import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from collections import OrderedDict
from super_gradients.training.models.sg_module import SgModule
"""Densenet-BC model class, based on
"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
Code source: https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py
Performance reproducibility (4 GPUs):
training params: {"max_epochs": 120, "lr_updates": [30, 60, 90, 100, 110], "lr_decay_factor": 0.1, "initial_lr": 0.025}
dataset_params: {"batch_size": 64}
"""
class _DenseLayer(nn.Module):
def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):
super(_DenseLayer, self).__init__()
self.add_module('norm1', nn.BatchNorm2d(num_input_features)),
self.add_module('relu1', nn.ReLU(inplace=True)),
self.add_module('conv1', nn.Conv2d(num_input_features, bn_size * growth_rate,
kernel_size=1, stride=1,
bias=False)),
self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),
self.add_module('relu2', nn.ReLU(inplace=True)),
self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate,
kernel_size=3, stride=1, padding=1,
bias=False)),
self.drop_rate = float(drop_rate)
def bn_function(self, inputs):
concated_features = torch.cat(inputs, 1)
bottleneck_output = self.conv1(self.relu1(self.norm1(concated_features))) # noqa: T484
return bottleneck_output
def forward(self, input): # noqa: F811
prev_features = [input] if isinstance(input, Tensor) else input
bottleneck_output = self.bn_function(prev_features)
new_features = self.conv2(self.relu2(self.norm2(bottleneck_output)))
if self.drop_rate > 0:
new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)
return new_features
class _DenseBlock(nn.ModuleDict):
def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate):
super(_DenseBlock, self).__init__()
for i in range(num_layers):
layer = _DenseLayer(
num_input_features + i * growth_rate,
growth_rate=growth_rate,
bn_size=bn_size,
drop_rate=drop_rate,
)
self.add_module('denselayer%d' % (i + 1), layer)
def forward(self, init_features):
features = [init_features]
for name, layer in self.items():
new_features = layer(features)
features.append(new_features)
return torch.cat(features, 1)
class _Transition(nn.Sequential):
def __init__(self, num_input_features, num_output_features):
super(_Transition, self).__init__()
self.add_module('norm', nn.BatchNorm2d(num_input_features))
self.add_module('relu', nn.ReLU(inplace=True))
self.add_module('conv', nn.Conv2d(num_input_features, num_output_features,
kernel_size=1, stride=1, bias=False))
self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))
[docs]class DenseNet(SgModule):
def __init__(self, growth_rate: int, structure: list, num_init_features: int, bn_size: int, drop_rate: float,
num_classes: int):
"""
:param growth_rate: number of filter to add each layer (noted as 'k' in the paper)
:param structure: how many layers in each pooling block - sequentially
:param num_init_features: the number of filters to learn in the first convolutional layer
:param bn_size: multiplicative factor for the number of bottle neck layers
(i.e. bn_size * k featurs in the bottleneck)
:param drop_rate: dropout rate after each dense layer
:param num_classes: number of classes in the classification task
"""
super(DenseNet, self).__init__()
# First convolution
self.features = nn.Sequential(OrderedDict([
('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)),
('norm0', nn.BatchNorm2d(num_init_features)),
('relu0', nn.ReLU(inplace=True)),
('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1))]))
# Each denseblock
num_features = num_init_features
for i, num_layers in enumerate(structure):
block = _DenseBlock(num_layers=num_layers, num_input_features=num_features, bn_size=bn_size,
growth_rate=growth_rate, drop_rate=drop_rate)
self.features.add_module('denseblock%d' % (i + 1), block)
num_features = num_features + num_layers * growth_rate
if i != len(structure) - 1:
trans = _Transition(num_input_features=num_features, num_output_features=num_features // 2)
self.features.add_module('transition%d' % (i + 1), trans)
num_features = num_features // 2
# Final batch norm
self.features.add_module('norm5', nn.BatchNorm2d(num_features))
# Linear layer
self.classifier = nn.Linear(num_features, num_classes)
# Official init from torch repo.
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.constant_(m.bias, 0)
[docs] def forward(self, x):
features = self.features(x)
out = F.relu(features, inplace=True)
out = F.adaptive_avg_pool2d(out, (1, 1))
out = torch.flatten(out, 1)
out = self.classifier(out)
return out
[docs]def CustomizedDensnet(arch_params):
return DenseNet(growth_rate=arch_params.growth_rate if hasattr(arch_params, "growth_rate") else 32,
structure=arch_params.structure if hasattr(arch_params, "structure") else [6, 12, 24, 16],
num_init_features=arch_params.num_init_features if hasattr(arch_params,
"num_init_features") else 64,
bn_size=arch_params.bn_size if hasattr(arch_params, "bn_size") else 4,
drop_rate=arch_params.drop_rate if hasattr(arch_params, "drop_rate") else 0,
num_classes=arch_params.num_classes)
[docs]def densenet121(arch_params):
return DenseNet(32, [6, 12, 24, 16], 64, 4, 0, arch_params.num_classes)
[docs]def densenet161(arch_params):
return DenseNet(48, [6, 12, 36, 24], 96, 4, 0, arch_params.num_classes)
[docs]def densenet169(arch_params):
return DenseNet(32, [6, 12, 32, 32], 64, 4, 0, arch_params.num_classes)
[docs]def densenet201(arch_params):
return DenseNet(32, [6, 12, 48, 32], 64, 4, 0, arch_params.num_classes)