veg/example_inference.py

# example_inference
import torch
from MultiTaskConvLSTM import ConvLSTMNetwork
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
import torch
import torch.nn as nn
from tqdm.auto import tqdm
from utils import (
    mse, mae, nash_sutcliffe_efficiency, r2_score, pearson_correlation,
    spearman_correlation, percentage_error, percentage_bias,
    kendall_tau, spatial_correlation
)
import torch.optim as optim


device = 'cpu'

height = 81
width = 97

set_lookback = 1
set_forecast_horizon = 1

#Define variables for evaluation
batch_size = 16
time_steps_out = set_forecast_horizon
channels = 14

#Variable names
variable_names = ['10 metre U wind component', '10 metre V wind component', '2 metre dewpoint temperature', '2 metre temperature', 'UV visible albedo for direct radiation (climatological)', 'Total column rain water', 'Volumetric soil water layer 1', 'Leaf area index, high vegetation', 'Leaf area index, low vegetation', 'Forecast surface roughness', 'Total precipitation', 'Time-integrated surface latent heat net flux', 'Evaporation']

# Adjust input_dim and output_channels according to your data specifics
model = ConvLSTMNetwork(
    input_dim=14 * set_lookback, 
    hidden_dims=[14, 32, 64], 
    kernel_size=(3,3), 
    num_layers=3, 
    output_channels=64 * set_forecast_horizon, 
    batch_first=True
).to(device)

# Define separate loss functions
loss_fn = nn.MSELoss()       # For regression output
bce_loss_fn = nn.BCELoss()       # For classification output

optimizer = optim.AdamW(model.parameters(), lr = 0.005) 

checkpoint = torch.load("MultiTaskConvLSTM_veg_variables.pth", map_location = device)
model.load_state_dict(checkpoint['model_state_dict'])

# If you want to move the model to the GPU (optional, depending on your setup)
model.to(device)  # Assuming you have a variable `device` for CUDA or CPU

# Ensure that the model is in evaluation mode if you're using it for inference
model.eval()

print("Model loaded successfully")


threshold = 0.1
precip_index = 10

def evaluate(model, test_loader, reg_loss_fn, class_loss_fn, device, variable_names, height, width):
    """
    Evaluate the model on the test set for both regression and classification tasks.
    """
    model.eval()  # Set the model to evaluation model

    # input_to_true = {'zero_to_non_zero': 0, 'non_zero_to_zero': 0}
    # input_to_pred_REG = {'zero_to_non_zero': 0, 'non_zero_to_zero': 0}
    # input_to_pred_CLASS = {'zero_to_non_zero': 0, 'non_zero_to_zero': 0}

    test_reg_loss = 0.0
    test_class_loss = 0.0
    test_total_loss = 0.0

    y_true_reg = []  # List to store true values for regression
    y_pred_reg = []  # List to store predicted values for regression

    y_pred_reg2 = []

    y_true_class = []  # List to store true values for classification
    y_pred_class = []  # List to store predicted probabilities for classification

    # Disable gradient computation
    with torch.no_grad():
        for X_test, y_test, y_zero_test in tqdm(test_loader, desc="Evaluating on Test Set"):
            # Move the batch to the device
            X_test, y_test, y_zero_test = X_test.to(device), y_test.to(device), y_zero_test.to(device)

            # Reshape inputs and targets
            batch_size, time_steps_in, channels_in, grid_points = X_test.shape
            batch_size, time_steps_out, channels_out, grid_points = y_test.shape
            X_test = X_test.view(batch_size, time_steps_in, channels_in, height, width)
            y_test = y_test.view(batch_size, time_steps_out, channels_out, height, width)
            y_zero_test = y_zero_test.view(batch_size, time_steps_out, channels_out, height, width)

            # Forward pass
            regression_output, classification_output = model(X_test)

            classification_predictions = (classification_output > 0.7).float()

            # Compute regression loss
            reg_loss = reg_loss_fn(regression_output, y_test)

            # Compute classification loss
            class_loss = class_loss_fn(classification_output, y_zero_test)

            # Total loss
            total_loss = reg_loss + class_loss

            regression_output2 = torch.where(classification_predictions == 0, regression_output, classification_predictions)

            # Accumulate losses
            test_reg_loss += reg_loss.item() * X_test.size(0)
            test_class_loss += class_loss.item() * X_test.size(0)
            test_total_loss += total_loss.item() * X_test.size(0)

            # Collect true and predicted values for regression and classification
            y_true_reg.append(y_test.cpu())
            y_pred_reg.append(regression_output.cpu())
            y_pred_reg2.append(regression_output2.cpu())
            y_true_class.append(y_zero_test.cpu())
            y_pred_class.append(classification_output.cpu())

    # Normalize losses by the total dataset size
    test_reg_loss /= len(test_loader)
    test_class_loss /= len(test_loader)
    test_total_loss /= len(test_loader)

    print(f"Test Regression Loss: {test_reg_loss:.16f}")
    print(f"Test Classification Loss: {test_class_loss:.16f}")
    print(f"Test Total Loss: {test_total_loss:.16f}")

    y_true_reg_flat = torch.cat(y_true_reg, dim=0).flatten()  # Keep as PyTorch tensor
    y_pred_reg_flat = torch.cat(y_pred_reg, dim=0).flatten()  # Keep as PyTorch tensor
    y_true_class_flat = torch.cat(y_true_class, dim=0).flatten()  # Keep as PyTorch tensor
    y_pred_class_flat = torch.cat(y_pred_class, dim=0).flatten()  # Keep as PyTorch tensor

    # Compute regression metrics
    regression_metrics = {
        "MSE": mse(y_true_reg_flat, y_pred_reg_flat),
        "MAE": mae(y_true_reg_flat, y_pred_reg_flat),
        "NSE": nash_sutcliffe_efficiency(y_true_reg_flat, y_pred_reg_flat),
        "R2": r2_score(y_true_reg_flat, y_pred_reg_flat),
        "Pearson": pearson_correlation(y_true_reg_flat, y_pred_reg_flat),
        "Spearman": spearman_correlation(y_true_reg_flat, y_pred_reg_flat),
        "NSE": nash_sutcliffe_efficiency(y_true_reg_flat, y_pred_reg_flat),
        "Percentage Bias": percentage_bias(y_true_reg_flat, y_pred_reg_flat),
        "Kendall Tau": kendall_tau(y_true_reg_flat, y_pred_reg_flat),
        "Spatial Correlation": spatial_correlation(y_true_reg_flat, y_pred_reg_flat)}

    print("\nRegression Metrics:")
    for metric, value in regression_metrics.items():
        print(f"{metric}: {value:.16f}")


    # Compute classification metrics
    classification_metrics = {
        "Accuracy": accuracy_score(y_true_class_flat, (y_pred_class_flat > 0.7)),
        "Precision": precision_score(y_true_class_flat, (y_pred_class_flat > 0.7)),
        "Recall": recall_score(y_true_class_flat, (y_pred_class_flat > 0.7)),
        "F1": f1_score(y_true_class_flat, (y_pred_class_flat > 0.7)),
        "ROC-AUC": roc_auc_score(y_true_class_flat, y_pred_class_flat),
    }

    print("\nClassification Metrics:")
    for metric, value in classification_metrics.items():
        print(f"{metric}: {value:.16f}")

    torch.save({
        'y_true_reg': y_true_reg_flat,
        'y_pred_reg': y_pred_reg_flat,
        'y_true_class': y_true_class_flat,
        'y_pred_class': y_pred_class_flat,
    }, 'results')

    return test_total_loss, regression_metrics, classification_metrics


"""
EXPECTED DATALOADER BATCH FORMAT (normalized_test_data):

Each batch must be a tuple: (X_batch, y_batch, y_zero_batch)

X_batch contains the previous hours variables. y_batch contains the next hour's precipitation.
y_zero_batch contains the next hour's precipitation thresholded as 0 for precipiation <=0.1mm/h and 
1 for precipitation >0.1mm.

Shapes BEFORE reshaping inside `evaluate`:
    X_batch:   (B, T_in, C_in, G)        # G = H*W = 81*97 = 7857
    y_batch:   (B, T_out, C_out, G)
    y_zero_batch: (B, T_out, C_out, G)   # binary 0/1 "zero-precip" targets

    If your preprocessing produces (B,T, C, H, W), reshape to (B, T, C, H*W) before inference.

DTypes:
    X_batch, y_batch:       torch.float32
    y_zero_batch:           torch.float32 (will be used with BCELoss)

Reshaping done in 'evaluate':
    X_test = X_batch.view(B, T_in, C_in, H, W)         -> (B, T_in, C_in, 81, 97)
    y_test = y_batch.view(B, T_out, C_out, H, W)       -> (B, T_out, C_out, 81, 97)
    y_zero_test = y_zero_batch.view(B, T_out, C_out, H, W)

Model input:
    model expects X_test shaped (B, T_in, input_dim, H, W)
    where input_dim == 9 * set_lookback  (with set_lookback=1 -> input_dim=9)

Notes:
  • Make sure G == H*W (i.e., 7857 for 81x97).
  • C_out for precipitation should be 1 (one target channel), and y_zero_batch
    is the 0/1 mask for “zero precipitation” at each pixel & time.
  • y_zero_batch should be probabilities/labels in {0,1} for BCELoss.
"""

normalized_test_data = torch.load("data/normalized_test_data_veg_input.pth")


test_total_loss, regression_metrics, classification_metrics = evaluate(
    model=model,
    test_loader=normalized_test_data,
    reg_loss_fn=loss_fn,
    class_loss_fn=bce_loss_fn,
    device=device,
    variable_names=variable_names,
    height=height,
    width=width,
)