### Install Multi-threaded Dataset Builder

Source: https://github.com/kvablack/dlimp/blob/main/rlds_converters/README.md

Installs the multithreaded dataset builder package. This command should be run from the root directory of the project. Additional dataset-specific requirements may be installed separately.

```bash
pip install .
```

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

### Complete DLataset Pipeline Example in Python

Source: https://context7.com/kvablack/dlimp/llms.txt

An end-to-end example demonstrating the DLataset pipeline, including loading data from TFRecords, trajectory-level filtering and transformations (like goal relabeling), frame-level image processing (decoding, resizing, augmentation), and finally shuffling, batching, and preparing the dataset for training. It utilizes method chaining for a functional programming style.

```python
from dlimp import DLataset
from dlimp.transforms.frame_transforms import decode_images, resize_images, augment
from dlimp.transforms import goal_relabeling
import tensorflow as tf

# Configuration
augment_config = {
    'augment_order': ['random_resized_crop', 'random_brightness', 'random_flip_left_right'],
    'random_resized_crop': {'scale': [0.9, 1.0], 'ratio': [0.9, 1.1]},
    'random_brightness': [0.1]
}

# Build pipeline
dataset = (
    DLataset.from_tfrecords(
        "/path/to/robotics/dataset/",
        shuffle=True,
        num_parallel_reads=8
    )
    .with_ram_budget(gb=16)
    # Trajectory-level: filter short trajectories
    .filter(lambda traj: traj['_len'][0] >= 10)
    # Trajectory-level: add next_obs
    .traj_map(lambda traj: {
        **traj,
        'next_obs': tf.nest.map_structure(
            lambda x: tf.concat([x[1:], x[-1:]], axis=0),
            traj['obs']
        )
    })
    # Trajectory-level: goal relabeling
    .traj_map(lambda x: goal_relabeling.uniform(x, reached_proportion=0.2))
    # Frame-level: decode and resize images
    .frame_map(decode_images)
    .frame_map(lambda x: resize_images(x, size=(224, 224)))
    # Frame-level: augmentation
    .frame_map(lambda x: augment(x, match='*image', augment_kwargs=augment_config))
    # Convert to frame-level dataset
    .flatten()
    # Shuffle and batch
    .shuffle(buffer_size=50000, seed=42)
    .batch(256)
    .repeat()
)

# Train model
for epoch in range(100):
    for batch in dataset.iterator(prefetch=2):
        # batch['obs'] shape: [256, 224, 224, 3]
        # batch['action'] shape: [256, action_dim]
        # batch['goals'] shape: [256, 224, 224, 3]
        # batch['rewards'] shape: [256]
        train_step(batch)
```

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

### Apply Trajectory-Level Transformations with DLIMP

Source: https://context7.com/kvablack/dlimp/llms.txt

Applies transformations at the trajectory level using DLIMP's `traj_map` and `filter` methods. Examples include adding computed fields like 'next_observations', filtering trajectories based on length, and truncating trajectories to a fixed maximum length.

```python
from dlimp import DLataset
import tensorflow as tf

dataset = DLataset.from_tfrecords("/path/to/dataset/")

# Add next observations to trajectory
def add_next_obs(traj):
    # Pad so last transition has next_obs == obs
    traj['next_observations'] = tf.nest.map_structure(
        lambda x: tf.concat([x[1:], x[-1:]], axis=0),
        traj['observations']
    )
    return traj

dataset = dataset.traj_map(add_next_obs)

# Filter trajectories by length
def filter_short_trajs(traj):
    return traj['_len'][0] >= 10

dataset = dataset.filter(filter_short_trajs)

# Truncate trajectories to fixed length
def truncate_traj(traj, max_len=50):
    if traj['_len'][0] > max_len:
        return tf.nest.map_structure(lambda x: x[:max_len], traj)
    return traj

dataset = dataset.traj_map(lambda x: truncate_traj(x, max_len=50))
```

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

### Build RLDS Dataset using tfds build

Source: https://github.com/kvablack/dlimp/blob/main/rlds_converters/README.md

Builds a specific RLDS dataset using the `tfds build` command. Requires specifying the path to the raw data and potentially modifying builder settings like `NUM_WORKERS` and `CHUNKSIZE`.

```bash
CUDA_VISIBLE_DEVICES="" tfds build --manual_dir <path_to_raw_data>
```

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

### Convert Kinetics Raw to TFRecord (Python)

Source: https://github.com/kvablack/dlimp/blob/main/legacy_converters/kinetics/downloading_kinetics.txt

This script converts raw Kinetics dataset files into the TFRecord format required by DLIMP. It takes the input path to the downloaded dataset and an output path for the TFRecord files. The 'aspect_ratio' flag should be set to True for proper data handling. Supports 400, 600, and 700 class splits.

```bash
python3 -m scripts.kinetics.raw_to_tfrecord --input_path /path-to-kinetics-dataset/k{num} --output_path /path-to-output/ --aspect_ratio True
```

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

### Load Datasets from RLDS Format using DLIMP

Source: https://context7.com/kvablack/dlimp/llms.txt

Loads datasets using the TensorFlow Datasets (TFDS) RLDS format. It automatically handles metadata, moving trajectory-level information to the 'traj_metadata' key. Supports TFDS dataset builders and specifies splits for loading.

```python
import tensorflow_datasets as tfds
from dlimp import DLataset

# Create TFDS dataset builder
builder = tfds.builder('bridge_dataset/1.0.0', data_dir='/path/to/data')

# Load dataset from RLDS format
dataset = DLataset.from_rlds(
    builder=builder,
    split='train[:80%]',
    shuffle=True,
    num_parallel_reads=8
)

# RLDS metadata is automatically moved from nested structure to top level
# with 'traj_metadata' key containing trajectory-level information
for traj in dataset.iterator():
    print(traj['traj_metadata'])  # Original trajectory metadata
    print(traj['observation'])     # Frame-level observations
    print(traj['action'])          # Frame-level actions
```

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

### Load Trajectory Datasets from TFRecords using DLIMP

Source: https://context7.com/kvablack/dlimp/llms.txt

Loads trajectory datasets from TFRecord files. Supports loading from a directory or specific files, with options for shuffling and parallel reading. It allows setting a RAM budget and iterating through trajectories, automatically adding metadata like trajectory length and frame index.

```python
from dlimp import DLataset

# Load from directory containing .tfrecord files
dataset = DLataset.from_tfrecords(
    "/path/to/dataset/",
    shuffle=True,
    num_parallel_reads=8
)

# Load from specific files
dataset = DLataset.from_tfrecords(
    ["/path/to/file1.tfrecord", "/path/to/file2.tfrecord"],
    shuffle=True,
    num_parallel_reads=4
)

# Set RAM budget for dataset processing
dataset = dataset.with_ram_budget(gb=8)

# Iterate through trajectories
for trajectory in dataset.iterator(prefetch=10):
    # Each trajectory is a dict with metadata added automatically
    print(trajectory['_len'])          # Trajectory length
    print(trajectory['_traj_index'])   # Trajectory index in dataset
    print(trajectory['_frame_index'])  # Frame index within trajectory
    print(trajectory.keys())           # All available keys
```

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

### Python: Mixing Multiple Datasets with Dlimp

Source: https://context7.com/kvablack/dlimp/llms.txt

Combines multiple DLataset objects using different sampling strategies, such as uniform sampling, weighted sampling, deterministic choice, or zipping. This is useful for multi-task learning or curriculum learning scenarios. Dependencies include 'dlimp' and 'tensorflow'.

```python
from dlimp import DLataset

# Load multiple datasets
dataset1 = DLataset.from_tfrecords("/path/to/dataset1/")
dataset2 = DLataset.from_tfrecords("/path/to/dataset2/")
dataset3 = DLataset.from_tfrecords("/path/to/dataset3/")

# Sample from datasets with uniform weights
mixed_dataset = DLataset.sample_from_datasets(
    datasets=[dataset1, dataset2, dataset3],
    weights=None,  # Uniform sampling
    seed=42,
    stop_on_empty_dataset=False
)

# Sample with custom weights (40%, 40%, 20%)
mixed_dataset = DLataset.sample_from_datasets(
    datasets=[dataset1, dataset2, dataset3],
    weights=[0.4, 0.4, 0.2],
    seed=42
)

# Choose dataset deterministically
import tensorflow as tf
choice_dataset = tf.data.Dataset.from_tensor_slices([0, 1, 2, 0, 1])
mixed_dataset = DLataset.choose_from_datasets(
    datasets=[dataset1, dataset2, dataset3],
    choice_dataset=choice_dataset,
    stop_on_empty_dataset=True
)

# Zip datasets together
zipped_dataset = DLataset.zip(dataset1, dataset2)
# Each element is tuple of (traj_from_dataset1, traj_from_dataset2)
```

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

### Python: Goal Relabeling for RL with Dlimp

Source: https://context7.com/kvablack/dlimp/llms.txt

Implements goal relabeling strategies for hindsight experience replay and goal-conditioned reinforcement learning. This involves adding a 'next_obs' field and then applying uniform, geometric, or last-state upweighted relabeling. Dependencies include 'dlimp', 'tensorflow', and its 'nest' module.

```python
from dlimp import DLataset
from dlimp.transforms import goal_relabeling
import tensorflow as tf

dataset = DLataset.from_tfrecords("/path/to/dataset/")

# Add next_obs field required for goal relabeling
def add_next_obs(traj):
    traj['next_obs'] = tf.nest.map_structure(
        lambda x: tf.concat([x[1:], x[-1:]], axis=0),
        traj['obs']
    )
    return traj

dataset = dataset.traj_map(add_next_obs)

# Uniform goal relabeling - samples goals uniformly from future
dataset = dataset.traj_map(
    lambda x: goal_relabeling.uniform(x, reached_proportion=0.2)
)
# Output: traj['goals'] and traj['rewards'] added
# reached_proportion=0.2 means 20% of transitions have reward=0

# Geometric goal relabeling - samples with exponential decay
dataset = dataset.traj_map(
    lambda x: goal_relabeling.geometric(
        x,
        reached_proportion=0.1,
        discount=0.95
    )
)
# Goals sampled with probability decaying by discount factor

# Last-state upweighted relabeling
dataset = dataset.traj_map(
    lambda x: goal_relabeling.last_state_upweighted(
        x,
        reached_proportion=0.15
    )
)
# Later transitions more likely to get final state as goal
```

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

### Python: Image Data Augmentation with Dlimp

Source: https://context7.com/kvablack/dlimp/llms.txt

Applies various deterministic or randomized augmentations to image data within a DLataset. It supports configurable augmentation pipelines and options for applying augmentations consistently across frames in a trajectory or independently. Dependencies include 'dlimp' and its transforms.

```python
from dlimp import DLataset
from dlimp.transforms.frame_transforms import decode_images, resize_images, augment

dataset = DLataset.from_tfrecords("/path/to/dataset/")
dataset = dataset.frame_map(decode_images)
dataset = dataset.frame_map(lambda x: resize_images(x, size=(224, 224)))

# Configure augmentation pipeline
augment_config = {
    'augment_order': [
        'random_resized_crop',
        'random_brightness',
        'random_contrast',
        'random_saturation',
        'random_flip_left_right'
    ],
    'random_resized_crop': {
        'scale': [0.8, 1.0],
        'ratio': [3/4, 4/3]
    },
    'random_brightness': [0.2],
    'random_contrast': [0.8, 1.2],
    'random_saturation': [0.8, 1.2]
}

# Apply augmentations (same seed for all images in trajectory by default)
dataset = dataset.frame_map(
    lambda x: augment(
        x,
        match='*image',  # Matches keys ending in 'image'
        traj_identical=True,  # Same augmentation for all frames in trajectory
        keys_identical=True,   # Same augmentation for all matching keys
        augment_kwargs=augment_config
    )
)

# Different augmentations per frame
dataset = dataset.frame_map(
    lambda x: augment(
        x,
        match='*image',
        traj_identical=False,
        keys_identical=True,
        augment_kwargs=augment_config
    )
)
```

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

### Frame-Level Image Processing with DLIMP Transforms

Source: https://context7.com/kvablack/dlimp/llms.txt

Applies frame-level transformations for image processing using DLIMP's `frame_map` and specialized transforms. Includes decoding images, resizing images and depth images, and chaining multiple transformations for image augmentation and preparation.

```python
from dlimp import DLataset
from dlimp.transforms.frame_transforms import decode_images, resize_images, resize_depth_images

dataset = DLataset.from_tfrecords("/path/to/dataset/")

# Decode all image fields (searches for 'image' in key names)
dataset = dataset.frame_map(decode_images)

# Decode specific image fields
dataset = dataset.frame_map(
    lambda x: decode_images(x, match=['rgb', 'camera'])
)

# Resize images to specific dimensions
dataset = dataset.frame_map(
    lambda x: resize_images(x, match='image', size=(128, 128))
)

# Resize depth images (handles float32 depth data)
dataset = dataset.frame_map(
    lambda x: resize_depth_images(x, match='depth', size=(64, 64))
)

# Chain multiple transformations
dataset = (
    dataset
    .frame_map(decode_images)
    .frame_map(lambda x: resize_images(x, size=(256, 256)))
    .frame_map(lambda x: resize_depth_images(x, size=(128, 128)))
)
```

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

### Python: Flattening Trajectories to Frames with Dlimp

Source: https://context7.com/kvablack/dlimp/llms.txt

Converts a trajectory-level dataset into a frame-level dataset, suitable for training on individual frames. This involves applying any necessary trajectory-level transformations before flattening and then performing frame-level transformations and batching. Only 'frame_map' is allowed after flattening.

```python
from dlimp import DLataset

dataset = DLataset.from_tfrecords("/path/to/dataset/")

# Apply trajectory-level transformations first
dataset = dataset.traj_map(lambda x: x)  # Any traj transformations

# Flatten to frame-level dataset
dataset = dataset.flatten(num_parallel_calls=8)

# After flattening, only frame_map is allowed (not traj_map)
dataset = dataset.frame_map(lambda x: x)  # Frame transformations

# Batch frames for training
dataset = dataset.batch(32)

# Shuffle at frame level
dataset = dataset.shuffle(buffer_size=10000)

# Iterate through frames
for batch in dataset.iterator(prefetch=10):
    # batch is dict with shape [batch_size, ...] for each field
    print(batch['observation'].shape)
    print(batch['action'].shape)
```

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

### Custom Frame Transformations with Parallel Processing in Python

Source: https://context7.com/kvablack/dlimp/llms.txt

Applies custom transformations to dataset frames using DLataset's frame_map, vmap, and parallel_vmap utilities. `normalize_action` is applied frame-wise, while `complex_frame_transform` demonstrates more involved processing suitable for parallel execution. `parallel_vmap` is used for efficient parallel processing, and `vmap` for sequential but fused processing.

```python
from dlimp import DLataset, vmap, parallel_vmap
import tensorflow as tf

dataset = DLataset.from_tfrecords("/path/to/dataset/")

# Custom transformation function for a single frame
def normalize_action(frame):
    frame['action'] = (frame['action'] - tf.constant([0.5, 0.5])) / tf.constant([0.2, 0.2])
    return frame

# Apply to all frames using frame_map
dataset = dataset.frame_map(normalize_action)

# For more complex transformations, use vmap utilities
def complex_frame_transform(frame):
    # Some expensive computation
    frame['processed'] = tf.nn.relu(frame['observation'])
    return frame

# Use parallel_vmap for parallelized frame processing
dataset = dataset.traj_map(
    parallel_vmap(complex_frame_transform, num_parallel_calls=8)
)

# Use vmap for sequential but fused frame processing
dataset = dataset.traj_map(
    vmap(complex_frame_transform)
)
```

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