### Configure MeaCap Inference Arguments

Source: https://context7.com/joeyz0z/meacap/llms.txt

Configures hyperparameters for MeaCap inference using an argument parser. This includes setting paths for various models (language, vision-language, parser, word-to-embedding), defining memory bank parameters, adjusting generation weights (fluency, image matching, memory alignment), and setting generation constraints like maximum length and repetition penalty. Example overrides demonstrate how to customize these parameters.

```python
from args import get_args

# Parse command-line arguments
args = get_args()

# Key configuration parameters:
# Model paths
# - args.lm_model_path: CBART model checkpoint path
# - args.vl_model: CLIP model identifier
# - args.parser_checkpoint: Scene graph parser model
# - args.wte_model_path: SentenceBERT model path

# Memory configuration
# - args.memory_id: Memory bank identifier (coco, cc3m, ss1m, flickr30k)
# - args.memory_caption_num: Number of captions to retrieve (default: 5)
# - args.use_memory: Enable memory-augmented generation (default: True)

# Generation weights
# - args.alpha: Weight for language model fluency (default: 0.1)
# - args.beta: Weight for image-matching score (default: 0.8)
# - args.gamma: Weight for memory alignment (default: 0.2)
# - args.conzic_top_k: Number of top-k candidates for ConZIC sampling (default: 200)

# Generation parameters
# - args.refinement_steps: Number of iterative refinement steps (default: 10)
# - args.max_len: Maximum caption length (default: 20)
# - args.min_len: Minimum caption length (default: 10)
# - args.repetition_penalty: Penalty for repeated tokens (default: 2.0)
# - args.temperature: Sampling temperature (default: 1.0)

# Prompt configuration
# - args.use_prompt: Enable prompt for training-free mode (default: False)
# - args.prompt: Prompt text (default: ['The image depicts that'])
# - args.prompt_ensembling: Use multiple prompts and select best (default: False)

# Example override
args.alpha = 0.15
args.beta = 0.75
args.memory_id = 'cc3m'
args.conzic_top_k = 150
```

--------------------------------

### Iterative Caption Refinement using Text Infilling

Source: https://context7.com/joeyz0z/meacap/llms.txt

Generates captions through an iterative text infilling process. It starts with extracted keywords and progressively refines the caption over multiple steps. The process uses a BART model for text infilling and incorporates various generation parameters and scoring mechanisms.

```python
from utils.generate_utils_ import Get_shuffle_score
from src.transformers import BartForTextInfill, BartTokenizer
import torch

# Initialize models and tokenizer
tokenizer = BartTokenizer.from_pretrained('./checkpoints/CBART_COCO')
lm_model = BartForTextInfill.from_pretrained('./checkpoints/CBART_COCO')
lm_model.to('cuda')

# Load stop/sub token filters
stop_tokens_tensor = torch.zeros(tokenizer.vocab_size).to('cuda')
sub_tokens_tensor = torch.zeros(tokenizer.vocab_size).to('cuda')

# Image embedding and retrieved concepts
batch_embeds = torch.randn(1, 512).to('cuda')
masked_sentences = ['dog', 'beach', 'running', 'water']  # Extracted concepts

# Memory embeddings for selected captions
select_memory_wte_embeddings = torch.randn(5, 384)  # 5 retrieved captions

# Generation arguments
class Args:
    batch_size = 1
    encoder_loss_type = 0
    max_insert_label = 1
    temperature = 1.0
    do_sample = 0
    top_k = 0
    top_p = 0.9
    refinement_steps = 10
    max_refinement_steps = 30
    adaptive = False
    repetition_penalty = 2.0
    threshold = 0
    decoder_chain = 1
    max_len = 20
    min_len = 1
    use_prompt = False
    prompt_len = 0
    conzic_top_k = 200
    alpha = 0.1
    beta = 0.8
    gamma = 0.2
    use_memory = True
    conzic_sample = True
    img_embeds = batch_embeds

args = Args()

```

--------------------------------

### Inference for MeaCap Training-Free (TF)

Source: https://github.com/joeyz0z/meacap/blob/main/README.md

This command demonstrates how to run the training-free version of MeaCap. It utilizes a pretrained CBART model and supports prompt ensembling. Key arguments include specifying the image path, language model path, and optionally enabling prompt usage.

```python
python inference.py --use_prompt  --memory_id cc3m --img_path ./image_example --lm_model_path ./checkpoints/CBART_one_billion 
```

--------------------------------

### Preparation: Memory Bank Embedding (Python)

Source: https://context7.com/joeyz0z/meacap/llms.txt

Preprocesses a textual corpus to compute CLIP and SentenceBERT embeddings for efficient memory retrieval. Requires specifying the memory ID, path to memory captions, and CLIP/SentenceBERT model names. Outputs are stored as `.pt` files for CLIP and SentenceBERT embeddings, alongside a copied caption file.

```python
python prepare_embedding.py \
    --memory_id coco \
    --memory_path data/memory/coco/memory_captions.json \
    --clip_model openai/clip-vit-base-patch32 \
    --wte_model_path sentence-transformers/all-MiniLM-L6-v2 \
    --gpu 0

# Input: data/memory/coco/memory_captions.json (JSON list of caption strings)
# Output:
#   - data/memory/coco/memory_clip_embeddings.pt (CLIP text embeddings)
#   - data/memory/coco/memory_wte_embeddings.pt (SentenceBERT embeddings)
#   - data/memory/coco/memory_captions.json (copied caption file)
```

--------------------------------

### Inference for MeaCap with ViECAP Baseline

Source: https://github.com/joeyz0z/meacap/blob/main/README.md

This command illustrates how to perform inference for MeaCap when integrating memory concepts with the ViECAP baseline. It involves replacing the entity module with a retrieve-then-filter module. Arguments include the memory ID, image path pattern, and the path to the model weights.

```python
python viecap_inference.py --memory_id coco --image_path "*.jpg" --weight_path "checkpoints/train_coco/coco_prefix-0014.pt"
```

--------------------------------

### Load Image Dataset for CLIP Batch Processing

Source: https://context7.com/joeyz0z/meacap/llms.txt

Loads images from a specified directory for batch processing using CLIP feature extraction. It initializes the CLIP model, creates a dataset instance, and then sets up a DataLoader for efficient batching. The code iterates through the DataLoader, printing the name and embedding shape of each processed image.

```python
from dataset.ImgDataset_img_return import Imgdata_img_return, collate_img_img_return
from torch.utils.data import DataLoader
from models.clip_utils import CLIP

# Initialize CLIP model
vl_model = CLIP('openai/clip-vit-base-patch32')
vl_model.to('cuda')

# Create dataset
img_data = Imgdata_img_return(
    dir_path='./image_example',
    match_model=vl_model
)

# Create dataloader
train_loader = DataLoader(
    img_data,
    batch_size=1,
    collate_fn=collate_img_img_return,
    shuffle=False,
    drop_last=False
)

# Iterate through images
for batch_idx, (batch_image_embeds, batch_name_list, batch_img_list) in enumerate(train_loader):
    # batch_image_embeds: [1, 512] CLIP embeddings
    # batch_name_list: ['image_001.jpg']
    # batch_img_list: [PIL.Image] image instances

    print(f"Processing image: {batch_name_list[0]}")
    print(f"Image embedding shape: {batch_image_embeds.shape}")

    # Process image with memory retrieval and caption generation
    # ... (caption generation code)
```

--------------------------------

### Integration: ViECAP with Memory Concepts (Python)

Source: https://context7.com/joeyz0z/meacap/llms.txt

Enhances the ViECAP baseline model by incorporating memory-augmented concepts for improved performance. This script requires paths to the image, ViECAP weights, and various language and vision-language models, along with parameters controlling prompt engineering and memory caption usage. The generated caption is printed to the console.

```python
python viecap_inference.py \
    --memory_id coco \
    --image_path image_example/COCO_val2014_000000027440.jpg \
    --weight_path checkpoints/train_coco/coco_prefix-0014.pt \
    --vl_model openai/clip-vit-base-patch32 \
    --parser_checkpoint lizhuang144/flan-t5-base-VG-factual-sg \
    --wte_model_path sentence-transformers/all-MiniLM-L6-v2 \
    --language_model openai-community/gpt2 \
    --clip_model ViT-B/32 \
    --using_hard_prompt \
    --continuous_prompt_length 10 \
    --clip_project_length 10 \
    --beam_width 5 \
    --memory_caption_num 5 \
    --device cuda:0

# Console output: "the generated caption: A woman sitting at a table with a laptop computer."
```

--------------------------------

### Utility: CLIP Image-Text Similarity Computation (Python)

Source: https://context7.com/joeyz0z/meacap/llms.txt

Computes similarity between image and text embeddings using a CLIP model wrapper. Requires initializing the CLIP model with a specified variant and moving it to the appropriate device (e.g., 'cuda'). It provides a function to compute image representations directly from file paths.

```python
from models.clip_utils import CLIP
import torch

# Initialize CLIP model
clip_model = CLIP('openai/clip-vit-base-patch32')
clip_model.to('cuda')

# Compute image representation from file path
image_embeds = clip_model.compute_image_representation_from_image_path('path/to/image.jpg')
# Output shape: [1, 512] for ViT-B/32
```

--------------------------------

### ConZIC Sampling for Token Generation

Source: https://context7.com/joeyz0z/meacap/llms.txt

Samples the next token during text generation using a combined objective function. This objective integrates language model probability, image-matching score (CLIP), and memory alignment. It requires language model logits, sequence information, and various model components for scoring.

```python
from utils.generate_utils_ import conzic_sample_function
import torch
import torch.nn.functional as F

# Language model logits for vocabulary at current position
lm_logits = torch.randn(50264)  # BART vocabulary size

# Unfinished sequence with mask token at position to generate
unfinish_seq = torch.tensor([[0, 250, 10, 50264, 2]])  # [BOS, 'a', 'dog', MASK, EOS]
mask_pos = 3

# Arguments configuration
class Args:
    img_embeds = torch.randn(1, 512).to('cuda')  # Image CLIP embedding
    use_memory = True
    conzic_top_k = 200
    alpha = 0.1  # Weight for LM fluency
    beta = 0.8   # Weight for image matching
    gamma = 0.2  # Weight for memory alignment

args = Args()

# Select top-k candidates and score with combined objective
generate_token = conzic_sample_function(
    lm_logits=lm_logits.to('cuda'),
    tokenizer=tokenizer,
    match_model=clip_model,
    wte_model=wte_model,
    unfinish_seq=unfinish_seq.to('cuda'),
    mask_pos=mask_pos,
    select_memory_wte_embeddings=memory_wte_embeddings,
    args=args
)

# Output: tensor([[431]]) - token ID with highest combined score
# Final score = 0.1 * P(token|context) + 0.8 * CLIP(image, caption) + 0.2 * similarity(caption, memory)
```

--------------------------------

### Inference: Text-Only-Training Image Captioning with MeaCap (Python)

Source: https://context7.com/joeyz0z/meacap/llms.txt

Generates image captions using a caption-finetuned CBART model for improved domain-specific performance. This approach requires specifying model paths, memory ID, and various generation parameters like batch size, max/min length, and refinement steps. Outputs are saved to a structured JSON file.

```python
python inference.py \
    --memory_id coco \
    --img_path ./image_example \
    --lm_model_path ./checkpoints/CBART_COCO \
    --vl_model openai/clip-vit-base-patch32 \
    --parser_checkpoint lizhuang144/flan-t5-base-VG-factual-sg \
    --wte_model_path sentence-transformers/all-MiniLM-L6-v2 \
    --alpha 0.1 \
    --beta 0.8 \
    --gamma 0.2 \
    --conzic_top_k 200 \
    --output_path ./outputs \
    --batch_size 1 \
    --max_len 20 \
    --min_len 10 \
    --refinement_steps 10

# Output saved to: ./outputs/MeaCap_{dataset}_memory_{memory_id}_lmTrainingCorpus_{lm_corpus}_{alpha}_{beta}_{gamma}_k{top_k}.json
```

--------------------------------

### Inference for MeaCap Text-Only-Training (ToT)

Source: https://github.com/joeyz0z/meacap/blob/main/README.md

This command shows the inference process for the text-only-training version of MeaCap. This version does not require explicit prompts. Arguments include the memory ID, image path, and the path to the finetuned CBART language model.

```python
python inference.py --memory_id coco --img_path ./image_example --lm_model_path ./checkpoints/CBART_COCO 
```

--------------------------------

### Inference: Training-Free Image Captioning with MeaCap (Python)

Source: https://context7.com/joeyz0z/meacap/llms.txt

Generates image captions using a pre-trained CBART model without caption-specific training. It utilizes memory-augmented concept extraction and requires specifying model paths, memory ID, and weighting parameters (alpha, beta, gamma). Outputs are saved as a JSON file.

```python
python inference.py \
    --use_prompt \
    --memory_id cc3m \
    --img_path ./image_example \
    --lm_model_path ./checkpoints/CBART_one_billion \
    --vl_model openai/clip-vit-base-patch32 \
    --parser_checkpoint lizhuang144/flan-t5-base-VG-factual-sg \
    --wte_model_path sentence-transformers/all-MiniLM-L6-v2 \
    --alpha 0.1 \
    --beta 0.8 \
    --gamma 0.2 \
    --conzic_top_k 200 \
    --output_path ./outputs

# Output: JSON file with image captions
# Example: {"image_001": "A man standing next to a red car on the street."}
```

--------------------------------

### Calculate Image-Text Similarity via Embeddings

Source: https://context7.com/joeyz0z/meacap/llms.txt

Calculates similarity scores between image embeddings and text embeddings using a CLIP model. It outputs both softmax probabilities and raw logits representing the similarity. This is used to find the best matching text for a given image.

```python
similarity_probs, similarity_logits = clip_model.compute_image_text_similarity_via_embeddings(
    image_embeds, text_embeds
)
# similarity_probs: softmax probabilities [1, 3]
# similarity_logits: raw similarity scores [1, 3]

best_match_idx = torch.argmax(similarity_probs, dim=-1)
print(f"Best matching text: {text_list[best_match_idx]}")
```

--------------------------------

### Generate Caption Iteratively

Source: https://context7.com/joeyz0z/meacap/llms.txt

Generates a caption through iterative refinement by calculating shuffle scores. It utilizes multiple models including a language model (lm_model), a matching model (clip_model), and a word-to-embedding model (wte_model), along with a tokenizer and various tensors for stop tokens and sub-tokens. The process is executed on a CUDA-enabled device.

```python
gen_text = Get_shuffle_score(
    batch_embeds=batch_embeds,
    masked_sentences=masked_sentences,
    model=lm_model,
    match_model=clip_model,
    wte_model=wte_model,
    tokenizer=tokenizer,
    select_memory_wte_embeddings=select_memory_wte_embeddings,
    stop_tokens_tensor=stop_tokens_tensor,
    sub_tokens_tensor=sub_tokens_tensor,
    rank_lm=None,
    logger=logger,
    args=args,
    device='cuda'
)

# Output: ["A dog running on the beach near the water."]
```

--------------------------------

### Memory-Based Concept Retrieval using Scene Graph Parsing

Source: https://context7.com/joeyz0z/meacap/llms.txt

Extracts key visual concepts from retrieved memory captions using scene graph parsing and semantic filtering. It initializes models like Flan-T5 for parsing and Sentence Transformers for embeddings, then uses a utility function to retrieve concepts based on image embeddings and memory captions.

```python
from utils.detect_utils import retrieve_concepts
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from sentence_transformers import SentenceTransformer
import torch

# Initialize models
parser_tokenizer = AutoTokenizer.from_pretrained('lizhuang144/flan-t5-base-VG-factual-sg')
parser_model = AutoModelForSeq2SeqLM.from_pretrained('lizhuang144/flan-t5-base-VG-factual-sg')
parser_model.eval()
parser_model.to('cuda')

wte_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')

# Top-5 retrieved memory captions based on CLIP similarity
select_memory_captions = [
    'a man standing next to a red car',
    'people walking on the street with vehicles',
    'urban scene with cars and pedestrians',
    'a person near a parked automobile',
    'city street with traffic and people'
]

# Image embeddings (from CLIP)
image_embeds = torch.randn(1, 512).to('cuda')  # Actual embeddings from your image

# Extract top-4 key concepts
concepts = retrieve_concepts(
    parser_model=parser_model,
    parser_tokenizer=parser_tokenizer,
    wte_model=wte_model,
    select_memory_captions=select_memory_captions,
    image_embeds=image_embeds,
    device='cuda',
    logger=None
)

# Output: ['man', 'car', 'street', 'person']
# These concepts are used as keywords for caption generation
```

--------------------------------

### Compute Text Representations with CLIP

Source: https://context7.com/joeyz0z/meacap/llms.txt

Computes text embeddings using a CLIP model. It takes a list of text strings as input and outputs a tensor representing the embeddings. The output shape is typically [number_of_texts, embedding_dimension]. This is a foundational step for many downstream tasks like similarity comparison.

```python
text_list = ['a dog on the beach', 'a cat in the house', 'a car on the road']
text_embeds = clip_model.compute_text_representation(text_list)
# Output shape: [3, 512]
```

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