def testSetNumpyBeforeTrain(self):
seed = 90
hidden_size = 10
vocab_size = 1000
num_layers = 1
num_steps = 3
init_scale = 0.1
batch_size = 4
batch_num = 200
with fluid.dygraph.guard():
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
# TODO: marsyang1993 Change seed to
ptb_model = PtbModel(hidden_size=hidden_size,
vocab_size=vocab_size,
num_layers=num_layers,
num_steps=num_steps,
init_scale=init_scale)
bd = []
lr_arr = [0.0]
# this a fake lr decay strategy
for i in range(1, 10):
bd.append(100 * i)
# set lr to 0.0, not update parameter
new_lr = 0.0
lr_arr.append(new_lr)
place = fluid.CPUPlace(
) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0)
adam = Adam(learning_rate=fluid.layers.piecewise_decay(
boundaries=bd, values=lr_arr),
beta1=0.8,
beta2=0.6,
parameter_list=ptb_model.parameters())
dy_param_updated = dict()
dy_param_init = dict()
dy_loss = None
last_hidden = None
last_cell = None
np_opti_dict = {}
np_state_dict = {}
for k, v in self.opti_dict.items():
np_opti_dict[v.name] = v.numpy()
for k, v in self.state_dict.items():
np_state_dict[k] = v.numpy()
adam.set_dict(np_opti_dict)
ptb_model.set_dict(np_state_dict)
for i in range(1):
x_data = np.arange(12).reshape(4, 3).astype('int64')
y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
y_data = y_data.reshape((-1, 1))
init_hidden_data = np.zeros(
(num_layers, batch_size, hidden_size), dtype='float32')
init_cell_data = np.zeros(
(num_layers, batch_size, hidden_size), dtype='float32')
x = to_variable(x_data)
y = to_variable(y_data)
init_hidden = to_variable(init_hidden_data)
init_cell = to_variable(init_cell_data)
dy_loss, last_hidden, last_cell = ptb_model(
x, y, init_hidden, init_cell)
dy_loss.backward()
adam.minimize(dy_loss)
ptb_model.clear_gradients()
opti_dict = adam.state_dict()
for k, v in opti_dict.items():
if k == "global_step":
self.assertTrue(
np.array_equal(v.numpy(), self.base_opti[v.name] + 1))
if k.find("beta1_pow_acc_0") > 0:
self.assertTrue(
np.array_equal(v.numpy(),
self.base_opti[v.name] * adam._beta1))
if k.find("beta2_pow_acc_0") > 0:
self.assertTrue(
np.array_equal(v.numpy(),
self.base_opti[v.name] * adam._beta2))
# check parameter
state_dict = ptb_model.state_dict()
for k, v in state_dict.items():
new_t = v.numpy()
base_t = self.model_base[k]
self.assertTrue(np.array_equal(new_t, base_t))
class Training(Base):
'''
Training the searched network
cf: config.yml path
cv_i: Which fold in the cross validation. If cv_i >= n_fold: use all the training dataset.
for_train: If True, for training process, otherwise for searching.
new_lr: if True, check_resume() will not load the saved states of optimizers and lr_schedulers.
'''
def __init__(self, cf='config.yml', cv_i=0, for_train=True, new_lr=False):
super().__init__(cf=cf, cv_i=cv_i, for_train=for_train)
self._init_model()
self.check_resume(new_lr=new_lr)
def _init_model(self):
geno_file = os.path.join(self.log_path,
self.config['search']['geno_file'])
with open(geno_file, 'rb') as f:
gene = eval(pickle.load(f)[0])
self.model = SearchedNet(
gene=gene,
in_channels=self.config['data']['in_channels'],
init_node_c=self.config['search']['init_node_c'],
out_channels=self.config['data']['out_channels'],
depth=self.config['search']['depth'],
n_nodes=self.config['search']['n_nodes'],
drop_rate=self.config['train']['drop_rate'])
print('Param size = {:.3f} MB'.format(
calc_param_size(self.model.parameters())))
self.loss = lambda props, y_truth: fluid.layers.reduce_mean(
fluid.layers.softmax_with_cross_entropy(props, y_truth))
self.optim = Adam(parameter_list=self.model.parameters())
self.scheduler = ReduceLROnPlateau(self.optim)
def check_resume(self, new_lr=False):
self.last_save = os.path.join(self.log_path,
self.config['train']['last_save'])
self.last_aux = os.path.join(self.log_path,
self.config['train']['last_aux'])
self.best_shot = os.path.join(self.log_path,
self.config['train']['best_shot'])
self.best_aux = os.path.join(self.log_path,
self.config['train']['best_aux'])
if os.path.exists(self.last_aux):
self.model.set_dict(fluid.dygraph.load_dygraph(self.last_save)[0])
with open(self.last_aux, 'rb') as f:
state_dicts = pickle.load(f)
self.epoch = state_dicts['epoch'] + 1
self.history = state_dicts['history']
if not new_lr:
self.optim.set_dict(
fluid.dygraph.load_dygraph(self.last_save)[1])
self.scheduler.load_state_dict(state_dicts['scheduler'])
self.best_val_loss = state_dicts['best_loss']
else:
self.epoch = 0
self.history = defaultdict(list)
self.best_val_loss = float('inf')
def main_run(self):
# pdb.set_trace()
n_epochs = self.config['train']['epochs']
for epoch in range(n_epochs):
is_best = False
loss, acc1, acc5 = self.train()
val_loss, val_acc1, val_acc5 = self.validate()
self.scheduler.step(val_loss)
self.history['loss'].append(loss)
self.history['acc1'].append(acc1)
self.history['acc5'].append(acc5)
self.history['val_loss'].append(val_loss)
self.history['val_acc1'].append(val_acc1)
self.history['val_acc5'].append(val_acc5)
if val_loss < self.best_val_loss:
is_best = True
self.best_val_loss = val_loss
# Save what the current epoch ends up with.
fluid.save_dygraph(self.model.state_dict(), self.last_save)
fluid.save_dygraph(self.optim.state_dict(), self.last_save)
state_dicts = {
'epoch': self.epoch,
'history': self.history,
'scheduler': self.scheduler.state_dict(),
'best_loss': self.best_val_loss
}
with open(self.last_aux, 'wb') as f:
pickle.dump(state_dicts, f)
if is_best:
shutil.copy(self.last_save + '.pdparams',
self.best_shot + '.pdparams')
shutil.copy(self.last_save + '.pdopt',
self.best_shot + '.pdopt')
shutil.copy(self.last_aux, self.best_aux)
self.epoch += 1
if self.epoch > n_epochs:
break
if DEBUG_FLAG and epoch >= 1:
break
print('Training Finished.')
return
def train(self):
'''
Training | Training process
'''
self.model.train()
n_steps = self.train_generator.steps_per_epoch
sum_loss = 0
sum_acc1 = 0
sum_acc5 = 0
with tqdm(self.train_generator.epoch(),
total=n_steps,
desc='Training | Epoch {} | Training'.format(
self.epoch)) as pbar:
for step, (x, y_truth) in enumerate(pbar):
x = fluid.dygraph.to_variable(x.astype('float32'))
y_truth = fluid.dygraph.to_variable(
y_truth.astype('int64')[:, np.newaxis])
y_pred = self.model(x)
loss = self.loss(y_pred, y_truth)
sum_loss += loss.numpy()[0]
acc1 = fluid.layers.accuracy(y_pred, y_truth, k=1)
acc5 = fluid.layers.accuracy(y_pred, y_truth, k=5)
sum_acc1 += acc1.numpy()[0]
sum_acc5 += acc5.numpy()[0]
loss.backward()
self.optim.minimize(loss)
self.optim.clear_gradients()
postfix = OrderedDict()
postfix['Loss'] = round(sum_loss / (step + 1), 3)
postfix['Top-1-Acc'] = round(sum_acc1 / (step + 1), 3)
postfix['Top-5-Acc'] = round(sum_acc5 / (step + 1), 3)
pbar.set_postfix(postfix)
if DEBUG_FLAG and step >= 1:
break
return [round(i / n_steps, 3) for i in [sum_loss, sum_acc1, sum_acc5]]
def validate(self):
'''
Training | Validation process
'''
self.model.eval()
n_steps = self.val_generator.steps_per_epoch
sum_loss = 0
sum_acc1 = 0
sum_acc5 = 0
with tqdm(self.val_generator.epoch(),
total=n_steps,
desc='Training | Epoch {} | Val'.format(self.epoch)) as pbar:
for step, (x, y_truth) in enumerate(pbar):
x = fluid.dygraph.to_variable(x.astype('float32'))
y_truth = fluid.dygraph.to_variable(
y_truth.astype('int64')[:, np.newaxis])
y_pred = self.model(x)
loss = self.loss(y_pred, y_truth)
sum_loss += loss.numpy()[0]
acc1 = fluid.layers.accuracy(y_pred, y_truth, k=1)
acc5 = fluid.layers.accuracy(y_pred, y_truth, k=5)
sum_acc1 += acc1.numpy()[0]
sum_acc5 += acc5.numpy()[0]
postfix = OrderedDict()
postfix['Loss'] = round(sum_loss / (step + 1), 3)
postfix['Top-1-Acc'] = round(sum_acc1 / (step + 1), 3)
postfix['Top-5-Acc'] = round(sum_acc5 / (step + 1), 3)
pbar.set_postfix(postfix)
if DEBUG_FLAG and step >= 1:
break
return [round(i / n_steps, 3) for i in [sum_loss, sum_acc1, sum_acc5]]
def testSetNumpy(self):
seed = 90
hidden_size = 10
vocab_size = 1000
num_layers = 1
num_steps = 3
init_scale = 0.1
batch_size = 4
batch_num = 200
with fluid.dygraph.guard():
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
# TODO: marsyang1993 Change seed to
ptb_model = PtbModel(hidden_size=hidden_size,
vocab_size=vocab_size,
num_layers=num_layers,
num_steps=num_steps,
init_scale=init_scale)
bd = []
lr_arr = [1.0]
# this a fake lr decay strategy
for i in range(1, 10):
bd.append(100 * i)
new_lr = 1.0
lr_arr.append(new_lr)
place = fluid.CPUPlace(
) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0)
adam = Adam(learning_rate=fluid.layers.piecewise_decay(
boundaries=bd, values=lr_arr),
parameter_list=ptb_model.parameters())
dy_param_updated = dict()
dy_param_init = dict()
dy_loss = None
last_hidden = None
last_cell = None
for i in range(batch_num):
x_data = np.arange(12).reshape(4, 3).astype('int64')
y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
y_data = y_data.reshape((-1, 1))
init_hidden_data = np.zeros(
(num_layers, batch_size, hidden_size), dtype='float32')
init_cell_data = np.zeros(
(num_layers, batch_size, hidden_size), dtype='float32')
x = to_variable(x_data)
y = to_variable(y_data)
init_hidden = to_variable(init_hidden_data)
init_cell = to_variable(init_cell_data)
dy_loss, last_hidden, last_cell = ptb_model(
x, y, init_hidden, init_cell)
if i == 0:
for param in ptb_model.parameters():
dy_param_init[param.name] = param.numpy()
dy_loss.backward()
adam.minimize(dy_loss)
ptb_model.clear_gradients()
if i == batch_num - 1:
for param in ptb_model.parameters():
dy_param_updated[param.name] = param.numpy()
# check optimizer
opti_dict = adam.state_dict()
np_opti_dict = {}
# set to zero
for k, v in opti_dict.items():
np_t = v.numpy()
np_opti_dict[v.name] = np_t
var = v.value().get_tensor()
var.set(np.zeros_like(np_t), place)
self.assertTrue(np.sum(np.abs(v.numpy())) == 0)
if isinstance(adam._learning_rate, LearningRateDecay):
adam._learning_rate.step_num = 0
adam.set_dict(np_opti_dict)
opti_dict = adam.state_dict()
for k, v in opti_dict.items():
self.assertTrue(
np.array_equal(v.numpy(), self.base_opti[v.name]))
# check parameter
state_dict = ptb_model.state_dict()
np_state_dict = {}
for k, v in state_dict.items():
np_t = v.numpy()
np_state_dict[k] = np_t
var = v.value().get_tensor()
var.set(np.zeros_like(np_t), place)
ptb_model.set_dict(np_state_dict)
state_dict = ptb_model.state_dict()
for k, v in state_dict.items():
new_t = v.numpy()
base_t = self.model_base[k]
self.assertTrue(np.array_equal(new_t, base_t))