From e7da965dc97fb8f0d958fc00ba8df1cae6982d79 Mon Sep 17 00:00:00 2001 From: thelfer1 Date: Wed, 7 Aug 2024 05:56:32 -0400 Subject: [PATCH] aded x2lvl8 run --- models/x2_lvl8/factor_2.yaml | 27 ++ models/x2_lvl8/learn_error.py | 405 ++++++++++++++++++ ...ounter_0000000015_data_time_1722656427.pth | Bin 0 -> 10384122 bytes models/x2_lvl8/warm-donkey-265 | 0 4 files changed, 432 insertions(+) create mode 100644 models/x2_lvl8/factor_2.yaml create mode 100644 models/x2_lvl8/learn_error.py create mode 100644 models/x2_lvl8/model_epoch_counter_0000000015_data_time_1722656427.pth create mode 100644 models/x2_lvl8/warm-donkey-265 diff --git a/models/x2_lvl8/factor_2.yaml b/models/x2_lvl8/factor_2.yaml new file mode 100644 index 0000000..4df0d56 --- /dev/null +++ b/models/x2_lvl8/factor_2.yaml @@ -0,0 +1,27 @@ +ADAMsteps: 2000 +n_steps: 2000 +batch_size: 15 +res_level: 8 +scaling_factor: 0.00001 +write_out_freq: 1 +# rescale factor +factor: 2 +# Data Source +filenamesX: "/home/thelfer1/scr4_tedwar42/thelfer1/high_end_data/outputXdata_level{res_level}_step00*.dat" +filenames_check: "/home/thelfer1/scr4_tedwar42/thelfer1/high_end_data_4/outputXdata_level{res_level}_step*.dat" +# Restarting +restart: False +file_path: "./Run15521648/model_epoch_counter_0000000399_data_time_1721913461.pth" +lambda_fac: 0.0 +loss: Ham_mom # Options Ham_mom Ham L1 +lr_adam: 0.00005 +lr_bfgs: 0.1 +train_ratio: 0.8 +kernel_size: 4 +padding: "same" +nonlinearity: "relu" #'relu' or 'gelu' +num_layers: 4 +masking_percentage: 0.25 +mask_type: checkers # "checkers" or "random" +downsample: 1 ## default one +align_corners: False \ No newline at end of file diff --git a/models/x2_lvl8/learn_error.py b/models/x2_lvl8/learn_error.py new file mode 100644 index 0000000..fc83bea --- /dev/null +++ b/models/x2_lvl8/learn_error.py @@ -0,0 +1,405 @@ +import sys +import os +import argparse + +import torch +import pandas as pd +import numpy as np +import time +import shutil +import yaml + +import wandb +import matplotlib.pylab as plt +from sklearn.preprocessing import StandardScaler +from sklearn.model_selection import train_test_split +from torch.autograd import Variable +import pandas as pd +from sklearn.metrics import r2_score +from torch.utils.tensorboard import SummaryWriter +from tqdm.auto import tqdm, trange +import torch.nn.init as init +from torch.utils.data import DataLoader, TensorDataset, random_split +from torch.utils.data import TensorDataset, DataLoader + + +from GeneralRelativity.Utils import ( + get_box_format, + TensorDict, + cut_ghosts, + keys, + keys_all, +) +from GeneralRelativity.FourthOrderDerivatives import diff1, diff2 +from pyinterpx.Interpolation import * +from GeneralRelativity.TensorAlgebra import compute_christoffel +from GeneralRelativity.Constraints import constraint_equations + +from SuperResolution.models import SuperResolution3DNet +from SuperResolution.losses import ( + Hamiltonian_loss, + Hamiltonian_and_momentum_loss, + Hamiltonian_and_momentum_loss_boundary_condition, +) +from SuperResolution.utils import load_config, copy_config_file + +time_stamp = int(time.time()) + + +def main(): + default_job_id = "local_run" + # Parse the arguments + slurm_job_id = os.getenv("SLURM_JOB_ID", default_job_id) + + folder_name = f"Run{slurm_job_id}" + + # Check if the folder exists + if not os.path.exists(folder_name): + # Create the folder if it doesn't exist + os.makedirs(folder_name) + print(f"Folder '{folder_name}' created.") + # sys.exit(1) # Exit the program with a non-zero exit code to indicate an error + + else: + print(f"Folder '{folder_name}' already exists.") + + # copy code into folder for reproducibility + shutil.copy("learn_error.py", folder_name) + print(f"Copied script to '{folder_name}'.") + + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + print("Executing the model on :", device) + + torch.manual_seed(3) + np.random.seed(6) + writer = SummaryWriter(f"{folder_name}") + + # Set up argument parsing + parser = argparse.ArgumentParser(description="Process some integers.") + parser.add_argument("config", type=str, help="Path to the configuration file") + + # Parse arguments + args = parser.parse_args() + + # Load configuration + config = load_config(args.config) + + # Initialize wandb + wandb.init(project="TorchGRTL", config=config) + # Log hyperparameters + wandb.config.update(config) + + # Copy the configuration file to the tracking directory + copy_config_file(args.config, folder_name) + + # Create an empty file with the wandb run name + run_name = wandb.run.name + run_name_file_path = os.path.join(folder_name, run_name) + with open(run_name_file_path, "w") as f: + pass + + # Access configuration variables + ADAMsteps = config["ADAMsteps"] + n_steps = config["n_steps"] + res_level = config["res_level"] + scaling_factor = config["scaling_factor"] + factor = config["factor"] + filenamesX = config["filenamesX"].format(res_level=res_level) + restart = config["restart"] + file_path = config["file_path"] + lambda_fac = config["lambda_fac"] + kernel_size = config["kernel_size"] + padding = config["padding"] + num_layers = config["num_layers"] + nonlinearity = config["nonlinearity"] + masking_percentage = config["masking_percentage"] + mask_type = config["mask_type"] + write_out_freq = config["write_out_freq"] + downsample = config["downsample"] + align_corners = config["align_corners"] + + print(f"lambda_fac {type(scaling_factor)}") + + num_varsX = 25 + dataX = get_box_format(filenamesX, num_varsX) + # Cutting out extra values added for validation + dataX = dataX[:, :, :, :, :25] + + plt.imshow( + dataX[0, 8, :, :, 0], cmap="viridis" + ) # 'viridis' is a colormap, you can choose others like 'plasma', 'inferno', etc. + plt.colorbar() # Add a colorbar to show the scale + plt.title("2D Array Plot") + plt.savefig("testarray.png") + # Instantiate the model + net = SuperResolution3DNet( + factor, + scaling_factor=scaling_factor, + num_layers=num_layers, + kernel_size=kernel_size, + padding=padding, + nonlinearity=nonlinearity, + masking_percentage=masking_percentage, + mask_type=mask_type, + align_corners=align_corners, + ).to(torch.double) + + # Create a random 3D low-resolution input tensor (batch size, channels, depth, height, width) + input_tensor = torch.randn( + 1, + dataX.shape[4], + dataX.shape[1] // downsample, + dataX.shape[2] // downsample, + dataX.shape[3] // downsample, + ).to( + torch.double + ) # Adjust dimensions as needed + # Forward pass to obtain the high-resolution output + output_tensor, _ = net(input_tensor) + print("mean", torch.mean(output_tensor)) + + # Check the shape of the output + print("Input Shape:", dataX.shape) + print("Output Shape:", output_tensor.shape) + + diff = (dataX.shape[-2] - output_tensor.shape[-1]) // 2 + print(f"diff {diff}") + + # global step counter + counter = 0 + + losses_train = [] + losses_val = [] + losses_val_interp = [] + steps_val = [] + + optimizerBFGS = torch.optim.LBFGS( + net.parameters(), lr=config["lr_bfgs"] + ) # Use LBFGS sometimes, it really does do magic sometimes, though its a bit of a diva + optimizerADAM = torch.optim.Adam(net.parameters(), lr=config["lr_adam"]) + + # Define the ratio for the split (e.g., 80% train, 20% test) + train_ratio = config["train_ratio"] + test_ratio = 1 - train_ratio + + # Calculate the number of samples for each split + num_samples = len(dataX) + num_train = int(train_ratio * num_samples) + num_test = num_samples - num_train + + # Permute data to put the channel as the second dimension (N, C, H, W, D) + dataX = dataX.permute(0, 4, 1, 2, 3) + + # Create a dataset from tensors + dataset = TensorDataset(dataX) + + # Split the dataset into training and testing datasets + train_dataset, test_dataset = random_split( + dataset, [num_train, num_test], generator=torch.Generator().manual_seed(3) + ) + batch_size = config["batch_size"] + + # Create DataLoader for batching -- in case data gets larger + train_loader = DataLoader( + dataset=train_dataset, + batch_size=batch_size, + shuffle=True, + pin_memory=False, + num_workers=0, + ) + test_loader = DataLoader( + dataset=test_dataset, + batch_size=batch_size, + shuffle=False, + num_workers=0, + ) + + if restart and os.path.exists(file_path): + net.load_state_dict(torch.load(file_path)) + + # oneoverdx = 64.0 / 16.0 + oneoverdx = (64.0 * 2**res_level) / 512.0 * float(factor) / float(downsample) + print(f"dx {1.0/oneoverdx}") + if config["loss"] == "Ham": + my_loss = Hamiltonian_loss(oneoverdx) + elif config["loss"] == "Ham_mom": + my_loss = Hamiltonian_and_momentum_loss(oneoverdx) + elif config["loss"] == "Ham_mom_boundary_simple": + my_loss = Hamiltonian_and_momentum_loss_boundary_condition(oneoverdx) + elif config["loss"] == "L1": + my_loss = torch.nn.L1Loss() + # raise Value error if downsample == factor + if downsample != factor: + raise ValueError("L1 loss not implemented for downsample == factor") + + net.train() + net.to(device) + net.to(torch.double) + + # For validation error + L1Loss = torch.nn.L1Loss() + ham_loss = Hamiltonian_and_momentum_loss(oneoverdx) + + print("training") + pbar = trange(n_steps) + for i in pbar: + total_loss_train = 0 + for (y_batch,) in train_loader: + net.train() + batchcounter = 0 + # for X_batch, y_batch in train_loader: + y_batch = y_batch.to(device) + X_batch = y_batch[:, :, ::downsample, ::downsample, ::downsample].clone() + if diff != 0: + y_batch = y_batch[ + :, + :25, + diff - 1 : -diff - 1, + diff - 1 : -diff - 1, + diff - 1 : -diff - 1, + ] + batchcounter += 1 + + # This is needed for LBFGS + def closure(): + if torch.is_grad_enabled(): + optimizerBFGS.zero_grad() + y_pred, y_interp = net(X_batch) + + loss_train = my_loss(y_pred, y_batch) + if loss_train.requires_grad: + loss_train.backward() + return loss_train + + # doing some ADAM first to warm up, sometimes BFGS fuckes up if you start too early + if counter < ADAMsteps: + y_pred, y_interp = net(X_batch) + + loss_train = my_loss(y_pred, y_batch) + optimizerADAM.zero_grad() + loss_train.backward() + optimizerADAM.step() + # print(f'ADAM {batchcounter}') + + else: + optimizerBFGS.step(closure) + # print(f'BFGS {batchcounter}') + + loss_train = closure() + total_loss_train += loss_train.item() + + # Calculate the average training loss + average_loss_train = total_loss_train / len(train_loader) + # Log the average training loss + wandb.log({"loss/train": average_loss_train, "step": counter}) + # Log the average training loss + writer.add_scalar("loss/train", average_loss_train, counter) + losses_train.append(average_loss_train) + if np.isnan(average_loss_train): + print("we got nans") + + # Validation + + if counter % 1 == 0: + with torch.no_grad(): + net.eval() + total_loss_val = 0.0 + interp_val = 0.0 + L1Loss_val = 0.0 + L1Loss_val_interp = 0.0 + loss_hard_base = 0.0 + Ham_loss = 0.0 + Ham_loss_interp = 0.0 + for (y_val_batch,) in test_loader: + # for X_val_batch, y_val_batch in test_loader: + # Transfer batch to GPU + y_val_batch = y_val_batch.to(device) + X_val_batch = y_val_batch[ + :, :, ::downsample, ::downsample, ::downsample + ].clone() + if diff != 0: + y_val_batch = y_val_batch[ + :, + :25, + diff - 1 : -diff - 1, + diff - 1 : -diff - 1, + diff - 1 : -diff - 1, + ] + y_val_pred, y_val_interp = net(X_val_batch) + loss_val = my_loss(y_val_pred, y_val_batch) + total_loss_val += loss_val.item() + interp_val += my_loss(y_val_interp, y_val_batch).item() + if downsample == factor: + print(y_val_pred[:, 0, :, :, :].shape) + print(y_val_batch[:, 0, :, :, :].shape) + L1Loss_val += L1Loss( + y_val_pred[:, 0, :, :, :], y_val_batch[:, 0, :, :, :] + ) + L1Loss_val_interp += L1Loss( + y_val_interp[:, 0, :, :, :], y_val_batch[:, 0, :, :, :] + ) + loss_hard_base += ham_loss(y_val_batch, None) + + if config["loss"] == "L1": + Ham_loss_interp += ham_loss(y_val_interp, None) + Ham_loss += ham_loss(y_val_pred, None) + # Calculate the average loss + average_loss_val = total_loss_val / len(test_loader) + average_interp_val = interp_val / len(test_loader) + losses_val_interp.append(average_interp_val) + losses_val.append(average_loss_val) + steps_val.append(counter) + writer.add_scalar("loss/test", loss_val.item(), counter) + wandb.log( + { + "loss/val": average_loss_val, + "loss/val_interp": average_interp_val, + "step": counter, + } + ) + if downsample == factor: + wandb.log( + { + "loss/val_hard_baseline": loss_hard_base / len(test_loader), + "L1Loss/val_interp_baseline": L1Loss_val_interp + / len(test_loader), + "L1Loss/val": L1Loss_val / len(test_loader), + "step": counter, + } + ) + if config["loss"] == "L1": + wandb.log( + { + "Hamloss/val": Ham_loss / len(test_loader), + "Hamloss/interp_val": Ham_loss_interp + / len(test_loader), + "step": counter, + } + ) + + if counter % write_out_freq == 0: + # Writing out network and scaler + torch.save( + net.state_dict(), + f"{folder_name}/model_epoch_counter_{counter:010d}_data_time_{time_stamp}.pth", + ) + # Advancing global counter + counter += 1 + + # Plotting shit at the end + plt.figure(figsize=(9, 6)) + print(f"final val loss {losses_val[-1]} relative {losses_val_interp[-1]}") + plt.plot(np.array(losses_train), label="Train") + plt.plot(steps_val, np.array(losses_val), label="Val", linewidth=0.5) + plt.plot(steps_val, np.array(losses_val_interp), label="baseline", linewidth=0.5) + plt.yscale("log") + plt.legend() + plt.savefig(f"{folder_name}/training.png") + plt.close() + + writer.flush() + writer.close() + + +if __name__ == "__main__": + main() diff --git a/models/x2_lvl8/model_epoch_counter_0000000015_data_time_1722656427.pth b/models/x2_lvl8/model_epoch_counter_0000000015_data_time_1722656427.pth new file mode 100644 index 0000000000000000000000000000000000000000..f9486ab54d67daae803c519f2e3fc3e0720870f3 GIT binary patch literal 10384122 zcmeEvcT^M47cYVeh@xTx6+vv+5Rjs%TtGymNRcK;@0~#C1QOB+-QEzfVM9ehMMW=S 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