import torch
def get_pseudo_labels(dataset, model, threshold=0.07):
# This functions generates pseudo-labels of a dataset using given model.
# It returns an instance of DatasetFolder containing images whose prediction confidences exceed a given threshold.
# You are NOT allowed to use any models trained on external data for pseudo-labeling.
device = "cuda" if torch.cuda.is_available() else "cpu"
x = []
y = []
# print(dataset.targets[0])
# Construct a data loader.
data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=False)
# Make sure the model is in eval mode.
model.eval()
# Define softmax function.
softmax = nn.Softmax()
counter = 0
# Iterate over the dataset by batches.
for batch in tqdm(data_loader):
img, _ = batch
# Forward the data
# Using torch.no_grad() accelerates the forward process.
with torch.no_grad():
logits = model(img.to(device))
# Obtain the probability distributions by applying softmax on logits.
probs = softmax(logits)
count = 0
# ---------- TODO ----------
# Filter the data and construct a new dataset.
dataset.targets = torch.tensor(dataset.targets)
for p in probs:
if torch.max(p) >= threshold:
if not(counter in x):
x.append(counter)
dataset.targets[counter] = torch.argmax(p)
counter += 1
# Turn off the eval mode.
model.train()
# dat = DataLoader(ImgDataset(x,y), batch_size=batch_size, shuffle=False)
print(dataset.targets[10])
new = torch.utils.data.Subset(dataset, x)
