polish: update pre-commit hooks; add: csv and markdown logging

.github/workflows/sync_to_private.yml

@@ -25,4 +25,4 @@ jobs:
       - name: Sync to private repository
         run: |
           git remote add private git@github.com:ryanxingql/${{ secrets.PRIVATE_REPO_NAME }}.git
-          git push private basicsr-based-dev --force
+          git push private basicsr-based-dev --force

.gitignore

@@ -168,4 +168,4 @@ cython_debug/
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-.idea/
+.idea/

.pre-commit-config.yaml

@@ -1,9 +1,37 @@
 repos:
-  - repo: https://github.com/psf/black
-    rev: 24.8.0
+  # flake8
+  - repo: https://github.com/PyCQA/flake8
+    rev: 7.1.1
     hooks:
-      - id: black
+      - id: flake8
+
+  # yapf
+  - repo: https://github.com/google/yapf
+    rev: v0.40.2
+    hooks:
+      - id: yapf
+
+  # isort
+  - repo: https://github.com/timothycrosley/isort
+    rev: 5.13.2
+    hooks:
+      - id: isort
+
+  # codespell
   - repo: https://github.com/codespell-project/codespell
     rev: v2.3.0
     hooks:
       - id: codespell
+
+  # pre-commit-hooks
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v5.0.0
+    hooks:
+      - id: trailing-whitespace  # Trim trailing whitespace
+      - id: check-yaml  # Attempt to load all yaml files to verify syntax
+      - id: check-merge-conflict  # Check for files that contain merge conflict strings
+      - id: double-quote-string-fixer  # Replace double-quoted strings with single quoted strings
+      - id: end-of-file-fixer  # Make sure files end in a newline and only a newline
+      - id: requirements-txt-fixer  # Sort entries in requirements.txt and remove incorrect entry for pkg-resources==0.0.0
+      - id: mixed-line-ending  # Replace or check mixed line ending
+        args: ["--fix=lf"]

powerqe/archs/__init__.py

@@ -1,7 +1,6 @@
 from copy import deepcopy
 
 from basicsr.utils import get_root_logger
-
 from .arcnn_arch import ARCNN
 from .cbdnet_arch import CBDNet
 from .dcad_arch import DCAD
@@ -14,24 +13,24 @@
 from .unet_arch import UNet
 
 __all__ = [
-    "ARCNN",
-    "CBDNet",
-    "DCAD",
-    "DnCNN",
-    "IdentityNet",
-    "MPRNet",
-    "RBQE",
-    "RDN",
-    "build_network",
-    "ARCH_REGISTRY",
-    "UNet",
+    'ARCNN',
+    'CBDNet',
+    'DCAD',
+    'DnCNN',
+    'IdentityNet',
+    'MPRNet',
+    'RBQE',
+    'RDN',
+    'build_network',
+    'ARCH_REGISTRY',
+    'UNet',
 ]
 
 
 def build_network(opt):
     opt = deepcopy(opt)
-    network_type = opt.pop("type")
+    network_type = opt.pop('type')
     net = ARCH_REGISTRY.get(network_type)(**opt)
     logger = get_root_logger()
-    logger.info(f"Network [{net.__class__.__name__}] is created.")
+    logger.info(f'Network [{net.__class__.__name__}] is created.')
     return net

powerqe/archs/arcnn_arch.py

@@ -37,9 +37,7 @@ def __init__(
         super().__init__()
 
         self.layers = nn.Sequential(
-            nn.Conv2d(
-                io_channels, mid_channels_1, in_kernel_size, padding=in_kernel_size // 2
-            ),
+            nn.Conv2d(io_channels, mid_channels_1, in_kernel_size, padding=in_kernel_size // 2),
             nn.ReLU(inplace=False),
             nn.Conv2d(
                 mid_channels_1,

powerqe/archs/cbdnet_arch.py

@@ -1,8 +1,8 @@
 import torch
 from torch import nn as nn
 
-from .unet_arch import UNet
 from .registry import ARCH_REGISTRY
+from .unet_arch import UNet
 
 
 @ARCH_REGISTRY.register()
@@ -31,41 +31,35 @@ def __init__(
         nf_gr_denoise=2,
         nl_base_denoise=1,
         nl_gr_denoise=2,
-        down_denoise="avepool2d",
-        up_denoise="transpose2d",
-        reduce_denoise="add",
+        down_denoise='avepool2d',
+        up_denoise='transpose2d',
+        reduce_denoise='add',
     ):
         super().__init__()
 
-        estimate_list = nn.ModuleList(
-            [
+        estimate_list = nn.ModuleList([
+            nn.Conv2d(
+                in_channels=io_channels,
+                out_channels=estimate_channels,
+                kernel_size=3,
+                padding=3 // 2,
+            ),
+            nn.ReLU(inplace=True),
+        ])
+        for _ in range(3):
+            estimate_list += nn.ModuleList([
                 nn.Conv2d(
-                    in_channels=io_channels,
+                    in_channels=estimate_channels,
                     out_channels=estimate_channels,
                     kernel_size=3,
                     padding=3 // 2,
                 ),
                 nn.ReLU(inplace=True),
-            ]
-        )
-        for _ in range(3):
-            estimate_list += nn.ModuleList(
-                [
-                    nn.Conv2d(
-                        in_channels=estimate_channels,
-                        out_channels=estimate_channels,
-                        kernel_size=3,
-                        padding=3 // 2,
-                    ),
-                    nn.ReLU(inplace=True),
-                ]
-            )
-        estimate_list += nn.ModuleList(
-            [
-                nn.Conv2d(estimate_channels, io_channels, 3, padding=3 // 2),
-                nn.ReLU(inplace=True),
-            ]
-        )
+            ])
+        estimate_list += nn.ModuleList([
+            nn.Conv2d(estimate_channels, io_channels, 3, padding=3 // 2),
+            nn.ReLU(inplace=True),
+        ])
         self.estimate = nn.Sequential(*estimate_list)
 
         self.denoise = UNet(

powerqe/archs/dncnn_arch.py

@@ -32,9 +32,7 @@ def __init__(self, io_channels=3, mid_channels=64, num_blocks=15, if_bn=False):
                 layers += [
                     # bias is unnecessary and off due to the following BN
                     nn.Conv2d(mid_channels, mid_channels, 3, padding=1, bias=False),
-                    nn.BatchNorm2d(
-                        num_features=mid_channels, momentum=0.9, eps=1e-04, affine=True
-                    ),
+                    nn.BatchNorm2d(num_features=mid_channels, momentum=0.9, eps=1e-04, affine=True),
                 ]
             else:
                 layers.append(nn.Conv2d(mid_channels, mid_channels, 3, padding=1))

powerqe/archs/identitynet_arch.py

@@ -8,7 +8,7 @@
 class IdentityNet(nn.Module):
     """Identity network used for testing benchmarks (in tensors). Support up-scaling."""
 
-    def __init__(self, scale=1, upscale_mode="nearest"):
+    def __init__(self, scale=1, upscale_mode='nearest'):
         super().__init__()
         self.scale = scale
         self.upscale_mode = upscale_mode