[pre-commit.ci] pre-commit suggestions (#7279)

* [pre-commit.ci] pre-commit suggestions

updates:
- [github.com/asottile/pyupgrade: v2.31.0 → v2.31.1](https://github.com/asottile/pyupgrade/compare/v2.31.0...v2.31.1)
- [github.com/pre-commit/mirrors-yapf: v0.31.0 → v0.32.0](https://github.com/pre-commit/mirrors-yapf/compare/v0.31.0...v0.32.0)

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update yolo.py

* Update activations.py

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update activations.py

* Update tf.py

* Update tf.py

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

utils/activations.py

@@ -8,29 +8,32 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 
-# SiLU https://arxiv.org/pdf/1606.08415.pdf ----------------------------------------------------------------------------
-class SiLU(nn.Module):  # export-friendly version of nn.SiLU()
+class SiLU(nn.Module):
+    # SiLU activation https://arxiv.org/pdf/1606.08415.pdf
     @staticmethod
     def forward(x):
         return x * torch.sigmoid(x)
 
 
-class Hardswish(nn.Module):  # export-friendly version of nn.Hardswish()
+class Hardswish(nn.Module):
+    # Hard-SiLU activation
     @staticmethod
     def forward(x):
         # return x * F.hardsigmoid(x)  # for TorchScript and CoreML
         return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0  # for TorchScript, CoreML and ONNX
 
 
-# Mish https://github.com/digantamisra98/Mish --------------------------------------------------------------------------
 class Mish(nn.Module):
+    # Mish activation https://github.com/digantamisra98/Mish
     @staticmethod
     def forward(x):
         return x * F.softplus(x).tanh()
 
 
 class MemoryEfficientMish(nn.Module):
+    # Mish activation memory-efficient
     class F(torch.autograd.Function):
+
         @staticmethod
         def forward(ctx, x):
             ctx.save_for_backward(x)
@@ -47,8 +50,8 @@ class MemoryEfficientMish(nn.Module):
         return self.F.apply(x)
 
 
-# FReLU https://arxiv.org/abs/2007.11824 -------------------------------------------------------------------------------
 class FReLU(nn.Module):
+    # FReLU activation https://arxiv.org/abs/2007.11824
     def __init__(self, c1, k=3):  # ch_in, kernel
         super().__init__()
         self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False)
@@ -58,12 +61,12 @@ class FReLU(nn.Module):
         return torch.max(x, self.bn(self.conv(x)))
 
 
-# ACON https://arxiv.org/pdf/2009.04759.pdf ----------------------------------------------------------------------------
 class AconC(nn.Module):
-    r""" ACON activation (activate or not).
+    r""" ACON activation (activate or not)
     AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter
     according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
     """
+
     def __init__(self, c1):
         super().__init__()
         self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))
@@ -76,10 +79,11 @@ class AconC(nn.Module):
 
 
 class MetaAconC(nn.Module):
-    r""" ACON activation (activate or not).
+    r""" ACON activation (activate or not)
     MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network
     according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
     """
+
     def __init__(self, c1, k=1, s=1, r=16):  # ch_in, kernel, stride, r
         super().__init__()
         c2 = max(r, c1 // r)

utils/callbacks.py

@@ -8,6 +8,7 @@ class Callbacks:
     """"
     Handles all registered callbacks for YOLOv5 Hooks
     """
+
     def __init__(self):
         # Define the available callbacks
         self._callbacks = {

utils/datasets.py

@@ -145,6 +145,7 @@ class InfiniteDataLoader(dataloader.DataLoader):
 
     Uses same syntax as vanilla DataLoader
     """
+
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler))
@@ -164,6 +165,7 @@ class _RepeatSampler:
     Args:
         sampler (Sampler)
     """
+
     def __init__(self, sampler):
         self.sampler = sampler
 
@@ -978,6 +980,7 @@ def dataset_stats(path='coco128.yaml', autodownload=False, verbose=False, profil
         autodownload:   Attempt to download dataset if not found locally
         verbose:        Print stats dictionary
     """
+
     def round_labels(labels):
         # Update labels to integer class and 6 decimal place floats
         return [[int(c), *(round(x, 4) for x in points)] for c, *points in labels]

utils/loggers/wandb/wandb_utils.py

@@ -116,6 +116,7 @@ class WandbLogger():
     For more on how this logger is used, see the Weights & Biases documentation:
     https://docs.wandb.com/guides/integrations/yolov5
     """
+
     def __init__(self, opt, run_id=None, job_type='Training'):
         """
         - Initialize WandbLogger instance

utils/metrics.py

@@ -260,6 +260,7 @@ def box_iou(box1, box2):
         iou (Tensor[N, M]): the NxM matrix containing the pairwise
             IoU values for every element in boxes1 and boxes2
     """
+
     def box_area(box):
         # box = 4xn
         return (box[2] - box[0]) * (box[3] - box[1])

utils/torch_utils.py

@@ -284,6 +284,7 @@ class ModelEMA:
     Keeps a moving average of everything in the model state_dict (parameters and buffers)
     For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
     """
+
     def __init__(self, model, decay=0.9999, tau=2000, updates=0):
         # Create EMA
         self.ema = deepcopy(de_parallel(model)).eval()  # FP32 EMA