debug_weights.py

import torch
import torch.nn as nn
import torchvision.models as models
import sys
import os
import pickle
import re
from collections import Counter

DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")

EMBED_DIM = 512
HIDDEN_DIM = 512
MAX_LEN = 25

# Vocabulary class
class Vocabulary:
    def __init__(self, freq_threshold=5):
        self.freq_threshold = freq_threshold
        self.itos = {0: "pad", 1: "startofseq", 2: "endofseq", 3: "unk"}
        self.stoi = {v: k for k, v in self.itos.items()}
        self.index = 4

    def __len__(self):
        return len(self.itos)

    def tokenizer(self, text):
        text = text.lower()
        tokens = re.findall(r"\w+", text)
        return tokens

    def build_vocabulary(self, sentence_list):
        frequencies = Counter()
        for sentence in sentence_list:
            tokens = self.tokenizer(sentence)
            frequencies.update(tokens)

        for word, freq in frequencies.items():
            if freq >= self.freq_threshold:
                self.stoi[word] = self.index
                self.itos[self.index] = word
                self.index += 1

    def numericalize(self, text):
        tokens = self.tokenizer(text)
        numericalized = []
        for token in tokens:
            if token in self.stoi:
                numericalized.append(self.stoi[token])
            else:
                numericalized.append(self.stoi["unk"])
        return numericalized


class Encoder(nn.Module):
    def __init__(self, embed_dim):
        super().__init__()
        resnet = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
        self.backbone = nn.Sequential(*list(resnet.children())[:-1])
        self.fc = nn.Linear(resnet.fc.in_features, embed_dim)
        self.bn = nn.BatchNorm1d(embed_dim)

    def forward(self, x):
        with torch.no_grad():
            features = self.backbone(x)
        features = features.reshape(features.size(0), -1)
        features = self.bn(self.fc(features))
        return features


class Decoder(nn.Module):
    def __init__(self, embed_dim, hidden_dim, vocab_size):
        super().__init__()
        self.embedding = nn.Embedding(vocab_size, embed_dim)
        self.lstm = nn.LSTM(
            embed_dim,
            hidden_dim,
            batch_first=True
        )
        self.fc = nn.Linear(hidden_dim, vocab_size)

    def forward(self, x, states=None):
        emb = self.embedding(x)
        outputs, states = self.lstm(emb, states)
        logits = self.fc(outputs)
        return logits, states


class CaptionModel(nn.Module):
    def __init__(self, embed_dim, hidden_dim, vocab_size):
        super().__init__()
        self.encoder = Encoder(embed_dim)
        self.decoder = Decoder(embed_dim, hidden_dim, vocab_size)


# Main debug
script_dir = os.path.dirname(os.path.abspath(__file__))
CHECKPOINT_PATH = os.path.join(script_dir, "best_checkpoint.pth")
VOCAB_PATH = os.path.join(script_dir, "vocab.pkl")

print("=" * 80)
print("LOADING CHECKPOINT")
print("=" * 80)

checkpoint = torch.load(CHECKPOINT_PATH, map_location=DEVICE)
print(f"\nCheckpoint keys: {list(checkpoint.keys())}")

print("\nCheckpoint model_state_dict keys:")
checkpoint_keys = set(checkpoint["model_state_dict"].keys())
for key in sorted(checkpoint_keys):
    shape = checkpoint["model_state_dict"][key].shape
    print(f"  {key}: {shape}")

# Load vocab
with open(VOCAB_PATH, "rb") as f:
    vocab = pickle.load(f)

vocab_size = len(vocab)
print(f"\nVocab size: {vocab_size}")

# Create model
model = CaptionModel(
    EMBED_DIM,
    HIDDEN_DIM,
    vocab_size
).to(DEVICE)

print("\n" + "=" * 80)
print("MODEL STATE DICT KEYS")
print("=" * 80)

model_keys = set(model.state_dict().keys())
for key in sorted(model_keys):
    shape = model.state_dict()[key].shape
    print(f"  {key}: {shape}")

# Check differences
print("\n" + "=" * 80)
print("COMPARISON")
print("=" * 80)

print("\nKeys in checkpoint but NOT in model:")
for key in sorted(checkpoint_keys - model_keys):
    print(f"  {key}")

print("\nKeys in model but NOT in checkpoint:")
for key in sorted(model_keys - checkpoint_keys):
    print(f"  {key}")

print("\nKeys in both but with different shapes:")
for key in sorted(checkpoint_keys & model_keys):
    cp_shape = checkpoint["model_state_dict"][key].shape
    model_shape = model.state_dict()[key].shape
    if cp_shape != model_shape:
        print(f"  {key}")
        print(f"    Checkpoint:  {cp_shape}")
        print(f"    Model:       {model_shape}")

print("\n" + "=" * 80)
print("ATTEMPTING TO LOAD WEIGHTS")
print("=" * 80)

try:
    model.load_state_dict(checkpoint["model_state_dict"])
    print("SUCCESS: Weights loaded successfully!")
except Exception as e:
    print(f"ERROR: {e}")