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mnist_baseline_drp.py
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mnist_baseline_drp.py
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# run as python trainer_minist.py with "observe=False" without sacred logging
# run as python trainer_mnist.py with sacred / neptune logging
# write over config parameters using the command line ie python trainer_mnist.py with "elbo_samples=10"
import os
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
from torch.utils.data import DataLoader
from torchvision import transforms
import numpy as np
from sacred import Experiment
from sacred.observers import FileStorageObserver
from neptune.new.integrations.sacred import NeptuneObserver
import neptune.new as neptune
import ddu_dirty_mnist
from models.mnist import BayesianMnist
from models.baselines import BaselineMnist
from models.baselines import BaselineEnsembleMnist
from models.baselines import BaselineMnistWithDropout
ex = Experiment()
@ex.config
def config():
observe = True
if observe:
token = os.environ.get("NEPTUNE_API_TOKEN")
nep_run = neptune.init(api_token=token, project="pgm/mnistbl")
ex.observers.append(FileStorageObserver("sacred_files"))
ex.observers.append(NeptuneObserver(run=nep_run))
print("*****Observing runs*****")
else:
print("*****Not oberving runs*****")
num_epochs = 100
batch_size = 100
elbo_samples = 4
train_samples = None
dirty_mnist = False
mixture_prior = True
lr = 0.01
cuda = True
model_param = "drp" # "det", "ens",
@ex.automain
def train(elbo_samples: int, batch_size: int, num_epochs: int, lr: float,
cuda: bool, model_param: str, mixture_prior: bool = False, checkpoint_name: str = None,
train_samples: int = None, dirty_mnist: bool = False):
print("starting train")
device = torch.device("cuda" if cuda else "cpu")
device_str = "cuda" if cuda else "cpu"
print(f"{device_str=}")
if dirty_mnist:
train_data = ddu_dirty_mnist.DirtyMNIST("~/data", train=True, download=True,
transform=transforms.Compose([
transforms.Normalize((-0.0651),(0.8897))
]), device=device_str)
test_data = ddu_dirty_mnist.DirtyMNIST("~/data", train=False, download=True,
transform=transforms.Compose([
transforms.Normalize((-0.0651),(0.8897))
]), device=device_str)
else:
train_data = torchvision.datasets.MNIST(root="~/data", train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor()]))
test_data = torchvision.datasets.MNIST(root="~/data", train=False,
download=True,
transform=transforms.Compose([
transforms.ToTensor()]))
print(f"{lr=}")
num_classes = 10
if model_param == "drp":
bl_mnist_drp_model = BaselineMnistWithDropout().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(bl_mnist_drp_model.parameters(), lr=lr)
loader_train = DataLoader(train_data, batch_size=batch_size, shuffle=True, drop_last=True)
loader_test = DataLoader(test_data, batch_size=batch_size, shuffle=True, drop_last=True)
correct_preds = 0
total_acc = 0
for epoch in range(num_epochs): # loop over the dataset multiple times
total_acc = 0
train_loss = 0
print(f"{loader_train=}")
bl_mnist_drp_model= bl_mnist_drp_model.train()
for x_train, y_train in loader_train:
x_train = x_train.reshape(batch_size, -1).to(device)
y_train = y_train.to(device)
optimizer.zero_grad()
output = bl_mnist_drp_model(x_train)
loss = criterion(output, y_train)
loss.backward()
optimizer.step()
train_loss += loss.item()
y_train_preds = output.max(1).indices
# print(f"{y_train_preds.shape=}")
# print(f"{y_train.shape=}")
correct_preds = sum(y_train_preds == y_train)
# print(f"{y_train_preds.shape=}")
total_acc += correct_preds/len(y_train)
# print(f"{total_acc=}")
epoch_accuracy = total_acc / len(loader_train)
print("epoch number: {} TRAIN loss: {} ".format(epoch, train_loss))
print("epoch number: {} TRAIN accuracy: {} ".format(epoch, epoch_accuracy))
ex.log_scalar("train loss", loss.item())
ex.log_scalar("train accuracy", epoch_accuracy)
ex.log_scalar("train error", 1 - epoch_accuracy)
# print('Finished Training')
test_loss = 0
total_test_acc = 0
bl_mnist_drp_model= bl_mnist_drp_model.eval()
for x_test, y_test in loader_test:
x_test = x_test.reshape(batch_size, -1).to(device)
y_test = y_test.to(device)
# optimizer.zero_grad()
output = bl_mnist_drp_model(x_test)
loss = criterion(output, y_test)
# loss.backward()
# optimizer.step()
test_loss += loss.item()
y_test_preds = output.max(1).indices
# print(f"{y_test_preds.shape=}")
# print(f"{y_test.shape=}")
correct_preds = sum(y_test_preds == y_test)
# print(f"{y_test_preds.shape=}")
total_test_acc += correct_preds/len(y_test)
# print(f"{total_test_acc=}")
epoch_accuracy = total_test_acc / len(loader_test)
print("epoch number: {} TEST loss: {} ".format(epoch, test_loss))
print("epoch number: {} TEST accuracy: {} ".format(epoch, epoch_accuracy))
ex.log_scalar("test loss", loss.item())
ex.log_scalar("test accuracy", epoch_accuracy)
ex.log_scalar("test error", 1 - epoch_accuracy)
print('Finished')
# num_classes = 10
# if train_samples != None:
# subset = list(range(0, train_samples))
# train_data = torch.utils.data.Subset(train_data, subset)
# loader_train = DataLoader(train_data, batch_size=batch_size, shuffle=True, drop_last=True)
# loader_test = DataLoader(test_data, batch_size=batch_size, shuffle=True, drop_last=True)
# num_train_batches = len(loader_train)
# num_test_batches = len(loader_test)
# for epoch in range(num_epochs):
# train_loss = 0
# total_accuracy = 0
# num_train_batches = len(loader_train)
# for x_train, y_train in loader_train:
# x_train = x_train.reshape(batch_size, -1).to(device)
# y_train = y_train.to(device)
# optimiser.zero_grad()
# loss, accuracy = model.energy_loss(
# x_train, y_train, num_train_batches, num_classes, elbo_samples)
# loss.backward()
# optimiser.step()
# train_loss += loss.item()
# total_accuracy += accuracy
# epoch_accuracy = total_accuracy / num_train_batches
# print("epoch number: {} TRAIN loss: {} ".format(epoch, train_loss))
# print("epoch number: {} TRAIN accuracy: {} ".format(epoch, epoch_accuracy))
# ex.log_scalar("train loss", loss.item())
# ex.log_scalar("train accuracy", epoch_accuracy)
# ex.log_scalar("train error", 1 - epoch_accuracy)
# test_loss = 0
# total_test_accuracy = 0
# total_entropy = 0
# num_test_batches = len(loader_test)
# for x_test, y_test in loader_test:
# x_test = x_test.reshape(batch_size, -1).to(device)
# y_test = y_test.to(device)
# test_loss, _ = model.energy_loss(
# x_test, y_test, num_test_batches, num_classes, elbo_samples)
# test_loss += test_loss.item()
# softmax_averaged, entropy = model.inference(x_test, 10, elbo_samples, batch_size)
# pred = softmax_averaged.argmax(axis=1)
# accuracy = sum(pred == y_test.to("cpu").numpy())
# total_test_accuracy += (accuracy / batch_size)
# total_entropy += np.sum(entropy)
# average_test_acc = total_test_accuracy/num_test_batches
# print(
# "epoch number {} TEST loss: {} ".format(epoch, test_loss))
# print(
# "epoch number {} TEST acc: {} ".format(
# epoch, average_test_acc.item()))
# print("epoch number {} entropy {}".format(epoch, total_entropy))
# ex.log_scalar("test loss", test_loss.item())
# ex.log_scalar("test accuracy", average_test_acc)
# ex.log_scalar("test error", 1 - average_test_acc)
# ex.log_scalar("test entropy", total_entropy )
# if checkpoint_name is not None:
# torch.save(model.state_dict(), checkpoint_name + ".checkpoint")
nep_run.stop()