hunk.code_window:     'nasnetalarge': {
           'url': 'http://data.lip6.fr/cadene/pretrainedmodels/nasnetalarge-a1897284.pth',
           'input_size': (3, 331, 331),
           'pool_size': (11, 11),
           'crop_pct': 0.875,
           'interpolation': 'bicubic',
           'mean': (0.5, 0.5, 0.5),
           'std': (0.5, 0.5, 0.5),
  
hunk.labels: keep keep keep keep replace keep keep keep
hunk.after_edit:         'crop_pct': 0.911,
  
hunk.file_path: timm/models/nasnet.py
hunk.type: replace
file: """
  ECA module from ECAnet
  
  paper: ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
  https://arxiv.org/abs/1910.03151
  
  Original ECA model borrowed from https://github.com/BangguWu/ECANet
  
  Modified circular ECA implementation and adaption for use in timm package
  by Chris Ha https://github.com/VRandme
  
  Original License:
  
  MIT License
  
  Copyright (c) 2019 BangguWu, Qilong Wang
  
  Permission is hereby granted, free of charge, to any person obtaining a copy
  of this software and associated documentation files (the "Software"), to deal
  in the Software without restriction, including without limitation the rights
  to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  copies of the Software, and to permit persons to whom the Software is
  furnished to do so, subject to the following conditions:
  
  The above copyright notice and this permission notice shall be included in all
  copies or substantial portions of the Software.
  
  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  SOFTWARE.
  """
  import math
  from torch import nn
  import torch.nn.functional as F
  
  
  class EcaModule(nn.Module):
      """Constructs an ECA module.
  
      Args:
          channels: Number of channels of the input feature map for use in adaptive kernel sizes
              for actual calculations according to channel.
              gamma, beta: when channel is given parameters of mapping function
              refer to original paper https://arxiv.org/pdf/1910.03151.pdf
              (default=None. if channel size not given, use k_size given for kernel size.)
          kernel_size: Adaptive selection of kernel size (default=3)
      """
      def __init__(self, channels=None, kernel_size=3, gamma=2, beta=1):
          super(EcaModule, self).__init__()
          assert kernel_size % 2 == 1
  
          if channels is not None:
              t = int(abs(math.log(channels, 2) + beta) / gamma)
              kernel_size = max(t if t % 2 else t + 1, 3)
  
          self.avg_pool = nn.AdaptiveAvgPool2d(1)
          self.conv = nn.Conv1d(1, 1, kernel_size=kernel_size, padding=(kernel_size - 1) // 2, bias=False)
  
      def forward(self, x):
          # Feature descriptor on the global spatial information
          y = self.avg_pool(x)
          # Reshape for convolution
          y = y.view(x.shape[0], 1, -1)
          # Two different branches of ECA module
          y = self.conv(y)
          # Multi-scale information fusion
          y = y.view(x.shape[0], -1, 1, 1).sigmoid()
          return x * y.expand_as(x)
  
  
  class CecaModule(nn.Module):
      """Constructs a circular ECA module.
  
      ECA module where the conv uses circular padding rather than zero padding.
      Unlike the spatial dimension, the channels do not have inherent ordering nor
      locality. Although this module in essence, applies such an assumption, it is unnecessary
      to limit the channels on either "edge" from being circularly adapted to each other.
      This will fundamentally increase connectivity and possibly increase performance metrics
      (accuracy, robustness), without significantly impacting resource metrics
      (parameter size, throughput,latency, etc)
  
      Args:
          channels: Number of channels of the input feature map for use in adaptive kernel sizes
              for actual calculations according to channel.
              gamma, beta: when channel is given parameters of mapping function
              refer to original paper https://arxiv.org/pdf/1910.03151.pdf
              (default=None. if channel size not given, use k_size given for kernel size.)
          kernel_size: Adaptive selection of kernel size (default=3)
      """
  
      def __init__(self, channels=None, kernel_size=3, gamma=2, beta=1):
          super(CecaModule, self).__init__()
          assert kernel_size % 2 == 1
  
          if channels is not None:
              t = int(abs(math.log(channels, 2) + beta) / gamma)
              kernel_size = max(t if t % 2 else t + 1, 3)
  
          self.avg_pool = nn.AdaptiveAvgPool2d(1)
          #pytorch circular padding mode is buggy as of pytorch 1.4
          #see https://github.com/pytorch/pytorch/pull/17240
  
          #implement manual circular padding
          self.conv = nn.Conv1d(1, 1, kernel_size=kernel_size, padding=0, bias=False)
          self.padding = (kernel_size - 1) // 2
  
      def forward(self, x):
          # Feature descriptor on the global spatial information
          y = self.avg_pool(x)
  
          # Manually implement circular padding, F.pad does not seemed to be bugged
          y = F.pad(y.view(x.shape[0], 1, -1), (self.padding, self.padding), mode='circular')
  
          # Two different branches of ECA module
          y = self.conv(y)
  
          # Multi-scale information fusion
          y = y.view(x.shape[0], -1, 1, 1).sigmoid()
  
          return x * y.expand_as(x)
  