Source code for torch_points3d.applications.kpconv

import os
from omegaconf import DictConfig, OmegaConf
import logging

from torch_points3d.applications.modelfactory import ModelFactory
from torch_points3d.core.common_modules import FastBatchNorm1d
from torch_points3d.modules.KPConv import *
from torch_points3d.core.base_conv.partial_dense import *
from torch_points3d.models.base_architectures.unet import UnwrappedUnetBasedModel
from torch_points3d.datasets.multiscale_data import MultiScaleBatch
from torch_points3d.core.common_modules.base_modules import MLP
from .utils import extract_output_nc


CUR_FILE = os.path.realpath(__file__)
DIR_PATH = os.path.dirname(os.path.realpath(__file__))
PATH_TO_CONFIG = os.path.join(DIR_PATH, "conf/kpconv")

log = logging.getLogger(__name__)


[docs]def KPConv( architecture: str = None, input_nc: int = None, num_layers: int = None, config: DictConfig = None, *args, **kwargs ): """ Create a KPConv backbone model based on the architecture proposed in https://arxiv.org/abs/1904.08889 Parameters ---------- architecture : str, optional Architecture of the model, choose from unet, encoder and decoder input_nc : int, optional Number of channels for the input output_nc : int, optional If specified, then we add a fully connected head at the end of the network to provide the requested dimension num_layers : int, optional Depth of the network in_grid_size : float, optional Size of the grid at the entry of the network. It is divided by two at each layer in_feat : int, optional Number of channels after the first convolution. Doubles at each layer config : DictConfig, optional Custom config, overrides the num_layers and architecture parameters """ factory = KPConvFactory( architecture=architecture, num_layers=num_layers, input_nc=input_nc, config=config, **kwargs ) return factory.build()
class KPConvFactory(ModelFactory): def _build_unet(self): if self._config: model_config = self._config else: path_to_model = os.path.join(PATH_TO_CONFIG, "unet_{}.yaml".format(self.num_layers)) model_config = OmegaConf.load(path_to_model) ModelFactory.resolve_model(model_config, self.num_features, self._kwargs) modules_lib = sys.modules[__name__] return KPConvUnet(model_config, None, None, modules_lib, **self.kwargs) def _build_encoder(self): if self._config: model_config = self._config else: path_to_model = os.path.join(PATH_TO_CONFIG, "encoder_{}.yaml".format(self.num_layers)) model_config = OmegaConf.load(path_to_model) ModelFactory.resolve_model(model_config, self.num_features, self._kwargs) modules_lib = sys.modules[__name__] return KPConvEncoder(model_config, None, None, modules_lib, **self.kwargs) class BaseKPConv(UnwrappedUnetBasedModel): CONV_TYPE = "partial_dense" def __init__(self, model_config, model_type, dataset, modules, *args, **kwargs): super(BaseKPConv, self).__init__(model_config, model_type, dataset, modules) try: default_output_nc = extract_output_nc(model_config) except: default_output_nc = -1 log.warning("Could not resolve number of output channels") self._output_nc = default_output_nc self._has_mlp_head = False if "output_nc" in kwargs: self._has_mlp_head = True self._output_nc = kwargs["output_nc"] self.mlp = MLP([default_output_nc, self.output_nc], activation=torch.nn.LeakyReLU(0.2), bias=False) @property def has_mlp_head(self): return self._has_mlp_head @property def output_nc(self): return self._output_nc def _set_input(self, data): """Unpack input data from the dataloader and perform necessary pre-processing steps. Parameters ----------- data: a dictionary that contains the data itself and its metadata information. """ data = data.to(self.device) if isinstance(data, MultiScaleBatch): self.pre_computed = data.multiscale self.upsample = data.upsample del data.upsample del data.multiscale else: self.upsample = None self.pre_computed = None self.input = data class KPConvEncoder(BaseKPConv): def forward(self, data, *args, **kwargs): """ Parameters ----------- data: A dictionary that contains the data itself and its metadata information. Should contain - pos [N, 3] - x [N, C] - multiscale (optional) precomputed data for the down convolutions - upsample (optional) precomputed data for the up convolutions Returns -------- data: - pos [1, 3] - Dummy pos - x [1, output_nc] """ self._set_input(data) data = self.input stack_down = [data] for i in range(len(self.down_modules) - 1): data = self.down_modules[i](data) stack_down.append(data) data = self.down_modules[-1](data) if not isinstance(self.inner_modules[0], Identity): stack_down.append(data) data = self.inner_modules[0](data) if self.has_mlp_head: data.x = self.mlp(data.x) return data class KPConvUnet(BaseKPConv): def forward(self, data, *args, **kwargs): """Run forward pass. Input --- D1 -- D2 -- D3 -- U1 -- U2 -- output | |_________| | |______________________| Parameters ----------- data: A dictionary that contains the data itself and its metadata information. Should contain - pos [N, 3] - x [N, C] - multiscale (optional) precomputed data for the down convolutions - upsample (optional) precomputed data for the up convolutions Returns -------- data: - pos [N, 3] - x [N, output_nc] """ self._set_input(data) data = super().forward(self.input, precomputed_down=self.pre_computed, precomputed_up=self.upsample) if self.has_mlp_head: data.x = self.mlp(data.x) return data