model.py

import torch
import torch.nn as nn
import torchvision.models as models


class EncoderCNN(nn.Module):
    def __init__(self, embed_size):
        super(EncoderCNN, self).__init__()
        resnet = models.resnet50(pretrained=True)
        for param in resnet.parameters():
            param.requires_grad_(False)
        
        modules = list(resnet.children())[:-1]
        self.resnet = nn.Sequential(*modules)
        self.embed = nn.Linear(resnet.fc.in_features, embed_size)

    def forward(self, images):
        features = self.resnet(images)
        features = features.view(features.size(0), -1)
        features = self.embed(features)
        return features
    

class DecoderRNN(nn.Module):
    def __init__(self, embed_size, hidden_size, vocab_size, num_layers=1):
        super(DecoderRNN, self).__init__()
        self.embed_size = embed_size
        self.hidden_size= hidden_size
        self.vocab_size = vocab_size
        self.num_layers = num_layers
        self.embed = nn.Embedding(vocab_size, embed_size)
        self.lstm = nn.LSTM(embed_size, hidden_size, num_layers,batch_first = True)
        self.linear = nn.Linear(hidden_size, vocab_size)
    
    def forward(self, features, captions):
        #embeddings = self.embed(captions[:,:-1])
        embeddings = self.embed(captions)
        embeddings = torch.cat((features.unsqueeze(1), embeddings),1)
        out, _ =  self.lstm(embeddings)
        outputs = self.linear(out[:,:-1,:])
        return outputs
    def sample(self, inputs, states=None, max_len=20):
        " accepts pre-processed image tensor (inputs) and returns predicted sentence (list of tensor ids of length max_len) "
        sampled_ids = []
        for i in range(max_len):
            hiddens, states = self.lstm(inputs, states)  
            outputs = self.linear(hiddens.squeeze(1))   
            max_idx=outputs.max(1)[1]
            sampled_ids.append(max_idx.item())
            inputs = self.embed(max_idx).unsqueeze(1) 
        return sampled_ids