add version

compute_dists_dirs.py

@@ -7,12 +7,13 @@
 parser.add_argument('-d0','--dir0', type=str, default='./imgs/ex_dir0')
 parser.add_argument('-d1','--dir1', type=str, default='./imgs/ex_dir1')
 parser.add_argument('-o','--out', type=str, default='./imgs/example_dists.txt')
+parser.add_argument('-v','--version', type=str, default='0.1')
 parser.add_argument('--use_gpu', action='store_true', help='turn on flag to use GPU')
 
 opt = parser.parse_args()
 
 ## Initializing the model
-model = models.PerceptualLoss(model='net-lin',net='alex',use_gpu=opt.use_gpu)
+model = models.PerceptualLoss(model='net-lin',net='alex',use_gpu=opt.use_gpu,version=opt.version)
 
 # crawl directories
 f = open(opt.out,'w')

compute_dists_pair.py

@@ -1,42 +1,58 @@
 import argparse
 import os
 import models
-import numpy as np
 from util import util
+import numpy as np
+from IPython import embed
 
 parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-parser.add_argument('-d','--dir', type=str, default='./imgs/ex_dir_pair')
+parser.add_argument('-d','--dir', type=str, default='./imgs/ex_dir0')
 parser.add_argument('-o','--out', type=str, default='./imgs/example_dists.txt')
+parser.add_argument('-v','--version', type=str, default='0.1')
+parser.add_argument('--all-pairs', action='store_true', help='turn on to test all N(N-1)/2 pairs, leave off to just do consecutive pairs (N-1)')
+parser.add_argument('-N', type=int, default=None)
 parser.add_argument('--use_gpu', action='store_true', help='turn on flag to use GPU')
 
 opt = parser.parse_args()
 
 ## Initializing the model
-model = models.PerceptualLoss(model='net-lin',net='alex',use_gpu=opt.use_gpu)
+model = models.PerceptualLoss(model='net-lin',net='alex',use_gpu=opt.use_gpu,version=opt.version)
 
 # crawl directories
 f = open(opt.out,'w')
 files = os.listdir(opt.dir)
+if(opt.N is not None):
+	files = files[:opt.N]
+F = len(files)
 
 dists = []
-for (ff,file0) in enumerate(files[:-1]):
-	img0 = util.im2tensor(util.load_image(os.path.join(opt.dir,file0))) # RGB image from [-1,1]
+for (ff,file) in enumerate(files[:-1]):
+	img0 = util.im2tensor(util.load_image(os.path.join(opt.dir,file))) # RGB image from [-1,1]
 	if(opt.use_gpu):
 		img0 = img0.cuda()
 
-	for (gg,file1) in enumerate(files[ff+1:]):
+	if(opt.all_pairs):
+		files1 = files[ff+1:]
+	else:
+		files1 = [files[ff+1],]
+
+	for file1 in files1:
 		img1 = util.im2tensor(util.load_image(os.path.join(opt.dir,file1)))
+
 		if(opt.use_gpu):
 			img1 = img1.cuda()
 
 		# Compute distance
-		dist01 = model.forward(img0,img1).item()
-		dists.append(dist01)
-		print('(%s, %s): %.3f'%(file0,file1,dist01))
-		f.writelines('(%s, %s): %.3f'%(file0,file1,dist01))
-
-dist_mean = np.mean(np.array(dists))
-print('Mean: %.3f'%dist_mean)
-f.writelines('Mean: %.3f'%dist_mean)
+		dist01 = model.forward(img0,img1)
+		print('(%s,%s): %.3f'%(file,file1,dist01))
+		f.writelines('(%s,%s): %.6f\n'%(file,file1,dist01))
+
+		dists.append(dist01.item())
+
+avg_dist = np.mean(np.array(dists))
+stderr_dist = np.std(np.array(dists))/np.sqrt(len(dists))
+
+print('Avg: %.5f +/- %.5f'%(avg_dist,stderr_dist))
+f.writelines('Avg: %.6f +/- %.6f'%(avg_dist,stderr_dist))
 
 f.close()

models/__init__.py

@@ -11,15 +11,15 @@
 from models import dist_model
 
 class PerceptualLoss(torch.nn.Module):
-    def __init__(self, model='net-lin', net='alex', colorspace='rgb', spatial=False, use_gpu=True, gpu_ids=[0]): # VGG using our perceptually-learned weights (LPIPS metric)
+    def __init__(self, model='net-lin', net='alex', colorspace='rgb', spatial=False, use_gpu=True, gpu_ids=[0], version='0.1'): # VGG using our perceptually-learned weights (LPIPS metric)
     # def __init__(self, model='net', net='vgg', use_gpu=True): # "default" way of using VGG as a perceptual loss
         super(PerceptualLoss, self).__init__()
         print('Setting up Perceptual loss...')
         self.use_gpu = use_gpu
         self.spatial = spatial
         self.gpu_ids = gpu_ids
         self.model = dist_model.DistModel()
-        self.model.initialize(model=model, net=net, use_gpu=use_gpu, colorspace=colorspace, spatial=self.spatial, gpu_ids=gpu_ids)
+        self.model.initialize(model=model, net=net, use_gpu=use_gpu, colorspace=colorspace, spatial=self.spatial, gpu_ids=gpu_ids, version=version)
         print('...[%s] initialized'%self.model.name())
         print('...Done')