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| import torchvision | |
| import torch.nn as nn | |
| import pretrainedmodels | |
| import torch.nn.functional as F | |
| from collections import OrderedDict | |
| from constant import SCALE_FACTOR | |
| import math | |
| class InceptionNet(nn.Module): | |
| def __init__(self, variant): | |
| super(InceptionNet, self).__init__() | |
| assert variant in ['inceptionv4', 'inceptionv3', 'inceptionresnetv2'] | |
| # load pretrain model | |
| model = pretrainedmodels.__dict__[variant](num_classes=1000, pretrained='imagenet') | |
| self.features = _get_features(model, variant) | |
| num_ftrs = model.last_linear.in_features | |
| self.classifier = nn.Sequential( | |
| nn.Linear(num_ftrs, 14), | |
| nn.Sigmoid() | |
| ) | |
| # load other info | |
| self.mean = model.mean | |
| self.std = model.std | |
| self.input_size = model.input_size[1] # assume every input is a square image | |
| self.input_range = model.input_range | |
| self.input_space = model.input_space | |
| self.resize_size = int(math.floor(self.input_size / SCALE_FACTOR)) | |
| def forward(self, x): | |
| x = self.features(x) # 1x1536x8x8 | |
| s = x.size()[3] # 8 if input image is 224x224 | |
| x = F.avg_pool2d(x, kernel_size=s, count_include_pad=False) # 1x1536x1x1, same for inceptionv4 and inceptionresnetv2 | |
| x = x.view(x.size(0), -1) # 1x1536 | |
| x = self.classifier(x) # 1x1000 | |
| return x | |
| def extract(self, x): | |
| return self.features(x) # 1x1536x8x8 | |
| def build(variant): | |
| net = InceptionNet(variant).cuda() | |
| return net | |
| def _get_features(model, variant): | |
| if variant == 'inceptionv4': | |
| features = model.features | |
| elif variant == 'inceptionv3': | |
| # TODO: Take a look on this | |
| features = nn.Sequential(OrderedDict([ | |
| ('Conv2d_1a_3x3', model.Conv2d_1a_3x3), | |
| ('Conv2d_2a_3x3', model.Conv2d_2a_3x3), | |
| ('Conv2d_2b_3x3', model.Conv2d_2b_3x3), | |
| ('max_pool2d_1', torch.nn.MaxPool2d(3, stride=2)), | |
| ('Conv2d_3b_1x1', model.Conv2d_3b_1x1), | |
| ('Conv2d_4a_3x3', model.Conv2d_4a_3x3), | |
| ('max_pool2d_2', torch.nn.MaxPool2d(3, stride=2)), | |
| ('Mixed_5b', model.Mixed_5b), | |
| ('Mixed_5c', model.Mixed_5c), | |
| ('Mixed_5d', model.Mixed_5d), | |
| ('Mixed_6a', model.Mixed_6a), | |
| ('Mixed_6b', model.Mixed_6b), | |
| ('Mixed_6c', model.Mixed_6c), | |
| ('Mixed_6d', model.Mixed_6b), | |
| # ('Mixed_6c', model.Mixed_6c), | |
| ])) | |
| elif variant == 'inceptionresnetv2': | |
| features = nn.Sequential(OrderedDict([ | |
| ('conv2d_1a', model.conv2d_1a), | |
| ('conv2d_2a', model.conv2d_2a), | |
| ('conv2d_2b', model.conv2d_2b), | |
| ('maxpool_3a', model.maxpool_3a), | |
| ('conv2d_3b', model.conv2d_3b), | |
| ('conv2d_4a', model.conv2d_4a), | |
| ('maxpool_5a', model.maxpool_5a), | |
| ('mixed_5b', model.mixed_5b), | |
| ('repeat', model.repeat), | |
| ('mixed_6a', model.mixed_6a), | |
| ('repeat_1', model.repeat_1), | |
| ('mixed_7a', model.mixed_7a), | |
| ('repeat_2', model.repeat_2), | |
| ('block8', model.block8), | |
| ('conv2d_7b', model.conv2d_7b) | |
| ])) | |
| else: | |
| raise "Unknown variant" | |
| return features | |
| architect='inception' |