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