Fixes and additional support for progressive zoom

codes/data/multiscale_dataset.py

@@ -22,7 +22,7 @@ class MultiScaleDataset(data.Dataset):
         self.num_scales = self.opt['num_scales']
         self.hq_size_cap = self.tile_size * 2 ** self.num_scales
         self.scale = self.opt['scale']
-        self.paths_hq, self.sizes_hq = util.get_image_paths(self.data_type, opt['paths'], [1])
+        self.paths_hq, self.sizes_hq = util.get_image_paths(self.data_type, opt['paths'], [1 for _ in opt['paths']])
         self.corruptor = ImageCorruptor(opt)
 
     # Selects the smallest dimension from the image and crops it randomly so the other dimension matches. The cropping

codes/models/ExtensibleTrainer.py

@@ -267,13 +267,14 @@ class ExtensibleTrainer(BaseModel):
         with torch.no_grad():
             # This can happen one of two ways: Either a 'validation injector' is provided, in which case we run that.
             # Or, we run the entire chain of steps in "train" mode and use eval.output_state.
-            if 'injector' in self.opt['eval'].keys():
+            if 'injectors' in self.opt['eval'].keys():
-                # Need to move from mega_batch mode to batch mode (remove chunks)
                 state = {}
-                for k, v in self.dstate.items():
+                for inj in self.opt['eval']['injectors'].values():
-                    state[k] = v[0]
+                    # Need to move from mega_batch mode to batch mode (remove chunks)
-                inj = create_injector(self.opt['eval']['injector'], self.env)
+                    for k, v in self.dstate.items():
-                state.update(inj(state))
+                        state[k] = v[0]
+                    inj = create_injector(inj, self.env)
+                    state.update(inj(state))
             else:
                 # Iterate through the steps, performing them one at a time.
                 state = self.dstate

codes/models/archs/RRDBNet_arch.py

@@ -188,6 +188,8 @@ class RRDBNet(nn.Module):
             if ref is None:
                 ref = torch.zeros_like(x_lg)
             x_lg = torch.cat([x_lg, ref], dim=1)
+        else:
+            x_lg = x
         feat = self.conv_first(x_lg)
         body_feat = self.conv_body(checkpoint_sequential(self.body, self.num_blocks // self.blocks_per_checkpoint, feat))
         feat = feat + body_feat

codes/models/steps/progressive_zoom.py

@@ -27,8 +27,12 @@ class ProgressiveGeneratorInjector(Injector):
         self.hq_output_key = opt['hq_output']  # The key where HQ images corresponding with generated images are stored.
         self.input_lq_index = opt['input_lq_index'] if 'input_lq_index' in opt.keys() else 0
         self.output_hq_index = opt['output_hq_index']
-        self.recurrent_output_index = opt['recurrent_output_index']
+        if 'recurrent_output_index' in opt.keys():
-        self.recurrent_index = opt['recurrent_index']
+            self.recurrent_output_index = opt['recurrent_output_index']
+            self.recurrent_index = opt['recurrent_index']
+            self.recurrence = True
+        else:
+            self.recurrence = False
         self.depth = opt['depth']
         self.number_branches = opt['num_branches']  # Number of input branches to randomly choose for generation. This defines the output shape.
         self.multiscale_leaves = build_multiscale_patch_index_map(self.depth)
@@ -52,7 +56,8 @@ class ProgressiveGeneratorInjector(Injector):
     def feed_forward(self, gen, inputs, results, lq_input, recurrent_input):
         ff_input = inputs.copy()
         ff_input[self.input_lq_index] = lq_input
-        ff_input[self.recurrent_index] = recurrent_input
+        if self.recurrence:
+            ff_input[self.recurrent_index] = recurrent_input
 
         with autocast(enabled=self.env['opt']['fp16']):
             gen_out = gen(*ff_input)
@@ -61,7 +66,10 @@ class ProgressiveGeneratorInjector(Injector):
             gen_out = [gen_out]
         for i, out_key in enumerate(self.output):
             results[out_key].append(gen_out[i])
-        return gen_out[self.output_hq_index], gen_out[self.recurrent_output_index]
+        recurrent = None
+        if self.recurrence:
+            recurrent = gen_out[self.recurrent_output_index]
+        return gen_out[self.output_hq_index], recurrent
 
     def forward(self, state):
         gen = self.env['generators'][self.gen_key]
@@ -73,6 +81,7 @@ class ProgressiveGeneratorInjector(Injector):
             inputs = [inputs]
         if not isinstance(self.output, list):
             output = [self.output]
+            self.output = output
         results = {}   # A list of outputs produced by feeding each progressive lq input into the generator.
         results_hq = []
         for out_key in output:
@@ -91,7 +100,8 @@ class ProgressiveGeneratorInjector(Injector):
             for link in chain:  # Remember, `link` is a MultiscaleTreeNode.
                 top = int(link.top * h)
                 left = int(link.left * w)
-                recurrent = torch.nn.functional.interpolate(recurrent[:, :, top:top+h//2, left:left+w//2], scale_factor=2, mode="nearest")
+                if recurrent is not None:
+                    recurrent = torch.nn.functional.interpolate(recurrent[:, :, top:top+h//2, left:left+w//2], scale_factor=2, mode="nearest")
                 if self.feed_gen_output_into_input:
                     top *= 2
                     left *= 2

codes/scripts/validate_data.py

@@ -13,7 +13,7 @@ from skimage import io
 def main():
     #### options
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../../options/train_imgset_spsr_switched2_xlbatch.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../../options/train_prog_mi1_rrdb_6bypass.yml')
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none',
                         help='job launcher')
     parser.add_argument('--local_rank', type=int, default=0)
@@ -50,6 +50,7 @@ def main():
                     len(train_set), train_size))
     assert train_loader is not None
 
+    '''
     tq_ldr = tqdm(train_set.get_paths())
     for path in tq_ldr:
         try:
@@ -58,6 +59,10 @@ def main():
         except Exception as e:
             print("Error with %s" % (path,))
             print(e)
+    '''
+    tq_ldr = tqdm(train_set)
+    for ds in tq_ldr:
+        pass
 
 
 if __name__ == '__main__':

codes/train.py

@@ -265,7 +265,7 @@ class Trainer:
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_mi1_rrdb4x_6bl_bypass.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_prog_mi1_rrdb_6bypass.yml')
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
     parser.add_argument('--local_rank', type=int, default=0)
     args = parser.parse_args()

codes/utils/options.py

@@ -23,8 +23,6 @@ def parse(opt_path, is_train=True):
         for phase, dataset in opt['datasets'].items():
             phase = phase.split('_')[0]
             dataset['phase'] = phase
-            if opt['distortion'] == 'sr' or opt['distortion'] == 'downsample':
-                dataset['scale'] = scale
             is_lmdb = False
             ''' LMDB is not supported at this point with the mods I've been making.
             if dataset.get('dataroot_GT', None) is not None:
@@ -67,11 +65,6 @@ def parse(opt_path, is_train=True):
         opt['path']['results_root'] = results_root
         opt['path']['log'] = results_root
 
-    # network
-    if opt['distortion'] == 'sr' or opt['distortion'] == 'downsample':
-        if 'network_G' in opt.keys():
-            opt['network_G']['scale'] = scale
-
     return opt