diff --git a/.gitignore b/.gitignore index f256990d..4ae20526 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,7 @@ ./data wandb/ lightning_logs/ +mmdetection3d/ *.pth # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/Dockerfile b/Dockerfile index f9ad35ba..6b595770 100755 --- a/Dockerfile +++ b/Dockerfile @@ -1,11 +1,12 @@ FROM python:3.8.3-slim -RUN apt-get update && apt-get upgrade -y -RUN apt-get install ffmpeg libsm6 libxext6 -y -RUN apt-get install curl -y -RUN pip3 install --upgrade pip +RUN apt-get update && apt-get upgrade -y && \ + apt-get install ffmpeg libsm6 libxext6 -y && \ + apt-get install curl -y && \ + apt-get -y install git && \ + pip3 install --upgrade pip # works with 1.2.2 -RUN /bin/bash -c 'curl -sSL https://install.python-poetry.org | python - --version 1.3.0' +RUN /bin/bash -c 'curl -sSL https://install.python-poetry.org | python - --version 1.6.1' COPY . /code WORKDIR code diff --git a/DockerfileMED b/DockerfileMED index 82263bb8..7391dbc2 100755 --- a/DockerfileMED +++ b/DockerfileMED @@ -6,7 +6,7 @@ RUN apt-get update \ && apt-get install ffmpeg libsm6 libxext6 curl iotop -y \ && rm -rf /var/lib/{apt,dpkg,cache,log}/ RUN pip3 install --upgrade pip pyopenssl cryptography -RUN /bin/bash -c 'curl -sSL https://install.python-poetry.org | python - --version 1.3.0' +RUN /bin/bash -c 'curl -sSL https://install.python-poetry.org | python - --version 1.6.1' COPY . /code RUN rm -r /code/config/experiments/* diff --git a/DockerfileNM b/DockerfileNM index 4b0549a4..ffd050d3 100755 --- a/DockerfileNM +++ b/DockerfileNM @@ -6,7 +6,7 @@ RUN apt-get update \ && apt-get install ffmpeg libsm6 libxext6 curl iotop -y \ && rm -rf /var/lib/{apt,dpkg,cache,log}/ RUN pip3 install --upgrade pip pyopenssl cryptography -RUN /bin/bash -c 'curl -sSL https://install.python-poetry.org | python - --version 1.3.0' +RUN /bin/bash -c 'curl -sSL https://install.python-poetry.org | python - --version 1.6.1' COPY . /code RUN rm -r /code/config/experiments/* diff --git a/DockerfilePROM b/DockerfilePROM index f3154c33..6def7167 100755 --- a/DockerfilePROM +++ b/DockerfilePROM @@ -6,7 +6,7 @@ RUN apt-get update \ && apt-get install ffmpeg libsm6 libxext6 curl iotop -y \ && rm -rf /var/lib/{apt,dpkg,cache,log}/ RUN pip3 install --upgrade pip pyopenssl cryptography -RUN /bin/bash -c 'curl -sSL https://install.python-poetry.org | python - --version 1.3.0' +RUN /bin/bash -c 'curl -sSL https://install.python-poetry.org | python - --version 1.6.1' COPY . /code RUN rm -r /code/config/experiments/* diff --git a/config/datasets/detection-3d/lep_3d_dataset.yaml b/config/datasets/detection-3d/lep_3d_dataset.yaml new file mode 100644 index 00000000..164e53e3 --- /dev/null +++ b/config/datasets/detection-3d/lep_3d_dataset.yaml @@ -0,0 +1,27 @@ +task: + - 3d-detection + +name: lep_3d +description: Набор данных содержит видео облетов c бпла над территорией линии электропередач + +markup_info: Набор данных содержит разметку bounding box, под формат детекции столбов ЛЭП. +date_time: 10.06.2024 + +_target_: innofw.core.integrations.mmdetection.datamodule.Mmdetection3DDataModuleAdapter + +train: + source: https://api.blackhole.ai.innopolis.university/public-datasets/lep_3d_detection/train.zip + target: ./data/lep3d + +test: + source: https://api.blackhole.ai.innopolis.university/public-datasets/lep_3d_detection/test.zip + target: ./data/lep3d + +infer: + source: https://api.blackhole.ai.innopolis.university/public-datasets/lep_3d_detection/test.zip + target: ./data/lep3d + +num_workers: 8 + +val_size: 0.2 +num_classes: 4 \ No newline at end of file diff --git a/config/experiments/detection-3d/KG_110624_oegq01.yaml b/config/experiments/detection-3d/KG_110624_oegq01.yaml new file mode 100644 index 00000000..cffa51be --- /dev/null +++ b/config/experiments/detection-3d/KG_110624_oegq01.yaml @@ -0,0 +1,14 @@ +# @package _global_ +defaults: + - override /models: detection-3d/base + - override /datasets: detection-3d/lep_3d_dataset + - override /optimizers: adam + +project: "lep_3d_det" +task: "3d-detection" +random_seed: 42 +epochs: 10 +batch_size: 2 +weights_freq: 1 +accelerator: gpu +devices: 0 \ No newline at end of file diff --git a/config/models/detection-3d/base.yaml b/config/models/detection-3d/base.yaml new file mode 100644 index 00000000..3b56c13d --- /dev/null +++ b/config/models/detection-3d/base.yaml @@ -0,0 +1,3 @@ +_target_: innofw.core.integrations.mmdetection.model_adapter.BaseMmdetModel +description: "base model for 3D point cloud detection" +name: "Base 3D model" \ No newline at end of file diff --git a/dui/pages/config_list.py b/dui/pages/config_list.py index 95781737..c2ef2f83 100644 --- a/dui/pages/config_list.py +++ b/dui/pages/config_list.py @@ -72,7 +72,7 @@ def layout(): selected_rows=[], page_action="native", page_current=0, - page_size=10, + page_size=20, style_header={'backgroundColor': 'white', 'fontWeight': 'bold', 'textAlign': 'left' diff --git a/innofw/constants.py b/innofw/constants.py index edc45a59..6ef0f2c9 100644 --- a/innofw/constants.py +++ b/innofw/constants.py @@ -19,6 +19,7 @@ class Frameworks(Enum): xgboost = "xgboost" catboost = "catboost" ultralytics = "ultralytics" + mmdetection = "mmdetection" none = "none" PathLike = TypeVar( diff --git a/innofw/core/integrations/__init__.py b/innofw/core/integrations/__init__.py index dae44cbd..1c7c9527 100755 --- a/innofw/core/integrations/__init__.py +++ b/innofw/core/integrations/__init__.py @@ -3,3 +3,4 @@ UltralyticsDataModuleAdapter as UltralyticsDataModule, ) from .ultralytics.ultralytics_adapter import UltralyticsAdapter as YOLOv5 +from .mmdetection.datamodule import Mmdetection3DDataModuleAdapter as Mmdetection3DDataModule \ No newline at end of file diff --git a/innofw/core/integrations/mmdetection/__init__.py b/innofw/core/integrations/mmdetection/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/innofw/core/integrations/mmdetection/datamodule.py b/innofw/core/integrations/mmdetection/datamodule.py new file mode 100644 index 00000000..0f941fc0 --- /dev/null +++ b/innofw/core/integrations/mmdetection/datamodule.py @@ -0,0 +1,375 @@ +from pckg_util import install_mmcv +install_mmcv() + +# standard libraries +import json +import os +import os.path as osp +import pickle as pkl +import pathlib +import random +from abc import ABC +from collections import namedtuple +from pathlib import Path +from typing import Optional + +import numpy as np +import open3d as o3d +from tqdm import tqdm + +import logging + +# local modules +from innofw.constants import Frameworks, Stages +from innofw.core.datamodules.base import BaseDataModule + + +ItemInfo = namedtuple('ItemInfo', ['img_path', 'name']) +target_maxes = np.array([[136.7853946685791, 135.2938232421875, 45.29965019226074]]) +target_mins = np.array([[-136.7853946685791, -135.2938232421875, -45.29965019226074]]) + + +class Mmdetection3DDataModuleAdapter(BaseDataModule, ABC): + """ + ... + ├── data + │ ├── custom + │ │ ├── ImageSets # just names lists, w/o extension or path + │ │ │ ├── train.txt + │ │ │ ├── val.txt + │ │ ├── points + │ │ │ ├── 000000.bin + │ │ │ ├── 000001.bin + │ │ │ ├── ... + │ │ ├── labels + │ │ │ ├── 000000.txt + │ │ │ ├── 000001.txt + │ │ │ ├── ... + """ + # Points clouds preprocessing code credit: https://github.com/sadjava + task = ["3d-detection"] + framework = [Frameworks.mmdetection] + + def __init__( + self, + train: Optional[str], + test: Optional[str], + infer: Optional[str], + num_classes: int, + val_size: float = 0.2, + stage=False, + random_state: int = 0, + *args, + **kwargs, + ): + super().__init__(train=train, test=test, infer=infer, stage=stage, *args, **kwargs) + self.train_source, self.test_source, self.infer_source = None, None, None + if self.train: + self.train_source = Path(self.train) + if self.test: + self.test_source = Path(self.test) + if self.infer: + self.infer_source = Path(self.infer) + + self.val_size = val_size + self.num_classes = num_classes + self.random_state = random_state + + self.train_set = None + self.val_set = None + self.state = { + 'data_path': self.train_source or self.test_source or self.infer_source, + 'save_path': None, + 'train_size': 0.9, + 'center_coords': [True, True, True], # x, y, z + 'window_size': [100., 100., 20.], + 'train_data_mode': 'full', + 'selectedClasses': ['LEP110_anchor', 'power_lines', 'forest', 'vegetation', + 'LEP110_prom'], + } + self.state['data_path'] = self.state['data_path'].parent + self.state['save_path'] = osp.join(self.state['data_path'].parent, 'processed_data') + + def get_train_val_sets(self): + all_items = [] + for p in os.listdir(osp.join(self.state['data_path'], 'pointcloud')): + all_items.append( + ItemInfo(name=p, img_path=osp.join(self.state['data_path'], 'pointcloud', p))) + random.shuffle(all_items) + train_count = int(len(all_items) * self.state['train_size']) + train_items = all_items[:train_count] + val_items = all_items[train_count:] + return train_items, val_items + + @staticmethod + def calculate_pcr(items): + point_cloud_range = [10000, 10000, 10000, -10000, -10000, -10000] + point_cloud_dim = [0, 0, 0] + for idx, item in tqdm(enumerate(items), total=len(items), desc='Calculating pcr'): + pcd = o3d.io.read_point_cloud(item.img_path) + pcd_np = np.asarray(pcd.points) + ptc_range = [ + pcd_np[:, 0].min(), + pcd_np[:, 1].min(), + pcd_np[:, 2].min(), + pcd_np[:, 0].max(), + pcd_np[:, 1].max(), + pcd_np[:, 2].max() + ] + for i in range(3): + if ptc_range[i] < point_cloud_range[i]: + point_cloud_range[i] = ptc_range[i] + if ptc_range[i + 3] > point_cloud_range[i + 3]: + point_cloud_range[i + 3] = ptc_range[i + 3] + if ptc_range[i + 3] - ptc_range[i] > point_cloud_dim[i]: + point_cloud_dim[i] = ptc_range[i + 3] - ptc_range[i] + print(f'Finished calculating pcr: {point_cloud_range}, pcd: {point_cloud_dim}') + return point_cloud_range, point_cloud_dim + + def create_splits(self): + self.train_set, self.val_set = self.get_train_val_sets() + pcr, pcd = Mmdetection3DDataModuleAdapter.calculate_pcr(self.train_set + self.val_set) + self.state['point_cloud_range'] = pcr + self.state['point_cloud_dim'] = pcd + + def centerize_ptc(self, points): + centerize_vec = [0, 0, 0] + for i in range(3): + if self.state['center_coords'][i]: + dim_trans = points[:, i].min() + (points[:, i].max() - points[:, i].min()) * 0.5 + points[:, i] -= dim_trans + centerize_vec[i] = -dim_trans + + return points, centerize_vec + + def get_ann_in_framework_format(self, item, sample_idx, pcd_np, pcd_sboxes, sly_ann, + annotations): + for slide_box_idx, sbox in enumerate(pcd_sboxes): + ptc_info = { + 'sample_idx': sample_idx, + 'lidar_points': {}, + 'instances': { + 'gt_bboxes_3d': [], + 'gt_names': [], + 'gt_labels_3d': [], + 'box_type_3d': 'LiDAR' + } + } + pcd_eps = 1e-3 + pcd_slide = pcd_np[ + (pcd_np[:, 0] > sbox[0] - pcd_eps) & + (pcd_np[:, 0] < sbox[1] + pcd_eps) & + (pcd_np[:, 1] > sbox[2] - pcd_eps) & + (pcd_np[:, 1] < sbox[3] + pcd_eps) & + (pcd_np[:, 2] > sbox[4] - pcd_eps) & + (pcd_np[:, 2] < sbox[5] + pcd_eps) + ] + if len(pcd_slide) == 0: + continue + slide_name = f"{item.name}_{slide_box_idx}" # .replace('.', '') + bin_filename = osp.join("points", f"{slide_name}.bin") + trans_vec = [0, 0, 0] + if any(self.state["center_coords"]): + pcd_slide, trans_vec = self.centerize_ptc(pcd_slide) + + intensity = np.zeros((pcd_slide.shape[0], 1), dtype=np.float32) + pcd_mins = np.array(self.state['point_cloud_range'][:3]) + pcd_maxes = np.array(self.state['point_cloud_range'][3:]) + pcd_slide = (pcd_slide - pcd_mins) / (pcd_maxes - pcd_mins) * (target_maxes - target_mins) + target_mins + pcd_slide = np.hstack((pcd_slide, intensity)) + pcd_slide.astype(np.float32).tofile(osp.join(self.state['save_path'], bin_filename)) + ptc_info['lidar_points']['lidar_path'] = bin_filename.split('/')[-1] + ptc_info['lidar_points']['num_pts_feats'] = 4 + objects2class = {obj['key']: obj['classTitle'] for obj in sly_ann['objects']} + for fig in sly_ann['figures']: + if objects2class[fig['objectKey']] not in self.state["selectedClasses"]: + continue + box_info = [] # x, y, z, dx, dy, dz, rot, [vel_x, vel_y] + pos = fig['geometry']['position'] + + if pos['x'] < sbox[0] or pos['x'] >= sbox[1] or \ + pos['y'] < sbox[2] or pos['y'] >= sbox[3] or \ + pos['z'] < sbox[4] or pos['z'] >= sbox[5]: + continue + pos_x = pos['x'] + trans_vec[0] + pos_y = pos['y'] + trans_vec[1] + pos_z = pos['z'] + trans_vec[2] + + box_pos = [pos_x, pos_y, pos_z] + box_info.extend(box_pos) + dim = fig['geometry']['dimensions'] + box_info.extend([dim['x'], dim['y'], dim['z']]) + box_info.extend([fig['geometry']['rotation']['z']]) + # box_info.extend([0, 0]) + + ptc_info['instances']['gt_names'].append(objects2class[fig['objectKey']]) + ptc_info['instances']['gt_bboxes_3d'].append(box_info) + ptc_info['instances']['gt_labels_3d'].append( + self.state["selectedClasses"].index(objects2class[fig['objectKey']])) + ptc_info['instances']['gt_bboxes_3d'] = np.array(ptc_info['instances']['gt_bboxes_3d'], + dtype=np.float32) + ptc_info['instances']['gt_bboxes_3d'][:, :3] = (ptc_info['instances']['gt_bboxes_3d'][:, :3] - pcd_mins) / (pcd_maxes - pcd_mins) * (target_maxes - target_mins) + target_mins + ptc_info['instances']['gt_bboxes_3d'][:, 3:6] = (ptc_info['instances']['gt_bboxes_3d'][:, 3:6] - pcd_mins) / (pcd_maxes - pcd_mins) * (target_maxes - target_mins) + target_mins + + + ptc_info['instances']['gt_labels_3d'] = np.array(ptc_info['instances']['gt_labels_3d'], + dtype=np.int32) + annotations.append(ptc_info) + return annotations + + def save_set_to_annotation(self, save_path, items, slide_boxes, subset): + os.makedirs(osp.dirname(save_path), exist_ok=True) + + annotations = [] + + for idx, item in enumerate(items): + + os.makedirs(osp.join(self.state['save_path'], "points"), exist_ok=True) + pcd = o3d.io.read_point_cloud(item.img_path) + pcd_np = np.asarray(pcd.points) + + pcd_sboxes = [] + pcdim = self.state["point_cloud_dim"] + for sbox in slide_boxes: + pcd_sboxes.append([ + pcd_np[:, 0].min() + (pcd_np[:, 0].max() - pcd_np[:, 0].min()) * 0.5 - pcdim[ + 0] * 0.5 + sbox[0], + pcd_np[:, 0].min() + (pcd_np[:, 0].max() - pcd_np[:, 0].min()) * 0.5 - pcdim[ + 0] * 0.5 + sbox[1], + pcd_np[:, 1].min() + (pcd_np[:, 1].max() - pcd_np[:, 1].min()) * 0.5 - pcdim[ + 1] * 0.5 + sbox[2], + pcd_np[:, 1].min() + (pcd_np[:, 1].max() - pcd_np[:, 1].min()) * 0.5 - pcdim[ + 1] * 0.5 + sbox[3], + pcd_np[:, 2].min() + (pcd_np[:, 2].max() - pcd_np[:, 2].min()) * 0.5 - pcdim[ + 2] * 0.5 + sbox[4], + pcd_np[:, 2].min() + (pcd_np[:, 2].max() - pcd_np[:, 2].min()) * 0.5 - pcdim[ + 2] * 0.5 + sbox[5] + ]) + + ann_path = osp.join(self.state['data_path'], "ann", f"{item.name}.json") + with open(ann_path, 'r') as f: + ann = json.load(f) + annotations = self.get_ann_in_framework_format(item, idx, pcd_np, pcd_sboxes, ann, + annotations) + + os.makedirs(osp.join(osp.dirname(save_path), 'labels'), exist_ok=True) + all_gt_names = set() + for ann in annotations: + name = ann['lidar_points']['lidar_path'][:-4] + bboxes = ann['instances']['gt_bboxes_3d'] + class_names = ann['instances']['gt_names'] + all_gt_names.update(class_names) + with open(osp.join(osp.dirname(save_path), 'labels', f"{name}.txt"), 'w') as f: + for bbox, class_name in zip(bboxes, class_names): + print(*bbox, class_name, file=f) + + all_gt_names = list(sorted(all_gt_names)) + os.makedirs(osp.join(osp.dirname(save_path), 'ImageSets'), exist_ok=True) + with open(osp.join(osp.dirname(save_path), 'ImageSets', f'{subset}.txt'), 'w') as f: + for ann in annotations: + print(ann['lidar_points']['lidar_path'][:-4], file=f) + + annotations = {'metainfo': {'categories': {cat: label for cat, label in enumerate(all_gt_names)}}, + 'data_list': annotations} + with open(save_path, 'wb') as f: + pkl.dump(annotations, f) + + def get_slide_boxes(self): + pcd = self.state["point_cloud_dim"].copy() + ws = self.state["window_size"].copy() + for i in range(3): + if pcd[i] < ws[i]: + ws[i] = pcd[i] + + slides_x, overlap_x = divmod(pcd[0], ws[0]) + slides_y, overlap_y = divmod(pcd[1], ws[1]) + slides_z, overlap_z = divmod(pcd[2], ws[2]) + if overlap_x != 0: + slides_x += 1 + if overlap_y != 0: + slides_y += 1 + if overlap_z != 0: + slides_z += 1 + + sboxes = [] + for z in range(int(slides_z)): + for y in range(int(slides_y)): + for x in range(int(slides_x)): + sboxes.append([ + 0 if x == 0 else ws[0] * x - overlap_x, + ws[0] if x == 0 else ws[0] * (x + 1) - overlap_x, + 0 if y == 0 else ws[1] * y - overlap_y, + ws[1] if y == 0 else ws[1] * (y + 1) - overlap_y, + 0 if z == 0 else ws[2] * z - overlap_z, + ws[2] if z == 0 else ws[2] * (z + 1) - overlap_z, + ]) + return sboxes, ws + + def prepare_data(self): + if self.state["train_data_mode"] == 'sliding_window': + sboxes, self.state["window_size"] = self.get_slide_boxes() + pcr = [ + -self.state["window_size"][0] * 0.5, + -self.state["window_size"][1] * 0.5, + -self.state["window_size"][2] * 0.5, + self.state["window_size"][0] * 0.5, + self.state["window_size"][1] * 0.5, + self.state["window_size"][2] * 0.5 + ] + elif self.state["train_data_mode"] == 'full': + sboxes = [[ + 0, self.state["point_cloud_dim"][0], + 0, self.state["point_cloud_dim"][1], + 0, self.state["point_cloud_dim"][2] + ]] + if any(self.state["center_coords"]): + pcr = [ + -self.state["point_cloud_dim"][0] * 0.5 if self.state["center_coords"][0] else + self.state["point_cloud_range"][0], + -self.state["point_cloud_dim"][1] * 0.5 if self.state["center_coords"][1] else + self.state["point_cloud_range"][1], + -self.state["point_cloud_dim"][2] * 0.5 if self.state["center_coords"][2] else + self.state["point_cloud_range"][2], + self.state["point_cloud_dim"][0] * 0.5 if self.state["center_coords"][0] else + self.state["point_cloud_range"][3], + self.state["point_cloud_dim"][1] * 0.5 if self.state["center_coords"][1] else + self.state["point_cloud_range"][4], + self.state["point_cloud_dim"][2] * 0.5 if self.state["center_coords"][2] else + self.state["point_cloud_range"][5] + ] + else: + pcr = self.state["point_cloud_range"] + self.state['point_cloud_range'] = pcr + + self.save_set_to_annotation(osp.join(self.state['save_path'], 'custom_infos_train.pkl'), self.train_set, + sboxes, 'train') + self.save_set_to_annotation(osp.join(self.state['save_path'], 'custom_infos_val.pkl'), self.val_set, + sboxes, 'val') + + logging.info('Finished data preparation') + + def setup_train_test_val(self, **kwargs): + self.create_splits() + self.prepare_data() + + def setup_infer(self, **kwargs): + self.create_splits() + self.prepare_data() + + def predict_dataloader(self): + pass + + def save_preds(self, preds, stage: Stages, dst_path: pathlib.Path): + pass + + def train_dataloader(self): + pass + + def test_dataloader(self): + pass + + +if __name__ == '__main__': + t = Mmdetection3DDataModuleAdapter(data={'source': '/home/karim/workdir/innofw/data/lep3d'}, + num_classes=4) + t.setup_train_test_val() \ No newline at end of file diff --git a/innofw/core/integrations/mmdetection/model_adapter.py b/innofw/core/integrations/mmdetection/model_adapter.py new file mode 100644 index 00000000..ea4fc94f --- /dev/null +++ b/innofw/core/integrations/mmdetection/model_adapter.py @@ -0,0 +1,143 @@ +import os +import logging +from pathlib import Path +from typing import Optional + +import torch +import yaml + +from ..base_integration_models import BaseIntegrationModel +from innofw.constants import Frameworks +from innofw.core.models import BaseModelAdapter +from innofw.core.models import register_models_adapter +from innofw.utils.checkpoint_utils import TorchCheckpointHandler + + +class BaseMmdetModel(BaseIntegrationModel): + framework = Frameworks.mmdetection + + def __init__(self, *args, **kwargs): + pass + + +@register_models_adapter('mmdetection_adapter') +class Mmdetection3DDataModel(BaseModelAdapter): + + @staticmethod + def is_suitable_model(model) -> bool: + return isinstance(model, BaseMmdetModel) + + def _test(self, data): + pass + + def _train(self, data): + pass + + def _predict(self, data): + pass + + framework = Frameworks.mmdetection + + def __init__( + self, + model, + log_dir, + trainer_cfg, + *args, + **kwargs, + ): + super().__init__(model, log_dir) + self.optimizer = map_optimizer_to_mmdet_optim(kwargs['optimizers_cfg']['_target_'].split('.')[-1].lower()) + self.optim_lr = kwargs['optimizers_cfg']['lr'] + self.device, self.epochs = trainer_cfg["accelerator"], trainer_cfg["max_epochs"] + self.devices = trainer_cfg.get('devices', []) + self.log_dir = Path(log_dir) + self.mmdet_path = setup_mmdetection() + + self.data_path = os.path.join(self.mmdet_path, 'configs/_base_/datasets/custom.py') + self.train_path = os.path.join(self.mmdet_path, 'configs/pointpillars/pointpillars_hv_secfpn_8xb6_custom.py') + + self.train_draft = '' + self.data_draft = '' + + def update_configs(self, processed_data_path: str): + with open(self.data_path, 'r') as d: + self.data_draft = d.read() + with open(self.data_path, 'w') as d: + print(self.data_draft.replace('DATA_ROOT', processed_data_path), file=d) + + with open(self.train_path, 'r') as tr: + self.train_draft = tr.read() + with open(self.train_path, 'w') as tr: + code = self.train_draft.replace('MAX_EPOCHS', str(self.epochs)) + code = code.replace('OPTIM_TYPE', self.optimizer) + code = code.replace('OPTIM_LR', str(self.optim_lr)) + print(code, file=tr) + + def rollback_configs(self): + with open(self.data_path, 'w') as d: + print(self.data_draft, file=d) + + with open(self.train_path, 'w') as tr: + print(self.train_draft, file=tr) + + def update_checkpoints_path(self): + try: + (self.log_dir / "weights").rename(self.log_dir / "checkpoints") + + try: + dst_path = list((self.log_dir / "checkpoints").iterdir())[0] + logging.info(f"Saved a checkpoint at: {dst_path}") + except: + pass + except Exception as e: + pass + + def train(self, data, ckpt_path=None): + data.setup() + self.update_configs(os.path.abspath(data.state['save_path'])) + logging.info('Training') + devices = [] if self.device == 'cpu' else self.devices + os.system( + f'cd {self.mmdet_path} && sudo -E env "PATH=$PATH" "PYTHONPATH=." "CUDA_VISIBLE_DEVICES={devices}" python tools/train.py configs/pointpillars/pointpillars_hv_secfpn_8xb6_custom.py --work-dir={self.log_dir}') + self.rollback_configs() + + def test(self, data, ckpt_path=None, flags=''): + data.setup() + self.update_configs(os.path.abspath(data.state['save_path'])) + devices = [] if self.device == 'cpu' else self.devices + try: + os.system( + f'cd {self.mmdet_path} && sudo -E env "PATH=$PATH" "PYTHONPATH=." "CUDA_VISIBLE_DEVICES={devices}" python tools/test.py configs/pointpillars/pointpillars_hv_secfpn_8xb6_custom.py {ckpt_path} {flags}') + except: + logging.info('Failed') + self.rollback_configs() + + def predict(self, data, ckpt_path=None): + data.setup() + self.update_configs(os.path.abspath(data.state['save_path'])) + devices = [] if self.device == 'cpu' else self.devices + try: + os.system( + f'cd {self.mmdet_path} && sudo -E env "PATH=$PATH" "PYTHONPATH=." "CUDA_VISIBLE_DEVICES={devices}" python tools/infer.py configs/pointpillars/pointpillars_hv_secfpn_8xb6_custom.py {ckpt_path} {data.state["save_path"]}/points {self.log_dir}') + except: + logging.info('Failed') + self.rollback_configs() + + +def setup_mmdetection(): + if os.path.exists('../mmdetection3d'): + logging.info('mmdetection-3d found') + os.environ['MMDET_FOR_INNOFW'] = os.path.join(Path(os.getcwd()).parent, 'mmdetection3d') + if 'MMDET_FOR_INNOFW' not in os.environ: + logging.info("Cloning mmdetection-3d") + os.system( + "cd .. && git clone https://github.com/BarzaH/mmdetection3d.git && cd mmdetection3d && git checkout innofw_mod") + os.environ['MMDET_FOR_INNOFW'] = os.path.join(Path(os.getcwd()).parent, 'mmdetection3d') + logging.info("mmdetection-3d path " + os.environ['MMDET_FOR_INNOFW']) + + return os.environ['MMDET_FOR_INNOFW'] + + +def map_optimizer_to_mmdet_optim(optim_name): + return {'adam': 'Adam', 'adamw': 'AdamW', 'sgd': 'SGD'}[optim_name] diff --git a/innofw/core/integrations/ultralytics/trainer.py b/innofw/core/integrations/ultralytics/trainer.py index 96fc399d..ef2b698e 100755 --- a/innofw/core/integrations/ultralytics/trainer.py +++ b/innofw/core/integrations/ultralytics/trainer.py @@ -1,3 +1,5 @@ +import os + from omegaconf import DictConfig from ..base_adapter import BaseAdapter @@ -28,6 +30,11 @@ def get_device(trainer_cfg): devices = ",".join(map(str, range(n_devices))) result = f"{devices}" + try: + if int(result) < 2: + return "" + except: + pass return result diff --git a/innofw/utils/framework.py b/innofw/utils/framework.py index e705a3df..a725b5ff 100755 --- a/innofw/utils/framework.py +++ b/innofw/utils/framework.py @@ -20,6 +20,7 @@ from innofw.core.datamodules.lightning_datamodules.concatenated_datamodule import ( ConcatenatedLightningDatamodule, ) +from innofw.core.integrations.mmdetection.model_adapter import BaseMmdetModel def map_model_to_framework(model): @@ -30,16 +31,18 @@ def map_model_to_framework(model): if isinstance(model, xgboost.XGBModel): return Frameworks.xgboost - elif isinstance(model, torch.nn.Module): - return Frameworks.torch + elif isinstance(model, YOLO): + return Frameworks.ultralytics + elif isinstance(model, BaseMmdetModel): + return Frameworks.mmdetection elif isinstance(model, sklearn.base.BaseEstimator): return Frameworks.sklearn elif isinstance(model, BaseIntegrationModel): return model.framework + elif isinstance(model, torch.nn.Module): + return Frameworks.torch elif isinstance(model, catboost.CatBoost): return Frameworks.catboost - elif isinstance(model, YOLO): - return Frameworks.ultralytics else: raise NotImplementedError(f"Framework is not supported. {model}") @@ -197,7 +200,7 @@ def get_optimizer( # Assume by default that torch optimizers are suitable for all tasks framework_consistent = ( - framework is Frameworks.torch or framework is Frameworks.ultralytics + framework is Frameworks.torch or framework is Frameworks.ultralytics or framework is Frameworks.mmdetection ) if framework_consistent: items = dict() diff --git a/pckg_util.py b/pckg_util.py index 4c63f565..847412d5 100644 --- a/pckg_util.py +++ b/pckg_util.py @@ -75,3 +75,13 @@ def check_gpu_and_torch_compatibility(): ) except OSError: logging.info("GPU device is not available") + + +def install_mmcv(): + import torch + torch_version = str(torch.__version__)[:4] + cuda_version = str(torch.version.cuda).replace(".", "") + install_and_import("mmcv", + version="2.1.0", + params="-f", + link=f"https://download.openmmlab.com/mmcv/dist/cu{cuda_version}/torch{torch_version}/index.html") \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index 67dc1439..7a5247f3 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2,13 +2,24 @@ [[package]] name = "absl-py" -version = "1.3.0" +version = "2.1.0" description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py." optional = false -python-versions = ">=3.6" +python-versions = ">=3.7" +files = [ + 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a/pyproject.toml +++ b/pyproject.toml @@ -4,17 +4,28 @@ version = "1.0.0" description = "" authors = ["Kazybek Askarbek "] +[[tool.poetry.source]] +name = "cudatorch" +url = "https://download.pytorch.org/whl/cu113" +priority = "supplemental" + [tool.poetry.dependencies] python = ">=3.8,<3.10" python-dotenv = "^0.20.0" -torch = "~1.12.0" + + +torch = {version = "1.11.0", source = "cudatorch"} +torchvision = {version = "0.12.0", source = "cudatorch"} + +numpy = "1.21.1" +pillow = "9.1.0" tqdm = "^4.64.0" h5py = "^3.7.0" streamlit = "^1.10.0" -albumentations = "^1.1.0" -pytorch-lightning = "^1.6.4" +albumentations = "1.1.0" +pytorch-lightning = "1.8.0" xgboost = "^1.6.1" hydra-core = "^1.2.0" segmentation-models-pytorch = "0.3.3" @@ -32,7 +43,7 @@ transformers = "^4.21.2" datasets = "^2.4.0" onvif-zeep = "^0.2.12" fire = "^0.4.0" -yolov5 = "^7.0.7" +yolov5 = "7.0.7" streamlit-pydantic = "^0.5.0" selfies = "^2.1.1" setuptools = "^65.4.1" @@ -41,7 +52,7 @@ lovely-tensors = "^0.1.14" lovely-numpy = "^0.2.8" wandb = "^0.13.9" aeronet = "^0.0.18" -ultralytics = "^8.0.124" +ultralytics = "8.0.124" clearml = "^1.13.0" dash-bootstrap-components = "^1.4.1" dash-core-components = "^2.0.0" @@ -53,6 +64,12 @@ Flask = "^2.2.3" dvc = "^3.30.1" dvc-s3 = "^2.23.0" +open3d = "^0.18.0" +mmengine = "^0.10.4" +numba = "^0.58.0" +mmdet3d = "^1.4.0" +mmdet = {git = "https://github.com/BarzaH/mmdetection"} + [tool.poetry.dev-dependencies] mock = "^4.0.3" pytest-mock = "^3.7.0" diff --git a/tests/fixtures/config/trainers.py b/tests/fixtures/config/trainers.py index 959dabeb..2d8f4f41 100644 --- a/tests/fixtures/config/trainers.py +++ b/tests/fixtures/config/trainers.py @@ -23,6 +23,9 @@ trainer_cfg_w_gpu_devices_1 = base_trainer_on_gpu_cfg.copy() trainer_cfg_w_gpu_devices_1["devices"] = 1 +trainer_cfg_w_gpu_devices_0 = base_trainer_on_gpu_cfg.copy() +trainer_cfg_w_gpu_devices_0["devices"] = 0 + # case: accelerator = 'gpu', devices = 2 # uses two gpu devices trainer_cfg_w_gpu_devices_2 = base_trainer_on_gpu_cfg.copy() trainer_cfg_w_gpu_devices_2["devices"] = 2 diff --git a/tests/integration/models/torch/lighting_modules/test_mmdetection3d.py b/tests/integration/models/torch/lighting_modules/test_mmdetection3d.py new file mode 100644 index 00000000..7e55186d --- /dev/null +++ b/tests/integration/models/torch/lighting_modules/test_mmdetection3d.py @@ -0,0 +1,41 @@ +import os +import shutil + +from omegaconf import DictConfig + +from innofw.constants import Stages +from innofw.core.integrations.mmdetection.datamodule import Mmdetection3DDataModuleAdapter +from innofw.core.integrations.mmdetection.model_adapter import Mmdetection3DDataModel, \ + BaseMmdetModel +from tests.fixtures.config import optimizers as fixt_optimizers +from tests.fixtures.config import trainers as fixt_trainers + + +def test_integration(): + data_path = { + 'source': 'https://api.blackhole.ai.innopolis.university/public-datasets/lep_3d_detection/test.zip', + 'target': './tmp'} + datamodule = Mmdetection3DDataModuleAdapter(data_path, data_path, data_path, 4) + datamodule.setup_train_test_val() + datamodule.setup_infer() + assert datamodule.predict_dataloader() is None + assert datamodule.train_dataloader() is None + assert datamodule.test_dataloader() is None + assert datamodule.save_preds(None, Stages.test, '') is None + + optimizer_cfg = DictConfig(fixt_optimizers.adam_optim_w_target) + os.makedirs('./tmp/logs') + model = Mmdetection3DDataModel(BaseMmdetModel(), './tmp/logs', + fixt_trainers.trainer_cfg_w_gpu_devices_0, + optimizers_cfg=optimizer_cfg) + model.train(datamodule) + ckpt_path = model.mmdet_path + '/work_dirs/pointpillars_hv_secfpn_8xb6_custom/epoch_10.pth' + model.test(datamodule, ckpt_path=ckpt_path) + model.predict(datamodule, ckpt_path=ckpt_path) + + for i in range(3): + try: + shutil.rmtree('./tmp') + break + except: + pass