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utils.py
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utils.py
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import numpy as np
from collections import namedtuple
import torch
from torch import nn
import torchvision
from torch.optim.optimizer import Optimizer, required
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
################################################################
## Components from https://github.com/davidcpage/cifar10-fast ##
################################################################
#####################
# data preprocessing
#####################
# equals np.mean(train_set.train_data, axis=(0,1,2))/255
cifar10_mean = (0.4914, 0.4822, 0.4465)
# equals np.std(train_set.train_data, axis=(0,1,2))/255
cifar10_std = (0.2471, 0.2435, 0.2616)
def pad(x, border=4):
return np.pad(x, [(0, 0), (border, border), (border, border), (0, 0)], mode='reflect')
def transpose(x, source='NHWC', target='NCHW'):
return x.transpose([source.index(d) for d in target])
#####################
# data augmentation
#####################
class Crop(namedtuple('Crop', ('h', 'w'))):
def __call__(self, x, x0, y0):
return x[:, y0:y0+self.h, x0:x0+self.w]
def options(self, x_shape):
C, H, W = x_shape
return {'x0': range(W+1-self.w), 'y0': range(H+1-self.h)}
def output_shape(self, x_shape):
C, H, W = x_shape
return (C, self.h, self.w)
class FlipLR(namedtuple('FlipLR', ())):
def __call__(self, x, choice):
return x[:, :, ::-1].copy() if choice else x
def options(self, x_shape):
return {'choice': [True, False]}
class Cutout(namedtuple('Cutout', ('h', 'w'))):
def __call__(self, x, x0, y0):
x = x.copy()
x[:, y0:y0+self.h, x0:x0+self.w].fill(0.0)
return x
def options(self, x_shape):
C, H, W = x_shape
return {'x0': range(W+1-self.w), 'y0': range(H+1-self.h)}
class Transform():
def __init__(self, dataset, transforms):
self.dataset, self.transforms = dataset, transforms
self.choices = None
def __len__(self):
return len(self.dataset)
def __getitem__(self, index):
data, labels = self.dataset[index]
for choices, f in zip(self.choices, self.transforms):
args = {k: v[index] for (k, v) in choices.items()}
data = f(data, **args)
return data, labels
def set_random_choices(self):
self.choices = []
x_shape = self.dataset[0][0].shape
N = len(self)
for t in self.transforms:
options = t.options(x_shape)
x_shape = t.output_shape(x_shape) if hasattr(
t, 'output_shape') else x_shape
self.choices.append({k: np.random.choice(v, size=N)
for (k, v) in options.items()})
#####################
# dataset
#####################
def cifar10(root):
train_set = torchvision.datasets.CIFAR10(
root=root, train=True, download=True)
test_set = torchvision.datasets.CIFAR10(
root=root, train=False, download=True)
return {
'train': {'data': train_set.data, 'labels': train_set.targets},
'test': {'data': test_set.data, 'labels': test_set.targets}
}
#####################
# data loading
#####################
class Batches():
def __init__(self, dataset, batch_size, shuffle, set_random_choices=False, num_workers=0, drop_last=False):
self.dataset = dataset
self.batch_size = batch_size
self.set_random_choices = set_random_choices
self.dataloader = torch.utils.data.DataLoader(
dataset, batch_size=batch_size, num_workers=num_workers, pin_memory=True, shuffle=shuffle, drop_last=drop_last
)
def __iter__(self):
if self.set_random_choices:
self.dataset.set_random_choices()
return ({'input': x.to(device).half(), 'target': y.to(device).long()} for (x, y) in self.dataloader)
def __len__(self):
return len(self.dataloader)
#####################
# new optimizer
#####################
class SGD_GCC(Optimizer):
def __init__(self, params, lr=required, momentum=0, dampening=0,
weight_decay=0, nesterov=False):
if lr is not required and lr < 0.0:
raise ValueError("Invalid learning rate: {}".format(lr))
if momentum < 0.0:
raise ValueError("Invalid momentum value: {}".format(momentum))
if weight_decay < 0.0:
raise ValueError(
"Invalid weight_decay value: {}".format(weight_decay))
defaults = dict(lr=lr, momentum=momentum, dampening=dampening,
weight_decay=weight_decay, nesterov=nesterov)
if nesterov and (momentum <= 0 or dampening != 0):
raise ValueError(
"Nesterov momentum requires a momentum and zero dampening")
super(SGD_GCC, self).__init__(params, defaults)
def __setstate__(self, state):
super(SGD_GCC, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('nesterov', False)
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
weight_decay = group['weight_decay']
momentum = group['momentum']
dampening = group['dampening']
nesterov = group['nesterov']
for p in group['params']:
if p.grad is None:
continue
d_p = p.grad.data
if weight_decay != 0:
d_p.add_(weight_decay, p.data)
# GC operation for Conv layers
if len(list(d_p.size())) > 3:
d_p.add_(-d_p.mean(dim=tuple(range(1,
len(list(d_p.size())))), keepdim=True))
if momentum != 0:
param_state = self.state[p]
if 'momentum_buffer' not in param_state:
buf = param_state['momentum_buffer'] = torch.clone(
d_p).detach()
else:
buf = param_state['momentum_buffer']
buf.mul_(momentum).add_(1 - dampening, d_p)
if nesterov:
d_p = d_p.add(momentum, buf)
else:
d_p = buf
p.data.add_(-group['lr'], d_p)
return loss
class SGD_GC(Optimizer):
def __init__(self, params, lr=required, momentum=0, dampening=0,
weight_decay=0, nesterov=False):
if lr is not required and lr < 0.0:
raise ValueError("Invalid learning rate: {}".format(lr))
if momentum < 0.0:
raise ValueError("Invalid momentum value: {}".format(momentum))
if weight_decay < 0.0:
raise ValueError(
"Invalid weight_decay value: {}".format(weight_decay))
defaults = dict(lr=lr, momentum=momentum, dampening=dampening,
weight_decay=weight_decay, nesterov=nesterov)
if nesterov and (momentum <= 0 or dampening != 0):
raise ValueError(
"Nesterov momentum requires a momentum and zero dampening")
super(SGD_GC, self).__init__(params, defaults)
def __setstate__(self, state):
super(SGD_GC, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('nesterov', False)
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
weight_decay = group['weight_decay']
momentum = group['momentum']
dampening = group['dampening']
nesterov = group['nesterov']
for p in group['params']:
if p.grad is None:
continue
d_p = p.grad.data
if weight_decay != 0:
d_p.add_(weight_decay, p.data)
# GC operation for Conv layers and FC layers
if len(list(d_p.size())) > 1:
d_p.add_(-d_p.mean(dim=tuple(range(1,
len(list(d_p.size())))), keepdim=True))
if momentum != 0:
param_state = self.state[p]
if 'momentum_buffer' not in param_state:
buf = param_state['momentum_buffer'] = torch.clone(
d_p).detach()
else:
buf = param_state['momentum_buffer']
buf.mul_(momentum).add_(1 - dampening, d_p)
if nesterov:
d_p = d_p.add(momentum, buf)
else:
d_p = buf
p.data.add_(-group['lr'], d_p)
return loss