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image_train.py
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import utils.csv_record as csv_record
import torch
import torch.nn as nn
import torch.nn.functional as F
import time
import main
import test
import copy
import config
def ImageTrain(helper, start_epoch, local_model, target_model, is_poison,agent_name_keys):
epochs_submit_update_dict = dict()
num_samples_dict = dict()
current_number_of_adversaries=0
for temp_name in agent_name_keys:
if temp_name in helper.params['adversary_list']:
current_number_of_adversaries+=1
for model_id in range(helper.params['no_models']):
epochs_local_update_list = []
last_local_model = dict()
client_grad = [] # only works for aggr_epoch_interval=1
for name, data in target_model.state_dict().items():
last_local_model[name] = target_model.state_dict()[name].clone()
agent_name_key = agent_name_keys[model_id]
## Synchronize LR and models
model = local_model
model.copy_params(target_model.state_dict())
optimizer = torch.optim.SGD(model.parameters(), lr=helper.params['lr'],
momentum=helper.params['momentum'],
weight_decay=helper.params['decay'])
model.train()
adversarial_index= -1
localmodel_poison_epochs = helper.params['poison_epochs']
if is_poison and agent_name_key in helper.params['adversary_list']:
for temp_index in range(0, len(helper.params['adversary_list'])):
if int(agent_name_key) == helper.params['adversary_list'][temp_index]:
adversarial_index= temp_index
localmodel_poison_epochs = helper.params[str(temp_index) + '_poison_epochs']
main.logger.info(
f'poison local model {agent_name_key} index {adversarial_index} ')
break
if len(helper.params['adversary_list']) == 1:
adversarial_index = -1 # the global pattern
for epoch in range(start_epoch, start_epoch + helper.params['aggr_epoch_interval']):
target_params_variables = dict()
for name, param in target_model.named_parameters():
target_params_variables[name] = last_local_model[name].clone().detach().requires_grad_(False)
if is_poison and agent_name_key in helper.params['adversary_list'] and (epoch in localmodel_poison_epochs):
main.logger.info('poison_now')
poison_lr = helper.params['poison_lr']
internal_epoch_num = helper.params['internal_poison_epochs']
step_lr = helper.params['poison_step_lr']
poison_optimizer = torch.optim.SGD(model.parameters(), lr=poison_lr,
momentum=helper.params['momentum'],
weight_decay=helper.params['decay'])
scheduler = torch.optim.lr_scheduler.MultiStepLR(poison_optimizer,
milestones=[0.2 * internal_epoch_num,
0.8 * internal_epoch_num], gamma=0.1)
temp_local_epoch = (epoch - 1) *internal_epoch_num
for internal_epoch in range(1, internal_epoch_num + 1):
temp_local_epoch += 1
_, data_iterator = helper.train_data[agent_name_key]
poison_data_count = 0
total_loss = 0.
correct = 0
dataset_size = 0
dis2global_list=[]
for batch_id, batch in enumerate(data_iterator):
data, targets, poison_num = helper.get_poison_batch(batch, adversarial_index=adversarial_index,evaluation=False)
poison_optimizer.zero_grad()
dataset_size += len(data)
poison_data_count += poison_num
output = model(data)
class_loss = nn.functional.cross_entropy(output, targets)
distance_loss = helper.model_dist_norm_var(model, target_params_variables)
# Lmodel = αLclass + (1 − α)Lano; alpha_loss =1 fixed
loss = helper.params['alpha_loss'] * class_loss + \
(1 - helper.params['alpha_loss']) * distance_loss
loss.backward()
# get gradients
if helper.params['aggregation_methods']==config.AGGR_FOOLSGOLD:
for i, (name, params) in enumerate(model.named_parameters()):
if params.requires_grad:
if internal_epoch == 1 and batch_id == 0:
client_grad.append(params.grad.clone())
else:
client_grad[i] += params.grad.clone()
poison_optimizer.step()
total_loss += loss.data
pred = output.data.max(1)[1] # get the index of the max log-probability
correct += pred.eq(targets.data.view_as(pred)).cpu().sum().item()
if helper.params["batch_track_distance"]:
# we can calculate distance to this model now.
temp_data_len = len(data_iterator)
distance_to_global_model = helper.model_dist_norm(model, target_params_variables)
dis2global_list.append(distance_to_global_model)
model.track_distance_batch_vis(vis=main.vis, epoch=temp_local_epoch,
data_len=temp_data_len,
batch=batch_id,distance_to_global_model= distance_to_global_model,
eid=helper.params['environment_name'],
name=str(agent_name_key),is_poisoned=True)
if step_lr:
scheduler.step()
main.logger.info(f'Current lr: {scheduler.get_lr()}')
acc = 100.0 * (float(correct) / float(dataset_size))
total_l = total_loss / dataset_size
main.logger.info(
'___PoisonTrain {} , epoch {:3d}, local model {}, internal_epoch {:3d}, Average loss: {:.4f}, '
'Accuracy: {}/{} ({:.4f}%), train_poison_data_count: {}'.format(model.name, epoch, agent_name_key,
internal_epoch,
total_l, correct, dataset_size,
acc, poison_data_count))
csv_record.train_result.append(
[agent_name_key, temp_local_epoch,
epoch, internal_epoch, total_l.item(), acc, correct, dataset_size])
if helper.params['vis_train']:
model.train_vis(main.vis, temp_local_epoch,
acc, loss=total_l, eid=helper.params['environment_name'], is_poisoned=True,
name=str(agent_name_key) )
num_samples_dict[agent_name_key] = dataset_size
if helper.params["batch_track_distance"]:
main.logger.info(
f'MODEL {model_id}. P-norm is {helper.model_global_norm(model):.4f}. '
f'Distance to the global model: {dis2global_list}. ')
# internal epoch finish
main.logger.info(f'Global model norm: {helper.model_global_norm(target_model)}.')
main.logger.info(f'Norm before scaling: {helper.model_global_norm(model)}. '
f'Distance: {helper.model_dist_norm(model, target_params_variables)}')
if not helper.params['baseline']:
main.logger.info(f'will scale.')
epoch_loss, epoch_acc, epoch_corret, epoch_total = test.Mytest(helper=helper, epoch=epoch,
model=model, is_poison=False,
visualize=False,
agent_name_key=agent_name_key)
csv_record.test_result.append(
[agent_name_key, epoch, epoch_loss, epoch_acc, epoch_corret, epoch_total])
epoch_loss, epoch_acc, epoch_corret, epoch_total = test.Mytest_poison(helper=helper,
epoch=epoch,
model=model,
is_poison=True,
visualize=False,
agent_name_key=agent_name_key)
csv_record.posiontest_result.append(
[agent_name_key, epoch, epoch_loss, epoch_acc, epoch_corret, epoch_total])
clip_rate = helper.params['scale_weights_poison']
main.logger.info(f"Scaling by {clip_rate}")
for key, value in model.state_dict().items():
target_value = last_local_model[key]
new_value = target_value + (value - target_value) * clip_rate
model.state_dict()[key].copy_(new_value)
distance = helper.model_dist_norm(model, target_params_variables)
main.logger.info(
f'Scaled Norm after poisoning: '
f'{helper.model_global_norm(model)}, distance: {distance}')
csv_record.scale_temp_one_row.append(epoch)
csv_record.scale_temp_one_row.append(round(distance, 4))
if helper.params["batch_track_distance"]:
temp_data_len = len(helper.train_data[agent_name_key][1])
model.track_distance_batch_vis(vis=main.vis, epoch=temp_local_epoch,
data_len=temp_data_len,
batch=temp_data_len-1,
distance_to_global_model=distance,
eid=helper.params['environment_name'],
name=str(agent_name_key), is_poisoned=True)
distance = helper.model_dist_norm(model, target_params_variables)
main.logger.info(f"Total norm for {current_number_of_adversaries} "
f"adversaries is: {helper.model_global_norm(model)}. distance: {distance}")
else:
temp_local_epoch = (epoch - 1) * helper.params['internal_epochs']
for internal_epoch in range(1, helper.params['internal_epochs'] + 1):
temp_local_epoch += 1
_, data_iterator = helper.train_data[agent_name_key]
total_loss = 0.
correct = 0
dataset_size = 0
dis2global_list = []
for batch_id, batch in enumerate(data_iterator):
optimizer.zero_grad()
data, targets = helper.get_batch(data_iterator, batch,evaluation=False)
dataset_size += len(data)
output = model(data)
loss = nn.functional.cross_entropy(output, targets)
loss.backward()
# get gradients
if helper.params['aggregation_methods'] == config.AGGR_FOOLSGOLD:
for i, (name, params) in enumerate(model.named_parameters()):
if params.requires_grad:
if internal_epoch == 1 and batch_id == 0:
client_grad.append(params.grad.clone())
else:
client_grad[i] += params.grad.clone()
optimizer.step()
total_loss += loss.data
pred = output.data.max(1)[1] # get the index of the max log-probability
correct += pred.eq(targets.data.view_as(pred)).cpu().sum().item()
if helper.params["vis_train_batch_loss"]:
cur_loss = loss.data
temp_data_len = len(data_iterator)
model.train_batch_vis(vis=main.vis,
epoch=temp_local_epoch,
data_len=temp_data_len,
batch=batch_id,
loss=cur_loss,
eid=helper.params['environment_name'],
name=str(agent_name_key) , win='train_batch_loss', is_poisoned=False)
if helper.params["batch_track_distance"]:
# we can calculate distance to this model now
temp_data_len = len(data_iterator)
distance_to_global_model = helper.model_dist_norm(model, target_params_variables)
dis2global_list.append(distance_to_global_model)
model.track_distance_batch_vis(vis=main.vis, epoch=temp_local_epoch,
data_len=temp_data_len,
batch=batch_id,distance_to_global_model= distance_to_global_model,
eid=helper.params['environment_name'],
name=str(agent_name_key),is_poisoned=False)
acc = 100.0 * (float(correct) / float(dataset_size))
total_l = total_loss / dataset_size
main.logger.info(
'___Train {}, epoch {:3d}, local model {}, internal_epoch {:3d}, Average loss: {:.4f}, '
'Accuracy: {}/{} ({:.4f}%)'.format(model.name, epoch, agent_name_key, internal_epoch,
total_l, correct, dataset_size,
acc))
csv_record.train_result.append([agent_name_key, temp_local_epoch,
epoch, internal_epoch, total_l.item(), acc, correct, dataset_size])
if helper.params['vis_train']:
model.train_vis(main.vis, temp_local_epoch,
acc, loss=total_l, eid=helper.params['environment_name'], is_poisoned=False,
name=str(agent_name_key))
num_samples_dict[agent_name_key] = dataset_size
if helper.params["batch_track_distance"]:
main.logger.info(
f'MODEL {model_id}. P-norm is {helper.model_global_norm(model):.4f}. '
f'Distance to the global model: {dis2global_list}. ')
# test local model after internal epoch finishing
epoch_loss, epoch_acc, epoch_corret, epoch_total = test.Mytest(helper=helper, epoch=epoch,
model=model, is_poison=False, visualize=True,
agent_name_key=agent_name_key)
csv_record.test_result.append([agent_name_key, epoch, epoch_loss, epoch_acc, epoch_corret, epoch_total])
if is_poison:
if agent_name_key in helper.params['adversary_list'] and (epoch in localmodel_poison_epochs):
epoch_loss, epoch_acc, epoch_corret, epoch_total = test.Mytest_poison(helper=helper,
epoch=epoch,
model=model,
is_poison=True,
visualize=True,
agent_name_key=agent_name_key)
csv_record.posiontest_result.append(
[agent_name_key, epoch, epoch_loss, epoch_acc, epoch_corret, epoch_total])
# test on local triggers
if agent_name_key in helper.params['adversary_list']:
if helper.params['vis_trigger_split_test']:
model.trigger_agent_test_vis(vis=main.vis, epoch=epoch, acc=epoch_acc, loss=None,
eid=helper.params['environment_name'],
name=str(agent_name_key) + "_combine")
epoch_loss, epoch_acc, epoch_corret, epoch_total = \
test.Mytest_poison_agent_trigger(helper=helper, model=model, agent_name_key=agent_name_key)
csv_record.poisontriggertest_result.append(
[agent_name_key, str(agent_name_key) + "_trigger", "", epoch, epoch_loss,
epoch_acc, epoch_corret, epoch_total])
if helper.params['vis_trigger_split_test']:
model.trigger_agent_test_vis(vis=main.vis, epoch=epoch, acc=epoch_acc, loss=None,
eid=helper.params['environment_name'],
name=str(agent_name_key) + "_trigger")
# update the model weight
local_model_update_dict = dict()
for name, data in model.state_dict().items():
local_model_update_dict[name] = torch.zeros_like(data)
local_model_update_dict[name] = (data - last_local_model[name])
last_local_model[name] = copy.deepcopy(data)
if helper.params['aggregation_methods'] == config.AGGR_FOOLSGOLD:
epochs_local_update_list.append(client_grad)
else:
epochs_local_update_list.append(local_model_update_dict)
epochs_submit_update_dict[agent_name_key] = epochs_local_update_list
return epochs_submit_update_dict, num_samples_dict