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pix2pixHD/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)