predict.py

import torch
import torch.nn as nn
from torchvision.models import resnet50, ResNet50_Weights
from torchvision import transforms
from PIL import Image

import os

# ===========
# Vocabulary
# ===========

class Vocabulary:
    def __init__(self):
        self.itos = {}
        self.stoi = {}

    def load(self, stoi, itos):
        self.stoi = stoi
        self.itos = itos

# ===========
# Encoder
# ===========

class EncoderCNN(nn.Module):
    def __init__(self, embed_size):
        super(EncoderCNN, self).__init__()
        resnet = resnet50(weights=ResNet50_Weights.DEFAULT)
        modules = list(resnet.children())[:-1]
        self.resnet = nn.Sequential(*modules)
        self.linear = nn.Linear(resnet.fc.in_features, embed_size)
        self.bn = nn.BatchNorm1d(embed_size, momentum=0.01)

    def forward(self, images):
        with torch.no_grad():
            features = self.resnet(images)  # [B, 2048, 1, 1]
        features = features.view(features.size(0), -1)  # [B, 2048]
        features = self.linear(features)                # [B, embed_size]
        features = self.bn(features)                    # [B, embed_size]
        return features

    def __init__(self, embed_size):
        super(EncoderCNN, self).__init__()
        resnet = resnet50(weights=ResNet50_Weights.DEFAULT)
        modules = list(resnet.children())[:-1]
        self.resnet = nn.Sequential(*modules)
        self.linear = nn.Linear(resnet.fc.in_features, embed_size)
        self.bn = nn.BatchNorm1d(embed_size, momentum=0.01)

    def forward(self, images):
        with torch.no_grad():
            features = self.resnet(images).squeeze()
        features = self.linear(features)
        features = self.bn(features)
        return features

# ===========
# Decoder
# ===========

class DecoderRNN(nn.Module):
    def __init__(self, embed_size, hidden_size, vocab_size, num_layers=1):
        super(DecoderRNN, self).__init__()
        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)
        inputs = torch.cat((features.unsqueeze(1), embeddings), 1)
        hiddens, _ = self.lstm(inputs)
        outputs = self.linear(hiddens)
        return outputs

    def sample(self, features, vocab, max_len=30):
        """
        Generates a caption for given image features using greedy search.
        """
        output_ids = []
        states = None

        inputs = features.unsqueeze(1)  # [B, 1, embed_size]

        for _ in range(max_len):
            hiddens, states = self.lstm(inputs, states)  # [B, 1, hidden]
            outputs = self.linear(hiddens.squeeze(1))    # [B, vocab_size]
            predicted = outputs.argmax(1)                # [B]
            output_ids.append(predicted.item())

            if vocab.itos[predicted.item()] == "<end>":
                break

            inputs = self.embed(predicted).unsqueeze(1)

        return output_ids

# ===========
# Predict block
# ===========

def predict(image_path, checkpoint_path):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    embed_size = 256
    hidden_size = 512
    num_layers = 1

    # === Load checkpoint ===
    checkpoint = torch.load(checkpoint_path, map_location=device)

    # === Load vocab ===
    vocab = Vocabulary()
    vocab.load(checkpoint['vocab_stoi'], checkpoint['vocab_itos'])
    vocab_size = len(vocab.stoi)

    # === Load models ===
    encoder = EncoderCNN(embed_size).to(device)
    decoder = DecoderRNN(embed_size, hidden_size, vocab_size, num_layers).to(device)

    encoder.load_state_dict(checkpoint['encoder_state_dict'])
    decoder.load_state_dict(checkpoint['decoder_state_dict'])

    encoder.eval()
    decoder.eval()

    # === Image transform ===
    transform = transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor()
    ])

    image = Image.open(image_path).convert("RGB")
    image = transform(image).unsqueeze(0).to(device)  # [1, 3, 224, 224]

    # === Encode ===
    features = encoder(image)

    # === Decode ===
    output_ids = decoder.sample(features, vocab)

    # === Convert IDs to words ===
    caption = []
    for idx in output_ids:
        word = vocab.itos[idx]
        if word == "<end>":
            break
        caption.append(word)

    final_caption = ' '.join(caption)
    print(f"\n📝 Predicted caption: {final_caption}\n")

if __name__ == "__main__":
    # ✅ Change these!
    image_path = r"C:\Users\Jayasimma D\Documents\Skin_Disease_Captioning\Dataset\images\train\Albinism\Albinism2.jpg"  # 🔍 your test image path
    checkpoint_path = "./checkpoints/caption_model_epoch5.pth"

    predict(image_path, checkpoint_path)