def __init__(self, path):
super().__init__('ann')
self.pot_rewards = {
11: 300, # Insure the agent values the goal suit. Handle seeing 11 cards elsewhere
10: 10 * 10 + 100, # [cards] * 10 + [10 pot]
9: 9 * 10 + 100, # [cards] * 10 + [10 pot]
8: 8 * 10 + .33 * 120 + .67 * 100, # [cards] * 10 + [prob. 8 goal] * [8 pot] + [prob. 10 goal] * [10 pot]
7: 7 * 10 + .33 * 120 + .67 * 100, # [cards] * 10 + [prob. 8 goal] * [8 pot] + [prob. 10 goal] * [10 pot]
6: 6 * 10 + .33 * 120 + .67 * 100, # [cards] * 10 + [prob. 8 goal] * [8 pot] + [prob. 10 goal] * [10 pot]
# [other hands 10] = C(n+r-1,r-1)) where n = 5 and r = 3 so [other hands 10] = 21
# [cards] * 10 + [prob 8 goal] * [8 pot] + [prob 10 goal] * (3/[other hands 10] * [10 pot]/2 + ([other hands 10] - 3)/[other hands 10] * [10 pot]])
5: 5 * 10 + .33 * 120 + .67 * (3/21 * 100/2 + 18/21 * 100),
# [other hands 10] = C(n+r-1,r-1)) where n = 6 and r = 3 so [other hands 10] = 28
# [other hands 8] = C(n+r-1,r-1)) where n = 4 and r = 3 so [other hands 8] = 15
# [cards] * 10 + [prob 8 goal] * (3/[other hands 8] * [8 pot] / 2 + ([other hands 8] - 3)/[other hands 8]) + [prob 10 goal] * (3/[other hands] * [10 pot]/2 * ([other hands 10] - 6)/[other hands 10] * [10 pot])
4: 4 * 10 + .33 * (3/15 * 120/2 + 12/15 * 120) + .67 * (3/28 * 100/2 + 22/28 * 100),
# [other hands 10] = C(n+r-1,r-1)) where n = 7 and r = 3 so [other hands 10] = 36
# [other hands 8] = C(n+r-1,r-1)) where n = 5 and r = 3 so [other hands 8] = 21
# [cards] * 10 + [prob 8 goal] * (3/[other hands 8] * [8 pot] / 3) + [prob 10 goal] * (3/[other hands 10] * [10 pot] / 2)
3: 3 * 10 + .33 * (3/21 * 120/3) + .67 * (3/36 * 100 / 2),
# [other hands 8] = C(n+r-1,r-1)) where n = 6 and r = 3 so [other hands 8] = 28
# [cards] * 10 + [prob 8 goal] * (1/[other hands 8] * [8 pot] / 4)
2: 2 * 10 + .33 * (1/28 * 120/4),
1: 10, # 1 * 10
0: 0, # 0 * 10
}
self.model = Net(16, 32, 64) # TODO don't hard code these
self.model.load_state_dict(torch.load(path))
self.model.eval()
images, labels = data
# if labels.data == 4:
# continue
# uncomment to test the adversarial patch
images = images.to(device)
labels = labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the %s test images: %10.5f %%' % (total,100 * correct / total))
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='test')
parser.add_argument("model", type=str, help="test_model")
args = parser.parse_args()
model = Net()
model.load_state_dict(torch.load('../donemodel/'+args.model))
print("test model is ", args.model)
dataloaders,dataset_sizes =data_process_lisa(batch_size =1)
test(model,dataloaders,dataset_sizes)
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))
# load best model weights
model.load_state_dict(best_model_wts)
return model
if __name__ == "__main__":
torch.manual_seed(123456)
dataloaders, dataset_sizes = data_process_lisa(batch_size=128)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_ft = Net()
model_ft.apply(weights_init)
#model_ft.load_state_dict(torch.load('../donemodel/'+args.model))
model_ft.to(device)
# model_ft = nn.DataParallel(model,device_ids=[0,1])
# use multiple gpus
criterion = nn.CrossEntropyLoss()
optimizer_ft = optim.Adam(model_ft.parameters(), lr=0.01)
# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft,
step_size=10,
gamma=0.1)
patch_type = opt.patch_type
#patch_size = opt.patch_size
image_size = opt.image_size
plot_all = opt.plot_all
print("=> creating model ")
netClassifier_par = {
'input_size': [3, 32, 32],
'input_range': [0, 1],
'mean': [0, 0, 0],
'std': [1, 1, 1],
'num_classes': 16,
'input_space': "RGB"
}
netClassifier = Net()
netClassifier.load_state_dict(torch.load('../donemodel/' + opt.model))
if torch.cuda.is_available():
netClassifier.cuda()
print('==> Preparing data..')
data_dir = '../LISA'
train_loader = torch.utils.data.DataLoader(dset.ImageFolder(
os.path.join(data_dir, 'Train'),
transforms.Compose([
transforms.Resize(
round(max(netClassifier_par["input_size"]) * 1.050)),
transforms.CenterCrop(max(netClassifier_par["input_size"])),
transforms.ToTensor()
delta.data *= epsilon / norms(delta.detach()).clamp(min=epsilon)
delta.grad.zero_()
all_loss = nn.CrossEntropyLoss(reduction='none')(model(X + delta), y)
max_delta[all_loss >= max_loss] = delta.detach()[all_loss >= max_loss]
max_loss = torch.max(max_loss, all_loss)
return max_delta
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='test')
parser.add_argument("model", type=str, help="test_model")
args = parser.parse_args()
model = Net()
model.load_state_dict(torch.load('../donemodel/' + args.model))
print("test model is ", args.model)
model.eval()
batch_size = 1
dataloaders, dataset_sizes = data_process_lisa(batch_size)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)
eps = [0.5, 1, 1.5, 2, 2.5, 3] # eps is epsilon of the l_2 bound
alpha = [0.05, 0.1, 0.15, 0.2, 0.25, 0.3] # alpha is learning rate
itera = [20, 20, 20, 20, 20, 20] # iterations to find optimal
restart = [
1, 1, 1, 1, 1, 1
] # restart times, since we just do some standard check of our model,
for x in ['train', 'val', 'test']
}
class_names = image_datasets['train'].classes
print(class_names)
print(dataset_sizes)
return dataloaders, dataset_sizes
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='test')
parser.add_argument("model", type=str, help="test_model")
args = parser.parse_args()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = Net()
model.load_state_dict(torch.load('../donemodel/' + args.model))
print("test model is ", args.model)
model.eval()
batch_size = 8
dataloaders, dataset_sizes = data_process(batch_size)
# model.to(device)
preprocessing = dict(mean=[0, 0, 0], std=[1, 1, 1], axis=-3)
fmodel = foolbox.models.PyTorchModel(model.eval(),
bounds=(0, 1),
num_classes=16,
preprocessing=preprocessing)
correct = 0
class AnnModel(UtilityModel):
def __init__(self, path):
super().__init__('ann')
self.pot_rewards = {
11: 300, # Insure the agent values the goal suit. Handle seeing 11 cards elsewhere
10: 10 * 10 + 100, # [cards] * 10 + [10 pot]
9: 9 * 10 + 100, # [cards] * 10 + [10 pot]
8: 8 * 10 + .33 * 120 + .67 * 100, # [cards] * 10 + [prob. 8 goal] * [8 pot] + [prob. 10 goal] * [10 pot]
7: 7 * 10 + .33 * 120 + .67 * 100, # [cards] * 10 + [prob. 8 goal] * [8 pot] + [prob. 10 goal] * [10 pot]
6: 6 * 10 + .33 * 120 + .67 * 100, # [cards] * 10 + [prob. 8 goal] * [8 pot] + [prob. 10 goal] * [10 pot]
# [other hands 10] = C(n+r-1,r-1)) where n = 5 and r = 3 so [other hands 10] = 21
# [cards] * 10 + [prob 8 goal] * [8 pot] + [prob 10 goal] * (3/[other hands 10] * [10 pot]/2 + ([other hands 10] - 3)/[other hands 10] * [10 pot]])
5: 5 * 10 + .33 * 120 + .67 * (3/21 * 100/2 + 18/21 * 100),
# [other hands 10] = C(n+r-1,r-1)) where n = 6 and r = 3 so [other hands 10] = 28
# [other hands 8] = C(n+r-1,r-1)) where n = 4 and r = 3 so [other hands 8] = 15
# [cards] * 10 + [prob 8 goal] * (3/[other hands 8] * [8 pot] / 2 + ([other hands 8] - 3)/[other hands 8]) + [prob 10 goal] * (3/[other hands] * [10 pot]/2 * ([other hands 10] - 6)/[other hands 10] * [10 pot])
4: 4 * 10 + .33 * (3/15 * 120/2 + 12/15 * 120) + .67 * (3/28 * 100/2 + 22/28 * 100),
# [other hands 10] = C(n+r-1,r-1)) where n = 7 and r = 3 so [other hands 10] = 36
# [other hands 8] = C(n+r-1,r-1)) where n = 5 and r = 3 so [other hands 8] = 21
# [cards] * 10 + [prob 8 goal] * (3/[other hands 8] * [8 pot] / 3) + [prob 10 goal] * (3/[other hands 10] * [10 pot] / 2)
3: 3 * 10 + .33 * (3/21 * 120/3) + .67 * (3/36 * 100 / 2),
# [other hands 8] = C(n+r-1,r-1)) where n = 6 and r = 3 so [other hands 8] = 28
# [cards] * 10 + [prob 8 goal] * (1/[other hands 8] * [8 pot] / 4)
2: 2 * 10 + .33 * (1/28 * 120/4),
1: 10, # 1 * 10
0: 0, # 0 * 10
}
self.model = Net(16, 32, 64) # TODO don't hard code these
self.model.load_state_dict(torch.load(path))
self.model.eval()
def get_card_values(self, figgie: Figgie, index: int) -> np.ndarray:
hand = figgie.cards[index]
result = np.zeros(4, dtype=int)
for s in SUITS:
if hand[s.value] > 10:
goal_suit = s.opposite()
result[goal_suit.value] = 100
return result
for s in SUITS:
result[s.value] = (self.pot_rewards[hand[s.value] + 1] - self.pot_rewards[hand[s.value]])
input = np.array([
[cards for cards in figgie.cards[figgie.active_player]],
[market.buying_price if market.buying_price is not None else 0 for market in figgie.markets],
[market.selling_price if market.selling_price is not None else 0 for market in figgie.markets],
[market.last_price if market.last_price is not None else 0 for market in figgie.markets],
[market.operations for market in figgie.markets],
[market.transactions for market in figgie.markets],
], dtype=np.float32).flatten()
input = torch.from_numpy(input).view(-1, 24)
percents = self.model(input)
percents = torch.exp(percents)
percents = percents.detach().numpy().flatten()
return (percents * result).astype(int)
parser.add_argument("--nums_choose",
type=int,
default=5,
help="number of potential positons for final search")
args = parser.parse_args()
for i in [5, 6, 7]:
seed = i
torch.manual_seed(seed)
torch.cuda.empty_cache()
print('Outout model name is ../donemodel/new_sticker_model0' +
str(args.out) + str(seed) + '.pt')
dataloaders, dataset_sizes = data_process_lisa(batch_size=256) #256
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_ft = Net()
model_ft.load_state_dict(torch.load('../donemodel/' + args.model))
#model_ft.load_weights()
model_ft.to(device)
# model_ft = nn.DataParallel(model,device_ids=[0,1])
criterion = nn.CrossEntropyLoss()
#optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
optimizer_ft = optim.Adam(model_ft.parameters(), lr=0.01)
#optimizer_ft = optim.Adadelta(model_ft.parameters(), lr=0.1, rho=0.9, eps=1e-06, weight_decay=0.01)
# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft,
step_size=100,