fix runtime error #87 and replace scipy by PIL

models/bicycle_gan_model.py

@@ -76,7 +76,7 @@ def get_z_random(self, batch_size, nz, random_type='gauss'):
             z = torch.rand(batch_size, nz) * 2.0 - 1.0
         elif random_type == 'gauss':
             z = torch.randn(batch_size, nz)
-        return z.to(self.device)
+        return z.detach().to(self.device)
 
     def encode(self, input_image):
         mu, logvar = self.netE.forward(input_image)
@@ -184,25 +184,24 @@ def update_D(self):
     def backward_G_alone(self):
         # 3, reconstruction |(E(G(A, z_random)))-z_random|
         if self.opt.lambda_z > 0.0:
-            self.loss_z_L1 = torch.mean(torch.abs(self.mu2 - self.z_random)) * self.opt.lambda_z
+            self.loss_z_L1 = self.criterionZ(self.mu2, self.z_random) * self.opt.lambda_z
             self.loss_z_L1.backward()
         else:
             self.loss_z_L1 = 0.0
 
     def update_G_and_E(self):
         # update G and E
-        self.set_requires_grad([self.netD, self.netD2], False)
-        self.optimizer_E.zero_grad()
-        self.optimizer_G.zero_grad()
-        self.backward_EG()
-        self.optimizer_G.step()
-        self.optimizer_E.step()
-        # update G only
-        if self.opt.lambda_z > 0.0:
-            self.optimizer_G.zero_grad()
+        with torch.autograd.set_detect_anomaly(True):
+            self.set_requires_grad([self.netD, self.netD2], False)
             self.optimizer_E.zero_grad()
-            self.backward_G_alone()
+            self.optimizer_G.zero_grad()
+            self.backward_EG()
+
+            # update G only
+            if self.opt.lambda_z > 0.0:
+                self.backward_G_alone()
             self.optimizer_G.step()
+            self.optimizer_E.step()
 
     def optimize_parameters(self):
         self.forward()

util/util.py

@@ -90,13 +90,20 @@ def interp_z(z0, z1, num_frames, interp_mode='linear'):
     return zs
 
 
-def save_image(image_numpy, image_path):
+def save_image(image_numpy, image_path, aspect_ratio=1.0):
     """Save a numpy image to the disk
     Parameters:
         image_numpy (numpy array) -- input numpy array
         image_path (str)          -- the path of the image
     """
+
     image_pil = Image.fromarray(image_numpy)
+    h, w, _ = image_numpy.shape
+
+    if aspect_ratio > 1.0:
+        image_pil = image_pil.resize((h, int(w * aspect_ratio)), Image.BICUBIC)
+    if aspect_ratio < 1.0:
+        image_pil = image_pil.resize((int(h / aspect_ratio), w), Image.BICUBIC)
     image_pil.save(image_path)
 
 

util/visualizer.py

@@ -6,7 +6,6 @@
 from . import util
 from . import html
 from subprocess import Popen, PIPE
-from scipy.misc import imresize
 
 
 if sys.version_info[0] == 2:
@@ -36,13 +35,7 @@ def save_images(webpage, images, names, image_path, aspect_ratio=1.0, width=256)
         im = util.tensor2im(im_data)
         image_name = '%s_%s.png' % (name, label)
         save_path = os.path.join(image_dir, image_name)
-        h, w, _ = im.shape
-        if aspect_ratio > 1.0:
-            im = imresize(im, (h, int(w * aspect_ratio)), interp='bicubic')
-        if aspect_ratio < 1.0:
-            im = imresize(im, (int(h / aspect_ratio), w), interp='bicubic')
-        util.save_image(im, save_path)
-
+        util.save_image(im, save_path, aspect_ratio=aspect_ratio)
         ims.append(image_name)
         txts.append(label)
         links.append(image_name)