class JRPGAttackTests(unittest.TestCase):
def setUp(self):
self.p1 = Character()
self.p2 = Character()
self.mwfar = rules.JRRPGRules.attack_resolution
# d20 = MagicMock(return_value=19) just not to forget
def test_main_weapon_full_attack_resolution_barehands(self):
rules.d20 = object()
rules.d20 = 3
self.assertEqual(self.mwfar(self.p1, self.p2), (-7, "miss"))
rules.d20 = 10
self.assertEqual(self.mwfar(self.p1, self.p2), (0, "hit"))
rules.d20 = 20
self.assertEqual(self.mwfar(self.p1, self.p2), (10, "critical hit"))
rules.d20 = 1 # mocking
self.assertEqual(self.mwfar(self.p1, self.p2), (-9, "critical miss"))
def test_dagger_full_attack(self):
self.p1.equip_primary_weapon(dagger)
rules.d20 = 19
self.assertEqual(self.mwfar(self.p1, self.p2), (9, "critical hit"))
def test_long_sword_full_attack(self):
self.p1.equip_primary_weapon(long_sword)
rules.d20 = 19
self.assertEqual(self.mwfar(self.p1, self.p2), (10, "hit"))
class TestServer(object):
def __init__(self, coordinator):
self.output_dir = coordinator.checkpoints_dir()
self.decoder = Character().from_txt(cfg.CHARACTER_TXT)
def evaluate(self, data_loader):
with tf.device('/cpu:0'):
input_images, input_labels, input_widths = data_loader.read_with_bucket_queue(
batch_size=cfg.TEST.BATCH_SIZE,
num_threads=cfg.TEST.THREADS,
num_epochs=1,
shuffle=False)
with tf.device('/gpu:0'):
logits = get_models(cfg.MODEL.BACKBONE)(cfg.MODEL.NUM_CLASSES).build(input_images, False)
seqlen = tf.cast(tf.floor_div(input_widths, 2), tf.int32, name='sequence_length')
softmax = tf.nn.softmax(logits, dim=-1, name='softmax')
decoded, log_prob = tf.nn.ctc_greedy_decoder(softmax, seqlen)
distance = tf.reduce_mean(tf.edit_distance(tf.cast(decoded[0], tf.int32), input_labels))
saver = tf.train.Saver(tf.global_variables())
gpu_options = tf.GPUOptions(allow_growth=True)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, allow_soft_placement=True)) as sess:
saver.restore(sess, tf.train.latest_checkpoint(self.output_dir))
sess.run(tf.local_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
cnt = 0
dm = 1e-5
while not coord.should_stop():
dt = sess.run([distance, ])[0]
cnt += 1
dm = (dm + dt) / cnt
if cfg.TEST.VIS:
dd, il, ii = sess.run([decoded, input_labels, input_images])
gts = self.decoder.sparse_to_strlist(il.indices, il.values, cfg.TEST.BATCH_SIZE)
pts = self.decoder.sparse_to_strlist(dd[0].indices, dd[0].values, cfg.TEST.BATCH_SIZE)
tb = PrettyTable()
tb.field_names = ['Index', 'GroundTruth', 'Predict', '{:.3f}/{:.3f}'.format(dt, dm)]
for i in range(len(gts)):
tb.add_row([i, gts[i], pts[i], ''])
print(tb)
else:
print('EditDistance: {:.3f}/{:.3f}'.format(dt, dm))
except tf.errors.OutOfRangeError:
print('Epochs Complete!')
finally:
coord.request_stop()
coord.join(threads)
def testAttackEnemy(self):
plop = Character("Plop", 10, 20, 30, 40)
yup = Character("Yup", 10, 20, 30, 40)
plop_xp_before_attack = plop.xp
yup_health_before_attack = yup.health
plop.attack(yup)
self.assertGreater(plop.xp, plop_xp_before_attack)
self.assertLess(yup.health, yup_health_before_attack)
def __init__(self, coordinator, images_dir, **kwargs):
self.images_dir = images_dir
self.output_dir = coordinator.checkpoints_dir()
self.decoder = Character().from_txt(cfg.CHARACTER_TXT)
self._allow_soft_placement = kwargs.get('allow_soft_placement', True)
self._log_device_placement = kwargs.get('log_device_placenebt', False)
self.device = kwargs.get('device', '/cpu:0')
self.model_path = kwargs.get('model_path', None)
self.graph = tf.Graph()
self.sess = tf.Session(graph=self.graph, config=tf.ConfigProto(allow_soft_placement=self._allow_soft_placement,
log_device_placement=self._log_device_placement))
self.model = self._load_weights(self.model_path)
def testCharacterConstructor(self):
c = Character("Toto", 10, 20, 30, 40)
self.assertEqual(c.name, "Toto")
self.assertEqual(c.health, 10)
self.assertEqual(c.mana, 20)
self.assertEqual(c.power, 30)
self.assertEqual(c.xp, 40)
self.assertListEqual(c.inventory, [])
class InferServer(object):
def __init__(self, coordinator, images_dir, **kwargs):
self.images_dir = images_dir
self.output_dir = coordinator.checkpoints_dir()
self.decoder = Character().from_txt(cfg.CHARACTER_TXT)
self._allow_soft_placement = kwargs.get('allow_soft_placement', True)
self._log_device_placement = kwargs.get('log_device_placenebt', False)
self.device = kwargs.get('device', '/cpu:0')
self.model_path = kwargs.get('model_path', None)
self.graph = tf.Graph()
self.sess = tf.Session(graph=self.graph, config=tf.ConfigProto(allow_soft_placement=self._allow_soft_placement,
log_device_placement=self._log_device_placement))
self.model = self._load_weights(self.model_path)
def _load_weights(self, weights=None):
with self.graph.as_default():
with tf.device(self.device):
input_images = tf.placeholder(tf.uint8, shape=[None, 32, None, 3], name='input_images')
input_widths = tf.placeholder(tf.uint8, shape=[None], name='input_widths')
with tf.device('/gpu:0'):
logits = get_models(cfg.MODEL.BACKBONE)(cfg.MODEL.NUM_CLASSES).build(input_images, False)
seqlen = tf.cast(tf.floor_div(input_widths, 2), tf.int32, name='sequence_length')
softmax = tf.nn.softmax(logits, dim=-1, name='softmax')
decoded, log_prob = tf.nn.ctc_greedy_decoder(softmax, seqlen)
prob = -tf.divide(tf.cast(log_prob, tf.int32), seqlen[0])
saver = tf.train.Saver(tf.global_variables())
if weights is None:
saver.restore(self.sess, tf.train.latest_checkpoint(self.output_dir))
else:
saver.restore(self.sess, weights)
return {'input_images': input_images, 'input_widths': input_widths, 'decoded': decoded, 'prob': prob}
@staticmethod
def resize_images_and_pad(images):
ws = list(map(lambda x: int(np.ceil(32.0 * x.shape[1] / x.shape[0])), images))
wmax = max(ws)
wmax = wmax if wmax % 32 == 0 else (wmax // 32 + 1) * 32
data = np.zeros(shape=(len(ws), 32, wmax, 3), dtype=np.uint8)
for i, img in enumerate(images):
tmp = cv2.resize(img, (ws[i], 32))
data[i, :32, : ws[i], :] = tmp
length = np.array([wmax], dtype=np.int32).repeat(len(ws))
return data, length
def predict(self, images):
imgs, widths = self.resize_images_and_pad(images)
output, prob = self.sess.run([self.model['decoded'], self.model['prob']],
feed_dict={self.model['input_images']: imgs,
self.model['input_widths']: widths})
context = self.decoder.sparse_to_strlist(output.indices, output.values, len(imgs))
return context, prob.reshape(-1)
def setup():
import os
os.environ['DIANJING_TEST'] = '1'
import dianjing.wsgi
teardown()
from core.mongo import MongoCharacter
from core.character import Character
Character.create(1, 1, "one", "club_one", 1)
MongoCharacter.db(1).update_one(
{'_id': 1},
{'$set': {
'club.gold': 0,
'club.diamond': 0
}}
)
class WeaponEquippedTest(unittest.TestCase):
def setUp(self):
self.p1 = Character()
w = Weapon("melee", 1, (1, 2))
self.p1.equip_primary_weapon(w)
def testEquip(self):
self.assertEqual(self.p1.attack_bonus, 1)
self.assertNotEqual(self.p1.attack_bonus, self.p1.base_attack_bonus)
def testUnequipWrongWay(self):
self.assertNotEqual(self.p1.base_attack_bonus, self.p1.attack_bonus)
# self.assertRaises(InvalidEquipmentError, self.p1.equip_primary_weapon, None)
self.assertNotEqual(self.p1.base_attack_bonus, self.p1.attack_bonus)
def testUnequip(self):
self.p1.unequip_primary_weapon()
self.assertEqual(self.p1.base_attack_bonus, self.p1.attack_bonus)
def testPickItem(self):
plop = Character("Plop", 10, 20, 30, 40)
item = Item("Poo", 50)
item2 = Item("Yak", 50)
item3 = Item("Prout", 50)
plop.pick(item)
self.assertListEqual(plop.inventory, [item])
plop.pick(item2)
plop.pick(item3)
self.assertListEqual(plop.inventory, [item, item2, item3])
import sys, pygame
from core.character import Character
from core.location import Location
from core.walkman import Walkman
pygame.init()
size = width, height = 800, 600
FPS = 60
current_frame = 0
MAX_FRAMES = 60
ANIMATION_SPEED = 15
clock = pygame.time.Clock()
location = Location()
character = Character()
walkman = Walkman(character.get_tapes())
walkman.load_songs()
time = 0
FULLTIME = 60
screen = pygame.display.set_mode(size)
while True:
keys = pygame.key.get_pressed()
if keys[ord('w')]:
character.move('up')
elif keys[ord('s')]:
character.move('down')
def setup_class(cls):
# 建立一个 id 为2 的角色
Character.create(1, 2, "two", "club_two", 1)
print("write tfrecords using time: {} s".format(end - start))
if __name__ == '__main__':
from core.character import Character
# TODO: windows platform has some bug
if os.name == 'nt':
SENTINEL = ("", [])
SAMPLE_QUEUE = Manager().Queue()
labels = char_count(
['/code/disk1/xupeichao/data/OCR/wecrash/rec/sim/0806_trainval.txt'],
'\t')
character = Character(labels)
data = txt_anno_to_data(
imgs_dir='/code/disk1/xupeichao/data/OCR/wecrash/rec/sim/imgs_0806',
label_file=
'/code/disk1/xupeichao/data/OCR/wecrash/rec/sim/0806_trainval.txt',
character=character)
# tfrecords_write(data, '/code/disk1/xupeichao/data/OCR/wecrash/rec/sim/tftest', 'train')
multi_tfrecords_write(
data,
'/code/disk1/xupeichao/data/OCR/wecrash/rec/sim/tftest',
'train',
num_process=10,
each_split=2000)
def setUp(self):
self.p1 = Character()
self.p2 = Character()
self.mwfar = rules.JRRPGRules.attack_resolution
def setUp(self):
self.p1 = Character()
w = Weapon("melee", 1, (1, 2))
self.p1.equip_primary_weapon(w)
def __init__(self, coordinator):
self.output_dir = coordinator.checkpoints_dir()
self.decoder = Character().from_txt(cfg.CHARACTER_TXT)
