| |
|
|
| from torchvision.models import resnet18, resnet50, resnet101, resnet152, vgg16, vgg19, inception_v3 |
| import torch |
| import torch.nn as nn |
| import random |
| import numpy as np |
|
|
|
|
| class EncoderCNN(nn.Module): |
| def __init__(self, embed_size, dropout=0.5, image_model='resnet101', pretrained=True): |
| """Load the pretrained ResNet-152 and replace top fc layer.""" |
| super(EncoderCNN, self).__init__() |
| resnet = globals()[image_model](pretrained=pretrained) |
| modules = list(resnet.children())[:-2] |
| self.resnet = nn.Sequential(*modules) |
|
|
| self.linear = nn.Sequential(nn.Conv2d(resnet.fc.in_features, embed_size, kernel_size=1, padding=0), |
| nn.Dropout2d(dropout)) |
|
|
| def forward(self, images, keep_cnn_gradients=False): |
| """Extract feature vectors from input images.""" |
|
|
| if keep_cnn_gradients: |
| raw_conv_feats = self.resnet(images) |
| else: |
| with torch.no_grad(): |
| raw_conv_feats = self.resnet(images) |
| features = self.linear(raw_conv_feats) |
| features = features.view(features.size(0), features.size(1), -1) |
|
|
| return features |
|
|
|
|
| class EncoderLabels(nn.Module): |
| def __init__(self, embed_size, num_classes, dropout=0.5, embed_weights=None, scale_grad=False): |
|
|
| super(EncoderLabels, self).__init__() |
| embeddinglayer = nn.Embedding(num_classes, embed_size, padding_idx=num_classes-1, scale_grad_by_freq=scale_grad) |
| if embed_weights is not None: |
| embeddinglayer.weight.data.copy_(embed_weights) |
| self.pad_value = num_classes - 1 |
| self.linear = embeddinglayer |
| self.dropout = dropout |
| self.embed_size = embed_size |
|
|
| def forward(self, x, onehot_flag=False): |
|
|
| if onehot_flag: |
| embeddings = torch.matmul(x, self.linear.weight) |
| else: |
| embeddings = self.linear(x) |
|
|
| embeddings = nn.functional.dropout(embeddings, p=self.dropout, training=self.training) |
| embeddings = embeddings.permute(0, 2, 1).contiguous() |
|
|
| return embeddings |
|
|
