Allow tecogan to be used in process_video

codes/data_scripts/use_discriminator_as_filter.py

@@ -61,19 +61,20 @@ if __name__ == "__main__":
 
         tq = tqdm(test_loader)
         removed = 0
+        means = []
+        dataset_mean = -7.133
         for data in tq:
             model.feed_data(data, need_GT=True)
             model.test()
             results = model.eval_state['discriminator_out'][0]
-            print(torch.mean(results), torch.max(results), torch.min(results))
+            means.append(torch.mean(results).item())
+            print(sum(means)/len(means), torch.mean(results), torch.max(results), torch.min(results))
             for i in range(results.shape[0]):
-                if results[i] < .8:
+                #if results[i] < .8:
-                    os.remove(data['GT_path'][i])
+                #    os.remove(data['GT_path'][i])
-                    removed += 1
+                #    removed += 1
-                #imname = osp.basename(data['GT_path'][i])
+                imname = osp.basename(data['GT_path'][i])
-                #if results[i] > .8:
+                if results[i]-dataset_mean > 1:
-                #    torchvision.utils.save_image(data['GT'][i], osp.join(good_path, imname))
+                    torchvision.utils.save_image(data['GT'][i], osp.join(bin_path, imname))
-                #else:
-                #    torchvision.utils.save_image(data['GT'][i], osp.join(bin_path, imname))
 
         print("Removed %i/%i images" % (removed, len(test_set)))

codes/models/ExtensibleTrainer.py

@@ -30,8 +30,9 @@ class ExtensibleTrainer(BaseModel):
         self.env = {'device': self.device,
                'rank': self.rank,
                'opt': opt,
-               'step': 0,
+               'step': 0}
-               'base_path': os.path.join(opt['path']['models'])}
+        if opt['path']['models'] is not None:
+               self.env['base_path'] = os.path.join(opt['path']['models'])
 
         self.mega_batch_factor = 1
         if self.is_train:

codes/models/steps/tecogan_losses.py

@@ -21,6 +21,8 @@ def create_teco_injector(opt, env):
     type = opt['type']
     if type == 'teco_recurrent_generated_sequence_injector':
         return RecurrentImageGeneratorSequenceInjector(opt, env)
+    elif type == 'teco_flow_adjustment':
+        return FlowAdjustment(opt, env)
     return None
 
 def create_teco_discriminator_sextuplet(input_list, lr_imgs, scale, index, flow_gen, resampler, margin):
@@ -132,6 +134,23 @@ class RecurrentImageGeneratorSequenceInjector(Injector):
         torchvision.utils.save_image(gen_input[:, 3:], osp.join(base_path, "%s_recurrent.png" % (it,)))
 
 
+class FlowAdjustment(Injector):
+    def __init__(self, opt, env):
+        super(FlowAdjustment, self).__init__(opt, env)
+        self.resample = Resample2d()
+        self.flow = opt['flow_network']
+        self.flow_target = opt['flow_target']
+        self.flowed = opt['flowed']
+
+    def forward(self, state):
+        flow = self.env['generators'][self.flow]
+        flow_target = state[self.flow_target]
+        flowed = state[self.flowed]
+        flow_input = torch.stack([flow_target, flowed], dim=2)
+        flowfield = flow(flow_input)
+        return {self.output: self.resample(flowed.float(), flowfield.float())}
+
+
 # This is the temporal discriminator loss from TecoGAN.
 #
 # It has a strict contract for 'real' and 'fake' inputs:

codes/process_video.py

@@ -28,6 +28,7 @@ class FfmpegBackedVideoDataset(data.Dataset):
         self.frame_rate = self.opt['frame_rate']
         self.start_at = self.opt['start_at_seconds']
         self.end_at = self.opt['end_at_seconds']
+        self.force_multiple = self.opt['force_multiple']
         self.frame_count = (self.end_at - self.start_at) * self.frame_rate
         # The number of (original) video frames that will be stored on the filesystem at a time.
         self.max_working_files = 20
@@ -69,6 +70,18 @@ class FfmpegBackedVideoDataset(data.Dataset):
 
         mask = torch.ones(1, img_LQ.shape[1], img_LQ.shape[2])
         ref = torch.cat([img_LQ, mask], dim=0)
+
+        if self.force_multiple > 1:
+            assert self.vertical_splits <= 1   # This is not compatible with vertical splits for now.
+            _, h, w = img_LQ.shape
+            height_removed = h % self.force_multiple
+            width_removed = w % self.force_multiple
+            if height_removed != 0:
+                img_LQ = img_LQ[:, :-height_removed, :]
+                ref = ref[:, :-height_removed, :]
+            if width_removed != 0:
+                img_LQ = img_LQ[:, :, :-width_removed]
+                ref = ref[:, :, :-width_removed]
         return {'LQ': img_LQ, 'lq_fullsize_ref': ref,
                 'lq_center': torch.tensor([img_LQ.shape[1] // 2, img_LQ.shape[2] // 2], dtype=torch.long) }
 
@@ -128,18 +141,30 @@ if __name__ == "__main__":
     vid_output = opt['mini_vid_output_folder'] if 'mini_vid_output_folder' in opt.keys() else dataset_dir
     vid_counter = opt['minivid_start_no'] if 'minivid_start_no' in opt.keys() else 0
     img_index = opt['generator_img_index']
+    recurrent_mode = opt['recurrent_mode']
+    first_frame = True
     ffmpeg_proc = None
 
     tq = tqdm(test_loader)
     for data in tq:
         need_GT = False if test_loader.dataset.opt['dataroot_GT'] is None else True
+
+        if recurrent_mode and first_frame:
+            recurrent_entry = data['LQ'].detach().clone()
+        first_frame = False
+        if recurrent_mode:
+            data['recurrent'] = recurrent_entry
+
         model.feed_data(data, need_GT=need_GT)
         model.test()
 
         if isinstance(model.fake_H, tuple):
-            visuals = model.fake_H[img_index].detach().float().cpu()
+            visuals = model.fake_H[img_index].detach()
         else:
-            visuals = model.fake_H.detach().float().cpu()
+            visuals = model.fake_H.detach()
+        if recurrent_mode:
+            recurrent_entry = torch.nn.functional.interpolate(visuals, scale_factor=1/opt['scale'], mode='bicubic')
+        visuals = visuals.cpu().float()
         for i in range(visuals.shape[0]):
             sr_img = util.tensor2img(visuals[i])  # uint8
 
@@ -148,7 +173,6 @@ if __name__ == "__main__":
             util.save_img(sr_img, save_img_path)
             frame_counter += 1
 
-
             if frame_counter % frames_per_vid == 0:
                 if ffmpeg_proc is not None:
                     print("Waiting for last encode..")