def get_learner_rnn_feedback(model_name):
# Training settings
bs = 1
seq_len = 1
# Datasets and Dataloaders
trn_lds = LinearFeedbackDataset(df_small_trn, trn_tfms, PATH,
IMAGES_FOLDER)
val_lds = LinearFeedbackDataset(df_small_val, trn_tfms, PATH,
IMAGES_FOLDER)
trn_dl = FastaiDataLoader(trn_lds, batch_sampler=trn_lds.batch_sampler())
val_dl = FastaiDataLoader(val_lds, batch_sampler=val_lds.batch_sampler())
# Model
model_folder = "CNNtoRNNFeedback"
model = CNNtoRNNFeedback(1024,
200,
2,
seq_len,
bs,
14,
use_ground_truth=False)
layer_groups = [
list(model.encoder.children())[:6],
list(model.encoder.children())[6:],
[model.lstm, model.linear],
]
# opt_fn is used like this: optimizer = opt_fn(trainable_params(model), lr=1e-1)
opt_fn = partial(optim.SGD, momentum=0.9)
criterion = F.l1_loss
learner = Learner(
MockedData(trn_dl, val_dl),
CustomModel(model, layer_groups),
metrics=METRICS,
opt_fn=opt_fn,
crit=criterion,
tmp_name=os.path.join(ROOT, PATH, 'tmp'),
models_name=os.path.join(ROOT, PATH, 'models', model_folder),
)
# clip and reg_fn needs shouldn't be passed to the constructor because it sets as None anyway...
# learner.reg_fn = partial(seq2seq_reg, alpha=2, beta=1)
learner.clip = 0.4
learner.load(model_name)
learner.model.eval()
return learner
def get_learner_rnn(model_name):
# mem comsuption: 7200 MB
bs = 1
seq_len = 1
# Datasets and Dataloaders
trn_ds = BatchifiedDataset(df_large_trn, bs, seq_len, trn_tfms, PATH,
IMAGES_FOLDER)
val_ds = BatchifiedDataset(df_large_val, bs, seq_len, trn_tfms, PATH,
IMAGES_FOLDER)
trn_dl = FastaiDataLoader(trn_ds, batch_sampler=trn_ds.batch_sampler())
val_dl = FastaiDataLoader(val_ds, batch_sampler=val_ds.batch_sampler())
# Model
model_folder = "CNNtoRNN_new"
model = CNNtoRNN(
encode_size=128, # 1024
hidden_size=32, # 200
num_layers=2,
bs=bs,
output_size=14)
layer_groups = [
list(model.encoder.children())[:6],
list(model.encoder.children())[6:],
[model.encoder_linear, model.lstm, model.linear],
]
# opt_fn is used like this: optimizer = opt_fn(trainable_params(model), lr=1e-1)
opt_fn = partial(optim.SGD, momentum=0.9)
criterion = F.mse_loss
learner = Learner(
MockedData(trn_dl, val_dl),
CustomModel(model, layer_groups),
metrics=METRICS,
opt_fn=opt_fn,
crit=criterion,
tmp_name=os.path.join(ROOT, PATH, 'tmp'),
models_name=os.path.join(ROOT, PATH, 'models', model_folder),
)
# clip and reg_fn needs shouldn't be passed to the constructor because it sets as None anyway...
# learner.reg_fn = partial(seq2seq_reg, alpha=2, beta=1)
learner.clip = 0.4
learner.load(model_name)
learner.model.eval()
return learner
learner = Learner(
MockedData(trn_dl, val_dl),
CustomModel(model, layer_groups),
metrics=METRICS,
opt_fn=opt_fn,
crit=criterion,
tmp_name=os.path.join(ROOT, PATH, 'tmp'),
models_name=os.path.join(ROOT, PATH, 'models', model_folder),
)
# no clipping
############
############ Model loading
############
learner.load('6ep_frz_12ep_unfrz')
learner.model.eval()
print_calced_metrics_from_dl(learner.model, val_dl)
######################
###################### Test driving
######################
# import pdb; pdb.set_trace()
def format_indicators(pred_indicators):
"""
Indicators are formatted differently on the controller
Cpp controller:
indicators[0] = shared->fast;
