def train(self, batch_size, epoches, out_name, mini_data):
print(mini_data)
# learning rate schedule
def step_decay(epoch):
initial_lrate = 1e-3
drop = 0.5
epochs_drop = 7.0
lrate = initial_lrate * math.pow(
drop, math.floor((1 + epoch) / epochs_drop))
return lrate
train_dataset = DataGen(self.IMG_SIZE, 5, True, mini=mini_data)
train_gen = train_dataset.generator(batch_size, True)
TIMESTAMP = "{0:%Y-%m-%dT%H-%M-%S}".format(datetime.datetime.now())
callbacks_list = [
EvalCallBack(out_name),
EarlyStopping(monitor='val_loss', mode='min', patience=6),
TensorBoard(log_dir='logs/' + TIMESTAMP,
batch_size=batch_size,
update_freq='epoch'),
LearningRateScheduler(step_decay)
]
self.model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['categorical_accuracy'])
self.model.fit_generator(
generator=train_gen,
steps_per_epoch=train_dataset.get_dataset_size() // batch_size,
epochs=epoches,
callbacks=callbacks_list)
def train(self, batch_size, model_path, epochs):
train_dataset = MPIIDataGen('../../data/mpii/mpii_annotations.json',
'../../data/mpii/images',
inres=self.inres,
outres=self.outres,
is_train=True)
train_gen = train_dataset.generator(batch_size=batch_size,
num_stack=self.num_stacks,
sigma=1,
is_shuffle=True,
rot_flag=True,
scale_flag=True,
flip_flag=True)
csvlogger = CSVLogger(
os.path.join(
model_path, "csv_train_" +
str(datetime.datetime.now().strftime('%H:%M')) + ".csv"))
checkpoint = EvalCallBack(model_path, self.inres, self.outres)
xcallbacks = [csvlogger, checkpoint]
self.model.fit_generator(
generator=train_gen,
steps_per_epoch=train_dataset.get_dataset_size() // batch_size,
epochs=epochs,
callbacks=xcallbacks)
def train(self, batch_size, model_path, epochs):
train_dataset = MPIIDataGen(
"/home/mike/Documents/stacked_hourglass_tf2/data/mpii/mpii_annotations.json",
"/home/mike/datasets/mpii_human_pose_v1/images",
inres=self.inres,
outres=self.outres,
is_train=True)
train_gen = train_dataset.generator(batch_size,
self.num_stacks,
sigma=1,
is_shuffle=True,
rot_flag=True,
scale_flag=True,
flip_flag=True)
# TypeError: expected str, bytes or os.PathLike object, not NoneType
csvlogger = CSVLogger(
os.path.join(
model_path, "csv_train_" +
str(datetime.datetime.now().strftime('%H:%M')) + ".csv"))
modelfile = os.path.join(model_path,
'weights_{epoch:02d}_{loss:.2f}.hdf5')
checkpoint = EvalCallBack(model_path, self.inres, self.outres)
xcallbacks = [csvlogger, checkpoint]
# ValueError: Failed to find data adapter that can handle input: <class 'NoneType'>, <class 'NoneType'>
self.model.fit_generator(
generator=train_gen,
steps_per_epoch=train_dataset.get_dataset_size() // batch_size,
epochs=epochs,
callbacks=xcallbacks)
def resume_train(self, batch_size, model_json, model_weights, init_epoch,
epochs):
self.load_model(model_json, model_weights)
self.model.compile(optimizer=Adam(lr=5e-2),
loss=mean_squared_error,
metrics=["accuracy"])
# dataset_path = os.path.join('D:\\', 'nyu_croped')
# dataset_path = '/home/tomas_bordac/nyu_croped'
dataset_path = config_reader.load_path()
train_dataset = NYUHandDataGen('joint_data.mat',
dataset_path,
inres=self.inres,
outres=self.outres,
is_train=True,
is_testtrain=True)
train_gen = train_dataset.generator(batch_size,
self.num_stacks,
sigma=3,
is_shuffle=True)
model_dir = os.path.dirname(os.path.abspath(model_json))
print(model_dir, model_json)
csvlogger = CSVLogger(
os.path.join(
model_dir, "csv_train_" +
str(datetime.datetime.now().strftime('%H:%M')) + ".csv"))
checkpoint = EvalCallBack(model_dir, self.inres, self.outres)
lr_reducer = ReduceLROnPlateau(monitor='loss',
factor=0.8,
patience=3,
verbose=1,
cooldown=2,
mode='auto')
xcallbacks = [csvlogger, checkpoint, lr_reducer]
self.model.fit_generator(
generator=train_gen,
steps_per_epoch=(train_dataset.get_dataset_size() // batch_size) *
4,
initial_epoch=init_epoch,
epochs=epochs,
callbacks=xcallbacks)
def train(self, batch_size, model_path, epochs):
train_dataset = MPIIDataGen("../../data/mpii/mpii_annotations.json", "../../data/mpii/images",
inres=self.inres, outres=self.outres, is_train=True)
train_gen = train_dataset.generator(batch_size, self.num_stacks, sigma=1, is_shuffle=True,
rot_flag=True, scale_flag=True, flip_flag=True)
csvlogger = CSVLogger(os.path.join(model_path, "csv_train_"+ str(datetime.datetime.now().strftime('%H:%M')) + ".csv"))
modelfile = os.path.join(model_path, 'weights_{epoch:02d}_{loss:.2f}.hdf5')
checkpoint = EvalCallBack(model_path)
xcallbacks = [csvlogger, checkpoint]
self.model.fit_generator(generator=train_gen, steps_per_epoch=train_dataset.get_dataset_size()//batch_size,
#validation_data=val_gen, validation_steps= val_dataset.get_dataset_size()//batch_size,
epochs=epochs, callbacks=xcallbacks)
def resume_train(self, batch_size, model_json, model_weights, init_epoch, epochs):
self.load_model(model_json, model_weights)
self.model.compile(optimizer=RMSprop(lr=5e-4), loss=mean_squared_error, metrics=["accuracy"])
train_dataset = MPIIDataGen("../../data/mpii/mpii_annotations.json", "../../data/mpii/images",
inres=self.inres, outres=self.outres, is_train=True)
train_gen = train_dataset.generator(batch_size, self.num_stacks, sigma=1, is_shuffle=True,
rot_flag=True, scale_flag=True, flip_flag=True)
model_dir = os.path.dirname(os.path.abspath(model_json))
print model_dir , model_json
csvlogger = CSVLogger(os.path.join(model_dir, "csv_train_" + str(datetime.datetime.now().strftime('%H:%M')) + ".csv"))
checkpoint = EvalCallBack(model_dir)
xcallbacks = [csvlogger, checkpoint]
self.model.fit_generator(generator=train_gen, steps_per_epoch=train_dataset.get_dataset_size() // batch_size,
initial_epoch=init_epoch, epochs=epochs, callbacks=xcallbacks)
def train(self, batch_size, model_path, epochs):
# dataset_path = os.path.join('D:\\', 'nyu_croped')
# dataset_path = '/home/tomas_bordac/nyu_croped'
# dataset_path = '../../data/nyu_croped/'
dataset_path = config_reader.load_path()
train_dataset = NYUHandDataGen('joint_data.mat',
dataset_path,
inres=self.inres,
outres=self.outres,
is_train=True,
is_testtrain=True)
train_gen = train_dataset.generator(batch_size,
self.num_stacks,
sigma=3,
is_shuffle=True)
csvlogger = CSVLogger(
os.path.join(
model_path, "csv_train_" +
str(datetime.datetime.now().strftime('%H:%M')) + ".csv"))
modelfile = os.path.join(model_path,
'weights_{epoch:02d}_{loss:.2f}.hdf5')
checkpoint = EvalCallBack(model_path, self.inres, self.outres)
lr_reducer = ReduceLROnPlateau(monitor='loss',
factor=0.8,
patience=3,
verbose=1,
cooldown=2,
mode='auto')
xcallbacks = [csvlogger, checkpoint, lr_reducer]
self.model.fit_generator(
generator=train_gen,
steps_per_epoch=(train_dataset.get_dataset_size() // batch_size) *
4,
epochs=epochs,
callbacks=xcallbacks)
def resume_train(self,
batch_size,
model_json,
model_weights,
init_epoch,
epochs,
out_name,
mini_data=True):
self.load_model(model_json, model_weights)
self.model.compile(optimizer=Adam(lr=5e-4),
loss='categorical_crossentropy',
metrics=["categorical_accuracy"])
train_dataset = DataGen(self.IMG_SIZE,
5,
is_train=True,
mini=mini_data)
train_gen = train_dataset.generator(batch_size, True)
model_dir = os.path.dirname(os.path.abspath(model_json))
print(model_dir, model_json)
TIMESTAMP = "{0:%Y-%m-%dT%H-%M-%S}".format(datetime.datetime.now())
callbacks_list = [
EvalCallBack(out_name),
EarlyStopping(monitor='val_loss', mode='min', patience=6),
TensorBoard(log_dir='logs/' + TIMESTAMP,
batch_size=batch_size,
update_freq='epoch')
]
self.model.fit_generator(
generator=train_gen,
steps_per_epoch=train_dataset.get_dataset_size() // batch_size,
initial_epoch=init_epoch,
epochs=epochs,
callbacks=callbacks_list)