# Dataloader of Gidaris & Komodakis, CVPR 2018 # Adapted from: # https://github.com/gidariss/FewShotWithoutForgetting/blob/master/dataloader.py from __future__ import print_function import os import os.path import numpy as npw 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 numpy as np import pandas as pd import h5py import cv2 from PIL import Image from PIL import ImageEnhance import matplotlib.pyplot as plt from torchvision.transforms.transforms import ToPILImage # Set the appropriate paths of the datasets here. # _CIFAR_FS_DATASET_DIR = './cifar/CIFAR-FS/' _CHEST_DATASET_DIR = './NIH' image_path = './NIH/images' label_dict = {'Cardiomegaly': 0, 'Edema': 1, 'Effusion': 2, 'Emphysema': 3, 'Infiltration': 4, 'Mass': 5, 'Atelectasis': 6, 'Consolidation': 7, 'Pleural_Thickening': 8, 'Fibrosis': 9, 'Hernia': 10, 'Pneumonia': 11, 'Nodule': 12, 'Pneumothorax': 13, 'No Finding': 14} def buildLabelIndex(labels): label2inds = {} for idx, label in enumerate(labels): label = label_dict[label] if label not in label2inds: label2inds[label] = [] label2inds[label].append(idx) return label2inds def load_data(file): try: with open(file, 'rb') as fo: data = pickle.load(fo) return data except: with open(file, 'rb') as f: u = pickle._Unpickler(f) u.encoding = 'latin1' data = u.load() return data class Chest(data.Dataset): def __init__(self, phase='train', idx = 1, do_not_use_random_transf=False): assert(phase == 'train' or phase == 'val' or phase == 'test' or phase == 'trainval') self.phase = phase # self.name = phase + '.csv' # idx = 3 # represents group for experimentation print('Loading Chest-XRay dataset - phase {0}'.format(phase)) train_path = os.path.join(_CHEST_DATASET_DIR, f'train{idx}.csv') val_path = os.path.join(_CHEST_DATASET_DIR, f'val{idx}.csv') test_path = os.path.join(_CHEST_DATASET_DIR, f'test{idx}.csv') 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'] file = pd.read_csv(train_path) self.data = file['image_id'].values self.labels = file['class_name'].values 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 == 'trainval': # # 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'] # data_base = load_data(file_train_categories_val_phase) # data_novel = load_data(file_val_categories_val_phase) # self.data = np.concatenate( # [self.data, data_novel['data']], axis=0) # self.data = np.concatenate( # [self.data, data_base['data']], axis=0) # self.labels = np.concatenate( # [self.labels, data_novel['labels']], axis=0) # self.labels = np.concatenate( # [self.labels, data_base['labels']], axis=0) # 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) train_file = pd.read_csv(train_path) file = pd.read_csv(test_path) 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) train_file = pd.read_csv(train_path) file = pd.read_csv(val_path) # self.data = np.concatenate( # [data_base['data'], data_novel['data']], axis=0) # self.labels = data_base['labels'] + data_novel['labels'] train_labels = train_file['class_name'].values novel_labels = file['class_name'].values self.data = np.concatenate( [train_file['image_id'].values, file['image_id'].values], axis=0) self.labels = np.concatenate( [train_file['class_name'].values, file['class_name'].values], axis=0) 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.labelIds_base = buildLabelIndex(train_labels).keys() self.labelIds_novel = buildLabelIndex(novel_labels).keys() print('='*60) print(self.labelIds_novel) print('='*60) self.num_cats_base = len(self.labelIds_base) self.num_cats_novel = len(self.labelIds_novel) # print(self.labelIds_novel) # print(self.num_cats_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 [129.37731888, # 124.10583864, 112.47758569]] # std_pix = [x/255.0 for x in [68.20947949, 65.43124043, 70.45866994]] mean_pix = [0.52024849, 0.52024849, 0.52024849] std_pix = [0.22699496, 0.22699496, 0.22699496] 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([ transforms.ToPILImage(), # lambda x: np.asarray(x), transforms.ToTensor(), # lambda x: x/255.0, normalize ]) else: self.transform = transforms.Compose([ transforms.ToPILImage(), # transforms.RandomCrop(32, padding=4), # transforms.ColorJitter( # brightness=0.4, contrast=0.4, saturation=0.4), transforms.RandomHorizontalFlip(), transforms.ToTensor(), # lambda x: np.asarray(x), # lambda x: x/255.0, normalize ]) def __getitem__(self, index): img, label = cv2.imread(os.path.join( image_path, self.data[index]))[:,:,::-1], self.labels[index] img = cv2.resize(img,(128,128)) # resize by Garvit # img = cv2.resize(img,(84, 84)) # resize by kshitiz # 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. # number of training examples per novel category. nExemplars=1, # number of test examples for all the novel categories. nTestNovel=15*5, # number of test examples for all the base categories. nTestBase=15*5, 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' or self.phase == 'trainval' 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' or self.phase == 'trainval') 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. # seed = random.randint(1,10000000) # random.seed(seed) 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 = int(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 int(self.epoch_size / self.batch_size)