def evaluate(data_source_f1, data_source_f2, batch_size=10):
with open(os.path.join(args.save, 'validvars_loop'), 'rb') as f:
# Aggregate exponents and outputs to calculate the validation set from the beginning of evaluation time
[total_f1, total_f1_exps, total_f2, total_f2_exps,
total_samples] = pickle.load(f)
# Turn on evaluation mode which disables dropout.
model_f1.eval()
model_f2.eval()
hidden_f1 = model_f1.init_hidden(batch_size, model_f1.ncell)
hidden_f2 = model_f2.init_hidden(batch_size, model_f2.ncell)
# Creating validation randata
_val_tmp_randata = data.new_process(args, valid_length)
# and setting uniform distribution for y~
valid_randata_f1 = batchify_f1(_val_tmp_randata,
batch_size,
args,
uniformly=True)
valid_randata_f2 = batchify_f2(_val_tmp_randata,
batch_size,
args,
uniformly=True)
for i in range(0, data_source_f1.size(0) - 1, args.bptt):
data_f1 = get_batch_dine(data_source_f1, i, args, evaluation=True)
data_f2 = get_batch_dine(data_source_f2, i, args, evaluation=True)
randata_f1 = get_batch_dine(valid_randata_f1, i, args, evaluation=True)
randata_f2 = get_batch_dine(valid_randata_f2, i, args, evaluation=True)
# forward
with torch.no_grad(): # added this line to overcome OOM error
out_f1, out_reused_f1, hidden_f1 = parallel_model_f1(
data_f1, randata_f1, hidden_f1)
out_f2, out_reused_f2, hidden_f2 = parallel_model_f2(
data_f2, randata_f2, hidden_f2)
# loss aggregation
total_f1 += torch.sum(out_f1)
total_f1_exps += torch.sum(torch.exp(out_reused_f1))
total_f2 += torch.sum(out_f2)
total_f2_exps += torch.sum(torch.exp(out_reused_f2))
total_samples += torch.numel(out_f1)
# hidden repackage
hidden_f1 = repackage_hidden(hidden_f1)
hidden_f2 = repackage_hidden(hidden_f2)
return [total_f1, total_f1_exps, total_f2, total_f2_exps, total_samples]
def evaluate(data_source_f1, data_source_f2, batch_size=10):
# Turn on evaluation mode which disables dropout.
model_f1.eval()
model_f2.eval()
total_loss_fi = [0, 0]
hidden_f1 = model_f1.init_hidden(batch_size, model_f1.ncell)
hidden_f2 = model_f2.init_hidden(batch_size, model_f2.ncell)
# Creating validation randata
_val_tmp_randata = data.new_process(args, valid_length)
# and setting uniform distribution for y~
valid_randata_f1 = batchify_f1(_val_tmp_randata,
batch_size,
args,
uniformly=True)
valid_randata_f2 = batchify_f2(_val_tmp_randata,
batch_size,
args,
uniformly=True)
for i in range(0, data_source_f1.size(0) - 1, args.bptt):
data_f1 = get_batch_dine(data_source_f1, i, args, evaluation=True)
data_f2 = get_batch_dine(data_source_f2, i, args, evaluation=True)
randata_f1 = get_batch_dine(valid_randata_f1, i, args, evaluation=True)
randata_f2 = get_batch_dine(valid_randata_f2, i, args, evaluation=True)
# forward
out_f1, out_reused_f1, hidden_f1 = parallel_model_f1(
data_f1, randata_f1, hidden_f1)
out_f2, out_reused_f2, hidden_f2 = parallel_model_f2(
data_f2, randata_f2, hidden_f2)
# loss calculation
raw_loss_f1 = torch.mean(out_f1) - torch.log(
torch.mean(torch.exp(out_reused_f1)))
raw_loss_f2 = torch.mean(out_f2) - torch.log(
torch.mean(torch.exp(out_reused_f2)))
total_loss_fi[0] += raw_loss_f1.data * len(data_f1)
total_loss_fi[1] += raw_loss_f2.data * len(data_f2)
# hidden repackage
hidden_f1 = repackage_hidden(hidden_f1)
hidden_f2 = repackage_hidden(hidden_f2)
total_loss_fi[0] = total_loss_fi[0].item() / (data_source_f1.size(0))
total_loss_fi[1] = total_loss_fi[1].item() / (data_source_f2.size(0))
return total_loss_fi
def evaluate_dist(x, support):
y_vec = np.arange(x - 7.5, x + 7.5, 0.05)
y_givenx = np.zeros(len(y_vec))
for k, y in enumerate(y_vec):
batch_size = 1
# Creating test data - for x,y respectively
data_xy = {
'features': (torch.ones(50, batch_size, args.ndim) * x),
'labels': (torch.ones(50, batch_size, args.ndim) * y)
}
test_data_f2 = batchify_f2(data_xy, batch_size, args)
# setting uniform distribution for y~
test_randata_f2 = batchify_f2(data_xy,
batch_size,
args,
uniformly=True)
# Turn on evaluation mode which disables dropout.
model_f2.eval()
hidden_f2 = model_f2.init_hidden(batch_size, model_f2.ncell)
dist_vector = torch.FloatTensor()
for i in range(0, test_data_f2.size(0) - 1, args.bptt):
data_f2 = get_batch_dine(test_data_f2, i, args, evaluation=True)
randata_f2 = get_batch_dine(test_randata_f2,
i,
args,
evaluation=True)
# forward
out_f2, out_reused_f2, hidden_f2 = parallel_model_f2(
data_f2, randata_f2, hidden_f2)
# distribution calculation
if i:
dist = torch.exp(out_f2) * (1 / support)
dist_vector = torch.cat((dist_vector, dist), 0)
else:
dist_vector = torch.exp(out_f2) * (1 / support)
# hidden repackage
hidden_f2 = repackage_hidden(hidden_f2)
y_givenx[k] = torch.mean(dist_vector).detach().cpu().numpy()
y_givenx = y_givenx / np.sum(y_givenx)
return y_vec, y_givenx
def evaluate(data_source_f1, data_source_f2, batch_size=10):
with open(os.path.join(args.save, 'validvars'), 'rb') as f:
# Aggregate exponents and outputs to calculate the validation set from the beginning of evaluation time
[total_f1, total_f1_exps, total_f2, total_f2_exps,
total_samples] = pickle.load(f)
# Turn on evaluation mode which disables dropout.
model_f1.eval()
model_f2.eval()
hidden_f1 = model_f1.init_hidden(batch_size, model_f1.ncell)
hidden_f2 = model_f2.init_hidden(batch_size, model_f2.ncell)
# Adjusting parameters for uniformly taken samples
model_f1.rand_num = model_f2.rand_num = 0 # args.ndim * 1
model_f1.sup_min = model_f2.sup_min = torch.min(data_source_f1)
model_f1.sup_max = model_f2.sup_max = torch.max(data_source_f1)
for i in range(0, data_source_f1.size(0) - 1, args.bptt):
data_f1 = get_batch_dine(data_source_f1, i, args, evaluation=True)
data_f2 = get_batch_dine(data_source_f2, i, args, evaluation=True)
# forward
with torch.no_grad(): # added this line to overcome OOM error
out_f1, out_reused_f1, hidden_f1 = parallel_model_f1(
data_f1, hidden_f1)
out_f2, out_reused_f2, hidden_f2 = parallel_model_f2(
data_f2, hidden_f2)
# loss aggregation
total_f1 += torch.sum(out_f1)
total_f1_exps += torch.sum(torch.exp(out_reused_f1))
total_f2 += torch.sum(out_f2)
total_f2_exps += torch.sum(torch.exp(out_reused_f2))
total_samples += torch.numel(out_f1)
# hidden repackage
hidden_f1 = repackage_hidden(hidden_f1)
hidden_f2 = repackage_hidden(hidden_f2)
return [total_f1, total_f1_exps, total_f2, total_f2_exps, total_samples]
def train():
assert args.batch_size % args.small_batch_size == 0, 'batch_size must be divisible by small_batch_size'
# Creating train data
_train_tmp_data = data.new_process(args, train_length)
train_data_f1 = batchify_f1(_train_tmp_data, args.batch_size, args)
train_data_f2 = batchify_f2(_train_tmp_data, args.batch_size, args)
# Turn on training mode which enables dropout.
total_loss_fi = [0, 0]
start_time = time.time()
hidden_f1 = [
model_f1.init_hidden(args.small_batch_size, args.ncell)
for _ in range(args.batch_size // args.small_batch_size)
]
hidden_f2 = [
model_f2.init_hidden(args.small_batch_size, args.ncell)
for _ in range(args.batch_size // args.small_batch_size)
]
# Shuffeling data for y~ (ONLY y!)
train_randata_f1 = batchify_f1(_train_tmp_data,
args.batch_size,
args,
uniformly=True)
train_randata_f2 = batchify_f2(_train_tmp_data,
args.batch_size,
args,
uniformly=True)
batch, i = 0, 0
while i < train_data_f1.size(0) - 1 - 1:
bptt = args.bptt if np.random.random() < 0.95 else args.bptt / 2.
# Prevent excessively small or negative sequence lengths
seq_len = max(5, int(np.random.normal(bptt, 5)))
# There's a very small chance that it could select a very long sequence length resulting in OOM
seq_len = min(seq_len, args.bptt + args.max_seq_len_delta)
# Adjusting learning rate to variable length sequence
lr_tmp_f1 = optimizer_f1.param_groups[0]['lr']
optimizer_f1.param_groups[0]['lr'] = lr_tmp_f1 * seq_len / args.bptt
lr_tmp_f2 = optimizer_f2.param_groups[0]['lr']
optimizer_f2.param_groups[0]['lr'] = lr_tmp_f2 * seq_len / args.bptt
# Activating train mode
model_f1.train()
model_f2.train()
data_f1 = get_batch_dine(train_data_f1, i, args, seq_len=seq_len)
data_f2 = get_batch_dine(train_data_f2, i, args, seq_len=seq_len)
randata_f1 = get_batch_dine(train_randata_f1, i, args, seq_len=seq_len)
randata_f2 = get_batch_dine(train_randata_f2, i, args, seq_len=seq_len)
optimizer_f1.zero_grad()
optimizer_f2.zero_grad()
start, end, s_id = 0, args.small_batch_size, 0
while start < args.batch_size:
cur_data_f1 = data_f1[:, start:end]
cur_data_f2 = data_f2[:, start:end]
cur_randata_f1 = randata_f1[:, start:end]
cur_randata_f2 = randata_f2[:, start:end]
# Starting each batch, we detach the hidden state from how it was previously produced.
# If we didn't, the model would try backpropagating all the way to start of the dataset.
hidden_f1[s_id] = repackage_hidden(hidden_f1[s_id])
hidden_f2[s_id] = repackage_hidden(hidden_f2[s_id])
out_f1, out_reused_f1, hidden_f1[s_id] = parallel_model_f1(
cur_data_f1, cur_randata_f1, hidden_f1[s_id])
out_f2, out_reused_f2, hidden_f2[s_id] = parallel_model_f2(
cur_data_f2, cur_randata_f2, hidden_f2[s_id])
raw_loss_f1 = torch.mean(out_f1) - torch.log(
torch.mean(torch.exp(out_reused_f1)))
raw_loss_f2 = torch.mean(out_f2) - torch.log(
torch.mean(torch.exp(out_reused_f2)))
loss = [raw_loss_f1, raw_loss_f2]
loss[0] *= args.small_batch_size / args.batch_size
loss[1] *= args.small_batch_size / args.batch_size
total_loss_fi[
0] += raw_loss_f1.data * args.small_batch_size / args.batch_size
total_loss_fi[
1] += raw_loss_f2.data * args.small_batch_size / args.batch_size
(-loss[0]).backward() # for gradient ascent we use -loss
(-loss[1]).backward()
# optimizer grad check... note: comment this if 'nan' problem solved
for j, p in enumerate(optimizer_f1.param_groups[0]['params']):
ans = p.grad.data[torch.isnan(p.grad.data)]
if ans.size()[0] > 0:
logging.info(
"some nan exists in optimizer_f1 parameter #{} !".
format(j))
raise KeyboardInterrupt
for j, p in enumerate(optimizer_f2.param_groups[0]['params']):
ans = p.grad.data[torch.isnan(p.grad.data)]
if ans.size()[0] > 0:
logging.info(
"some nan exists in optimizer_f2 parameter #{} !".
format(j))
raise KeyboardInterrupt
s_id += 1
start = end
end = start + args.small_batch_size
gc.collect()
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
torch.nn.utils.clip_grad_norm_(model_f1.parameters(), args.clip)
torch.nn.utils.clip_grad_norm_(model_f2.parameters(), args.clip)
optimizer_f1.step()
optimizer_f2.step()
#learning rate - back to normal
optimizer_f1.param_groups[0]['lr'] = lr_tmp_f1
optimizer_f2.param_groups[0]['lr'] = lr_tmp_f2
if batch < 1:
log_interval = np.ceil((len(train_data_f1) // args.bptt) / 5)
if batch % log_interval == 0 and batch > 0:
curr_loss_fi = [
total_loss_fi[0].item() / args.log_interval,
total_loss_fi[1].item() / args.log_interval
]
elapsed = time.time() - start_time
logging.info(
'| epoch {:3d} | {:3d}/{:3d} batches | lr1 {:f} | lr2 {:f} | ms/batch {:5.4f} | '
'loss F1 {:6.4f} | loss F2 {:6.4f} | loss {:6.4f}'.format(
epoch, batch,
len(train_data_f1) // args.bptt,
optimizer_f1.param_groups[0]['lr'],
optimizer_f2.param_groups[0]['lr'],
elapsed * 1000 / args.log_interval, curr_loss_fi[0],
curr_loss_fi[1], curr_loss_fi[1] - curr_loss_fi[0]))
total_loss_fi = [0, 0]
start_time = time.time()
###
batch += 1
i += seq_len