b2txt25/model_training_nnn/test_dual_gru.py

#!/usr/bin/env python3
"""
Test script for the dual-GRU architecture
"""

import torch
import sys
import os

# Add the current directory to the path to import rnn_model
sys.path.append(os.path.dirname(os.path.abspath(__file__)))

from rnn_model import GRUDecoder

def test_dual_gru_architecture():
    """Test the dual-GRU model with synthetic data"""

    # Model parameters (matching the original configuration)
    neural_dim = 512
    n_units = 768
    n_days = 5
    n_classes = 41
    batch_size = 4
    seq_len = 100

    print("=== Dual-GRU Architecture Test ===")
    print(f"Neural dim: {neural_dim}")
    print(f"Hidden units: {n_units}")
    print(f"Regression GRU layers: 2")
    print(f"Residual GRU layers: 3")
    print(f"Classes: {n_classes}")
    print()

    # Create model instance
    model = GRUDecoder(
        neural_dim=neural_dim,
        n_units=n_units,
        n_days=n_days,
        n_classes=n_classes,
        rnn_dropout=0.1,
        input_dropout=0.1,
        n_layers_regression=2,
        n_layers_residual=3,
        patch_size=0,  # Start without patching for simpler test
        patch_stride=0
    )

    print(f"Model created successfully!")
    print(f"Total parameters: {sum(p.numel() for p in model.parameters()):,}")
    print()

    # Create synthetic input data
    x = torch.randn(batch_size, seq_len, neural_dim)
    day_idx = torch.randint(0, n_days, (batch_size,))

    print(f"Input shape: {x.shape}")
    print(f"Day indices: {day_idx}")
    print()

    # Test forward pass without states
    print("Testing forward pass (without states)...")
    with torch.no_grad():
        logits = model(x, day_idx)
        print(f"Output logits shape: {logits.shape}")
        print(f"Expected shape: [{batch_size}, {seq_len}, {n_classes}]")
        assert logits.shape == (batch_size, seq_len, n_classes), f"Shape mismatch! Got {logits.shape}"
        print("✓ Forward pass successful")
        print()

    # Test forward pass with state return
    print("Testing forward pass (with state return)...")
    with torch.no_grad():
        logits, states = model(x, day_idx, return_state=True)
        regression_states, residual_states = states
        print(f"Output logits shape: {logits.shape}")
        print(f"Regression states shape: {regression_states.shape}")
        print(f"Residual states shape: {residual_states.shape}")
        print("✓ Forward pass with states successful")
        print()

    # Test with patch processing
    print("Testing with patch processing...")
    model_with_patches = GRUDecoder(
        neural_dim=neural_dim,
        n_units=n_units,
        n_days=n_days,
        n_classes=n_classes,
        rnn_dropout=0.1,
        input_dropout=0.1,
        n_layers_regression=2,
        n_layers_residual=3,
        patch_size=14,
        patch_stride=4
    )

    with torch.no_grad():
        logits_patches = model_with_patches(x, day_idx)
        expected_patches = (seq_len - 14) // 4 + 1  # Number of patches
        print(f"Output logits shape (with patches): {logits_patches.shape}")
        print(f"Expected patches: {expected_patches}")
        print("✓ Patch processing successful")
        print()

    # Test gradient flow
    print("Testing gradient flow...")
    model.train()
    x_grad = torch.randn(batch_size, seq_len, neural_dim, requires_grad=True)
    logits = model(x_grad, day_idx)
    loss = logits.sum()
    loss.backward()

    # Check if gradients exist
    regression_grad_exists = any(p.grad is not None for p in model.gru_regression.parameters())
    residual_grad_exists = any(p.grad is not None for p in model.gru_residual.parameters())
    day_grad_exists = any(p.grad is not None for p in model.day_weights)

    print(f"Regression GRU gradients: {'✓' if regression_grad_exists else '✗'}")
    print(f"Residual GRU gradients: {'✓' if residual_grad_exists else '✗'}")
    print(f"Day-specific layer gradients: {'✓' if day_grad_exists else '✗'}")
    print("✓ Gradient flow test successful")
    print()

    print("=== All tests passed! ===")
    print()

    # Print architecture summary
    print("=== Architecture Summary ===")
    print("Data Flow:")
    print("1. Input → Day-specific layers (512 → 512)")
    print("2. Day output → Regression GRU (2 layers, 512 hidden)")
    print("3. Residual = Day output - Regression output")
    print("4. Residual → Residual GRU (3 layers, 768 hidden)")
    print("5. Residual GRU output → Linear classifier (768 → 41)")
    print()

    return True

if __name__ == "__main__":
    test_dual_gru_architecture()