Jupyter Notebook - App¶

Run Application interactively without using FastAPI¶

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import warnings
from typing import List, Tuple

from pydantic import BaseModel, Field
from torch import rand
from torch.nn import Sequential
from typing_extensions import Annotated

from lit_auto_encoder.auto_encoder import LitAutoEncoder
from lit_auto_encoder.train_autoencoder import train_litautoencoder

warnings.filterwarnings("ignore")

import warnings from typing import List, Tuple from pydantic import BaseModel, Field from torch import rand from torch.nn import Sequential from typing_extensions import Annotated from lit_auto_encoder.auto_encoder import LitAutoEncoder from lit_auto_encoder.train_autoencoder import train_litautoencoder warnings.filterwarnings("ignore")

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# Input validation model
class NumberFakeImages(BaseModel):
    n_fake_images: Annotated[int, Field(ge=1, le=10)]  # type: ignore # Between 1 and 10 fake images allowed

# Input validation model class NumberFakeImages(BaseModel): n_fake_images: Annotated[int, Field(ge=1, le=10)] # type: ignore # Between 1 and 10 fake images allowed

Train the Model¶

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def train_model() -> Tuple[Sequential, Sequential]:
    """Train the autoencoder model.

    Returns:
        tuple[Sequential, Sequential]: Encoder and decoder models.
    """

    encoder, decoder, _is_model_trained = train_litautoencoder()
    return encoder, decoder

def train_model() -> Tuple[Sequential, Sequential]: """Train the autoencoder model. Returns: tuple[Sequential, Sequential]: Encoder and decoder models. """ encoder, decoder, _is_model_trained = train_litautoencoder() return encoder, decoder

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# Train encoder and decoder
encoder, decoder = train_model()

# Train encoder and decoder encoder, decoder = train_model()

GPU available: True (mps), used: True
TPU available: False, using: 0 TPU cores
HPU available: False, using: 0 HPUs

  | Name    | Type       | Params | Mode 
-----------------------------------------------
0 | encoder | Sequential | 50.4 K | train
1 | decoder | Sequential | 51.2 K | train
-----------------------------------------------
101 K     Trainable params
0         Non-trainable params
101 K     Total params
0.407     Total estimated model params size (MB)
8         Modules in train mode
0         Modules in eval mode

Epoch 0: 100%|██████████| 100/100 [00:00<00:00, 203.43it/s, v_num=80]

`Trainer.fit` stopped: `max_epochs=1` reached.

Epoch 0: 100%|██████████| 100/100 [00:00<00:00, 198.91it/s, v_num=80]

Create Embeddings¶

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def create_embed(
    input_data: NumberFakeImages, encoder: Sequential, decoder: Sequential, checkpoint_path: str
) -> List[List[float]]:
    """Embed fake images using the trained autoencoder.

    Args:
        input_data (NumberFakeImages): Input data containing the number of fake images to embed.
        encoder (Sequential): Encoder model.
        decoder (Sequential): Decoder model.
        checkpoint_path (str): Path to the checkpoint file.

    Returns:
        List[List[float]]: A list containing the embeddings of each fake images as a list.
    """

    n_fake_images = input_data.n_fake_images

    # Load the trained autoencoder from the checkpoint
    autoencoder = LitAutoEncoder.load_from_checkpoint(
        checkpoint_path, encoder=encoder, decoder=decoder
    )
    encoder_model = autoencoder.encoder
    encoder_model.eval()

    # Generate fake image embeddings based on user input
    fake_image_batch = rand(n_fake_images, 28 * 28, device=autoencoder.device)
    embeddings = encoder_model(fake_image_batch)

    return embeddings.tolist()

def create_embed( input_data: NumberFakeImages, encoder: Sequential, decoder: Sequential, checkpoint_path: str ) -> List[List[float]]: """Embed fake images using the trained autoencoder. Args: input_data (NumberFakeImages): Input data containing the number of fake images to embed. encoder (Sequential): Encoder model. decoder (Sequential): Decoder model. checkpoint_path (str): Path to the checkpoint file. Returns: List[List[float]]: A list containing the embeddings of each fake images as a list. """ n_fake_images = input_data.n_fake_images # Load the trained autoencoder from the checkpoint autoencoder = LitAutoEncoder.load_from_checkpoint( checkpoint_path, encoder=encoder, decoder=decoder ) encoder_model = autoencoder.encoder encoder_model.eval() # Generate fake image embeddings based on user input fake_image_batch = rand(n_fake_images, 28 * 28, device=autoencoder.device) embeddings = encoder_model(fake_image_batch) return embeddings.tolist()

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# Create embeddings
embeddings = create_embed(
    NumberFakeImages(n_fake_images=2),
    encoder,
    decoder,
    checkpoint_path="./lightning_logs/LitAutoEncoder/version_0/checkpoints/epoch=0-step=100.ckpt",
)

# Print the embeddings
print("⚡" * 20, "\nPredictions (image embeddings):\n", embeddings, "\n", "⚡" * 20, sep="")

# Create embeddings embeddings = create_embed( NumberFakeImages(n_fake_images=2), encoder, decoder, checkpoint_path="./lightning_logs/LitAutoEncoder/version_0/checkpoints/epoch=0-step=100.ckpt", ) # Print the embeddings print("⚡" * 20, "\nPredictions (image embeddings):\n", embeddings, "\n", "⚡" * 20, sep="")

⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡
Predictions (image embeddings):
[[0.3581562042236328, 0.14595450460910797, 0.47827404737472534], [0.5079143643379211, 0.09660176187753677, 0.5766311287879944]]
⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡

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