Source code for cleanlab.experimental.cifar_cnn

# Copyright (C) 2017-2023  Cleanlab Inc.
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"""
A PyTorch CNN which can be used for finding label issues in CIFAR-10 and CleanLearning with co-teaching.

Code adapted from: https://github.com/bhanML/Co-teaching/blob/master/model.py

You must have PyTorch installed: https://pytorch.org/get-started/locally/
"""


import torch.nn as nn
import torch.nn.functional as F


[docs]def call_bn(bn, x): return bn(x)
[docs]class CNN(nn.Module): """A CNN architecture shown to be a good baseline for a CIFAR-10 benchmark. Parameters ---------- input_channel : int n_outputs : int dropout_rate : float top_bn : bool Methods ------- forward forward pass in PyTorch""" def __init__(self, input_channel=3, n_outputs=10, dropout_rate=0.25, top_bn=False): self.dropout_rate = dropout_rate self.top_bn = top_bn super(CNN, self).__init__() self.c1 = nn.Conv2d(input_channel, 128, kernel_size=3, stride=1, padding=1) self.c2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1) self.c3 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1) self.c4 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1) self.c5 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1) self.c6 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1) self.c7 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=0) self.c8 = nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=0) self.c9 = nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=0) self.l_c1 = nn.Linear(128, n_outputs) self.bn1 = nn.BatchNorm2d(128) self.bn2 = nn.BatchNorm2d(128) self.bn3 = nn.BatchNorm2d(128) self.bn4 = nn.BatchNorm2d(256) self.bn5 = nn.BatchNorm2d(256) self.bn6 = nn.BatchNorm2d(256) self.bn7 = nn.BatchNorm2d(512) self.bn8 = nn.BatchNorm2d(256) self.bn9 = nn.BatchNorm2d(128)
[docs] def forward( self, x, ): h = x h = self.c1(h) h = F.leaky_relu(call_bn(self.bn1, h), negative_slope=0.01) h = self.c2(h) h = F.leaky_relu(call_bn(self.bn2, h), negative_slope=0.01) h = self.c3(h) h = F.leaky_relu(call_bn(self.bn3, h), negative_slope=0.01) h = F.max_pool2d(h, kernel_size=2, stride=2) h = F.dropout2d(h, p=self.dropout_rate) h = self.c4(h) h = F.leaky_relu(call_bn(self.bn4, h), negative_slope=0.01) h = self.c5(h) h = F.leaky_relu(call_bn(self.bn5, h), negative_slope=0.01) h = self.c6(h) h = F.leaky_relu(call_bn(self.bn6, h), negative_slope=0.01) h = F.max_pool2d(h, kernel_size=2, stride=2) h = F.dropout2d(h, p=self.dropout_rate) h = self.c7(h) h = F.leaky_relu(call_bn(self.bn7, h), negative_slope=0.01) h = self.c8(h) h = F.leaky_relu(call_bn(self.bn8, h), negative_slope=0.01) h = self.c9(h) h = F.leaky_relu(call_bn(self.bn9, h), negative_slope=0.01) h = F.avg_pool2d(h, kernel_size=h.data.shape[2]) h = h.view(h.size(0), h.size(1)) logit = self.l_c1(h) if self.top_bn: logit = call_bn(self.bn_c1, logit) return logit