#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()
])),
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)
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)