def test_get_triangles_return_trianlge_with_arg_normals(self):
model = Model(vertices=[
[0.0, 2.0, 2.0],
[0.0, 0.0, 0.0],
[2.0, 0.0, 0.0],
],
normals=[
[1.0, 2.0, 3.0],
[4.0, 5.0, 6.0],
],
faces=[
[[1, 0, 1], [2, 0, 2], [3, 0, 2]],
[[2, 0, 1], [1, 0, 1], [3, 0, 1]],
])
expected_triangles = [[
[3.0, 4.0, 5.0],
[0.0, 2.0, 2.0],
[0.0, 0.0, 0.0],
[2.0, 0.0, 0.0],
],
[
[1.0, 2.0, 3.0],
[0.0, 0.0, 0.0],
[0.0, 2.0, 2.0],
[2.0, 0.0, 0.0],
]]
triangles = model.get_triangles()
self.assertEqual(str(triangles), str(expected_triangles))
def test_get_triangles_return_fan_triangles(self):
model = Model(vertices=[
[0.0, 2.0, 2.0],
[0.0, 0.0, 0.0],
[2.0, 0.0, 0.0],
[2.0, 2.0, 0.0],
],
faces=[
[[1, 0, 0], [2, 0, 0], [3, 0, 0], [4, 0, 0]],
])
expected_triangles = [[
[0.0, 0.0, 0.0],
[0.0, 2.0, 2.0],
[0.0, 0.0, 0.0],
[2.0, 0.0, 0.0],
],
[
[0.0, 0.0, 0.0],
[0.0, 2.0, 2.0],
[2.0, 0.0, 0.0],
[2.0, 2.0, 0.0],
]]
triangles = model.get_triangles()
self.assertEqual(str(triangles), str(expected_triangles))
def setUp(self):
self.model = Model(model_nm="Test model", grp_struct=DEF_GRP_STRUCT)
self.exec_key = self.model.exec_key
self.agent = Agent("Test agent", exec_key=self.model.exec_key)
self.agent2 = Agent("Test agent 2", exec_key=self.model.exec_key)
self.blue_grp = get_agent(BLUE_GRP_NM, self.exec_key)
self.red_grp = get_agent(RED_GRP_NM, self.exec_key)
def test_model_save_load_run_from_disk(self, dump, load):
DEF_GRP[GRP_ACTION] = self.complex_agent_action
DEF_GRP[MBR_CREATOR] = self.complex_agent_create
SECOND_GRP = DEF_GRP.copy()
SECOND_GRP[COLOR] = RED
GRP_STRUCT = {
"COMPLEX_RED_GRP": SECOND_GRP,
"COMPLEX_BLUE_GRP": DEF_GRP
}
complexModel = Model(grp_struct=GRP_STRUCT, model_nm="Basic")
complexModel.run(5)
registry.save_reg(key=complexModel.exec_key)
registry.load_reg(complexModel.exec_key)
loaded_object = get_model(complexModel.exec_key)
self.assertTrue(type(loaded_object) == Model)
self.assertTrue("Basic" == loaded_object.module)
all_red_members_have_attribute_5 = True
all_blue_memebrs_have_attribute_10 = True
deserialized_model = loaded_object
deserialized_model.run(5)
for grp in deserialized_model.groups:
for member in grp.members:
if grp.color == BLUE:
all_blue_memebrs_have_attribute_10 = \
all_blue_memebrs_have_attribute_10 and (
grp[member].get_attr("value") != 5)
else:
all_red_members_have_attribute_5 = \
all_red_members_have_attribute_5 and (
grp[member].get_attr("value") == 5)
self.assertTrue(all_red_members_have_attribute_5)
self.assertTrue(all_blue_memebrs_have_attribute_10)
def __init__(self, protector):
self.protector = protector
self.db_model = Model()
self.vendor_name = 'nasys'
self.model_name = 'ul2011'
self.initialize_notifier()
def __init__(self, num_classes, learning_rate=0.01):
self.num_classes = num_classes
self.learning_rate = learning_rate
self._create_placeholders()
self._build_model()
Model.__init__(self)
def main(opts):
cap = cv2.VideoCapture(opts.input)
model = Model(66, opts.size)
model.load(opts.weights)
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter('data/res.mp4', fourcc, 10, (640, 480))
while True:
_, img = cap.read()
# Предикт лица (по дефолту тут HOG)
inp_img = img[:, :, ::-1]
face_locations = face_recognition.face_locations(inp_img)
for (top, right, bottom, left) in face_locations:
# Расширяем ббокс и смещаем вертикально
bbox_width = abs(bottom - top)
bbox_height = abs(right - left)
left -= int(2 * bbox_width / 4)
right += int(2 * bbox_width / 4)
top -= int(3 * bbox_height / 4)
bottom += int(bbox_height / 4)
# Выход за пределы
top = max(top, 0)
left = max(left, 0)
bottom = min(img.shape[0], bottom)
right = min(img.shape[1], right)
crop = img[top:bottom, left:right]
# Ресайз по меньшей стороне и кроп от центра
crop = resize_center_crop(crop, opts.size)
# Нормализация
normed_img = normalize(crop)
imgs = []
imgs.append(normed_img)
# Предикт
res = model.test_online(imgs)
# Отрисовка
img = draw_axis(img,
*res,
tdx=(left + right) / 2,
tdy=(top + bottom) / 2,
size=100)
cv2.rectangle(img, (left, top), (right, bottom), (255, 0, 0), 1)
out.write(img)
cv2.imshow('img', img)
if cv2.waitKey(1) == 27:
break
out.release()
def test_model():
sample_image_path = os.getcwd(
) + "/app/frontend/build/assets/semsearch/datasets/fashion200/0.jpg"
efficientnet_model = Model()
image_array = image_to_np_array(sample_image_path,
efficientnet_model.image_size)
features = efficientnet_model.get_features(np.asarray([image_array]))
assert (features.shape == (1, 7, 7, 1280))
def start(self, model_checker):
Model.start(self, model_checker)
mac1 = (0x00, 0x00, 0x00, 0x00, 0x01, 0x00)
mac2 = (0x00, 0x00, 0x00, 0x00, 0x01, 0x01)
mac3 = (0x00, 0x00, 0x00, 0x01, 0x01, 0x01)
ip1 = "128.0.0.11"
ip2 = "128.0.0.12"
ip3 = "128.0.0.13"
self.clients[0].enableAction("send_packet", (mac1, ip1))
self.clients[0].enableAction("send_packet", (mac2, ip2))
self.clients[0].enableAction("send_packet", (mac3, ip3))
def start(self, model_checker):
Model.start(self, model_checker)
mac1 = (0x00, 0x00, 0x00, 0x00, 0x01, 0x00)
mac2 = (0x00, 0x00, 0x00, 0x00, 0x01, 0x01)
mac3 = (0x00, 0x00, 0x00, 0x01, 0x01, 0x01)
ip1 = "128.0.0.11"
ip2 = "128.0.0.12"
ip3 = "128.0.0.13"
self.clients[0].enableAction("send_packet", (mac1, ip1))
self.clients[0].enableAction("send_packet", (mac2, ip2))
self.clients[0].enableAction("send_packet", (mac3, ip3))
def runTests(X_test, X_train, y_test, y_train):
classifiers = []
train_times = []
gs_times = []
pred_times = []
f1_trains = []
f1_tests = []
for classifier, parameters, gs_params in CLASSIFIERS:
print(classifier, parameters, gs_params)
this_model = Model(classifier, parameters, gs_params)
this_model(X_train, y_train, X_test, y_test)
# Append vanilla values
classifiers.append(this_model.classifier.__name__)
train_times.append(this_model.training_time)
pred_times.append(this_model.train_prediction_time)
f1_trains.append(this_model.f1_train)
f1_tests.append(this_model.f1_test)
gs_times.append(0)
# Append optimized classifier values
classifiers.append(this_model.classifier.__name__ + ' (Optimized)')
train_times.append(this_model.optimal_training_time)
pred_times.append(this_model.optimal_train_prediction_time)
f1_trains.append(this_model.f1_optimal_train)
f1_tests.append(this_model.f1_optimal_test)
gs_times.append(this_model.gs_time)
return classifiers, train_times, pred_times, f1_trains, f1_tests, gs_times
def test_get_model(self):
"""
Register a model and fetch it back.
"""
self.model = Model(exec_key=self.exec_key)
reg_model(self.model, self.exec_key)
self.assertEqual(self.model, get_model(self.exec_key))
def parse(self, path):
self.vertices = []
self.faces = []
self.normals = []
self.textures = []
with open(path, 'r') as file:
for line in file.readlines():
parts = line.strip().split(' ')
dataType = parts[0]
if dataType == ObjParsingStrategy.VERTEX_TYPE:
vertex = self._create_vertex_from_line_parts(parts)
self.vertices.append(vertex)
elif dataType == ObjParsingStrategy.NORMAL_TYPE:
normal = self._create_normal_from_line_parts(parts)
self.normals.append(normal)
elif dataType == ObjParsingStrategy.TEXTURE_TYPE:
texture = self._create_texture_from_line_parts(parts)
self.textures.append(texture)
elif dataType == ObjParsingStrategy.FACE_TYPE:
face = self._create_face_from_line_parts(parts)
self.faces.append(face)
return Model(vertices=self.vertices,
textures=self.textures,
normals=self.normals,
faces=self.faces)
def test_parse_should_return_with_parser_strategy_returns(self):
expectedModel = Model([], [])
self._parsing_strategy_mock.parse.return_value = expectedModel
path = 'dummy/path'
model = self._parser.parse(path)
self.assertEqual(model, expectedModel)
def train(opts):
# Select device
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
# Define model
model = Model().to(device)
# Define dataloaders
train_loader, val_loader = split_trainval(opts.data, opts.bs)
# Define loss
loss_criter = nn.L1Loss().to(device)
# Define optimizer
optimizer = Adam(model.parameters(), lr=opts.lr, weight_decay=1e-6)
scheduler = StepLR(optimizer, step_size=int(opts.epoch/2), gamma=0.1)
# Training loop
for epoch in range(opts.epoch):
# Train cycle
running_loss = 0.0
model.train()
for batch_num, (inputs, labels) in enumerate(train_loader):
inputs = inputs.to(device)
labels = labels.to(device)
outputs = model(inputs)
loss = loss_criter(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
running_loss += loss.item() * inputs.size(0)
print(f'epoch num {epoch:02d} batch num {batch_num:04d} train loss {loss:02.04f}', end='\r')
epoch_loss = running_loss / len(train_loader.dataset)
# Val cycle
running_loss = 0.0
model.eval()
for inputs, labels in val_loader:
inputs = inputs.to(device)
labels = labels.to(device)
with torch.no_grad():
outputs = model(inputs)
loss = loss_criter(outputs, labels)
running_loss += loss.item() * inputs.size(0)
epoch_val_loss = running_loss / len(val_loader.dataset)
print(f'\n\nepoch num {epoch:02d} train loss {epoch_loss:02.04f} val loss {epoch_val_loss:02.04f}')
scheduler.step()
if (epoch + 1) % opts.save_every == 0:
torch.save(model.state_dict(), os.path.join(opts.output, f'checkpoint_size{opts.size}_e{epoch+1}of{opts.epoch}_lr{opts.lr:.01E}.pth'))
def __json_to_object(self, serial_obj, exec_key):
"""
Takes a serial JSON object back into a live Python object.
"""
restored_obj = dict()
restored_groups = []
model_deserialized = False
for obj_name in serial_obj:
should_restore_object = isinstance(serial_obj[obj_name],
dict) and "type" in serial_obj[
obj_name]
if should_restore_object:
if serial_obj[obj_name]["type"] == "TestUser":
restored_obj[obj_name] = TermUser(name=obj_name,
serial_obj=serial_obj[
obj_name],
exec_key=exec_key)
if serial_obj[obj_name]["type"] == "APIUser":
restored_obj[obj_name] = APIUser(name=obj_name,
serial_obj=serial_obj[
obj_name],
exec_key=exec_key)
if serial_obj[obj_name]["type"] == "Agent":
restored_obj[obj_name] = Agent(name=obj_name,
serial_obj=serial_obj[
obj_name],
exec_key=exec_key)
elif serial_obj[obj_name]["type"] == "Model":
from lib.model import Model
print(f'restoring model for key {exec_key}')
restored_obj[obj_name] = Model(exec_key=exec_key,
serial_obj=serial_obj[
obj_name])
model_deserialized = True
elif serial_obj[obj_name]["type"] == "Group":
from lib.group import Group
restored_obj[obj_name] = Group(exec_key=exec_key,
serial_obj=serial_obj[
obj_name],
name=serial_obj[obj_name][
'name'])
restored_groups.append(restored_obj[obj_name])
elif serial_obj[obj_name]["type"] == "Env":
restored_obj[obj_name] = Env(exec_key=exec_key,
serial_obj=serial_obj[
obj_name],
name=serial_obj[obj_name][
'name'])
else:
restored_obj[obj_name] = serial_obj[obj_name]
self.registries[exec_key][obj_name] = restored_obj[obj_name]
if model_deserialized:
restored_obj['model'].groups = restored_groups
restored_obj['model'].env = restored_obj['env']
self.registries[exec_key]['model'] = restored_obj['model']
return restored_obj
class EncoderTrainer(ApplicationSession):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.batch_size = 2
self.needStop = False
self.codec = Codec()
self.model = Model('train')
@asyncio.coroutine
def train(self):
self.publish('semanticaio.encoder.trainer.started')
print('[emit] semanticaio.encoder.trainer.started')
input_dataset = np.zeros((self.batch_size, self.codec.seq_len, self.codec.n_chars), dtype = np.bool)
output_dataset = np.zeros((self.batch_size, self.codec.seq_len, self.codec.n_chars), dtype = np.bool)
while not self.needStop :
yield from asyncio.sleep(0.1)
batch = yield from self.call('semanticaio.db.batch.get', size = self.batch_size)
for i, question in enumerate(batch) :
self.codec.encode(question['sentence'], input_dataset[i])
if question['correctFormId'] == None :
self.codec.encode(question['sentence'], output_dataset[i])
else :
correctQuestion = yield from self.call('semanticaio.db.get', id = question['correctFormId'])
self.codec.encode(correctQuestion['sentence'], output_dataset[i])
(loss, accuracy) = self.model.train(input_dataset, output_dataset)
print('training:', loss, accuracy)
self.needStop = False
self.publish('semanticaio.encoder.trainer.stopped')
print('[emit] semanticaio.encoder.trainer.stopped')
def load(self, *args, **kwargs):
print('[call] semanticaio.encoder.trainer.load')
try :
self.model.load()
except :
print('[error] semanticaio.encoder.trainer.load')
self.model.compile()
def save(self, *args, **kwargs):
print('[call] semanticaio.encoder.trainer.save')
self.model.save()
@asyncio.coroutine
def start(self, *args, **kwargs):
print('[event received] semanticaio.encoder.trainer.start')
yield from self.train()
def stop(self, *args, **kwargs):
print('[event received] semanticaio.encoder.trainer.stop')
self.needStop = True
@asyncio.coroutine
def onJoin(self, details):
yield from self.register(self.load, 'semanticaio.encoder.trainer.load')
yield from self.register(self.save, 'semanticaio.encoder.trainer.save')
yield from self.subscribe(self.start, 'semanticaio.encoder.trainer.start')
yield from self.subscribe(self.stop, 'semanticaio.encoder.trainer.stop')
print('[encoder-trainer started]')
def setUp(self):
self.exec_key = get_exec_key()
self.newton = create_newton()
self.calcs = create_calcguys(self.exec_key, [])
self.cambs = create_cambguys(self.exec_key)
self.pop_hist = PopHist()
self.model = Model(exec_key=self.exec_key)
self.env = self.model.env
self.env.action = env_action
def __init__(self,
max_length,
embedding_size,
num_hidden,
num_classes,
learning_rate=0.01,
sentences=None,
sequence_lengths=None):
self.max_length = max_length
self.embedding_size = embedding_size
self.num_hidden = num_hidden
self.num_classes = num_classes
self.learning_rate = learning_rate
with tf.variable_scope("SentenceClassifier"):
self._create_placeholders()
self._create_weights()
self._build_model(sentences, sequence_lengths)
Model.__init__(self)
def update_cache():
json = {'command_names': {}}
db_model = Model()
# command id - names
all_commands = db_model.get_commands()
for cmd in all_commands:
json['command_names'][cmd['cmd_id']] = cmd['cmd_name']
# device names and vendors
all_devices = db_model.get_all_device_names()
json['devices'] = {}
for dev in all_devices:
json['devices'][dev['dev_name']] = {
'vendor_name': dev['vendor_name'],
'model_name': dev['model_name']
}
# find all models, their vendor and their connection info
for m in db_model.get_models_w_cnx_info():
vendor_name = m['vendor_name']
model_name = m['model_name']
if vendor_name not in json:
json[vendor_name] = {}
if model_name not in json[vendor_name]:
json[vendor_name][model_name] = {}
# aliases to the dictionaries
m_dict = json[vendor_name][model_name]
# connection info
m_dict['cnx'] = {
'name': m['cnx_name'],
'type': m['cnxt_name'],
'server': m['cnx_server'],
'port': m['cnx_port'],
'username': m['cnx_username'],
'msg_template': m['cnx_msg_template']
}
if m['cnxt_name'] == 'mqtt':
m_dict['cnx']['tx_topic'] = m['cnx_mqtt_tx_topic']
m_dict['cnx']['rx_topic'] = m['cnx_mqtt_rx_topic']
# generate model commands
m = import_module(f'drivers.{vendor_name}.{model_name}.funcs')
m_dict['commands'] = m.generate_cmd_table(all_commands)
db_model.cleanup()
print(cache_json_path)
with open(cache_json_path, 'w') as fout:
json_dump(json, fout, indent=4)
def __init__(self,
data_processor,
bottleneck_dim=128,
num_codebooks=16,
hidden_dim=512,
decoder_layers=2,
encoder_layers=2,
**kwargs):
super().__init__()
self.data_processor = data_processor
self.encoder1 = nn.Sequential(
Feedforward(self.data_processor.input_dim,
hidden_dim,
num_layers=encoder_layers,
**kwargs), nn.Linear(hidden_dim, bottleneck_dim))
self.quntizer = Model(input_dim=bottleneck_dim,
hidden_dim=1024,
bottleneck_dim=256,
encoder_layers=2,
decoder_layers=2,
Activation=nn.ReLU,
num_codebooks=8,
codebook_size=256,
initial_entropy=3.0,
share_codewords=True).cuda()
self.distance = DISTANCES['euclidian_squared']
self.triplet_delta = 5
all_parameters = list(self.encoder1.parameters()) + list(
self.quntizer.parameters())
self.optimizer = OneCycleSchedule(QHAdam(all_parameters,
nus=(0.8, 0.7),
betas=(0.95, 0.998)),
learning_rate_base=1e-3,
warmup_steps=10000,
decay_rate=0.2)
self.experiment_path = 'logs'
self.writer = SummaryWriter(self.experiment_path, comment='Cora')
def main(opts):
model = Model(66, opts.size)
model.model.summary()
model.load(opts.weights)
train_list, val_list = split(opts.data)
val_dataset = AFLW2000(val_list, batch_size=1, input_size=opts.size)
err, times = [], []
for idx, (x, y) in enumerate(val_dataset.data_generator()):
print(f'{idx}/{val_dataset.epoch_steps}')
t1 = time()
res = model.test_online(x)
times.append(time() - t1)
ypr = np.array(y)[:, 0, 1]
err.append(abs(ypr - res))
print(f'YPR: {np.mean(np.array(err), axis=0)}')
print(f'TIME: {np.mean(times)}')
if idx == val_dataset.epoch_steps:
break
class Encoder(ApplicationSession):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.codec = Codec()
self.model = Model('encode')
def load(self, *args, **kwargs):
print('[call] semanticaio.encoder.load')
try :
self.model.load()
except :
print('[error] semanticaio.encoder.load')
self.model.compile()
def _encode(self, question) :
coded_question = np.zeros((self.codec.seq_len, self.codec.n_chars), dtype = np.bool)
self.codec.encode(question, coded_question)
return self.model.encode(coded_question).tolist()
def encode(self, *args, **kwargs):
print('[call] semanticaio.encoder.encode:', kwargs)
result = {}
if 'question' in kwargs :
result['encoded'] = self._encode(kwargs['question'])
elif 'questions' in kwargs :
result['encoded'] = []
for question in kwargs['questions'] :
result['encoded'].append(self._encode(question))
return result
@coroutine
def onJoin(self, details):
yield from self.register(self.load, 'semanticaio.encoder.load')
yield from self.register(self.encode, 'semanticaio.encoder.encode')
print('[encoder started]')
def cronjob(sched_id, cmd_id):
db_model = Model()
devices = db_model.get_schedule_active_devices(sched_id, 'iled', 'iled')
if not devices:
return
with open(cache_json_path, 'r') as fin:
json = json_load(fin)
cmd_name = json['command_names'][str(cmd_id)]
errors = []
succeeded = []
dev_ids_to_update = []
for dev_id, dev_name, password in devices:
# call worker.py
res = handle_request(
vendor='iled', model='iled', dev_name=dev_name,
password=password, command=cmd_id
)
if res['status'] != 'OK':
errors.append({
'dev_name': dev_name,
'status': res['status'],
'reason': res['message']
})
else:
dev_ids_to_update.append(dev_id)
succeeded.append(dev_name)
if errors:
json = json_dumps({'schedule_id' : sched_id, 'vendor' : 'iled', 'model' : 'iled', 'command': cmd_name, 'errors' : errors})
db_model.insert_log('schedule_error', json, 1)
if dev_ids_to_update:
db_model.update_device_status(dev_ids_to_update, cmd_id=cmd_id, dstat_name='active')
db_model.cleanup()
def runTests(X_test, X_train, y_test, y_train):
classifiers = []
train_times = []
pred_times = []
f1_trains = []
f1_tests = []
for classifier, parameters in CLASSIFIERS:
this_model = Model(classifier, parameters)
this_model(X_train, y_train, X_test, y_test)
classifiers.append(this_model.classifier.__class__.__name__)
train_times.append(this_model.training_time)
pred_times.append(this_model.train_prediction_time)
f1_trains.append(this_model.f1_train)
f1_tests.append(this_model.f1_test)
return classifiers, train_times, pred_times, f1_trains, f1_tests
def _cronjob(sched_id):
db_model = Model()
devices = db_model.get_schedule_active_devices(sched_id, 'nasys', 'ul2011')
sched_cmd_data = generate_schedule(sched_id)
cmd = 'send_custom_command'
msg = json_dumps(sched_cmd_data)
errors = []
succeeded = []
for _dev_id, dev_name, password in devices:
res = handle_request(vendor='nasys',
model='ul2011',
dev_name=dev_name,
password=password,
command=cmd,
parameter=msg)
if res['status'] != 'OK':
errors.append({
'dev_name': dev_name,
'status': res['status'],
'reason': res['message']
})
else:
succeeded.append(dev_name)
if errors:
json = json_dumps({
'schedule_id': sched_id,
'vendor': 'nasys',
'model': 'ul2011',
'errors': errors
})
db_model.insert_log('schedule_error', json, 1)
db_model.cleanup()
def main(opts):
model = Model(66, opts.size)
# Если есть предобучение, то возьмем
if opts.pretrain is not None:
print(f'Initial weights from {opts.pretrain}')
model.load(opts.pretrain)
train_list, val_list = split(opts.data)
# Обучающий лоадер с аугментациями, но там их не сильно много
train_dataset = AFLW2000(train_list,
augment=True,
batch_size=opts.bs,
input_size=opts.size)
val_dataset = AFLW2000(val_list, batch_size=opts.bs, input_size=opts.size)
# Учим
chkpt_name = f'model_size{opts.size}_e{opts.epoch}_lr{opts.lr:.01E}.h5'
model.train(chkpt_name, train_dataset, val_dataset, opts.epoch)
def main(opts):
# Select device
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
# Define model
model = Model().to(device)
model.load_state_dict(torch.load(opts.weights))
model.eval()
# Define dataloader
test_loader = get_test(opts.input)
preds = []
for img in test_loader:
img = img.to(device)
with torch.no_grad():
predict = model(img)
predict = predict.cpu().detach().numpy().squeeze()
preds.append(predict)
preds = np.array(preds)
np.save(os.path.join(opts.input, 'ytest.npy'), preds)
def update_cron():
times = get_sunrise_sunset_times()
db_model = Model()
schedule_items = db_model.get_all_schedule_items(times)
schedule_models = db_model.get_all_schedule_models()
db_model.cleanup()
# print(schedule_items)
# print(schedule_models)
with open(cron_txt_path, 'w') as fout:
write_crons_header(fout)
for sched_id in schedule_items:
items = schedule_items[sched_id]
models = schedule_models[sched_id]
# FIXME: the lines probably belond in a separate function for each model in drivers/model/funcs.py
# NASys controllers
if ('nasys', 'ul2011') in models:
fout.write(
f'{crontimes["nasys_update_schedule"]} /usr/bin/python3 {basepath}/drivers/nasys/ul2011/cron.py {sched_id}\n'
)
# iLED controllers
if ('iled', 'iled') in models:
for time, cmd_id, _cmd_name in items:
fout.write(
f'{time[3:5]} {time[0:2]} * * * /usr/bin/python3 {basepath}/drivers/iled/iled/cron.py {sched_id} {cmd_id}\n'
)
fout.flush()
username = getenv('USER')
run([f'crontab -u {username} {cron_txt_path} <<EOL'], shell=True)
from flask import Flask, send_from_directory, jsonify
from lib.model import Model
from lib.prezi_indexer import PerziIndexer
app = Flask(__name__, static_folder="prezi-app/dist", static_url_path="")
model = Model(
PerziIndexer.index_json(open("data/prezis.json").read(), index_by='id'))
@app.route('/')
def index():
return app.send_static_file('index.html')
@app.route('/<path:path>')
def static_proxy(path):
return app.send_static_file(path)
@app.route('/prezi/<id>')
def get_prezi_data(id):
return jsonify(model[id])
@app.route('/search/<phrase>')
def search_name(phrase):
return jsonify(model.search(phrase, field_key="title"))
if __name__ == '__main__':
def __init__(self):
Model.__init__(self)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.batch_size = 2
self.needStop = False
self.codec = Codec()
self.model = Model('train')
def test_parse_should_call_given_parser_strategy(self):
path = 'dummy/path'
model = Model([], [])
self._writer.write(path, model)
self._writing_strategy_mock.write.assert_called_once_with(path, model)
from data import load_data
from lib.model import Model
from util import Logger, train, validation, AdamOptimizer
# os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
# os.environ["CUDA_VISIBLE_DEVICES"]=str(exp_config['device'])
# torch.cuda.set_device(0)
# data
train_data, val_data = load_data(data_config, exp_config['batch_size'])
eval_length = data_config['eval_length']
# logger
# model
model = Model(**model_config).to(0)
# optimizer
optimizer = AdamOptimizer(params=model.parameters(), lr=exp_config['lr'],
grad_clip_value=exp_config['grad_clip_value'],
grad_clip_norm=exp_config['grad_clip_norm'])
logger_on = True
if logger_on:
logger = Logger(exp_config, model_config, data_config)
# train / val loop
for epoch in range(exp_config['n_epochs']):
print('Epoch:', epoch)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.codec = Codec()
self.model = Model('encode')
