def __init__(self, model):
self.model_ = model
self.lstm_stack_enc_ = lstm_spatial.LSTMStack()
self.lstm_stack_dec_ = lstm.LSTMStack()
self.lstm_stack_pre_ = lstm.LSTMStack()
for l in model.lstm:
self.lstm_stack_enc_.Add(lstm_spatial.LSTM(l))
if model.dec_seq_length > 0:
for l in model.lstm_dec:
self.lstm_stack_dec_.Add(lstm.LSTM(l))
if model.pre_seq_length > 0:
for l in model.lstm_pre:
self.lstm_stack_pre_.Add(lstm.LSTM(l))
assert model.dec_seq_length > 0
self.is_conditional_dec_ = model.dec_conditional
if self.is_conditional_dec_ and model.dec_seq_length > 0:
assert self.lstm_stack_dec_.HasInputs()
self.squash_relu_ = False #model.squash_relu
self.squash_relu_lambda_ = 0 #model.squash_relu_lambda
self.relu_data_ = False #model.relu_data
self.binary_data_ = True #model.binary_data or self.squash_relu_
self.only_occ_predict_ = model.only_occ_predict
if len(model.timestamp) > 0:
old_st = model.timestamp[-1]
ckpt = os.path.join(model.checkpoint_dir,
'%s_%s.h5' % (model.name, old_st))
f = h5py.File(ckpt)
self.lstm_stack_enc_.Load(f)
if model.dec_seq_length > 0:
self.lstm_stack_dec_.Load(f)
if model.pre_seq_length > 0 and not self.only_occ_predict_:
self.lstm_stack_pre_.Load(f)
f.close()
def __init__(self, model, board, board_ladv, board_sup):
self.model_ = model
self.board_ = board
self.board_ladv_ = board_ladv
self.board_sup_ = board_sup
self.lstm_stack_enc_ = lstm_spatial.LSTMStack()
self.lstm_stack_dec_ = lstm.LSTMStack()
self.lstm_stack_pre_ = lstm.LSTMStack()
model_file_sup = './data/bk20151009/part_1/bvlc_googlenet_quick_iter_231760.caffemodel'
solver_file = './data/googlenet_ladv_solver.prototxt'
prototxt_file_sup = './data/train_val_quick_grad.prototxt'
mean_file = './data/bk20151009/part_1/lmdb_casia_full_part1_mean.binaryproto'
self.cnn_solver = caffe.SGDSolver(solver_file)
self.cnn_solver.net.copy_from(model_file_sup)
self.cnn_net_sup = caffe.Net(prototxt_file_sup, model_file_sup,
caffe.TRAIN)
mean = read_mean(mean_file)
mean = mean.reshape((1, 128, 128))
self.cnn_mean_ = mean
caffe.set_mode_gpu()
if self.board_ladv_ == self.board_:
caffe.set_device(self.board_ladv_)
for l in model.lstm:
self.lstm_stack_enc_.Add(lstm_spatial.LSTM(l))
if model.dec_seq_length > 0:
for l in model.lstm_dec:
self.lstm_stack_dec_.Add(lstm.LSTM(l))
if model.pre_seq_length > 0:
for l in model.lstm_pre:
self.lstm_stack_pre_.Add(lstm.LSTM(l))
assert model.dec_seq_length > 0
self.is_conditional_dec_ = model.dec_conditional
if self.is_conditional_dec_ and model.dec_seq_length > 0:
assert self.lstm_stack_dec_.HasInputs()
self.squash_relu_ = False #model.squash_relu
self.squash_relu_lambda_ = 0 #model.squash_relu_lambda
self.relu_data_ = False #model.relu_data
self.binary_data_ = True #model.binary_data or self.squash_relu_
self.only_occ_predict_ = model.only_occ_predict
if len(model.timestamp) > 0:
old_st = model.timestamp[-1]
ckpt = os.path.join(model.checkpoint_dir,
'%s_%s.h5' % (model.name, old_st))
f = h5py.File(ckpt)
self.lstm_stack_enc_.Load(f)
if model.dec_seq_length > 0:
self.lstm_stack_dec_.Load(f)
if model.pre_seq_length > 0 and not self.only_occ_predict_:
self.lstm_stack_pre_.Load(f)
f.close()
def __init__(self, model):
self.model_ = model # keeps the model configurations alongside global configurations
# stacks of encoder, decoder and future predictions
self.lstm_stack_enc_ = lstm.LSTMStack()
self.lstm_stack_dec_ = lstm.LSTMStack()
self.lstm_stack_fut_ = lstm.LSTMStack()
self.decoder_copy_init_state_ = model.decoder_copy_init_state
self.future_copy_init_state_ = model.future_copy_init_state
# add LSTM blocks for encoder, decoder and future predictor
for l in model.lstm:
# get LSTM encoder model according to specifications
self.lstm_stack_enc_.Add(lstm.LSTM(l))
if model.dec_seq_length > 0:
for l in model.lstm_dec:
# get LSTM decoder model according to specifications
self.lstm_stack_dec_.Add(lstm.LSTM(l))
if model.future_seq_length > 0:
for l in model.lstm_future:
# get LSTM future predictor model according to specifications
self.lstm_stack_fut_.Add(lstm.LSTM(l))
# do other initialization stuff
assert model.dec_seq_length > 0 or model.future_seq_length > 0
# get specification of whether decoder and future predictors are conditional on inputs
self.is_conditional_dec_ = model.dec_conditional
self.is_conditional_fut_ = model.future_conditional
if self.is_conditional_dec_ and model.dec_seq_length > 0:
assert self.lstm_stack_dec_.HasInputs()
if self.is_conditional_fut_ and model.future_seq_length > 0:
assert self.lstm_stack_fut_.HasInputs()
self.squash_relu_ = model.squash_relu
self.binary_data_ = model.binary_data or model.squash_relu
self.squash_relu_lambda_ = model.squash_relu_lambda
self.relu_data_ = model.relu_data
# load model if available
if len(model.timestamp) > 0:
old_st = model.timestamp[-1]
ckpt = os.path.join(model.checkpoint_dir,
'%s_%s.h5' % (model.name, old_st))
f = h5py.File(ckpt)
self.lstm_stack_enc_.Load(f)
self.lstm_stack_dec_.Load(f)
self.lstm_stack_fut_.Load(f)
f.close()
def __init__(self, model):
self.model_ = model
self.lstm_stack_ = lstm.LSTMStack()
for l in model.lstm:
self.lstm_stack_.Add(lstm.LSTM(l))
self.squash_relu_ = model.squash_relu
self.squash_relu_lambda_ = model.squash_relu_lambda
if len(model.timestamp) > 0:
old_st = model.timestamp[-1]
ckpt = os.path.join(model.checkpoint_dir, '%s_%s.h5' % (model.name, old_st))
f = h5py.File(ckpt)
self.lstm_stack_.Load(f)
f.close()