Merge pull request #18 from modelscope/v0.0.4_dev

V0.0.4 dev

readme.md

@@ -176,7 +176,6 @@ python run.py --cfg classifier.yaml
 ```
 
 
-
 ## 🖥️ SCEPTER Studio
 
 ### Launch

scepter/modules/inference/diffusion_inference.py

@@ -395,7 +395,7 @@ class DiffusionInference():
             return self.first_stage_model['paras']['scale_factor'] * z
 
     def decode_first_stage(self, z):
-        _, dtype = self.get_function_info(self.first_stage_model, 'encode')
+        _, dtype = self.get_function_info(self.first_stage_model, 'decode')
         with torch.autocast('cuda',
                             enabled=dtype == 'float16',
                             dtype=getattr(torch, dtype)):

scepter/modules/model/network/autoencoder/ae_kl.py

@@ -52,7 +52,7 @@ class DiagonalGaussianDistribution(object):
                                dim=dims)
 
     def mode(self):
-        print('*** use DiagonalGaussianDistribution.mode() ***')
+        # print('*** use DiagonalGaussianDistribution.mode() ***')
         return self.mean
 
 

scepter/studio/self_train/self_train_ui/model_ui.py

@@ -171,16 +171,26 @@ class ModelUI(UIBase):
             status = trainer_ui.trainer_ins.get_status(model_name)
             ckpt_list = self.get_ckpt_list(model_name)
             ckpt_value = ckpt_list[-1] if len(ckpt_list) > 0 else ''
+            if ckpt_value is not None and len(ckpt_value) > 0:
+                gallery_value = self.get_gallery_list(model_name, ckpt_value)
+            else:
+                gallery_value = []
+            select_index = 0 if len(gallery_value) > 0 else None
             return (message, gr.Column(visible=status in ('running',
                                                           'success')),
-                    gr.Dropdown(choices=ckpt_list, value=ckpt_value))
+                    gr.Dropdown(choices=ckpt_list, value=ckpt_value),
+                    gr.Gallery(value=gallery_value,
+                               preview=True,
+                               selected_index=select_index)
+                    )
 
         self.output_model_name.change(fn=model_name_change,
                                       inputs=[self.output_model_name],
                                       outputs=[
                                           self.log_message,
                                           self.export_log_panel,
-                                          self.output_ckpt_name
+                                          self.output_ckpt_name,
+                                          self.eval_gallery
                                       ],
                                       queue=False)
 
@@ -243,8 +253,9 @@ class ModelUI(UIBase):
             ckpt_list = self.get_ckpt_list(model_name)
             ckpt_value = ckpt_list[-1] if len(ckpt_list) > 0 else ''
             ret_gallery = ckpt_name_change(model_name, ckpt_value)
-            return (message, gr.Column(visible=status in ('running',
-                                                          'success')),
+            return (message,
+                    gr.Column(visible=status in ('running', 'success')),
+                    gr.Dropdown(choices=self.model_list, value=model_name),
                     gr.Dropdown(choices=ckpt_list,
                                 value=ckpt_value), ret_gallery)
 
@@ -254,7 +265,7 @@ class ModelUI(UIBase):
             outputs=[
                 self.log_message,
                 self.export_log_panel,
-                # self.output_model_name,
+                self.output_model_name,
                 self.output_ckpt_name,
                 self.eval_gallery
             ],
@@ -305,10 +316,13 @@ class ModelUI(UIBase):
                 image_dir = os.path.join(self.work_dir, output_model,
                                          'eval_probe', output_ckpt_name,
                                          'image')
-                image_path = [
-                    os.path.join(image_dir, name)
-                    for name in os.listdir(image_dir)
-                ]
+                if os.path.exists(image_dir):
+                    image_path = [
+                        os.path.join(image_dir, name)
+                        for name in os.listdir(image_dir)
+                    ]
+                else:
+                    image_path = []
             else:
                 _, base_model, base_model_revision, tuner_name, _ = output_model.split(
                     '@', 4)