Source code for super_gradients.training.models.googlenet

"""
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)