def main(replays_folder, output_folder, player, max_replays, double_input,
prev_frame):
if replays_folder[-1] == '/':
replays_folder = replays_folder[:-1]
out_dir = "{}/{}".format(output_folder, replays_folder.split('/')[-1])
os.mkdir(out_dir)
np.random.seed(0)
replay_files = [
fname for fname in os.listdir(replays_folder) if fname[-4:] == '.hlt'
]
# replay_files = replay_files[:50]
n_replays = len(replay_files)
progbar = ProgressBar(n_replays)
print('Selecting winning replays')
selected = []
for replay_name in replay_files:
progbar.update()
replay = json.load(open('{}/{}'.format(replays_folder, replay_name)))
frames = get_frames(replay)
target_id = get_winner(frames)
if target_id == 0: continue
if player not in replay['player_names'][target_id - 1].lower():
continue
selected.append(replay_name)
del replay
print("{}/{} replays have been selected.".format(len(selected), n_replays))
print('Shuffling Replays')
np.random.shuffle(selected)
print("Computing chunk sizes")
chunks = get_chunks(len(selected), max_replays)
print("Chunk sizes will be: {}".format(', '.join(
[str(chunk) for chunk in chunks])))
print('Processing replays')
progbar2 = ProgressBar(len(selected))
cursor = 0
for i_chunk, chunk in enumerate(chunks):
# print("Chunk {}".format(i_chunk))
# print("{} to {}".format(cursor,cursor+chunk))
training_input = []
training_target = []
if double_input:
training_input_alt = []
if prev_frame:
training_input_prev = []
test_input = []
test_target = []
if double_input:
test_input_alt = []
if prev_frame:
test_input_prev = []
for i, replay_name in enumerate(selected[cursor:cursor + chunk]):
progbar2.update()
is_training = i < 0.8 * chunk
replay = json.load(
open('{}/{}'.format(replays_folder, replay_name)))
frames = get_frames(replay)
target_id = get_winner(frames)
moves = np.array(replay['moves'])
is_player = frames[:, :, :, 0] == target_id
filtered_moves = np.where(is_player[:-1], moves,
np.zeros_like(moves))
categorical_moves = (
np.arange(5) == filtered_moves[:, :, :, None]).astype(int)
# wrapped_frames = np.empty(shape=(frames.shape[0],input_shape[0],input_shape[1],frames.shape[3]))
# wrapped_moves = np.empty(shape=(categorical_moves.shape[0],input_shape[0],input_shape[1],categorical_moves.shape[3]))
iframes = np.empty(shape=frames.shape[:3] + (4, ))
iframes[:, :, :, 0] = frames[:, :, :, 0] == target_id
iframes[:, :, :, 1] = (frames[:, :, :, 0] !=
target_id) & (frames[:, :, :, 0] != 0)
iframes[:, :, :, 2] = frames[:, :, :, 1] / 20.
iframes[:, :, :, 3] = frames[:, :, :, 2] / 255.
for j, (iframe, move) in enumerate(zip(iframes,
categorical_moves)):
centroid = get_centroid(iframe)
wframe = center_frame(iframe, centroid, wrap_size=input_shape)
wmoves = center_frame(move, centroid, wrap_size=input_shape)
if double_input:
prev_move = categorical_moves[j - 1] if j > 0 else move
wprev_move = center_frame(prev_move,
centroid,
wrap_size=input_shape)
if prev_frame:
prev_iframe = iframes[j - 1] if j > 0 else iframe
wprev_iframe = center_frame(prev_iframe,
centroid,
wrap_size=input_shape)
if is_training:
if double_input:
training_input_alt.append(wprev_move)
if prev_frame:
training_input_prev.append(wprev_iframe)
training_input.append(wframe)
training_target.append(wmoves)
else:
if double_input:
test_input_alt.append(wprev_move)
if prev_frame:
test_input_prev.append(wprev_iframe)
test_input.append(wframe)
test_target.append(wmoves)
del replay
np.save("{}/training_input_{}.npy".format(out_dir, i_chunk),
training_input)
np.save("{}/training_target_{}.npy".format(out_dir, i_chunk),
training_target)
np.save("{}/test_input_{}.npy".format(out_dir, i_chunk), test_input)
np.save("{}/test_target_{}.npy".format(out_dir, i_chunk), test_target)
if double_input:
np.save("{}/training_input_alt_{}.npy".format(out_dir, i_chunk),
training_input_alt)
np.save("{}/test_input_alt_{}.npy".format(out_dir, i_chunk),
test_input_alt)
if prev_frame:
np.save("{}/training_input_prev_{}.npy".format(out_dir, i_chunk),
training_input_prev)
np.save("{}/test_input_prev_{}.npy".format(out_dir, i_chunk),
test_input_prev)
cursor += chunk
model.add(Convolution2D(64, 5, 5, activation='relu', border_mode='same'))
model.add(Convolution2D(5, 5, 5, activation='relu', border_mode='same'))
opt = RMSprop(lr=0.00001)
model.compile(loss='mse', optimizer=opt, metrics=['accuracy'])
n_replays = len(os.listdir(REPLAY_FOLDER))
print('Loading')
for i, replay_name in enumerate(os.listdir(REPLAY_FOLDER)):
if i <= 120: # or i>600:
continue
if replay_name[-4:] != '.hlt': continue
print('Loading {}/{}'.format(i + 1, n_replays))
replay = json.load(open('{}/{}'.format(REPLAY_FOLDER, replay_name)))
frames = get_frames(replay)
player = frames[:, :, :, 0]
players, counts = np.unique(player[-1], return_counts=True)
target_id = players[counts.argmax()]
if target_id == 0: continue
if 'nmalaguti' not in replay['player_names'][target_id - 1].lower():
print "not using replay..."
continue
print "using replay"
moves = np.array(replay['moves'])
is_player = frames[:, :, :, 0] == target_id
filtered_moves = np.where(is_player[:-1], moves, np.zeros_like(moves))
categorical_moves = (np.arange(5) == filtered_moves[:, :, :,
split_ratio = 0.8
input_ = []
target_ = []
np.random.seed(0)
n_replays = len(os.listdir(REPLAY_FOLDER))
for i, replay_name in enumerate(os.listdir(REPLAY_FOLDER)):
if i > 300:
break
if replay_name[-4:] != '.hlt': continue
print('Loading {}/{}'.format(i + 1, n_replays))
replay = json.load(open('{}/{}'.format(REPLAY_FOLDER, replay_name)))
frames = mlutils.get_frames(replay)
player = frames[:, :, :, 0]
players, counts = np.unique(player[-1], return_counts=True)
target_id = players[counts.argmax()]
if target_id == 0: continue
# if 'erdman' not in replay['player_names'][target_id-1].lower():
# continue
n_max = min(len(replay['frames']), 20)
frames = frames[:n_max]
moves = np.array(replay['moves'])[:n_max - 1]
is_player = frames[:, :, :, 0] == target_id
filtered_moves = np.where(is_player[:-1], moves, np.zeros_like(moves))
categorical_moves = (np.arange(5) == filtered_moves[:, :, :,