<filename>tests/test_shapenet_core.py<gh_stars>1-10
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
"""
Sanity checks for loading ShapeNetCore.
"""
import os
import unittest

import numpy as np
import torch
from common_testing import TestCaseMixin, get_tests_dir, load_rgb_image
from PIL import Image
from pytorch3d.datasets import ShapeNetCore, collate_batched_meshes
from pytorch3d.renderer import (
    FoVPerspectiveCameras,
    PointLights,
    RasterizationSettings,
    look_at_view_transform,
)
from torch.utils.data import DataLoader


# Set the SHAPENET_PATH to the local path to the dataset
SHAPENET_PATH = None
VERSION = 1
# If DEBUG=True, save out images generated in the tests for debugging.
# All saved images have prefix DEBUG_
DEBUG = False
DATA_DIR = get_tests_dir() / "data"


class TestShapenetCore(TestCaseMixin, unittest.TestCase):
    def setUp(self):
        """
        Check if the ShapeNet dataset is provided in the repo.
        If not, download this separately and update the shapenet_path`
        with the location of the dataset in order to run the tests.
        """
        if SHAPENET_PATH is None or not os.path.exists(SHAPENET_PATH):
            url = "https://www.shapenet.org/"
            msg = (
                "ShapeNet data not found, download from %s, update "
                "SHAPENET_PATH at the top of the file, and rerun."
            )

            self.skipTest(msg % url)

    def test_load_shapenet_core(self):
        """
        Test loading both the entire ShapeNetCore dataset and a subset of the ShapeNetCore
        dataset. Check the loaded datasets return items of the correct shapes and types.
        """
        # Try loading ShapeNetCore with an invalid version number and catch error.
        with self.assertRaises(ValueError) as err:
            ShapeNetCore(SHAPENET_PATH, version=3)
        self.assertTrue("Version number must be either 1 or 2." in str(err.exception))

        # Load ShapeNetCore without specifying any particular categories.
        shapenet_dataset = ShapeNetCore(SHAPENET_PATH, version=VERSION)

        # Count the number of grandchildren directories (which should be equal to
        # the total number of objects in the dataset) by walking through the given
        # directory.
        wnsynset_list = [
            wnsynset
            for wnsynset in os.listdir(SHAPENET_PATH)
            if os.path.isdir(os.path.join(SHAPENET_PATH, wnsynset))
        ]
        model_num_list = [
            (len(next(os.walk(os.path.join(SHAPENET_PATH, wnsynset)))[1]))
            for wnsynset in wnsynset_list
        ]
        # Check total number of objects in the dataset is correct.
        self.assertEqual(len(shapenet_dataset), sum(model_num_list))

        # Randomly retrieve an object from the dataset.
        rand_obj = shapenet_dataset[torch.randint(len(shapenet_dataset), (1,))]
        # Check that data types and shapes of items returned by __getitem__ are correct.
        verts, faces = rand_obj["verts"], rand_obj["faces"]
        self.assertTrue(verts.dtype == torch.float32)
        self.assertTrue(faces.dtype == torch.int64)
        self.assertEqual(verts.ndim, 2)
        self.assertEqual(verts.shape[-1], 3)
        self.assertEqual(faces.ndim, 2)
        self.assertEqual(faces.shape[-1], 3)

        # Load six categories from ShapeNetCore.
        # Specify categories with a combination of offsets and labels.
        shapenet_subset = ShapeNetCore(
            SHAPENET_PATH,
            synsets=[
                "04330267",
                "guitar",
                "02801938",
                "birdhouse",
                "03991062",
                "tower",
            ],
            version=1,
        )
        subset_offsets = [
            "04330267",
            "03467517",
            "02801938",
            "02843684",
            "03991062",
            "04460130",
        ]
        subset_model_nums = [
            (len(next(os.walk(os.path.join(SHAPENET_PATH, offset)))[1]))
            for offset in subset_offsets
        ]
        self.assertEqual(len(shapenet_subset), sum(subset_model_nums))

    def test_collate_models(self):
        """
        Test collate_batched_meshes returns items of the correct shapes and types.
        Check that when collate_batched_meshes is passed to Dataloader, batches of
        the correct shapes and types are returned.
        """
        # Load ShapeNetCore without specifying any particular categories.
        shapenet_dataset = ShapeNetCore(SHAPENET_PATH)
        # Randomly retrieve several objects from the dataset.
        rand_idxs = torch.randint(len(shapenet_dataset), (6,))
        rand_objs = [shapenet_dataset[idx] for idx in rand_idxs]

        # Collate the randomly selected objects
        collated_meshes = collate_batched_meshes(rand_objs)
        verts, faces = (collated_meshes["verts"], collated_meshes["faces"])
        self.assertEqual(len(verts), 6)
        self.assertEqual(len(faces), 6)

        # Pass the custom collate_fn function to DataLoader and check elements
        # in batch have the correct shape.
        batch_size = 12
        shapenet_core_loader = DataLoader(
            shapenet_dataset, batch_size=batch_size, collate_fn=collate_batched_meshes
        )
        it = iter(shapenet_core_loader)
        object_batch = next(it)
        self.assertEqual(len(object_batch["synset_id"]), batch_size)
        self.assertEqual(len(object_batch["model_id"]), batch_size)
        self.assertEqual(len(object_batch["label"]), batch_size)
        self.assertEqual(object_batch["mesh"].verts_padded().shape[0], batch_size)
        self.assertEqual(object_batch["mesh"].faces_padded().shape[0], batch_size)

    def test_catch_render_arg_errors(self):
        """
        Test rendering ShapeNetCore with invalid model_ids, categories or indices,
        and catch corresponding errors.
        """
        # Load ShapeNetCore.
        shapenet_dataset = ShapeNetCore(SHAPENET_PATH)

        # Try loading with an invalid model_id and catch error.
        with self.assertRaises(ValueError) as err:
            shapenet_dataset.render(model_ids=["piano0"])
        self.assertTrue("not found in the loaded dataset" in str(err.exception))

        # Try loading with an index out of bounds and catch error.
        with self.assertRaises(IndexError) as err:
            shapenet_dataset.render(idxs=[100000])
        self.assertTrue("are out of bounds" in str(err.exception))

    def test_render_shapenet_core(self):
        """
        Test rendering objects from ShapeNetCore.
        """
        # Setup device and seed for random selections.
        device = torch.device("cuda:0")
        torch.manual_seed(39)

        # Load category piano from ShapeNetCore.
        piano_dataset = ShapeNetCore(SHAPENET_PATH, synsets=["piano"])

        # Rendering settings.
        R, T = look_at_view_transform(1.0, 1.0, 90)
        cameras = FoVPerspectiveCameras(R=R, T=T, device=device)
        raster_settings = RasterizationSettings(image_size=512)
        lights = PointLights(
            location=torch.tensor([0.0, 1.0, -2.0], device=device)[None],
            # TODO: debug the source of the discrepancy in two images when rendering on GPU.
            diffuse_color=((0, 0, 0),),
            specular_color=((0, 0, 0),),
            device=device,
        )

        # Render first three models in the piano category.
        pianos = piano_dataset.render(
            idxs=list(range(3)),
            device=device,
            cameras=cameras,
            raster_settings=raster_settings,
            lights=lights,
        )
        # Check that there are three images in the batch.
        self.assertEqual(pianos.shape[0], 3)

        # Compare the rendered models to the reference images.
        for idx in range(3):
            piano_rgb = pianos[idx, ..., :3].squeeze().cpu()
            if DEBUG:
                Image.fromarray((piano_rgb.numpy() * 255).astype(np.uint8)).save(
                    DATA_DIR / ("DEBUG_shapenet_core_render_piano_by_idxs_%s.png" % idx)
                )
            image_ref = load_rgb_image(
                "test_shapenet_core_render_piano_%s.png" % idx, DATA_DIR
            )
            self.assertClose(piano_rgb, image_ref, atol=0.05)

        # Render the same piano models but by model_ids this time.
        pianos_2 = piano_dataset.render(
            model_ids=[
                "13394ca47c89f91525a3aaf903a41c90",
                "14755c2ee8e693aba508f621166382b0",
                "156c4207af6d2c8f1fdc97905708b8ea",
            ],
            device=device,
            cameras=cameras,
            raster_settings=raster_settings,
            lights=lights,
        )

        # Compare the rendered models to the reference images.
        for idx in range(3):
            piano_rgb_2 = pianos_2[idx, ..., :3].squeeze().cpu()
            if DEBUG:
                Image.fromarray((piano_rgb_2.numpy() * 255).astype(np.uint8)).save(
                    DATA_DIR / ("DEBUG_shapenet_core_render_piano_by_ids_%s.png" % idx)
                )
            image_ref = load_rgb_image(
                "test_shapenet_core_render_piano_%s.png" % idx, DATA_DIR
            )
            self.assertClose(piano_rgb_2, image_ref, atol=0.05)

        #######################
        # Render by categories
        #######################

        # Load ShapeNetCore.
        shapenet_dataset = ShapeNetCore(SHAPENET_PATH)

        # Render a mixture of categories and specify the number of models to be
        # randomly sampled from each category.
        mixed_objs = shapenet_dataset.render(
            categories=["faucet", "chair"],
            sample_nums=[2, 1],
            device=device,
            cameras=cameras,
            raster_settings=raster_settings,
            lights=lights,
        )
        # Compare the rendered models to the reference images.
        for idx in range(3):
            mixed_rgb = mixed_objs[idx, ..., :3].squeeze().cpu()
            if DEBUG:
                Image.fromarray((mixed_rgb.numpy() * 255).astype(np.uint8)).save(
                    DATA_DIR
                    / ("DEBUG_shapenet_core_render_mixed_by_categories_%s.png" % idx)
                )
            image_ref = load_rgb_image(
                "test_shapenet_core_render_mixed_by_categories_%s.png" % idx, DATA_DIR
            )
            self.assertClose(mixed_rgb, image_ref, atol=0.05)

        # Render a mixture of categories without specifying sample_nums.
        mixed_objs_2 = shapenet_dataset.render(
            categories=["faucet", "chair"],
            device=device,
            cameras=cameras,
            raster_settings=raster_settings,
            lights=lights,
        )
        # Compare the rendered models to the reference images.
        for idx in range(2):
            mixed_rgb_2 = mixed_objs_2[idx, ..., :3].squeeze().cpu()
            if DEBUG:
                Image.fromarray((mixed_rgb_2.numpy() * 255).astype(np.uint8)).save(
                    DATA_DIR
                    / ("DEBUG_shapenet_core_render_without_sample_nums_%s.png" % idx)
                )
            image_ref = load_rgb_image(
                "test_shapenet_core_render_without_sample_nums_%s.png" % idx, DATA_DIR
            )
            self.assertClose(mixed_rgb_2, image_ref, atol=0.05)

<filename>tests/test_shapenet_core.py<gh_stars>1-10
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
"""
Sanity checks for loading ShapeNetCore.
"""
import os
import unittest

import numpy as np
import torch
from common_testing import TestCaseMixin, get_tests_dir, load_rgb_image
from PIL import Image
from pytorch3d.datasets import ShapeNetCore, collate_batched_meshes
from pytorch3d.renderer import (
    FoVPerspectiveCameras,
    PointLights,
    RasterizationSettings,
    look_at_view_transform,
)
from torch.utils.data import DataLoader


# Set the SHAPENET_PATH to the local path to the dataset
SHAPENET_PATH = None
VERSION = 1
# If DEBUG=True, save out images generated in the tests for debugging.
# All saved images have prefix DEBUG_
DEBUG = False
DATA_DIR = get_tests_dir() / "data"


class TestShapenetCore(TestCaseMixin, unittest.TestCase):
    def setUp(self):
        """
        Check if the ShapeNet dataset is provided in the repo.
        If not, download this separately and update the shapenet_path`
        with the location of the dataset in order to run the tests.
        """
        if SHAPENET_PATH is None or not os.path.exists(SHAPENET_PATH):
            url = "https://www.shapenet.org/"
            msg = (
                "ShapeNet data not found, download from %s, update "
                "SHAPENET_PATH at the top of the file, and rerun."
            )

            self.skipTest(msg % url)

    def test_load_shapenet_core(self):
        """
        Test loading both the entire ShapeNetCore dataset and a subset of the ShapeNetCore
        dataset. Check the loaded datasets return items of the correct shapes and types.
        """
        # Try loading ShapeNetCore with an invalid version number and catch error.
        with self.assertRaises(ValueError) as err:
            ShapeNetCore(SHAPENET_PATH, version=3)
        self.assertTrue("Version number must be either 1 or 2." in str(err.exception))

        # Load ShapeNetCore without specifying any particular categories.
        shapenet_dataset = ShapeNetCore(SHAPENET_PATH, version=VERSION)

        # Count the number of grandchildren directories (which should be equal to
        # the total number of objects in the dataset) by walking through the given
        # directory.
        wnsynset_list = [
            wnsynset
            for wnsynset in os.listdir(SHAPENET_PATH)
            if os.path.isdir(os.path.join(SHAPENET_PATH, wnsynset))
        ]
        model_num_list = [
            (len(next(os.walk(os.path.join(SHAPENET_PATH, wnsynset)))[1]))
            for wnsynset in wnsynset_list
        ]
        # Check total number of objects in the dataset is correct.
        self.assertEqual(len(shapenet_dataset), sum(model_num_list))

        # Randomly retrieve an object from the dataset.
        rand_obj = shapenet_dataset[torch.randint(len(shapenet_dataset), (1,))]
        # Check that data types and shapes of items returned by __getitem__ are correct.
        verts, faces = rand_obj["verts"], rand_obj["faces"]
        self.assertTrue(verts.dtype == torch.float32)
        self.assertTrue(faces.dtype == torch.int64)
        self.assertEqual(verts.ndim, 2)
        self.assertEqual(verts.shape[-1], 3)
        self.assertEqual(faces.ndim, 2)
        self.assertEqual(faces.shape[-1], 3)

        # Load six categories from ShapeNetCore.
        # Specify categories with a combination of offsets and labels.
        shapenet_subset = ShapeNetCore(
            SHAPENET_PATH,
            synsets=[
                "04330267",
                "guitar",
                "02801938",
                "birdhouse",
                "03991062",
                "tower",
            ],
            version=1,
        )
        subset_offsets = [
            "04330267",
            "03467517",
            "02801938",
            "02843684",
            "03991062",
            "04460130",
        ]
        subset_model_nums = [
            (len(next(os.walk(os.path.join(SHAPENET_PATH, offset)))[1]))
            for offset in subset_offsets
        ]
        self.assertEqual(len(shapenet_subset), sum(subset_model_nums))

    def test_collate_models(self):
        """
        Test collate_batched_meshes returns items of the correct shapes and types.
        Check that when collate_batched_meshes is passed to Dataloader, batches of
        the correct shapes and types are returned.
        """
        # Load ShapeNetCore without specifying any particular categories.
        shapenet_dataset = ShapeNetCore(SHAPENET_PATH)
        # Randomly retrieve several objects from the dataset.
        rand_idxs = torch.randint(len(shapenet_dataset), (6,))
        rand_objs = [shapenet_dataset[idx] for idx in rand_idxs]

        # Collate the randomly selected objects
        collated_meshes = collate_batched_meshes(rand_objs)
        verts, faces = (collated_meshes["verts"], collated_meshes["faces"])
        self.assertEqual(len(verts), 6)
        self.assertEqual(len(faces), 6)

        # Pass the custom collate_fn function to DataLoader and check elements
        # in batch have the correct shape.
        batch_size = 12
        shapenet_core_loader = DataLoader(
            shapenet_dataset, batch_size=batch_size, collate_fn=collate_batched_meshes
        )
        it = iter(shapenet_core_loader)
        object_batch = next(it)
        self.assertEqual(len(object_batch["synset_id"]), batch_size)
        self.assertEqual(len(object_batch["model_id"]), batch_size)
        self.assertEqual(len(object_batch["label"]), batch_size)
        self.assertEqual(object_batch["mesh"].verts_padded().shape[0], batch_size)
        self.assertEqual(object_batch["mesh"].faces_padded().shape[0], batch_size)

    def test_catch_render_arg_errors(self):
        """
        Test rendering ShapeNetCore with invalid model_ids, categories or indices,
        and catch corresponding errors.
        """
        # Load ShapeNetCore.
        shapenet_dataset = ShapeNetCore(SHAPENET_PATH)

        # Try loading with an invalid model_id and catch error.
        with self.assertRaises(ValueError) as err:
            shapenet_dataset.render(model_ids=["piano0"])
        self.assertTrue("not found in the loaded dataset" in str(err.exception))

        # Try loading with an index out of bounds and catch error.
        with self.assertRaises(IndexError) as err:
            shapenet_dataset.render(idxs=[100000])
        self.assertTrue("are out of bounds" in str(err.exception))

    def test_render_shapenet_core(self):
        """
        Test rendering objects from ShapeNetCore.
        """
        # Setup device and seed for random selections.
        device = torch.device("cuda:0")
        torch.manual_seed(39)

        # Load category piano from ShapeNetCore.
        piano_dataset = ShapeNetCore(SHAPENET_PATH, synsets=["piano"])

        # Rendering settings.
        R, T = look_at_view_transform(1.0, 1.0, 90)
        cameras = FoVPerspectiveCameras(R=R, T=T, device=device)
        raster_settings = RasterizationSettings(image_size=512)
        lights = PointLights(
            location=torch.tensor([0.0, 1.0, -2.0], device=device)[None],
            # TODO: debug the source of the discrepancy in two images when rendering on GPU.
            diffuse_color=((0, 0, 0),),
            specular_color=((0, 0, 0),),
            device=device,
        )

        # Render first three models in the piano category.
        pianos = piano_dataset.render(
            idxs=list(range(3)),
            device=device,
            cameras=cameras,
            raster_settings=raster_settings,
            lights=lights,
        )
        # Check that there are three images in the batch.
        self.assertEqual(pianos.shape[0], 3)

        # Compare the rendered models to the reference images.
        for idx in range(3):
            piano_rgb = pianos[idx, ..., :3].squeeze().cpu()
            if DEBUG:
                Image.fromarray((piano_rgb.numpy() * 255).astype(np.uint8)).save(
                    DATA_DIR / ("DEBUG_shapenet_core_render_piano_by_idxs_%s.png" % idx)
                )
            image_ref = load_rgb_image(
                "test_shapenet_core_render_piano_%s.png" % idx, DATA_DIR
            )
            self.assertClose(piano_rgb, image_ref, atol=0.05)

        # Render the same piano models but by model_ids this time.
        pianos_2 = piano_dataset.render(
            model_ids=[
                "13394ca47c89f91525a3aaf903a41c90",
                "14755c2ee8e693aba508f621166382b0",
                "156c4207af6d2c8f1fdc97905708b8ea",
            ],
            device=device,
            cameras=cameras,
            raster_settings=raster_settings,
            lights=lights,
        )

        # Compare the rendered models to the reference images.
        for idx in range(3):
            piano_rgb_2 = pianos_2[idx, ..., :3].squeeze().cpu()
            if DEBUG:
                Image.fromarray((piano_rgb_2.numpy() * 255).astype(np.uint8)).save(
                    DATA_DIR / ("DEBUG_shapenet_core_render_piano_by_ids_%s.png" % idx)
                )
            image_ref = load_rgb_image(
                "test_shapenet_core_render_piano_%s.png" % idx, DATA_DIR
            )
            self.assertClose(piano_rgb_2, image_ref, atol=0.05)

        #######################
        # Render by categories
        #######################

        # Load ShapeNetCore.
        shapenet_dataset = ShapeNetCore(SHAPENET_PATH)

        # Render a mixture of categories and specify the number of models to be
        # randomly sampled from each category.
        mixed_objs = shapenet_dataset.render(
            categories=["faucet", "chair"],
            sample_nums=[2, 1],
            device=device,
            cameras=cameras,
            raster_settings=raster_settings,
            lights=lights,
        )
        # Compare the rendered models to the reference images.
        for idx in range(3):
            mixed_rgb = mixed_objs[idx, ..., :3].squeeze().cpu()
            if DEBUG:
                Image.fromarray((mixed_rgb.numpy() * 255).astype(np.uint8)).save(
                    DATA_DIR
                    / ("DEBUG_shapenet_core_render_mixed_by_categories_%s.png" % idx)
                )
            image_ref = load_rgb_image(
                "test_shapenet_core_render_mixed_by_categories_%s.png" % idx, DATA_DIR
            )
            self.assertClose(mixed_rgb, image_ref, atol=0.05)

        # Render a mixture of categories without specifying sample_nums.
        mixed_objs_2 = shapenet_dataset.render(
            categories=["faucet", "chair"],
            device=device,
            cameras=cameras,
            raster_settings=raster_settings,
            lights=lights,
        )
        # Compare the rendered models to the reference images.
        for idx in range(2):
            mixed_rgb_2 = mixed_objs_2[idx, ..., :3].squeeze().cpu()
            if DEBUG:
                Image.fromarray((mixed_rgb_2.numpy() * 255).astype(np.uint8)).save(
                    DATA_DIR
                    / ("DEBUG_shapenet_core_render_without_sample_nums_%s.png" % idx)
                )
            image_ref = load_rgb_image(
                "test_shapenet_core_render_without_sample_nums_%s.png" % idx, DATA_DIR
            )
            self.assertClose(mixed_rgb_2, image_ref, atol=0.05)

# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. Sanity checks for loading ShapeNetCore. # Set the SHAPENET_PATH to the local path to the dataset # If DEBUG=True, save out images generated in the tests for debugging. # All saved images have prefix DEBUG_ Check if the ShapeNet dataset is provided in the repo.         If not, download this separately and update the shapenet_path`         with the location of the dataset in order to run the tests. Test loading both the entire ShapeNetCore dataset and a subset of the ShapeNetCore         dataset. Check the loaded datasets return items of the correct shapes and types. # Try loading ShapeNetCore with an invalid version number and catch error. # Load ShapeNetCore without specifying any particular categories. # Count the number of grandchildren directories (which should be equal to # the total number of objects in the dataset) by walking through the given # directory. # Check total number of objects in the dataset is correct. # Randomly retrieve an object from the dataset. # Check that data types and shapes of items returned by __getitem__ are correct. # Load six categories from ShapeNetCore. # Specify categories with a combination of offsets and labels. Test collate_batched_meshes returns items of the correct shapes and types.         Check that when collate_batched_meshes is passed to Dataloader, batches of         the correct shapes and types are returned. # Load ShapeNetCore without specifying any particular categories. # Randomly retrieve several objects from the dataset. # Collate the randomly selected objects # Pass the custom collate_fn function to DataLoader and check elements # in batch have the correct shape. Test rendering ShapeNetCore with invalid model_ids, categories or indices,         and catch corresponding errors. # Load ShapeNetCore. # Try loading with an invalid model_id and catch error. # Try loading with an index out of bounds and catch error. Test rendering objects from ShapeNetCore. # Setup device and seed for random selections. # Load category piano from ShapeNetCore. # Rendering settings. # TODO: debug the source of the discrepancy in two images when rendering on GPU. # Render first three models in the piano category. # Check that there are three images in the batch. # Compare the rendered models to the reference images. # Render the same piano models but by model_ids this time. # Compare the rendered models to the reference images. ####################### # Render by categories ####################### # Load ShapeNetCore. # Render a mixture of categories and specify the number of models to be # randomly sampled from each category. # Compare the rendered models to the reference images. # Render a mixture of categories without specifying sample_nums. # Compare the rendered models to the reference images.