### Install amplify-pytorch

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

Install the amplify-pytorch library using pip.

```bash
pip install amplify-pytorch
```

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

### Initialize and Train Amplify Model

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

Configures the Amplify model architecture and demonstrates the forward pass for training, including loss breakdown retrieval.

```python
amplify = Amplify(
    tokenizer=tokenizer,
    llm=llm,
    dim_proprio=17,                          # Proprioceptive state dimension
    dim_image_embed=256,                     # Image embedding dimension
    action_chunk_size=20,                    # Number of actions to predict
    dim_action=20,                           # Action dimension
    video_time_seq_len=16,                   # Video temporal length
    motion_max_seq_len=1024,                 # Max motion sequence length
    inverse_dynamics_transformer_depth=2,   # Depth of inverse dynamics transformer
    pred_action_loss_weight=1.0,             # Weight for action prediction loss
    vit=dict(
        image_size=224,
        patch_size=14,
        num_classes=1000,
        heads=8,
        dim=256,
        depth=2,
        mlp_dim=1024,
    ),
    decoder=dict(
        dim=512,
        depth=2
    )
)

# Prepare training data
batch_size = 2
trajectories = torch.randn(batch_size, 16, 3, 16, 16, 2)  # Motion trajectories
commands = torch.randint(0, 20000, (batch_size, 512))      # Language commands (tokenized)
videos = torch.randn(batch_size, 3, 16, 224, 224)          # Video frames (C, T, H, W)
proprio = torch.randn(batch_size, 17)                       # Proprioceptive state
actions = torch.randn(batch_size, 20, 20)                   # Ground truth actions

# Training forward pass
loss = amplify(
    trajectories=trajectories,
    commands=commands,
    videos=videos,
    proprio=proprio,
    actions=actions
)
loss.backward()
print(f"Training loss: {loss.item():.4f}")

# Get detailed loss breakdown
total_loss, (autoregressive_loss, action_loss) = amplify(
    trajectories=trajectories,
    commands=commands,
    videos=videos,
    proprio=proprio,
    actions=actions,
    return_loss_breakdown=True
)
print(f"Autoregressive loss: {autoregressive_loss.item():.4f}")
print(f"Action prediction loss: {action_loss.item():.4f}")
```

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

### Initialize Amplify Model Components

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

Initialize the language model backbone using `TransformerWrapper` and the `MotionTokenizer` for use within the Amplify model.

```python
import torch
from amplify_pytorch.amplify import Amplify, MotionTokenizer
from x_transformers import Decoder, TransformerWrapper

# Initialize the language model backbone
llm = TransformerWrapper(
    num_tokens=20000,           # Vocabulary size
    max_seq_len=1024,           # Max sequence length
    attn_layers=Decoder(
        dim=512,
        depth=6,
        heads=8
    )
)

# Initialize motion tokenizer
tokenizer = MotionTokenizer(dim=32)
```

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

### Initialize and Use MotionTokenizer

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

Initialize the MotionTokenizer with specified dimensions and parameters. Use it to compute reconstruction loss and tokenize trajectories for motion prediction.

```python
import torch
from amplify_pytorch.amplify import MotionTokenizer

# Initialize tokenizer with default parameters
tokenizer = MotionTokenizer(
    dim=512,                    # Model dimension
    channels=2,                 # Number of channels (velocity/position)
    height=16,                  # Spatial height
    width=16,                   # Spatial width
    patch_size=4,               # Patch size for patchification
    num_views=3,                # Number of camera views
    channel_splits=1,           # Number of codebook splits
    codebook_size=64,           # Size of quantization codebook
    max_time_seq_len=16,        # Maximum temporal sequence length
    encoder_kwargs=dict(
        depth=2,
        attn_dim_head=64,
        heads=8
    ),
    decoder_kwargs=dict(
        depth=2,
        attn_dim_head=64,
        heads=8
    ),
    fsq_kwargs=dict(
        levels=[8, 5, 5, 5]     # FSQ quantization levels
    )
)

# Create sample trajectory data: (batch, time, views, height, width, channels)
trajectories = torch.randn(2, 16, 3, 16, 16, 2)

# Training: compute reconstruction loss
loss = tokenizer(trajectories)
loss.backward()

print(f"Reconstruction loss: {loss.item():.4f}")

# Get reconstructed velocities and trajectories
loss, (recon_velocities, recon_trajectories) = tokenizer(
    trajectories,
    return_recon_trajectories=True,
    return_recons=True
)

print(f"Reconstructed velocities shape: {recon_velocities.shape}")
print(f"Reconstructed trajectories shape: {recon_trajectories.shape}")

# Tokenize trajectories for motion prediction (inference mode)
with torch.no_grad():
    token_ids = tokenizer.tokenize(trajectories)
    print(f"Token IDs shape: {token_ids.shape}")
```

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

### Perform Amplify Inference

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

Demonstrates loading a trained model and generating action chunks from observations without ground truth data.

```python
import torch
from amplify_pytorch.amplify import Amplify, MotionTokenizer
from x_transformers import Decoder, TransformerWrapper

# Initialize model (same as training setup)
llm = TransformerWrapper(
    num_tokens=20000,
    max_seq_len=1024,
    attn_layers=Decoder(dim=512, depth=6, heads=8)
)

tokenizer = MotionTokenizer(dim=32)

amplify = Amplify(
    tokenizer=tokenizer,
    llm=llm,
    dim_proprio=17,
    dim_image_embed=256,
    action_chunk_size=20,
    vit=dict(
        image_size=224,
        patch_size=14,
        num_classes=1000,
        heads=8,
        dim=256,
        depth=2,
        mlp_dim=1024,
    ),
    decoder=dict(dim=512, depth=2)
)

# Load trained weights (example)
# amplify.load_state_dict(torch.load('amplify_checkpoint.pt'))
amplify.eval()

# Inference: predict actions from observations
with torch.no_grad():
    pred_action_chunk = amplify(
        commands=torch.randint(0, 20000, (1, 512)),    # Language command
        videos=torch.randn(1, 3, 16, 224, 224),        # Video observation
        proprio=torch.randn(1, 17),                     # Robot proprioception
        generate_motion_max_seq_len=768                 # Max generated motion length
    )

print(f"Predicted action chunk shape: {pred_action_chunk.shape")
# Output: torch.Size([1, 20, 20]) - (batch, action_chunk_size, dim_action)
```

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

### Convert Trajectories to Velocities and Back

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

Use `trajectory_to_velocities` to convert position trajectories to velocity representations and `velocities_to_trajectory` to convert back. This is useful as the motion tokenizer processes velocities.

```python
import torch
from amplify_pytorch.amplify import trajectory_to_velocities, velocities_to_trajectory

# Create sample trajectory: (batch, time, ...)
trajectories = torch.randn(2, 16, 3, 16, 16, 2)

# Convert trajectories to velocities
velocities = trajectory_to_velocities(trajectories)
print(f"Input trajectories shape: {trajectories.shape}")
print(f"Output velocities shape: {velocities.shape}")

# Convert back to trajectories (cumulative sum)
reconstructed = velocities_to_trajectory(velocities)
print(f"Reconstructed trajectories shape: {reconstructed.shape}")
```

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