debug

app.py

@@ -144,6 +144,7 @@ Note: Currently, only 5 types of textures are supported, i.e., pure color, strip
         enable_queue=args.enable_queue,
         server_port=args.port,
         share=args.share,
+        debug=True,
     )
 
 

model.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import logging
 import os
 import pathlib
 import sys
@@ -18,6 +19,17 @@ from utils.language_utils import (generate_shape_attributes,
 from utils.options import dict_to_nonedict, parse
 from utils.util import set_random_seed
 
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.DEBUG)
+logger.propagate = False
+formatter = logging.Formatter(
+    '[%(asctime)s] %(name)s %(levelname)s: %(message)s',
+    datefmt='%Y-%m-%d %H:%M:%S')
+handler = logging.StreamHandler(stream=sys.stdout)
+handler.setLevel(logging.DEBUG)
+handler.setFormatter(formatter)
+logger.addHandler(handler)
+
 COLOR_LIST = [
     (0, 0, 0),
     (255, 250, 250),
@@ -73,6 +85,7 @@ class Model:
 
     @staticmethod
     def preprocess_pose_image(image: PIL.Image.Image) -> torch.Tensor:
+        logger.debug(f'{image.size=}')
         image = np.array(
             image.resize(
                 size=(256, 512),
@@ -109,6 +122,7 @@ class Model:
                              shape_text: str) -> np.ndarray:
         if pose_data is None:
             return
+        logger.debug(f'{len(shape_text)=}')
         self.model.feed_pose_data(pose_data)
         shape_attributes = generate_shape_attributes(shape_text)
         shape_attributes = torch.LongTensor(shape_attributes).unsqueeze(0)
@@ -122,6 +136,8 @@ class Model:
                        sample_steps: int, seed: int) -> np.ndarray:
         if label_image is None:
             return
+        logger.debug(f'{len(texture_text)=}')
+        logger.debug(f'{sample_steps=}')
         mask = label_image.copy()
         seg_map = self.process_mask(mask)
         self.model.segm = torch.from_numpy(seg_map).unsqueeze(0).unsqueeze(