def evaluate_class_agnostic(self):
""" Treat all masks as one category.
"""
lvis_dt = self.lvis_dt
lvis_gt = self.lvis_gt
feats_ann = self.feats_ann
cluster_to_coco = self.cluster_to_coco
# by default, none of the predictions gets evaluated.
for _, dt in lvis_dt.anns.items(): dt['category_id'] = -2
for _, dt in lvis_gt.anns.items(): dt['category_id'] = -2
print('Updating category ids')
for i in tqdm(range(len(feats_ann))):
cluster_id = self.clusters[i]
ann_id = int(feats_ann[i])
if cluster_id in cluster_to_coco:
lvis_dt.anns[ann_id]['category_id'] = -1 # the assigned ones are included in the eval
cocoEval = LVISEval(lvis_gt, lvis_dt, 'segm')
cocoEval.params.catIds = [-1] # only evaluate on the category -1.
cocoEval.params.useCats = 0
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
def evaluate(self,
results,
metric=['track'],
logger=None,
resfile_path=None):
if isinstance(metric, list):
metrics = metric
elif isinstance(metric, str):
metrics = [metric]
else:
raise TypeError('metric must be a list or a str.')
allowed_metrics = ['bbox', 'track']
for metric in metrics:
if metric not in allowed_metrics:
raise KeyError(f'metric {metric} is not supported.')
result_files, tmp_dir = self.format_results(results, resfile_path)
eval_results = dict()
if 'track' in metrics:
from tao.toolkit.tao import TaoEval
print_log('Evaluating TAO results...', logger)
tao_eval = TaoEval(self.ann_file, result_files['track'])
tao_eval.params.img_ids = self.img_ids
tao_eval.params.cat_ids = self.cat_ids
tao_eval.params.iou_thrs = np.array([0.5, 0.75])
tao_eval.run()
tao_eval.print_results()
tao_results = tao_eval.get_results()
for k, v in tao_results.items():
if isinstance(k, str) and k.startswith('AP'):
key = 'track_{}'.format(k)
val = float('{:.3f}'.format(float(v)))
eval_results[key] = val
if 'bbox' in metrics:
print_log('Evaluating detection results...', logger)
lvis_gt = LVIS(self.ann_file)
lvis_dt = LVISResults(lvis_gt, result_files['bbox'])
lvis_eval = LVISEval(lvis_gt, lvis_dt, 'bbox')
lvis_eval.params.imgIds = self.img_ids
lvis_eval.params.catIds = self.cat_ids
lvis_eval.evaluate()
lvis_eval.accumulate()
lvis_eval.summarize()
lvis_eval.print_results()
lvis_results = lvis_eval.get_results()
for k, v in lvis_results.items():
if k.startswith('AP'):
key = '{}_{}'.format('bbox', k)
val = float('{:.3f}'.format(float(v)))
eval_results[key] = val
ap_summary = ' '.join([
'{}:{:.3f}'.format(k, float(v))
for k, v in lvis_results.items() if k.startswith('AP')
])
eval_results['bbox_mAP_copypaste'] = ap_summary
if tmp_dir is not None:
tmp_dir.cleanup()
return eval_results
def evaluate(self,
results,
metric='bbox',
logger=None,
jsonfile_prefix=None,
classwise=False,
proposal_nums=(100, 300, 1000),
iou_thrs=np.arange(0.5, 0.96, 0.05)):
"""Evaluation in LVIS protocol.
Args:
results (list[list | tuple]): Testing results of the dataset.
metric (str | list[str]): Metrics to be evaluated. Options are
'bbox', 'segm', 'proposal', 'proposal_fast'.
logger (logging.Logger | str | None): Logger used for printing
related information during evaluation. Default: None.
jsonfile_prefix (str | None):
classwise (bool): Whether to evaluating the AP for each class.
proposal_nums (Sequence[int]): Proposal number used for evaluating
recalls, such as recall@100, recall@1000.
Default: (100, 300, 1000).
iou_thrs (Sequence[float]): IoU threshold used for evaluating
recalls. If set to a list, the average recall of all IoUs will
also be computed. Default: 0.5.
Returns:
dict[str, float]: LVIS style metrics.
"""
try:
import lvis
assert lvis.__version__ >= '10.5.3'
from lvis import LVISResults, LVISEval
except AssertionError:
raise AssertionError('Incompatible version of lvis is installed. '
'Run pip uninstall lvis first. Then run pip '
'install mmlvis to install open-mmlab forked '
'lvis. ')
except ImportError:
raise ImportError('Package lvis is not installed. Please run pip '
'install mmlvis to install open-mmlab forked '
'lvis.')
assert isinstance(results, list), 'results must be a list'
assert len(results) == len(self), (
'The length of results is not equal to the dataset len: {} != {}'.
format(len(results), len(self)))
metrics = metric if isinstance(metric, list) else [metric]
allowed_metrics = ['bbox', 'segm', 'proposal', 'proposal_fast']
for metric in metrics:
if metric not in allowed_metrics:
raise KeyError('metric {} is not supported'.format(metric))
if jsonfile_prefix is None:
tmp_dir = tempfile.TemporaryDirectory()
jsonfile_prefix = osp.join(tmp_dir.name, 'results')
else:
tmp_dir = None
result_files = self.results2json(results, jsonfile_prefix)
eval_results = {}
# get original api
lvis_gt = self.coco
for metric in metrics:
msg = 'Evaluating {}...'.format(metric)
if logger is None:
msg = '\n' + msg
print_log(msg, logger=logger)
if metric == 'proposal_fast':
ar = self.fast_eval_recall(results,
proposal_nums,
iou_thrs,
logger='silent')
log_msg = []
for i, num in enumerate(proposal_nums):
eval_results['AR@{}'.format(num)] = ar[i]
log_msg.append('\nAR@{}\t{:.4f}'.format(num, ar[i]))
log_msg = ''.join(log_msg)
print_log(log_msg, logger=logger)
continue
if metric not in result_files:
raise KeyError('{} is not in results'.format(metric))
try:
lvis_dt = LVISResults(lvis_gt, result_files[metric])
except IndexError:
print_log('The testing results of the whole dataset is empty.',
logger=logger,
level=logging.ERROR)
break
iou_type = 'bbox' if metric == 'proposal' else metric
lvis_eval = LVISEval(lvis_gt, lvis_dt, iou_type)
lvis_eval.params.imgIds = self.img_ids
if metric == 'proposal':
lvis_eval.params.useCats = 0
lvis_eval.params.maxDets = list(proposal_nums)
lvis_eval.evaluate()
lvis_eval.accumulate()
lvis_eval.summarize()
for k, v in lvis_eval.get_results().items():
if k.startswith('AR'):
val = float('{:.3f}'.format(float(v)))
eval_results[k] = val
else:
lvis_eval.evaluate()
lvis_eval.accumulate()
lvis_eval.summarize()
lvis_results = lvis_eval.get_results()
if classwise: # Compute per-category AP
# Compute per-category AP
# from https://github.com/facebookresearch/detectron2/
precisions = lvis_eval.eval['precision']
# precision: (iou, recall, cls, area range, max dets)
assert len(self.cat_ids) == precisions.shape[2]
results_per_category = []
for idx, catId in enumerate(self.cat_ids):
# area range index 0: all area ranges
# max dets index -1: typically 100 per image
nm = self.coco.load_cats(catId)[0]
precision = precisions[:, :, idx, 0, -1]
precision = precision[precision > -1]
if precision.size:
ap = np.mean(precision)
else:
ap = float('nan')
results_per_category.append(
(f'{nm["name"]}', f'{float(ap):0.3f}'))
num_columns = min(6, len(results_per_category) * 2)
results_flatten = list(
itertools.chain(*results_per_category))
headers = ['category', 'AP'] * (num_columns // 2)
results_2d = itertools.zip_longest(*[
results_flatten[i::num_columns]
for i in range(num_columns)
])
table_data = [headers]
table_data += [result for result in results_2d]
table = AsciiTable(table_data)
print_log('\n' + table.table, logger=logger)
for k, v in lvis_results.items():
if k.startswith('AP'):
key = '{}_{}'.format(metric, k)
val = float('{:.3f}'.format(float(v)))
eval_results[key] = val
ap_summary = ' '.join([
'{}:{:.3f}'.format(k, float(v))
for k, v in lvis_results.items() if k.startswith('AP')
])
eval_results['{}_mAP_copypaste'.format(metric)] = ap_summary
lvis_eval.print_results()
if tmp_dir is not None:
tmp_dir.cleanup()
return eval_results
class LVISEvaluator(object):
def __init__(self, run_path, model_ckpt):
self.lvis_gt = LVIS(ANNOTATION_PATH)
self.lvis_dt = LVISResults(self.lvis_gt, PREDICTION_PATH)
self.run_path = run_path
self.model_ckpt = model_ckpt
self._build_coco_to_lvis_map()
cocoEval = LVISEval(self.lvis_gt, self.lvis_dt, 'segm')
self.freq_groups = cocoEval._prepare_freq_group()
config_path = os.path.join(self.run_path, 'config_lvis.yaml')
self.config = yaml.load(open(config_path, "r"), Loader=yaml.FullLoader)
def _build_coco_to_lvis_map(self):
coco_map = json.load(open(os.path.join(LVIS_API_PATH, 'data/coco_to_synset.json')))
synset_to_lvis = {cat['synset']: cat['id'] for cat in self.lvis_gt.cats.values()}
synset_to_lvis['oven.n.01'] = synset_to_lvis['toaster_oven.n.01']
synset_to_lvis['frank.n.02'] = synset_to_lvis['sausage.n.01']
coco_to_lvis = {}
lvis_to_coco = {}
for item in coco_map.values():
coco_id, lvis_id = item['coco_cat_id'], synset_to_lvis[item['synset']]
coco_to_lvis[coco_id] = lvis_id
lvis_to_coco[lvis_id] = coco_id
self.coco_to_lvis = coco_to_lvis
self.lvis_to_coco = lvis_to_coco
def reload_annotations(self):
self.lvis_gt = LVIS(ANNOTATION_PATH)
self.dt_path = PREDICTION_PATH
self.lvis_dt = LVISResults(self.lvis_gt, self.dt_path)
def _save_gt_features(self):
# This takes a long time and shoud only be run once.
from eval.feature_saver import LvisSaver
import torch
config = self.config
if self.config['hyperbolic']:
from models.hyperbolic_resnet import HResNetSimCLR
model = HResNetSimCLR(config['model']['base_model'], config['model']['out_dim'])
else:
from models.resnet_simclr import ResNetSimCLR
model = ResNetSimCLR(config['model']['base_model'], config['model']['out_dim'])
state_dict = torch.load(os.path.join(self.run_path, self.model_ckpt)) # , map_location=device)i
model.load_state_dict(state_dict)
model.eval()
saver = LvisSaver(model, self.lvis_gt, GT_FEATS)
saver.save()
def _save_dt_features(self):
from eval.feature_saver import LvisSaver
import torch
config = self.config
if self.config['hyperbolic']:
from models.hyperbolic_resnet import HResNetSimCLR
model = HResNetSimCLR(config['model']['base_model'], config['model']['out_dim'])
else:
from models.resnet_simclr import ResNetSimCLR
model = ResNetSimCLR(config['model']['base_model'], config['model']['out_dim'])
state_dict = torch.load(os.path.join(self.run_path, self.model_ckpt)) # , map_location=device)i
model.load_state_dict(state_dict)
model.eval()
saver = LvisSaver(model, self.lvis_dt, GT_FEATS)
saver.save()
def load_gt_features(self, coco_only=False, k=100, freq_groups=None):
""" Load gt features from GT_FEATS folder.
:param coco_only: only load categories that are in COCO.
:param k: only load k masks for each category.
:param freq_groups: only load categories in the specified freq_groups. e.g. ['f', 'r']
"""
feats = []
y = []
files = os.listdir(GT_FEATS)
if len(files) == 0:
self._save_gt_features()
print('Found {} files.'.format(len(files)))
for f in files:
if f.endswith('_x.npy'):
feats.append(np.load(os.path.join(GT_FEATS, f)))
elif f.endswith('_y.npy'):
y.extend(np.load(os.path.join(GT_FEATS, f)))
feats = np.concatenate(feats)
print(feats.shape)
self.feats_gt = feats
self.feats_gt_y = np.array(y)
if freq_groups is not None:
print('Filter by freq groups', freq_groups)
freqs = (np.concatenate([self.freq_groups[i] for i in freq_groups]) + 1).astype(np.int)
idx = np.isin(self.feats_gt_y, freqs)
self.feats_gt_y = self.feats_gt_y[idx]
self.feats_gt = self.feats_gt[idx]
print('After:', self.feats_gt.shape)
if coco_only:
coco_cats = self.lvis_to_coco.keys()
idx = np.array([y in coco_cats for y in self.feats_gt_y])
self.feats_gt = self.feats_gt[idx]
self.feats_gt_y = self.feats_gt_y[idx]
print('Keeping objects in COCO', self.feats_gt.shape)
if k:
print('Keeping only {} masks for each class'.format(k))
new_feats_gt = []
new_feats_gt_y = []
counts = Counter(self.feats_gt_y)
for i, c in counts.items():
if c > k:
idx = np.random.choice(np.arange(len(self.feats_gt_y))[self.feats_gt_y == i], k, replace=False)
# print(self.feats_gt_y[idx])
new_feats_gt.append(self.feats_gt[idx])
new_feats_gt_y.extend([i] * k)
else:
new_feats_gt.append(self.feats_gt[self.feats_gt_y == i])
new_feats_gt_y.extend([i] * c)
self.feats_gt = np.concatenate(new_feats_gt)
self.feats_gt_y = new_feats_gt_y
print(self.feats_gt.shape)
def fit_knn(self, k=5, weights='distance'):
""" Fit a KNN model on the ground truth mask features to see whether the embeddings
makes sense.
"""
feats = self.feats_gt
y = self.feats_gt_y
if self.config['hyperbolic']:
from hyperbolic_knn import HyperbolicKNN
self.neigh = HyperbolicKNN(k, feats, y)
pred_y = self.neigh.predict(feats)
print('KNN accuracy', accuracy_score(y, pred_y))
else:
from sklearn.neighbors import KNeighborsClassifier
self.neigh = KNeighborsClassifier(n_neighbors=k, weights=weights)
self.neigh.fit(feats, y)
print('KNN accuracy', self.neigh.score(feats, y))
def load_dt_features(self):
feats = []
feats_ann = []
files = os.listdir(DT_FEATS)
if len(files) == 0:
self._save_dt_features()
print('Found {} files.'.format(len(files)))
for f in files:
if f.endswith('_x.npy'):
feats.append(np.load(os.path.join(DT_FEATS, f)))
elif f.endswith('_ann_id.npy'):
feats_ann.extend(np.load(os.path.join(DT_FEATS, f)))
self.feats = np.concatenate(feats)
self.feats_ann = np.array(feats_ann)
print(self.feats.shape, self.feats_ann.shape)
def run_kmeans(self, C=1500):
feats = self.feats
if self.config['hyperbolic']:
print('Running Hyperbolic KMeans...')
from poincare_kmeans import PoincareKMeans as HKMeans
assert self.feats.shape[1] == 2, 'only supports hkmeans in 2d.'
kmeans = HKMeans(self.feats.shape[1], C)
clusters = kmeans.fit_predict(self.feats)
self.clusters = clusters
else:
print('Running Euclidean KMeans...')
from sklearn.cluster import MiniBatchKMeans
kmeans = MiniBatchKMeans(C)
clusters = kmeans.fit_predict(feats)
self.clusters = clusters
def assign_labels(self):
"""
Take the clusters assigned by kmeans and assign labels to the clusters.
:return:
"""
neigh = self.neigh
clusters = self.clusters
feats = self.feats
C = len(set(clusters))
coco_clusters = {}
cluster_to_coco = {}
print('Assigning labels using KNN ...')
for i in tqdm(range(C)):
idx = np.where(clusters == i)[0]
if len(idx) == 0: continue
predicted = neigh.predict(feats[idx])
votes = sorted(Counter(predicted).items(), key=lambda tup: -tup[1])
best_ratio = votes[0][1] / len(predicted)
if len(predicted) < 3: continue # ignore clusters with fewer than 5
if best_ratio < 0.95: continue
cluster_to_coco[i] = (votes[0][0], best_ratio, len(predicted))
self.cluster_to_coco = cluster_to_coco
self.coco_clusters = coco_clusters
print('Number of assigned clusters:', len(cluster_to_coco))
def evaluate(self):
cluster_to_coco = self.cluster_to_coco
lvis_dt = self.lvis_dt
clusters = self.clusters
feats_ann = self.feats_ann
# by default everything is -1.
for _, dt in lvis_dt.anns.items(): dt['category_id'] = -1
print('Updating category ids')
for i in tqdm(range(len(feats_ann))):
ann_id = int(feats_ann[i])
cluster_id = clusters[i]
if cluster_id in cluster_to_coco:
lvis_dt.anns[ann_id]['category_id'] = cluster_to_coco[cluster_id][0]
# print('assigned ', cluster_to_coco[cluster_id][0])
print('Finally, evaluate!!')
self.lvisEval = LVISEval(self.lvis_gt, lvis_dt, 'segm')
# img_ids = cocoDt.get_img_ids()[:100]
# lvisEval.params.catIds = [1, 2, 3, 4]# 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 33, 34, 35, 37, 40, 41, 42, 43, 44, 46, 47, 49, 50, 51, 52, 54, 56, 57, 59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 88, 90]
# lvisEval.params.imgIds = img_ids
# lvisEval.params.iouThrs = np.linspace(.25, 0.95, int(np.round((0.95 - .25) / .05)) + 1, endpoint=True)
self.lvisEval.lvis_gt.cats[-1] = {'frequency': 'f',
'id': -1,
'synset': 'all',
'image_count': 0,
'instance_count': 0,
'synonyms': ['all'],
'def': 'dummy category',
'name': 'all'}
self.lvisEval.evaluate()
self.lvisEval.accumulate()
self.lvisEval.summarize()
def evaluate_class_agnostic(self):
""" Treat all masks as one category.
"""
lvis_dt = self.lvis_dt
lvis_gt = self.lvis_gt
feats_ann = self.feats_ann
cluster_to_coco = self.cluster_to_coco
# by default, none of the predictions gets evaluated.
for _, dt in lvis_dt.anns.items(): dt['category_id'] = -2
for _, dt in lvis_gt.anns.items(): dt['category_id'] = -2
print('Updating category ids')
for i in tqdm(range(len(feats_ann))):
cluster_id = self.clusters[i]
ann_id = int(feats_ann[i])
if cluster_id in cluster_to_coco:
lvis_dt.anns[ann_id]['category_id'] = -1 # the assigned ones are included in the eval
cocoEval = LVISEval(lvis_gt, lvis_dt, 'segm')
cocoEval.params.catIds = [-1] # only evaluate on the category -1.
cocoEval.params.useCats = 0
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
class Eval_KMeans(object):
def __init__(self):
self.lvis = LVIS('/scratch/users/zzweng/datasets/lvis/lvis_v0.5_val.json')
self.dt_path = 'output/inference/lvis_instances_results.json'
self.lvis_dt = LVISResults(self.lvis, self.dt_path)
coco_map = json.load(open('lvis-api/data/coco_to_synset.json'))
synset_to_lvis = {cat['synset']: cat['id'] for cat in self.lvis.cats.values()}
synset_to_lvis['oven.n.01'] = synset_to_lvis['toaster_oven.n.01']
synset_to_lvis['frank.n.02'] = synset_to_lvis['sausage.n.01']
coco_to_lvis = {}
lvis_to_coco = {}
for item in coco_map.values():
coco_id, lvis_id = item['coco_cat_id'], synset_to_lvis[item['synset']]
coco_to_lvis[coco_id] = lvis_id
lvis_to_coco[lvis_id] = coco_id
self.coco_to_lvis = coco_to_lvis
self.lvis_to_coco = lvis_to_coco
cocoEval = LVISEval(self.lvis, self.lvis_dt,'segm')
self.freq_groups = cocoEval._prepare_freq_group()
# just making sure I am not using any labels in DT annotations, let's wipe them all
# for _, dt in self.cocoDt.anns.items(): dt['category_id'] = -1
def reload_annotations(self):
self.lvis = LVIS('/scratch/users/zzweng/datasets/lvis/lvis_v0.5_val.json')
self.dt_path = 'output/inference/lvis_instances_results.json'
self.lvis_dt = LVISResults(self.lvis, self.dt_path)
def load_gt_features(self, coco_only=False, k=100, freq_groups=None):
feats = []
y = []
rng = np.linspace(0, 5000, 51, dtype=int)
args = list(zip(rng[:-1], rng[1:]))
# args = [(400, 500), (500, 600), (600, 700), (700, 800),
# (800, 900), (900, 1000), (1000, 1100), (1100, 1200)]
for rng in args:
start, end = rng[0], rng[1]
try:
feats.append(np.load(r'{}/val_feats_{}_{}_x.npy'.format(val_feat_folder, start, end)))
y.extend(np.load(r'{}/val_feats_{}_{}_y.npy'.format(val_feat_folder, start, end)))
except FileNotFoundError:
print('File {}/{}_{}.npy not found. Skipped.'.format(val_feat_folder, start, end))
feats = np.concatenate(feats)
print(feats.shape)
self.feats_gt = feats
self.feats_gt_y = np.array(y)
if freq_groups is not None:
print('Filter by freq groups', freq_groups)
freqs = (np.concatenate([self.freq_groups[i] for i in freq_groups])+1).astype(np.int)
idx = np.isin(self.feats_gt_y, freqs)
self.feats_gt_y = self.feats_gt_y[idx]
self.feats_gt = self.feats_gt[idx]
print('After:', self.feats_gt.shape)
if coco_only:
coco_cats = self.lvis_to_coco.keys()
idx = np.array([y in coco_cats for y in self.feats_gt_y])
self.feats_gt = self.feats_gt[idx]
self.feats_gt_y = self.feats_gt_y[idx]
print('Keeping objects in COCO', self.feats_gt.shape)
if k:
print('Keeping only {} masks for each class'.format(k))
new_feats_gt = []
new_feats_gt_y = []
counts = Counter(self.feats_gt_y)
for i, c in counts.items():
if c > k:
idx = np.random.choice(np.arange(len(self.feats_gt_y))[self.feats_gt_y==i], k, replace=False)
# print(self.feats_gt_y[idx])
new_feats_gt.append(self.feats_gt[idx])
new_feats_gt_y.extend([i]*k)
else:
new_feats_gt.append(self.feats_gt[self.feats_gt_y==i])
new_feats_gt_y.extend([i]*c)
self.feats_gt = np.concatenate(new_feats_gt)
self.feats_gt_y = new_feats_gt_y
print(self.feats_gt.shape)
def fit_knn(self, k=5, weights='distance'):
feats = self.feats_gt
y = self.feats_gt_y
if 'hyperbolic' in dt_feat_folder:
self.neigh = HyperbolicKNN(k, feats, y)
pred_y = self.neigh.predict(feats[:50])
print('KNN accuracy', accuracy_score(y[:50], pred_y))
else:
self.neigh = KNeighborsClassifier(n_neighbors=k, weights=weights)
self.neigh.fit(feats, y)
print('KNN accuracy', self.neigh.score(feats, y))
def load_dt_features(self):
feats = []
feats_ann = []
rng = np.linspace(0, 5000, 51, dtype=int)
args = list(zip(rng[:-1], rng[1:]))
for rng in args:
start, end = rng[0], rng[1]
try:
feats.append(np.load(r'{}/{}_{}.npy'.format(dt_feat_folder, start, end)))
feats_ann.extend(np.load(r'{}/{}_{}_ann.npy'.format(dt_feat_folder, start, end)))
except FileNotFoundError:
print('File {}/{}_{}.npy not found. Skipped.'.format(dt_feat_folder, start, end))
self.feats = np.concatenate(feats)
self.feats_ann = feats_ann
print(self.feats.shape)
def run_kmeans(self, C=1500):
feats = self.feats
feats_ann = self.feats_ann
print('Running KMeans ...')
kmeans = MiniBatchKMeans(C)
clusters = kmeans.fit_predict(feats)
self.clusters = clusters
def run_hyperbolic_kmeans(self, C=200):
from poincare_kmeans import PoincareKMeans as HKMeans
kmeans = HKMeans(self.feats.shape[1], C)
clusters = kmeans.fit_predict(self.feats)
self.clusters = clusters
def assign_labels(self):
neigh = self.neigh
cocoDt = self.lvis_dt
clusters = self.clusters
feats = self.feats
feats_ann = self.feats_ann
C = len(set(clusters))
# feats = self.pca.transform(feats)
print(feats.shape)
coco_clusters = {}
cluster_to_coco = {}
print('Assigning labels using KNN ...')
for i in tqdm(range(C)):
idx = np.where(clusters==i)[0]
if len(idx) == 0: continue
predicted = neigh.predict(feats[idx])
# neighbors = neigh.kneighbors(feats[np.where(clusters==i)])[1]
# distances = neigh.kneighbors(feats[np.where(clusters==i)])[0]
votes = sorted(Counter(predicted).items(), key=lambda tup:-tup[1])
best_ratio = votes[0][1] / len(predicted)
# if len(predicted) < 3: continue # ignore clusters with fewer than 5
if best_ratio < 0.95: continue
cluster_to_coco[i] = (votes[0][0], best_ratio, len(predicted))
# if votes[0][0] not in coco_clusters or coco_clusters[votes[0][0]][1] < best_ratio:
# coco_clusters[votes[0][0]] = (i, best_ratio, len(predicted))
# cluster_to_coco[i] = (votes[0][0], best_ratio, len(predicted))
# for j in range(1, len(votes)):
# if votes[j][0] not in coco_clusters or coco_clusters[votes[j][0]][1] < votes[j][1] / len(predicted):
# coco_clusters[votes[j][0]] = (i, votes[j][1] / len(predicted), len(predicted))
# cluster_to_coco[i] = votes[j][0]
self.cluster_to_coco = cluster_to_coco
self.coco_clusters = coco_clusters
print('Number of assigned clusters:', len(cluster_to_coco))
def evaluate_plain(self):
cocoEval = COCOeval(self.cocoGt, self.cocoDt,'segm')
img_ids = self.cocoDt.getImgIds()[:100]
cocoEval.params.imgIds = img_ids
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
self.cocoEval = cocoEval
def evaluate(self):
# self.reload_annotation()
cluster_to_coco = self.cluster_to_coco
coco_clusters = self.coco_clusters
cocoDt = self.lvis_dt
clusters = self.clusters
feats_ann = self.feats_ann
# by default everything is -1.
for _, dt in cocoDt.anns.items(): dt['category_id'] = -1
print('Updating category ids')
for i in tqdm(range(len(feats_ann))):
ann_id = int(feats_ann[i])
cluster_id = clusters[i]
if cluster_id in cluster_to_coco:
cocoDt.anns[ann_id]['category_id'] = cluster_to_coco[cluster_id][0]
# print('assigned ', cluster_to_coco[cluster_id][0])
print('Finally, evaluate!!')
self.cocoEval = LVISEval(self.lvis, cocoDt,'segm')
img_ids = cocoDt.get_img_ids()[:100]
# cocoEval.params.catIds = [1, 2, 3, 4]# 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 33, 34, 35, 37, 40, 41, 42, 43, 44, 46, 47, 49, 50, 51, 52, 54, 56, 57, 59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 88, 90]
# cocoEval.params.imgIds = img_ids
# cocoEval.params.iouThrs = np.linspace(.25, 0.95, int(np.round((0.95 - .25) / .05)) + 1, endpoint=True)
self.cocoEval.lvis_gt.cats[-1] = {'frequency': 'f',
'id': -1,
'synset': 'all',
'image_count': 0,
'instance_count': 0,
'synonyms': ['all'],
'def': 'nut from an oak tree',
'name': 'all'}
import pdb
pdb.set_trace()
self.cocoEval.evaluate()
self.cocoEval.accumulate()
self.cocoEval.summarize()
def evaluate_class_agnostic_all(self):
cocoDt = self.cocoDt
cocoGt = self.cocoGt
feats_ann = self.feats_ann
clusters = self.clusters
cluster_to_coco = self.cluster_to_coco
for _, dt in cocoDt.anns.items(): dt['category_id'] = -1
for _, dt in cocoGt.anns.items(): dt['category_id'] = -1
cocoEval = COCOeval(cocoGt, cocoDt,'segm')
img_ids = cocoDt.getImgIds()[:100]
print(len(img_ids))
cocoEval.params.imgIds = img_ids
cocoEval.params.catIds = [-1]
cocoEval.params.useCats = 0
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
def evaluate_class_agnostic(self):
cocoDt = self.cocoDt
cocoGt = self.cocoGt
feats_ann = self.feats_ann
clusters = self.clusters
cluster_to_coco = self.cluster_to_coco
for _, dt in cocoDt.anns.items(): dt['category_id'] = -2
for _, dt in cocoGt.anns.items(): dt['category_id'] = -1
for i in range(len(feats_ann)):
ann_id = int(feats_ann[i])
cluster_id = clusters[i]
ann = cocoDt.loadAnns(ann_id)[0]
if cluster_id in cluster_to_coco:
ann['category_id'] = -1
else:
ann['category_id'] = -2
cocoEval = COCOeval(cocoGt, cocoDt,'segm')
img_ids = cocoDt.getImgIds()[:100]
print(len(img_ids))
cocoEval.params.imgIds = img_ids
cocoEval.params.catIds = [-1]
cocoEval.params.useCats = 0
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
def _evaluate_predictions_on_lvis_per_class(lvis_gt,
lvis_results,
iou_type,
class_names=None):
"""
Args:
iou_type (str):
kpt_oks_sigmas (list[float]):
class_names (None or list[str]): if provided, will use it to predict
per-category AP.
Returns:
a dict of {metric name: score}
"""
metrics = {
"bbox":
["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"],
"segm":
["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"],
}[iou_type]
logger = logging.getLogger(__name__)
if len(lvis_results) == 0: # TODO: check if needed
logger.warn("No predictions from the model! Set scores to -1")
return {metric: -1 for metric in metrics}
if iou_type == "segm":
lvis_results = copy.deepcopy(lvis_results)
# When evaluating mask AP, if the results contain bbox, LVIS API will
# use the box area as the area of the instance, instead of the mask area.
# This leads to a different definition of small/medium/large.
# We remove the bbox field to let mask AP use mask area.
for c in lvis_results:
c.pop("bbox", None)
from lvis import LVISEval, LVISResults
lvis_results = LVISResults(lvis_gt, lvis_results)
lvis_eval = LVISEval(lvis_gt, lvis_results, iou_type)
lvis_eval.evaluate()
lvis_eval.accumulate()
precisions = lvis_eval.eval["precision"]
results_per_category = []
for idx, name in enumerate(class_names):
# area range index 0: all area ranges
# max dets index -1: typically 100 per image
precision = precisions[:, :, idx, 0]
precision = precision[precision > -1]
ap = np.mean(precision) if precision.size else float("nan")
results_per_category.append(("{}".format(name), float(ap * 100)))
lvis_eval.summarize()
lvis_eval.print_results()
# Pull the standard metrics from the LVIS results
results = lvis_eval.get_results()
results = {metric: float(results[metric] * 100) for metric in metrics}
logger.info("Evaluation results for {}: \n".format(iou_type) +
create_small_table(results))
results.update({"AP-" + name: ap for name, ap in results_per_category})
return results
def eval_cocofied_lvis_result(self, gt_file, result_file, metric='segm'):
def get_lvis_format_result(lvis_params, lvis_results):
template = " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} catIds={:>3s}] = {:0.3f}"
result_list = []
for key, value in lvis_results.items():
max_dets = lvis_params.max_dets
if "AP" in key:
title = "Average Precision"
_type = "(AP)"
else:
title = "Average Recall"
_type = "(AR)"
if len(key) > 2 and key[2].isdigit():
iou_thr = (float(key[2:]) / 100)
iou = "{:0.2f}".format(iou_thr)
else:
iou = "{:0.2f}:{:0.2f}".format(
lvis_params.iou_thrs[0], lvis_params.iou_thrs[-1]
)
if len(key) > 2 and key[2] in ["r", "c", "f"]:
cat_group_name = key[2]
else:
cat_group_name = "all"
if len(key) > 2 and key[2] in ["s", "m", "l"]:
area_rng = key[2]
else:
area_rng = "all"
result_list.append(template.format(title, _type, iou, area_rng, max_dets, cat_group_name, value))
return result_list
print('load gt json')
lvis_gt = LVIS(gt_file)
cat_ids = lvis_gt.get_cat_ids()
print('load pred json')
lvis_dt = LVISResults(lvis_gt, result_file)
print('evaluating')
lvis_eval = LVISEval(lvis_gt, lvis_dt, metric)
lvis_eval.params.imgIds = lvis_gt.get_img_ids()
lvis_eval.evaluate()
lvis_eval.accumulate()
lvis_eval.summarize()
# Compute per-category AP
precisions = lvis_eval.eval['precision']
assert len(cat_ids) == precisions.shape[2]
results_per_category = []
for idx, catId in enumerate(cat_ids):
nm = lvis_gt.load_cats([catId])[0]
precision = precisions[:, :, idx, 0]
precision = precision[precision > -1]
if precision.size:
ap = np.mean(precision)
else:
ap = float('nan')
results_per_category.append(
(f'{nm["name"]}', f'{float(ap):0.3f}'))
num_columns = min(6, len(results_per_category) * 2)
results_flatten = list(
itertools.chain(*results_per_category))
headers = ['category', 'AP'] * (num_columns // 2)
results_2d = itertools.zip_longest(*[
results_flatten[i::num_columns]
for i in range(num_columns)
])
table_data = [headers]
table_data += [result for result in results_2d]
table = AsciiTable(table_data)
print_log('\n' + table.table)
format_summary_result_list = get_lvis_format_result(lvis_eval.params, lvis_eval.results)
format_summary_result = "\n".join(format_summary_result_list)
with open(f"cocofied_per-category-ap-{metric}.txt", 'w') as f:
f.write(table.table + "\n" + format_summary_result)
lvis_eval.print_results()
