delete print model.device

app.py

@@ -361,6 +361,7 @@ def construction_all():
 @torch.no_grad()   
 @spaces.GPU(duration=120) 
 def evaluate_v1(inputs, model, quantizer, tokenizer, width, height, do_sample=False, temperature=1.0, top_p=1.0, top_k=50):
+    print(f"evaluate_v1")
     print(f"evaluate_v1 {model.device} {model.lm.device} {pipeline.device}")
     model = model.to("cuda")
     print(f"after evaluate_v1 {model.device} {model.lm.device} {pipeline.device}")
@@ -406,6 +407,7 @@ def evaluate_v1(inputs, model, quantizer, tokenizer, width, height, do_sample=Fa
     return pred_json_example
 
 def inference(generate_method, intention, model, quantizer, tokenizer, width, height, do_sample=True, temperature=1.0, top_p=1.0, top_k=50):
+    print(f"start inference")
     rawdata = {}
     rawdata["wholecaption"] = intention
     rawdata["layout"] = []
@@ -414,7 +416,7 @@ def inference(generate_method, intention, model, quantizer, tokenizer, width, he
         max_try_time = 5
         preddata = None
         while preddata is None and max_try_time > 0:
-            print(f"inference {model.device} {model.lm.device} {pipeline.device}")
+            print(f"preddata = evaluate_v1")
             preddata = evaluate_v1(rawdata, model, quantizer, tokenizer, width, height, do_sample=do_sample, temperature=temperature, top_p=top_p, top_k=top_k)
             max_try_time -= 1
     else:
@@ -423,6 +425,50 @@ def inference(generate_method, intention, model, quantizer, tokenizer, width, he
 
     return preddata
 
+def process_preddate(intention, temperature, top_p, generate_method='v1'):
+    intention = intention.replace('\n', '').replace('\r', '').replace('\\', '')
+    intention = ensure_space_after_period(intention)
+    print(f"process_preddate")
+    if temperature == 0.0:
+        # print("looking for greedy decoding strategies, set `do_sample=False`.")
+        # preddata = inference_partial(generate_method, intention, do_sample=False)
+        print(f"preddata = inference temperatrue = 0.0")
+        preddata = inference(generate_method, intention, model=model, quantizer=quantizer, tokenizer=tokenizer, width=512, height=512, do_sample=False)
+    else:
+        # preddata = inference_partial(generate_method, intention, temperature=temperature, top_p=top_p)
+        print(f"preddata = inference temperatrue != 0.0")
+        preddata = inference(generate_method, intention, model=model, quantizer=quantizer, tokenizer=tokenizer, width=512, height=512, temperature=temperature, top_p=top_p)
+        
+    layouts = preddata["layout"]
+    list_box = []
+    for i, layout in enumerate(layouts):
+        x, y = layout["x"], layout["y"]
+        width, height = layout["width"], layout["height"]
+        if i == 0:
+            list_box.append((0, 0, width, height))
+            list_box.append((0, 0, width, height))
+        else:
+            left = x - width // 2
+            top = y - height // 2
+            right = x + width // 2
+            bottom = y + height // 2
+            list_box.append((left, top, right, bottom))
+            
+    # print(list_box)
+    filtered_boxes = list_box[:2]
+    for i in range(2, len(list_box)):
+        keep = True
+        for j in range(1, len(filtered_boxes)):
+            iou = calculate_iou(list_box[i], filtered_boxes[j])
+            if iou > 0.65:
+                print(list_box[i], filtered_boxes[j])
+                keep = False
+                break
+        if keep:
+            filtered_boxes.append(list_box[i])
+        
+    return str(filtered_boxes), intention, str(filtered_boxes)
+
 @spaces.GPU(duration=120)
 def test_one_sample(validation_box, validation_prompt, true_gs, inference_steps, pipeline, generator, transp_vae):
     print(validation_box)
@@ -515,48 +561,6 @@ def process_svg(text_input, tuple_input, seed, true_gs, inference_steps):
         """
     
     return result_images, svg_file_path, svg_editor
-
-def process_preddate(intention, temperature, top_p, generate_method='v1'):
-    intention = intention.replace('\n', '').replace('\r', '').replace('\\', '')
-    intention = ensure_space_after_period(intention)
-    print(f"process_preddate: {model.lm.device}")
-    if temperature == 0.0:
-        # print("looking for greedy decoding strategies, set `do_sample=False`.")
-        # preddata = inference_partial(generate_method, intention, do_sample=False)
-        preddata = inference(generate_method, intention, model=model, quantizer=quantizer, tokenizer=tokenizer, width=512, height=512, do_sample=False)
-    else:
-        # preddata = inference_partial(generate_method, intention, temperature=temperature, top_p=top_p)
-        preddata = inference(generate_method, intention, model=model, quantizer=quantizer, tokenizer=tokenizer, width=512, height=512, temperature=temperature, top_p=top_p)
-        
-    layouts = preddata["layout"]
-    list_box = []
-    for i, layout in enumerate(layouts):
-        x, y = layout["x"], layout["y"]
-        width, height = layout["width"], layout["height"]
-        if i == 0:
-            list_box.append((0, 0, width, height))
-            list_box.append((0, 0, width, height))
-        else:
-            left = x - width // 2
-            top = y - height // 2
-            right = x + width // 2
-            bottom = y + height // 2
-            list_box.append((left, top, right, bottom))
-            
-    # print(list_box)
-    filtered_boxes = list_box[:2]
-    for i in range(2, len(list_box)):
-        keep = True
-        for j in range(1, len(filtered_boxes)):
-            iou = calculate_iou(list_box[i], filtered_boxes[j])
-            if iou > 0.65:
-                print(list_box[i], filtered_boxes[j])
-                keep = False
-                break
-        if keep:
-            filtered_boxes.append(list_box[i])
-        
-    return str(filtered_boxes), intention, str(filtered_boxes)
     
 def main():
     construction_all()