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standalone_eval_v2.py
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standalone_eval_v2.py
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# Adapted from https://github.com/ScanNet/ScanNet/blob/master/BenchmarkScripts/3d_evaluation/evaluate_semantic_instance.py # noqa E501
# Modified by Thang Vu
# Collected by Jiahui Lei 2022, Sep
# ! warning, different to scannet official metric, here we don't ignore the false positive that are matched to invalid labels like floor and walls
# ! version 2
from copyreg import pickle
import multiprocessing as mp
from copy import deepcopy
import numpy as np
from tqdm import tqdm
import os
import os.path as osp
import torch
import json
import pickle
import sys
from matplotlib import pyplot as plt
class Instance(object):
instance_id = 0
label_id = 0
vert_count = 0
med_dist = -1
dist_conf = 0.0
def __init__(self, mesh_vert_instances, instance_id):
if instance_id == -1:
return
self.instance_id = int(instance_id)
self.label_id = int(self.get_label_id(instance_id))
self.vert_count = int(self.get_instance_verts(mesh_vert_instances, instance_id))
def get_label_id(self, instance_id):
return int(instance_id // 1000)
def get_instance_verts(self, mesh_vert_instances, instance_id):
return (mesh_vert_instances == instance_id).sum()
def to_json(self):
return json.dumps(self, default=lambda o: o.__dict__, sort_keys=True, indent=4)
def to_dict(self):
dict = {}
dict["instance_id"] = self.instance_id
dict["label_id"] = self.label_id
dict["vert_count"] = self.vert_count
dict["med_dist"] = self.med_dist
dict["dist_conf"] = self.dist_conf
return dict
def from_json(self, data):
self.instance_id = int(data["instance_id"])
self.label_id = int(data["label_id"])
self.vert_count = int(data["vert_count"])
if "med_dist" in data:
self.med_dist = float(data["med_dist"])
self.dist_conf = float(data["dist_conf"])
def __str__(self):
return "(" + str(self.instance_id) + ")"
def get_instances(ids, class_ids, class_labels, id2label):
instances = {}
for label in class_labels:
instances[label] = []
instance_ids = np.unique(ids)
for id in instance_ids:
if id == 0:
continue
inst = Instance(ids, id)
if inst.label_id in class_ids:
instances[id2label[inst.label_id]].append(inst.to_dict())
return instances
def rle_encode(mask):
"""Encode RLE (Run-length-encode) from 1D binary mask.
Args:
mask (np.ndarray): 1D binary mask
Returns:
rle (dict): encoded RLE
"""
length = mask.shape[0]
mask = np.concatenate([[0], mask, [0]])
runs = np.where(mask[1:] != mask[:-1])[0] + 1
runs[1::2] -= runs[::2]
counts = " ".join(str(x) for x in runs)
rle = dict(length=length, counts=counts)
return rle
def rle_decode(rle):
"""Decode rle to get binary mask.
Args:
rle (dict): rle of encoded mask
Returns:
mask (np.ndarray): decoded mask
"""
length = rle["length"]
counts = rle["counts"]
s = counts.split()
starts, nums = [np.asarray(x, dtype=np.int32) for x in (s[0:][::2], s[1:][::2])]
starts -= 1
ends = starts + nums
mask = np.zeros(length, dtype=np.uint8)
for lo, hi in zip(starts, ends):
mask[lo:hi] = 1
return mask
class ScanNetEval(object):
def __init__(self, class_labels, min_npoint=None, iou_type=None, use_label=True):
self.valid_class_labels = class_labels
self.valid_class_ids = (
np.arange(len(class_labels)) + 1
) # ! waring, the bg is 0, fg cate id are starting from 1
self.id2label = {}
self.label2id = {}
for i in range(len(self.valid_class_ids)):
self.label2id[self.valid_class_labels[i]] = self.valid_class_ids[i]
self.id2label[self.valid_class_ids[i]] = self.valid_class_labels[i]
self.ious = np.append(np.arange(0.5, 0.95, 0.05), 0.25)
if min_npoint:
# ! warning, note here is a parameter
self.min_region_sizes = np.array([min_npoint])
else:
self.min_region_sizes = np.array([100])
# ! Ours always produce enough points mask, but using 100 will help other baselines to get better performance since they might output les than 100 pts masks!
# ! turn this to small numbers like 10 will decrease baseline performance, but ours stays the same, so we leave it as 100
print("MIN_REGION_SIZE", self.min_region_sizes)
self.distance_threshes = np.array([float("inf")])
self.distance_confs = np.array([-float("inf")])
self.iou_type = iou_type
self.use_label = use_label
if self.use_label:
self.eval_class_labels = self.valid_class_labels
else:
# ! TODO: check this!!
assert NotImplementedError("JH need to check this!")
self.eval_class_labels = ["class_agnostic"]
def evaluate_matches(self, matches, prcurv_save_dir, scannet_flag=False):
ious = self.ious
min_region_sizes = [self.min_region_sizes[0]]
dist_threshes = [self.distance_threshes[0]]
dist_confs = [self.distance_confs[0]]
# results: class x iou
ap = np.zeros((len(dist_threshes), len(self.eval_class_labels), len(ious)), np.float)
rc = np.zeros((len(dist_threshes), len(self.eval_class_labels), len(ious)), np.float)
for di, (min_region_size, distance_thresh, distance_conf) in enumerate(
zip(min_region_sizes, dist_threshes, dist_confs)
):
for oi, iou_th in enumerate(ious):
pred_visited = {}
for m in matches:
for p in matches[m]["pred"]:
for label_name in self.eval_class_labels:
for p in matches[m]["pred"][label_name]:
if "filename" in p:
pred_visited[p["filename"]] = False
for li, label_name in enumerate(self.eval_class_labels):
y_true = np.empty(0)
y_score = np.empty(0)
hard_false_negatives = 0
has_gt = False
has_pred = False
for m in matches:
pred_instances = matches[m]["pred"][label_name]
gt_instances = matches[m]["gt"][label_name]
# filter groups in ground truth
gt_instances = [
gt
for gt in gt_instances
if gt["instance_id"] >= 1000
and gt["vert_count"] >= min_region_size
and gt["med_dist"] <= distance_thresh
and gt["dist_conf"] >= distance_conf
]
if gt_instances:
has_gt = True
if pred_instances:
has_pred = True
cur_true = np.ones(len(gt_instances))
cur_score = np.ones(len(gt_instances)) * (-float("inf"))
cur_match = np.zeros(len(gt_instances), dtype=np.bool)
# collect matches
for gti, gt in enumerate(gt_instances):
found_match = False
for pred in gt["matched_pred"]:
# greedy assignments
if pred_visited[pred["filename"]]:
continue
# TODO change to use compact iou
iou = pred["iou"]
if iou > iou_th:
confidence = pred["confidence"]
# if already have a prediction for this gt,
# the prediction with the lower score is
# automatically a FP
if cur_match[gti]:
max_score = max(cur_score[gti], confidence)
min_score = min(cur_score[gti], confidence)
cur_score[gti] = max_score
# append false positive
cur_true = np.append(cur_true, 0)
cur_score = np.append(cur_score, min_score)
cur_match = np.append(cur_match, True)
# otherwise set score
else:
found_match = True
cur_match[gti] = True
cur_score[gti] = confidence
pred_visited[pred["filename"]] = True
if not found_match:
hard_false_negatives += 1
# remove non-matched ground truth instances
cur_true = cur_true[cur_match == True] # noqa E712
cur_score = cur_score[cur_match == True] # noqa E712
# collect non-matched predictions as false positive
for pred in pred_instances:
found_gt = False
for gt in pred["matched_gt"]:
iou = gt["iou"]
if iou > iou_th:
found_gt = True
break
if not found_gt:
if (
scannet_flag
): # ! here is the original scannet script, ignoring invalid classes false positive
num_ignore = pred["void_intersection"]
for gt in pred["matched_gt"]:
# group?
if gt["instance_id"] < 1000:
num_ignore += gt["intersection"]
# small ground truth instances
if (
gt["vert_count"] < min_region_size
or gt["med_dist"] > distance_thresh
or gt["dist_conf"] < distance_conf
):
num_ignore += gt["intersection"]
proportion_ignore = float(num_ignore) / pred["vert_count"]
# if not ignored append false positive
if proportion_ignore <= iou_th:
cur_true = np.append(cur_true, 0)
confidence = pred["confidence"]
cur_score = np.append(cur_score, confidence)
else:
# ! always append as FP, no ignore if this pred is matched to invalid mask, e.g. the floor and wall
cur_true = np.append(cur_true, 0)
confidence = pred["confidence"]
cur_score = np.append(cur_score, confidence)
# append to overall results
y_true = np.append(y_true, cur_true)
y_score = np.append(y_score, cur_score)
# compute average precision
if has_gt and has_pred:
# compute precision recall curve first
# sorting and cumsum
score_arg_sort = np.argsort(y_score)
y_score_sorted = y_score[score_arg_sort]
y_true_sorted = y_true[score_arg_sort]
y_true_sorted_cumsum = np.cumsum(y_true_sorted)
# unique thresholds
(thresholds, unique_indices) = np.unique(y_score_sorted, return_index=True)
num_prec_recall = len(unique_indices) + 1
# prepare precision recall
num_examples = len(y_score_sorted)
num_true_examples = y_true_sorted_cumsum[-1]
precision = np.zeros(num_prec_recall)
recall = np.zeros(num_prec_recall)
# deal with the first point
y_true_sorted_cumsum = np.append(y_true_sorted_cumsum, 0)
# deal with remaining
for idx_res, idx_scores in enumerate(unique_indices):
cumsum = y_true_sorted_cumsum[idx_scores - 1]
tp = num_true_examples - cumsum
fp = num_examples - idx_scores - tp
fn = cumsum + hard_false_negatives
p = float(tp) / (tp + fp)
r = float(tp) / (tp + fn)
precision[idx_res] = p
recall[idx_res] = r
# recall is the first point on recall curve
rc_current = recall[0]
# first point in curve is artificial
precision[-1] = 1.0
recall[-1] = 0.0
# plot and save
fig = plt.figure(figsize=(15, 5))
plt.subplot(1, 3, 1)
plt.plot(recall, precision)
plt.plot(recall, precision, "r*")
plt.grid()
plt.xlabel("Recall")
plt.xlim((0.0, 1.0))
plt.ylabel("Precision")
plt.ylim((0.0, 1.0))
plt.title(f"PR di={di} iou={iou_th:.3f} {label_name}")
plt.subplot(1, 3, 2)
plt.plot(thresholds, precision[:-1])
plt.plot(thresholds, precision[:-1], "r*")
plt.grid()
plt.xlabel("conf TH")
plt.xlim((0.0, 1.0))
plt.ylabel("Precision")
plt.ylim((0.0, 1.0))
plt.title(f"P-TH di={di} iou={iou_th:.3f} {label_name}")
plt.subplot(1, 3, 3)
plt.plot(thresholds, recall[:-1])
plt.plot(thresholds, recall[:-1], "r*")
plt.grid()
plt.xlabel("conf TH")
plt.xlim((0.0, 1.0))
plt.ylabel("Recall")
plt.ylim((0.0, 1.0))
plt.title(f"R-TH di={di} iou={iou_th:.3f} {label_name}")
plt.savefig(
osp.join(prcurv_save_dir, f"{di}_iou={iou_th:.3f}_{label_name}.png")
)
np.savez_compressed(
osp.join(prcurv_save_dir, f"{di}_iou={iou_th:.3f}_{label_name}.npz"),
precision=precision,
recall=recall,
thresholds=thresholds,
)
plt.close()
# compute average of precision-recall curve
recall_for_conv = np.copy(recall)
recall_for_conv = np.append(recall_for_conv[0], recall_for_conv)
recall_for_conv = np.append(recall_for_conv, 0.0)
stepWidths = np.convolve(recall_for_conv, [-0.5, 0, 0.5], "valid")
# integrate is now simply a dot product
ap_current = np.dot(precision, stepWidths)
elif has_gt:
ap_current = 0.0
rc_current = 0.0
else:
ap_current = float("nan")
rc_current = float("nan")
ap[di, li, oi] = ap_current
rc[di, li, oi] = rc_current
return ap, rc
def compute_averages(self, aps, rcs):
d_inf = 0
o50 = np.where(np.isclose(self.ious, 0.5))
o25 = np.where(np.isclose(self.ious, 0.25))
oAllBut25 = np.where(np.logical_not(np.isclose(self.ious, 0.25)))
avg_dict = {}
# avg_dict['all_ap'] = np.nanmean(aps[ d_inf,:,: ])
avg_dict["all_ap"] = np.nanmean(aps[d_inf, :, oAllBut25])
avg_dict["all_ap_50%"] = np.nanmean(aps[d_inf, :, o50])
avg_dict["all_ap_25%"] = np.nanmean(aps[d_inf, :, o25])
avg_dict["all_rc"] = np.nanmean(rcs[d_inf, :, oAllBut25])
avg_dict["all_rc_50%"] = np.nanmean(rcs[d_inf, :, o50])
avg_dict["all_rc_25%"] = np.nanmean(rcs[d_inf, :, o25])
avg_dict["classes"] = {}
for li, label_name in enumerate(self.eval_class_labels):
avg_dict["classes"][label_name] = {}
avg_dict["classes"][label_name]["ap"] = np.average(aps[d_inf, li, oAllBut25])
avg_dict["classes"][label_name]["ap50%"] = np.average(aps[d_inf, li, o50])
avg_dict["classes"][label_name]["ap25%"] = np.average(aps[d_inf, li, o25])
avg_dict["classes"][label_name]["rc"] = np.average(rcs[d_inf, li, oAllBut25])
avg_dict["classes"][label_name]["rc50%"] = np.average(rcs[d_inf, li, o50])
avg_dict["classes"][label_name]["rc25%"] = np.average(rcs[d_inf, li, o25])
return avg_dict
def assign_instances_for_scan(self, preds, gts):
"""get gt instances, only consider the valid class labels even in class
agnostic setting."""
gt_instances = get_instances(
gts, self.valid_class_ids, self.valid_class_labels, self.id2label
)
# associate
if self.use_label:
gt2pred = deepcopy(gt_instances)
for label in gt2pred:
for gt in gt2pred[label]:
gt["matched_pred"] = []
else:
gt2pred = {}
agnostic_instances = []
# concat all the instances label to agnostic label
for _, instances in gt_instances.items():
agnostic_instances += deepcopy(instances)
for gt in agnostic_instances:
gt["matched_pred"] = []
gt2pred[self.eval_class_labels[0]] = agnostic_instances
pred2gt = {}
for label in self.eval_class_labels:
pred2gt[label] = []
num_pred_instances = 0
# mask of void labels in the groundtruth
bool_void = np.logical_not(np.in1d(gts // 1000, self.valid_class_ids))
# go thru all prediction masks
for pred in preds:
if self.use_label:
label_id = pred["label_id"]
if label_id not in self.id2label:
continue
label_name = self.id2label[label_id]
else:
label_name = self.eval_class_labels[0] # class agnostic label
conf = pred["conf"]
pred_mask = pred["pred_mask"]
# pred_mask can be np.array or rle dict
if isinstance(pred_mask, dict):
pred_mask = rle_decode(pred_mask)
assert (
pred_mask.shape[0] == gts.shape[0]
), f"pred={pred_mask.shape[0]} but gt={gts.shape[0]}"
# convert to binary
pred_mask = np.not_equal(pred_mask, 0)
num = np.count_nonzero(pred_mask)
if num < self.min_region_sizes[0]:
continue # skip if empty
pred_instance = {}
pred_instance["filename"] = "{}_{}".format(pred["scan_id"], num_pred_instances) # dummy
pred_instance["pred_id"] = num_pred_instances
pred_instance["label_id"] = label_id if self.use_label else None
pred_instance["vert_count"] = num
pred_instance["confidence"] = conf
pred_instance["void_intersection"] = np.count_nonzero(
np.logical_and(bool_void, pred_mask)
)
# matched gt instances
matched_gt = []
# go thru all gt instances with matching label
for gt_num, gt_inst in enumerate(gt2pred[label_name]):
intersection = np.count_nonzero(
np.logical_and(gts == gt_inst["instance_id"], pred_mask)
)
if intersection > 0:
gt_copy = gt_inst.copy()
pred_copy = pred_instance.copy()
gt_copy["intersection"] = intersection
pred_copy["intersection"] = intersection
iou = float(intersection) / (
gt_copy["vert_count"] + pred_copy["vert_count"] - intersection
)
gt_copy["iou"] = iou
pred_copy["iou"] = iou
matched_gt.append(gt_copy)
gt2pred[label_name][gt_num]["matched_pred"].append(pred_copy)
pred_instance["matched_gt"] = matched_gt
num_pred_instances += 1
pred2gt[label_name].append(pred_instance)
return gt2pred, pred2gt
def print_results(self, avgs):
sep = ""
col1 = ":"
lineLen = 64
print()
print("#" * lineLen)
line = ""
line += "{:<15}".format("what") + sep + col1
line += "{:>8}".format("AP") + sep
line += "{:>8}".format("AP_50%") + sep
line += "{:>8}".format("AP_25%") + sep
line += "{:>8}".format("AR") + sep
line += "{:>8}".format("RC_50%") + sep
line += "{:>8}".format("RC_25%") + sep
print(line)
print("#" * lineLen)
for li, label_name in enumerate(self.eval_class_labels):
ap_avg = avgs["classes"][label_name]["ap"]
ap_50o = avgs["classes"][label_name]["ap50%"]
ap_25o = avgs["classes"][label_name]["ap25%"]
rc_avg = avgs["classes"][label_name]["rc"]
rc_50o = avgs["classes"][label_name]["rc50%"]
rc_25o = avgs["classes"][label_name]["rc25%"]
line = "{:<15}".format(label_name) + sep + col1
line += sep + "{:>8.3f}".format(ap_avg) + sep
line += sep + "{:>8.3f}".format(ap_50o) + sep
line += sep + "{:>8.3f}".format(ap_25o) + sep
line += sep + "{:>8.3f}".format(rc_avg) + sep
line += sep + "{:>8.3f}".format(rc_50o) + sep
line += sep + "{:>8.3f}".format(rc_25o) + sep
print(line)
all_ap_avg = avgs["all_ap"]
all_ap_50o = avgs["all_ap_50%"]
all_ap_25o = avgs["all_ap_25%"]
all_rc_avg = avgs["all_rc"]
all_rc_50o = avgs["all_rc_50%"]
all_rc_25o = avgs["all_rc_25%"]
print("-" * lineLen)
line = "{:<15}".format("average") + sep + col1
line += "{:>8.3f}".format(all_ap_avg) + sep
line += "{:>8.3f}".format(all_ap_50o) + sep
line += "{:>8.3f}".format(all_ap_25o) + sep
line += "{:>8.3f}".format(all_rc_avg) + sep
line += "{:>8.3f}".format(all_rc_50o) + sep
line += "{:>8.3f}".format(all_rc_25o) + sep
print(line)
print("#" * lineLen)
print()
def print_fb_results(self, REPORT):
print("=" * 64)
for label in self.valid_class_labels:
print(f"OBJ-IOU {label} mean={REPORT['obj_iou'][label]:.3f}")
print("- " * 32)
for label in self.valid_class_labels:
print(f"F-B-ACC {label} mean={REPORT['fb_acc'][label]:.3f}")
print("=" * 64)
print()
return
def write_result_file(self, avgs, filename):
_SPLITTER = ","
with open(filename, "w") as f:
f.write(_SPLITTER.join(["class", "class id", "ap", "ap50", "ap25"]) + "\n")
for class_name in self.eval_class_labels:
ap = avgs["classes"][class_name]["ap"]
ap50 = avgs["classes"][class_name]["ap50%"]
ap25 = avgs["classes"][class_name]["ap25%"]
f.write(_SPLITTER.join([str(x) for x in [class_name, ap, ap50, ap25]]) + "\n")
def evaluate(self, pred_list, gt_list, prcurv_save_dir, scannet_flag=False):
"""
Args:
pred_list:
for each scan:
for each instance
instance = dict(scan_id, label_id, mask, conf)
gt_list:
for each scan:
for each point:
gt_id = class_id * 1000 + instance_id
"""
pool = mp.Pool()
results = pool.starmap(self.assign_instances_for_scan, zip(pred_list, gt_list))
pool.close()
pool.join()
matches = {}
for i, (gt2pred, pred2gt) in enumerate(results):
matches_key = f"gt_{i}"
matches[matches_key] = {}
matches[matches_key]["gt"] = gt2pred
matches[matches_key]["pred"] = pred2gt
ap_scores, rc_scores = self.evaluate_matches(
matches, prcurv_save_dir=prcurv_save_dir, scannet_flag=scannet_flag
)
avgs = self.compute_averages(ap_scores, rc_scores)
# print
self.print_results(avgs)
return avgs
def load_scannet_format_results(load_dir, semantic_fn=""):
# https://kaldir.vc.in.tum.de/scannet_benchmark/documentation#format-instance3d
if len(semantic_fn) == 0:
semantic_fn = osp.join(load_dir, "semantic.npz")
semantic_data = np.load(semantic_fn, allow_pickle=True)
class_name = semantic_data["class_names"]
print(f"load sem from semantic.npz")
print(class_name)
# ! in the semantic npz file, "class_names" is required, "nyu_id" is optional
if "nyu_id" in semantic_data.files:
nyu_id = semantic_data["nyu_id"].tolist() # scannet use nyu id as label for eval
else:
nyu_id = None
mask_results_dir = osp.join(load_dir, "scannet_format")
recovered_pred_insts = []
scan_id_list = [f[:-4] for f in os.listdir(mask_results_dir) if f.endswith(".txt")]
scan_id_list.sort()
args = []
for scan_id in tqdm(scan_id_list):
meta_fn = osp.join(mask_results_dir, f"{scan_id}.txt")
args.append((scan_id, meta_fn, nyu_id))
print("loading sem")
try:
sem_pred = []
for scan_id in tqdm(scan_id_list):
fn = osp.join(load_dir, "semantic_pred", f"{scan_id}.npy")
data = np.load(fn)
sem_pred.append(data)
except:
print("Warning, can't load sem pred, skip sem loading")
sem_pred = None
# pool = mp.Pool()
# ret = pool.map(__load_single_scannet_format, args)
# pool.close()
# # sort
# ret_scan_id = [r[0]["scan_id"] for r in ret]
# for scan_id in scan_id_list:
# recovered_pred_insts.append(ret[ret_scan_id.index(scan_id)])
for arg in tqdm(args):
recovered_pred_insts.append(__load_single_scannet_format(arg))
return scan_id_list, recovered_pred_insts, class_name, nyu_id, sem_pred
# !the recover_pred_insts lable are form 1-K, not in nyu format
def __load_single_scannet_format(args):
scan_id, meta_fn, nyu_id = args
with open(meta_fn, "r") as f:
meta_info = f.readlines()
load_dir = osp.dirname(meta_fn)
results = []
for line in meta_info:
mask_fn, label, score = line.split("\n")[0].split(" ")
label, score = int(label), float(score)
if nyu_id is not None:
# * convert from nyu id back to compact 1-K id
label = nyu_id.index(label) + 1
mask_fn = osp.join(load_dir, mask_fn)
if osp.exists(mask_fn + ".npy"):
mask = np.load(mask_fn + ".npy")
else:
with open(mask_fn, "r") as f:
data = f.readlines()
mask = [int(l[0]) for l in data]
mask = np.asarray(mask, dtype=np.uint8)
# mask = np.loadtxt(mask_fn, dtype=np.uint8)
results.append({"scan_id": scan_id, "label_id": label, "conf": score, "pred_mask": mask})
return results
def load_gt_from_scannet_preprocessed(
gt_dir,
semantic_classes=20, # mugs: 2
instance_classes=18, # mugs: 1
postfix="_inst_nostuff.pth", # mugs: .pth
):
# ! the data format follow the scannet preprocessing of softgroup and dknet etc
scan_id_list = [f[: -len(postfix)] for f in os.listdir(gt_dir) if f.endswith(postfix)]
scan_id_list.sort()
ins_ret = []
sem_ret = []
for scan_id in tqdm(scan_id_list):
data_fn = osp.join(gt_dir, f"{scan_id}{postfix}")
_, _, sem, ins = torch.load(data_fn)
gt = get_gt_instances(
sem, ins, semantic_classes=semantic_classes, instance_classes=instance_classes
).astype(np.int32)
ins_ret.append(gt)
sem_ret.append(sem.astype(np.int8))
return scan_id_list, ins_ret, sem_ret
def get_gt_instances(semantic_labels, instance_labels, semantic_classes=20, instance_classes=18):
# convert to evaluation format 0: ignore, 1->N: valid
label_shift = semantic_classes - instance_classes
semantic_labels = semantic_labels - label_shift + 1
semantic_labels[semantic_labels < 0] = 0
instance_labels += 1
ignore_inds = instance_labels < 0
# scannet encoding rule
gt_ins = semantic_labels * 1000 + instance_labels
gt_ins[ignore_inds] = 0
return gt_ins
def evaluate_semantic_acc(pred_list, gt_list, ignore_label=-100):
gt = np.concatenate(gt_list, axis=0)
pred = np.concatenate(pred_list, axis=0)
assert gt.shape == pred.shape
correct = (gt[gt != ignore_label] == pred[gt != ignore_label]).sum()
whole = (gt != ignore_label).sum()
acc = correct.astype(float) / whole * 100
print(f"Total Acc: {acc:.1f}")
return acc
def evaluate_semantic_miou(pred_list, gt_list, class_names, ignore_label=-100):
gt = np.concatenate(gt_list, axis=0)
pred = np.concatenate(pred_list, axis=0)
pos_inds = gt != ignore_label
gt = gt[pos_inds]
pred = pred[pos_inds]
assert gt.shape == pred.shape
iou_list = []
for _index in np.unique(gt):
if _index != ignore_label:
intersection = ((gt == _index) & (pred == _index)).sum()
union = ((gt == _index) | (pred == _index)).sum()
iou = intersection.astype(float) / union * 100
iou_list.append(iou)
miou = np.mean(iou_list)
for _iou, name in zip(iou_list, class_names):
print(f"{name} mIoU: {_iou:.1f}")
print(f"mIoU: {miou:.1f}")
return miou, iou_list
def eval_main(
gt_dir,
results_dir,
eval_min_npoint=None,
semantic_classes=2,
instance_classes=1,
postfix=".pth",
strict=True,
semantic_fn="",
scannet_flag=False,
):
# eval_min_npoint defaulat is 100 in ScanNetEval
if scannet_flag:
print("Ignoring the invalid background, following the original SCANNET eval script")
# * load gt
loaded_gt_scan_id_list, gt_insts, sem_labels = load_gt_from_scannet_preprocessed(
gt_dir,
semantic_classes=semantic_classes,
instance_classes=instance_classes,
postfix=postfix,
)
# * load the prediction from scannet format
(
loaded_results_scan_id_list,
pred_insts,
CLASSES_NAME,
NYU_ID,
sem_preds,
) = load_scannet_format_results(load_dir=results_dir, semantic_fn=semantic_fn)
if strict:
assert loaded_gt_scan_id_list == loaded_results_scan_id_list
else:
print(
f"WARNING, ONLY EVALUATE PARTIAL DATASET! {len(loaded_results_scan_id_list)}/{len(loaded_gt_scan_id_list)}"
)
_gt_insts = []
for scan_id in loaded_results_scan_id_list:
_gt_insts.append(gt_insts[loaded_gt_scan_id_list.index(scan_id)])
gt_insts = _gt_insts
loaded_gt_scan_id_list = loaded_results_scan_id_list
# * eval
# * semantic
sem_class_name = [
f"label_{i}" for i in range(semantic_classes - instance_classes)
] + CLASSES_NAME.tolist()
if sem_preds is not None:
miou, sem_iou_list = evaluate_semantic_miou(
sem_preds, sem_labels, sem_class_name, ignore_label=-100
)
acc = evaluate_semantic_acc(sem_preds, sem_labels, ignore_label=-100)
else:
miou, acc = np.nan, np.nan
# * instance
scannet_eval = ScanNetEval(CLASSES_NAME, eval_min_npoint)
prcurv_save_dir = osp.join(results_dir, "pr_curve")
os.makedirs(prcurv_save_dir, exist_ok=True)
avgs = scannet_eval.evaluate(
pred_insts, gt_insts, prcurv_save_dir=prcurv_save_dir, scannet_flag=scannet_flag
)
# * save detailed report to results dir
out_fn = osp.join(results_dir, "results.txt")
if osp.exists(out_fn):
os.rename(out_fn, out_fn + ".bck")
with open(out_fn, "w") as f:
original_stdout = sys.stdout
sys.stdout = f
print("-" * 20 + "instnace" + "-" * 20)
scannet_eval.print_results(avgs)
if sem_preds is not None:
print("-" * 20 + "semantic" + "-" * 20)
for _iou, name in zip(sem_iou_list, sem_class_name):
print(f"{name} mIoU: {_iou:.1f}")
print(f"mIoU: {miou:.1f}")
sys.stdout = original_stdout
# * save a xls report
import pandas as pd
report = {
"name": ["ave"],
"sem_iou": [miou],
"ap": [avgs["all_ap"]],
"ap50%": [avgs["all_ap_50%"]],
"ap25%": [avgs["all_ap_25%"]],
"rc": [avgs["all_rc"]],
"rc50%": [avgs["all_rc_50%"]],
"rc25%": [avgs["all_rc_25%"]],
"sem_acc": [acc],
}
per_class_ins = avgs["classes"]
for _id, k in enumerate(sem_class_name):
report["name"].append(k)
if sem_preds is not None:
report["sem_iou"].append(sem_iou_list[_id])
else:
report["sem_iou"].append(None)
report["sem_acc"].append(None)
if k in per_class_ins.keys():
for metric_name, value in per_class_ins[k].items():
report[metric_name].append(value)
else:
n = len(report["name"])
for k, v in report.items():
if len(v) < n:
assert len(v) + 1 == n, f"{k} {len(v)}+1 != {n}"
report[k].append(None)
# for k, v in report.items():
# print(f"{k} len={len(v)}")
out_fn = osp.join(results_dir, "results.xlsx")
df = pd.DataFrame(report)
df.to_excel(out_fn)
return
if __name__ == "__main__":
import argparse
"""
IMPORTANT
The saved prediction's semantic label either
a.) start from 1, which corresponds to the semantic.npz ["class_names"] file list [0]
b.) corresponds to NYU id for scannet, need to translate back to compact 1-K id for eval
The predicted_masks can either contain standard scannet format txt (which is quite slow) or npy files,
whose name is just xxxx.txt.npy, NOTE, the name in the scene_id.txt description stays the scannet format,
i.e. the file path is .txt/
"""
parser = argparse.ArgumentParser("SoftGroup")
parser.add_argument(
"--results_dir",
required=True,
type=str,
help="This dir must contain a dir named scannet_format and a file called semantic.npz",
)
parser.add_argument(
"--gt_dir",
required=True,
type=str,
help="The (SoftGroup/DKNet) preprocessed dataset dir",
)
parser.add_argument(
"--n_sem",
required=True,
type=int,
help="number of sem classes including bg like floor or walls, Scannet=20, Sapien=2 or larger",
)
parser.add_argument(
"--n_ins",
required=True,
type=int,
help="number of sem classes of fg instances, Scannet=18, Sapien=1 (mugs) or larger",
)
parser.add_argument(
"--postfix",
default=".pth",
type=str,
help="the post fix of the dataset, scannet=_inst_nostuff.pth, default sapien is .pth",
)
parser.add_argument(
"--non_strict",
default=False,
action="store_true",
help="if set, only eval the existed results, not require the whole dataset are finished",
)
parser.add_argument(
"--semantic_fn",
default="",
type=str,
help="a path to .npz file that has a file called classes",
)
parser.add_argument(
"--scannet_flag",
default=False,
action="store_true",
help="Whether to ignore invalid background, if on scannet set true, otherwise leave it false",
)
args = parser.parse_args()
eval_main(
results_dir=args.results_dir,
gt_dir=args.gt_dir,
semantic_classes=args.n_sem,
instance_classes=args.n_ins,
postfix=args.postfix,
strict=not args.non_strict,
semantic_fn=args.semantic_fn,
scannet_flag=args.scannet_flag,
)
# debug
# eval_main(
# results_dir="/home/ray/projects/SoftGroup/work_dirs/debug/official_scannet/eval_ins_seg_results",
# gt_dir="/home/ray/datasets/ScanNet/DKNet/val",
# semantic_classes=20,
# instance_classes=18,
# postfix="_inst_nostuff.pth",
# )