def evaluate(FLAGS,
model,
eval_set,
log_entry,
logger,
trainer,
vocabulary=None,
show_sample=False,
eval_index=0):
filename, dataset = eval_set
A = Accumulator()
eval_log = log_entry.evaluation.add()
reporter = EvalReporter()
tree_strs = None
# Evaluate
total_batches = len(dataset)
progress_bar = SimpleProgressBar(msg="Run Eval",
bar_length=60,
enabled=FLAGS.show_progress_bar)
progress_bar.step(0, total=total_batches)
total_tokens = 0
start = time.time()
model.eval()
for i, dataset_batch in enumerate(dataset):
batch = get_batch(dataset_batch)
eval_X_batch, eval_transitions_batch, eval_y_batch, eval_num_transitions_batch, eval_ids = batch
# Run model.
output = model(eval_X_batch,
eval_transitions_batch,
eval_y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions,
store_parse_masks=show_sample,
example_lengths=eval_num_transitions_batch)
can_sample = (FLAGS.model_type == "RLSPINN"
and FLAGS.use_internal_parser)
if show_sample and can_sample:
tmp_samples = model.get_samples(
eval_X_batch, vocabulary, only_one=not FLAGS.write_eval_report)
tree_strs = prettyprint_trees(tmp_samples)
if not FLAGS.write_eval_report:
# Only show one sample, regardless of the number of batches.
show_sample = False
# Calculate class accuracy.
target = torch.from_numpy(eval_y_batch).long()
# get the index of the max log-probability
pred = output.data.max(1, keepdim=False)[1].cpu()
eval_accumulate(model, A, batch)
A.add('class_correct', pred.eq(target).sum())
A.add('class_total', target.size(0))
# Update Aggregate Accuracies
total_tokens += sum([(nt + 1) / 2
for nt in eval_num_transitions_batch.reshape(-1)])
if FLAGS.write_eval_report:
transitions_per_example, _ = model.spinn.get_transitions_per_example(
style="preds" if FLAGS.eval_report_use_preds else "given") if (
FLAGS.model_type == "SPINN"
and FLAGS.use_internal_parser) else (None, None)
if model.use_sentence_pair:
batch_size = pred.size(0)
sent1_transitions = transitions_per_example[:
batch_size] if transitions_per_example is not None else None
sent2_transitions = transitions_per_example[
batch_size:] if transitions_per_example is not None else None
sent1_trees = tree_strs[:
batch_size] if tree_strs is not None else None
sent2_trees = tree_strs[
batch_size:] if tree_strs is not None else None
else:
sent1_transitions = transitions_per_example if transitions_per_example is not None else None
sent2_transitions = None
sent1_trees = tree_strs if tree_strs is not None else None
sent2_trees = None
reporter.save_batch(pred, target, eval_ids,
output.data.cpu().numpy(), sent1_transitions,
sent2_transitions, sent1_trees, sent2_trees)
# Print Progress
progress_bar.step(i + 1, total=total_batches)
progress_bar.finish()
if tree_strs is not None:
logger.Log('Sample: ' + tree_strs[0])
end = time.time()
total_time = end - start
A.add('total_tokens', total_tokens)
A.add('total_time', total_time)
eval_stats(model, A, eval_log)
eval_log.filename = filename
if FLAGS.write_eval_report:
eval_report_path = os.path.join(
FLAGS.log_path,
FLAGS.experiment_name + ".eval_set_" + str(eval_index) + ".report")
reporter.write_report(eval_report_path)
eval_class_acc = eval_log.eval_class_accuracy
eval_trans_acc = eval_log.eval_transition_accuracy
return eval_class_acc, eval_trans_acc
def evaluate(FLAGS, model, data_manager, eval_set, index, logger, step, vocabulary=None):
filename, dataset = eval_set
A = Accumulator()
M = MetricsWriter(os.path.join(FLAGS.metrics_path, FLAGS.experiment_name))
reporter = EvalReporter()
eval_str = eval_format(model)
eval_extra_str = eval_extra_format(model)
# Evaluate
total_batches = len(dataset)
progress_bar = SimpleProgressBar(msg="Run Eval", bar_length=60, enabled=FLAGS.show_progress_bar)
progress_bar.step(0, total=total_batches)
total_tokens = 0
invalid = 0
start = time.time()
model.eval()
for i, dataset_batch in enumerate(dataset):
batch = get_batch(dataset_batch)
eval_X_batch, eval_transitions_batch, eval_y_batch, eval_num_transitions_batch, eval_ids = batch
# Run model.
output = model(eval_X_batch, eval_transitions_batch, eval_y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions)
# Normalize output.
logits = F.log_softmax(output)
# Calculate class accuracy.
target = torch.from_numpy(eval_y_batch).long()
pred = logits.data.max(1)[1].cpu() # get the index of the max log-probability
eval_accumulate(model, data_manager, A, batch)
A.add('class_correct', pred.eq(target).sum())
A.add('class_total', target.size(0))
# Optionally calculate transition loss/acc.
transition_loss = model.transition_loss if hasattr(model, 'transition_loss') else None
# Update Aggregate Accuracies
total_tokens += sum([(nt+1)/2 for nt in eval_num_transitions_batch.reshape(-1)])
if FLAGS.write_eval_report:
reporter_args = [pred, target, eval_ids, output.data.cpu().numpy()]
if hasattr(model, 'transition_loss'):
transitions_per_example, _ = model.spinn.get_transitions_per_example(
style="preds" if FLAGS.eval_report_use_preds else "given")
if model.use_sentence_pair:
batch_size = pred.size(0)
sent1_transitions = transitions_per_example[:batch_size]
sent2_transitions = transitions_per_example[batch_size:]
reporter_args.append(sent1_transitions)
reporter_args.append(sent2_transitions)
else:
reporter_args.append(transitions_per_example)
reporter.save_batch(*reporter_args)
# Print Progress
progress_bar.step(i+1, total=total_batches)
progress_bar.finish()
end = time.time()
total_time = end - start
A.add('total_tokens', total_tokens)
A.add('total_time', total_time)
stats_args = eval_stats(model, A, step)
stats_args['filename'] = filename
logger.Log(eval_str.format(**stats_args))
logger.Log(eval_extra_str.format(**stats_args))
if FLAGS.write_eval_report:
eval_report_path = os.path.join(FLAGS.log_path, FLAGS.experiment_name + ".report")
reporter.write_report(eval_report_path)
eval_class_acc = stats_args['class_acc']
eval_trans_acc = stats_args['transition_acc']
if index == 0:
eval_metrics(M, stats_args, step)
return eval_class_acc, eval_trans_acc
def evaluate(FLAGS, model, data_manager, eval_set, log_entry,
logger, step, vocabulary=None, show_sample=False, eval_index=0):
filename, dataset = eval_set
A = Accumulator()
index = len(log_entry.evaluation)
eval_log = log_entry.evaluation.add()
reporter = EvalReporter()
tree_strs = None
# Evaluate
total_batches = len(dataset)
progress_bar = SimpleProgressBar(msg="Run Eval", bar_length=60, enabled=FLAGS.show_progress_bar)
progress_bar.step(0, total=total_batches)
total_tokens = 0
start = time.time()
if FLAGS.model_type in ["Pyramid", "ChoiPyramid"]:
pyramid_temperature_multiplier = FLAGS.pyramid_temperature_decay_per_10k_steps ** (
step / 10000.0)
if FLAGS.pyramid_temperature_cycle_length > 0.0:
min_temp = 1e-5
pyramid_temperature_multiplier *= (math.cos((step) /
FLAGS.pyramid_temperature_cycle_length) + 1 + min_temp) / 2
else:
pyramid_temperature_multiplier = None
model.eval()
for i, dataset_batch in enumerate(dataset):
batch = get_batch(dataset_batch)
eval_X_batch, eval_transitions_batch, eval_y_batch, eval_num_transitions_batch, eval_ids = batch
# Run model.
output = model(eval_X_batch, eval_transitions_batch, eval_y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions,
pyramid_temperature_multiplier=pyramid_temperature_multiplier,
store_parse_masks=show_sample,
example_lengths=eval_num_transitions_batch)
can_sample = FLAGS.model_type in ["ChoiPyramid"] or (FLAGS.model_type == "SPINN" and FLAGS.use_internal_parser) # TODO: Restore support in Pyramid if using.
if show_sample and can_sample:
tmp_samples = model.get_samples(eval_X_batch, vocabulary, only_one=not FLAGS.write_eval_report)
tree_strs = prettyprint_trees(tmp_samples)
if not FLAGS.write_eval_report:
show_sample = False # Only show one sample, regardless of the number of batches.
# Normalize output.
logits = F.log_softmax(output)
# Calculate class accuracy.
target = torch.from_numpy(eval_y_batch).long()
# get the index of the max log-probability
pred = logits.data.max(1, keepdim=False)[1].cpu()
eval_accumulate(model, data_manager, A, batch)
A.add('class_correct', pred.eq(target).sum())
A.add('class_total', target.size(0))
# Optionally calculate transition loss/acc.
model.transition_loss if hasattr(model, 'transition_loss') else None
# Update Aggregate Accuracies
total_tokens += sum([(nt + 1) / 2 for nt in eval_num_transitions_batch.reshape(-1)])
if FLAGS.write_eval_report:
transitions_per_example, _ = model.spinn.get_transitions_per_example(
style="preds" if FLAGS.eval_report_use_preds else "given") if (FLAGS.model_type == "SPINN" and FLAGS.use_internal_parser) else (None, None)
if model.use_sentence_pair:
batch_size = pred.size(0)
sent1_transitions = transitions_per_example[:batch_size] if transitions_per_example is not None else None
sent2_transitions = transitions_per_example[batch_size:] if transitions_per_example is not None else None
sent1_trees = tree_strs[:batch_size] if tree_strs is not None else None
sent2_trees = tree_strs[batch_size:] if tree_strs is not None else None
else:
sent1_transitions = transitions_per_example if transitions_per_example is not None else None
sent2_transitions = None
sent1_trees = tree_strs if tree_strs is not None else None
sent2_trees = None
reporter.save_batch(pred, target, eval_ids, output.data.cpu().numpy(), sent1_transitions, sent2_transitions, sent1_trees, sent2_trees)
# Print Progress
progress_bar.step(i + 1, total=total_batches)
progress_bar.finish()
if tree_strs is not None:
logger.Log('Sample: ' + tree_strs[0])
end = time.time()
total_time = end - start
A.add('total_tokens', total_tokens)
A.add('total_time', total_time)
eval_stats(model, A, eval_log)
eval_log.filename = filename
if FLAGS.write_eval_report:
eval_report_path = os.path.join(FLAGS.log_path, FLAGS.experiment_name + ".eval_set_" + str(eval_index) + ".report")
reporter.write_report(eval_report_path)
eval_class_acc = eval_log.eval_class_accuracy
eval_trans_acc = eval_log.eval_transition_accuracy
return eval_class_acc, eval_trans_acc
def evaluate(FLAGS,
model,
data_manager,
eval_set,
log_entry,
logger,
step,
vocabulary=None,
show_sample=False,
eval_index=0):
filename, dataset = eval_set
A = Accumulator()
index = len(log_entry.evaluation)
eval_log = log_entry.evaluation.add()
reporter = EvalReporter()
tree_strs = None
# Evaluate
total_batches = len(dataset)
progress_bar = SimpleProgressBar(msg="Run Eval",
bar_length=60,
enabled=FLAGS.show_progress_bar)
progress_bar.step(0, total=total_batches)
total_tokens = 0
start = time.time()
if FLAGS.model_type in ["Pyramid", "ChoiPyramid"]:
pyramid_temperature_multiplier = FLAGS.pyramid_temperature_decay_per_10k_steps**(
step / 10000.0)
if FLAGS.pyramid_temperature_cycle_length > 0.0:
min_temp = 1e-5
pyramid_temperature_multiplier *= (math.cos(
(step) / FLAGS.pyramid_temperature_cycle_length) + 1 +
min_temp) / 2
else:
pyramid_temperature_multiplier = None
model.eval()
for i, dataset_batch in enumerate(dataset):
batch = get_batch(dataset_batch)
eval_X_batch, eval_transitions_batch, eval_y_batch, eval_num_transitions_batch, eval_ids, _, silver_tree = batch
# eval_X_batch: <batch x maxlen x 2>
# eval_y_batch: <batch >
# silver_tree:
# the dist is invalid for val
# Run model.
output = model(
eval_X_batch,
eval_transitions_batch,
eval_y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions,
pyramid_temperature_multiplier=pyramid_temperature_multiplier,
store_parse_masks=True,
example_lengths=eval_num_transitions_batch)
# TODO: Restore support in Pyramid if using.
can_sample = FLAGS.model_type in [
"ChoiPyramid"
] or (FLAGS.model_type == "SPINN" and FLAGS.use_internal_parser)
if show_sample and can_sample:
tmp_samples = model.get_samples(
eval_X_batch, vocabulary, only_one=not FLAGS.write_eval_report)
# tree_strs = prettyprint_trees(tmp_samples)
tree_strs = [tree for tree in tmp_samples]
tmp_samples = model.get_samples(eval_X_batch,
vocabulary,
only_one=False)
# def get_max(s):
# # test f1
# max = 0
# for x in s:
# _, idx = x.split(',')
# if int(idx) > max:
# max = int(idx)
# return max
for s in (range(int(model.use_sentence_pair) + 1)):
for b in (range(silver_tree.shape[0])):
model_out = tmp_samples[s * silver_tree.shape[0] + b]
std_out = silver_tree[b, :, s]
std_out = set([x for x in std_out if x != '-1,-1'])
model_out_brackets, model_out_max_l = get_brackets(model_out)
model_out = set(convert_brackets_to_string(model_out_brackets))
outmost_bracket = '{:d},{:d}'.format(0, model_out_max_l)
std_out.add(outmost_bracket)
model_out.add(outmost_bracket)
# print get_max(model_out), get_max(std_out)
# print model_out
# print std_out
# print '=' * 30
# assert get_max(model_out) == get_max(std_out)
overlap = model_out & std_out
prec = float(len(overlap)) / (len(model_out) + 1e-8)
reca = float(len(overlap)) / (len(std_out) + 1e-8)
if len(std_out) == 0:
reca = 1.
if len(model_out) == 0:
prec = 1.
f1 = 2 * prec * reca / (prec + reca + 1e-8)
A.add('f1', f1)
if not FLAGS.write_eval_report:
# Only show one sample, regardless of the number of batches.
show_sample = False
# Normalize output.
logits = F.log_softmax(output)
# Calculate class accuracy.
target = torch.from_numpy(eval_y_batch).long()
# get the index of the max log-probability
pred = logits.data.max(1, keepdim=False)[1].cpu()
eval_accumulate(model, data_manager, A, batch)
A.add('class_correct', pred.eq(target).sum())
A.add('class_total', target.size(0))
# Optionally calculate transition loss/acc.
#model.transition_loss if hasattr(model, 'transition_loss') else None
# TODO: review this. the original line seems to have no effect
# Update Aggregate Accuracies
total_tokens += sum([(nt + 1) / 2
for nt in eval_num_transitions_batch.reshape(-1)])
if FLAGS.write_eval_report:
transitions_per_example, _ = model.spinn.get_transitions_per_example(
style="preds" if FLAGS.eval_report_use_preds else "given") if (
FLAGS.model_type == "SPINN"
and FLAGS.use_internal_parser) else (None, None)
if model.use_sentence_pair:
batch_size = pred.size(0)
sent1_transitions = transitions_per_example[:
batch_size] if transitions_per_example is not None else None
sent2_transitions = transitions_per_example[
batch_size:] if transitions_per_example is not None else None
sent1_trees = tree_strs[:
batch_size] if tree_strs is not None else None
sent2_trees = tree_strs[
batch_size:] if tree_strs is not None else None
else:
sent1_transitions = transitions_per_example if transitions_per_example is not None else None
sent2_transitions = None
sent1_trees = tree_strs if tree_strs is not None else None
sent2_trees = None
reporter.save_batch(pred, target, eval_ids,
output.data.cpu().numpy(), sent1_transitions,
sent2_transitions, sent1_trees, sent2_trees)
# Print Progress
progress_bar.step(i + 1, total=total_batches)
progress_bar.finish()
if tree_strs is not None:
logger.Log('Sample: ' + str(tree_strs[0]))
end = time.time()
total_time = end - start
A.add('total_tokens', total_tokens)
A.add('total_time', total_time)
eval_stats(model, A, eval_log) # get the eval statistics (e.g. average F1)
eval_log.filename = filename
if FLAGS.write_eval_report:
eval_report_path = os.path.join(
FLAGS.log_path,
FLAGS.experiment_name + ".eval_set_" + str(eval_index) + ".report")
reporter.write_report(eval_report_path)
eval_class_acc = eval_log.eval_class_accuracy
eval_trans_acc = eval_log.eval_transition_accuracy
eval_f1 = eval_log.f1
return eval_class_acc, eval_trans_acc, eval_f1
def evaluate(FLAGS,
model,
eval_set,
log_entry,
logger,
trainer,
vocabulary=None,
show_sample=False,
eval_index=0,
target_vocabulary=None):
filename, dataset = eval_set
A = Accumulator()
len(log_entry.evaluation)
eval_log = log_entry.evaluation.add()
reporter = EvalReporter()
tree_strs = None
# Evaluate
total_batches = len(dataset)
progress_bar = SimpleProgressBar(msg="Run Eval",
bar_length=60,
enabled=FLAGS.show_progress_bar)
progress_bar.step(0, total=total_batches)
total_tokens = 0
start = time.time()
model.eval()
ref_file_name = FLAGS.log_path + "/ref_file"
pred_file_name = FLAGS.log_path + "/pred_file"
reference_file = open(ref_file_name, "w")
predict_file = open(pred_file_name, "w")
full_ref = []
full_pred = []
for i, dataset_batch in enumerate(dataset):
batch = get_batch(dataset_batch)
eval_X_batch, eval_transitions_batch, eval_y_batch, eval_num_transitions_batch, eval_ids = batch
# Run model.
output = model(eval_X_batch,
eval_transitions_batch,
eval_y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions,
example_lengths=eval_num_transitions_batch)
can_sample = (FLAGS.model_type == "RLSPINN"
and FLAGS.use_internal_parser)
if show_sample and can_sample:
tmp_samples = model.encoder.get_samples(
eval_X_batch, vocabulary, only_one=not FLAGS.write_eval_report)
tree_strs = prettyprint_trees(tmp_samples)
if not FLAGS.write_eval_report:
# Only show one sample, regardless of the number of batches.
show_sample = False
# Get reference translation
ref_out = [" ".join(map(str, k[:-1])) + " ." for k in eval_y_batch]
full_ref += ref_out
# Get predicted translation
predicted = [[] for i in range(len(eval_y_batch))]
done = []
for x in output:
index = -1
for x_0 in x:
index += 1
val = int(x_0)
if val == 1:
if index in done:
continue
done.append(index)
elif index not in done:
predicted[index].append(val)
pred_out = [" ".join(map(str, k)) + " ." for k in predicted]
full_pred += pred_out
eval_accumulate(model, A, batch)
# Optionally calculate transition loss/acc.
model.encoder.transition_loss if hasattr(model.encoder,
'transition_loss') else None
# Update Aggregate Accuracies
total_tokens += sum([(nt + 1) / \
2 for nt in eval_num_transitions_batch.reshape(-1)])
if FLAGS.write_eval_report:
transitions_per_example, _ = model.encoder.spinn.get_transitions_per_example(
style="preds" if FLAGS.eval_report_use_preds else "given") if (
FLAGS.model_type == "SPINN"
and FLAGS.use_internal_parser) else (None, None)
sent1_transitions = transitions_per_example if transitions_per_example is not None else None
sent2_transitions = None
sent1_trees = tree_strs if tree_strs is not None else None
sent2_trees = None
reporter.save_batch(full_pred,
full_ref,
eval_ids, [None],
sent1_transitions,
sent2_transitions,
sent1_trees,
sent2_trees,
mt=True)
# Print Progress
progress_bar.step(i + 1, total=total_batches)
progress_bar.finish()
if tree_strs is not None:
logger.Log('Sample: ' + tree_strs[0])
reference_file.write("\n".join(full_ref))
reference_file.close()
predict_file.write("\n".join(full_pred))
predict_file.close()
bleu_score = os.popen("perl spinn/util/multi-bleu.perl " + ref_file_name +
" < " + pred_file_name).read()
try:
bleu_score = float(bleu_score)
except:
bleu_score = 0.0
end = time.time()
total_time = end - start
A.add('class_correct', bleu_score)
A.add('class_total', 1)
A.add('total_tokens', total_tokens)
A.add('total_time', total_time)
eval_stats(model, A, eval_log)
eval_log.filename = filename
if FLAGS.write_eval_report:
eval_report_path = os.path.join(
FLAGS.log_path,
FLAGS.experiment_name + ".eval_set_" + str(eval_index) + ".report")
reporter.write_report(eval_report_path)
stats = parse_comparison.run_main(
data_type="mt",
main_report_path_template=FLAGS.log_path + "/" +
FLAGS.experiment_name + ".eval_set_0.report",
main_data_path=FLAGS.source_eval_path)
# To-do: include the following into lgog-formatter so it's reported in standard format.
if tree_strs is not None:
logger.Log(
'F1 w/ GT: ' + str(stats['gt']) + '\n' +\
'F1 w/ LB: ' + str(stats['lb']) + '\n' +\
'F1 w/ RB: ' + str(stats['rb']) + '\n' +\
'Avg. tree depth: ' + str(stats['depth'])
)
eval_class_acc = eval_log.eval_class_accuracy
eval_trans_acc = eval_log.eval_transition_accuracy
return eval_class_acc, eval_trans_acc
def evaluate(model, eval_set, logger, metrics_logger, step, vocabulary=None):
filename, dataset = eval_set
reporter = EvalReporter()
# Evaluate
class_correct = 0
class_total = 0
total_batches = len(dataset)
progress_bar = SimpleProgressBar(msg="Run Eval", bar_length=60, enabled=FLAGS.show_progress_bar)
progress_bar.step(0, total=total_batches)
total_tokens = 0
start = time.time()
model.eval()
transition_preds = []
transition_targets = []
for i, (eval_X_batch, eval_transitions_batch, eval_y_batch, eval_num_transitions_batch, eval_ids) in enumerate(dataset):
if FLAGS.truncate_eval_batch:
eval_X_batch, eval_transitions_batch = truncate(
eval_X_batch, eval_transitions_batch, eval_num_transitions_batch)
if FLAGS.saving_eval_attention_matrix:
model.set_recording_attention_weight_matrix(True)
# Run model.
output = model(eval_X_batch, eval_transitions_batch, eval_y_batch,
use_internal_parser=FLAGS.use_internal_parser,
validate_transitions=FLAGS.validate_transitions,)
if FLAGS.saving_eval_attention_matrix:
# WARNING: only attention SPINN model have attention matrix
attention_matrix = model.get_attention_matrix_from_last_forward()
with open(os.path.join(FLAGS.metrics_path, FLAGS.experiment_name, 'attention-matrix-{}.txt'.format(step)), 'a') as txtfile:
for eval_id, attmat in izip(eval_ids, attention_matrix):
txtfile.write('{}\n'.format(eval_id))
txtfile.write('{},{}\n'.format(len(attmat), len(attmat[0])))
for row in attmat:
txtfile.write(','.join(['{:.1f}'.format(x*100.0) for x in row]))
txtfile.write('\n')
model.set_recording_attention_weight_matrix(False) # reset it after run
# Normalize output.
logits = F.log_softmax(output)
# Calculate class accuracy.
target = torch.from_numpy(eval_y_batch).long()
pred = logits.data.max(1)[1].cpu() # get the index of the max log-probability
class_correct += pred.eq(target).sum()
class_total += target.size(0)
# Optionally calculate transition loss/acc.
transition_loss = model.transition_loss if hasattr(model, 'transition_loss') else None
# Update Aggregate Accuracies
total_tokens += eval_num_transitions_batch.ravel().sum()
# Accumulate stats for transition accuracy.
if transition_loss is not None:
transition_preds.append([m["t_preds"] for m in model.spinn.memories])
transition_targets.append([m["t_given"] for m in model.spinn.memories])
if FLAGS.write_eval_report:
reporter_args = [pred, target, eval_ids, output.data.cpu().numpy()]
if hasattr(model, 'transition_loss'):
transition_preds_per_example = model.spinn.get_transition_preds_per_example()
if model.use_sentence_pair:
batch_size = pred.size(0)
sent1_preds = transition_preds_per_example[:batch_size]
sent2_preds = transition_preds_per_example[batch_size:]
reporter_args.append(sent1_preds)
reporter_args.append(sent2_preds)
else:
reporter_args.append(transition_preds_per_example)
reporter.save_batch(*reporter_args)
# Print Progress
progress_bar.step(i+1, total=total_batches)
progress_bar.finish()
end = time.time()
total_time = end - start
# Get time per token.
time_metric = time_per_token([total_tokens], [total_time])
# Get class accuracy.
eval_class_acc = class_correct / float(class_total)
# Get transition accuracy if applicable.
if len(transition_preds) > 0:
all_preds = np.array(flatten(transition_preds))
all_truth = np.array(flatten(transition_targets))
eval_trans_acc = (all_preds == all_truth).sum() / float(all_truth.shape[0])
else:
eval_trans_acc = 0.0
logger.Log("Step: %i Eval acc: %f %f %s Time: %5f" %
(step, eval_class_acc, eval_trans_acc, filename, time_metric))
metrics_logger.Log('eval_class_acc', eval_class_acc, step)
metrics_logger.Log('eval_trans_acc', eval_trans_acc, step)
if FLAGS.write_eval_report:
eval_report_path = os.path.join(FLAGS.log_path, FLAGS.experiment_name + ".report")
reporter.write_report(eval_report_path)
return eval_class_acc