121 lines
4.4 KiB
Python
121 lines
4.4 KiB
Python
import torch
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import torch.optim as optim
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from torchdiffeq import odeint
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import numpy as np
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import os
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import shutil
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import csv
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import inspect
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from PendulumController import PendulumController
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from PendulumDynamics import PendulumDynamics
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from time_weighting_functions import weight_functions
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# Device setup
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device = torch.device("cpu")
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base_controller_path = f"/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/controller_base.pth"
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# Initial conditions (theta0, omega0, alpha0, desired_theta)
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from initial_conditions import initial_conditions
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state_0 = torch.tensor(initial_conditions, dtype=torch.float32, device=device)
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# Constants
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m = 10.0
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g = 9.81
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R = 1.0
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# Time grid
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t_start, t_end, t_points = 0, 10, 1000
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t_span = torch.linspace(t_start, t_end, t_points, device=device)
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# Specify directory for storing results
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output_dir = "time_weighting"
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os.makedirs(output_dir, exist_ok=True)
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# Optimizer values
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learning_rate = 1e-1
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weight_decay = 1e-4
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# Training parameters
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num_epochs = 1000
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# Define loss functions
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def make_loss_fn(weight_fn):
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def loss_fn(state_traj, t_span):
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theta = state_traj[:, :, 0] # Size: [batch_size, t_points]
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desired_theta = state_traj[:, :, 3] # Size: [batch_size, t_points]
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min_weight = 0.01 # Weights are on the range [min_weight, 1]
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weights = weight_fn(t_span, min_val=min_weight) # Initially Size: [t_points]
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# Reshape or expand weights to match theta dimensions
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weights = weights.view(-1, 1) # Now Size: [batch_size, t_points]
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# Calculate the weighted loss
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return torch.mean(weights * (theta - desired_theta) ** 2)
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return loss_fn
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# Training loop for each weight function
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for name, weight_fn in weight_functions.items():
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controller = PendulumController().to(device)
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controller.load_state_dict(torch.load(base_controller_path))
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pendulum_dynamics = PendulumDynamics(controller, m, R, g).to(device)
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print(f"Loaded {base_controller_path} as base controller")
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optimizer = optim.Adam(controller.parameters(), lr=learning_rate, weight_decay=weight_decay)
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loss_fn = make_loss_fn(weight_fn)
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# File paths
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function_output_dir = os.path.join(output_dir, name)
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controllers_dir = os.path.join(function_output_dir, "controllers")
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# Check if controllers directory exists and remove it
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if os.path.exists(controllers_dir):
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shutil.rmtree(controllers_dir)
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os.makedirs(controllers_dir, exist_ok=True)
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config_file = os.path.join(function_output_dir, "training_config.txt")
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log_file = os.path.join(function_output_dir, "training_log.csv")
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# Overwrite configuration and log files
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with open(config_file, "w") as f:
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f.write(f"Base controller path: {base_controller_path}\n")
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f.write(f"Time Span: {t_start} to {t_end}, Points: {t_points}\n")
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f.write(f"Learning Rate: {learning_rate}\n")
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f.write(f"Weight Decay: {weight_decay}\n")
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f.write("\nLoss Function:\n")
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f.write(inspect.getsource(loss_fn))
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f.write("\nWeight Function:\n")
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f.write(inspect.getsource(weight_fn))
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f.write("\nTraining Cases:\n")
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f.write("[theta0, omega0, alpha0, desired_theta]\n")
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for case in state_0.cpu().numpy():
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f.write(f"{case.tolist()}\n")
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with open(log_file, "w", newline="") as csvfile:
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csv_writer = csv.writer(csvfile)
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csv_writer.writerow(["Epoch", "Loss"])
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# Training loop
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for epoch in range(0, num_epochs+1):
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optimizer.zero_grad()
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state_traj = odeint(pendulum_dynamics, state_0, t_span, method='rk4')
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loss = loss_fn(state_traj, t_span)
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loss.backward()
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# Save the model before training on this epoch
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# Therefore, controller_epoch represents the controller after {epoch} training iterations
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model_file = os.path.join(controllers_dir, f"controller_{epoch}.pth")
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torch.save(controller.state_dict(), model_file)
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print(f"{model_file} saved with loss: {loss}")
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# Update the weights and biases
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optimizer.step()
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# Logging
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with open(log_file, "a", newline="") as csvfile:
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csv_writer = csv.writer(csvfile)
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csv_writer.writerow([epoch, loss.item()])
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print("Training complete. Models and logs are saved under respective directories for each loss function.") |