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main.py
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main.py
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#!/usr/bin/env python3
# Copyright 2021 Alexander Meulemans, Matilde Tristany Farinha, Javier Garcia Gordonez
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import random
import torch
import torchvision
import torchvision.transforms as transforms
from torchvision import datasets
from torch.utils.data import DataLoader
from utils.utils import FastMNIST, FastFashionMNIST
from utils.train import train, train_bp
from utils.args import parse_cmd_arguments
from utils import builders, utils
from tensorboardX import SummaryWriter
import os.path
import pickle
def run():
"""
- Parsing command-line arguments
- Creating synthetic regression data
- Initiating training process
- Testing final network
"""
args = parse_cmd_arguments()
torch.manual_seed(args.random_seed)
torch.cuda.manual_seed_all(args.random_seed)
np.random.seed(args.random_seed)
random.seed(args.random_seed)
if args.cuda_deterministic:
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
use_cuda = (not args.no_cuda) and torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
if use_cuda:
torch.set_default_tensor_type('torch.cuda.FloatTensor')
print('Using cuda: ' + str(use_cuda))
if args.double_precision:
torch.set_default_dtype(torch.float64)
if args.dataset in ['mnist', 'mnist_autoencoder']:
if torchvision.__version__ != '0.9.1':
datasets.MNIST.resources = [
(
'https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz',
'f68b3c2dcbeaaa9fbdd348bbdeb94873'),
(
'https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz',
'd53e105ee54ea40749a09fcbcd1e9432'),
(
'https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz',
'9fb629c4189551a2d022fa330f9573f3'),
(
'https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz',
'ec29112dd5afa0611ce80d1b7f02629c')
]
if args.dataset == 'mnist':
print('### Training on MNIST ###')
elif args.dataset == 'mnist_autoencoder':
print('### Training on MNIST Autoencoder ###')
if args.multiple_hpsearch:
data_dir = '../../../../../data'
elif args.hpsearch:
data_dir = '../../../../../data'
else:
data_dir = './data'
if args.no_preprocessing_mnist:
print('This option is deprecated.')
train_dataset = FastMNIST(data_dir, device, args.double_precision, train=True, download=True)
if args.no_val_set:
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=0)
val_loader = None
else:
if torch.__version__ == '1.4.0': # quickfix to avoid error
trainset, valset = torch.utils.data.random_split(train_dataset,
[55000, 5000])
else:
if (torch.__version__ == '1.8.1' or torch.__version__ == '1.8.1+cu102')\
and device.type == 'cuda':
g_cuda = torch.Generator(device='cuda:0')
else:
g_cuda = torch.Generator(device=device)
trainset, valset = torch.utils.data.random_split(train_dataset,
[55000, 5000],
g_cuda)
train_loader = torch.utils.data.DataLoader(trainset,
batch_size=args.batch_size,
shuffle=True, num_workers=0)
val_loader = torch.utils.data.DataLoader(valset,
batch_size=args.batch_size,
shuffle=False, num_workers=0)
test_dataset = FastMNIST(data_dir, device, args.double_precision, train=False, download=True)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=False, num_workers=0)
elif args.dataset == 'fashion_mnist':
print('### Training on Fashion-MNIST ###')
if args.multiple_hpsearch:
data_dir = '../../../../../data'
elif args.hpsearch:
data_dir = '../../../../../data'
else:
data_dir = './data'
train_dataset = FastFashionMNIST(data_dir, device, args.double_precision,
train=True, download=True)
if args.no_val_set:
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=0)
val_loader = None
else:
if torch.__version__ == '1.4.0': # quickfix to avoid error
trainset, valset = torch.utils.data.random_split(train_dataset,
[55000, 5000])
else:
if (torch.__version__ == '1.8.1' or torch.__version__ == '1.8.1+cu102') \
and device.type == 'cuda':
g_cuda = torch.Generator(device='cuda:0')
else:
g_cuda = torch.Generator(device=device)
trainset, valset = torch.utils.data.random_split(train_dataset,
[55000, 5000],
g_cuda)
train_loader = torch.utils.data.DataLoader(trainset,
batch_size=args.batch_size,
shuffle=True, num_workers=0)
val_loader = torch.utils.data.DataLoader(valset,
batch_size=args.batch_size,
shuffle=False, num_workers=0)
test_dataset = FastFashionMNIST(data_dir, device, args.double_precision,
train=False, download=True)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=False, num_workers=0)
elif args.dataset == 'cifar10':
print('### Training on CIFAR10 ###')
if args.multiple_hpsearch:
data_dir = '../../../../../data'
elif args.hpsearch:
data_dir = '../../../../../data'
else:
data_dir = './data'
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset_total = torchvision.datasets.CIFAR10(root=data_dir,
train=True,
download=True,
transform=transform)
if args.no_val_set:
train_loader = torch.utils.data.DataLoader(trainset_total,
batch_size=args.batch_size,
shuffle=True,
num_workers=0)
val_loader = None
else:
if (torch.__version__ == '1.8.1' or torch.__version__ == '1.8.1+cu102') \
and device.type == 'cuda':
g_cuda = torch.Generator(device='cuda:0')
else:
g_cuda = torch.Generator(device=device)
trainset, valset = torch.utils.data.random_split(trainset_total,
[45000, 5000],
g_cuda)
train_loader = torch.utils.data.DataLoader(trainset,
batch_size=args.batch_size,
shuffle=True, num_workers=0)
val_loader = torch.utils.data.DataLoader(valset,
batch_size=args.batch_size,
shuffle=False, num_workers=0)
testset = torchvision.datasets.CIFAR10(root=data_dir, train=False,
download=True,
transform=transform)
test_loader = torch.utils.data.DataLoader(testset,
batch_size=args.batch_size,
shuffle=False, num_workers=0)
elif args.dataset == 'student_teacher':
print('### Training a student_teacher regression model ###')
torch.set_default_tensor_type('torch.FloatTensor')
if args.double_precision:
torch.set_default_dtype(torch.float64)
if not args.load_ST_dataset:
train_x, test_x, val_x, train_y, test_y, val_y = \
builders.generate_data_from_teacher(
n_in=args.size_input, n_out=args.size_output,
n_hidden=[1000, 1000, 1000, 1000], device=device,
num_train=args.num_train, num_test=args.num_test,
num_val=args.num_val,
args=args, activation='relu')
else:
train_x = np.load('./data/train_x.npy')
test_x = np.load('./data/test_x.npy')
val_x = np.load('./data/val_x.npy')
train_y = np.load('./data/train_y.npy')
test_y = np.load('./data/test_y.npy')
val_y = np.load('./data/val_y.npy')
if use_cuda:
torch.set_default_tensor_type('torch.cuda.FloatTensor')
if args.double_precision:
torch.set_default_dtype(torch.float64)
train_loader = DataLoader(utils.RegressionDataset(train_x, train_y,
args.double_precision),
batch_size=args.batch_size, shuffle=True)
test_loader = DataLoader(utils.RegressionDataset(test_x, test_y,
args.double_precision),
batch_size=args.batch_size, shuffle=False)
if args.no_val_set:
val_loader = None
else:
val_loader = DataLoader(utils.RegressionDataset(val_x, val_y,
args.double_precision),
batch_size=args.batch_size, shuffle=False)
else:
raise ValueError('The provided dataset {} is not supported.'.format(
args.dataset
))
if args.log_interval is None:
args.log_interval = max(1, int(len(train_loader)/100))
if args.save_logs:
writer = SummaryWriter(logdir=args.out_dir)
else:
writer = None
summary = utils.setup_summary_dict(args)
net = builders.build_network(args)
net.to(device)
if (args.network_type == "DFC" and not args.ndi) or (args.network_type == "TPDI" and not net._fast_di and not net._ndi):
args.save_convergence = True
print('Saving how many samples converge/diverge/neither.')
else:
args.save_convergence = False
print('Not saving convergence results.')
if not args.network_type in ('BP', 'BPConv'):
summary = train(args=args,
device=device,
train_loader=train_loader,
net=net,
writer=writer,
test_loader=test_loader,
summary=summary,
val_loader=val_loader)
else:
summary = train_bp(args=args, device=device, train_loader=train_loader, net=net, writer=writer,
test_loader=test_loader, summary=summary, val_loader=val_loader)
if (args.save_df and args.network_type != 'BP'):
summary['bp_angles'] = net.bp_angles
summary['gnt_angles'] = net.gnt_angles
summary['gn_angles'] = net.gn_angles
summary['gn_angles_network'] = net.gn_angles_network
summary['gnt_angles_network'] = net.gnt_angles_network
summary['nullspace_relative_norm_angles'] = net.nullspace_relative_norm
if args.network_type == 'DFC':
summary['ndi_angles'] = net.ndi_angles
summary['ndi_angles_network'] = net.ndi_angles_network
summary['condition_gn_angles'] = net.condition_gn
summary['condition_gn_angles_init'] = net.condition_gn_init
summary['jac_transpose_angles'] = net.jac_transpose_angles
summary['jac_transpose_angles_init'] = net.jac_transpose_angles_init
summary['jac_pinv_angles'] = net.jac_pinv_angles
summary['jac_pinv_angles_init'] = net.jac_pinv_angles_init
if summary['finished'] == 0:
summary['finished'] = 1
utils.save_summary_dict(args, summary)
if writer is not None:
writer.close()
filename = os.path.join(args.out_dir, 'results.pickle')
with open(filename, 'wb') as f:
pickle.dump(summary, f)
return summary
if __name__ == '__main__':
run()