def new_reserved_lab():
json = request.get_json(force=True)
if not validate(
json,
'fullName',
'department',
'labNumber',
'deviceQuantity',
'date',
'block',
):
abort(400)
full_name = json['fullName']
department = json['department']
lab_number = json['labNumber']
device_quantity = json['deviceQuantity']
date = json['date']
block = json['block']
reserved_lab = ReservedLabModel(
full_name=full_name,
department=department,
lab_number=lab_number,
device_quantity=device_quantity,
date=date,
block=block,
)
reserved_lab.push()
return ''
def delete_lab():
json = request.get_json(force=True)
if not validate(json, 'labNumber'):
abort(403)
LabModel.delete_one(json['labNumber'])
return ''
def delete_cart():
json = request.get_json(force=True)
if not validate(json, 'cartNumber'):
abort(403)
CartModel.delete_one(json['cartNumber'])
return ''
def delete_reserved_lab():
json = request.get_json(force=True)
if not validate(json, 'labNumber', 'date', 'block'):
abort(403)
ReservedLabModel.delete_one(json['labNumber'], json['date'], json['block'])
return ''
def delete_teacher():
json = request.get_json(force=True)
if not validate(json, 'firstName', 'lastName', 'department'):
abort(403)
TeacherModel.delete_one(json['firstName'], json['lastName'],
json['department'])
return ''
def auth():
a = request.get_json()
if validate(a) == True:
if check(a) == True:
new_ticket = db_manager.ticket_number()
return
else:
return make_error_message("1")
else:
return make_error_message("2")
def new_lab():
json = request.get_json(force=True)
if not validate(json, 'labNumber', 'deviceQuantity'):
abort(400)
lab_number = json['labNumber']
device_quantity = json['deviceQuantity']
lab = LabModel(lab_number=lab_number, device_quantity=device_quantity)
lab.push()
return ''
def new_cart():
json = request.get_json(force=True)
if not validate(json, 'cartNumber', 'type', 'deviceQuantity'):
abort(400)
cart_number = json['cartNumber']
device_type = json['type']
device_quantity = json['deviceQuantity']
cart = CartModel(cart_number=cart_number,
device_type=device_type,
device_quantity=device_quantity)
cart.push()
return ''
def new_teacher():
json = request.get_json(force=True)
if not validate(json, 'firstName', 'lastName', 'department'):
abort(400)
first_name = json['firstName']
last_name = json['lastName']
department = json['department']
teacher = LabModel(first_name=first_name,
last_name=last_name,
department=department)
teacher.push()
return ''
def validate():
data = json.loads(request.data)
results = utilities.validate(data)
if results:
row = results[0]
if(row[3] == data["password"]):
# do session stuff
session.clear()
if(data["role"] == "student"):
session['s_id'] = row[0]
else:
session['p_id'] = row[0]
session['name'] = row[1]
return "Correct"
else:
# wrong password, tell user
session.clear()
return "Wrong"
# If we still reach here, it means that the user is not a registered one
return "Missing"
def new_reserved_cart():
json = request.get_json(force=True)
if not validate(
json,
'fullName',
'department',
'cartNumber',
'deviceType',
'deviceQuantity',
'date',
'block',
):
abort(400)
full_name = json['fullName']
department = json['department']
cart_number = json['cartNumber']
device_type = json['deviceType']
device_quantity = json['deviceQuantity']
date = json['date']
block = json['block']
reserved_cart = ReservedCartModel(
full_name=full_name,
department=department,
cart_number=cart_number,
device_type=device_type,
device_quantity=device_quantity,
date=date,
block=block,
)
reserved_cart.push()
return ''
def main(model_path, model_name, log_path):
torch.manual_seed(0)
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
num_gpu = torch.cuda.device_count()
print("There are {} gups.".format(num_gpu))
kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
if not os.path.isdir("../data"):
os.makedirs("../data")
if not os.path.isdir(model_path):
os.makedirs(model_path)
if not os.path.isfile(os.path.join(model_path, 'params.json')):
with open('./params.json') as json_file:
params = json.load(json_file)
else:
with open(os.path.join(model_path, 'params.json')) as json_file:
params = json.load(json_file)
data_params = params['data']
train_params = params['train']
dataset = LJSPEECH_MEL(
'../data',
params['data'],
url='https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2',
download=True)
train_dataset, test_dataset = torch.utils.data.random_split(
dataset, [12000, 1100])
train_loader = data.DataLoader(
dataset=train_dataset,
batch_size=train_params['batch_size'],
shuffle=True,
collate_fn=lambda x: data_mel_processing(x, params['data']),
**kwargs)
rand_sampler = torch.utils.data.RandomSampler(test_dataset,
num_samples=100,
replacement=True)
val_loader = data.DataLoader(
dataset=test_dataset,
batch_size=1,
shuffle=False,
sampler=rand_sampler,
collate_fn=lambda x: data_mel_processing(x, params['data']),
**kwargs)
model = Model(params, device).to(device)
# if num_gpu > 1:
# model = nn.DataParallel(model)
criteriate = TTSLoss(device).to(device)
# print(model)
# for name, param in model.named_parameters():
# if param.requires_grad:
# print("parameter {} is {}".format(name, param.shape))
print('Num Model Parameters',
sum([param.nelement() for param in model.parameters()]))
epochs = train_params['epochs']
writer = get_writer(model_path, log_path)
optimizer = torch.optim.Adam(model.parameters(),
lr=train_params['lr'],
betas=(0.9, 0.98),
eps=1e-09)
iteration = 0
model_file = os.path.join(model_path, 'model.pt')
if os.path.isfile(model_file):
file = torch.load(model_file)
iteration = file['iteration']
optimizer.load_state_dict(file['optimizer'])
model.load_state_dict(file['model_dict'])
best_valid_loss = validate(model, criteriate, device, val_loader,
iteration, writer, train_params)
else:
best_valid_loss = float("inf")
for epoch in range(1, epochs + 1):
iteration = train(model, criteriate, device, train_loader, optimizer,
iteration, train_params, writer)
test_loss = validate(model, criteriate, device, val_loader, iteration,
writer, train_params)
if test_loss < best_valid_loss:
print("The validation loss is improved by {}, new model is saving".
format(best_valid_loss - test_loss))
best_valid_loss = test_loss
save_model(model, optimizer, iteration, params, model_path,
model_name)
if epoch % 10 == 0:
temp_model_path = os.path.join(model_path, str(iteration))
os.makedirs(temp_model_path)
temp_model_name = 'model' + str(iteration) + '.pt'
save_model(model, optimizer, iteration, params, temp_model_path,
temp_model_name)
