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data.py
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from abc import ABC, abstractmethod
from collections import Iterator, OrderedDict
from functools import reduce
import os
import math
import torch
from torch.utils.data import (
Dataset,
ConcatDataset,
Subset,
DataLoader,
RandomSampler,
)
from torch.utils.data.dataloader import default_collate
import torchvision
import torchvision.transforms as transforms
from tensorboardX import SummaryWriter
from models.base import Model
from models.ndpm_model import NdpmModel
# =====================
# Base Classes and ABCs
# =====================
class DataScheduler(Iterator):
def __init__(self, config):
self.config = config
self.schedule = config['data_schedule']
self.datasets = OrderedDict()
self.eval_datasets = OrderedDict()
self.total_step = 0
self.stage = -1
# Prepare datasets
for i, stage in enumerate(self.schedule):
stage_total = 0
for j, subset in enumerate(stage['subsets']):
dataset_name, subset_name = subset
if dataset_name in self.datasets:
stage_total += len(
self.datasets[dataset_name].subsets[subset_name])
continue
self.datasets[dataset_name] = DATASET[dataset_name](config)
self.eval_datasets[dataset_name] = DATASET[dataset_name](
config, train=False
)
stage_total += len(
self.datasets[dataset_name].subsets[subset_name])
if 'steps' in stage:
self.total_step += stage['steps']
elif 'epochs' in stage:
self.total_step += int(
stage['epochs'] * (stage_total // config['batch_size']))
if stage_total % config['batch_size'] > 0:
self.total_step += 1
elif 'steps' in stage:
self.total_step += sum(stage['steps'])
else:
self.total_step += stage_total // config['batch_size']
self.iterator = None
def __next__(self):
try:
if self.iterator is None:
raise StopIteration
data = next(self.iterator)
except StopIteration:
# Progress to next stage
self.stage += 1
print('\nProgressing to stage %d' % self.stage)
if self.stage >= len(self.schedule):
raise StopIteration
stage = self.schedule[self.stage]
collate_fn = list(self.datasets.values())[0].collate_fn
subsets = []
for dataset_name, subset_name in stage['subsets']:
subsets.append(
self.datasets[dataset_name].subsets[subset_name])
dataset = ConcatDataset(subsets)
# Determine sampler
if 'samples' in stage:
sampler = RandomSampler(
dataset,
replacement=True,
num_samples=stage['samples']
)
elif 'steps' in stage:
sampler = RandomSampler(
dataset,
replacement=True,
num_samples=stage['steps'] * self.config['batch_size']
)
elif 'epochs' in stage:
sampler = RandomSampler(
dataset,
replacement=True,
num_samples=(int(stage['epochs'] * len(dataset))
+ len(dataset) % self.config['batch_size'])
)
else:
sampler = RandomSampler(dataset)
self.iterator = iter(DataLoader(
dataset,
batch_size=self.config['batch_size'],
num_workers=self.config['num_workers'],
collate_fn=collate_fn,
sampler=sampler,
drop_last=True,
))
data = next(self.iterator)
# Get next data
return data[0], data[1], self.stage
def __len__(self):
return self.total_step
def eval(self, model, writer, step, eval_title):
for i, eval_dataset in enumerate(self.eval_datasets.values()):
# NOTE: we assume that each task is a dataset in multi-dataset
# episode
eval_dataset.eval(
model, writer, step, eval_title,
task_index=(i if len(self.eval_datasets) > 1 else None)
)
class BaseDataset(Dataset, ABC):
name = 'base'
def __init__(self, config, train=True):
self.config = config
self.subsets = {}
self.train = train
def eval(self, model: Model, writer: SummaryWriter, step, eval_title,
task_index=None):
if self.config['eval_d']:
self._eval_discriminative_model(model, writer, step, eval_title)
if self.config['eval_g']:
self._eval_generative_model(model, writer, step, eval_title)
if 'eval_t' in self.config and self.config['eval_t']:
self._eval_hard_assign(
model, writer, step, eval_title,
task_index=task_index
)
@abstractmethod
def _eval_hard_assign(
self,
model,
writer: SummaryWriter,
step, eval_titlem, task_index=None):
raise NotImplementedError
@abstractmethod
def _eval_discriminative_model(
self,
model,
writer: SummaryWriter,
step, eval_title):
raise NotImplementedError
@abstractmethod
def _eval_generative_model(
self,
model: Model,
writer: SummaryWriter,
step, eval_title):
raise NotImplementedError
def collate_fn(self, batch):
return default_collate(batch)
class CustomSubset(Subset):
def __init__(self, dataset, indices, transform):
super().__init__(dataset, indices)
self.transform = transform
def __getitem__(self, idx):
return self.transform(self.dataset[self.indices[idx]])
# ================
# Generic Datasets
# ================
class ClassificationDataset(BaseDataset, ABC):
num_classes = NotImplemented
targets = NotImplemented
def __init__(self, config, train=True):
super().__init__(config, train)
def _eval_hard_assign(
self,
model: NdpmModel,
writer: SummaryWriter,
step, eval_title, task_index=None,
):
tasks = [
tuple([c for _, c in t['subsets']])
for t in self.config['data_schedule']
]
if task_index is not None:
tasks = [tasks[task_index]]
k = 5
# Overall counts
total_overall = 0.
correct_1_overall = 0.
correct_k_overall = 0.
correct_expert_overall = 0.
correct_assign_overall = 0.
# Loop over each task
for task_index, task_subsets in enumerate(tasks, task_index or 0):
# Task-wise counts
total = 0.
correct_1 = 0.
correct_k = 0.
correct_expert = 0.
correct_assign = 0.
# Loop over each subset
for subset in task_subsets:
data = DataLoader(
self.subsets[subset],
batch_size=self.config['eval_batch_size'],
num_workers=self.config['eval_num_workers'],
collate_fn=self.collate_fn,
)
for x, y in iter(data):
with torch.no_grad():
logits, assignments = model(
x, return_assignments=True)
total += x.size(0)
correct_assign += (assignments == task_index).float().sum()
if not self.config['disable_d']:
# NDPM accuracy
_, pred_topk = logits.topk(k, dim=1)
correct_topk = (
pred_topk.cpu()
== y.unsqueeze(1).expand_as(pred_topk)
).float()
correct_1 += correct_topk[:, :1].view(-1).sum()
correct_k += correct_topk[:, :k].view(-1).sum()
# Hard-assigned expert accuracy
num_experts = len(model.ndpm.experts) - 1
if num_experts > task_index:
expert = model.ndpm.experts[task_index + 1]
with torch.no_grad():
logits = expert(x)
correct = (y == logits.argmax(dim=1).cpu()).float()
correct_expert += correct.sum()
# Add to overall counts
total_overall += total
correct_1_overall += correct_1
correct_k_overall += correct_k
correct_expert_overall += correct_expert
correct_assign_overall += correct_assign
# Task-wise accuracies
accuracy_1 = correct_1 / total
accuracy_k = correct_k / total
accuracy_expert = correct_expert / total
accuracy_assign = correct_assign / total
# Summarize task-wise accuracies
writer.add_scalar(
'accuracy_1/%s/%s/%s' % (eval_title, self.name, task_index),
accuracy_1, step
)
writer.add_scalar(
'accuracy_%s/%s/%s/%s' %
(k, eval_title, self.name, task_index), accuracy_k, step
)
writer.add_scalar(
'accuracy_expert/%s/%s/%s' %
(eval_title, self.name, task_index), accuracy_expert, step
)
writer.add_scalar(
'accuracy_assign/%s/%s/%s' %
(eval_title, self.name, task_index), accuracy_assign, step
)
# Overall accuracies
accuracy_1 = correct_1_overall / total_overall
accuracy_k = correct_k_overall / total_overall
accuracy_expert = correct_expert_overall / total_overall
accuracy_assign = correct_assign_overall / total_overall
# Summarize overall accuracies
writer.add_scalar(
'accuracy_1/%s/%s/overall' % (eval_title, self.name),
accuracy_1, step
)
writer.add_scalar(
'accuracy_%s/%s/%s/overall' % (k, eval_title, self.name),
accuracy_k, step
)
writer.add_scalar(
'accuracy_expert/%s/%s/overall' %
(eval_title, self.name), accuracy_expert, step
)
writer.add_scalar(
'accuracy_assign/%s/%s/overall' %
(eval_title, self.name), accuracy_assign, step
)
def _eval_discriminative_model(
self,
model: Model,
writer: SummaryWriter,
step, eval_title):
training = model.training
model.eval()
K = 5
totals = []
corrects_1 = []
corrects_k = []
# Accuracy of each subset
for subset_name, subset in self.subsets.items():
data = DataLoader(
subset,
batch_size=self.config['eval_batch_size'],
num_workers=self.config['eval_num_workers'],
collate_fn=self.collate_fn,
)
total = 0.
correct_1 = 0.
correct_k = 0.
for x, y in iter(data):
b = x.size(0)
with torch.no_grad():
logits = model(x).view(b, -1)
# [B, K]
_, pred_topk = logits.topk(K, dim=1)
correct_topk = (
pred_topk.cpu() == y.view(b, -1).expand_as(pred_topk)
).float()
correct_1 += correct_topk[:, :1].view(-1).cpu().sum()
correct_k += correct_topk[:, :K].view(-1).cpu().sum()
total += x.size(0)
totals.append(total)
corrects_1.append(correct_1)
corrects_k.append(correct_k)
accuracy_1 = correct_1 / total
accuracy_k = correct_k / total
writer.add_scalar(
'accuracy_1/%s/%s/%s' % (eval_title, self.name, subset_name),
accuracy_1, step
)
writer.add_scalar(
'accuracy_%d/%s/%s/%s' %
(K, eval_title, self.name, subset_name), accuracy_k, step
)
# Overall accuracy
total = sum(totals)
correct_1 = sum(corrects_1)
correct_k = sum(corrects_k)
accuracy_1 = correct_1 / total
accuracy_k = correct_k / total
writer.add_scalar(
'accuracy_1/%s/%s/overall' % (eval_title, self.name),
accuracy_1, step
)
writer.add_scalar(
'accuracy_%d/%s/%s/overall' % (K, eval_title, self.name),
accuracy_k, step
)
model.train(training)
def _eval_generative_model(
self,
model: Model,
writer: SummaryWriter,
step, eval_title):
# change the model to eval mode
training = model.training
z_samples = model.config['z_samples']
model.eval()
model.config['z_samples'] = 16
# evaluate generative model on each subset
subset_counts = []
subset_cumulative_bpds = []
for subset_name, subset in self.subsets.items():
data = DataLoader(
subset,
batch_size=self.config['eval_batch_size'],
num_workers=self.config['eval_num_workers'],
collate_fn=self.collate_fn,
)
subset_count = 0
subset_cumulative_bpd = 0
# evaluate on a subset
for x, _ in iter(data):
dim = reduce(lambda x, y: x * y, x.size()[1:])
with torch.no_grad():
ll = model(x)
bpd = -ll / math.log(2) / dim
subset_count += x.size(0)
subset_cumulative_bpd += bpd.sum()
# append the subset evaluation result
subset_counts.append(subset_count)
subset_cumulative_bpds.append(subset_cumulative_bpd)
subset_bpd = subset_cumulative_bpd / subset_count
writer.add_scalar(
'bpd/%s/%s/%s' % (eval_title, self.name, subset_name),
subset_bpd, step
)
# Overall accuracy
overall_bpd = sum(subset_cumulative_bpds) / sum(subset_counts)
writer.add_scalar(
'bpd/%s/%s/overall' % (eval_title, self.name), overall_bpd, step
)
# roll back the mode
model.train(training)
model.config['z_samples'] = z_samples
def offset_label(self):
if 'label_offset' not in self.config:
return
if isinstance(self.targets, torch.Tensor):
self.targets += self.config['label_offset'][self.name]
else:
for i in range(len(self.targets)):
self.targets[i] += self.config['label_offset'][self.name]
# =================
# Concrete Datasets
# =================
class MNIST(torchvision.datasets.MNIST, ClassificationDataset):
name = 'mnist'
num_classes = 10
def __init__(self, config, train=True):
# Compose transformation
transform_list = [
transforms.Resize((config['x_h'], config['x_w'])),
transforms.ToTensor(),
]
if config['recon_loss'] == 'bernoulli':
transform_list.append(
lambda x: (torch.rand_like(x) < x).to(torch.float)
)
if config['x_c'] > 1:
transform_list.append(
lambda x: x.expand(config['x_c'], -1, -1)
)
transform = transforms.Compose(transform_list)
# Initialize super classes
torchvision.datasets.MNIST.__init__(
self, root=os.path.join(config['data_root'], 'mnist'),
train=train, transform=transform, download=True)
ClassificationDataset.__init__(self, config, train)
# Create subset for each class
for y in range(self.num_classes):
self.subsets[y] = Subset(
self,
list((self.targets == y).nonzero().squeeze(1).numpy())
)
self.offset_label()
class SVHN(torchvision.datasets.SVHN, ClassificationDataset):
name = 'svhn'
num_classes = 10
def __init__(self, config, train=True):
# Compose transformation
transform_list = [
transforms.Resize((config['x_h'], config['x_w'])),
transforms.ToTensor(),
]
transform = transforms.Compose(transform_list)
# Initialize super classes
split = 'train' if train else 'test'
torchvision.datasets.SVHN.__init__(
self, root=os.path.join(config['data_root'], 'svhn'),
split=split, transform=transform, download=True)
ClassificationDataset.__init__(self, config, train)
# Create subset for each class
self.targets = torch.Tensor(self.labels)
for y in range(self.num_classes):
self.subsets[y] = Subset(
self,
list((self.targets == y
).nonzero().squeeze(1).numpy())
)
self.offset_label()
class CIFAR10(torchvision.datasets.CIFAR10, ClassificationDataset):
name = 'cifar10'
num_classes = 10
def __init__(self, config, train=True):
if config.get('augment_cifar'):
transform = transforms.Compose([
transforms.Resize((config['x_h'], config['x_w'])),
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(15),
transforms.ToTensor(),
])
else:
transform = transforms.Compose([
transforms.Resize((config['x_h'], config['x_w'])),
transforms.ToTensor(),
])
torchvision.datasets.CIFAR10.__init__(
self, root=os.path.join(config['data_root'], 'cifar10'),
train=train, transform=transform, download=True)
ClassificationDataset.__init__(self, config, train)
# Create subset for each class
for y in range(self.num_classes):
self.subsets[y] = Subset(
self,
list((torch.Tensor(self.targets) == y
).nonzero().squeeze(1).numpy())
)
self.offset_label()
class CIFAR100(torchvision.datasets.CIFAR100, ClassificationDataset):
name = 'cifar100'
num_classes = 100
def __init__(self, config, train=True):
if config.get('augment_cifar'):
transform = transforms.Compose([
transforms.Resize((config['x_h'], config['x_w'])),
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(15),
transforms.ToTensor(),
])
else:
transform = transforms.Compose([
transforms.Resize((config['x_h'], config['x_w'])),
transforms.ToTensor(),
])
torchvision.datasets.CIFAR100.__init__(
self, root=os.path.join(config['data_root'], 'cifar100'),
train=train, transform=transform, download=True)
ClassificationDataset.__init__(self, config, train)
# Create subset for each class
for y in range(self.num_classes):
self.subsets[y] = Subset(
self,
list((torch.Tensor(self.targets) == y
).nonzero().squeeze(1).numpy())
)
self.offset_label()
DATASET = {
MNIST.name: MNIST,
SVHN.name: SVHN,
CIFAR10.name: CIFAR10,
CIFAR100.name: CIFAR100,
}