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ml/tests/test_dilated_encoder.py
Validates Multi-Scale Dilated 1D-CNN Encoder architecture and multi-wavelength capture.
"""
import pytest
import torch
from ml.models.geometry.sequence_vae import DilatedEncoder, SequenceVAE
def test_dilated_encoder_multi_scale_branches():
"""Verify short (dilation=1), medium (dilation=4), and long (dilation=10) branches and shapes."""
batch_size = 4
n_features = 5
seq_len = 80
latent_dim = 16
encoder = DilatedEncoder(n_features=n_features, seq_len=seq_len, latent_dim=latent_dim)
x = torch.randn(batch_size, n_features, seq_len)
mu, logvar = encoder(x)
assert mu.shape == (batch_size, latent_dim)
assert logvar.shape == (batch_size, latent_dim)
def test_dilated_sequence_vae_forward_and_reconstruction():
"""Verify complete SequenceVAE with dilated encoder reconstructs to exact 80 spatial bins."""
batch_size = 6
seq_len = 80
n_features = 5
model = SequenceVAE(seq_len=seq_len, n_features=n_features, latent_dim=16)
x = torch.randn(batch_size, seq_len, n_features)
recon_x, mu, logvar = model(x)
assert recon_x.shape == (batch_size, seq_len, n_features)
assert not torch.isnan(recon_x).any()
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