from __future__ import print_function
import os
import os.path
import numpy as np
import random
import pickle
import json
import math
import torch
import torch.utils.data as data
import torchvision
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import torchnet as tnt
import h5py
from PIL import Image
from PIL import ImageEnhance
from pdb import set_trace as breakpoint
# Set the appropriate paths of the datasets here.
_MINI_IMAGENET_DATASET_DIR = './datasets/MiniImagenet'
_IMAGENET_DATASET_DIR = './datasets/IMAGENET/ILSVRC2012'
_IMAGENET_LOWSHOT_BENCHMARK_CATEGORY_SPLITS_PATH = './data/IMAGENET_LOWSHOT_BENCHMARK_CATEGORY_SPLITS.json'
transformtypedict=dict(
Brightness=ImageEnhance.Brightness, Contrast=ImageEnhance.Contrast,
Sharpness=ImageEnhance.Sharpness, Color=ImageEnhance.Color
)
def get_label_ids(class_to_idx, class_names, inside=True):
label_ids = []
if inside:
for cname in class_names:
label_ids.append(class_to_idx[cname])
else:
for cname, clabel in class_to_idx.items():
if cname not in class_names:
label_ids.append(clabel)
return label_ids
class ImageJitter(object):
def __init__(self, transformdict):
self.transforms = [(transformtypedict[k], transformdict[k]) for k in transformdict]
def __call__(self, img):
out = img
randtensor = torch.rand(len(self.transforms))
for i, (transformer, alpha) in enumerate(self.transforms):
r = alpha*(randtensor[i]*2.0 -1.0) + 1
out = transformer(out).enhance(r).convert('RGB')
return out
class Denormalize(object):
def __init__(self, mean, std):
self.mean = mean
self.std = std
def __call__(self, tensor):
for t, m, s in zip(tensor, self.mean, self.std):
t.mul_(s).add_(m)
return tensor
def buildLabelIndex(labels):
label2inds = {}
for idx, label in enumerate(labels):
if label not in label2inds:
label2inds[label] = []
label2inds[label].append(idx)
return label2inds
def load_data(file):
with open(file, 'rb') as fo:
data = pickle.load(fo)
return data
class MiniImageNet(data.Dataset):
def __init__(self, phase='train', do_not_use_random_transf=False):
self.base_folder = 'miniImagenet'
assert(phase=='train' or phase=='val' or phase=='test')
self.phase = phase
self.name = 'MiniImageNet_' + phase
print('Loading mini ImageNet dataset - phase {0}'.format(phase))
file_train_categories_train_phase = os.path.join(
_MINI_IMAGENET_DATASET_DIR,
'miniImageNet_category_split_train_phase_train.pickle')
file_train_categories_val_phase = os.path.join(
_MINI_IMAGENET_DATASET_DIR,
'miniImageNet_category_split_train_phase_val.pickle')
file_train_categories_test_phase = os.path.join(
_MINI_IMAGENET_DATASET_DIR,
'miniImageNet_category_split_train_phase_test.pickle')
file_val_categories_val_phase = os.path.join(
_MINI_IMAGENET_DATASET_DIR,
'miniImageNet_category_split_val.pickle')
file_test_categories_test_phase = os.path.join(
_MINI_IMAGENET_DATASET_DIR,
'miniImageNet_category_split_test.pickle')
if self.phase=='train':
# During training phase we only load the training phase images
# of the training categories (aka base categories).
data_train = load_data(file_train_categories_train_phase)
self.data = data_train['data']
self.labels = data_train['labels']
self.label2ind = buildLabelIndex(self.labels)
self.labelIds = sorted(self.label2ind.keys())
self.num_cats = len(self.labelIds)
self.labelIds_base = self.labelIds
self.num_cats_base = len(self.labelIds_base)
elif self.phase=='val' or self.phase=='test':
if self.phase=='test':
# load data that will be used for evaluating the recognition
# accuracy of the base categories.
data_base = load_data(file_train_categories_test_phase)
# load data that will be use for evaluating the few-shot recogniton
# accuracy on the novel categories.
data_novel = load_data(file_test_categories_test_phase)
else: # phase=='val'
# load data that will be used for evaluating the recognition
# accuracy of the base categories.
data_base = load_data(file_train_categories_val_phase)
# load data that will be use for evaluating the few-shot recogniton
# accuracy on the novel categories.
data_novel = load_data(file_val_categories_val_phase)
self.data = np.concatenate(
[data_base['data'], data_novel['data']], axis=0)
self.labels = data_base['labels'] + data_novel['labels']
self.label2ind = buildLabelIndex(self.labels)
self.labelIds = sorted(self.label2ind.keys())
self.num_cats = len(self.labelIds)
self.labelIds_base = buildLabelIndex(data_base['labels']).keys()
self.labelIds_novel = buildLabelIndex(data_novel['labels']).keys()
self.num_cats_base = len(self.labelIds_base)
self.num_cats_novel = len(self.labelIds_novel)
intersection = set(self.labelIds_base) & set(self.labelIds_novel)
assert(len(intersection) == 0)
else:
raise ValueError('Not valid phase {0}'.format(self.phase))
mean_pix = [x/255.0 for x in [120.39586422, 115.59361427, 104.54012653]]
std_pix = [x/255.0 for x in [70.68188272, 68.27635443, 72.54505529]]
normalize = transforms.Normalize(mean=mean_pix, std=std_pix)
if (self.phase=='test' or self.phase=='val') or (do_not_use_random_transf==True):
self.transform = transforms.Compose([
lambda x: np.asarray(x),
transforms.ToTensor(),
normalize
])
else:
self.transform = transforms.Compose([
transforms.RandomCrop(84, padding=8),
transforms.RandomHorizontalFlip(),
lambda x: np.asarray(x),
transforms.ToTensor(),
normalize
])
def __getitem__(self, index):
img, label = self.data[index], self.labels[index]
# doing this so that it is consistent with all other datasets
# to return a PIL Image
img = Image.fromarray(img)
if self.transform is not None:
img = self.transform(img)
return img, label
def __len__(self):
return len(self.data)
class FewShotDataloader():
def __init__(self,
dataset,
nKnovel=5, # number of novel categories.
nKbase=-1, # number of base categories.
nExemplars=1, # number of training examples per novel category.
nTestNovel=15*5, # number of test examples for all the novel categories.
nTestBase=15*5, # number of test examples for all the base categories.
batch_size=1, # number of training episodes per batch.
num_workers=4,
epoch_size=2000, # number of batches per epoch.
):
self.dataset = dataset
self.phase = self.dataset.phase
max_possible_nKnovel = (self.dataset.num_cats_base if self.phase=='train'
else self.dataset.num_cats_novel)
assert(nKnovel >= 0 and nKnovel < max_possible_nKnovel)
self.nKnovel = nKnovel
max_possible_nKbase = self.dataset.num_cats_base
nKbase = nKbase if nKbase >= 0 else max_possible_nKbase
if self.phase=='train' and nKbase > 0:
nKbase -= self.nKnovel
max_possible_nKbase -= self.nKnovel
assert(nKbase >= 0 and nKbase <= max_possible_nKbase)
self.nKbase = nKbase
self.nExemplars = nExemplars
self.nTestNovel = nTestNovel
self.nTestBase = nTestBase
self.batch_size = batch_size
self.epoch_size = epoch_size
self.num_workers = num_workers
self.is_eval_mode = (self.phase=='test') or (self.phase=='val')
def sampleImageIdsFrom(self, cat_id, sample_size=1):
"""
Samples `sample_size` number of unique image ids picked from the
category `cat_id` (i.e., self.dataset.label2ind[cat_id]).
Args:
cat_id: a scalar with the id of the category from which images will
be sampled.
sample_size: number of images that will be sampled.
Returns:
image_ids: a list of length `sample_size` with unique image ids.
"""
assert(cat_id in self.dataset.label2ind)
assert(len(self.dataset.label2ind[cat_id]) >= sample_size)
# Note: random.sample samples elements without replacement.
return random.sample(self.dataset.label2ind[cat_id], sample_size)
def sampleCategories(self, cat_set, sample_size=1):
"""
Samples `sample_size` number of unique categories picked from the
`cat_set` set of categories. `cat_set` can be either 'base' or 'novel'.
Args:
cat_set: string that specifies the set of categories from which
categories will be sampled.
sample_size: number of categories that will be sampled.
Returns:
cat_ids: a list of length `sample_size` with unique category ids.
"""
if cat_set=='base':
labelIds = self.dataset.labelIds_base
elif cat_set=='novel':
labelIds = self.dataset.labelIds_novel
else:
raise ValueError('Not recognized category set {}'.format(cat_set))
assert(len(labelIds) >= sample_size)
# return sample_size unique categories chosen from labelIds set of
# categories (that can be either self.labelIds_base or self.labelIds_novel)
# Note: random.sample samples elements without replacement.
return random.sample(labelIds, sample_size)
def sample_base_and_novel_categories(self, nKbase, nKnovel):
"""
Samples `nKbase` number of base categories and `nKnovel` number of novel
categories.
Args:
nKbase: number of base categories
nKnovel: number of novel categories
Returns:
Kbase: a list of length 'nKbase' with the ids of the sampled base
categories.
Knovel: a list of lenght 'nKnovel' with the ids of the sampled novel
categories.
"""
if self.is_eval_mode:
assert(nKnovel <= self.dataset.num_cats_novel)
# sample from the set of base categories 'nKbase' number of base
# categories.
Kbase = sorted(self.sampleCategories('base', nKbase))
# sample from the set of novel categories 'nKnovel' number of novel
# categories.
Knovel = sorted(self.sampleCategories('novel', nKnovel))
else:
# sample from the set of base categories 'nKnovel' + 'nKbase' number
# of categories.
cats_ids = self.sampleCategories('base', nKnovel+nKbase)
assert(len(cats_ids) == (nKnovel+nKbase))
# Randomly pick 'nKnovel' number of fake novel categories and keep
# the rest as base categories.
random.shuffle(cats_ids)
Knovel = sorted(cats_ids[:nKnovel])
Kbase = sorted(cats_ids[nKnovel:])
return Kbase, Knovel
def sample_test_examples_for_base_categories(self, Kbase, nTestBase):
"""
Sample `nTestBase` number of images from the `Kbase` categories.
Args:
Kbase: a list of length `nKbase` with the ids of the categories from
where the images will be sampled.
nTestBase: the total number of images that will be sampled.
Returns:
Tbase: a list of length `nTestBase` with 2-element tuples. The 1st
element of each tuple is the image id that was sampled and the
2nd elemend is its category label (which is in the range
[0, len(Kbase)-1]).
"""
Tbase = []
if len(Kbase) > 0:
# Sample for each base category a number images such that the total
# number sampled images of all categories to be equal to `nTestBase`.
KbaseIndices = np.random.choice(
np.arange(len(Kbase)), size=nTestBase, replace=True)
KbaseIndices, NumImagesPerCategory = np.unique(
KbaseIndices, return_counts=True)
for Kbase_idx, NumImages in zip(KbaseIndices, NumImagesPerCategory):
imd_ids = self.sampleImageIdsFrom(
Kbase[Kbase_idx], sample_size=NumImages)
Tbase += [(img_id, Kbase_idx) for img_id in imd_ids]
assert(len(Tbase) == nTestBase)
return Tbase
def sample_train_and_test_examples_for_novel_categories(
self, Knovel, nTestNovel, nExemplars, nKbase):
"""Samples train and test examples of the novel categories.
Args:
Knovel: a list with the ids of the novel categories.
nTestNovel: the total number of test images that will be sampled
from all the novel categories.
nExemplars: the number of training examples per novel category that
will be sampled.
nKbase: the number of base categories. It is used as offset of the
category index of each sampled image.
Returns:
Tnovel: a list of length `nTestNovel` with 2-element tuples. The
1st element of each tuple is the image id that was sampled and
the 2nd element is its category label (which is in the range
[nKbase, nKbase + len(Knovel) - 1]).
Exemplars: a list of length len(Knovel) * nExemplars of 2-element
tuples. The 1st element of each tuple is the image id that was
sampled and the 2nd element is its category label (which is in
the ragne [nKbase, nKbase + len(Knovel) - 1]).
"""
if len(Knovel) == 0:
return [], []
nKnovel = len(Knovel)
Tnovel = []
Exemplars = []
assert((nTestNovel % nKnovel) == 0)
nEvalExamplesPerClass = nTestNovel / nKnovel
for Knovel_idx in range(len(Knovel)):
imd_ids = self.sampleImageIdsFrom(
Knovel[Knovel_idx],
sample_size=(nEvalExamplesPerClass + nExemplars))
imds_tnovel = imd_ids[:nEvalExamplesPerClass]
imds_ememplars = imd_ids[nEvalExamplesPerClass:]
Tnovel += [(img_id, nKbase+Knovel_idx) for img_id in imds_tnovel]
Exemplars += [(img_id, nKbase+Knovel_idx) for img_id in imds_ememplars]
assert(len(Tnovel) == nTestNovel)
assert(len(Exemplars) == len(Knovel) * nExemplars)
random.shuffle(Exemplars)
return Tnovel, Exemplars
def sample_episode(self):
"""Samples a training episode."""
nKnovel = self.nKnovel
nKbase = self.nKbase
nTestNovel = self.nTestNovel
nTestBase = self.nTestBase
nExemplars = self.nExemplars
Kbase, Knovel = self.sample_base_and_novel_categories(nKbase, nKnovel)
Tbase = self.sample_test_examples_for_base_categories(Kbase, nTestBase)
Tnovel, Exemplars = self.sample_train_and_test_examples_for_novel_categories(
Knovel, nTestNovel, nExemplars, nKbase)
# concatenate the base and novel category examples.
Test = Tbase + Tnovel
random.shuffle(Test)
Kall = Kbase + Knovel
return Exemplars, Test, Kall, nKbase
def createExamplesTensorData(self, examples):
"""
Creates the examples image and label tensor data.
Args:
examples: a list of 2-element tuples, each representing a
train or test example. The 1st element of each tuple
is the image id of the example and 2nd element is the
category label of the example, which is in the range
[0, nK - 1], where nK is the total number of categories
(both novel and base).
Returns:
images: a tensor of shape [nExamples, Height, Width, 3] with the
example images, where nExamples is the number of examples
(i.e., nExamples = len(examples)).
labels: a tensor of shape [nExamples] with the category label
of each example.
"""
images = torch.stack(
[self.dataset[img_idx][0] for img_idx, _ in examples], dim=0)
labels = torch.LongTensor([label for _, label in examples])
return images, labels
def get_iterator(self, epoch=0):
rand_seed = epoch
random.seed(rand_seed)
np.random.seed(rand_seed)
def load_function(iter_idx):
Exemplars, Test, Kall, nKbase = self.sample_episode()
Xt, Yt = self.createExamplesTensorData(Test)
Kall = torch.LongTensor(Kall)
if len(Exemplars) > 0:
Xe, Ye = self.createExamplesTensorData(Exemplars)
return Xe, Ye, Xt, Yt, Kall, nKbase
else:
return Xt, Yt, Kall, nKbase
tnt_dataset = tnt.dataset.ListDataset(
elem_list=range(self.epoch_size), load=load_function)
data_loader = tnt_dataset.parallel(
batch_size=self.batch_size,
num_workers=(0 if self.is_eval_mode else self.num_workers),
shuffle=(False if self.is_eval_mode else True))
return data_loader
def __call__(self, epoch=0):
return self.get_iterator(epoch)
def __len__(self):
return (self.epoch_size / self.batch_size)
class ImageNetLowShot(data.Dataset):
def __init__(self,
phase='train',
split='train',
do_not_use_random_transf=False):
self.phase = phase
self.split = split
assert(phase=='train' or phase=='test' or phase=='val')
assert(split=='train' or split=='val')
self.name = 'ImageNetLowShot_Phase_' + phase + '_Split_' + split
print('Loading ImageNet dataset (for few-shot benchmark) - phase {0}'.
format(phase))
#***********************************************************************
with open(_IMAGENET_LOWSHOT_BENCHMARK_CATEGORY_SPLITS_PATH, 'r') as f:
label_idx = json.load(f)
base_classes = label_idx['base_classes']
novel_classes_val_phase = label_idx['novel_classes_1']
novel_classes_test_phase = label_idx['novel_classes_2']
#***********************************************************************
transforms_list = []
if (phase!='train') or (do_not_use_random_transf==True):
transforms_list.append(transforms.Scale(256))
transforms_list.append(transforms.CenterCrop(224))
else:
transforms_list.append(transforms.RandomSizedCrop(224))
jitter_params = {'Brightness': 0.4, 'Contrast': 0.4, 'Color': 0.4}
transforms_list.append(ImageJitter(jitter_params))
transforms_list.append(transforms.RandomHorizontalFlip())
transforms_list.append(lambda x: np.asarray(x))
transforms_list.append(transforms.ToTensor())
mean_pix = [0.485, 0.456, 0.406]
std_pix = [0.229, 0.224, 0.225]
transforms_list.append(transforms.Normalize(mean=mean_pix, std=std_pix))
self.transform = transforms.Compose(transforms_list)
traindir = os.path.join(_IMAGENET_DATASET_DIR, 'train')
valdir = os.path.join(_IMAGENET_DATASET_DIR, 'val')
self.data = datasets.ImageFolder(
traindir if split=='train' else valdir, self.transform)
self.labels = [item[1] for item in self.data.imgs]
self.label2ind = buildLabelIndex(self.labels)
self.labelIds = sorted(self.label2ind.keys())
self.num_cats = len(self.labelIds)
assert(self.num_cats==1000)
self.labelIds_base = base_classes
self.num_cats_base = len(self.labelIds_base)
if self.phase=='val' or self.phase=='test':
self.labelIds_novel = (
novel_classes_val_phase if (self.phase=='val') else
novel_classes_test_phase)
self.num_cats_novel = len(self.labelIds_novel)
intersection = set(self.labelIds_base) & set(self.labelIds_novel)
assert(len(intersection) == 0)
def __getitem__(self, index):
img, label = self.data[index]
return img, label
def __len__(self):
return len(self.data)
class ImageNet(data.Dataset):
def __init__(self, split='train'):
self.split = split
assert(split=='train' or split=='val')
self.name = 'ImageNet_Split_' + split
print('Loading ImageNet dataset - split {0}'.format(split))
transforms_list = []
transforms_list.append(transforms.Scale(256))
transforms_list.append(transforms.CenterCrop(224))
transforms_list.append(lambda x: np.asarray(x))
transforms_list.append(transforms.ToTensor())
mean_pix = [0.485, 0.456, 0.406]
std_pix = [0.229, 0.224, 0.225]
transforms_list.append(transforms.Normalize(mean=mean_pix, std=std_pix))
self.transform = transforms.Compose(transforms_list)
traindir = os.path.join(_IMAGENET_DATASET_DIR, 'train')
valdir = os.path.join(_IMAGENET_DATASET_DIR, 'val')
self.data = datasets.ImageFolder(
traindir if split=='train' else valdir, self.transform)
self.labels = [item[1] for item in self.data.imgs]
def __getitem__(self, index):
img, label = self.data[index]
return img, label
def __len__(self):
return len(self.data)
class SimpleDataloader():
def __init__(self, dataset, batch_size, num_workers=4):
self.dataset = dataset
self.batch_size = batch_size
self.num_workers = num_workers
self.epoch_size = len(dataset)
def get_iterator(self):
def load_fun_(idx):
img, label = self.dataset[idx]
return img, label
tnt_dataset = tnt.dataset.ListDataset(
elem_list=range(self.epoch_size), load=load_fun_)
data_loader = tnt_dataset.parallel(
batch_size=self.batch_size,
num_workers=self.num_workers,
shuffle=False, drop_last=False)
return data_loader
def __call__(self):
return self.get_iterator()
def __len__(self):
return (self.epoch_size / self.batch_size)
class ImageNetLowShotFeaturesLegacy():
def __init__(
self,
data_dir,
phase='train',
add_novel_split='val'):
self.phase = phase
assert(phase=='train' or phase=='test' or phase=='val')
self.name = 'ImageNetLowShotFeatures_Phase_' + phase
split = 'train' if (phase=='train') else 'val'
dataset_file = os.path.join(data_dir, 'feature_dataset_'+split+'.json')
self.data_file = h5py.File(dataset_file, 'r')
self.count = self.data_file['count'][0]
self.features = self.data_file['all_features'][...]
self.labels = self.data_file['all_labels'][:self.count].tolist()
#***********************************************************************
with open(_IMAGENET_LOWSHOT_BENCHMARK_CATEGORY_SPLITS_PATH, 'r') as f:
label_idx = json.load(f)
base_classes = label_idx['base_classes']
base_classes_val_phase = label_idx['base_classes_1']
base_classes_test_phase = label_idx['base_classes_2']
novel_classes_val_phase = label_idx['novel_classes_1']
novel_classes_test_phase = label_idx['novel_classes_2']
#***********************************************************************
self.label2ind = buildLabelIndex(self.labels)
self.labelIds = sorted(self.label2ind.keys())
self.num_cats = len(self.labelIds)
assert(self.num_cats==1000)
self.labelIds_base = base_classes
self.num_cats_base = len(self.labelIds_base)
novel_split = add_novel_split if (self.phase=='train') else self.phase
if novel_split=='val' or novel_split=='test':
self.labelIds_novel = (
novel_classes_val_phase if (novel_split=='val') else
novel_classes_test_phase)
self.num_cats_novel = len(self.labelIds_novel)
intersection = set(self.labelIds_base) & set(self.labelIds_novel)
assert(len(intersection) == 0)
self.base_classes_subset = (
base_classes_val_phase if (novel_split=='val') else
base_classes_test_phase)
else:
self.base_classes_subset = base_classes
def __getitem__(self, index):
features_this = torch.Tensor(self.features[index]).view(-1,1,1)
label_this = self.labels[index]
return features_this, label_this
def __len__(self):
return int(self.count)
class ImageNetLowShotFeatures():
def __init__(
self,
data_dir, # path to the directory with the saved ImageNet features.
image_split='train', # the image split of the ImageNet that will be loaded.
phase='train', # whether the dataset will be used for training, validating, or testing a model.
):
assert(image_split=='train' or image_split=='val')
assert(phase=='train' or phase=='val' or phase=='test')
self.phase = phase
self.image_split = image_split
self.name = ('ImageNetLowShotFeatures_ImageSplit_' + self.image_split
+'_Phase_' + self.phase)
dataset_file = os.path.join(
data_dir, 'feature_dataset_' + self.image_split + '.json')
self.data_file = h5py.File(dataset_file, 'r')
self.count = self.data_file['count'][0]
self.features = self.data_file['all_features'][...]
self.labels = self.data_file['all_labels'][:self.count].tolist()
#***********************************************************************
with open(_IMAGENET_LOWSHOT_BENCHMARK_CATEGORY_SPLITS_PATH, 'r') as f:
label_idx = json.load(f)
base_classes = label_idx['base_classes']
base_classes_val_split = label_idx['base_classes_1']
base_classes_test_split = label_idx['base_classes_2']
novel_classes_val_split = label_idx['novel_classes_1']
novel_classes_test_split = label_idx['novel_classes_2']
#***********************************************************************
self.label2ind = buildLabelIndex(self.labels)
self.labelIds = sorted(self.label2ind.keys())
self.num_cats = len(self.labelIds)
assert(self.num_cats==1000)
self.labelIds_base = base_classes
self.num_cats_base = len(self.labelIds_base)
if self.phase=='val' or self.phase=='test':
self.labelIds_novel = (
novel_classes_val_split if (self.phase=='val') else
novel_classes_test_split)
self.num_cats_novel = len(self.labelIds_novel)
intersection = set(self.labelIds_base) & set(self.labelIds_novel)
assert(len(intersection) == 0)
self.base_classes_eval_split = (
base_classes_val_split if (self.phase=='val') else
base_classes_test_split)
self.base_classes_subset = self.base_classes_eval_split
def __getitem__(self, index):
features_this = torch.Tensor(self.features[index]).view(-1,1,1)
label_this = self.labels[index]
return features_this, label_this
def __len__(self):
return int(self.count)
class LowShotDataloader():
def __init__(
self,
dataset_train_novel,
dataset_evaluation,
nExemplars=1,
batch_size=1,
num_workers=4):
self.nExemplars = nExemplars
self.batch_size = batch_size
self.num_workers = num_workers
self.dataset_train_novel = dataset_train_novel
self.dataset_evaluation = dataset_evaluation
assert(self.dataset_evaluation.labelIds_novel ==
self.dataset_train_novel.labelIds_novel)
assert(self.dataset_evaluation.labelIds_base ==
self.dataset_train_novel.labelIds_base)
assert(self.dataset_evaluation.base_classes_eval_split ==
self.dataset_train_novel.base_classes_eval_split)
self.nKnovel = self.dataset_evaluation.num_cats_novel
self.nKbase = self.dataset_evaluation.num_cats_base
# Category ids of the base categories.
self.Kbase = sorted(self.dataset_evaluation.labelIds_base)
assert(self.nKbase == len(self.Kbase))
# Category ids of the novel categories.
self.Knovel = sorted(self.dataset_evaluation.labelIds_novel)
assert(self.nKnovel == len(self.Knovel))
self.Kall = self.Kbase + self.Knovel
self.CategoryId2LabelIndex = {
category_id: label_index for label_index, category_id in enumerate(self.Kall)
}
self.Kbase_eval_split = self.dataset_train_novel.base_classes_eval_split
Kbase_set = set(self.Kall[:self.nKbase])
Kbase_eval_split_set = set(self.Kbase_eval_split)
assert(len(set.intersection(Kbase_set, Kbase_eval_split_set)) == len(Kbase_eval_split_set))
self.base_eval_split_labels = sorted(
[self.CategoryId2LabelIndex[category_id] for category_id in self.Kbase_eval_split]
)
# Collect the image indices of the evaluation set for both the base and
# the novel categories.
data_indices = []
for category_id in self.Kbase_eval_split:
data_indices += self.dataset_evaluation.label2ind[category_id]
for category_id in self.Knovel:
data_indices += self.dataset_evaluation.label2ind[category_id]
self.eval_data_indices = sorted(data_indices)
self.epoch_size = len(self.eval_data_indices)
def base_category_label_indices(self):
return self.base_eval_split_labels
def novel_category_label_indices(self):
return range(self.nKbase, len(self.Kall))
def sampleImageIdsFrom(self, cat_id, sample_size=1):
"""
Samples `sample_size` number of unique image ids picked from the
category `cat_id` (i.e., self.dataset_train_novel.label2ind[cat_id]).
Args:
cat_id: a scalar with the id of the category from which images will
be sampled.
sample_size: number of images that will be sampled.
Returns:
image_ids: a list of length `sample_size` with unique image ids.
"""
assert(cat_id in self.dataset_train_novel.label2ind)
assert(len(self.dataset_train_novel.label2ind[cat_id]) >= sample_size)
# Note: random.sample samples elements without replacement.
return random.sample(self.dataset_train_novel.label2ind[cat_id], sample_size)
def sample_training_examples_for_novel_categories(
self, Knovel, nExemplars, nKbase):
"""Samples (a few) training examples for the novel categories.
Args:
Knovel: a list with the ids of the novel categories.
nExemplars: the number of training examples per novel category.
nKbase: the number of base categories.
Returns:
Exemplars: a list of length len(Knovel) * nExemplars of 2-element
tuples. The 1st element of each tuple is the image id that was
sampled and the 2nd element is its category label (which is in
the ragne [nKbase, nKbase + len(Knovel) - 1]).
"""
Exemplars = []
for knovel_idx, knovel_label in enumerate(Knovel):
imds = self.sampleImageIdsFrom(knovel_label, sample_size=nExemplars)
Exemplars += [(img_id, nKbase + knovel_idx) for img_id in imds]
random.shuffle(Exemplars)
return Exemplars
def create_examples_tensor_data(self, examples):
"""
Creates the examples image and label tensor data.
Args:
examples: a list of 2-element tuples, each representing a
train or test example. The 1st element of each tuple
is the image id of the example and 2nd element is the
category label of the example, which is in the range
[0, nK - 1], where nK is the total number of categories
(both novel and base).
Returns:
images: a tensor of shape [nExamples, Height, Width, 3] with the
example images, where nExamples is the number of examples
(i.e., nExamples = len(examples)).
labels: a tensor of shape [nExamples] with the category label
of each example.
"""
images = torch.stack(
[self.dataset_train_novel[img_idx][0] for img_idx, _ in examples],
dim=0)
labels = torch.LongTensor([label for _, label in examples])
return images, labels
def sample_training_data_for_novel_categories(self, exp_id=0):
nKnovel = self.nKnovel
nKbase = self.nKbase
random.seed(exp_id) # fix the seed for this experiment.
# Sample `nExemplars` number of training examples per novel category.
train_examples = self.sample_training_examples_for_novel_categories(
self.Knovel, self.nExemplars, nKbase)
Kall = torch.LongTensor(self.Kall)
images_train, labels_train = self.create_examples_tensor_data(
train_examples)
return images_train, labels_train, Kall, nKbase, nKnovel
def get_iterator(self, epoch=0):
def load_fun_(idx):
img_idx = self.eval_data_indices[idx]
img, category_id = self.dataset_evaluation[img_idx]
label = (self.CategoryId2LabelIndex[category_id]
if (category_id in self.CategoryId2LabelIndex) else -1)
return img, label
tnt_dataset = tnt.dataset.ListDataset(
elem_list=range(self.epoch_size), load=load_fun_)
data_loader = tnt_dataset.parallel(
batch_size=self.batch_size, num_workers=self.num_workers,
shuffle=False, drop_last=False)
return data_loader
def __call__(self, epoch=0):
return self.get_iterator(epoch)
def __len__(self):
return int(math.ceil(float(self.epoch_size)/self.batch_size))
class LowShotDataloaderLegacy():
def __init__(
self,
dataset_train_novel,
dataset_evaluation,
nExemplars=1,
batch_size=1,
num_workers=4):
self.dataset_train_novel = dataset_train_novel
self.dataset_evaluation = dataset_evaluation
assert(self.dataset_evaluation.labelIds_novel ==
self.dataset_train_novel.labelIds_novel)
# Collect the image indices of the evaluation set for both the base and
# the novel categories.
data_inds = []
for kid in self.dataset_evaluation.labelIds_base:
data_inds += self.dataset_evaluation.label2ind[kid]
for kid in self.dataset_evaluation.labelIds_novel:
data_inds += self.dataset_evaluation.label2ind[kid]
self.eval_data_indices = sorted(data_inds)
self.nKnovel = self.dataset_evaluation.num_cats_novel
self.nKbase = self.dataset_evaluation.num_cats_base
self.nExemplars = nExemplars
self.batch_size = batch_size
self.epoch_size = len(self.eval_data_indices)
self.num_workers = num_workers
def sampleImageIdsFrom(self, cat_id, sample_size=1):
"""
Samples `sample_size` number of unique image ids picked from the
category `cat_id` (i.e., self.dataset_train_novel.label2ind[cat_id]).
Args:
cat_id: a scalar with the id of the category from which images will
be sampled.
sample_size: number of images that will be sampled.
Returns:
image_ids: a list of length `sample_size` with unique image ids.
"""
assert(cat_id in self.dataset_train_novel.label2ind)
assert(len(self.dataset_train_novel.label2ind[cat_id]) >= sample_size)
# Note: random.sample samples elements without replacement.
return random.sample(self.dataset_train_novel.label2ind[cat_id], sample_size)
def sampleCategories(self, cat_set, sample_size=1):
"""
Samples `sample_size` number of unique categories picked from the
`cat_set` set of categories. `cat_set` can be either 'base' or 'novel'.
Args:
cat_set: string that specifies the set of categories from which
categories will be sampled.
sample_size: number of categories that will be sampled.
Returns:
cat_ids: a list of length `sample_size` with unique category ids.
"""
if cat_set=='base':
labelIds = self.dataset_evaluation.labelIds_base
elif cat_set=='novel':
labelIds = self.dataset_evaluation.labelIds_novel
else:
raise ValueError('Not recognized category set {}'.format(cat_set))
assert(len(labelIds) >= sample_size)
# return sample_size unique categories chosen from labelIds set of
# categories (that can be either self.labelIds_base or self.labelIds_novel)
# Note: random.sample samples elements without replacement.
return random.sample(labelIds, sample_size)
def sample_novel_data(self):
"""Samples a few training examples for each novel category."""
nKnovel = self.nKnovel
nKbase = self.nKbase
nExemplars = self.nExemplars
# Kbase = sorted(self.dataset_evaluation.labelIds_base)
# Knovel = sorted(self.dataset_evaluation.labelIds_novel)
Kbase = sorted(self.sampleCategories('base', nKbase))
Knovel = sorted(self.sampleCategories('novel', nKnovel))
Exemplars = []
for knovel_idx, knovel_label in enumerate(Knovel):
imds = self.sampleImageIdsFrom(knovel_label, sample_size=nExemplars)
Exemplars += [(img_id, nKbase + knovel_idx) for img_id in imds]
random.shuffle(Exemplars)
Kids = Kbase + Knovel
return Exemplars, Kids, nKbase, nKnovel
def create_examples_tensor_data(self, examples):
"""
Creates the examples image and label tensor data.
Args:
examples: a list of 2-element tuples, each representing a
train or test example. The 1st element of each tuple
is the image id of the example and 2nd element is the
category label of the example, which is in the range
[0, nK - 1], where nK is the total number of categories
(both novel and base).
Returns:
images: a tensor of shape [nExamples, Height, Width, 3] with the
example images, where nExamples is the number of examples
(i.e., nExamples = len(examples)).
labels: a tensor of shape [nExamples] with the category label
of each example.
"""
images = torch.stack(
[self.dataset_train_novel[img_idx][0] for img_idx, _ in examples],
dim=0)
labels = torch.LongTensor([label for _, label in examples])
return images, labels
def getNovelCategoriesTrainingData(self, exp_id=0):
random.seed(exp_id) # fix the seed for this experiment.
# Sample training examples for each novel category.
Exemplars, Kids, nKbase, nKnovel = self.sample_novel_data()
self.Kid2Label = {kid: label_idx for label_idx, kid in enumerate(Kids)}
base_classes_subset = self.dataset_train_novel.base_classes_subset
assert(len(set.intersection(set(Kids[:nKbase]),set(base_classes_subset))) == len(base_classes_subset))
self.Kids_base_subset = sorted([self.Kid2Label[kid] for kid in base_classes_subset])
Kids = torch.LongTensor(Kids)
Xe, Ye = self.create_examples_tensor_data(Exemplars)
return Xe, Ye, Kids, nKbase, nKnovel
def sample_training_data_of_novel_categories(self, exp_id=0):
nKnovel = self.nKnovel
nKbase = self.nKbase
nExemplars = self.nExemplars
random.seed(exp_id) # fix the seed for this experiment.
breakpoint()
# Ids of the base categories.
Kbase = sorted(self.dataset_evaluation.labelIds_base)
# Ids of the novel categories.
Knovel = sorted(self.dataset_evaluation.labelIds_novel)
assert(len(Kbase) == nKnovel and len(Knovel) == nKbase)
Kall = Kbase + Knovel
# Sample `nExemplars` number of training examples for each novel
# category.
train_examples = self.sample_training_examples_for_novel_categories(
Knovel, nExemplars)
breakpoint()
self.Kid2Label = {kid: label_idx for label_idx, kid in enumerate(Kall)}
breakpoint()
base_classes_subset = self.dataset_train_novel.base_classes_subset
assert(len(set.intersection(set(Kall[:nKbase]),set(base_classes_subset))) == len(base_classes_subset))
self.Kids_base_subset = sorted([self.Kid2Label[kid] for kid in base_classes_subset])
Kall = torch.LongTensor(Kall)
images_train, labels_train = self.create_examples_tensor_data(train_examples)
return images_train, labels_train, Kall, nKbase, nKnovel
def get_iterator(self, epoch=0):
def load_fun_(idx):
img_idx = self.eval_data_indices[idx]
img, kid = self.dataset_evaluation[img_idx]
label = self.Kid2Label[kid] if (kid in self.Kid2Label) else -1
return img, label
tnt_dataset = tnt.dataset.ListDataset(
elem_list=range(self.epoch_size), load=load_fun_)
data_loader = tnt_dataset.parallel(
batch_size=self.batch_size, num_workers=self.num_workers,
shuffle=False, drop_last=False)
return data_loader
def __call__(self, epoch=0):
return self.get_iterator(epoch)
def __len__(self):
return int(math.ceil(float(self.epoch_size)/self.batch_size))
