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coco_eval.py
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coco_eval.py
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import logging
import tempfile
import os
import torch
from collections import OrderedDict
from tqdm import tqdm
from maskrcnn_benchmark.modeling.roi_heads.mask_head.inference import Masker
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou
def do_coco_evaluation(
dataset,
predictions,
box_only,
output_folder,
iou_types,
expected_results,
expected_results_sigma_tol,
):
logger = logging.getLogger("maskrcnn_benchmark.inference")
if box_only:
logger.info("Evaluating bbox proposals")
areas = {"all": "", "small": "s", "medium": "m", "large": "l"}
res = COCOResults("box_proposal")
for limit in [100, 1000]:
for area, suffix in areas.items():
stats = evaluate_box_proposals(
predictions, dataset, area=area, limit=limit
)
key = "AR{}@{:d}".format(suffix, limit)
res.results["box_proposal"][key] = stats["ar"].item()
logger.info(res)
check_expected_results(res, expected_results, expected_results_sigma_tol)
if output_folder:
torch.save(res, os.path.join(output_folder, "box_proposals.pth"))
return
logger.info("Preparing results for COCO format")
coco_results = {}
if "bbox" in iou_types:
logger.info("Preparing bbox results")
coco_results["bbox"] = prepare_for_coco_detection(predictions, dataset)
if "segm" in iou_types:
logger.info("Preparing segm results")
coco_results["segm"] = prepare_for_coco_segmentation(predictions, dataset)
if "keypoints" in iou_types:
logger.info("Preparing keypoints results")
coco_results["keypoints"] = prepare_for_coco_keypoint(predictions, dataset)
results = COCOResults(*iou_types)
logger.info("Evaluating predictions")
for iou_type in iou_types:
with tempfile.NamedTemporaryFile() as f:
file_path = f.name
if output_folder:
file_path = os.path.join(output_folder, iou_type + ".json")
for catId in dataset.coco.getCatIds():
res = evaluate_predictions_on_coco(
dataset.coco, coco_results[iou_type], file_path, iou_type, catId
)
results.update(res)
res = evaluate_predictions_on_coco(
dataset.coco, coco_results[iou_type], file_path, iou_type
)
results.update(res)
logger.info(results)
check_expected_results(results, expected_results, expected_results_sigma_tol)
if output_folder:
torch.save(results, os.path.join(output_folder, "coco_results.pth"))
return results, coco_results
def prepare_for_coco_detection(predictions, dataset):
# assert isinstance(dataset, COCODataset)
coco_results = []
for image_id, prediction in enumerate(predictions):
original_id = dataset.id_to_img_map[image_id]
if len(prediction) == 0:
continue
img_info = dataset.get_img_info(image_id)
image_width = img_info["width"]
image_height = img_info["height"]
prediction = prediction.resize((image_width, image_height))
prediction = prediction.convert("xywh")
boxes = prediction.bbox.tolist()
scores = prediction.get_field("scores").tolist()
labels = prediction.get_field("labels").tolist()
mapped_labels = [dataset.contiguous_category_id_to_json_id[i] for i in labels]
coco_results.extend(
[
{
"image_id": original_id,
"category_id": mapped_labels[k],
"bbox": box,
"score": scores[k],
}
for k, box in enumerate(boxes)
]
)
return coco_results
def prepare_for_coco_segmentation(predictions, dataset):
import pycocotools.mask as mask_util
import numpy as np
masker = Masker(threshold=0.5, padding=1)
# assert isinstance(dataset, COCODataset)
coco_results = []
for image_id, prediction in tqdm(enumerate(predictions)):
original_id = dataset.id_to_img_map[image_id]
if len(prediction) == 0:
continue
img_info = dataset.get_img_info(image_id)
image_width = img_info["width"]
image_height = img_info["height"]
prediction = prediction.resize((image_width, image_height))
masks = prediction.get_field("mask")
# t = time.time()
# Masker is necessary only if masks haven't been already resized.
if list(masks.shape[-2:]) != [image_height, image_width]:
masks = masker(masks.expand(1, -1, -1, -1, -1), prediction)
masks = masks[0]
# logger.info('Time mask: {}'.format(time.time() - t))
# prediction = prediction.convert('xywh')
# boxes = prediction.bbox.tolist()
scores = prediction.get_field("scores").tolist()
labels = prediction.get_field("labels").tolist()
# rles = prediction.get_field('mask')
rles = [
mask_util.encode(np.array(mask[0, :, :, np.newaxis], order="F"))[0]
for mask in masks
]
for rle in rles:
rle["counts"] = rle["counts"].decode("utf-8")
mapped_labels = [dataset.contiguous_category_id_to_json_id[i] for i in labels]
coco_results.extend(
[
{
"image_id": original_id,
"category_id": mapped_labels[k],
"segmentation": rle,
"score": scores[k],
}
for k, rle in enumerate(rles)
]
)
return coco_results
def prepare_for_coco_keypoint(predictions, dataset):
# assert isinstance(dataset, COCODataset)
coco_results = []
for image_id, prediction in enumerate(predictions):
original_id = dataset.id_to_img_map[image_id]
if len(prediction.bbox) == 0:
continue
# TODO replace with get_img_info?
image_width = dataset.coco.imgs[original_id]["width"]
image_height = dataset.coco.imgs[original_id]["height"]
prediction = prediction.resize((image_width, image_height))
prediction = prediction.convert("xywh")
boxes = prediction.bbox.tolist()
scores = prediction.get_field("scores").tolist()
labels = prediction.get_field("labels").tolist()
keypoints = prediction.get_field("keypoints")
keypoints = keypoints.resize((image_width, image_height))
keypoints = keypoints.keypoints.view(keypoints.keypoints.shape[0], -1).tolist()
mapped_labels = [dataset.contiguous_category_id_to_json_id[i] for i in labels]
coco_results.extend(
[
{
"image_id": original_id,
"category_id": mapped_labels[k],
"keypoints": keypoint,
"score": scores[k],
}
for k, keypoint in enumerate(keypoints)
]
)
return coco_results
# inspired from Detectron
def evaluate_box_proposals(
predictions, dataset, thresholds=None, area="all", limit=None
):
"""Evaluate detection proposal recall metrics. This function is a much
faster alternative to the official COCO API recall evaluation code. However,
it produces slightly different results.
"""
# Record max overlap value for each gt box
# Return vector of overlap values
areas = {
"all": 0,
"small": 1,
"medium": 2,
"large": 3,
"96-128": 4,
"128-256": 5,
"256-512": 6,
"512-inf": 7,
}
area_ranges = [
[0 ** 2, 1e5 ** 2], # all
[0 ** 2, 32 ** 2], # small
[32 ** 2, 96 ** 2], # medium
[96 ** 2, 1e5 ** 2], # large
[96 ** 2, 128 ** 2], # 96-128
[128 ** 2, 256 ** 2], # 128-256
[256 ** 2, 512 ** 2], # 256-512
[512 ** 2, 1e5 ** 2],
] # 512-inf
assert area in areas, "Unknown area range: {}".format(area)
area_range = area_ranges[areas[area]]
gt_overlaps = []
num_pos = 0
for image_id, prediction in enumerate(predictions):
original_id = dataset.id_to_img_map[image_id]
img_info = dataset.get_img_info(image_id)
image_width = img_info["width"]
image_height = img_info["height"]
prediction = prediction.resize((image_width, image_height))
# sort predictions in descending order
# TODO maybe remove this and make it explicit in the documentation
inds = prediction.get_field("objectness").sort(descending=True)[1]
prediction = prediction[inds]
ann_ids = dataset.coco.getAnnIds(imgIds=original_id)
anno = dataset.coco.loadAnns(ann_ids)
gt_boxes = [obj["bbox"] for obj in anno if obj["iscrowd"] == 0]
gt_boxes = torch.as_tensor(gt_boxes).reshape(-1, 4) # guard against no boxes
gt_boxes = BoxList(gt_boxes, (image_width, image_height), mode="xywh").convert(
"xyxy"
)
gt_areas = torch.as_tensor([obj["area"] for obj in anno if obj["iscrowd"] == 0])
if len(gt_boxes) == 0:
continue
valid_gt_inds = (gt_areas >= area_range[0]) & (gt_areas <= area_range[1])
gt_boxes = gt_boxes[valid_gt_inds]
num_pos += len(gt_boxes)
if len(gt_boxes) == 0:
continue
if len(prediction) == 0:
continue
if limit is not None and len(prediction) > limit:
prediction = prediction[:limit]
overlaps = boxlist_iou(prediction, gt_boxes)
_gt_overlaps = torch.zeros(len(gt_boxes))
for j in range(min(len(prediction), len(gt_boxes))):
# find which proposal box maximally covers each gt box
# and get the iou amount of coverage for each gt box
max_overlaps, argmax_overlaps = overlaps.max(dim=0)
# find which gt box is 'best' covered (i.e. 'best' = most iou)
gt_ovr, gt_ind = max_overlaps.max(dim=0)
assert gt_ovr >= 0
# find the proposal box that covers the best covered gt box
box_ind = argmax_overlaps[gt_ind]
# record the iou coverage of this gt box
_gt_overlaps[j] = overlaps[box_ind, gt_ind]
assert _gt_overlaps[j] == gt_ovr
# mark the proposal box and the gt box as used
overlaps[box_ind, :] = -1
overlaps[:, gt_ind] = -1
# append recorded iou coverage level
gt_overlaps.append(_gt_overlaps)
gt_overlaps = torch.cat(gt_overlaps, dim=0)
gt_overlaps, _ = torch.sort(gt_overlaps)
if thresholds is None:
step = 0.05
thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32)
recalls = torch.zeros_like(thresholds)
# compute recall for each iou threshold
for i, t in enumerate(thresholds):
recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos)
# ar = 2 * np.trapz(recalls, thresholds)
ar = recalls.mean()
return {
"ar": ar,
"recalls": recalls,
"thresholds": thresholds,
"gt_overlaps": gt_overlaps,
"num_pos": num_pos,
}
def evaluate_predictions_on_coco(
coco_gt, coco_results, json_result_file, iou_type="bbox", catId=None
):
import json
with open(json_result_file, "w") as f:
json.dump(coco_results, f)
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
coco_dt = coco_gt.loadRes(str(json_result_file)) if coco_results else COCO()
# coco_dt = coco_gt.loadRes(coco_results)
coco_eval = COCOeval(coco_gt, coco_dt, iou_type)
if catId:
coco_eval.params.catIds = [catId]
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
return coco_eval
class COCOResults(object):
METRICS = {
"bbox": ["AP", "AP50", "AP75", "APs", "APm", "APl"],
"segm": ["AP", "AP50", "AP75", "APs", "APm", "APl"],
"box_proposal": [
"AR@100",
"ARs@100",
"ARm@100",
"ARl@100",
"AR@1000",
"ARs@1000",
"ARm@1000",
"ARl@1000",
],
"keypoints": ["AP", "AP50", "AP75", "APm", "APl"],
}
def __init__(self, *iou_types):
allowed_types = ("box_proposal", "bbox", "segm", "keypoints")
assert all(iou_type in allowed_types for iou_type in iou_types)
results = OrderedDict()
for iou_type in iou_types:
results[iou_type] = OrderedDict(
[(metric, -1) for metric in COCOResults.METRICS[iou_type]]
)
self.results = results
def update(self, coco_eval):
if coco_eval is None:
return
from pycocotools.cocoeval import COCOeval
assert isinstance(coco_eval, COCOeval)
s = coco_eval.stats
iou_type = coco_eval.params.iouType
catIds = coco_eval.params.catIds
res = self.results[iou_type]
metrics = COCOResults.METRICS[iou_type]
# if current eval is single catId, add to results
if len(catIds) is 1:
res[catIds[0]] = {}
for idx, metric in enumerate(metrics):
res[catIds[0]][metric] = s[idx]
else:
for idx, metric in enumerate(metrics):
res[metric] = s[idx]
def __repr__(self):
# TODO make it pretty
return repr(self.results)
def check_expected_results(results, expected_results, sigma_tol):
if not expected_results:
return
logger = logging.getLogger("maskrcnn_benchmark.inference")
for task, metric, (mean, std) in expected_results:
actual_val = results.results[task][metric]
lo = mean - sigma_tol * std
hi = mean + sigma_tol * std
ok = (lo < actual_val) and (actual_val < hi)
msg = (
"{} > {} sanity check (actual vs. expected): "
"{:.3f} vs. mean={:.4f}, std={:.4}, range=({:.4f}, {:.4f})"
).format(task, metric, actual_val, mean, std, lo, hi)
if not ok:
msg = "FAIL: " + msg
logger.error(msg)
else:
msg = "PASS: " + msg
logger.info(msg)