opdmulti-demo / inference.py
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"""
inference.py
------------
Provides functionality to run the OPDMulti model on an input image, independent of dataset and ground truth, and
visualize the output. Large portions of the code originate from get_prediction.py, rgbd_to_pcd_vis.py,
evaluate_on_log.py, and other related files. The primary goal was to create a more standalone script which could be
converted more easily into a public demo, thus the goal was to sever most dependencies on existing ground truth or
datasets.
Example usage:
python inference.py \
--rgb path/to/59-4860.png \
--depth path/to/59-4860_d.png \
--model path/to/model.pth \
--output path/to/output_dir
"""
import argparse
import logging
import os
import time
from typing import Any
import imageio
import open3d as o3d
import numpy as np
import torch
import torch.nn as nn
from detectron2 import engine, evaluation
from detectron2.modeling import build_model
from detectron2.config import get_cfg, CfgNode
from detectron2.projects.deeplab import add_deeplab_config
from detectron2.structures import instances
from detectron2.utils import comm
from detectron2.utils.logger import setup_logger
from mask2former import (
add_maskformer2_config,
add_motionnet_config,
)
from utilities import prediction_to_json
from visualization import (
draw_line,
generate_rotation_visualization,
generate_translation_visualization,
batch_trim,
create_gif,
)
# import based on torch version. Required for model loading. Code is taken from fvcore.common.checkpoint, in order to
# replicate model loading without the overhead of setting up an OPDTrainer
TORCH_VERSION: tuple[int, ...] = tuple(int(x) for x in torch.__version__.split(".")[:2])
if TORCH_VERSION >= (1, 11):
from torch.ao import quantization
from torch.ao.quantization import FakeQuantizeBase, ObserverBase
elif (
TORCH_VERSION >= (1, 8)
and hasattr(torch.quantization, "FakeQuantizeBase")
and hasattr(torch.quantization, "ObserverBase")
):
from torch import quantization
from torch.quantization import FakeQuantizeBase, ObserverBase
# TODO: find a global place for this instead of in many places in code
TYPE_CLASSIFICATION = {
0: "rotation",
1: "translation",
}
ARROW_COLOR = [0, 1, 0] # green
def get_parser() -> argparse.ArgumentParser:
"""
Specfy command-line arguments.
The primary inputs to the script should be the image paths (RGBD) and camera intrinsics. Other arguments are
provided to facilitate script testing and model changes. Run file with -h/--help to see all arguments.
:return: parser for extracting command-line arguments
"""
parser = argparse.ArgumentParser(description="Inference for OPDMulti")
# The main arguments which should be specified by the user
parser.add_argument(
"--rgb",
dest="rgb_image",
metavar="FILE",
help="path to RGB image file on which to run model",
)
parser.add_argument(
"--depth",
dest="depth_image",
metavar="FILE",
help="path to depth image file on which to run model",
)
parser.add_argument( # FIXME: might make more sense to make this a path
"-i",
"--intrinsics",
nargs=9,
default=[
214.85935872395834,
0.0,
0.0,
0.0,
214.85935872395834,
0.0,
125.90160319010417,
95.13726399739583,
1.0,
],
dest="intrinsics",
help="camera intrinsics matrix, as a list of values",
)
# optional parameters for user to specify
parser.add_argument(
"-n",
"--num-samples",
default=10,
dest="num_samples",
metavar="NUM",
help="number of sample states to generate in visualization",
)
parser.add_argument(
"--crop",
action="store_true",
dest="crop",
help="crop whitespace out of images for visualization",
)
# local script development arguments
parser.add_argument(
"-m",
"--model",
default="path/to/model/file", # FIXME: set a good default path
dest="model",
metavar="FILE",
help="path to model file to run",
)
parser.add_argument(
"-c",
"--config",
default="configs/coco/instance-segmentation/swin/opd_v1_real.yaml",
metavar="FILE",
dest="config_file",
help="path to config file",
)
parser.add_argument(
"-o",
"--output",
default="output", # FIXME: set a good default path
dest="output",
help="path to output directory in which to save results",
)
parser.add_argument(
"--num-processes",
default=1,
dest="num_processes",
help="number of processes per machine. When using GPUs, this should be the number of GPUs.",
)
parser.add_argument(
"-s",
"--score-threshold",
default=0.8,
type=float,
dest="score_threshold",
help="threshold between 0.0 and 1.0 by which to filter out bad predictions",
)
parser.add_argument(
"--input-format",
default="RGB",
dest="input_format",
help="input format of image. Must be one of RGB, RGBD, or depth",
)
parser.add_argument(
"--cpu",
action="store_true",
help="flag to require code to use CPU only",
)
return parser
def setup_cfg(args: argparse.Namespace) -> CfgNode:
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
# add model configurations
add_deeplab_config(cfg)
add_maskformer2_config(cfg)
add_motionnet_config(cfg)
cfg.merge_from_file(args.config_file)
# set additional config parameters
cfg.MODEL.WEIGHTS = args.model
cfg.OBJ_DETECT = False # TODO: figure out if this is needed, and parameterize it
cfg.MODEL.MOTIONNET.VOTING = "none"
# Output directory
cfg.OUTPUT_DIR = args.output
cfg.MODEL.DEVICE = "cpu" if args.cpu else "cuda"
cfg.MODEL.MODELATTRPATH = None
# Input format
cfg.INPUT.FORMAT = args.input_format
if args.input_format == "RGB":
cfg.MODEL.PIXEL_MEAN = cfg.MODEL.PIXEL_MEAN[0:3]
cfg.MODEL.PIXEL_STD = cfg.MODEL.PIXEL_STD[0:3]
elif args.input_format == "depth":
cfg.MODEL.PIXEL_MEAN = cfg.MODEL.PIXEL_MEAN[3:4]
cfg.MODEL.PIXEL_STD = cfg.MODEL.PIXEL_STD[3:4]
elif args.input_format == "RGBD":
pass
else:
raise ValueError("Invalid input format")
cfg.freeze()
engine.default_setup(cfg, args)
# Setup logger for "mask_former" module
setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name="opdformer")
return cfg
def format_input(rgb_path: str) -> list[dict[str, Any]]:
"""
Read and format input image into detectron2 form so that it can be passed to the model.
:param rgb_path: path to RGB image file
:return: list of dictionaries per image, where each dictionary is of the form
{
"file_name": path to RGB image,
"image": torch.Tensor of dimensions [channel, height, width] representing the image
}
"""
image = imageio.imread(rgb_path).astype(np.float32)
image_tensor = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1))) # dim: [channel, height, width]
return [{"file_name": rgb_path, "image": image_tensor}]
def load_model(model: nn.Module, checkpoint: Any) -> None:
"""
Load weights from a checkpoint.
The majority of the function definition is taken from the DetectionCheckpointer implementation provided in
detectron2. While not all of this code is necessarily needed for model loading, it was ported with the intention
of keeping the implementation and output as close to the original as possible, and reusing the checkpoint class here
in isolation was determined to be infeasible.
:param model: model for which to load weights
:param checkpoint: checkpoint contains the weights.
"""
def _strip_prefix_if_present(state_dict: dict[str, Any], prefix: str) -> None:
"""If prefix is found on all keys in state dict, remove prefix."""
keys = sorted(state_dict.keys())
if not all(len(key) == 0 or key.startswith(prefix) for key in keys):
return
for key in keys:
newkey = key[len(prefix) :]
state_dict[newkey] = state_dict.pop(key)
checkpoint_state_dict = checkpoint.pop("model")
# convert from numpy to tensor
for k, v in checkpoint_state_dict.items():
if not isinstance(v, np.ndarray) and not isinstance(v, torch.Tensor):
raise ValueError("Unsupported type found in checkpoint! {}: {}".format(k, type(v)))
if not isinstance(v, torch.Tensor):
checkpoint_state_dict[k] = torch.from_numpy(v)
# if the state_dict comes from a model that was wrapped in a
# DataParallel or DistributedDataParallel during serialization,
# remove the "module" prefix before performing the matching.
_strip_prefix_if_present(checkpoint_state_dict, "module.")
# workaround https://github.com/pytorch/pytorch/issues/24139
model_state_dict = model.state_dict()
incorrect_shapes = []
for k in list(checkpoint_state_dict.keys()): # state dict is modified in loop, so list op is necessary
if k in model_state_dict:
model_param = model_state_dict[k]
# Allow mismatch for uninitialized parameters
if TORCH_VERSION >= (1, 8) and isinstance(model_param, nn.parameter.UninitializedParameter):
continue
shape_model = tuple(model_param.shape)
shape_checkpoint = tuple(checkpoint_state_dict[k].shape)
if shape_model != shape_checkpoint:
has_observer_base_classes = (
TORCH_VERSION >= (1, 8)
and hasattr(quantization, "ObserverBase")
and hasattr(quantization, "FakeQuantizeBase")
)
if has_observer_base_classes:
# Handle the special case of quantization per channel observers,
# where buffer shape mismatches are expected.
def _get_module_for_key(model: torch.nn.Module, key: str) -> torch.nn.Module:
# foo.bar.param_or_buffer_name -> [foo, bar]
key_parts = key.split(".")[:-1]
cur_module = model
for key_part in key_parts:
cur_module = getattr(cur_module, key_part)
return cur_module
cls_to_skip = (
ObserverBase,
FakeQuantizeBase,
)
target_module = _get_module_for_key(model, k)
if isinstance(target_module, cls_to_skip):
# Do not remove modules with expected shape mismatches
# them from the state_dict loading. They have special logic
# in _load_from_state_dict to handle the mismatches.
continue
incorrect_shapes.append((k, shape_checkpoint, shape_model))
checkpoint_state_dict.pop(k)
model.load_state_dict(checkpoint_state_dict, strict=False)
def predict(model: nn.Module, inp: list[dict[str, Any]]) -> list[dict[str, instances.Instances]]:
"""
Compute model predictions.
:param model: model to run on input
:param inp: input, in the form
{
"image_file": path to image,
"image": float32 torch.tensor of dimensions [channel, height, width] as RGB/RGBD/depth image
}
:return: list of detected instances and predicted openable parameters
"""
with torch.no_grad(), evaluation.inference_context(model):
out = model(inp)
return out
def main(
cfg: CfgNode,
rgb_image: str,
depth_image: str,
intrinsics: list[float],
num_samples: int,
crop: bool,
score_threshold: float,
) -> None:
"""
Main inference method.
:param cfg: configuration object
:param rgb_image: local path to RGB image
:param depth_image: local path to depth image
:param intrinsics: camera intrinsics matrix as a list of 9 values
:param num_samples: number of sample visualization states to generate
:param crop: if True, images will be cropped to remove whitespace before visualization
:param score_threshold: float between 0 and 1 representing threshold at which to filter instances based on score
"""
logger = logging.getLogger("detectron2")
# setup data
logger.info("Loading image.")
inp = format_input(rgb_image)
# setup model
logger.info("Loading model.")
model = build_model(cfg)
weights = torch.load(cfg.MODEL.WEIGHTS, map_location=torch.device("cpu"))
if "model" not in weights:
weights = {"model": weights}
load_model(model, weights)
# run model on data
logger.info("Running model.")
prediction = predict(model, inp)[0] # index 0 since there is only one image
pred_instances = prediction["instances"]
# log results
image_id = os.path.splitext(os.path.basename(rgb_image))[0]
pred_dict = {"image_id": image_id}
instances = pred_instances.to(torch.device("cpu"))
pred_dict["instances"] = prediction_to_json(instances, image_id)
torch.save(pred_dict, os.path.join(cfg.OUTPUT_DIR, f"{image_id}_prediction.pth"))
# select best prediction to visualize
score_ranking = np.argsort([-1 * pred_instances[i].scores.item() for i in range(len(pred_instances))])
score_ranking = [idx for idx in score_ranking if pred_instances[int(idx)].scores.item() > score_threshold]
if len(score_ranking) == 0:
logging.warning("The model did not predict any moving parts above the score threshold.")
return
for idx in score_ranking: # iterate through all best predictions, by score threshold
pred = pred_instances[int(idx)] # take highest predicted one
logger.info("Rendering prediction for instance %d", int(idx))
output_dir = os.path.join(cfg.OUTPUT_DIR, str(idx))
os.makedirs(output_dir, exist_ok=True)
# extract predicted values for visualization
mask = np.squeeze(pred.pred_masks.cpu().numpy()) # dim: [height, width]
origin = pred.morigin.cpu().numpy().flatten() # dim: [3, ]
axis_vector = pred.maxis.cpu().numpy().flatten() # dim: [3, ]
pred_type = TYPE_CLASSIFICATION.get(pred.mtype.item())
range_min = 0 - pred.mstate.cpu().numpy()
range_max = pred.mstatemax.cpu().numpy() - pred.mstate.cpu().numpy()
# process visualization
color = o3d.io.read_image(rgb_image)
depth = o3d.io.read_image(depth_image)
rgbd_image = o3d.geometry.RGBDImage.create_from_color_and_depth(color, depth, convert_rgb_to_intensity=False)
color_np = np.asarray(color)
height, width = color_np.shape[:2]
# generate intrinsics
intrinsic_matrix = np.reshape(intrinsics, (3, 3), order="F")
intrinsic_obj = o3d.camera.PinholeCameraIntrinsic(
width,
height,
intrinsic_matrix[0, 0],
intrinsic_matrix[1, 1],
intrinsic_matrix[0, 2],
intrinsic_matrix[1, 2],
)
# Convert the RGBD image to a point cloud
pcd = o3d.geometry.PointCloud.create_from_rgbd_image(rgbd_image, intrinsic_obj)
# Create a LineSet to visualize the direction vector
axis_arrow = draw_line(origin, axis_vector + origin)
axis_arrow.paint_uniform_color(ARROW_COLOR)
# if USE_GT:
# anno_path = f"/localhome/atw7/projects/opdmulti/data/data_demo_dev/59-4860.json"
# part_id = 32
# # get annotation for the frame
# import json
# with open(anno_path, "r") as f:
# anno = json.load(f)
# articulations = anno["articulation"]
# for articulation in articulations:
# if articulation["partId"] == part_id:
# range_min = articulation["rangeMin"] - articulation["state"]
# range_max = articulation["rangeMax"] - articulation["state"]
# break
if pred_type == "rotation":
generate_rotation_visualization(
pcd,
axis_arrow,
mask,
axis_vector,
origin,
range_min,
range_max,
num_samples,
output_dir,
)
elif pred_type == "translation":
generate_translation_visualization(
pcd,
axis_arrow,
mask,
axis_vector,
range_min,
range_max,
num_samples,
output_dir,
)
else:
raise ValueError(f"Invalid motion prediction type: {pred_type}")
if pred_type:
if crop: # crop images to remove shared extraneous whitespace
output_dir_cropped = f"{output_dir}_cropped"
if not os.path.isdir(output_dir_cropped):
os.makedirs(output_dir_cropped)
batch_trim(output_dir, output_dir_cropped, identical=True)
# create_gif(output_dir_cropped, num_samples)
else: # leave original dimensions of image as-is
# create_gif(output_dir, num_samples)
pass
if __name__ == "__main__":
# parse arguments
start_time = time.time()
args = get_parser().parse_args()
cfg = setup_cfg(args)
# run main code
engine.launch(
main,
args.num_processes,
args=(
cfg,
args.rgb_image,
args.depth_image,
args.intrinsics,
args.num_samples,
args.crop,
args.score_threshold,
),
)
end_time = time.time()
print(f"Inference time: {end_time - start_time:.2f} seconds")