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import os
import torch
import torchvision.transforms as transforms
from loss import TripletMarginLoss
from model import EmbeddingNet
from sampler import PKSampler
from torch.optim import Adam
from torch.utils.data import DataLoader
from torchvision.datasets import FashionMNIST
def train_epoch(model, optimizer, criterion, data_loader, device, epoch, print_freq):
model.train()
running_loss = 0
running_frac_pos_triplets = 0
for i, data in enumerate(data_loader):
optimizer.zero_grad()
samples, targets = data[0].to(device), data[1].to(device)
embeddings = model(samples)
loss, frac_pos_triplets = criterion(embeddings, targets)
loss.backward()
optimizer.step()
running_loss += loss.item()
running_frac_pos_triplets += float(frac_pos_triplets)
if i % print_freq == print_freq - 1:
i += 1
avg_loss = running_loss / print_freq
avg_trip = 100.0 * running_frac_pos_triplets / print_freq
print(f"[{epoch:d}, {i:d}] | loss: {avg_loss:.4f} | % avg hard triplets: {avg_trip:.2f}%")
running_loss = 0
running_frac_pos_triplets = 0
def find_best_threshold(dists, targets, device):
best_thresh = 0.01
best_correct = 0
for thresh in torch.arange(0.0, 1.51, 0.01):
predictions = dists <= thresh.to(device)
correct = torch.sum(predictions == targets.to(device)).item()
if correct > best_correct:
best_thresh = thresh
best_correct = correct
accuracy = 100.0 * best_correct / dists.size(0)
return best_thresh, accuracy
@torch.inference_mode()
def evaluate(model, loader, device):
model.eval()
embeds, labels = [], []
dists, targets = None, None
for data in loader:
samples, _labels = data[0].to(device), data[1]
out = model(samples)
embeds.append(out)
labels.append(_labels)
embeds = torch.cat(embeds, dim=0)
labels = torch.cat(labels, dim=0)
dists = torch.cdist(embeds, embeds)
labels = labels.unsqueeze(0)
targets = labels == labels.t()
mask = torch.ones(dists.size()).triu() - torch.eye(dists.size(0))
dists = dists[mask == 1]
targets = targets[mask == 1]
threshold, accuracy = find_best_threshold(dists, targets, device)
print(f"accuracy: {accuracy:.3f}%, threshold: {threshold:.2f}")
def save(model, epoch, save_dir, file_name):
file_name = "epoch_" + str(epoch) + "__" + file_name
save_path = os.path.join(save_dir, file_name)
torch.save(model.state_dict(), save_path)
def main(args):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if args.use_deterministic_algorithms:
torch.backends.cudnn.benchmark = False
torch.use_deterministic_algorithms(True)
else:
torch.backends.cudnn.benchmark = True
p = args.labels_per_batch
k = args.samples_per_label
batch_size = p * k
model = EmbeddingNet()
if args.resume:
model.load_state_dict(torch.load(args.resume, weights_only=True))
model.to(device)
criterion = TripletMarginLoss(margin=args.margin)
optimizer = Adam(model.parameters(), lr=args.lr)
transform = transforms.Compose(
[
transforms.Lambda(lambda image: image.convert("RGB")),
transforms.Resize((224, 224)),
transforms.PILToTensor(),
transforms.ConvertImageDtype(torch.float),
]
)
# Using FMNIST to demonstrate embedding learning using triplet loss. This dataset can
# be replaced with any classification dataset.
train_dataset = FashionMNIST(args.dataset_dir, train=True, transform=transform, download=True)
test_dataset = FashionMNIST(args.dataset_dir, train=False, transform=transform, download=True)
# targets is a list where the i_th element corresponds to the label of i_th dataset element.
# This is required for PKSampler to randomly sample from exactly p classes. You will need to
# construct targets while building your dataset. Some datasets (such as ImageFolder) have a
# targets attribute with the same format.
targets = train_dataset.targets.tolist()
train_loader = DataLoader(
train_dataset, batch_size=batch_size, sampler=PKSampler(targets, p, k), num_workers=args.workers
)
test_loader = DataLoader(test_dataset, batch_size=args.eval_batch_size, shuffle=False, num_workers=args.workers)
if args.test_only:
# We disable the cudnn benchmarking because it can noticeably affect the accuracy
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
evaluate(model, test_loader, device)
return
for epoch in range(1, args.epochs + 1):
print("Training...")
train_epoch(model, optimizer, criterion, train_loader, device, epoch, args.print_freq)
print("Evaluating...")
evaluate(model, test_loader, device)
print("Saving...")
save(model, epoch, args.save_dir, "ckpt.pth")
def parse_args():
import argparse
parser = argparse.ArgumentParser(description="PyTorch Embedding Learning")
parser.add_argument("--dataset-dir", default="/tmp/fmnist/", type=str, help="FashionMNIST dataset directory path")
parser.add_argument(
"-p", "--labels-per-batch", default=8, type=int, help="Number of unique labels/classes per batch"
)
parser.add_argument("-k", "--samples-per-label", default=8, type=int, help="Number of samples per label in a batch")
parser.add_argument("--eval-batch-size", default=512, type=int, help="batch size for evaluation")
parser.add_argument("--epochs", default=10, type=int, metavar="N", help="number of total epochs to run")
parser.add_argument("-j", "--workers", default=4, type=int, metavar="N", help="number of data loading workers")
parser.add_argument("--lr", default=0.0001, type=float, help="initial learning rate")
parser.add_argument("--margin", default=0.2, type=float, help="Triplet loss margin")
parser.add_argument("--print-freq", default=20, type=int, help="print frequency")
parser.add_argument("--save-dir", default=".", type=str, help="Model save directory")
parser.add_argument("--resume", default="", type=str, help="path of checkpoint")
parser.add_argument(
"--test-only",
dest="test_only",
help="Only test the model",
action="store_true",
)
parser.add_argument(
"--use-deterministic-algorithms", action="store_true", help="Forces the use of deterministic algorithms only."
)
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
main(args)
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