Latent space playground

codes/scripts/srflow_latent_space_playground.py

@@ -128,6 +128,36 @@ def extract_center_latent(ref, lat):
     return ref[:, :, math.floor(dh):-math.ceil(dh), math.floor(dw):-math.ceil(dw)]
 
 
+def linear_interpolation(latents1, latents2, proportion):
+    return [l1*proportion+l2*(1-proportion) for l1, l2 in zip(latents1, latents2)]
+
+
+def slerp(latents1, latents2, proportion):
+    res = []
+    for low, high in zip(latents1, latents2):
+        low_norm = low / torch.norm(low, dim=[2,3], keepdim=True)
+        high_norm = high / torch.norm(high, dim=[2,3], keepdim=True)
+        omega = torch.acos((low_norm * high_norm).sum(1))
+        so = torch.sin(omega)
+        res.append((torch.sin((1.0 - proportion) * omega) / so).unsqueeze(1) * low + (torch.sin(proportion * omega) / so).unsqueeze(1) * high)
+    return res
+
+
+def create_interpolation_video(gen, lq, output_file, latents1, latents2, steps=10):
+    for i in range(steps):
+        proportion = i / (steps-1)
+        lats = linear_interpolation(latents1, latents2, proportion)
+        hr, _ = gen(lr=lq,
+                    z=lats[0],
+                    reverse=True,
+                    epses=lats,
+                    add_gt_noise=False)
+        torchvision.transforms.ToPILImage()(hr.squeeze(0).cpu())
+        torchvision.utils.save_image(hr.cpu(), os.path.join(output_file, "%i.png" % (i,)))
+    # Stopped using this because PILs animated gif output is total crap.
+    #images[0].save(output_file, save_all=True, append_images=images[1:], duration=80, loop=0)
+
+
 if __name__ == "__main__":
     #### options
     torch.backends.cudnn.benchmark = True
@@ -150,17 +180,18 @@ if __name__ == "__main__":
     gen = model.networks['generator']
     gen.eval()
 
-    mode = "feed_through"  # restore | latent_transfer | feed_through
+    mode = "temperature"  # temperature | restore | latent_transfer | feed_through
     #imgs_to_resample_pattern = "F:\\4k6k\\datasets\\ns_images\\adrianna\\val2\\lr\\*"
-    imgs_to_resample_pattern = "F:\\4k6k\\datasets\\ns_images\\adrianna\\pure_adrianna_full\\images\\*"
+    #imgs_to_resample_pattern = "F:\\4k6k\\datasets\\ns_images\\adrianna\\analyze\\analyze_xx\\*"
+    imgs_to_resample_pattern = "F:\\4k6k\\datasets\\ns_images\\imagesets\\images-half\\*lanette*"
     scale = 2
-    resample_factor = 2  # When != 1, the HR image is upsampled by this factor using a bicubic to get the local latents.
+    resample_factor = 1  # When != 1, the HR image is upsampled by this factor using a bicubic to get the local latents.
-    temperature = .3
+    temperature = 1
     output_path = "E:\\4k6k\\mmsr\\results\\latent_playground"
 
     # Data types <- used to perform latent transfer.
     data_path = "F:\\4k6k\\datasets\\ns_images\\imagesets\\images-half"
-    data_type_filters = ["*alexa*", "*lanette*", "*80755*", "*x-art-1912*", "*joli_high*", "*stacy-cruz*"]
+    data_type_filters = ["*alexa*", "*lanette*", "*80755*", "*joli_high*"]
     #data_type_filters = ["*lanette*"]
     max_size = 1100  # Should be set to 2x the largest single dimension of the input space, otherwise an error will occur.
     max_ref_datatypes = 30  # Only picks this many images from the above data types to sample from.
@@ -183,7 +214,6 @@ if __name__ == "__main__":
                 cvimg = torch2cv(img)
                 cvimg = corruptor.corrupt_images([cvimg])[0]
                 img = cv2torch(cvimg)
-                torchvision.utils.save_image(img, "corrupted_lq_%i.png" % (random.randint(0, 100),))
                 return img
             dt_latents = [fetch_latents_for_image(gen, i, scale, corrupt_and_downsample) for i in dt_transfers]
 
@@ -214,6 +244,8 @@ if __name__ == "__main__":
                     dts.append([extract_center_latent(sl, l) * temperature for l, sl in zip(latents, slat)])
                 latents = dts
                 multiple_latents = True
+            elif mode == "temperature":
+                latents = [l * temperature for l in latents]
 
             # Re-compute each image with the new metrics
             if not multiple_latents:
@@ -221,13 +253,8 @@ if __name__ == "__main__":
             else:
                 lats = latents
             for j in range(len(lats)):
-                hr, _ = gen(lr=F.interpolate(resample_img, scale_factor=1/scale, mode="area"),
+                path = os.path.join(output_path, "%i_%i" % (im_it, j))
-                         z=lats[j][0],
+                os.makedirs(path, exist_ok=True)
-                         reverse=True,
+                torchvision.utils.save_image(resample_img, os.path.join(path, "%i_orig.jpg" %(im_it)))
-                         epses=lats[j],
+                create_interpolation_video(gen, F.interpolate(resample_img, scale_factor=1/scale, mode="area"),
-                         add_gt_noise=False)
+                                           path, [torch.zeros_like(l) for l in lats[j]], lats[j])
-                if torch.isnan(torch.max(hr)):
-                    continue
-                os.makedirs(os.path.join(output_path), exist_ok=True)
-                torchvision.utils.save_image(resample_img, os.path.join(output_path, "%i_orig.jpg" %(im_it)))
-                torchvision.utils.save_image(hr, os.path.join(output_path, "%i_%i.jpg" % (im_it,j)))