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encode_features.py
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encode_features.py
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from options.train_options import TrainOptions
from data.data_loader import CreateDataLoader
from models.models import create_model
import numpy as np
import os
opt = TrainOptions().parse()
opt.nThreads = 1
opt.batchSize = 1
opt.serial_batches = True
opt.no_flip = True
opt.instance_feat = True
opt.continue_train = True
name = "features"
save_path = os.path.join(opt.checkpoints_dir, opt.name)
############ Initialize #########
data_loader = CreateDataLoader(opt)
dataset = data_loader.load_data()
dataset_size = len(data_loader)
model = create_model(opt)
########### Encode features ###########
reencode = True
if reencode:
features = {}
for label in range(opt.label_nc):
features[label] = np.zeros((0, opt.feat_num + 1))
for i, data in enumerate(dataset):
feat = model.module.encode_features(data["image"], data["inst"])
for label in range(opt.label_nc):
features[label] = np.append(features[label], feat[label], axis=0)
print("%d / %d images" % (i + 1, dataset_size))
save_name = os.path.join(save_path, name + ".npy")
np.save(save_name, features)
############## Clustering ###########
n_clusters = opt.n_clusters
load_name = os.path.join(save_path, name + ".npy")
features = np.load(load_name).item()
from sklearn.cluster import KMeans
centers = {}
for label in range(opt.label_nc):
feat = features[label]
feat = feat[feat[:, -1] > 0.5, :-1]
if feat.shape[0]:
n_clusters = min(feat.shape[0], opt.n_clusters)
kmeans = KMeans(n_clusters=n_clusters, random_state=0).fit(feat)
centers[label] = kmeans.cluster_centers_
save_name = os.path.join(save_path, name + "_clustered_%03d.npy" % opt.n_clusters)
np.save(save_name, centers)
print("saving to %s" % save_name)