# 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, )