def main(args):
# do not track lambda param, it can be changed after train
exp = Experiment(args, ignore=('lambda_', ))
print(exp)
if exp.found:
print('Already exists: SKIPPING')
exit(0)
np.random.seed(args.seed)
tf.random.set_seed(args.seed)
# get data
train_dataset = get_train_data(args.category,
image_size=args.image_size,
patch_size=args.patch_size,
batch_size=args.batch_size,
n_batches=args.n_batches,
rotation_range=args.rotation_range,
seed=args.seed)
test_dataset, test_labels = get_test_data(args.category,
image_size=args.image_size,
patch_size=args.patch_size,
batch_size=args.batch_size)
is_object = args.category in objects
# build models
generator = make_generator(args.latent_size,
channels=args.channels,
upsample_first=is_object,
upsample_type=args.ge_up,
bn=args.ge_bn,
act=args.ge_act)
encoder = make_encoder(args.patch_size,
args.latent_size,
channels=args.channels,
bn=args.ge_bn,
act=args.ge_act)
discriminator = make_discriminator(args.patch_size,
args.latent_size,
channels=args.channels,
bn=args.d_bn,
act=args.d_act)
# feature extractor model for evaluation
discriminator_features = get_discriminator_features_model(discriminator)
# build optimizers
generator_encoder_optimizer = O.Adam(args.lr,
beta_1=args.ge_beta1,
beta_2=args.ge_beta2)
discriminator_optimizer = O.Adam(args.lr,
beta_1=args.d_beta1,
beta_2=args.d_beta2)
# reference to the models to use in eval
generator_eval = generator
encoder_eval = encoder
# for smoothing generator and encoder evolution
if args.ge_decay > 0:
ema = tf.train.ExponentialMovingAverage(decay=args.ge_decay)
generator_ema = tf.keras.models.clone_model(generator)
encoder_ema = tf.keras.models.clone_model(encoder)
generator_eval = generator_ema
encoder_eval = encoder_ema
# checkpointer
checkpoint = tf.train.Checkpoint(
generator=generator,
encoder=encoder,
discriminator=discriminator,
generator_encoder_optimizer=generator_encoder_optimizer,
discriminator_optimizer=discriminator_optimizer)
best_ckpt_path = exp.ckpt(f'ckpt_{args.category}_best')
last_ckpt_path = exp.ckpt(f'ckpt_{args.category}_last')
# log stuff
log, log_file = exp.require_csv(f'log_{args.category}.csv.gz')
metrics, metrics_file = exp.require_csv(f'metrics_{args.category}.csv')
best_metric = 0.
best_recon = float('inf')
best_recon_file = exp.path_to(f'best_recon_{args.category}.png')
last_recon_file = exp.path_to(f'last_recon_{args.category}.png')
# animate generation during training
n_preview = 6
train_batch = next(iter(train_dataset))[:n_preview]
test_batch = next(iter(test_dataset))[0][:n_preview]
latent_batch = tf.random.normal([n_preview, args.latent_size])
if not is_object: # take random patches from test images
patch_location = np.random.randint(0,
args.image_size - args.patch_size,
(n_preview, 2))
test_batch = [
x[i:i + args.patch_size, j:j + args.patch_size, :]
for x, (i, j) in zip(test_batch, patch_location)
]
test_batch = K.stack(test_batch)
video_out = exp.path_to(f'{args.category}.mp4')
video_options = dict(fps=30, codec='libx265',
quality=4) # see imageio FFMPEG options
video_saver = VideoSaver(train_batch, test_batch, latent_batch, video_out,
**video_options)
video_saver.generate_and_save(generator, encoder)
# train loop
progress = tqdm(train_dataset, desc=args.category, dynamic_ncols=True)
try:
for step, image_batch in enumerate(progress, start=1):
if step == 1 or args.d_iter == 0: # only for JIT compilation (tf.function) to work
d_train = True
ge_train = True
elif args.d_iter:
n_iter = step % (abs(args.d_iter) + 1) # can be in [0, d_iter]
d_train = (n_iter != 0) if (args.d_iter > 0) else (
n_iter == 0) # True in [1, d_iter]
ge_train = not d_train # True when step == d_iter + 1
else: # d_iter == None: dynamic adjustment
d_train = (scores['fake_score'] > 0) or (scores['real_score'] <
0)
ge_train = (scores['real_score'] > 0) or (scores['fake_score']
< 0)
losses, scores = train_step(image_batch,
generator,
encoder,
discriminator,
generator_encoder_optimizer,
discriminator_optimizer,
d_train,
ge_train,
alpha=args.alpha,
gp_weight=args.gp_weight)
if (args.ge_decay > 0) and (step % 10 == 0):
ge_vars = generator.variables + encoder.variables
ema.apply(ge_vars) # update exponential moving average
# tensor to numpy
losses = {
n: l.numpy() if l is not None else l
for n, l in losses.items()
}
scores = {
n: s.numpy() if s is not None else s
for n, s in scores.items()
}
# log step metrics
entry = {
'step': step,
'timestamp': pd.to_datetime('now'),
**losses,
**scores
}
log = log.append(entry, ignore_index=True)
if step % 100 == 0:
if args.ge_decay > 0:
ge_ema_vars = generator_ema.variables + encoder_ema.variables
for v_ema, v in zip(ge_ema_vars, ge_vars):
v_ema.assign(ema.average(v))
preview = video_saver.generate_and_save(
generator_eval, encoder_eval)
if step % 1000 == 0:
log.to_csv(log_file, index=False)
checkpoint.write(file_prefix=last_ckpt_path)
auc, balanced_accuracy = evaluate(generator_eval,
encoder_eval,
discriminator_features,
test_dataset,
test_labels,
patch_size=args.patch_size,
lambda_=args.lambda_)
entry = {
'step': step,
'auc': auc,
'balanced_accuracy': balanced_accuracy
}
metrics = metrics.append(entry, ignore_index=True)
metrics.to_csv(metrics_file, index=False)
if auc > best_metric:
best_metric = auc
checkpoint.write(file_prefix=best_ckpt_path)
# save last image to inspect it during training
imageio.imwrite(last_recon_file, preview)
recon = losses['images_reconstruction_loss']
if recon < best_recon:
best_recon = recon
imageio.imwrite(best_recon_file, preview)
progress.set_postfix({
'AUC': f'{auc:.1%}',
'BalAcc': f'{balanced_accuracy:.1%}',
'BestAUC': f'{best_metric:.1%}',
})
except KeyboardInterrupt:
checkpoint.write(file_prefix=last_ckpt_path)
finally:
log.to_csv(log_file, index=False)
video_saver.close()
# score the test set
checkpoint.read(best_ckpt_path)
auc, balanced_accuracy = evaluate(generator,
encoder,
discriminator_features,
test_dataset,
test_labels,
patch_size=args.patch_size,
lambda_=args.lambda_)
print(f'{args.category}: AUC={auc}, BalAcc={balanced_accuracy}')
def main(args):
es = Elasticsearch(timeout=30, max_retries=10, retry_on_timeout=True)
dataset, q, x = utils.load_benchmark(args.dataset, args.features)
q = utils.load_features(q, chunks=(5000, 2048))
x = utils.load_features(x, chunks=(5000, 2048))
n_queries, n_samples = q.shape[0], x.shape[0]
if args.limit:
x = x[:args.limit]
if args.crelu:
q = crelu(q)
x = crelu(x)
params = vars(args)
ignore = ('output', 'force')
progress = tqdm(zip(args.threshold, args.sq_factor), total=len(args.threshold))
for thr, s in progress:
params['threshold'] = thr
params['sq_factor'] = s
progress.set_postfix({k: v for k, v in params.items() if k not in ignore})
exp = Experiment(params, root=args.output, ignore=ignore)
density, density_file = exp.require_csv(f'density.csv')
if 'query_density' not in density:
progress.write('Computing query density ...')
q_sq = thr_sq(q, thr, s)
q_density = (q_sq != 0).mean(axis=0)
q_density = utils.compute_if_dask(q_density)
density['query_density'] = q_density
density.to_csv(density_file, index=False)
if 'database_density' not in density:
progress.write('Computing database density ...')
x_sq = thr_sq(x, thr, s)
x_density = (x_sq != 0).mean(axis=0)
x_density = utils.compute_if_dask(x_density)
density['database_density'] = x_density
density.to_csv(density_file, index=False)
index_name = exp.name.lower()
if not es.indices.exists(index_name) or es.count(index=index_name)['count'] < n_samples or args.force:
# x_sq = thr_sq(x, thr, s)
x_ids, _ = dataset.images()
index_actions = generate_index_actions(es, index_name, x, x_ids, thr, s, 50)
# index_actions = tqdm(index_actions, total=n_samples)
progress.write(f'Indexing: {index_name}')
index_config = {
"mappings": {
"_source": {"enabled": False}, # do not store STR
"properties": {"repr": {"type": "text"}} # FULLTEXT
},
"settings": {
"index": {"number_of_shards": 1, "number_of_replicas": 0},
"analysis": {"analyzer": {"first": {"type": "whitespace"}}}
}
}
# es.indices.delete(index_name, ignore=(400, 404))
es.indices.create(index_name, index_config, ignore=400)
es.indices.put_settings({"index": {"refresh_interval": "-1", "number_of_replicas": 0}}, index_name)
indexing = parallel_bulk(es, index_actions, thread_count=4, chunk_size=150, max_chunk_bytes=2**26)
indexing = tqdm(indexing, total=n_samples)
start = time.time()
deque(indexing, maxlen=0)
add_time = time.time() - start
progress.write(f'Index time: {add_time}')
es.indices.put_settings({"index": {"refresh_interval": "1s"}}, index_name)
es.indices.refresh()
index_stats_file = exp.path_to('index_stats.csv')
index_stats = pd.DataFrame({'add_time': add_time}, index=[0])
index_stats.to_csv(index_stats_file, index=False)
metrics, metrics_file = exp.require_csv(f'metrics.csv')
scores = None
scores_file = exp.path_to(f'scores.h5')
if not os.path.exists(scores_file):
progress.write('Computing scores...')
xid2idx = {k: i for i, k in enumerate(dataset.images()[0])}
q_sq = thr_sq(q, thr, s)
q_sq = utils.compute_if_dask(q_sq, progress=False)
scores = np.zeros((n_queries, n_samples), dtype=np.float32)
query_times = []
for i, qi in enumerate(tqdm(q_sq)):
query = {
"query": {"query_string": {"default_field": "repr", "query": surrogate_text(qi, boost=True)}},
# "from": 0, "size": n_samples
}
start = time.time()
for hit in tqdm(scan(es, query, index=index_name, preserve_order=True), total=n_samples):
j = xid2idx[hit['_id']]
scores[i, j] = hit['_score']
query_times.append(time.time() - start)
metrics['query_time'] = query_times
metrics.to_csv(metrics_file, index=False)
progress.write(f'Query time: {metrics.query_time.sum()}')
utils.save_as_hdf5(scores, scores_file, progress=True)
if 'ap' not in metrics:
if scores is None:
progress.write('Loading scores...')
scores = utils.load_features(scores_file)[...]
progress.write('Computing mAP...')
metrics['ap'] = dataset.score(scores, reduction=False, progress=True)
metrics.to_csv(metrics_file, index=False)
progress.write(f'mAP: {metrics.ap.mean()}')
if 'ndcg' not in metrics:
dataset._load() # TODO in y_true getter
if scores is None:
progress.write('Loading scores...')
scores = utils.load_features(scores_file)[...]
progress.write('Computing nDCG...')
metrics['ndcg'] = dcg(dataset.y_true, scores, normalized=True)
metrics.to_csv(metrics_file, index=False)
progress.write(f'nDCG: {metrics.ndcg.mean()}')
def main(args):
dataset, q, x = utils.load_benchmark(args.dataset, args.features)
q = utils.load_features(q, chunks=(2500, 2048))
x = utils.load_features(x, chunks=(2500, 2048))
if args.limit:
x = x[:args.limit]
n_points, dim = x.shape
if args.n_cells is None:
step_k = 2500
min_points_per_centroid = 39.0
max_points_per_centroid = 256.0
# n_train_points = min(n_points, 120000) # train index with less points or it crashes..
min_k = np.ceil(
n_points / (step_k * max_points_per_centroid)).astype(int) * step_k
max_k = np.floor(
n_points / (step_k * min_points_per_centroid)).astype(int) * step_k
args.n_cells = min_k
print('Using min suggested cells:', args.n_cells)
exp = Experiment(args, root=args.output, ignore=('output', 'pretrained'))
print(exp)
# create or load faiss index
index_file = exp.path_to('index.faiss')
if not os.path.exists(index_file):
if args.pretrained:
print('Loading pre-trained empty index ...')
index = faiss.read_index(args.pretrained)
train_time = None
else:
tmp = utils.compute_if_dask(x)
print('Creating index: training ...')
index = faiss.index_factory(
dim, 'IVF{},PQ{}'.format(args.n_cells, args.code_size))
# index = faiss.index_factory(dim, 'IVF{},Flat'.format(args.n_cells))
start = time.time()
index.train(tmp)
train_time = time.time() - start
del tmp
print('Creating index: adding ...')
start = time.time()
bs = 2**14
for i in trange(0, x.shape[0], bs):
batch = utils.compute_if_dask(x[i:i + bs])
index.add(batch)
add_time = time.time() - start
faiss.write_index(index, index_file)
size = os.path.getsize(index_file)
index_stats_file = exp.path_to('index_stats.csv')
index_stats = pd.DataFrame(
{
'size': size,
'train_time': train_time,
'add_time': add_time
},
index=[0])
index_stats.to_csv(index_stats_file, index=False)
else:
print('Loading pre-built index ...')
index = faiss.read_index(index_file)
n_probes = (1, 2, 5, 10, 25) # , 50, 100, 250, 500, 1000, 2500, 5000)
n_probes = filter(lambda x: x <= args.n_cells, n_probes)
params = vars(args)
progress = tqdm(n_probes)
for p in progress:
index.nprobe = p
params['nprobe'] = p
progress.set_postfix(
{k: v
for k, v in params.items() if k != 'output'})
scores = None
scores_file = exp.path_to(f'scores_np{p}.h5')
if not os.path.exists(scores_file):
print('Computing scores:', scores_file)
q = utils.compute_if_dask(q)
# execute kNN search using k = dataset size
ranked_sim, ranked_ids = index.search(q, n_points)
# we need a similarity matrix, we construct it from the ranked results.
# we fill it initially with the lowest score (not recovered IDs has infinity score)
if False: # XXX OPTIMIZED VERSION NOT WORKING!!!!
ranked_ids = np.ma.array(ranked_ids, mask=(ranked_ids < 0))
id_order = ranked_ids.argsort(axis=1)
scores = -ranked_sim[np.arange(q.shape[0]).reshape(-1, 1),
id_order]
del ranked_sim, ranked_ids, id_order
else:
scores = np.full((q.shape[0], n_points), np.inf)
for i, (rsims, rids) in enumerate(zip(ranked_sim, ranked_ids)):
for rsim, rid in zip(rsims, rids):
if rid > 0:
scores[i, rid] = rsim
scores = -scores
utils.save_as_hdf5(scores, scores_file, progress=True)
query_times, query_times_file = exp.require_csv('query_times.csv',
index='n_probes')
for i in trange(1, 6):
if utils.value_missing(query_times, p, f'query_time_run{i}'):
q = utils.compute_if_dask(q)
start = time.time()
index.search(q, n_points)
query_time = time.time() - start
query_times.at[p, f'query_time_run{i}'] = query_time
query_times.to_csv(query_times_file)
metrics, metrics_file = exp.require_csv(f'metrics_np{p}.csv')
if 'ap' not in metrics:
if scores is None:
print('Loading scores...')
scores = utils.load_features(scores_file)
print('Computing mAP...')
metrics['ap'] = dataset.score(scores[...],
reduction=False,
progress=True)
metrics.to_csv(metrics_file, index=False)
if 'ndcg' not in metrics:
dataset._load() # TODO in y_true getter
if scores is None:
print('Loading scores...')
scores = utils.load_features(scores_file)
print('Computing nDCG...')
y_true = dataset.y_true[:, :args.
limit] if args.limit else dataset.y_true
bs = 5
ndcg = []
for i in trange(0, y_true.shape[0], bs):
ndcg.append(
dcg(y_true[i:i + bs], scores[i:i + bs], normalized=True))
ndcg = np.concatenate(ndcg)
# metrics['ndcg'] = dcg(y_true, scores, normalized=True)
metrics['ndcg'] = ndcg
metrics.to_csv(metrics_file, index=False)
if 'ndcg@25' not in metrics:
dataset._load() # TODO in y_true getter
if scores is None:
progress.write('Loading scores...')
scores = utils.load_features(scores_file)[...]
progress.write('Computing nDCG@25...')
y_true = dataset.y_true[:, :args.
limit] if args.limit else dataset.y_true
bs = 50
ndcg = []
for i in trange(0, y_true.shape[0], bs):
ndcg.append(
dcg(y_true[i:i + bs],
scores[i:i + bs],
p=25,
normalized=True))
metrics['ndcg@25'] = np.concatenate(ndcg)
# metrics['ndcg'] = dcg(dataset.y_true, scores, normalized=True)
metrics.to_csv(metrics_file, index=False)
progress.write(f'nDCG@25: {metrics["ndcg@25"].mean()}')
metrics['n_probes'] = p
metrics.to_csv(metrics_file, index=False)
def main(args):
lucene_vm = lucene.initVM(vmargs=['-Djava.awt.headless=true'])
lucene_vm.attachCurrentThread()
dataset, q, x = utils.load_benchmark(args.dataset, args.features)
q = utils.load_features(q, chunks=(5000, 2048))
x = utils.load_features(x, chunks=(5000, 2048))
if args.limit:
x = x[:args.limit]
n_queries, n_samples = q.shape[0], x.shape[0]
if args.crelu:
q = crelu(q)
x = crelu(x)
params = vars(args)
ignore = ('output', 'force')
progress = tqdm(zip(args.threshold, args.sq_factor),
total=len(args.threshold))
for thr, s in progress:
params['threshold'] = thr
params['sq_factor'] = s
progress.set_postfix(
{k: v
for k, v in params.items() if k not in ignore})
exp = Experiment(params, root=args.output, ignore=ignore)
density, density_file = exp.require_csv(f'density.csv')
if 'query_density' not in density:
progress.write('Computing query density ...')
q_re = q.rechunk({0: -1, 1: 'auto'}) if utils.is_dask(q) else q
q_sq = threshold(q_re, thr, s)
q_density = (q_sq != 0).mean(axis=0)
q_density = utils.compute_if_dask(q_density)
density['query_density'] = q_density
density.to_csv(density_file, index=False)
if 'database_density' not in density:
progress.write('Computing database density ...')
x_re = q.rechunk({0: -1, 1: 'auto'}) if utils.is_dask(x) else x
x_sq = threshold(x_re, thr, s)
x_density = (x_sq != 0).mean(axis=0)
x_density = utils.compute_if_dask(x_density)
density['database_density'] = x_density
density.to_csv(density_file, index=False)
index_stats, index_stats_file = exp.require_csv('index_stats.csv')
index_name = exp.name.lower()
index_path = exp.path_to('lucene_index')
with LuceneIndex(index_path) as idx:
if idx.count() < n_samples:
x_sq = threshold(x, thr, s)
x_sq = batch_features(x_sq, 5000)
# x_str = features_to_str(x_sq, 5000)
progress.write(f'Indexing: {index_name}')
start = time.time()
for i, xi in enumerate(tqdm(x_sq, total=n_samples)):
idx.add(str(i), xi)
add_time = time.time() - start
progress.write(f'Index time: {add_time}')
index_stats.at[0, 'add_time'] = add_time
if 'size' not in index_stats.columns:
index_stats.at[0, 'size'] = utils.get_folder_size(index_path)
index_stats.to_csv(index_stats_file, index=False)
metrics, metrics_file = exp.require_csv(f'metrics.csv')
scores = None
scores_file = exp.path_to(f'scores.h5')
if not os.path.exists(scores_file):
progress.write('Computing scores...')
q_sq = threshold(q, thr, s)
q_sq = utils.compute_if_dask(q_sq, progress=False)
# q_str = features_to_str(q_sq, n_queries, boost=True)
scores = np.zeros((n_queries, n_samples), dtype=np.float32)
query_times = []
if True: # sequential version
for i, qi in enumerate(tqdm(q_sq, total=n_queries)):
start = time.time()
if qi.any():
for j, score in tqdm(idx.query(qi, n_samples),
total=n_samples):
scores[i, int(j)] = score
query_times.append(time.time() - start)
else:
query_times.append(None)
else: # Parallel version (currently slower)
idx._init_searcher()
def _search(i, qi):
lucene_vm.attachCurrentThread()
scores_i = np.zeros(n_samples, dtype=np.float32)
start = time.time()
if qi.any():
for j, score in idx.query(qi, n_samples):
scores_i[int(j)] = score
query_time = time.time() - start
else:
query_time = None
return scores_i, query_time
queries = enumerate(tqdm(q_sq, total=n_queries))
scores_n_times = Parallel(n_jobs=6, prefer="threads")(
delayed(_search)(i, qi) for i, qi in queries)
scores, query_times = zip(*scores_n_times)
scores = np.vstack(scores)
metrics['query_time'] = query_times
metrics.to_csv(metrics_file, index=False)
progress.write(f'Query time: {metrics.query_time.sum()}')
utils.save_as_hdf5(scores, scores_file, progress=True)
if 'ap' not in metrics:
dataset._load() # TODO in y_true getter
if scores is None:
progress.write('Loading scores...')
scores = utils.load_features(scores_file)[...]
progress.write('Computing mAP...')
metrics['ap'] = dataset.score(scores,
reduction=False,
progress=True)
metrics.to_csv(metrics_file, index=False)
progress.write(f'mAP: {metrics.ap.mean()}')
if 'ndcg' not in metrics:
dataset._load() # TODO in y_true getter
if scores is None:
progress.write('Loading scores...')
scores = utils.load_features(scores_file)[...]
progress.write('Computing nDCG...')
y_true = dataset.y_true[:, :args.
limit] if args.limit else dataset.y_true
bs = 50
ndcg = []
for i in trange(0, y_true.shape[0], bs):
ndcg.append(
dcg(y_true[i:i + bs], scores[i:i + bs], normalized=True))
metrics['ndcg'] = np.concatenate(ndcg)
# metrics['ndcg'] = dcg(dataset.y_true, scores, normalized=True)
metrics.to_csv(metrics_file, index=False)
progress.write(f'nDCG: {metrics.ndcg.mean()}')
if 'ndcg@25' not in metrics:
dataset._load() # TODO in y_true getter
if scores is None:
progress.write('Loading scores...')
scores = utils.load_features(scores_file)[...]
progress.write('Computing nDCG@25...')
y_true = dataset.y_true[:, :args.
limit] if args.limit else dataset.y_true
bs = 50
ndcg = []
for i in trange(0, y_true.shape[0], bs):
ndcg.append(
dcg(y_true[i:i + bs],
scores[i:i + bs],
p=25,
normalized=True))
metrics['ndcg@25'] = np.concatenate(ndcg)
# metrics['ndcg'] = dcg(dataset.y_true, scores, normalized=True)
metrics.to_csv(metrics_file, index=False)
progress.write(f'nDCG@25: {metrics["ndcg@25"].mean()}')