eval running - offline logger implemented -> Test it!
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Si11ium
parent
ba7c0280ae
commit
8d0577b756
@ -11,7 +11,7 @@ main_arg_parser = ArgumentParser(description="parser for fast-neural-style")
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# Main Parameters
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main_arg_parser.add_argument("--main_debug", type=strtobool, default=False, help="")
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main_arg_parser.add_argument("--main_eval", type=strtobool, default=True, help="")
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main_arg_parser.add_argument("--main_eval", type=strtobool, default=False, help="")
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main_arg_parser.add_argument("--main_seed", type=int, default=69, help="")
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# Project
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@ -21,7 +21,6 @@ main_arg_parser.add_argument("--project_neptune_key", type=str, default=os.geten
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# Data Parameters
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main_arg_parser.add_argument("--data_worker", type=int, default=10, help="")
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main_arg_parser.add_argument("--data_dataset_length", type=int, default=10000, help="")
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main_arg_parser.add_argument("--data_root", type=str, default='data', help="")
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main_arg_parser.add_argument("--data_use_preprocessed", type=strtobool, default=True, help="")
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@ -29,25 +28,28 @@ main_arg_parser.add_argument("--data_use_preprocessed", type=strtobool, default=
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# main_arg_parser.add_argument("--transformations_to_tensor", type=strtobool, default=False, help="")
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# main_arg_parser.add_argument("--transformations_normalize", type=strtobool, default=False, help="")
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# Transformations
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# Training
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main_arg_parser.add_argument("--train_outpath", type=str, default="output", help="")
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main_arg_parser.add_argument("--train_version", type=strtobool, required=False, help="")
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main_arg_parser.add_argument("--train_epochs", type=int, default=500, help="")
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main_arg_parser.add_argument("--train_batch_size", type=int, default=200, help="")
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main_arg_parser.add_argument("--train_batch_size", type=int, default=10, help="")
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main_arg_parser.add_argument("--train_lr", type=float, default=1e-3, help="")
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main_arg_parser.add_argument("--train_weight_decay", type=float, default=1e-8, help="")
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main_arg_parser.add_argument("--train_num_sanity_val_steps", type=int, default=0, help="")
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main_arg_parser.add_argument("--train_sto_weight_avg", type=strtobool, default=False, help="")
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main_arg_parser.add_argument("--train_opt_reset_interval", type=strtobool, default=False, help="")
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# Model
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main_arg_parser.add_argument("--model_type", type=str, default="PN2", help="")
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main_arg_parser.add_argument("--model_norm_as_feature", type=strtobool, default=True, help="")
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main_arg_parser.add_argument("--model_activation", type=str, default="leaky_relu", help="")
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main_arg_parser.add_argument("--model_use_bias", type=strtobool, default=True, help="")
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main_arg_parser.add_argument("--model_use_norm", type=strtobool, default=False, help="")
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main_arg_parser.add_argument("--model_use_norm", type=strtobool, default=True, help="")
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main_arg_parser.add_argument("--model_dropout", type=float, default=0.00, help="")
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# Model 2: Layer Specific Stuff
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main_arg_parser.add_argument("--model_filters", type=str, default="[16, 32, 64]", help="")
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main_arg_parser.add_argument("--model_features", type=int, default=16, help="")
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# Parse it
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@ -5,7 +5,7 @@ from abc import ABC
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from pathlib import Path
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from torch.utils.data import Dataset
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from ml_lib.point_toolset.sampling import FarthestpointSampling
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from ml_lib.point_toolset.sampling import FarthestpointSampling, RandomSampling
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import numpy as np
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@ -22,16 +22,21 @@ class _Point_Dataset(ABC, Dataset):
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raise NotImplementedError
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headers = ['x', 'y', 'z', 'xn', 'yn', 'zn', 'label', 'cl_idx']
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samplers = dict(fps=FarthestpointSampling, rnd=RandomSampling)
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def __init__(self, root=Path('data'), sampling_k=2048, transforms=None, load_preprocessed=True, *args, **kwargs):
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def __init__(self, root=Path('data'), norm_as_feature=True, sampling_k=2048, sampling='rnd',
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transforms=None, load_preprocessed=True, split='train', dense_output=False, *args, **kwargs):
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super(_Point_Dataset, self).__init__()
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self.dense_output = dense_output
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self.split = split
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self.norm_as_feature = norm_as_feature
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self.load_preprocessed = load_preprocessed
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self.transforms = transforms if transforms else lambda x: x
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self.sampling_k = sampling_k
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self.sampling = FarthestpointSampling(K=self.sampling_k)
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self.sampling = self.samplers[sampling](K=self.sampling_k)
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self.root = Path(root)
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self.raw = self.root / 'raw'
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self.raw = self.root / 'raw' / self.split
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self.processed_ext = '.pik'
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self.raw_ext = '.xyz'
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self.processed = self.root / self.setting
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@ -9,6 +9,7 @@ from ._point_dataset import _Point_Dataset
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class FullCloudsDataset(_Point_Dataset):
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setting = 'pc'
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split: str
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def __init__(self, *args, **kwargs):
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super(FullCloudsDataset, self).__init__(*args, **kwargs)
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@ -21,13 +22,15 @@ class FullCloudsDataset(_Point_Dataset):
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with processed_file_path.open('rb') as processed_file:
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pointcloud = pickle.load(processed_file)
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points = np.stack((pointcloud['x'], pointcloud['y'], pointcloud['z'],
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pointcloud['xn'], pointcloud['yn'], pointcloud['zn']
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),
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axis=-1)
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# When yopu want to return points and normal seperately
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# normal = np.stack((pointcloud['xn'], pointcloud['yn'], pointcloud['zn']), axis=-1)
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label = pointcloud['label']
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sample_idxs = self.sampling(points)
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return points[sample_idxs].astype(np.float), label[sample_idxs].astype(np.int)
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position = np.stack((pointcloud['x'], pointcloud['y'], pointcloud['z']), axis=-1)
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normal = np.stack((pointcloud['xn'], pointcloud['yn'], pointcloud['zn']), axis=-1)
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label = pointcloud['label']
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sample_idxs = self.sampling(position)
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return (normal[sample_idxs].astype(np.float),
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position[sample_idxs].astype(np.float),
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label[sample_idxs].astype(np.int))
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@ -26,7 +26,7 @@ class FullCloudsDataset(_Point_Dataset):
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# When yopu want to return points and normal seperately
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# normal = np.stack((pointcloud['xn'], pointcloud['yn'], pointcloud['zn']), axis=-1)
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label = np.stack((pointcloud['label'], pointcloud['cl_idx']))
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label = pointcloud['cl_idx']
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sample_idxs = self.sampling(points)
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return points[sample_idxs], label[sample_idxs]
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@ -26,7 +26,7 @@ class FullCloudsDataset(_Point_Dataset):
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# When yopu want to return points and normal seperately
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# normal = np.stack((pointcloud['xn'], pointcloud['yn'], pointcloud['zn']), axis=-1)
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label = np.stack((pointcloud['label'], pointcloud['cl_idx']))
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label = pointcloud['cl_idx']
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sample_idxs = self.sampling(points)
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return points[sample_idxs], label[sample_idxs]
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4
main.py
4
main.py
@ -66,8 +66,8 @@ def run_lightning_loop(config_obj):
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trainer.fit(model)
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# Save the last state & all parameters
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trainer.save_checkpoint(config_obj.exp_path.log_dir / 'weights.ckpt')
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model.save_to_disk(config_obj.exp_path)
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trainer.save_checkpoint(logger.log_dir / 'weights.ckpt')
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model.save_to_disk(logger.log_dir)
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# Evaluate It
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if config_obj.main.eval:
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@ -1,15 +1,16 @@
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from argparse import Namespace
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import torch.nn.functional as F
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import torch
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from torch.optim import Adam
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from torch import nn
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from torch_geometric.data import Data
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from datasets.full_pointclouds import FullCloudsDataset
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from ml_lib.modules.geometric_blocks import SAModule, GlobalSAModule, MLP
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from ml_lib.modules.geometric_blocks import SAModule, GlobalSAModule, MLP, FPModule
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from ml_lib.modules.util import LightningBaseModule, F_x
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from utils.module_mixins import BaseValMixin, BaseTrainMixin, BaseOptimizerMixin, BaseDataloadersMixin, DatasetMixin
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from utils.project_config import GlobalVar
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class PointNet2(BaseValMixin,
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@ -31,31 +32,27 @@ class PointNet2(BaseValMixin,
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# =============================================================================
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# Additional parameters
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self.in_shape = self.dataset.train_dataset.sample_shape
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self.channels = self.in_shape[-1]
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# Modules
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self.sa1_module = SAModule(0.5, 0.2, MLP([self.channels, 64, 64, 128]))
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self.sa2_module = SAModule(0.25, 0.4, MLP([128 + self.channels, 128, 128, 256]))
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self.sa3_module = GlobalSAModule(MLP([256 + self.channels, 256, 512, 1024]))
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self.sa1_module = SAModule(0.2, 0.2, MLP([3 + 3, 64, 64, 128]))
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self.sa2_module = SAModule(0.25, 0.4, MLP([128 + 3, 128, 128, 256]))
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self.sa3_module = GlobalSAModule(MLP([256 + 3, 256, 512, 1024]))
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self.lin1 = nn.Linear(1024, 512)
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self.lin2 = nn.Linear(512, 256)
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self.lin3 = nn.Linear(256, 10)
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self.fp3_module = FPModule(1, MLP([1024 + 256, 256, 256]))
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self.fp2_module = FPModule(3, MLP([256 + 128, 256, 128]))
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self.fp1_module = FPModule(3, MLP([128 + 3, 128, 128, 128]))
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self.lin1 = torch.nn.Linear(128, 128)
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self.lin2 = torch.nn.Linear(128, 128)
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self.lin3 = torch.nn.Linear(128, len(GlobalVar.classes))
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# Utility
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self.dropout = nn.Dropout(self.params.dropout) if self.params.dropout else F_x(None)
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self.activation = self.params.activation()
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self.log_softmax = nn.LogSoftmax(dim=-1)
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def configure_optimizers(self):
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return Adam(self.parameters(), lr=self.params.lr, weight_decay=self.params.weight_decay)
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def forward(self, data, **kwargs):
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"""
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data: a batch of input, torch.Tensor or torch_geometric.data.Data type
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- torch.Tensor: (batch_size, 3, num_points), as common batch input
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data: a batch of input torch_geometric.data.Data type
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- torch_geometric.data.Data, as torch_geometric batch input:
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data.x: (batch_size * ~num_points, C), batch nodes/points feature,
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~num_points means each sample can have different number of points/nodes
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@ -66,37 +63,22 @@ class PointNet2(BaseValMixin,
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idendifiers for all nodes of all graphs/pointclouds in the batch. See
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pytorch_gemometric documentation for more information
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"""
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dense_input = True if isinstance(data, torch.Tensor) else False
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if dense_input:
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# Convert to torch_geometric.data.Data type
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# data = data.transpose(1, 2).contiguous()
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batch_size, N, _ = data.shape # (batch_size, num_points, 6)
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pos = data.view(batch_size*N, -1)
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batch = torch.zeros((batch_size, N), device=pos.device, dtype=torch.long)
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for i in range(batch_size):
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batch[i] = i
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batch = batch.view(-1)
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data = Data()
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data.pos, data.batch = pos, batch
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if not hasattr(data, 'x'):
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data.x = None
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sa0_out = (data.x, data.pos, data.batch)
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sa1_out = self.sa1_module(*sa0_out)
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sa2_out = self.sa2_module(*sa1_out)
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sa3_out = self.sa3_module(*sa2_out)
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tensor, pos, batch = sa3_out
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fp3_out = self.fp3_module(*sa3_out, *sa2_out)
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fp2_out = self.fp2_module(*fp3_out, *sa1_out)
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tensor, _, _ = self.fp1_module(*fp2_out, *sa0_out)
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tensor = tensor.float()
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tensor = self.lin1(tensor)
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tensor = self.activation(tensor)
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tensor = self.lin1(tensor)
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tensor = self.dropout(tensor)
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tensor = self.lin2(tensor)
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tensor = self.activation(tensor)
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tensor = self.dropout(tensor)
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tensor = self.lin3(tensor)
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tensor = self.log_softmax(tensor)
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@ -1,10 +1,17 @@
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from collections import defaultdict
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from itertools import cycle
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from abc import ABC
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from argparse import Namespace
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import torch
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import numpy as np
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from numpy import interp
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from sklearn.metrics import roc_curve, auc, confusion_matrix, ConfusionMatrixDisplay, f1_score, roc_auc_score
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import matplotlib.pyplot as plt
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from torch import nn
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from torch.optim import Adam
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from torch.utils.data import DataLoader
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@ -12,7 +19,9 @@ from torchcontrib.optim import SWA
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from torchvision.transforms import Compose
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from ml_lib.modules.util import LightningBaseModule
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from ml_lib.utils.tools import to_one_hot
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from ml_lib.utils.transforms import ToTensor
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from ml_lib.point_toolset.point_io import BatchToData
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from .project_config import GlobalVar
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@ -43,16 +52,21 @@ class BaseOptimizerMixin:
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class BaseTrainMixin:
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# Absolute Error
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absolute_loss = nn.L1Loss()
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# negative Log Likelyhood
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nll_loss = nn.NLLLoss()
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# Binary Cross Entropy
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bce_loss = nn.BCELoss()
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# Batch To Data
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batch_to_data = BatchToData()
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def training_step(self, batch_xy, batch_nb, *_, **__):
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def training_step(self, batch_pos_x_y, batch_nb, *_, **__):
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assert isinstance(self, LightningBaseModule)
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batch_x, batch_y = batch_xy
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y = self(batch_x).main_out
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bce_loss = self.bce_loss(y, batch_y)
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return dict(loss=bce_loss, log=dict(batch_nb=batch_nb))
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data = self.batch_to_data(*batch_pos_x_y)
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y = self(data).main_out
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nll_loss = self.nll_loss(y, data.y)
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return dict(loss=nll_loss, log=dict(batch_nb=batch_nb))
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def training_epoch_end(self, outputs):
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assert isinstance(self, LightningBaseModule)
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@ -66,17 +80,20 @@ class BaseTrainMixin:
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class BaseValMixin:
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# Absolute Error
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absolute_loss = nn.L1Loss()
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# negative Log Likelyhood
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nll_loss = nn.NLLLoss()
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# Binary Cross Entropy
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bce_loss = nn.BCELoss()
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def validation_step(self, batch_xy, batch_idx, _, *__, **___):
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def validation_step(self, batch_pos_x_y, batch_idx, *_, **__):
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assert isinstance(self, LightningBaseModule)
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batch_x, batch_y = batch_xy
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y = self(batch_x).main_out
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val_bce_loss = self.bce_loss(y, batch_y)
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return dict(val_bce_loss=val_bce_loss,
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batch_idx=batch_idx, y=y, batch_y=batch_y)
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data = self.batch_to_data(*batch_pos_x_y)
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y = self(data).main_out
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nll_loss = self.nll_loss(y, data.y)
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return dict(val_nll_loss=nll_loss,
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batch_idx=batch_idx, y=y, batch_y=data.y)
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def validation_epoch_end(self, outputs, *_, **__):
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assert isinstance(self, LightningBaseModule)
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@ -84,25 +101,107 @@ class BaseValMixin:
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# In case of Multiple given dataloader this will outputs will be: list[list[dict[]]]
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# for output_idx, output in enumerate(outputs):
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# else:list[dict[]]
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keys = list(outputs.keys())
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keys = list(outputs[0].keys())
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# Add Every Value das has a "loss" in it, by calc. mean over all occurences.
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summary_dict['log'].update({f'mean_{key}': torch.mean(torch.stack([output[key]
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for output in outputs]))
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for key in keys if 'loss' in key}
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)
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"""
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# Additional Score like the unweighted Average Recall:
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# UnweightedAverageRecall
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#######################################################################################
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# Additional Score - UAR - ROC - Conf. Matrix - F1
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#######################################################################################
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#
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# INIT
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y_true = torch.cat([output['batch_y'] for output in outputs]) .cpu().numpy()
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y_true_one_hot = to_one_hot(y_true)
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y_pred = torch.cat([output['y'] for output in outputs]).squeeze().cpu().numpy()
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y_pred_max = np.argmax(y_pred, axis=1)
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y_pred = (y_pred >= 0.5).astype(np.float32)
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class_names = {val: key for key, val in GlobalVar.classes.__dict__().items()}
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######################################################################################
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#
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# F1 SCORE
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micro_f1_score = f1_score(y_true, y_pred_max, labels=None, pos_label=1, average='micro', sample_weight=None,
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zero_division=True)
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macro_f1_score = f1_score(y_true, y_pred_max, labels=None, pos_label=1, average='macro', sample_weight=None,
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zero_division=True)
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summary_dict['log'].update(dict(micro_f1_score=micro_f1_score, macro_f1_score=macro_f1_score))
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uar_score = sklearn.metrics.recall_score(y_true, y_pred, labels=[0, 1], average='macro',
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sample_weight=None, zero_division='warn')
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#######################################################################################
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#
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# ROC Curve
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summary_dict['log'].update({f'uar_score': uar_score})
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"""
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# Compute ROC curve and ROC area for each class
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fpr = dict()
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tpr = dict()
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roc_auc = dict()
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for i in range(len(GlobalVar.classes)):
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fpr[i], tpr[i], _ = roc_curve(y_true_one_hot[:, i], y_pred[:, i])
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roc_auc[i] = auc(fpr[i], tpr[i])
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# Compute micro-average ROC curve and ROC area
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fpr["micro"], tpr["micro"], _ = roc_curve(y_true_one_hot.ravel(), y_pred.ravel())
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roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])
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|
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# First aggregate all false positive rates
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all_fpr = np.unique(np.concatenate([fpr[i] for i in range(len(GlobalVar.classes))]))
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# Then interpolate all ROC curves at this points
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mean_tpr = np.zeros_like(all_fpr)
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for i in range(len(GlobalVar.classes)):
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mean_tpr += interp(all_fpr, fpr[i], tpr[i])
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# Finally average it and compute AUC
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mean_tpr /= len(GlobalVar.classes)
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fpr["macro"] = all_fpr
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tpr["macro"] = mean_tpr
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roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])
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|
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# Plot all ROC curves
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plt.figure()
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plt.plot(fpr["micro"], tpr["micro"],
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label=f'micro ROC ({round(roc_auc["micro"], 2)})',
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color='deeppink', linestyle=':', linewidth=4)
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|
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plt.plot(fpr["macro"], tpr["macro"],
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label=f'macro ROC({round(roc_auc["macro"], 2)})]',
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color='navy', linestyle=':', linewidth=4)
|
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|
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colors = cycle(['firebrick', 'orangered', 'gold', 'olive', 'limegreen', 'aqua',
|
||||
'dodgerblue', 'slategrey', 'royalblue', 'indigo', 'fuchsia'], )
|
||||
|
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for i, color in zip(range(len(GlobalVar.classes)), colors):
|
||||
plt.plot(fpr[i], tpr[i], color=color, lw=2, label=f'{class_names[i]} ({round(roc_auc[i],2 )})')
|
||||
|
||||
plt.plot([0, 1], [0, 1], 'k--', lw=2)
|
||||
plt.xlim([0.0, 1.0])
|
||||
plt.ylim([0.0, 1.05])
|
||||
plt.xlabel('False Positive Rate')
|
||||
plt.ylabel('True Positive Rate')
|
||||
plt.legend(loc="lower right")
|
||||
|
||||
self.logger.log_image('ROC', image=plt.gcf(), step=self.current_epoch)
|
||||
plt.clf()
|
||||
|
||||
#######################################################################################
|
||||
#
|
||||
# ROC SCORE
|
||||
|
||||
macro_roc_auc_ovr = roc_auc_score(y_true_one_hot, y_pred, multi_class="ovr",
|
||||
average="macro")
|
||||
summary_dict['log'].update(macro_roc_auc_ovr=macro_roc_auc_ovr)
|
||||
|
||||
#######################################################################################
|
||||
#
|
||||
# Confusion matrix
|
||||
|
||||
cm = confusion_matrix(y_true, y_pred_max, labels=[class_name for class_name in class_names], normalize='all')
|
||||
disp = ConfusionMatrixDisplay(confusion_matrix=cm)
|
||||
disp.plot(include_values=True)
|
||||
self.logger.log_image('Confusion Matrix', image=plt.gcf(), step=self.current_epoch)
|
||||
|
||||
return summary_dict
|
||||
|
||||
@ -122,18 +221,17 @@ class DatasetMixin:
|
||||
dataset = Namespace(
|
||||
**dict(
|
||||
# TRAIN DATASET
|
||||
train_dataset=dataset_class(self.params.root, setting=GlobalVar.train,
|
||||
transforms=transforms
|
||||
),
|
||||
|
||||
# VALIDATION DATASET
|
||||
val_dataset=dataset_class(self.params.root, setting=GlobalVar.vali,
|
||||
train_dataset=dataset_class(self.params.root, setting=GlobalVar.data_split.train,
|
||||
transforms=transforms
|
||||
),
|
||||
|
||||
# TEST DATASET
|
||||
test_dataset=dataset_class(self.params.root, setting=GlobalVar.test,
|
||||
),
|
||||
# VALIDATION DATASET
|
||||
val_dataset=dataset_class(self.params.root, setting=GlobalVar.data_split.devel,
|
||||
),
|
||||
|
||||
# TEST DATASET
|
||||
test_dataset=dataset_class(self.params.root, setting=GlobalVar.data_split.test,
|
||||
),
|
||||
)
|
||||
)
|
||||
return dataset
|
||||
|
||||
@ -3,24 +3,44 @@ from argparse import Namespace
|
||||
from ml_lib.utils.config import Config
|
||||
|
||||
|
||||
class GlobalVar(Namespace):
|
||||
# Labels for classes
|
||||
LEFT = 1
|
||||
RIGHT = 0
|
||||
WRONG = -1
|
||||
class DataClass(Namespace):
|
||||
|
||||
# Colors for img files
|
||||
WHITE = 255
|
||||
BLACK = 0
|
||||
def __len__(self):
|
||||
return len(self.__dict__())
|
||||
|
||||
def __dict__(self):
|
||||
return {key: val for key, val in self.__class__.__dict__.items() if '__' not in key}
|
||||
|
||||
def __repr__(self):
|
||||
return f'{self.__class__.__name__}({self.__dict__().__repr__()})'
|
||||
|
||||
|
||||
class Classes(DataClass):
|
||||
# Object Classes for Point Segmentation
|
||||
Sphere = 0
|
||||
Cylinder = 1
|
||||
Cone = 2
|
||||
Box = 3
|
||||
Polytope = 4
|
||||
Torus = 5
|
||||
Plane = 6
|
||||
|
||||
|
||||
class DataSplit(DataClass):
|
||||
# DATA SPLIT OPTIONS
|
||||
train = 'train',
|
||||
devel = 'devel',
|
||||
test = 'test'
|
||||
|
||||
|
||||
class GlobalVar(DataClass):
|
||||
# Variables for plotting
|
||||
PADDING = 0.25
|
||||
DPI = 50
|
||||
|
||||
# DATAOPTIONS
|
||||
train ='train',
|
||||
vali ='vali',
|
||||
test ='test'
|
||||
data_split = DataSplit()
|
||||
|
||||
classes = Classes()
|
||||
|
||||
|
||||
from models import *
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user