def apply_model(params):
"""
Function for using a previously trained model for predicting.
"""
########### Load data
dataset = build_dataset(params)
###########
########### Load model
ing_model = loadModel(params['STORE_PATH'], params['RELOAD'])
ing_model.setOptimizer()
###########
########### Apply sampling
for s in params["EVAL_ON_SETS"]:
# Apply model predictions
params_prediction = {
'batch_size': params['BATCH_SIZE'],
'n_parallel_loaders': params['PARALLEL_LOADERS'],
'predict_on_sets': [s],
'normalize': params['NORMALIZE_IMAGES'],
'mean_substraction': params['MEAN_SUBSTRACTION']
}
predictions = ing_model.predictNet(dataset, params_prediction)[s]
# Format predictions
predictions = decode_multilabel(
predictions, # not used
dataset.extra_variables['idx2word_binary'],
min_val=params['MIN_PRED_VAL'],
verbose=1)
# Store result
filepath = ing_model.model_path + '/' + s + '_labels.pred' # results file
listoflists2file(filepath, predictions)
## Evaluate result
extra_vars = dict()
extra_vars[s] = dict()
extra_vars[s]['word2idx'] = dataset.extra_variables['word2idx_binary']
exec("extra_vars[s]['references'] = dataset.Y_" + s +
"[params['OUTPUTS_IDS_DATASET'][0]]")
for metric in params['METRICS']:
logging.info('Evaluating on metric ' + metric)
# Evaluate on the chosen metric
metrics = evaluation.select[metric](pred_list=predictions,
verbose=1,
extra_vars=extra_vars,
split=s)
def evaluate(self, epoch, counter_name='epoch', logs=None):
if logs is None:
logs = {}
# Change inputs and outputs mappings for evaluation
self.changeInOutMappings()
# Evaluate on each set separately
all_metrics = []
for s in self.set_name:
# Apply model predictions
if self.beam_search:
params_prediction = {'max_batch_size': self.batch_size,
'n_parallel_loaders': self.extra_vars[
'n_parallel_loaders'],
'predict_on_sets': [s],
'beam_batch_size': self.beam_batch_size if
self.beam_batch_size is not None else self.batch_size,
'pos_unk': False,
'normalize': self.normalize,
'normalization_type': self.normalization_type,
'max_eval_samples': self.max_eval_samples
}
params_prediction.update(checkDefaultParamsBeamSearch(self.extra_vars))
predictions_all = self.model_to_eval.predictBeamSearchNet(self.ds, params_prediction)[s]
else:
orig_size = self.extra_vars.get('eval_orig_size', False)
params_prediction = {'batch_size': self.batch_size,
'n_parallel_loaders': self.extra_vars.get(
'n_parallel_loaders', 8),
'predict_on_sets': [s],
'normalize': self.normalize,
'normalization_type': self.normalization_type,
'max_eval_samples': self.max_eval_samples,
'model_name': self.model_name,
}
# Convert predictions
postprocess_fun = None
if self.is_3DLabel:
postprocess_fun = [self.ds.convert_3DLabels_to_bboxes,
self.extra_vars[s]['references_orig_sizes']]
elif orig_size:
postprocess_fun = [self.ds.resize_semantic_output,
self.extra_vars[s]['eval_orig_size_id']]
predictions_all = \
self.model_to_eval.predictNet(self.ds, params_prediction,
postprocess_fun=postprocess_fun)[s]
# Single-output model
if not self.gt_pos or self.gt_pos == 0 or len(self.gt_pos) == 1:
if len(predictions_all) != 2:
predictions_all = [predictions_all]
gt_positions = [0]
# Multi-output model
else:
gt_positions = self.gt_pos
# Select each output to evaluate separately
for gt_pos, type, these_metrics, gt_id, write_type, index2word_y, index2word_x in zip(
gt_positions,
self.output_types,
self.metric_name,
self.gt_id,
self.write_type,
self.index2word_y,
self.index2word_x):
predictions = predictions_all[gt_pos]
if self.verbose > 0:
print('')
logging.info('Prediction output ' + str(gt_pos) + ': ' + str(
gt_id) + ' (' + str(type) + ')')
# Postprocess outputs of type text
if type == 'text':
if params_prediction.get('pos_unk', False):
samples = predictions[0]
alphas = predictions[1]
if eval('self.ds.loaded_raw_' + s + '[0]'):
sources = predictions[2]
else:
sources = []
for preds in predictions[2]:
for src in preds[self.input_text_id]:
sources.append(src)
sources = decode_predictions_beam_search(sources,
index2word_x,
pad_sequences=True,
verbose=self.verbose)
heuristic = self.extra_vars['heuristic']
else:
samples = predictions
alphas = None
heuristic = None
sources = None
if self.out_pred_idx is not None:
samples = samples[self.out_pred_idx]
# Convert predictions into sentences
if self.beam_search:
predictions = decode_predictions_beam_search(samples,
index2word_y,
alphas=alphas,
x_text=sources,
heuristic=heuristic,
mapping=self.extra_vars.get(
'mapping',
None),
verbose=self.verbose)
else:
probs = predictions
predictions = decode_predictions(predictions,
1,
# always set temperature to 1
index2word_y,
self.sampling_type,
verbose=self.verbose)
# Apply detokenization function if needed
if self.extra_vars.get('apply_detokenization', False):
predictions = map(self.extra_vars['detokenize_f'],
predictions)
# Postprocess outputs of type binary
elif type == 'binary':
predictions = decode_multilabel(predictions,
index2word_y,
min_val=self.min_pred_multilabel[
gt_pos],
verbose=self.verbose)
# Prepare references
# exec ("y_raw = self.ds.Y_" + s + "[gt_id]")
y_split = getattr(self.ds, 'Y_' + s)
y_raw = y_split[gt_id]
self.extra_vars[gt_pos][s]['references'] = self.ds.loadBinary(y_raw, gt_id)
# Postprocess outputs of type 3DLabel
elif type == '3DLabel':
self.extra_vars[gt_pos][s] = dict()
# exec ('ref=self.ds.Y_' + s + '["' + gt_id + '"]')
y_split = getattr(self.ds, 'Y_' + s)
ref = y_split[gt_id]
[ref, original_sizes] = self.ds.convert_GT_3DLabels_to_bboxes(
ref)
self.extra_vars[gt_pos][s]['references'] = ref
self.extra_vars[gt_pos][s]['references_orig_sizes'] = original_sizes
# Postprocess outputs of type 3DSemanticLabel
elif type == '3DSemanticLabel':
self.extra_vars[gt_pos]['eval_orig_size'] = self.eval_orig_size
self.extra_vars[gt_pos][s] = dict()
# exec ('ref=self.ds.Y_' + s + '["' + gt_id + '"]')
y_split = getattr(self.ds, 'Y_' + s)
ref = y_split[gt_id]
if self.eval_orig_size:
old_crop = copy.deepcopy(self.ds.img_size_crop)
self.ds.img_size_crop = copy.deepcopy(self.ds.img_size)
self.extra_vars[gt_pos][s]['eval_orig_size_id'] = np.array([gt_id] * len(ref))
ref = self.ds.load_GT_3DSemanticLabels(ref, gt_id)
if self.eval_orig_size:
self.ds.img_size_crop = copy.deepcopy(old_crop)
self.extra_vars[gt_pos][s]['references'] = ref
# Other output data types
else:
# exec ("self.extra_vars[gt_pos][s]['references'] = self.ds.Y_" + s + "[gt_id]")
y_split = getattr(self.ds, 'Y_' + s)
self.extra_vars[gt_pos][s]['references'] = y_split[gt_id]
# Store predictions
if self.write_samples:
# Store result
filepath = self.save_path + '/' + s + '_' + counter_name + '_' + str(epoch) + '_output_' + str(gt_pos) + '.pred' # results file
if write_type == 'list':
list2file(filepath, predictions)
elif write_type == 'vqa':
try:
# exec ('refs = self.ds.Y_' + s + '[gt_id]')
y_split = getattr(self.ds, 'Y_' + s)
refs = y_split[gt_id]
except Exception:
refs = ['N/A' for _ in range(probs.shape[0])]
extra_data_plot = {'reference': refs,
'probs': probs,
'vocab': index2word_y}
list2vqa(filepath, predictions,
self.extra_vars[gt_pos][s]['question_ids'],
extra=extra_data_plot)
elif write_type == 'listoflists':
listoflists2file(filepath, predictions)
elif write_type == 'numpy':
numpy2file(filepath, predictions)
elif write_type == '3DLabels':
raise NotImplementedError(
'Write 3DLabels function is not implemented')
elif write_type == '3DSemanticLabel':
folder_path = self.save_path + '/' + s + '_' + counter_name + '_' + str(
epoch) # results folder
numpy2imgs(folder_path,
predictions,
eval('self.ds.X_' + s + '["' + self.input_id + '"]'),
self.ds)
else:
raise NotImplementedError('The store type "' + self.write_type + '" is not implemented.')
# Evaluate on each metric
for metric in these_metrics:
if self.verbose > 0:
logging.info('Evaluating on metric ' + metric)
filepath = self.save_path + '/' + s + '.' + metric # results file
if s == 'train':
logging.info(
"WARNING: evaluation results on 'train' split might be incorrect when"
"applying random image shuffling.")
# Evaluate on the chosen metric
metrics = evaluation.select[metric](
pred_list=predictions,
verbose=self.verbose,
extra_vars=self.extra_vars[gt_pos],
split=s)
# Print results to file and store in model log
with open(filepath, 'a') as f:
header = counter_name + ','
line = str(epoch) + ','
# Store in model log
self.model_to_eval.log(s, counter_name, epoch)
for metric_ in sorted(metrics):
value = metrics[metric_]
# Multiple-output model
if self.gt_pos and self.gt_pos != 0:
metric_ += '_output_' + str(gt_pos)
all_metrics.append(metric_)
header += metric_ + ','
line += str(value) + ','
# Store in model log
self.model_to_eval.log(s, metric_, value)
if not self.written_header:
f.write(header + '\n')
self.written_header = True
f.write(line + '\n')
if self.verbose > 0:
logging.info('Done evaluating on metric ' + metric)
# Store losses
if logs.get('loss') is not None:
self.model_to_eval.log('train', 'train_loss', logs['loss'])
if logs.get('valid_loss') is not None:
self.model_to_eval.log('val', 'val_loss', logs['valid_loss'])
# Plot results so far
if self.do_plot:
if self.metric_name:
self.model_to_eval.plot(counter_name, set(all_metrics),
self.set_name, upperbound=self.max_plot)
# Save the model
if self.save_each_evaluation:
from keras_wrapper.cnn_model import saveModel
saveModel(self.model_to_eval, epoch, store_iter=not self.eval_on_epochs)
# Recover inputs and outputs mappings for resume training
self.recoverInOutMappings()
def evaluate(self, epoch, counter_name='epoch', logs=None):
if logs is None:
logs = {}
# Change inputs and outputs mappings for evaluation
self.changeInOutMappings()
# Evaluate on each set separately
all_metrics = []
for s in self.set_name:
# Apply model predictions
if self.beam_search:
params_prediction = {'max_batch_size': self.batch_size,
'n_parallel_loaders': self.extra_vars.get('n_parallel_loaders', 1),
'predict_on_sets': [s],
'beam_batch_size': self.beam_batch_size if
self.beam_batch_size is not None else self.batch_size,
'pos_unk': False,
'normalize': self.normalize,
'normalization_type': self.normalization_type,
'max_eval_samples': self.max_eval_samples
}
params_prediction.update(checkDefaultParamsBeamSearch(self.extra_vars))
predictions_all = self.model_to_eval.predictBeamSearchNet(self.ds, params_prediction)[s]
else:
orig_size = self.extra_vars.get('eval_orig_size', False)
params_prediction = {'batch_size': self.batch_size,
'n_parallel_loaders': self.extra_vars.get('n_parallel_loaders', 1),
'predict_on_sets': [s],
'normalize': self.normalize,
'normalization_type': self.normalization_type,
'max_eval_samples': self.max_eval_samples,
'model_name': self.model_name,
}
# Convert predictions
postprocess_fun = None
if self.is_3DLabel:
postprocess_fun = [self.ds.convert_3DLabels_to_bboxes,
self.extra_vars[s]['references_orig_sizes']]
elif orig_size:
postprocess_fun = [self.ds.resize_semantic_output,
self.extra_vars[s]['eval_orig_size_id']]
predictions_all = \
self.model_to_eval.predictNet(self.ds, params_prediction,
postprocess_fun=postprocess_fun)[s]
# Single-output model
if not self.gt_pos or self.gt_pos == 0 or len(self.gt_pos) == 1:
if len(predictions_all) != 2:
predictions_all = [predictions_all]
gt_positions = [0]
# Multi-output model
else:
gt_positions = self.gt_pos
# Select each output to evaluate separately
for gt_pos, type_out, these_metrics, gt_id, write_type, index2word_y, index2word_x in zip(
gt_positions,
self.output_types,
self.metric_name,
self.gt_id,
self.write_type,
self.index2word_y,
self.index2word_x):
predictions = predictions_all[gt_pos]
if self.verbose > 0:
print('')
logging.info('Prediction output ' + str(gt_pos) + ': ' + str(
gt_id) + ' (' + str(type_out) + ')')
# Postprocess outputs of type text
if type_out == 'text':
if params_prediction.get('pos_unk', False):
samples = predictions[0]
alphas = predictions[1]
if eval('self.ds.loaded_raw_' + s + '[0]'):
sources = predictions[2]
else:
sources = []
for preds in predictions[2]:
for src in preds[self.input_text_id]:
sources.append(src)
sources = decode_predictions_beam_search(sources,
index2word_x,
pad_sequences=True,
verbose=self.verbose)
heuristic = self.extra_vars['heuristic']
else:
samples = predictions
alphas = None
heuristic = None
sources = None
if self.out_pred_idx is not None:
samples = samples[self.out_pred_idx]
# Convert predictions into sentences
if self.beam_search:
predictions = decode_predictions_beam_search(samples,
index2word_y,
glossary=self.extra_vars.get('glossary', None),
alphas=alphas,
x_text=sources,
heuristic=heuristic,
mapping=self.extra_vars.get('mapping', None),
verbose=self.verbose)
else:
probs = predictions
predictions = decode_predictions(predictions,
1,
# always set temperature to 1
index2word_y,
self.sampling_type,
verbose=self.verbose)
# Apply detokenization function if needed
if self.extra_vars.get('apply_detokenization', False):
predictions = list(map(self.extra_vars['detokenize_f'], predictions))
# Postprocess outputs of type binary
elif type_out == 'binary':
predictions = decode_multilabel(predictions,
index2word_y,
min_val=self.min_pred_multilabel[
gt_pos],
verbose=self.verbose)
# Prepare references
# exec ("y_raw = self.ds.Y_" + s + "[gt_id]")
y_split = getattr(self.ds, 'Y_' + s)
y_raw = y_split[gt_id]
self.extra_vars[gt_pos][s]['references'] = self.ds.loadBinary(y_raw, gt_id)
# Postprocess outputs of type 3DLabel
elif type_out == '3DLabel':
self.extra_vars[gt_pos][s] = dict()
# exec ('ref=self.ds.Y_' + s + '["' + gt_id + '"]')
y_split = getattr(self.ds, 'Y_' + s)
ref = y_split[gt_id]
[ref, original_sizes] = self.ds.convert_GT_3DLabels_to_bboxes(
ref)
self.extra_vars[gt_pos][s]['references'] = ref
self.extra_vars[gt_pos][s]['references_orig_sizes'] = original_sizes
# Postprocess outputs of type 3DSemanticLabel
elif type_out == '3DSemanticLabel':
self.extra_vars[gt_pos]['eval_orig_size'] = self.eval_orig_size
self.extra_vars[gt_pos][s] = dict()
# exec ('ref=self.ds.Y_' + s + '["' + gt_id + '"]')
y_split = getattr(self.ds, 'Y_' + s)
ref = y_split[gt_id]
if self.eval_orig_size:
old_crop = copy.deepcopy(self.ds.img_size_crop)
self.ds.img_size_crop = copy.deepcopy(self.ds.img_size)
self.extra_vars[gt_pos][s]['eval_orig_size_id'] = np.array([gt_id] * len(ref))
ref = self.ds.load_GT_3DSemanticLabels(ref, gt_id)
if self.eval_orig_size:
self.ds.img_size_crop = copy.deepcopy(old_crop)
self.extra_vars[gt_pos][s]['references'] = ref
# Other output data types
else:
# exec ("self.extra_vars[gt_pos][s]['references'] = self.ds.Y_" + s + "[gt_id]")
y_split = getattr(self.ds, 'Y_' + s)
self.extra_vars[gt_pos][s]['references'] = y_split[gt_id]
# Store predictions
if self.write_samples:
# Store result
filepath = self.save_path + '/' + s + '_' + counter_name + '_' + str(epoch) + '_output_' + str(gt_pos) + '.pred' # results file
if write_type == 'list':
list2file(filepath, predictions)
elif write_type == 'vqa':
try:
# exec ('refs = self.ds.Y_' + s + '[gt_id]')
y_split = getattr(self.ds, 'Y_' + s)
refs = y_split[gt_id]
except Exception:
refs = ['N/A' for _ in range(probs.shape[0])]
extra_data_plot = {'reference': refs,
'probs': probs,
'vocab': index2word_y}
list2vqa(filepath, predictions,
self.extra_vars[gt_pos][s]['question_ids'],
extra=extra_data_plot)
elif write_type == 'listoflists':
listoflists2file(filepath, predictions)
elif write_type == 'numpy':
numpy2file(filepath, predictions)
elif write_type == '3DLabels':
raise NotImplementedError(
'Write 3DLabels function is not implemented')
elif write_type == '3DSemanticLabel':
folder_path = self.save_path + '/' + s + '_' + counter_name + '_' + str(
epoch) # results folder
numpy2imgs(folder_path,
predictions,
eval('self.ds.X_' + s + '["' + self.input_id + '"]'),
self.ds)
else:
raise NotImplementedError('The store type "' + self.write_type + '" is not implemented.')
# Evaluate on each metric
for metric in these_metrics:
if self.verbose > 0:
logging.info('Evaluating on metric ' + metric)
filepath = self.save_path + '/' + s + '.' + metric # results file
if s == 'train':
logging.info(
"WARNING: evaluation results on 'train' split might be incorrect when"
"applying random image shuffling.")
# Evaluate on the chosen metric
metrics = evaluation.select[metric](
pred_list=predictions,
verbose=self.verbose,
extra_vars=self.extra_vars[gt_pos],
split=s)
# Print results to file and store in model log
with open(filepath, 'a') as f:
header = counter_name + ','
line = str(epoch) + ','
# Store in model log
self.model_to_eval.log(s, counter_name, epoch)
for metric_ in sorted(metrics):
value = metrics[metric_]
# Multiple-output model
if self.gt_pos and self.gt_pos != 0:
metric_ += '_output_' + str(gt_pos)
all_metrics.append(metric_)
header += metric_ + ','
line += str(value) + ','
# Store in model log
self.model_to_eval.log(s, metric_, value)
if not self.written_header:
f.write(header + '\n')
self.written_header = True
f.write(line + '\n')
if self.verbose > 0:
logging.info('Done evaluating on metric ' + metric)
# Store losses
if logs.get('loss') is not None:
self.model_to_eval.log('train', 'train_loss', logs['loss'])
if logs.get('valid_loss') is not None:
self.model_to_eval.log('val', 'val_loss', logs['valid_loss'])
# Plot results so far
if self.do_plot:
if self.metric_name:
self.model_to_eval.plot(counter_name, set(all_metrics),
self.set_name, upperbound=self.max_plot)
# Save the model
if self.save_each_evaluation:
from keras_wrapper.cnn_model import saveModel
saveModel(self.model_to_eval, epoch, store_iter=not self.eval_on_epochs)
# Recover inputs and outputs mappings for resume training
self.recoverInOutMappings()