class Decoder():
def __init__(self,path):
self.model = Autoencoder().float()
# self.model.eval()
checkpoint = load_checkpoint(path)
self.model.load_state_dict(checkpoint['model_state'])
self.model.eval()
def decompress(self, in_path, out_path):
dw = dh = y = S2 = S3 = None
with lzma.open(in_path, 'rb') as fp:
dw = int.from_bytes(fp.read(1), byteorder='big', signed=False)
dh = int.from_bytes(fp.read(1), byteorder='big', signed=False)
S2 = int.from_bytes(fp.read(2), byteorder='big', signed=False)
S3 = int.from_bytes(fp.read(2), byteorder='big', signed=False)
y = np.empty((1,128,S2,S3)).ravel()
temp = None;
j = 0
print('reading matrix')
byte = fp.read(1)
while byte != b"":
temp = BitArray(byte).bin
# print(temp)
for i in range(len(temp)):
y[j] = int(temp[i])
j += 1
byte = fp.read(1)
y[y<0.5] = -1
y = torch.from_numpy(y.reshape(1,128,S2,S3)).float()
output = self.model.dec(y)
img = TF.to_pil_image(output.squeeze(0))
width, height = img.size
img = img.crop((dw,dh,width,height));
img.save(out_path, "PNG")
return y
def find_similar(fen, num=1, similarity_function=nearest):
"""
Finds x smallest values in a table.
:param fen: String with chess position in Fen notation
:param num: Number of games to be found
:param similarity_function: Function that measures the similarity of vectors
:return: list of games with similar positions
"""
coder = Autoencoder(settings.BOARD_SHAPE, settings.LATENT_SIZE).to(
settings.DEVICE
)
coder.load_state_dict(torch.load(settings.CODER_PATH, map_location=settings.DEVICE))
coder = coder.coder
coder.eval()
inf = Inference(settings.DEVICE, coder)
target = inf.predict([fen]).tolist()[0]
conn = sqlite3.connect(settings.DATABASE)
cursor = conn.cursor()
cursor.execute("select Embeding FROM positions_lite")
matrix = cursor.fetchall()
matrix = [json.loads(x[0])[0] for x in matrix]
scores = similarity_function(matrix, target)
idx = find_lowest(scores, num)
return Games(find_game(idx, cursor))
def main():
parser = argparse.ArgumentParser(
description='Simple training script for training model')
parser.add_argument(
'--epochs', help='Number of epochs (default: 75)', type=int, default=75)
parser.add_argument(
'--batch-size', help='Batch size of the data (default: 16)', type=int, default=16)
parser.add_argument(
'--learning-rate', help='Learning rate (default: 0.001)', type=float, default=0.001)
parser.add_argument(
'--seed', help='Random seed (default:1)', type=int, default=1)
parser.add_argument(
'--data-path', help='Path for the downloaded dataset (default: ../dataset/)', default='../dataset/')
parser.add_argument(
'--dataset', help='Dataset name. Must be one of MNIST, STL10, CIFAR10')
parser.add_argument(
'--use-cuda', help='CUDA usage (default: False)', type=bool, default=False)
parser.add_argument(
'--network-type', help='Type of the network layers. Must be one of Conv, FC (default: FC)', default='FC')
parser.add_argument(
'--weight-decay', help='weight decay (L2 penalty) (default: 1e-5)', type=float, default=1e-5)
parser.add_argument(
'--log-interval', help='No of batches to wait before logging training status (default: 50)', type=int, default=50)
parser.add_argument(
'--save-model', help='For saving the current model (default: True)', type=bool, default=True)
args = parser.parse_args()
epochs = args.epochs # number of epochs
batch_size = args.batch_size # batch size
learning_rate = args.learning_rate # learning rate
torch.manual_seed(args.seed) # seed value
# Creating dataset path if it doesn't exist
if args.data_path is None:
raise ValueError('Must provide dataset path')
else:
data_path = args.data_path
if not os.path.isdir(data_path):
os.mkdir(data_path)
# Downloading proper dataset and creating data loader
if args.dataset == 'MNIST':
T = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
train_data = torchvision.datasets.MNIST(
data_path, train=True, download=True, transform=T)
test_data = torchvision.datasets.MNIST(
data_path, train=False, download=True, transform=T)
ip_dim = 1 * 28 * 28 # input dimension
h1_dim = int(ip_dim / 2) # hidden layer 1 dimension
op_dim = int(ip_dim / 4) # output dimension
elif args.dataset == 'STL10':
T = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_data = torchvision.datasets.STL10(
data_path, split='train', download=True, transform=T)
test_data = torchvision.datasets.STL10(
data_path, split='test', download=True, transform=T)
ip_dim = 3 * 96 * 96 # input dimension
h1_dim = int(ip_dim / 2) # hidden layer 1 dimension
op_dim = int(ip_dim / 4) # output dimension
elif args.dataset == 'CIFAR10':
T = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_data = torchvision.datasets.CIFAR10(
data_path, train=True, download=True, transform=T)
test_data = torchvision.datasets.CIFAR10(
data_path, train=False, download=True, transform=T)
ip_dim = 3 * 32 * 32 # input dimension
h1_dim = int(ip_dim / 2) # hidden layer 1 dimension
op_dim = int(ip_dim / 4) # output dimension
elif args.dataset is None:
raise ValueError('Must provide dataset')
else:
raise ValueError('Dataset name must be MNIST, STL10 or CIFAR10')
train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=False)
# use CUDA or not
device = 'cpu'
if args.use_cuda is False:
if torch.cuda.is_available():
warnings.warn(
'CUDA is available, please use for faster convergence')
else:
device = 'cpu'
else:
if torch.cuda.is_available():
device = 'cuda'
else:
raise ValueError('CUDA is not available, please set it False')
# Type of layer
if args.network_type == 'FC':
auto_encoder = Autoencoder(ip_dim, h1_dim, op_dim).to(device)
elif args.network_type == 'Conv':
auto_encoder = ConvolutionAE().to(device)
else:
raise ValueError('Network type must be either FC or Conv type')
# Train the model
auto_encoder.train()
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(
lr=learning_rate, params=auto_encoder.parameters(), weight_decay=args.weight_decay)
for n_epoch in range(epochs): # loop over the dataset multiple times
reconstruction_loss = 0.0
for batch_idx, (X, Y) in enumerate(train_loader):
X = X.view(X.size()[0], -1)
X = Variable(X).to(device)
encoded, decoded = auto_encoder(X)
optimizer.zero_grad()
loss = criterion(X, decoded)
loss.backward()
optimizer.step()
reconstruction_loss += loss.item()
if (batch_idx + 1) % args.log_interval == 0:
print('[%d, %5d] Reconstruction loss: %.5f' %
(n_epoch + 1, batch_idx + 1, reconstruction_loss / args.log_interval))
reconstruction_loss = 0.0
if args.save_model:
torch.save(auto_encoder.state_dict(), "Autoencoder.pth")
# Save real images
data_iter = iter(test_loader)
images, labels = data_iter.next()
torchvision.utils.save_image(torchvision.utils.make_grid(
images, nrow=4), 'images/actual_img.jpeg')
# Load trained model and get decoded images
auto_encoder.load_state_dict(torch.load('Autoencoder.pth'))
auto_encoder.eval()
images = images.view(images.size()[0], -1)
images = Variable(images).to(device)
encoded, decoded = auto_encoder(images)
# Save decoded images
if args.dataset == 'MNIST':
decoded = decoded.view(decoded.size()[0], 1, 28, 28)
elif args.dataset == 'STL10':
decoded = decoded.view(decoded.size()[0], 3, 96, 96)
elif args.dataset == 'CIFAR10':
decoded = decoded.view(decoded.size()[0], 3, 32, 32)
torchvision.utils.save_image(torchvision.utils.make_grid(
decoded, nrow=4), 'images/decoded_img.jpeg')
def train():
g = Generator(Zdim)
g.apply(weights_init)
print(g)
# load pretrained Autoencoder
if opt.ae:
ae = Autoencoder()
ae.load_state_dict(torch.load(os.path.join(MODEL_PATH, opt.ae)))
# custom loss function
# ==========================
criterion = MQSymKLLoss(th=opt.threshold)
# setup optimizer
# ==========================
optimizer = optim.Adam(g.parameters(), lr=opt.lr, weight_decay=opt.decay)
z = torch.FloatTensor(BS, Zdim, 1, 1).normal_(0, 1)
z_pred = torch.FloatTensor(64, Zdim, 1, 1).normal_(0, 1)
# cuda
if cuda:
g.cuda()
criterion.cuda()
z, z_pred = z.cuda(), z_pred.cuda()
z_pred = Variable(z_pred)
if opt.ae:
if cuda:
ae.cuda()
ae.eval()
# load dataset
# ==========================
kwargs = dict(num_workers=1, pin_memory=True) if cuda else {}
dataloader = DataLoader(
datasets.MNIST(
'MNIST',
download=True,
transform=transforms.Compose([
transforms.ToTensor()
# transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=BS,
shuffle=True,
**kwargs)
N = len(dataloader)
if opt.history:
loss_history = np.empty(N * opt.epochs, dtype=np.float32)
# train
# ==========================
for epoch in range(opt.epochs):
loss_mean = 0.0
for i, (imgs, _) in enumerate(dataloader):
if cuda:
imgs = imgs.cuda()
imgs = Variable(imgs)
g.zero_grad()
# forward & backward & update params
z.resize_(BS, Zdim, 1, 1).normal_(0, 1)
zv = Variable(z)
outputs = g(zv)
if opt.ae:
imgs_enc, _ = ae(imgs)
out_enc, _ = ae(outputs)
loss = criterion(out_enc, imgs_enc)
else:
loss = criterion(outputs, imgs)
loss.backward()
optimizer.step()
loss_mean += loss.data[0]
if opt.history:
loss_history[N * epoch + i] = loss.data[0]
show_progress(epoch + 1, i + 1, N, loss.data[0])
print('\ttotal loss (mean): %f' % (loss_mean / N))
# generate fake images
vutils.save_image(g(z_pred).data,
os.path.join(IMAGE_PATH, '%d.png' % (epoch + 1)),
normalize=False)
# normalize=True)
# save models
torch.save(g.state_dict(), MODEL_FULLPATH)
# save loss history
if opt.history:
np.save('history' + opt.name, loss_history)
optimizer = torch.optim.Adam(AE.parameters(), lr=0.1)
epochs = 1000
for i in range(epochs):
optimizer.zero_grad()
output = AE(data_torch)
loss = loss_fn(output, data_torch)
loss.backward()
optimizer.step()
if i % 100 == 0:
print('Epoch {}: {:.4f}'.format(i, loss))
AE.eval()
with torch.no_grad():
data_transformed = AE.encode(data_torch).detach().numpy()
else:
raise Exception('Please specify a valid solver!')
edges2d = []
for e in edge_list:
edges2d.append(data_transformed[[e[0], e[1]]])
# check crosses
crosses = cross(edges2d, edge_list, findall=True)
print('{}: {} crosses'.format(method, len(crosses)))
plt.scatter(data_transformed[:, 0],
# print statistics
total_samples = input.numel()
running_loss += loss.detach().item() * total_samples
time_step_count += total_samples
i += 1
if i % report_every == 0: # print every 500 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch, i, running_loss / time_step_count))
running_loss = 0.0
time_step_count = 0
if i % (report_every * 10) == 0:
# print()
# print("REPORTING")
# print()
model.eval()
with torch.no_grad():
total_loss = 0.
count = 0
for data in data_stream(valid_filenames[:20],
shuffle=False,
batch_size=32):
# get the inputs
input = torch.from_numpy(data.astype(
np.float32)).cuda()
loss = model(input)
# print(loss)
total_loss += loss.data.item()
count += 1
valid_loss = total_loss / count
if valid_loss < best_loss:
class Main(Frame):
def __init__(
self,
master,
color_palette,
home_path,
pieces_name,
pieces_path,
arrows_path,
images_sizes,
selected_piece,
pieces_padding,
header_height = 60,
option_width = 100,
message_width = 600,
message_height = 200,
find_options_width = 600,
find_options_height = 400,
):
Frame.__init__(self, master, bg=color_palette[0])
master.rowconfigure(0, weight=1)
master.columnconfigure(0, weight=1)
self.main = self
self.coder_set = False
self.home_path = home_path
self.current_pages = 0
self.pages_fens = ["rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1"]
self.pieces_name = pieces_name
self.pieces_path = pieces_path
self.arrows_path = arrows_path
self.images_sizes = images_sizes
self.selected_piece = selected_piece
self.pieces_padding = pieces_padding
self.color_palette = color_palette
self.entering = True
self.follow_fen = True
self.fen_placement = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR"
self.fen_player = "w"
self.fen_castling = "KQkq"
self.header_height = header_height
self.option_width = option_width
self._create_widgets(message_width, message_height, find_options_width, find_options_height)
self.bind("<Configure>", self._resize)
self.winfo_toplevel().minsize(600, 600)
self.display_fen()
self.coder = None
self.games = None
self.store_games = None
self.lichess_set = False
self.coder_launcher = None
self.set_fen("rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1")
self.set_coder(settings.CODER_PATH)
def _create_widgets(self, message_width, message_height,find_options_width, find_options_height):
self.board_box = BoardBox(self)
self.option_box = Options(self, self.option_width)
self.header = Header(self, header_height=self.header_height)
self.pgn_box = PGNOptions(self, self.option_width)
self.tensor_message = TensorDisplayer(self, message_width, message_height)
self.find_option = FindOptions(self,find_options_width, find_options_height)
self.lichess_set_option = LichessSetOptions(self,find_options_width,find_options_height)
self.board_box.grid(row=1, column=0, sticky=N + S + E + W)
self.option_box.grid(row=1, column=1, sticky=N + S + E + W)
self.header.grid(row=0, column=0, columnspan=2, sticky=N + S + E + W)
self.rowconfigure(1, weight=1)
self.columnconfigure(0, weight=1)
def show_lichess_options(self):
self.lichess_set_option.place(relx=0.5, rely=0.5, anchor=CENTER)
self.find_option.place_forget()
def set_lichess(self,name,n_games):
try:
with urlopen("https://lichess.org/api/games/user/{}?max={}&perfType=ultraBullet,bullet,blitz,rapid,classical,correspondence".format(name, n_games)) as pgn:
self.games = pgn_games(pgn,n_games)
self.lichess_set_option.place_forget()
self.header.coder_label["text"]="Account set"
self.follow_fen = True
self.entering = False
self.lichess_set = True
self.set_fen()
self.option_box.grid_forget()
self.pgn_box.grid(row=1, column=1, sticky=N + S + E + W)
self.pgn_box.set_game_number()
except:
self.header.display_fen("Lichess Account couldn't be set", "", "")
def _resize(self, event):
"""Modify padding when window is resized."""
w, h = event.width, event.height
self.rowconfigure(1, weight=h - self.header_height)
self.columnconfigure(0, weight=w - self.option_width)
def display_fen(self):
self.header.display_fen(self.fen_placement, self.fen_player, self.fen_castling)
if self.follow_fen:
self.pages_fens[self.current_pages] = " ".join(
[self.fen_placement, self.fen_player, self.fen_castling, "- 0 0"]
)
def set_fen(self, fen=None):
if self.entering == False:
fen = self.games.board.fen()
split_fen = fen.split()
self.fen_placement = split_fen[0]
self.fen_player = split_fen[1]
self.fen_castling = split_fen[2]
self.board_box.board.set_board(self.fen_placement)
if self.follow_fen:
self.pages_fens[self.current_pages] = fen
self.display_fen()
return
try:
a = chess.Board(fen)
fen = a.fen()
del a
split_fen = fen.split()
self.fen_placement = split_fen[0]
self.fen_player = split_fen[1]
self.fen_castling = split_fen[2]
self.option_box.set_option(self.fen_player, self.fen_castling)
self.board_box.board.set_board(self.fen_placement)
self.pages_fens[self.current_pages] = fen
except ValueError:
self.header.display_fen("Incorrect fen", "", "")
def set_coder(self, filename):
try:
self.coder = Autoencoder(settings.BOARD_SHAPE, settings.LATENT_SIZE).to(
settings.DEVICE
)
self.coder.load_state_dict(torch.load(filename, map_location=settings.DEVICE))
self.coder = self.coder.coder
self.coder.eval()
self.coder_launcher = Inference(
settings.DEVICE,
self.coder,
)
self.coder_set = True
self.header.coder_label["text"]="Coder Set"
return True
except:
return False
def show_find_option(self):
if self.coder_set:
self.find_option.place(relx=0.5, rely=0.5, anchor=CENTER)
self.lichess_set_option.place_forget()
else:
self.header.coder_label["text"]="Set Coder first"
def run_coder(self,number,comparison):
if self.coder_set:
if self.lichess_set:
self.store_games = self.games
output = str(
self.coder_launcher.predict([self.pages_fens[self.current_pages]])
)
self.find_option.place_forget()
self.display_tensor(output)
self.games = find_similar(self.pages_fens[self.current_pages], number, similarity_functions[comparison])
self.entering = False
self.follow_fen = False
self.set_fen()
self.option_box.grid_forget()
self.pgn_box.grid(row=1, column=1, sticky=N + S + E + W)
self.pgn_box.set_game_number()
def exit_pgn_options(self):
if self.lichess_set:
if self.follow_fen :
self.lichess_set = False
else:
self.games = self.store_games
self.follow_fen = True
self.set_fen()
self.option_box.grid_forget()
self.pgn_box.set_game_number()
return
self.pgn_box.grid_forget()
self.option_box.grid(row=1, column=1, sticky=N + S + E + W)
self.entering = True
self.follow_fen = True
self.set_fen(self.pages_fens[self.current_pages])
def display_tensor(self, message):
self.tensor_message.set_message(message)
self.tensor_message.place(relx=0.5, rely=0.5, anchor=CENTER)
def stop_display_tensor(self):
self.tensor_message.place_forget()