def pop_card_from_list(self, card_id: str, location: List[Card]):
card = util.shrink([card for card in location if card.id == card_id])
other_cards = [card for card in location if card.id != card_id]
location.clear()
for other_card in other_cards:
location.append(other_card)
return card
def reject_trade(self, player: Player, trade_id: str):
trade: Trade = util.shrink([trade for trade in self.trades if trade.id == trade_id])
if not trade:
return util.error("Trade does not exist")
if player is not trade.p2:
return util.error("You are not in this trade")
# Remove trade from trades
self.trades = [trade for trade in self.trades if trade.id != trade_id]
return util.success("Trade successfully rejected")
def create_trade(self, p1: Player, p2_name: str, card_ids: List[str], wants: List[str]):
tcs: List[TradingCard] = self.ids_to_tcs(p1, card_ids)
new_trades: List[Trade] = []
p2: Player = util.shrink([player for player in self.players if player.name == p2_name])
if not p2:
return util.error("Player chosen is not in game")
new_trades += [Trade(p1, p2, tcs, wants)]
self.trades += new_trades
return util.success('Successfully created trade')
def accept_trade(self, player: Player, trade_id: str, card_ids: List[str]):
tcs: List[TradingCard] = self.ids_to_tcs(player, card_ids)
trade: Trade = util.shrink([trade for trade in self.trades if trade.id == trade_id])
if not trade:
return util.error("Trade does not exist")
if player is not trade.p2:
return util.error("You are not in this trade")
result = trade.accept(tcs)
if not result.get('error'):
self.trades = [trade for trade in self.trades if trade.id != trade_id]
return result
def access() -> Dict:
'''
Checks if request has a cookie of a logged in user. If cookie exists it
sends user their game info. If not, it takes them to the login page.
'''
cookie: str = request.cookies.get('tbg_token')
try:
game_id: str = clients[cookie]
except KeyError:
abort(400, util.error('Access denied'))
if games[game_id].status == 'Completed':
abort(400, util.error('Game already completed'))
player: Player = util.shrink([
player for player in games[game_id].players
if player.token == request.cookies.get('tbg_token')
])
return jsonify({'game': game_id, "player_name": player.name})
def login() -> Dict:
'''
Adds user to the requested game as long as game isn't full or already started,
game exists, and user name isn't already used in game.
'''
post_data: Dict = request.get_json()
try:
name: str = post_data['name']
game_id: str = post_data['game']
except KeyError:
abort(400, util.error('Incorrect JSON data'))
# Get first public game
game: Game = util.shrink([
games[game_id] for game_id in games
if games[game_id].game_type == 'public'
])
# If game game_id isn't a blank string, use that game
if game_id in games:
game = games[game_id]
if name in [player.name for player in game.players]:
abort(400, util.error('User already exists with that name'))
if game.status != 'Awaiting':
abort(400, util.error('Game has already started or ended'))
if game.is_full():
abort(400, util.error('Game is full'))
player: Player = Player(name)
game.add_player(player)
clients[player.token] = game_id
response = make_response(
jsonify(util.success('Successfully logged into game')))
response.set_cookie('tbg_token', player.token, max_age=6000)
update_client(game)
return response
def run():
for epoch in range(config.epoch):
print(epoch)
while config.input_dim + 1 <= len(config.columns):
current_feature_name = config.columns[config.input_dim]
textLoader = DataLoader(text,
batch_size=config.batch_size,
shuffle=True,
num_workers=config.num_workers,
drop_last=config.drop_last)
model = netLSTM_withbn()
with torch.no_grad():
model = model.cuda()
net_enn_train = enn.ENN(model, NE)
# If pre_existent epoch found, set net_enn_train parameters with pre_existent epoch record.
# Only processed if current epoch count is 0
epoch_list = [
i for i in os.listdir(PATH) if i.startswith(
"parameters_{}_epoch_".format(current_feature_name))
]
if len(epoch_list) > 0 and epoch == 0:
print("Pre_existent epoch found: {}".format(
sorted(epoch_list)[-1]))
epoch_pre_existent = pickle.load(
open(os.path.join(PATH,
sorted(epoch_list)[-1]), 'rb'))
net_enn_train.set_parameter(epoch_pre_existent)
if epoch > 0:
parameter_path = os.path.join(
PATH,
"parameters_{}_epoch_{}".format(current_feature_name,
epoch - 1))
print("Setting checkpoint {}".format(parameter_path))
parameter_checkpoint = pickle.load(open(parameter_path, 'rb'))
net_enn_train.set_parameter(parameter_checkpoint)
for i, data in enumerate(textLoader):
print('#' * 30)
print("{}: batch{}".format(time.strftime('%Y%m%d_%H_%M_%S'),
i))
# preparing the train data
input_, target = data
input_ = torch.from_numpy(
np.stack(list(shrink(input_, config.shrink_len)), axis=1))
target = torch.from_numpy(
np.stack(list(shrink(target, config.shrink_len)), axis=1))
with torch.no_grad():
input_, target = map(Variable,
(input_.float(), target.float()))
target = target.reshape(-1, config.output_dim)
input_ = input_.cuda()
target = target.cuda()
# train the model
net_enn_train, loss, pred_data = train(
net_enn_train,
input_,
target,
feature_name=current_feature_name)
with torch.no_grad():
params = net_enn_train.get_parameter()
filename = PATH + "/parameters_{}_epoch_{}".format(
current_feature_name, epoch)
save_var(params, filename)
del params
test(net_enn_train,
feature_name=current_feature_name,
draw_result=(epoch == config.epoch - 1))
config.input_dim += 1
text.reset_train_dataset()
config.input_dim -= 3
text.reset_train_dataset()
text.reset_test_dataset()
indices1 = np.where(scores1 > score_threshold1)[0]
scores1 = scores1[indices1]
boxes_pred1 = boxes_pred1[indices1]
boxes_pred1, scores1 = util.nms(boxes_pred1, scores1, nms_threshold)
indices2 = np.where(scores2 > score_threshold2)[0]
scores2 = scores2[indices2]
boxes_pred2 = boxes_pred2[indices2]
boxes_pred2, scores2 = util.nms(boxes_pred2, scores2, nms_threshold)
boxes_pred = np.concatenate((boxes_pred1, boxes_pred2))
scores = np.concatenate((scores1, scores2))
boxes_pred, scores = util.averages(boxes_pred, scores, wt_overlap,
solo_min)
util.shrink(boxes_pred, shrink_factor)
output = ''
for j, bb in enumerate(boxes_pred):
x1 = int(bb[0])
y1 = int(bb[1])
w = int(bb[2] - x1 + 1)
h = int(bb[3] - y1 + 1)
output += f'{scores[j]:.3f} {x1} {y1} {w} {h} '
test_ids.append(fid)
test_outputs.append(output)
print()
end = time.time()
# print execution time
print(f"Elapsed time = {end-start:.3f} seconds")
