llflow(fix): works from llflow directory

models/llflow/inference.py

@@ -1,12 +1,17 @@
+
+import os
+os.environ['KMP_DUPLICATE_LIB_OK']='TRUE'
+
 import glob
 import sys
 from collections import OrderedDict
 import tqdm
 from natsort import natsort
 import argparse
-import models.llflow.option_ as option
-from models.llflow import Measure, psnr
-from models.llflow import imresize
+import option_ as option
+# import option_ as option
+from Measure import Measure, psnr
+from imresize import imresize
 from models import create_model
 import torch
 from util import opt_get
@@ -92,8 +97,8 @@ def auto_padding(img, times=16):
 
 def main(path:str):
     parser = argparse.ArgumentParser()
-    # parser.add_argument("--opt", default="./confs/LOL_smallNet.yml")
-    parser.add_argument("--opt", default="./models/llflow/LOL_smallNet.yml")
+    parser.add_argument("--opt", default="./confs/LOL_smallNet.yml")
+    # parser.add_argument("--opt", default="E:\startup\image_enhancement\models\llflow\LOL_smallNet.yml")
     parser.add_argument("-n", "--name", default="unpaired")
     
     # Namespace(opt="./models/llflow/LOL_smallNet.yml", name="unpaired")
@@ -139,7 +144,8 @@ def main(path:str):
     assert raw_shape == sr.shape
     path_out_sr = os.path.join(test_dir, os.path.basename(lr_path))
     # imwrite(path_out_sr, sr)
-    # cv2.imwrite(path_out_sr, sr[:, :, [2, 1, 0]])
+    cv2.imwrite(path_out_sr, sr[:, :, [2, 1, 0]])
+    Console().print(f"image saved 💾 to {path_out_sr}")
     
     return sr[:, :, [2, 1, 0]]
 
@@ -154,4 +160,4 @@ def format_measurements(meas):
 
 
 if __name__ == "__main__":
-    main()
+    main(r"E:\startup\image_enhancement\n000129\0026_01.jpg")

models/llflow/models/__init__.py

@@ -1,6 +1,7 @@
 import importlib
 import logging
 import os
+from rich.console import Console
 
 try:
     import local_config
@@ -16,7 +17,9 @@ def find_model_using_name(model_name):
     # the file "models/modelname_model.py"
     # will be imported.
     model_filename = "models." + model_name + "_model"
-    modellib = importlib.import_module(model_filename)
+    Console().log(f"⚡\t model_filename: {model_filename}")
+    modellib = importlib.import_module(model_filename) # "models.llflow.models.LLFlow_model"
+    # modellib = importlib.import_module("models.llflow.models.LLFlow_model") # "models.llflow.models.LLFlow_model"
 
     # In the file, the class called ModelNameModel() will
     # be instantiated. It has to be a subclass of torch.nn.Module,

models/llflow/models/modules/ConditionEncoder.py

@@ -7,7 +7,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 import models.modules.module_util as mutil
 # from utils.util import opt_get
-from models.llflow.models.modules.flow import Conv2dZeros
+from models.modules.flow import Conv2dZeros
 
 
 def opt_get(opt, keys, default=None):

models/llflow/option_.py

@@ -2,7 +2,7 @@ import os
 import os.path as osp
 import logging
 import yaml
-from models.llflow.util import OrderedYaml
+from util import OrderedYaml
 
 Loader, Dumper = OrderedYaml()