{ "cells": [ { "cell_type": "markdown", "id": "3c00934d-0f14-4c1a-a563-94d627b377e5", "metadata": {}, "source": [ "## Notebook to visualize a dataset\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "b15dfdc1-5054-40bc-9631-4c7426eecfd9", "metadata": {}, "outputs": [], "source": [ "from notebook_imports import *\n", "\n", "import torch\n", "from torch.utils.data import DataLoader\n", "\n", "from pyrad.data.dataloader import TrainDataloader\n", "from pyrad.data.image_dataset import ImageDataset, PanopticImageDataset\n", "from pyrad.data.image_sampler import CacheImageSampler\n", "from pyrad.data.pixel_sampler import PixelSampler\n", "from pyrad.data.utils import DatasetInputs, get_dataset_inputs_from_dataset_config\n", "from pyrad.graphs.modules.ray_generator import RayGenerator\n", "from pyrad.graphs.modules.scene_colliders import SceneBoundsCollider, AABBBoxCollider\n", "from pyrad.cameras.rays import RayBundle\n", "from pyrad.utils.io import get_absolute_path\n", "from pyrad.viewer.plotly import get_line_segments_from_lines\n", "from pyrad.cameras.cameras import get_camera_model\n", "from pyrad.utils.misc import get_dict_to_torch, instantiate_from_dict_config\n", "\n", "\n", "from hydra import compose, initialize\n", "from omegaconf import open_dict\n", "import pprint\n", "from tqdm import tqdm\n", "import random" ] }, { "cell_type": "code", "execution_count": 2, "id": "7fe7478d", "metadata": {}, "outputs": [], "source": [ "with initialize(version_base=None, config_path=\"../configs\"):\n", " config = compose(config_name=\"graph_default.yaml\")" ] }, { "cell_type": "code", "execution_count": 3, "id": "642b0a25-0e88-4eac-b661-a57a74fb10fc", "metadata": {}, "outputs": [], "source": [ "dataset_inputs = get_dataset_inputs_from_dataset_config(**config.data.dataset_inputs_train, split=\"train\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "001bdf3b-60a0-4bb2-9f88-ae36f2036c80", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 0%| | 0/10 [00:00, ?it/s]\n" ] } ], "source": [ "device = \"cpu\"\n", "\n", "# ImageDataset\n", "image_dataset_train = instantiate_from_dict_config(config.data.image_dataset_train, **dataset_inputs.as_dict())\n", "# ImageSampler\n", "image_sampler_train = instantiate_from_dict_config(\n", " config.data.dataloader_train.image_sampler, dataset=image_dataset_train, device=device\n", ")\n", "# PixelSampler\n", "pixel_sampler_train = instantiate_from_dict_config(config.data.dataloader_train.pixel_sampler, keep_full_image=True)\n", "\n", "dataloader_train = TrainDataloader(image_sampler_train, pixel_sampler_train)\n", "\n", "ray_generator = RayGenerator(dataset_inputs.intrinsics, dataset_inputs.camera_to_world)\n", "\n", "iter_dataloader_train = iter(dataloader_train)\n", "\n", "num_batches = 10\n", "for _ in tqdm(range(num_batches)):\n", " ray_indices, batch = next(iter_dataloader_train)\n", " ray_bundle = ray_generator.forward(ray_indices)\n", " break" ] }, { "cell_type": "code", "execution_count": 5, "id": "19b85061-a1b0-458b-9d00-96cde6f37dca", "metadata": {}, "outputs": [ { "data": { "text/html": [ "