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test.py
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test.py
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import torch
import torch.nn as nn
import config
import main
def Mytest(helper, epoch,
model, is_poison=False, visualize=True, agent_name_key=""):
model.eval()
total_loss = 0
correct = 0
dataset_size = 0
if helper.params['type'] == config.TYPE_LOAN:
for i in range(0, len(helper.allStateHelperList)):
state_helper = helper.allStateHelperList[i]
data_iterator = state_helper.get_testloader()
for batch_id, batch in enumerate(data_iterator):
data, targets = state_helper.get_batch(data_iterator, batch, evaluation=True)
dataset_size += len(data)
output = model(data)
total_loss += nn.functional.cross_entropy(output, targets,
reduction='sum').item() # sum up batch loss
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()
elif helper.params['type'] == config.TYPE_CIFAR \
or helper.params['type'] == config.TYPE_MNIST \
or helper.params['type'] == config.TYPE_TINYIMAGENET:
data_iterator = helper.test_data
for batch_id, batch in enumerate(data_iterator):
data, targets = helper.get_batch(data_iterator, batch, evaluation=True)
dataset_size += len(data)
output = model(data)
total_loss += nn.functional.cross_entropy(output, targets,
reduction='sum').item() # sum up batch loss
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()
acc = 100.0 * (float(correct) / float(dataset_size)) if dataset_size!=0 else 0
total_l = total_loss / dataset_size if dataset_size!=0 else 0
main.logger.info('___Test {} poisoned: {}, epoch: {}: Average loss: {:.4f}, '
'Accuracy: {}/{} ({:.4f}%)'.format(model.name, is_poison, epoch,
total_l, correct, dataset_size,
acc))
if visualize: # loss =total_l
model.test_vis(vis=main.vis, epoch=epoch, acc=acc, loss=None,
eid=helper.params['environment_name'],
agent_name_key=str(agent_name_key))
model.train()
return (total_l, acc, correct, dataset_size)
def Mytest_poison(helper, epoch,
model, is_poison=False, visualize=True, agent_name_key=""):
model.eval()
total_loss = 0.0
correct = 0
dataset_size = 0
poison_data_count = 0
if helper.params['type'] == config.TYPE_LOAN:
trigger_names = []
trigger_values = []
for j in range(0, helper.params['trigger_num']):
for name in helper.params[str(j) + '_poison_trigger_names']:
trigger_names.append(name)
for value in helper.params[str(j) + '_poison_trigger_values']:
trigger_values.append(value)
for i in range(0, len(helper.allStateHelperList)):
state_helper = helper.allStateHelperList[i]
data_source = state_helper.get_testloader()
data_iterator = data_source
for batch_id, batch in enumerate(data_iterator):
for index in range(len(batch[0])):
batch[1][index] = helper.params['poison_label_swap']
for j in range(0, len(trigger_names)):
name = trigger_names[j]
value = trigger_values[j]
batch[0][index][helper.feature_dict[name]] = value
poison_data_count += 1
data, targets = state_helper.get_batch(data_source, batch, evaluation=True)
dataset_size += len(data)
output = model(data)
total_loss += nn.functional.cross_entropy(output, targets,
reduction='sum').item() # sum up batch loss
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()
elif helper.params['type'] == config.TYPE_CIFAR \
or helper.params['type'] == config.TYPE_MNIST \
or helper.params['type'] == config.TYPE_TINYIMAGENET:
data_iterator = helper.test_data_poison
for batch_id, batch in enumerate(data_iterator):
data, targets, poison_num = helper.get_poison_batch(batch, adversarial_index=-1, evaluation=True)
poison_data_count += poison_num
dataset_size += len(data)
output = model(data)
total_loss += nn.functional.cross_entropy(output, targets,
reduction='sum').item() # sum up batch loss
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()
acc = 100.0 * (float(correct) / float(poison_data_count)) if poison_data_count!=0 else 0
total_l = total_loss / poison_data_count if poison_data_count!=0 else 0
main.logger.info('___Test {} poisoned: {}, epoch: {}: Average loss: {:.4f}, '
'Accuracy: {}/{} ({:.4f}%)'.format(model.name, is_poison, epoch,
total_l, correct, poison_data_count,
acc))
if visualize: #loss = total_l
model.poison_test_vis(vis=main.vis, epoch=epoch, acc=acc, loss=None, eid=helper.params['environment_name'],agent_name_key=str(agent_name_key))
model.train()
return total_l, acc, correct, poison_data_count
def Mytest_poison_trigger(helper, model, adver_trigger_index):
model.eval()
total_loss = 0.0
correct = 0
dataset_size = 0
poison_data_count = 0
if helper.params['type'] == config.TYPE_LOAN:
trigger_names = []
trigger_values = []
if adver_trigger_index == -1:
for j in range(0, helper.params['trigger_num']):
for name in helper.params[str(j) + '_poison_trigger_names']:
trigger_names.append(name)
for value in helper.params[str(j) + '_poison_trigger_values']:
trigger_values.append(value)
else:
trigger_names = helper.params[str(adver_trigger_index) + '_poison_trigger_names']
trigger_values = helper.params[str(adver_trigger_index) + '_poison_trigger_values']
for i in range(0, len(helper.allStateHelperList)):
state_helper = helper.allStateHelperList[i]
data_source = state_helper.get_testloader()
data_iterator = data_source
for batch_id, batch in enumerate(data_iterator):
for index in range(len(batch[0])):
batch[1][index] = helper.params['poison_label_swap']
for j in range(0, len(trigger_names)):
name = trigger_names[j]
value = trigger_values[j]
batch[0][index][helper.feature_dict[name]] = value
poison_data_count += 1
data, targets = state_helper.get_batch(data_source, batch, evaluation=True)
dataset_size += len(data)
output = model(data)
total_loss += nn.functional.cross_entropy(output, targets,
reduction='sum').item() # sum up batch loss
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()
elif helper.params['type'] == config.TYPE_CIFAR \
or helper.params['type'] == config.TYPE_MNIST \
or helper.params['type'] == config.TYPE_TINYIMAGENET:
data_iterator = helper.test_data_poison
adv_index = adver_trigger_index
for batch_id, batch in enumerate(data_iterator):
data, targets, poison_num = helper.get_poison_batch(batch, adversarial_index=adv_index, evaluation=True)
poison_data_count += poison_num
dataset_size += len(data)
output = model(data)
total_loss += nn.functional.cross_entropy(output, targets,
reduction='sum').item() # sum up batch loss
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()
acc = 100.0 * (float(correct) / float(poison_data_count)) if poison_data_count!=0 else 0
total_l = total_loss / poison_data_count if poison_data_count!=0 else 0
model.train()
return total_l, acc, correct, poison_data_count
def Mytest_poison_agent_trigger(helper, model, agent_name_key):
model.eval()
total_loss = 0.0
correct = 0
dataset_size = 0
poison_data_count = 0
if helper.params['type'] == config.TYPE_LOAN:
adv_index = -1
for temp_index in range(0, len(helper.params['adversary_list'])):
if agent_name_key == helper.params['adversary_list'][temp_index]:
adv_index = temp_index
break
trigger_names = helper.params[str(adv_index) + '_poison_trigger_names']
trigger_values = helper.params[str(adv_index) + '_poison_trigger_values']
for i in range(0, len(helper.allStateHelperList)):
state_helper = helper.allStateHelperList[i]
data_source = state_helper.get_testloader()
data_iterator = data_source
for batch_id, batch in enumerate(data_iterator):
for index in range(len(batch[0])):
batch[1][index] = helper.params['poison_label_swap']
for j in range(0, len(trigger_names)):
name = trigger_names[j]
value = trigger_values[j]
batch[0][index][helper.feature_dict[name]] = value
poison_data_count += 1
data, targets = state_helper.get_batch(data_source, batch, evaluation=True)
dataset_size += len(data)
output = model(data)
total_loss += nn.functional.cross_entropy(output, targets,
reduction='sum').item() # sum up batch loss
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()
elif helper.params['type'] == config.TYPE_CIFAR \
or helper.params['type'] == config.TYPE_MNIST \
or helper.params['type'] == config.TYPE_TINYIMAGENET:
data_iterator = helper.test_data_poison
adv_index = -1
for temp_index in range(0, len(helper.params['adversary_list'])):
if int(agent_name_key) == helper.params['adversary_list'][temp_index]:
adv_index = temp_index
break
for batch_id, batch in enumerate(data_iterator):
data, targets, poison_num = helper.get_poison_batch(batch, adversarial_index=adv_index, evaluation=True)
poison_data_count += poison_num
dataset_size += len(data)
output = model(data)
total_loss += nn.functional.cross_entropy(output, targets,
reduction='sum').item() # sum up batch loss
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()
acc = 100.0 * (float(correct) / float(poison_data_count)) if poison_data_count!=0 else 0
total_l = total_loss / poison_data_count if poison_data_count!=0 else 0
model.train()
return total_l, acc, correct, poison_data_count