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Supervised_FedCollaborator.py
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Supervised_FedCollaborator.py
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"""
Supervised_FedCollaborator.py
Class for running local training (FedDis); delivered to client
"""
import torch
import numpy as np
import wandb
import logging
from time import time
from monai.losses import DiceCELoss
from monai.optimizers import Adam
from monai.optimizers.lr_scheduler import ExponentialLRDecay
from monai.transforms import ToRGB
from monai.ILIA.core.FedCollaborator import FedCollaborator
class Supervised_FedDisCollaborator(FedCollaborator):
def __init__(self, client_idx, client_name, training_params, model, device, log_wandb=False):
"""
Init function for Client
:param client_idx: int
Id of the client
:param client_name: str
Name of the client
:param training_params: list
parameters for local training routine
:param device: torch.device
GPU | CPU
:param model: torch.nn.module
Neural network
"""
super(Supervised_FedDisCollaborator, self).\
__init__(client_idx=client_idx, client_name=client_name, training_params=training_params, model=model,
device=device, log_wandb=log_wandb)
self.eval_step = 0
self.min_val_loss = np.inf
self.train_step = 0
self.criterion = DiceCELoss(ce_weight=torch.cuda.FloatTensor([0.8]))
self.img2RGB = ToRGB(255, 255, 255)
self.optimizer = Adam(self.model.parameters(), lr=training_params['optimizer_params']['learning_rate'])
self.decayRate = training_params['optimizer_params']['decay_rate']
self.my_lr_scheduler = ExponentialLRDecay(optimizer=self.optimizer, gamma=self.decayRate)
def load_data(self, dataset_name):
"""
:param dataset_name: str
name of the dataset(s) to load
:return:
local_training_data: monai.DataLoader
local_test_data: monai.DataLoader
num_train_samples: int
number of training samples
"""
if not isinstance(dataset_name, list):
dataset_name = [dataset_name]
local_training_data = self.data_loader.load_data(dataset_current_list=dataset_name)
return local_training_data, local_training_data
def get_nr_train_samples(self):
return len(self.local_training_data) * self.local_training_data.batch_size
def train(self, w_global, opt_state, round_idx=0):
"""
Train local client
:param w_global: weights
weights of the global model
:param opt_state: state
state of the optimizer
:param round_idx: int
the round number
:return:
self.model.state_dict():
self.optimizer.state_dict():
epoch_loss: float
the loss of the epoch
"""
# return self.client_name, self.model.state_dict(), self.optimizer.state_dict(), epoch_loss
for param in self.model.state_dict():
check_equal = torch.equal(self.model.state_dict()[param], w_global[param])
if not check_equal:
logging.info("************************** model params equal? ({}): {}".format(param, 'different'))
self.model.load_state_dict(w_global) # load weights
self.model.train()
self.model.to(self.device)
if opt_state is not None:
self.optimizer.load_state_dict(opt_state) # load optimizer
# self.optimizer.state = collections.defaultdict(dict) # reset optimizer
epoch_losses = []
for epoch in range(self.training_params['nr_epochs']):
start_time = time()
batch_loss = 1.0
count_images = 0
ct_train = np.floor(int(0.7 * len(self.local_training_data)))
print('Number of training batches: {}'.format(ct_train))
ct = 0
for data in self.local_training_data:
# Input
ct += 1
if ct > ct_train:
break
input_all_slices = data[0]
gt_all_slices = data[1]
images = input_all_slices
gt = gt_all_slices
b, sl, w, h = images.shape[0], images.shape[1], images.shape[2], images.shape[3]
images = images.view(b*sl, 1, w, h)
gt = gt.view(b*sl, 1, w, h)
count_images += images.shape[0]
images, gt = images.to(self.device), gt.to(self.device)
# Forward Pass
self.optimizer.zero_grad()
segmented_images = self.model(images)['x_rec']
# Reconstruction Loss
loss = self.criterion(segmented_images, gt)
loss.backward()
self.optimizer.step()
batch_loss += loss.item() * images.size(0)
if count_images >= self.training_params['max_iterations']:
logging.info('Break due to maximal number of local iterations...')
break
epoch_loss = batch_loss / count_images if count_images > 0 else batch_loss
epoch_losses.append(epoch_loss)
end_time = time()
mid_slice = int(sl / 2 / b)
grid_image = np.hstack([self.img2RGB(images.detach().cpu()[mid_slice].numpy())[0, :, :128],
self.img2RGB(gt.detach().cpu()[mid_slice].numpy())[0, :, :128],
self.img2RGB(segmented_images.detach().cpu()[mid_slice].numpy())[0, :, :128]])
wandb.log({"Train\Example_" + self.client_name: [
wandb.Image(grid_image, caption="Reconstruction_" + str(self.train_step))]})
print('Epoch: {} \tTraining Loss: {:.6f} , computed in {} seconds for {} samples'.format(
epoch, epoch_loss, end_time - start_time, count_images))
wandb.log({"Train/Loss_" + self.client_name: epoch_loss, '_step_' + self.client_name: self.train_step})
torch.save(self.model.state_dict(), self.client_path + '/latest_model.pt')
self.train_step += 1
if epoch % 3 == 0:
self.test(self.model.state_dict())
return self.client_name, self.model.state_dict(), self.optimizer.state_dict(), sum(epoch_losses) / len(
epoch_losses)
def test(self, model_weights):
"""
:param model_weights: weights of the global model
:return: dict
metric_name : value
e.g.:
metrics = {
'test_loss_l1': 0,
'test_total': 0
}
"""
# return metrics
self.test_model.load_state_dict(model_weights)
self.test_model.to(self.device)
self.test_model.eval()
metrics = {
'test_loss': 0,
}
test_total = 0
with torch.no_grad():
ct_test = np.floor(int(0.7 * len(self.local_training_data)))
ct = 0
for data in self.local_test_data:
ct += 1
if ct <= ct_test:
continue
x_all = data[0]
gt_all = data[1]
x = x_all
gt = gt_all
b, sl, w, h = x.shape[0], x.shape[1], x.shape[2], x.shape[3]
x = x.view(b * sl, 1, w, h)
gt = gt.view(b * sl, 1, w, h)
test_total += x.shape[0]
x, gt = x.to(self.device), gt.to(self.device)
# Forward pass
y, _ = self.test_model(x)
loss = self.criterion(y, gt)
metrics['test_loss'] += loss.item() * x.size(0)
mid_slice = int(x.shape[0] / 2 / b)
grid_image = np.hstack([self.img2RGB(x.detach().cpu()[mid_slice].numpy())[0, :, :128],
self.img2RGB(gt.detach().cpu()[mid_slice].numpy())[0, :, :128],
self.img2RGB(y.detach().cpu()[mid_slice].numpy())[0, :, :128]])
wandb.log({"Test\Example_" + self.client_name: [wandb.Image(
grid_image, caption="Reconstruction_" + str(self.train_step))]})
for metric_key in metrics.keys():
metric_name = 'Test/' + str(metric_key) + '_' + self.client_name
metric_score = metrics[metric_key] / test_total
wandb.log({metric_name: metric_score, '_step_' + self.client_name: self.train_step})
if loss < self.min_val_loss:
self.min_val_loss = loss
torch.save(model_weights, self.client_path + '/best_model.pt')
self.my_lr_scheduler.step()
return metrics, test_total