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client.py
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client.py
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import numpy as np
import sys
import argparse
from collections import OrderedDict
import os
import matplotlib.pyplot as plt
import torch
from torch import nn
from torch._C import device
import torchvision.transforms as transforms
import flwr as fl
from losses import FocalLoss, mIoULoss
from model import UNet
from dataloader import segDataset
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--data", type=str, required=True, help="path to your train dataset"
)
parser.add_argument("--test", type=str, help="path to your test dataset")
parser.add_argument("--meta", type=str, required=True, help="path to your metadata")
parser.add_argument(
"--name", type=str, default="unet", help="name to be appended to checkpoints"
)
parser.add_argument("--num_epochs", type=int, default=100, help="dnumber of epochs")
parser.add_argument("--batch", type=int, default=1, help="batch size")
parser.add_argument("--save_step", type=int, default=5, help="epochs to skip")
parser.add_argument(
"--loss",
type=str,
default="focalloss",
help="focalloss | iouloss | crossentropy",
)
return parser.parse_args()
def acc(y, pred_mask):
seg_acc = (y.cpu() == torch.argmax(pred_mask, axis=1).cpu()).sum() / torch.numel(
y.cpu()
)
return seg_acc
if __name__ == "__main__":
args = get_args()
N_EPOCHS = args.num_epochs
BACH_SIZE = args.batch
color_shift = transforms.ColorJitter(0.1, 0.1, 0.1, 0.1)
blurriness = transforms.GaussianBlur(3, sigma=(0.1, 2.0))
t = transforms.Compose([color_shift, blurriness])
if not args.test:
dataset = segDataset(args.data, args.meta, training=True, transform=t)
n_classes = len(dataset.bin_classes) + 1
print("Number of data : " + str(len(dataset)))
test_num = int(0.1 * len(dataset))
print(f"Test data : {test_num}")
print(f"Number of classes : {n_classes}")
train_dataset, test_dataset = torch.utils.data.random_split(
dataset,
[len(dataset) - test_num, test_num],
generator=torch.Generator().manual_seed(101),
)
N_DATA, N_TEST = len(train_dataset), len(test_dataset)
train_dataloader = torch.utils.data.DataLoader(
train_dataset, batch_size=BACH_SIZE, shuffle=True, num_workers=2
)
test_dataloader = torch.utils.data.DataLoader(
test_dataset, batch_size=BACH_SIZE, shuffle=False, num_workers=1
)
else:
dataset = segDataset(args.data, args.meta, training=True, transform=t)
dataset2 = segDataset(args.test, args.meta, training=False, transform=t)
n_classes = len(dataset.bin_classes) + 1
print("Number of train data : " + str(len(dataset)))
test_num = len(dataset2)
print(f"Test data : {test_num}")
print(f"Number of classes : {n_classes}")
N_DATA, N_TEST = len(dataset), len(dataset2)
train_dataloader = torch.utils.data.DataLoader(
dataset, batch_size=BACH_SIZE, shuffle=True, num_workers=2
)
test_dataloader = torch.utils.data.DataLoader(
dataset2, batch_size=BACH_SIZE, shuffle=False, num_workers=1
)
if args.loss == "focalloss":
criterion = FocalLoss(gamma=3 / 4).to(device)
elif args.loss == "iouloss":
criterion = mIoULoss(n_classes=n_classes).to(device)
elif args.loss == "crossentropy":
criterion = nn.CrossEntropyLoss().to(device)
else:
print("Loss function not found!")
model = UNet(n_channels=3, n_classes=n_classes, bilinear=True).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.5)
min_loss = torch.tensor(float("inf"))
scheduler_counter = 0
round = 0
class UNetClient(fl.client.NumPyClient):
def get_parameters(self):
return [val.cpu().numpy() for _, val in model.state_dict().items()]
def set_parameters(self, parameters):
params_dict = zip(model.state_dict().keys(), parameters)
state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict})
it1 = model.state_dict().items()
it2 = state_dict.items()
l1 = len(it1)
l2 = len(it2)
if l1 != l2:
print(f"{l1} : {l2} length do not match")
else:
for i in model.state_dict():
if not model.state_dict()[i].shape == state_dict[i].shape:
print(
i,
model.state_dict()[i].shape,
state_dict[i].shape,
"Different",
)
model.load_state_dict(state_dict, strict=False)
def fit(self, parameters, config):
print("Fiting started on Client...")
self.set_parameters(parameters)
global scheduler_counter, round
os.makedirs("./saved_models", exist_ok=True)
plot_losses = []
plot_accuracies = []
for epoch in range(N_EPOCHS):
model.train()
loss_list = []
acc_list = []
for batch_i, (x, y) in enumerate(train_dataloader):
pred_mask = model(x.to(device))
loss = criterion(pred_mask, y.to(device))
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_list.append(loss.cpu().detach().numpy())
acc_list.append(acc(y, pred_mask).numpy())
sys.stdout.write(
"\r[Epoch %d/%d] [Batch %d/%d] [Loss: %f (%f)]"
% (
epoch,
N_EPOCHS,
batch_i,
len(train_dataloader),
loss.cpu().detach().numpy(),
np.mean(loss_list),
)
)
scheduler_counter += 1
if (epoch + 1) % args.save_step == 0:
torch.save(
model.state_dict(),
"./saved_models/{}_{}_epoch_{}_{:.5f}.pt".format(
args.name, round, epoch, np.mean(loss_list)
),
)
plot_losses.append([epoch, np.mean(loss_list)])
plot_accuracies.append([epoch, np.mean(acc_list)])
print(
" epoch {} - loss : {:.5f} - acc : {:.2f}".format(
epoch, np.mean(loss_list), np.mean(acc_list)
)
)
plot_losses = np.array(plot_losses)
plot_accuracies = np.array(plot_accuracies)
plt.figure(figsize=(12, 8))
plt.plot(plot_losses[:, 0], plot_losses[:, 1], color="b", linewidth=4)
plt.title(args.loss, fontsize=20)
plt.xlabel("epoch", fontsize=20)
plt.ylabel("loss", fontsize=20)
plt.grid()
plt.savefig(f"loss_plots_{args.name}_{round}.png")
plt.figure(figsize=(12, 8))
plt.plot(
plot_accuracies[:, 0], plot_accuracies[:, 1], color="b", linewidth=4
)
plt.title("accuracy", fontsize=20)
plt.xlabel("epoch", fontsize=20)
plt.ylabel("accuracy", fontsize=20)
plt.grid()
plt.savefig(f"accuracy_plots_{args.name}_{round}.png")
round += 1
return self.get_parameters(), len(train_dataloader), {}
def evaluate(self, parameters, config):
print("Evaluation started on Client...")
self.set_parameters(parameters)
model.eval()
val_loss_list = []
val_acc_list = []
for batch_i, (x, y) in enumerate(test_dataloader):
with torch.no_grad():
pred_mask = model(x.to(device))
val_loss = criterion(pred_mask, y.to(device))
val_loss_list.append(val_loss.cpu().detach().numpy())
val_acc_list.append(acc(y, pred_mask).numpy())
print(
" val loss : {:.5f} - val acc : {:.2f}".format(
np.mean(val_loss_list), np.mean(val_acc_list)
)
)
return (
np.mean(val_loss_list).item(),
len(test_dataloader),
{"accuracy": np.mean(val_acc_list).item()},
)
fl.client.start_numpy_client(
server_address="localhost:5000",
client=UNetClient(),
)