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# Copyright (c) Meta Platforms, Inc. and affiliates
from termcolor import colored
import itertools
from tabulate import tabulate
import logging
logger = logging.getLogger(__name__)
def print_ap_category_histogram(dataset, results):
"""
Prints AP performance for each category.
Args:
results: dictionary; each entry contains information for a dataset
"""
num_classes = len(results)
N_COLS = 9
data = list(
itertools.chain(
*[
[
cat,
out["AP2D"],
out["AP3D"],
]
for cat, out in results.items()
]
)
)
data.extend([None] * (N_COLS - (len(data) % N_COLS)))
data = itertools.zip_longest(*[data[i::N_COLS] for i in range(N_COLS)])
table = tabulate(
data,
headers=["category", "AP2D", "AP3D"] * (N_COLS // 2),
tablefmt="pipe",
numalign="left",
stralign="center",
)
logger.info(
"Performance for each of {} categories on {}:\n".format(num_classes, dataset)
+ colored(table, "cyan")
)
def print_ap_analysis_histogram(results):
"""
Prints AP performance for various IoU thresholds and (near, medium, far) objects.
Args:
results: dictionary. Each entry in results contains outputs for a dataset
"""
metric_names = ["AP2D", "AP3D", "AP3D@15", "AP3D@25", "AP3D@50", "AP3D-N", "AP3D-M", "AP3D-F"]
N_COLS = 10
data = []
for name, metrics in results.items():
data_item = [name, metrics["iters"], metrics["AP2D"], metrics["AP3D"], metrics["AP3D@15"], metrics["AP3D@25"], metrics["AP3D@50"], metrics["AP3D-N"], metrics["AP3D-M"], metrics["AP3D-F"]]
data.append(data_item)
table = tabulate(
data,
headers=["Dataset", "#iters", "AP2D", "AP3D", "AP3D@15", "AP3D@25", "AP3D@50", "AP3D-N", "AP3D-M", "AP3D-F"],
tablefmt="grid",
numalign="left",
stralign="center",
)
logger.info(
"Per-dataset performance analysis on test set:\n"
+ colored(table, "cyan")
)
def print_ap_dataset_histogram(results):
"""
Prints AP performance for each dataset.
Args:
results: list of dicts. Each entry in results contains outputs for a dataset
"""
metric_names = ["AP2D", "AP3D"]
N_COLS = 4
data = []
for name, metrics in results.items():
data_item = [name, metrics["iters"], metrics["AP2D"], metrics["AP3D"]]
data.append(data_item)
table = tabulate(
data,
headers=["Dataset", "#iters", "AP2D", "AP3D"],
tablefmt="grid",
numalign="left",
stralign="center",
)
logger.info(
"Per-dataset performance on test set:\n"
+ colored(table, "cyan")
)
def print_ap_omni_histogram(results):
"""
Prints AP performance for Omni3D dataset.
Args:
results: list of dicts. Each entry in results contains outputs for a dataset
"""
metric_names = ["AP2D", "AP3D"]
N_COLS = 4
data = []
for name, metrics in results.items():
data_item = [name, metrics["iters"], metrics["AP2D"], metrics["AP3D"]]
data.append(data_item)
table = tabulate(
data,
headers=["Dataset", "#iters", "AP2D", "AP3D"],
tablefmt="grid",
numalign="left",
stralign="center",
)
logger.info("Omni3D performance on test set. The numbers below should be used to compare to others approaches on Omni3D, such as Cube R-CNN")
logger.info(
"Performance on Omni3D:\n"
+ colored(table, "magenta")
)
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