Spaces:
Running
Running
File size: 5,397 Bytes
7088d16 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import contextlib
import math
import os
import unittest
from typing import Tuple
import torch
from pytorch3d.implicitron.dataset.json_index_dataset import JsonIndexDataset
from pytorch3d.implicitron.dataset.visualize import get_implicitron_sequence_pointcloud
from pytorch3d.implicitron.models.visualization.render_flyaround import render_flyaround
from pytorch3d.implicitron.tools.config import expand_args_fields
from pytorch3d.implicitron.tools.point_cloud_utils import render_point_cloud_pytorch3d
from pytorch3d.renderer.cameras import CamerasBase
from tests.common_testing import interactive_testing_requested
from visdom import Visdom
from .common_resources import get_skateboard_data
class TestModelVisualize(unittest.TestCase):
def test_flyaround_one_sequence(
self,
image_size: int = 256,
):
if not interactive_testing_requested():
return
category = "skateboard"
stack = contextlib.ExitStack()
dataset_root, path_manager = stack.enter_context(get_skateboard_data())
self.addCleanup(stack.close)
frame_file = os.path.join(dataset_root, category, "frame_annotations.jgz")
sequence_file = os.path.join(dataset_root, category, "sequence_annotations.jgz")
subset_lists_file = os.path.join(dataset_root, category, "set_lists.json")
expand_args_fields(JsonIndexDataset)
train_dataset = JsonIndexDataset(
frame_annotations_file=frame_file,
sequence_annotations_file=sequence_file,
subset_lists_file=subset_lists_file,
dataset_root=dataset_root,
image_height=image_size,
image_width=image_size,
box_crop=True,
load_point_clouds=True,
path_manager=path_manager,
subsets=[
"train_known",
],
)
# select few sequences to visualize
sequence_names = list(train_dataset.seq_annots.keys())
# select the first sequence name
show_sequence_name = sequence_names[0]
output_dir = os.path.split(os.path.abspath(__file__))[0]
visdom_show_preds = Visdom().check_connection()
for load_dataset_pointcloud in [True, False]:
model = _PointcloudRenderingModel(
train_dataset,
show_sequence_name,
device="cuda:0",
load_dataset_pointcloud=load_dataset_pointcloud,
)
video_path = os.path.join(
output_dir,
f"load_pcl_{load_dataset_pointcloud}",
)
os.makedirs(output_dir, exist_ok=True)
for output_video_frames_dir in [None, video_path]:
render_flyaround(
train_dataset,
show_sequence_name,
model,
video_path,
n_flyaround_poses=10,
fps=5,
max_angle=2 * math.pi,
trajectory_type="circular_lsq_fit",
trajectory_scale=1.1,
scene_center=(0.0, 0.0, 0.0),
up=(0.0, 1.0, 0.0),
traj_offset=1.0,
n_source_views=1,
visdom_show_preds=visdom_show_preds,
visdom_environment="test_model_visalize",
visdom_server="http://127.0.0.1",
visdom_port=8097,
num_workers=10,
seed=None,
video_resize=None,
visualize_preds_keys=[
"images_render",
"depths_render",
"masks_render",
"_all_source_images",
],
output_video_frames_dir=output_video_frames_dir,
)
class _PointcloudRenderingModel(torch.nn.Module):
def __init__(
self,
train_dataset: JsonIndexDataset,
sequence_name: str,
render_size: Tuple[int, int] = (400, 400),
device=None,
load_dataset_pointcloud: bool = False,
max_frames: int = 30,
num_workers: int = 10,
):
super().__init__()
self._render_size = render_size
point_cloud, _ = get_implicitron_sequence_pointcloud(
train_dataset,
sequence_name=sequence_name,
mask_points=True,
max_frames=max_frames,
num_workers=num_workers,
load_dataset_point_cloud=load_dataset_pointcloud,
)
self._point_cloud = point_cloud.to(device)
def forward(
self,
camera: CamerasBase,
**kwargs,
):
image_render, mask_render, depth_render = render_point_cloud_pytorch3d(
camera[0],
self._point_cloud,
render_size=self._render_size,
point_radius=1e-2,
topk=10,
bg_color=0.0,
)
return {
"images_render": image_render.clamp(0.0, 1.0),
"masks_render": mask_render,
"depths_render": depth_render,
}
|