a runable version

data/pano_dataset.py

@@ -106,7 +106,7 @@ class PanoDataset(object):
         """
         self.c = c
         self.device = device
-        self._load_desc(desc, res, views_to_load, load_images)
+        self._load_desc(desc, data_dir, res, views_to_load, load_images)
 
     def get_data(self):
         return {

data/view_dataset.py

@@ -91,7 +91,7 @@ class ViewDataset(object):
             rays_o = self.centers[view_idx]
             rays_d = self.dataset.cam_rays[pix_idx] # (N, 3)
             r = self.rots[view_idx].movedim(-1, -2) # (N, 3, 3)
-            rays_d = torch.matmul(rays_d, r)
+            rays_d = (rays_d[:, None] @ r)[:, 0]
             extra_data = {}
             if self.colors is not None:
                 extra_data['colors'] = self.colors[idx]
@@ -150,15 +150,15 @@ class ViewDataset(object):
                    load_depths: bool,
                    load_bins: bool):
         if load_images and desc.get('view_file_pattern'):
-            self.image_path = os.path.join(self.data_dir, desc['view_file_pattern'])
+            self.image_path = os.path.join(os.getcwd(), desc['view_file_pattern'])
         else:
             self.image_path = None
         if load_depths and desc.get('depth_file_pattern'):
-            self.depth_path = os.path.join(self.data_dir, desc['depth_file_pattern'])
+            self.depth_path = os.path.join(os.getcwd(), desc['depth_file_pattern'])
         else:
             self.depth_path = None
         if load_bins and desc.get('bins_file_pattern'):
-            self.bins_path = os.path.join(self.data_dir, desc['bins_file_pattern'])
+            self.bins_path = os.path.join(os.getcwd(), desc['bins_file_pattern'])
         else:
             self.bins_path = None
         self.res = res if res else misc.values(desc['view_res'], 'y', 'x')

modules/sampler.py

@@ -164,24 +164,3 @@ class PdfSampler(nn.Module):
         return samples
 
         
-class VoxelSampler(nn.Module):
-
-    def __init__(self, *, depth_range: Tuple[float, float], n_samples: int, perturb_sample: bool,
-                 lindisp: bool, space):
-        """
-        Initialize a Sampler module
-
-        :param depth_range: depth range for sampler
-        :param n_samples: count to sample along ray
-        :param perturb_sample: perturb the sample depths
-        :param lindisp: If True, sample linearly in inverse depth rather than in depth
-        """
-        super().__init__()
-        self.lindisp = lindisp
-        self.perturb_sample = perturb_sample
-        self.n_samples = n_samples
-        self.space = space
-        self.s_range = (1 / depth_range[0], 1 / depth_range[1]) if self.lindisp else depth_range
-
-    def forward(self, rays_o, rays_d, *, weights, s_vals=None, include_s_vals=False):
-        
\ No newline at end of file

run_spherical_view_syn.py

@@ -341,6 +341,7 @@ def test():
                                       load_images=args.output_flags['perf'])
         data_loader = DataLoader(dataset, TEST_BATCH_SIZE, chunk_max_items=TEST_MAX_CHUNK_ITEMS,
                                  shuffle=False)
+        print(dataset.image_path)
 
         # 2. Load trained model
         netio.load(test_model_path, model)
@@ -367,7 +368,10 @@ def test():
 
         if args.output_flags['perf']:
             perf = Perf(True, start=True)
-        for _, rays_o, rays_d, _ in data_loader:
+            gt = []
+        for _, rays_o, rays_d, extra_data in data_loader:
+            if args.output_flags["perf"] and "colors" in extra_data:
+                gt.append(extra_data["colors"])
             n_rays = rays_o.size(0)
             ret = model(rays_o, rays_d,
                         ret_depth=args.output_flags['depth'],
@@ -388,8 +392,8 @@ def test():
                                * 0.5 + 0.5) * (vals > 0.1)
             idx = slice(offset, offset + n_rays)
             for key in out:
-                print("key ", key, ", idx ", idx, ", out is ",
-                      out[key].shape, ", ret is ", ret[key].shape, ", rays is ", n_rays)
+                #print("key ", key, ", idx ", idx, ", out is ",
+                #      out[key].shape, ", ret is ", ret[key].shape, ", rays is ", n_rays)
                 out[key][idx] = ret[key]
             if not args.log_redirect:
                 progress_bar(i, math.ceil(total_pixels / n_rays), 'Inferring...')
@@ -416,11 +420,11 @@ def test():
         if args.output_flags['perf']:
             perf_errors = torch.ones(n) * NaN
             perf_ssims = torch.ones(n) * NaN
-            if dataset.images != None:
+            if len(gt) > 0:
+                gt = torch.cat(gt).reshape(n, *dataset.res, -1).movedim(-1, -3)
                 for i in range(n):
-                    perf_errors[i] = loss_mse(dataset.images[i], out['color'][i]).item()
-                    perf_ssims[i] = ssim(dataset.images[i:i + 1],
-                                         out['color'][i:i + 1]).item() * 100
+                    perf_errors[i] = loss_mse(gt[i], out['color'][i]).item()
+                    perf_ssims[i] = ssim(gt[i:i + 1], out['color'][i:i + 1]).item() * 100
             perf_mean_time = tot_time / n
             perf_mean_error = torch.mean(perf_errors).item()
             perf_name = 'perf_%s_%.1fms_%.2e.csv' % (