + list_data_source = [] + list_data_target = [] + with torch.no_grad(): + count_iter = 0 + t = tqdm( + test_loader, + desc=" Test " + str(epoch), + ncols=200, + disable=disable_log, + ) + for data in t: + + data = dict_to_device(data, device) + if config["dual_seg_head"]: + if target_flag: + if config["get_latent"]: + output_data, output_seg, data = net.forward_test_target(data, get_latent=config["get_latent"]) # TARGET data + latents = data["latents"].detach().cpu().numpy() + label = data["y"].detach().cpu().numpy() + pos = data["pos"].detach().cpu().numpy() + if count_iter % 5 == 0: + list_data_target.append(list([pos, label, latents])) + else: + output_data, output_seg = net.forward_test_target(data, get_latent=config["get_latent"]) # TARGET data + else: + if config["get_latent"]: + output_data, output_seg, data = net.forward_pretraining(data, get_latent=config["get_latent"]) # SOURCE data + latents = data["latents"].detach().cpu().numpy() + label = data["y"].detach().cpu().numpy() + pos = data["pos"].detach().cpu().numpy() + if count_iter % 5 == 0: + list_data_source.append(list([pos, label, latents])) + else: + output_data, output_seg = net.forward_pretraining(data, get_latent=config["get_latent"]) # SOURCE data + + if config["network_backbone"] == "MinkEng_Res16UNet34C": + loss_seg = ce_loss_layer(output_seg, data["y"]) + else: + loss_seg = ce_loss_layer(output_seg, data["y"][:, None]) + + else: + output_data = net.forward_pretraining(data) + outputs = output_data["predictions"].squeeze(-1) + occupancies = output_data["occupancies"].float() + recons_loss = loss_layer(outputs, occupancies) + loss = recons_loss + if "loss" in output_data: + additionnal_loss = output_data["loss"] + loss = loss + additionnal_loss + else: + additionnal_loss = torch.zeros((1,)) + + output_np = (torch.sigmoid(outputs).cpu().detach().numpy() > 0.5).astype(int) + target_np = occupancies.cpu().numpy().astype(int) + cm_ = confusion_matrix( + target_np.ravel(), output_np.ravel(), labels=list(range(N_LABELS)) + ) + cm += cm_ + + error += loss.item() + error_additional += additionnal_loss.item() + error_recons += recons_loss.item() + + # point-wise scores on testing + test_oa = metrics.stats_overall_accuracy(cm) + test_aa, _ = metrics.stats_accuracy_per_class(cm) + test_iou, iou_per_class = metrics.stats_iou_per_class(cm) + test_aloss = error / cm.sum() + test_aloss_recons = error_recons / cm.sum() + test_aloss_additional = error_additional / cm.sum() + + # Batch Norm calculation + bnm_value_sum = bnm_value_sum + torch.norm(torch_softmax(output_seg[:, :, 0]),p='nuc').detach().cpu()/(output_seg.size()[0]) + + + # Entropy and Information maximization calculation + softmaxed_value = scipy.special.softmax(output_seg.cpu().detach().numpy(), axis=1) + entropy_calculated = entropy(softmaxed_value, axis=1) + entropy_mean = np.average(entropy_calculated, axis=0) + entropy_on_average_prob = entropy(np.average(softmaxed_value, axis=0)) + information_maximation = entropy_on_average_prob - entropy_mean + + entropy_mean_sum = entropy_mean_sum + entropy_mean + information_maximization_sum = information_maximization_sum + information_maximation + + if config["dual_seg_head"]: + output_seg_np = np.argmax(output_seg[:,1:].cpu().detach().numpy(), axis=1) + 1 # As 0 is ignored, only looked in the non 0 part + target_seg_np = data["y"].cpu().numpy().astype(int) + cm_seg_head_ = confusion_matrix(target_seg_np.ravel(), output_seg_np.ravel(), labels=list(range(config["nb_classes"]))) + cm_seg_head += cm_seg_head_ + if config["network_backbone"] == "MinkEng_Res16UNet34C": + error_seg_head += loss_seg.item() + else: + error_seg_head += loss_seg.item() + # point wise scores on training segmentation head + test_seg_head_maa, accuracy_per_class = metrics.stats_accuracy_per_class(cm_seg_head, ignore_list=list_ignore_classes) + test_seg_head_miou, seg_iou_per_class = metrics.stats_iou_per_class(cm_seg_head, ignore_list=list_ignore_classes) + test_seg_head_loss = error_seg_head / cm_seg_head.sum() + else: + test_seg_head_maa = 0 + test_seg_head_miou = 0 + test_seg_head_loss = 0 + + count_iter += 1 + if count_iter % 10 == 0: + torch.cuda.empty_cache() + + if config["dual_seg_head"]: + description = f"Epoch {epoch}|| TARGET {target_flag}|| Rec-IoU {test_iou*100:.2f} | Rec-Loss {test_aloss:.3e} | LossR {test_aloss_recons:.3e} | LossA {test_aloss_additional:.3e} | Seg-IoU {test_seg_head_miou*100:.2f}| Seg-AA {test_seg_head_maa*100:.2f} | Seg-Loss {test_seg_head_loss:.3e}" + + else: + description = f"Epoch {epoch}|| TARGET {target_flag}|| Rec-IoU {test_iou*100:.2f} | Rec-Loss {test_aloss:.3e} | LossR {test_aloss_recons:.3e} | LossA {test_aloss_additional:.3e} |" + t.set_description_str(wgreen(description)) + + entropy_mean_avg = entropy_mean_sum / count_iter + information_maximization_avg = information_maximization_sum / count_iter + bnm_value_avg = bnm_value_sum.numpy() / count_iter + print("Confusion matrix") + np.set_printoptions(threshold=np.inf) + print(cm_seg_head) + np.set_printoptions(threshold=1000) + lists_latents = (list_data_source, list_data_target) + if config["dual_seg_head"]: + return_data = {"test_oa": test_oa, "test_aa": test_aa, "test_iou": test_iou, "test_aloss": test_aloss, "test_aloss_recons": test_aloss_recons, + "test_aloss_additional": test_aloss_additional, "test_seg_head_miou": test_seg_head_miou, "test_seg_head_maa": test_seg_head_maa, + "test_seg_head_loss": test_seg_head_loss, "seg_iou_per_class": seg_iou_per_class, "accuracy_per_class": accuracy_per_class, + "lists_latents": lists_latents, "cm_seg_head": cm_seg_head, "entropy_mean_avg": entropy_mean_avg[0], + "information_maximization_avg": information_maximization_avg[0], "bnm_value_avg": bnm_value_avg[0]} + return return_data + + else: + return test_oa, test_aa, test_iou, test_aloss, test_aloss_recons, test_aloss_additional, lists_latents

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