### Install rewind-reward-pytorch

Source: https://github.com/lucidrains/rewind-reward-pytorch/blob/main/README.md

Install the library using pip. This is the initial setup step before using the library.

```bash
pip install rewind-reward-pytorch
```

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

### Complete Training Example with RewindReward Pytorch

Source: https://context7.com/lucidrains/rewind-reward-pytorch/llms.txt

An end-to-end example covering model initialization with custom decoder, optimizer setup, a training loop using RewindTrainWrapper, and inference for predicting progress rewards. Ensure 'num_epochs' and 'dataloader' are defined.

```python
import torch
from torch.optim import AdamW
from rewind_reward_pytorch import RewardModel, RewindTrainWrapper

# Initialize model and wrapper
reward_model = RewardModel(
    decoder=dict(dim=768, depth=4, heads=8, attn_dim_head=64),
    categorical_rewards=False,
    use_hl_gauss_loss=True
)

train_wrapper = RewindTrainWrapper(
    reward_model,
    rewind_augmentation_prob=0.5
)

# Setup optimizer (only train specific parameters)
optimizer = AdamW(reward_model.parameters(), lr=1e-4)

# Training loop
for epoch in range(num_epochs):
    for batch in dataloader:
        commands = batch['commands']  # List of natural language commands
        video = batch['video']        # (B, C, T, H, W) tensor
        video_lens = batch['lengths'] # (B,) actual video lengths

        optimizer.zero_grad()

        # Forward pass with rewind augmentation
        loss = train_wrapper(
            commands,
            video,
            video_lens=video_lens
        )

        loss.backward()
        optimizer.step()

        print(f"Epoch {epoch}, Loss: {loss.item():.4f}")

# Inference
reward_model.eval()
with torch.no_grad():
    test_commands = ['grasp the yellow block and place it on the shelf']
    test_video = torch.rand(1, 3, 16, 224, 224)

    # Predict per-frame progress rewards
    progress_rewards = reward_model(test_commands, test_video)
    # Shape: (1, 16) - rewards for each of the 16 frames

    print(f"Predicted progress: {progress_rewards[0].tolist()}")
```

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

### Initialize and Use RewindTrainWrapper

Source: https://context7.com/lucidrains/rewind-reward-pytorch/llms.txt

Demonstrates initializing the RewardModel with continuous rewards and wrapping it with RewindTrainWrapper for data augmentation. Shows basic training loop usage with and without specified video lengths, and how to retrieve augmentation details.

```python
import torch
from rewind_reward_pytorch import RewardModel, RewindTrainWrapper

# Initialize reward model (must use continuous rewards)
reward_model = RewardModel(
    categorical_rewards=False
)

# Wrap with ReWiND augmentation
wrapper = RewindTrainWrapper(
    reward_model,
    rewind_augmentation_prob=0.25  # 25% chance of applying rewind augmentation
)

commands = [
    'pick up the blue ball and put it in the red tray',
    'pick up the red cube and put it in the green bin',
]

video = torch.rand(2, 3, 16, 224, 224)

# Basic training loop
loss = wrapper(commands, video)
loss.backward()

# Training with video lengths specified
loss = wrapper(
    commands,
    video,
    video_lens=torch.randint(5, 16, (2,))
)
loss.backward()

# Get augmentation details for debugging/visualization
loss, (is_augmented, aug_video, aug_lens, progress) = wrapper(
    commands,
    video,
    return_maybe_augmented=True
)

print(f"Rewind augmentation applied: {is_augmented}")
print(f"Augmented video shape: {aug_video.shape}")
print(f"Video lengths after augmentation: {aug_lens}")
print(f"Progress labels: {progress}")
```

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

### Initialize and Train ReWiND Reward Model

Source: https://github.com/lucidrains/rewind-reward-pytorch/blob/main/README.md

Initialize the RewardModel and RewindTrainWrapper. The wrapper is used for training, accepting commands and video data to compute a loss. Ensure commands and video tensors are correctly formatted.

```python
import torch

from rewind_reward_pytorch import (
    RewardModel,
    RewindTrainWrapper
)

reward_model = RewardModel()

wrapper = RewindTrainWrapper(
    reward_model,
    rewind_augmentation_prob = 0.25
)

commands = [
  'pick up the blue ball and put it in the red tray',
  'pick up the red cube and put it in the green bin',
]

video = torch.rand(2, 3, 16, 224, 224)

loss = wrapper(
    commands,
    video
)

loss.backward()
```

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

### Initialize and Use RewardModel for Continuous Rewards

Source: https://context7.com/lucidrains/rewind-reward-pytorch/llms.txt

Initialize the RewardModel for continuous reward prediction and perform inference or training. Ensure ground truth rewards are provided for training.

```python
import torch
from rewind_reward_pytorch import RewardModel

# Initialize reward model with default settings
reward_model = RewardModel(
    decoder=dict(
        dim=768,
        depth=4,
        heads=8,
        attn_dim_head=64
    ),
    mlp_predictor_depth=3,
    reward_bins=10,
    max_video_frames=16,
    dim_image_embed=768,
    num_register_tokens=4,
    lang_per_token_embed=True,
    sentence_transformer_path='sentence-transformers/all-MiniLM-L12-v2',
    categorical_rewards=False,
    use_hl_gauss_loss=True,
    reward_min_value=0.,
    reward_max_value=1.,
    reward_hl_gauss_loss_num_bins=20
)

# Natural language task commands
commands = [
    'pick up the blue ball and put it in the red tray',
    'pick up the red cube and put it in the green bin',
]

# Video input: (batch, channels, frames, height, width)
video = torch.rand(2, 3, 16, 224, 224)

# Inference: predict per-frame progress rewards
pred_rewards = reward_model(commands, video)
# Output shape: (2, 16) - per frame progress prediction for each video
assert pred_rewards.shape == (2, 16)

# Training: compute loss with ground truth rewards
rewards = torch.rand(2, 16)  # Ground truth progress values [0, 1]
loss = reward_model(
    commands,
    video,
    rewards=rewards,
    video_lens=torch.tensor([16, 14])  # Optional: actual video lengths
)
loss.backward()
```

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

### Initialize and Use RewardModel for Categorical Rewards

Source: https://context7.com/lucidrains/rewind-reward-pytorch/llms.txt

Configure the RewardModel for categorical reward prediction using discrete bins and cross-entropy loss. Inference returns logits over reward bins.

```python
import torch
from rewind_reward_pytorch import RewardModel

# Initialize with categorical rewards (discrete bins)
reward_model = RewardModel(
    reward_bins=10,
    lang_per_token_embed=True,
    categorical_rewards=True
)

commands = [
    'pick up the blue ball and put it in the red tray',
    'pick up the red cube and put it in the green bin'
]

video = torch.rand(2, 3, 16, 224, 224)

# Inference returns logits over reward bins
logits = reward_model(commands, video)
# Output shape: (batch, frames, reward_bins)
assert logits.shape == (2, 16, 10)

# Get predicted reward class per frame
predicted_bins = logits.argmax(dim=-1)  # (2, 16)
```

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

### Predict Rewards with ReWiND Model

Source: https://github.com/lucidrains/rewind-reward-pytorch/blob/main/README.md

After training, use the RewardModel to predict rewards for given commands and video data. The output is a tensor of per-frame progress predictions.

```python
# after much training

pred_rewards = reward_model(
    commands,
    video
) # (2, 16) - per frame progress prediction

assert pred_rewards.shape == (2, 16)
```

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

### Use RewardModel with Extra Embedding Tokens

Source: https://context7.com/lucidrains/rewind-reward-pytorch/llms.txt

Pass additional embedding tokens, such as from RGBD or tactile sensors, to enrich the model's input representation during training or inference.

```python
import torch
from rewind_reward_pytorch import RewardModel

reward_model = RewardModel(
    reward_bins=10,
    lang_per_token_embed=True,
    categorical_rewards=False
)

commands = [
    'pick up the blue ball and put it in the red tray',
    'pick up the red cube and put it in the green bin',
]

video = torch.rand(2, 3, 16, 224, 224)

# Extra embedding tokens (e.g., from Adapt3R RGBD or tactile sensors)
# Shape: (batch, num_tokens, embedding_dim)
extra_embed_tokens = torch.randn(2, 7, 768)

# Training with extra embeddings
loss = reward_model(
    commands,
    video,
    extra_embed_tokens=extra_embed_tokens,
    video_lens=torch.randint(5, 16, (2,)),
    rewards=torch.randn((2, 16))
)

loss.backward()

# Inference with extra embeddings
pred = reward_model(commands, video, extra_embed_tokens=extra_embed_tokens)
assert pred.shape == (2, 16)
```

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