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router.py
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router.py
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import os, sys, inspect
sys.path.insert(1, os.path.join(sys.path[0], '../../'))
import wandb
import random
import copy
import numpy as np
import pickle as pkl
import torch
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as T
import warnings
import yaml
from core.scripts.train import train_net
from core.scripts.eval import get_images, eval_net, get_loss_table, eval_set_metrics
from core.models.add_uncertainty import add_uncertainty
from core.calibration.calibrate_model import calibrate_model
from core.utils import fix_randomness
from core.datasets.utils import normalize_dataset
# Models
from core.models.trunks.unet import UNet
# Datasets
from core.datasets.CAREDrosophila import CAREDrosophilaDataset
from core.datasets.bsbcm import BSBCMDataset
from core.datasets.fastmri import FastMRIDataset
from core.datasets.temca import TEMCADataset
if __name__ == "__main__":
# Fix the randomness
fix_randomness()
warnings.filterwarnings("ignore")
print("Entered main method.")
wandb.init()
print("wandb init.")
# Check if results exist already
output_dir = wandb.config['output_dir']
results_fname = output_dir + f'/results_' + wandb.config['dataset'] + "_" + wandb.config['uncertainty_type'] + "_" + str(wandb.config['batch_size']) + "_" + str(wandb.config['lr']) + "_" + wandb.config['input_normalization'] + "_" + wandb.config['output_normalization'].replace('.','_') + '.pkl'
if os.path.exists(results_fname):
print(f"Results already precomputed and stored in {results_fname}!")
os._exit(os.EX_OK)
else:
print("Computing the results from scratch!")
# Otherwise compute results
curr_method = wandb.config["uncertainty_type"]
curr_lr = wandb.config["lr"]
curr_dataset = wandb.config["dataset"]
wandb.run.name = f"{curr_method}, {curr_dataset}, lr{curr_lr}"
wandb.run.save()
params = { key: wandb.config[key] for key in wandb.config.keys() }
batch_size = wandb.config['batch_size']
params['batch_size'] = batch_size
print("wandb save run.")
# DATASET LOADING
if wandb.config["dataset"] == "CIFAR10":
normalize = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transform = T.Compose([ T.Resize(256), T.CenterCrop(224), T.ToTensor(), normalize ])
dataset = torchvision.datasets.CIFAR10('/clusterfs/abc/angelopoulos/CIFAR10', download=True, transform=transform)
elif wandb.config["dataset"] == "bsbcm":
path = '/home/aa/data/bsbcm'
dataset = BSBCMDataset(path, num_instances='all', normalize=wandb.config["output_normalization"])
elif wandb.config["dataset"] == "CAREDrosophila":
path = '/clusterfs/abc/angelopoulos/care/Isotropic_Drosophila/train_data/data_label.npz'
dataset = CAREDrosophilaDataset(path, num_instances='all', normalize=wandb.config["output_normalization"])
elif wandb.config["dataset"] == "fastmri":
path = '/clusterfs/abc/angelopoulos/fastmri/knee/singlecoil_train/'
mask_info = {'type': 'equispaced', 'center_fraction' : [0.08], 'acceleration' : [4]}
dataset = FastMRIDataset(path, normalize_input=wandb.config["input_normalization"], normalize_output = wandb.config["output_normalization"], mask_info=mask_info)
dataset = normalize_dataset(dataset)
wandb.config.update(dataset.norm_params)
params.update(dataset.norm_params)
elif wandb.config["dataset"] == "temca":
path = '/clusterfs/fiona/amit/temca_data/'
dataset = TEMCADataset(path, patch_size=[wandb.config["side_length"], wandb.config["side_length"]], downsampling=[wandb.config["downsampling_factor"],wandb.config["downsampling_factor"]], num_imgs='all', buffer_size=wandb.config["num_buffer"], normalize='01')
else:
raise NotImplementedError
# MODEL LOADING
if wandb.config["dataset"] == "CIFAR10":
if wandb.config["model"] == "ResNet18":
trunk = torchvision.models.resnet18(num_classes=wandb.config["num_classes"])
if wandb.config["model"] == "UNet":
trunk = UNet(wandb.config["num_inputs"],1)
# ADD LAST LAYER OF MODEL
model = add_uncertainty(trunk, params)
# DATA SPLITTING
if wandb.config["dataset"] == "temca":
img_paths = dataset.img_paths
lengths = np.round(len(img_paths)*np.array(wandb.config["data_split_percentages"])).astype(int)
lengths[-1] = len(img_paths)-(lengths.sum()-lengths[-1])
random.shuffle(img_paths)
train_dataset = copy.deepcopy(dataset)
calib_dataset = copy.deepcopy(dataset)
val_dataset = copy.deepcopy(dataset)
train_dataset.img_paths = img_paths[:lengths[0]]
calib_dataset.img_paths = img_paths[lengths[0]:(lengths[0]+lengths[1])]
val_dataset.img_paths = img_paths[(lengths[0]+lengths[1]):(lengths[0]+lengths[1]+lengths[2])]
else:
lengths = np.round(len(dataset)*np.array(wandb.config["data_split_percentages"])).astype(int)
lengths[-1] = len(dataset)-(lengths.sum()-lengths[-1])
train_dataset, calib_dataset, val_dataset, _ = torch.utils.data.random_split(dataset, lengths.tolist())
model = train_net(model,
train_dataset,
val_dataset,
wandb.config['device'],
wandb.config['epochs'],
batch_size,
wandb.config['lr'],
wandb.config['load_from_checkpoint'],
wandb.config['checkpoint_dir'],
wandb.config['checkpoint_every'],
wandb.config['validate_every'],
params)
print("Done training!")
model.eval()
with torch.no_grad():
#val_loader = DataLoader(val_dataset, batch_size=1, shuffle=False, num_workers=0)
#val_loss = eval_net(model,val_loader,wandb.config['device'])
#print(f"Done validating! Validation Loss: {val_loss}")
# Save the loss tables for later experiments
print("Get the validation loss table.") # Doing this first, so I can save it for later experiments.
val_loss_table = get_loss_table(model,val_dataset,wandb.config)
print("Calibrate the model.")
model, calib_loss_table = calibrate_model(model, calib_dataset, params)
print(f"Model calibrated! lambda hat = {model.lhat}")
# Save the loss tables
if output_dir != None:
try:
os.makedirs(output_dir,exist_ok=True)
print('Created output directory')
except OSError:
pass
torch.save(torch.cat((calib_loss_table,val_loss_table),dim=0),output_dir + f'/loss_table_' + wandb.config['dataset'] + "_" + wandb.config['uncertainty_type'] + "_" + str(wandb.config['batch_size']) + "_" + str(wandb.config['lr']) + "_" + wandb.config['input_normalization'] + "_" + wandb.config['output_normalization'].replace('.','_') + '.pth')
print("Loss table saved!")
# Get the prediction sets and properly organize them
examples_input, examples_lower_edge, examples_prediction, examples_upper_edge, examples_ground_truth, examples_ll, examples_ul, raw_images_dict = get_images(model,
val_dataset,
wandb.config['device'],
list(range(wandb.config['num_validation_images'])),
params)
# Log everything
wandb.log({"epoch": wandb.config['epochs']+1, "examples_input": examples_input})
wandb.log({"epoch": wandb.config['epochs']+1, "Lower edge": examples_lower_edge})
wandb.log({"epoch": wandb.config['epochs']+1, "Predictions": examples_prediction})
wandb.log({"epoch": wandb.config['epochs']+1, "Upper edge": examples_upper_edge})
wandb.log({"epoch": wandb.config['epochs']+1, "Ground truth": examples_ground_truth})
wandb.log({"epoch": wandb.config['epochs']+1, "Lower length": examples_ll})
wandb.log({"epoch": wandb.config['epochs']+1, "Upper length": examples_ul})
# Get the risk and set size
print("GET THE RISK AND SET SIZE")
risk, sizes, spearman, stratified_risk, mse = eval_set_metrics(model, val_dataset, params)
print("DONE")
#data = [[label, val] for (label, val) in zip(["Easy","Easy-medium", "Medium-Hard", "Hard"], stratified_risk.numpy())]
#table = wandb.Table(data=data, columns = ["Difficulty", "Empirical Risk"])
#wandb.log({"Size-Stratified Risk Barplot" : wandb.plot.bar(table, "Difficulty","Empirical Risk", title="Size-Stratified Risk") })
print(f"Risk: {risk} | Mean size: {sizes.mean()} | Spearman: {spearman} | Size-stratified risk: {stratified_risk} | MSE: {mse}")
wandb.log({"epoch": wandb.config['epochs']+1, "risk": risk, "mean_size":sizes.mean(), "Spearman":spearman, "Size-Stratified Risk":stratified_risk, "mse":mse})
# Save outputs for later plotting
print("Saving outputs for plotting")
if output_dir != None:
try:
os.makedirs(output_dir,exist_ok=True)
print('Created output directory')
except OSError:
pass
results = { "risk": risk, "sizes": sizes, "spearman": spearman, "size-stratified risk": stratified_risk, "mse": mse }
results.update(raw_images_dict)
with open(results_fname, 'wb') as handle:
pkl.dump(results, handle, protocol=pkl.HIGHEST_PROTOCOL)
print(f'Results saved to file {results_fname}!')
print(f"Done with {str(params)}")