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network.py
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network.py
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"""Model Code."""
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models import (squeezenet1_1,
efficientnet_b1,
convnext_tiny,
mobilenet_v3_small,
shufflenet_v2_x0_5)
def get_network(network='squeezenet', num_classes=10):
"""Get a model."""
if network == 'squeezenet':
model = squeezenet1_1(num_classes=num_classes)
elif network == 'efficientnet':
model = efficientnet_b1(num_classes=num_classes)
elif network == 'convnext':
model = convnext_tiny(num_classes=num_classes)
elif network == 'mobilenet':
model = mobilenet_v3_small(num_classes=num_classes)
elif network == 'shufflenet':
model = shufflenet_v2_x0_5(num_classes=num_classes)
elif network == 'wrn_28_2':
model = WideResNet(10, 28, 2)
elif network == 'wrn_28_8':
model = WideResNet(10, 28, 8)
return model
# Wide ResNet
# Reference: https://github.com/kekmodel/FixMatch-pytorch
def mish(x):
"""Mish: A Self Regularized Non-Monotonic Neural Activation Function."""
return x * torch.tanh(F.softplus(x))
class PSBatchNorm2d(nn.BatchNorm2d):
"""How Does BN Increase Collapsed Neural Network Filters? (https://arxiv.org/abs/2001.11216)"""
def __init__(self, num_features, alpha=0.1, eps=1e-05, momentum=0.001, affine=True, track_running_stats=True):
super().__init__(num_features, eps, momentum, affine, track_running_stats)
self.alpha = alpha
def forward(self, x):
return super().forward(x) + self.alpha
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, drop_rate=0.0, activate_before_residual=False):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes, momentum=0.001)
self.relu1 = nn.LeakyReLU(negative_slope=0.1, inplace=True)
self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_planes, momentum=0.001)
self.relu2 = nn.LeakyReLU(negative_slope=0.1, inplace=True)
self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1,
padding=1, bias=False)
self.drop_rate = drop_rate
self.equalInOut = (in_planes == out_planes)
self.convShortcut = (not self.equalInOut) and nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
padding=0, bias=False) or None
self.activate_before_residual = activate_before_residual
def forward(self, x):
if not self.equalInOut and self.activate_before_residual == True:
x = self.relu1(self.bn1(x))
else:
out = self.relu1(self.bn1(x))
out = self.relu2(self.bn2(self.conv1(out if self.equalInOut else x)))
if self.drop_rate > 0:
out = F.dropout(out, p=self.drop_rate, training=self.training)
out = self.conv2(out)
return torch.add(x if self.equalInOut else self.convShortcut(x), out)
class NetworkBlock(nn.Module):
def __init__(self, nb_layers, in_planes, out_planes, block, stride, drop_rate=0.0, activate_before_residual=False):
super(NetworkBlock, self).__init__()
self.layer = self._make_layer(
block, in_planes, out_planes, nb_layers, stride, drop_rate, activate_before_residual)
def _make_layer(self, block, in_planes, out_planes, nb_layers, stride, drop_rate, activate_before_residual):
layers = []
for i in range(int(nb_layers)):
layers.append(block(i == 0 and in_planes or out_planes, out_planes,
i == 0 and stride or 1, drop_rate, activate_before_residual))
return nn.Sequential(*layers)
def forward(self, x):
return self.layer(x)
class WideResNet(nn.Module):
def __init__(self, num_classes, depth=28, widen_factor=2, drop_rate=0.0):
super(WideResNet, self).__init__()
channels = [16, 16*widen_factor, 32*widen_factor, 64*widen_factor]
assert((depth - 4) % 6 == 0)
n = (depth - 4) / 6
block = BasicBlock
# 1st conv before any network block
self.conv1 = nn.Conv2d(3, channels[0], kernel_size=3, stride=1,
padding=1, bias=False)
# 1st block
self.block1 = NetworkBlock(
n, channels[0], channels[1], block, 1, drop_rate, activate_before_residual=True)
# 2nd block
self.block2 = NetworkBlock(
n, channels[1], channels[2], block, 2, drop_rate)
# 3rd block
self.block3 = NetworkBlock(
n, channels[2], channels[3], block, 2, drop_rate)
# global average pooling and classifier
self.bn1 = nn.BatchNorm2d(channels[3], momentum=0.001)
self.relu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
self.fc = nn.Linear(channels[3], num_classes)
self.channels = channels[3]
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight,
mode='fan_out',
nonlinearity='leaky_relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1.0)
nn.init.constant_(m.bias, 0.0)
elif isinstance(m, nn.Linear):
nn.init.xavier_normal_(m.weight)
nn.init.constant_(m.bias, 0.0)
def forward(self, x):
out = self.conv1(x)
out = self.block1(out)
out = self.block2(out)
out = self.block3(out)
out = self.relu(self.bn1(out))
out = F.adaptive_avg_pool2d(out, 1)
out = out.view(-1, self.channels)
return self.fc(out)