polish

doc.md

@@ -6,7 +6,9 @@ Chinese users may use mirrors:
 
 ```bash
 # for Conda: https://mirrors.tuna.tsinghua.edu.cn/help/anaconda
-pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/  # pip
+
+# for PyPI: https://mirrors.tuna.tsinghua.edu.cn/help/pypi/
+pip config set global.index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
 ```
 
 First, create a Conda environment:

powerqe/archs/identitynet_arch.py

@@ -9,7 +9,7 @@ class IdentityNet(nn.Module):
     """Identity network used for testing benchmarks (in tensors). Support up-scaling."""
 
     def __init__(self, scale=1, upscale_mode="nearest"):
-        super(IdentityNet, self).__init__()
+        super().__init__()
         self.scale = scale
         self.upscale_mode = upscale_mode
 

powerqe/losses/__init__.py

@@ -2,7 +2,6 @@
 
 
 from basicsr.utils import get_root_logger
-from basicsr.utils.registry import LOSS_REGISTRY as LOSS_REGISTRY_BASICSR
 
 from .registry import LOSS_REGISTRY
 

powerqe/models/qe_model.py

@@ -10,23 +10,4 @@ class QEModel(SRModel):
     """Base QE model for single image quality enhancement."""
 
     def __init__(self, opt):
-        super(SRModel, self).__init__(opt)
-
-        # define network
-        self.net_g = build_network(opt["network_g"])
-        self.net_g = self.model_to_device(self.net_g)
-        self.print_network(self.net_g)
-
-        # load pretrained models
-        load_path = self.opt["path"].get("pretrain_network_g", None)
-        if load_path is not None:
-            param_key = self.opt["path"].get("param_key_g", "params")
-            self.load_network(
-                self.net_g,
-                load_path,
-                self.opt["path"].get("strict_load_g", True),
-                param_key,
-            )
-
-        if self.is_train:
-            self.init_training_settings()
+        super().__init__(opt)