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sampler.py
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sampler.py
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import torch
import random
import numpy as np
class UncertaintySampler:
def __init__(self, budget):
self.budget = budget
def sample(self, task_learner, data, cuda):
all_indices = []
all_preds = []
for images, _, indices in data:
if cuda:
images = images.cuda()
with torch.no_grad():
out = task_learner(images)
preds = out.max(1)[0] #if the max was 1, then we will select it, and then it will be -1 => not taken
preds = preds.cpu().data
all_preds.extend(preds)
all_indices.extend(indices)
all_preds = torch.stack(all_preds)
all_preds = all_preds.view(-1)
all_preds *= -1
_, querry_indices = torch.topk(all_preds, int(self.budget))
querry_pool_indices = np.asarray(all_indices)[querry_indices]
print("uncertinaty sampler")
print(querry_pool_indices)
return querry_pool_indices
class EESampler:
def __init__(self, budget):
self.budget = budget
def sample(self, task_learner, data, cuda):
all_indices = []
all_preds = []
for images, _, indices in data:
if cuda:
images = images.cuda()
with torch.no_grad():
out = task_learner(images)
preds = out.max(1)[0] # if the max was 1, then we will select it, and then it will be -1 => not taken
preds = preds.cpu().data
all_preds.extend(preds)
all_indices.extend(indices)
all_preds = torch.stack(all_preds)
all_preds = all_preds.view(-1)
# all_preds *= -1
_, querry_indices = torch.topk(all_preds, int(self.budget))
querry_pool_indices = np.asarray(all_indices)[querry_indices]
print("uncertinaty sampler")
print(querry_pool_indices)
return querry_pool_indices
class RandomSampler:
def __init__(self, budget):
self.budget = budget
def sample(self, data):
all_indices = []
for _, _, indices in data:
all_indices.extend(indices)
all_indices = [int(x) for x in all_indices]
random.seed("csc2547")
new_indices = random.sample(all_indices, self.budget)
return new_indices
class AdversarySamplerSingleClass:
def __init__(self, budget):
self.budget = budget
def sample(self, vae, discriminator, data, cuda):
all_preds = []
all_indices = []
for images, _, indices in data:
if cuda:
images = images.cuda()
with torch.no_grad():
_, _, mu, _ = vae(images)
preds = discriminator(mu)
preds = preds.cpu().data
all_preds.extend(preds)
all_indices.extend(indices)
all_preds = torch.stack(all_preds)
all_preds = all_preds.view(-1)
# need to multiply by -1 to be able to use torch.topk
all_preds *= -1
# select the points which the discriminator things are the most likely to be unlabeled
_, querry_indices = torch.topk(all_preds, int(self.budget))
querry_pool_indices = np.asarray(all_indices)[querry_indices]
return querry_pool_indices
class AdversarySampler:
def __init__(self, budget):
self.budget = budget
def sample(self, vae, discriminator, data, cuda):
all_preds = []
all_indices = []
for images, _, indices in data:
if cuda:
images = images.cuda()
with torch.no_grad():
_, _, mu, _ = vae(images)
discrim_out = discriminator(mu)
preds = discrim_out[0] #look at only the probability of the class zero
preds = preds.cpu().data
all_preds.extend(preds)
all_indices.extend(indices)
all_preds = torch.stack(all_preds)
all_preds = all_preds.view(-1)
# need to multiply by -1 to be able to use torch.topk
# all_preds *= -1
# select the points which the discriminator things are the most likely to be unlabeled
_, querry_indices = torch.topk(all_preds, int(self.budget))
querry_pool_indices = np.asarray(all_indices)[querry_indices]
return querry_pool_indices