"""
Googlenet code based on https://pytorch.org/vision/stable/_modules/torchvision/models/googlenet.html
"""
from collections import namedtuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
from super_gradients.training.models.sg_module import SgModule
GoogLeNetOutputs = namedtuple('GoogLeNetOutputs', ['log_', 'aux_logits2', 'aux_logits1'])
[docs]class GoogLeNet(SgModule):
def __init__(self, num_classes=1000, aux_logits=True, init_weights=True,
backbone_mode=False, dropout=0.3):
super(GoogLeNet, self).__init__()
self.num_classes = num_classes
self.backbone_mode = backbone_mode
self.aux_logits = aux_logits
self.dropout_p = dropout
self.conv1 = BasicConv2d(3, 64, kernel_size=7, stride=2, padding=3)
self.maxpool1 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
self.conv2 = BasicConv2d(64, 64, kernel_size=1)
self.conv3 = BasicConv2d(64, 192, kernel_size=3, padding=1)
self.maxpool2 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
self.inception3a = Inception(192, 64, 96, 128, 16, 32, 32)
self.inception3b = Inception(256, 128, 128, 192, 32, 96, 64)
self.maxpool3 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
self.inception4a = Inception(480, 192, 96, 208, 16, 48, 64)
self.inception4b = Inception(512, 160, 112, 224, 24, 64, 64)
self.inception4c = Inception(512, 128, 128, 256, 24, 64, 64)
self.inception4d = Inception(512, 112, 144, 288, 32, 64, 64)
self.inception4e = Inception(528, 256, 160, 320, 32, 128, 128)
self.maxpool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.inception5a = Inception(832, 256, 160, 320, 32, 128, 128)
self.inception5b = Inception(832, 384, 192, 384, 48, 128, 128)
if aux_logits:
self.aux1 = InceptionAux(512, num_classes)
self.aux2 = InceptionAux(528, num_classes)
else:
self.aux1 = None
self.aux2 = None
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
if not self.backbone_mode:
self.dropout = nn.Dropout(self.dropout_p)
self.fc = nn.Linear(1024, num_classes)
if init_weights:
self._initialize_weights()
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
import scipy.stats as stats
x = stats.truncnorm(-2, 2, scale=0.01)
values = torch.as_tensor(x.rvs(m.weight.numel()), dtype=m.weight.dtype)
values = values.view(m.weight.size())
with torch.no_grad():
m.weight.copy_(values)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def _forward(self, x):
# N x 3 x 224 x 224
x = self.conv1(x)
# N x 64 x 112 x 112
x = self.maxpool1(x)
# N x 64 x 56 x 56
x = self.conv2(x)
# N x 64 x 56 x 56
x = self.conv3(x)
# N x 192 x 56 x 56
x = self.maxpool2(x)
# N x 192 x 28 x 28
x = self.inception3a(x)
# N x 256 x 28 x 28
x = self.inception3b(x)
# N x 480 x 28 x 28
x = self.maxpool3(x)
# N x 480 x 14 x 14
x = self.inception4a(x)
# N x 512 x 14 x 14
aux1 = None
if self.aux1 is not None and self.training:
aux1 = self.aux1(x)
x = self.inception4b(x)
# N x 512 x 14 x 14
x = self.inception4c(x)
# N x 512 x 14 x 14
x = self.inception4d(x)
# N x 528 x 14 x 14
aux2 = None
if self.aux2 is not None and self.training:
aux2 = self.aux2(x)
x = self.inception4e(x)
# N x 832 x 14 x 14
x = self.maxpool4(x)
# N x 832 x 7 x 7
x = self.inception5a(x)
# N x 832 x 7 x 7
x = self.inception5b(x)
# N x 1024 x 7 x 7
x = self.avgpool(x)
# N x 1024 x 1 x 1
x = torch.flatten(x, 1)
# N x 1024
if not self.backbone_mode:
x = self.dropout(x)
x = self.fc(x)
# N x num_classes
return x, aux2, aux1
[docs] def forward(self, x):
x, aux1, aux2 = self._forward(x)
if self.training and self.aux_logits:
return GoogLeNetOutputs(x, aux2, aux1)
else:
return x
[docs] def load_state_dict(self, state_dict, strict=True):
"""
load_state_dict - Overloads the base method and calls it to load a modified dict for usage as a backbone
:param state_dict: The state_dict to load
:param strict: strict loading (see super() docs)
"""
pretrained_model_weights_dict = state_dict.copy()
if self.backbone_mode:
# FIRST LET'S POP THE LAST TWO LAYERS - NO NEED TO LOAD THEIR VALUES SINCE THEY ARE IRRELEVANT AS A BACKBONE
pretrained_model_weights_dict.popitem()
pretrained_model_weights_dict.popitem()
pretrained_backbone_weights_dict = OrderedDict()
for layer_name, weights in pretrained_model_weights_dict.items():
# GET THE LAYER NAME WITHOUT THE 'module.' PREFIX
name_without_module_prefix = layer_name.split('module.')[1]
# MAKE SURE THESE ARE NOT THE FINAL LAYERS
pretrained_backbone_weights_dict[name_without_module_prefix] = weights
c_temp = torch.nn.Linear(1024, self.num_classes)
torch.nn.init.xavier_uniform(c_temp.weight)
pretrained_backbone_weights_dict['fc.weight'] = c_temp.weight
pretrained_backbone_weights_dict['fc.bias'] = c_temp.bias
# RETURNING THE UNMODIFIED/MODIFIED STATE DICT DEPENDING ON THE backbone_mode VALUE
super().load_state_dict(pretrained_backbone_weights_dict, strict)
else:
super().load_state_dict(pretrained_model_weights_dict, strict)
[docs]class Inception(nn.Module):
def __init__(self, in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj,
conv_block=None):
super(Inception, self).__init__()
if conv_block is None:
conv_block = BasicConv2d
self.branch1 = conv_block(in_channels, ch1x1, kernel_size=1)
self.branch2 = nn.Sequential(
conv_block(in_channels, ch3x3red, kernel_size=1),
conv_block(ch3x3red, ch3x3, kernel_size=3, padding=1)
)
self.branch3 = nn.Sequential(
conv_block(in_channels, ch5x5red, kernel_size=1),
conv_block(ch5x5red, ch5x5, kernel_size=3, padding=1)
)
self.branch4 = nn.Sequential(
nn.MaxPool2d(kernel_size=3, stride=1, padding=1, ceil_mode=True),
conv_block(in_channels, pool_proj, kernel_size=1)
)
def _forward(self, x):
branch1 = self.branch1(x)
branch2 = self.branch2(x)
branch3 = self.branch3(x)
branch4 = self.branch4(x)
outputs = [branch1, branch2, branch3, branch4]
return outputs
[docs] def forward(self, x):
outputs = self._forward(x)
return torch.cat(outputs, 1)
[docs]class InceptionAux(nn.Module):
def __init__(self, in_channels, num_classes, conv_block=None):
super(InceptionAux, self).__init__()
if conv_block is None:
conv_block = BasicConv2d
self.conv = conv_block(in_channels, 128, kernel_size=1)
self.fc1 = nn.Linear(2048, 1024)
self.fc2 = nn.Linear(1024, num_classes)
[docs] def forward(self, x):
# aux1: N x 512 x 14 x 14, aux2: N x 528 x 14 x 14
x = F.adaptive_avg_pool2d(x, (4, 4))
# aux1: N x 512 x 4 x 4, aux2: N x 528 x 4 x 4
x = self.conv(x)
# N x 128 x 4 x 4
x = torch.flatten(x, 1)
# N x 2048
x = F.relu(self.fc1(x), inplace=True)
# N x 1024
x = F.dropout(x, 0.7, training=self.training)
# N x 1024
x = self.fc2(x)
# N x 1000 (num_classes)
return x
[docs]class BasicConv2d(nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
self.relu = nn.ReLU()
[docs] def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.relu(x)
return x
[docs]def googlenet_v1(arch_params):
return GoogLeNet(aux_logits=False, num_classes=arch_params.num_classes, dropout=arch_params.dropout)