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import torch
import torch.nn as nn
import torchvision.models as models
class Encoder(nn.Module):
def __init__(self, embed_size):
super(Encoder, self).__init__()
resnet = models.resnet50(pretrained=True)
for param in list(resnet.parameters())[:-6]:
param.requires_grad_(False)
for param in list(resnet.parameters())[-6:]:
param.requires_grad_(True)
modules = list(resnet.children())[:-1]
self.resnet = nn.Sequential(*modules)
self.embed = nn.Linear(resnet.fc.in_features, embed_size)
self.batch_norm = nn.BatchNorm1d(embed_size, momentum=0.01)
self.dropout = nn.Dropout(0.5)
def forward(self, images):
features = self.resnet(images)
features = features.view(features.size(0), -1)
features = self.embed(features)
features = self.batch_norm(features)
return features
class Decoder(nn.Module):
def __init__(self, embed_size, hidden_size, vocab_size, num_layers=1, dropout=0.5):
super(Decoder, self).__init__()
self.embed_size = embed_size
self.hidden_size = hidden_size
self.vocab_size = vocab_size
self.num_layers = num_layers
self.word_embedding = nn.Embedding(self.vocab_size, self.embed_size)
self.lstm = nn.LSTM(self.embed_size,
self.hidden_size,
self.num_layers,
batch_first=True,
dropout=dropout if num_layers > 1 else 0)
self.layer_norm = nn.LayerNorm(self.hidden_size)
self.dropout = nn.Dropout(0.5)
self.fc = nn.Linear(self.hidden_size, self.vocab_size)
def forward(self, features, captions):
caption_embed = self.word_embedding(captions[:, :-1])
caption_embed = torch.cat((features.unsqueeze(dim=1), caption_embed), 1)
output, _ = self.lstm(caption_embed)
output = self.fc(output)
return output
def sample(self, inputs, states=None, max_len=20):
output = []
(h, c) = (torch.randn(self.num_layers, 1, self.hidden_size).to(inputs.device), torch.randn(self.num_layers, 1, self.hidden_size).to(inputs.device))
for i in range(max_len):
x, (h, c) = self.lstm(inputs, (h, c))
x = self.fc(x)
x = x.squeeze(1)
predict = x.argmax(dim=1)
if predict.item() == 1:
break
output.append(predict.item())
inputs = self.word_embedding(predict.unsqueeze(0))
return output |