def resume_training(out: ModelDir,
notes: str = None,
dry_run=False,
start_eval=False):
""" Resume training an existing model """
train_params = out.get_last_train_params()
model = out.get_model()
train_data = train_params["data"]
evaluators = train_params["evaluators"]
params = train_params["train_params"]
params.num_epochs = 24 * 3
if isinstance(train_data, PreprocessedData):
# TODO don't hard code # of processes
train_data.preprocess(6, 1000)
latest = tf.train.latest_checkpoint(out.save_dir)
if latest is None:
raise ValueError("No checkpoint to resume from found in " +
out.save_dir)
_train(model, train_data, latest, None, False, params, evaluators, out,
notes, dry_run, start_eval)
def init():
global model, model_dir
print("Loading Model...")
# Load the model
model_dir = ModelDir(
"pretrained_models/models/triviaqa-unfiltered-shared-norm")
model = model_dir.get_model()
if not isinstance(model, ParagraphQuestionModel):
raise ValueError(
"This script is built to work for ParagraphQuestionModel models only"
)
def main():
print('Starting...')
model_dir = ModelDir(OPTS.model)
model = model_dir.get_model()
if not isinstance(model, ParagraphQuestionModel):
raise ValueError("This script is built to work for ParagraphQuestionModel models only")
input_data, vocab = read_input_data(model)
print('Loading word vectors...')
model.set_input_spec(ParagraphAndQuestionSpec(batch_size=None), vocab)
print('Starting Tensorflow session...')
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
with sess.as_default():
prediction = model.get_prediction()
# Take 0-th here because we know we only truncate to one paragraph
start_logits_tf = prediction.start_logits[0]
end_logits_tf = prediction.end_logits[0]
none_logit_tf = prediction.none_logit[0]
context_rep_tf = model.context_rep[0]
m1_tf = model.predictor.m1[0]
m2_tf = model.predictor.m2[0]
model_dir.restore_checkpoint(sess)
with open(OPTS.output_file, 'w') as f:
for doc_raw, q_raw, context, ex in tqdm(input_data):
encoded = model.encode(ex, is_train=False)
start_logits, end_logits, none_logit, context_rep, m1, m2 = sess.run(
[start_logits_tf, end_logits_tf, none_logit_tf, context_rep_tf,
m1_tf, m2_tf],
feed_dict=encoded)
beam, p_na = logits_to_probs(
doc_raw, context[0], start_logits, end_logits, none_logit,
beam_size=OPTS.beam_size)
inputs = [context_rep, m1, m2]
vec = np.concatenate([np.amax(x, axis=0) for x in inputs] +
[np.amin(x, axis=0) for x in inputs] +
[np.mean(x, axis=0) for x in inputs])
#span_logits = np.add.outer(start_logits, end_logits)
#all_logits = np.concatenate((np.array([none_logit]), span_logits.flatten()))
#log_partition = scipy.special.logsumexp(all_logits)
#vec = np.concatenate([
# np.amax(context_rep, axis=0),
# np.amin(context_rep, axis=0),
# np.mean(context_rep, axis=0),
# [np.amax(start_logits), scipy.special.logsumexp(start_logits),
# np.amax(end_logits), scipy.special.logsumexp(end_logits),
# none_logit, log_partition]
#])
out_obj = {'paragraph': doc_raw, 'question': q_raw,
'beam': beam, 'p_na': p_na}
if not OPTS.no_vec:
out_obj['vec'] = vec.tolist()
print(json.dumps(out_obj), file=f)
def main():
print('Starting...')
model_dir = ModelDir(OPTS.model)
model = model_dir.get_model()
if isinstance(model, ParagraphQuestionModel):
run_paragraph_model(model_dir, model)
elif isinstance(model, ElmoQaModel):
run_elmo_model(model_dir, model)
else:
raise ValueError(
"This script is built to work for ParagraphQuestionModel and ElmoQaModel models only"
)
def resume_training(model_to_resume: str,
dataset_oversampling: Dict[str, int],
checkpoint: Optional[str] = None,
epochs: Optional[int] = None):
"""Resume training on a partially trained model (or finetune an existing model)
:param model_to_resume: path to the model directory of the model to resume training
:param dataset_oversampling: dictionary mapping dataset names to integer counts of how much
to oversample them
:param checkpoint: optional string to specify which checkpoint to resume from. Uses the latest
if not specified
:param epochs: Optional int specifying how many epochs to train for. If not detailed, runs for 24
"""
out = ModelDir(model_to_resume)
train_params = out.get_last_train_params()
evaluators = train_params["evaluators"]
params = train_params["train_params"]
params.num_epochs = epochs if epochs is not None else 24
model = out.get_model()
notes = None
dry_run = False
data = prepare_data(model, TrainConfig(), dataset_oversampling)
if checkpoint is None:
checkpoint = tf.train.latest_checkpoint(out.save_dir)
_train_async(model=model,
data=data,
checkpoint=checkpoint,
parameter_checkpoint=None,
save_start=False,
train_params=params,
evaluators=evaluators,
out=out,
notes=notes,
dry_run=dry_run,
start_eval=False)
def convert_model_pickle(model_dir, output_dir):
print("Updating model...")
md = ModelDir(model_dir)
model = md.get_model()
# remove the lm models word embeddings - cpu model will use Char-CNN
model.lm_model.embed_weights_file = None
dim = model.embed_mapper.layers[1].n_units
model.embed_mapper.layers = [
model.embed_mapper.layers[0],
BiRecurrentMapper(CompatGruCellSpec(dim)), model.embed_mapper.layers[2]
]
model.match_encoder.layers = list(model.match_encoder.layers)
other = model.match_encoder.layers[1].other
other.layers = list(other.layers)
other.layers[1] = BiRecurrentMapper(CompatGruCellSpec(dim))
pred = model.predictor.predictor
pred.first_layer = BiRecurrentMapper(CompatGruCellSpec(dim))
pred.second_layer = BiRecurrentMapper(CompatGruCellSpec(dim))
with open(join(output_dir, "model.pkl"), "wb") as f:
pickle.dump(model, f)
def main():
parser = argparse.ArgumentParser(description='Evaluate a model on SQuAD')
parser.add_argument('model', help='model directory to evaluate')
parser.add_argument("-o", "--official_output", type=str,
help="where to output an official result file")
parser.add_argument('-n', '--sample_questions', type=int, default=None,
help="(for testing) run on a subset of questions")
parser.add_argument('--answer_bounds', nargs='+', type=int, default=[17],
help="Max size of answer")
parser.add_argument('-b', '--batch_size', type=int, default=200,
help="Batch size, larger sizes can be faster but uses more memory")
parser.add_argument('-s', '--step', default=None,
help="Weights to load, can be a checkpoint step or 'latest'")
# Add ja_test choice to test Multilingual QA dataset.
parser.add_argument(
'-c', '--corpus', choices=["dev", "train", "ja_test", "pred"], default="dev")
parser.add_argument('--no_ema', action="store_true",
help="Don't use EMA weights even if they exist")
# Add ja_test choice to test Multilingual QA pipeline.
parser.add_argument('-p', '--pred_filepath', default=None,
help="The csv file path if you try pred mode")
args = parser.parse_args()
model_dir = ModelDir(args.model)
corpus = SquadCorpus()
if args.corpus == "dev":
questions = corpus.get_dev()
# Add ja_test choice to test Multilingual QA pipeline.
elif args.corpus == "ja_test":
questions = corpus.get_ja_test()
# This is for prediction mode for MLQA pipeline.
elif args.corpus == "pred":
questions = create_pred_dataset(args.pred_filepath)
else:
questions = corpus.get_train()
questions = split_docs(questions)
if args.sample_questions:
np.random.RandomState(0).shuffle(
sorted(questions, key=lambda x: x.question_id))
questions = questions[:args.sample_questions]
questions.sort(key=lambda x: x.n_context_words, reverse=True)
dataset = ParagraphAndQuestionDataset(
questions, FixedOrderBatcher(args.batch_size, True))
evaluators = [SpanEvaluator(args.answer_bounds, text_eval="squad")]
if args.official_output is not None:
evaluators.append(RecordSpanPrediction(args.answer_bounds[0]))
if args.step is not None:
if args.step == "latest":
checkpoint = model_dir.get_latest_checkpoint()
else:
checkpoint = model_dir.get_checkpoint(int(args.step))
else:
checkpoint = model_dir.get_best_weights()
if checkpoint is not None:
print("Using best weights")
else:
print("Using latest checkpoint")
checkpoint = model_dir.get_latest_checkpoint()
model = model_dir.get_model()
evaluation = trainer.test(model, evaluators, {args.corpus: dataset},
corpus.get_resource_loader(), checkpoint, not args.no_ema)[args.corpus]
# Print the scalar results in a two column table
scalars = evaluation.scalars
cols = list(sorted(scalars.keys()))
table = [cols]
header = ["Metric", ""]
table.append([("%s" % scalars[x] if x in scalars else "-") for x in cols])
print_table([header] + transpose_lists(table))
# Save the official output
if args.official_output is not None:
quid_to_para = {}
for x in questions:
quid_to_para[x.question_id] = x.paragraph
q_id_to_answers = {}
q_ids = evaluation.per_sample["question_id"]
spans = evaluation.per_sample["predicted_span"]
for q_id, (start, end) in zip(q_ids, spans):
text = quid_to_para[q_id].get_original_text(start, end)
q_id_to_answers[q_id] = text
with open(args.official_output, "w") as f:
json.dump(q_id_to_answers, f)
def run():
parser = argparse.ArgumentParser()
parser.add_argument("squad_path", help="path to squad dev data file")
parser.add_argument("output_path",
help="path where evaluation json file will be written")
parser.add_argument("--model-path",
default="model",
help="path to model directory")
parser.add_argument("--n", type=int, default=None)
parser.add_argument("-b", "--batch_size", type=int, default=100)
parser.add_argument("--ema", action="store_true")
args = parser.parse_args()
squad_path = args.squad_path
output_path = args.output_path
model_dir = ModelDir(args.model_path)
nltk.data.path.append("nltk_data")
print("Loading data")
docs = parse_squad_data(squad_path, "", NltkAndPunctTokenizer(), False)
pairs = split_docs(docs)
dataset = ParagraphAndQuestionDataset(
pairs, ClusteredBatcher(args.batch_size, ContextLenKey(), False, True))
print("Done, init model")
model = model_dir.get_model()
loader = ResourceLoader(lambda a, b: load_word_vector_file(
join(VEC_DIR, "glove.840B.300d.txt"), b))
lm_model = model.lm_model
basedir = join(LM_DIR, "squad-context-concat-skip")
lm_model.lm_vocab_file = join(basedir,
"squad_train_dev_all_unique_tokens.txt")
lm_model.options_file = join(
basedir, "options_squad_lm_2x4096_512_2048cnn_2xhighway_skip.json")
lm_model.weight_file = join(
basedir,
"squad_context_concat_lm_2x4096_512_2048cnn_2xhighway_skip.hdf5")
lm_model.embed_weights_file = None
model.set_inputs([dataset], loader)
print("Done, building graph")
sess = tf.Session()
with sess.as_default():
pred = model.get_prediction()
best_span = pred.get_best_span(17)[0]
all_vars = tf.global_variables() + tf.get_collection(
tf.GraphKeys.SAVEABLE_OBJECTS)
dont_restore_names = {
x.name
for x in all_vars if x.name.startswith("bilm")
}
print(sorted(dont_restore_names))
vars = [x for x in all_vars if x.name not in dont_restore_names]
print("Done, loading weights")
checkpoint = model_dir.get_best_weights()
if checkpoint is None:
print("Loading most recent checkpoint")
checkpoint = model_dir.get_latest_checkpoint()
else:
print("Loading best weights")
saver = tf.train.Saver(vars)
saver.restore(sess, checkpoint)
if args.ema:
ema = tf.train.ExponentialMovingAverage(0)
saver = tf.train.Saver(
{ema.average_name(x): x
for x in tf.trainable_variables()})
saver.restore(sess, checkpoint)
sess.run(
tf.variables_initializer(
[x for x in all_vars if x.name in dont_restore_names]))
print("Done, starting evaluation")
out = {}
for i, batch in enumerate(dataset.get_epoch()):
if args.n is not None and i == args.n:
break
print("On batch: %d" % (i + 1))
enc = model.encode(batch, False)
spans = sess.run(best_span, feed_dict=enc)
for (s, e), point in zip(spans, batch):
out[point.question_id] = point.get_original_text(s, e)
sess.close()
print("Done, saving")
with open(output_path, "w") as f:
json.dump(out, f)
print("Mission accomplished!")
def main():
parser = argparse.ArgumentParser(description='Evaluate a model on SQuAD')
parser.add_argument('model', help='model directory to evaluate')
parser.add_argument("-o", "--official_output", type=str, help="where to output an official result file")
parser.add_argument('-n', '--sample_questions', type=int, default=None,
help="(for testing) run on a subset of questions")
parser.add_argument('--answer_bounds', nargs='+', type=int, default=[17],
help="Max size of answer")
parser.add_argument('-b', '--batch_size', type=int, default=200,
help="Batch size, larger sizes can be faster but uses more memory")
parser.add_argument('-s', '--step', default=None,
help="Weights to load, can be a checkpoint step or 'latest'")
parser.add_argument('-c', '--corpus', choices=["dev", "train"], default="dev")
parser.add_argument('--no_ema', action="store_true", help="Don't use EMA weights even if they exist")
parser.add_argument('--none_prob', action="store_true", help="Output none probability for samples")
parser.add_argument('--elmo', action="store_true", help="Use elmo model")
parser.add_argument('--per_question_loss_file', type=str, default=None,
help="Run question by question and output a question_id -> loss output to this file")
args = parser.parse_known_args()[0]
model_dir = ModelDir(args.model)
corpus = SquadCorpus()
if args.corpus == "dev":
questions = corpus.get_dev()
else:
questions = corpus.get_train()
questions = split_docs(questions)
if args.sample_questions:
np.random.RandomState(0).shuffle(sorted(questions, key=lambda x: x.question_id))
questions = questions[:args.sample_questions]
questions.sort(key=lambda x:x.n_context_words, reverse=True)
dataset = ParagraphAndQuestionDataset(questions, FixedOrderBatcher(args.batch_size, True))
evaluators = [SpanEvaluator(args.answer_bounds, text_eval="squad")]
if args.official_output is not None:
evaluators.append(RecordSpanPrediction(args.answer_bounds[0]))
if args.per_question_loss_file is not None:
evaluators.append(RecordSpanPredictionScore(args.answer_bounds[0], args.batch_size, args.none_prob))
if args.step is not None:
if args.step == "latest":
checkpoint = model_dir.get_latest_checkpoint()
else:
checkpoint = model_dir.get_checkpoint(int(args.step))
else:
checkpoint = model_dir.get_best_weights()
if checkpoint is not None:
print("Using best weights")
else:
print("Using latest checkpoint")
checkpoint = model_dir.get_latest_checkpoint()
model = model_dir.get_model()
if args.elmo:
model.lm_model.lm_vocab_file = './elmo-params/squad_train_dev_all_unique_tokens.txt'
model.lm_model.options_file = './elmo-params/options_squad_lm_2x4096_512_2048cnn_2xhighway_skip.json'
model.lm_model.weight_file = './elmo-params/squad_context_concat_lm_2x4096_512_2048cnn_2xhighway_skip.hdf5'
model.lm_model.embed_weights_file = None
evaluation = trainer.test(model, evaluators, {args.corpus: dataset},
corpus.get_resource_loader(), checkpoint, not args.no_ema)[args.corpus]
# Print the scalar results in a two column table
scalars = evaluation.scalars
cols = list(sorted(scalars.keys()))
table = [cols]
header = ["Metric", ""]
table.append([("%s" % scalars[x] if x in scalars else "-") for x in cols])
print_table([header] + transpose_lists(table))
# Save the official output
if args.official_output is not None:
quid_to_para = {}
for x in questions:
quid_to_para[x.question_id] = x.paragraph
q_id_to_answers = {}
q_ids = evaluation.per_sample["question_id"]
spans = evaluation.per_sample["predicted_span"]
for q_id, (start, end) in zip(q_ids, spans):
text = quid_to_para[q_id].get_original_text(start, end)
q_id_to_answers[q_id] = text
with open(args.official_output, "w") as f:
json.dump(q_id_to_answers, f)
if args.per_question_loss_file is not None:
print("Saving result")
output_file = args.per_question_loss_file
ids = evaluation.per_sample["question_ids"]
f1s = evaluation.per_sample["text_f1"]
ems = evaluation.per_sample["text_em"]
losses = evaluation.per_sample["loss"]
if args.none_prob:
none_probs = evaluation.per_sample["none_probs"]
"""
results = {question_id: {'f1': float(f1), 'em': float(em), 'loss': float(loss), 'none_prob': float(none_prob)} for question_id, f1, em, loss, none_prob in zip(ids, f1s, ems, losses, none_probs)}
"""
results = {question_id: float(none_prob) for question_id, none_prob in zip(ids, none_probs)}
else:
results = {question_id: {'f1': float(f1), 'em': float(em), 'loss': float(loss)} for question_id, f1, em, loss in zip(ids, f1s, ems, losses)}
with open(output_file, 'w') as f:
json.dump(results, f)
def main():
parser = argparse.ArgumentParser(description="Run an ELMo model on user input")
# parser.add_argument("model", type=int, help="Model directory")
parser.add_argument("question", help="Question to answer")
parser.add_argument("documents", help="List of text documents to answer the question with", nargs='+')
args = parser.parse_args()
# Models path
SQUAD_MODEL_DIRECTORY_PATH = 'docqa/models-cpu/squad'
SQUAD_SHARED_NORM_MODEL_DIRECTORY_PATH = 'docqa/models-cpu/squad-shared-norm'
TRIVIAQA_MODEL_DIRECTORY_PATH = 'docqa/models-cpu/triviaqa-unfiltered-shared-norm'
TRIVIAQA_SHARED_NORM_MODEL_DIRECTORY_PATH = 'docqa/models-cpu/triviaqa-web-shared-norm'
models_directory = [
SQUAD_MODEL_DIRECTORY_PATH,
SQUAD_SHARED_NORM_MODEL_DIRECTORY_PATH,
TRIVIAQA_MODEL_DIRECTORY_PATH,
TRIVIAQA_SHARED_NORM_MODEL_DIRECTORY_PATH
]
print("Preprocessing...")
# Load the model
# model_dir = ModelDir(args.model)
model_dir = ModelDir(models_directory[0])
model = model_dir.get_model()
if not isinstance(model, ParagraphQuestionModel):
raise ValueError("This script is built to work for ParagraphQuestionModel models only")
# Read the documents
documents = []
for doc in args.documents:
if not isfile(doc):
raise ValueError(doc + " does not exist")
with open(doc, "r") as f:
documents.append(f.read())
print("Loaded %d documents" % len(documents))
# Split documents into lists of paragraphs
documents = [re.split("\s*\n\s*", doc) for doc in documents]
# Tokenize the input, the models expects data to be tokenized using `NltkAndPunctTokenizer`
# Note the model expects case-sensitive input
tokenizer = NltkAndPunctTokenizer()
question = tokenizer.tokenize_paragraph_flat(args.question) # List of words
# Now list of document->paragraph->sentence->word
documents = [[tokenizer.tokenize_paragraph(p) for p in doc] for doc in documents]
# Now group the document into paragraphs, this returns `ExtractedParagraph` objects
# that additionally remember the start/end token of the paragraph within the source document
splitter = MergeParagraphs(400)
# splitter = PreserveParagraphs() # Uncomment to use the natural paragraph grouping
documents = [splitter.split(doc) for doc in documents]
# Now select the top paragraphs using a `ParagraphFilter`
if len(documents) == 1:
# Use TF-IDF to select top paragraphs from the document
selector = TopTfIdf(NltkPlusStopWords(True), n_to_select=5)
context = selector.prune(question, documents[0])
else:
# Use a linear classifier to select top paragraphs among all the documents
selector = ShallowOpenWebRanker(n_to_select=10)
context = selector.prune(question, flatten_iterable(documents))
print("Select %d paragraph" % len(context))
if model.preprocessor is not None:
# Models are allowed to define an additional pre-processing step
# This will turn the `ExtractedParagraph` objects back into simple lists of tokens
context = [model.preprocessor.encode_text(question, x) for x in context]
else:
# Otherwise just use flattened text
context = [flatten_iterable(x.text) for x in context]
print("Setting up model")
# Tell the model the batch size (can be None) and vocab to expect, This will load the
# needed word vectors and fix the batch size to use when building the graph / encoding the input
voc = set(question)
for txt in context:
voc.update(txt)
model.set_input_spec(ParagraphAndQuestionSpec(batch_size=len(context)), voc)
# Now we build the actual tensorflow graph, `best_span` and `conf` are
# tensors holding the predicted span (inclusive) and confidence scores for each
# element in the input batch, confidence scores being the pre-softmax logit for the span
print("Build tf graph")
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
# We need to use sess.as_default when working with the cuNND stuff, since we need an active
# session to figure out the # of parameters needed for each layer. The cpu-compatible models don't need this.
with sess.as_default():
# 8 means to limit the span to size 8 or less
best_spans, conf = model.get_prediction().get_best_span(10)
# Loads the saved weights
model_dir.restore_checkpoint(sess)
# Now the model is ready to run
# The model takes input in the form of `ContextAndQuestion` objects, for example:
data = [ParagraphAndQuestion(x, question, None, "user-question%d"%i)
for i, x in enumerate(context)]
print("Starting run")
# The model is run in two steps, first it "encodes" a batch of paragraph/context pairs
# into numpy arrays, then we use `sess` to run the actual model get the predictions
encoded = model.encode(data, is_train=False) # batch of `ContextAndQuestion` -> feed_dict
best_spans, conf = sess.run([best_spans, conf], feed_dict=encoded) # feed_dict -> predictions
best_para = np.argmax(conf) # We get output for each paragraph, select the most-confident one to print
print("Best Paragraph: " + str(best_para))
para_id = int(str(best_para))
# print("Best Paragraph: \n" + (" ".join((paras[para_id].text)[0])))
print("Best Paragraph: \n" + " ".join(context[para_id]))
print("Best span: " + str(best_spans[best_para]))
print("Answer text: " + " ".join(context[best_para][best_spans[best_para][0]:best_spans[best_para][1]+1]))
print("Confidence: " + str(conf[best_para]))
def main():
parser = argparse.ArgumentParser(description='Evaluate a model on SQuAD')
parser.add_argument('model', help='model directory to evaluate')
parser.add_argument("-o", "--official_output", type=str, help="where to output an official result file")
parser.add_argument('-n', '--sample_questions', type=int, default=None,
help="(for testing) run on a subset of questions")
parser.add_argument('--answer_bounds', nargs='+', type=int, default=[17],
help="Max size of answer")
parser.add_argument('-b', '--batch_size', type=int, default=45,
help="Batch size, larger sizes can be faster but uses more memory")
parser.add_argument('-s', '--step', default=None,
help="Weights to load, can be a checkpoint step or 'latest'")
parser.add_argument('-c', '--corpus', choices=["dev", "train"], default="dev")
parser.add_argument('--no_ema', action="store_true", help="Don't use EMA weights even if they exist")
args = parser.parse_args()
num_choices = 4
model_dir = ModelDir(args.model)
corpus = SquadCorpus()
if args.corpus == "dev":
questions = corpus.get_dev()
else:
questions = corpus.get_train()
questions = split_docs(questions)
if args.sample_questions:
np.random.RandomState(0).shuffle(sorted(questions, key=lambda x: x.question_id))
questions = questions[:args.sample_questions]
questions.sort(key=lambda x:x.n_context_words, reverse=True)
#pdb.set_trace()
#print(args.batch_size)
#dataset = ParagraphAndQuestionDataset(questions, FixedOrderBatcher(args.batch_size, False),None,num_choices)
dataset = ParagraphAndQuestionDataset(questions, ClusteredBatcher(45, ContextLenKey(), False, False),None,num_choices)
#ClusteredBatcher(45, ContextLenKey(), False, False)
evaluators = [MultiChoiceEvaluator(num_choices)]
#if args.official_output is not None:
#evaluators.append(RecordSpanPrediction(args.answer_bounds[0]))
#pdb.set_trace()
if args.step is not None:
if args.step == "latest":
checkpoint = model_dir.get_latest_checkpoint()
else:
checkpoint = model_dir.get_checkpoint(int(args.step))
else:
checkpoint = model_dir.get_best_weights()
if checkpoint is not None:
print("Using best weights")
else:
print("Using latest checkpoint")
checkpoint = model_dir.get_latest_checkpoint()
model = model_dir.get_model()
#pdb.set_trace()
evaluation = trainer.test(model, evaluators, {args.corpus: dataset},
corpus.get_resource_loader(), checkpoint, not args.no_ema)[args.corpus]
#pdb.set_trace()
# Print the scalar results in a two column table
scalars = evaluation.scalars
cols = list(sorted(scalars.keys()))
table = [cols]
header = ["Metric", ""]
table.append([("%s" % scalars[x] if x in scalars else "-") for x in cols])
print_table([header] + transpose_lists(table))
# Save the official output
if args.official_output is not None:
data_to_dump = {}
list_of_choices = ['A','B','C','D']
q_ids = evaluation.per_sample["question_id"]
correct_ans = evaluation.per_sample["correct answer"]
correct_ids = evaluation.per_sample["correct index"]
pred_ids = evaluation.per_sample["predictied index"]
pred_ans = evaluation.per_sample["predictied answer"]
is_correct = evaluation.per_sample["is correct"]
#pdb.set_trace()
for ix, q_ids in enumerate(q_ids):
if(is_correct[ix]):
data_to_dump[q_ids] = {'Is Correct' : 'True',
'predictied' : [' '.join(pred_ans[ix]),list_of_choices[pred_ids[ix]]],
'correct' : [' '.join(correct_ans[ix]),list_of_choices[correct_ids[ix]]]
}
else:
data_to_dump[q_ids] = {'Is Correct' : 'False',
'predictied' : [' '.join(pred_ans[ix]),list_of_choices[pred_ids[ix]]],
'correct' : [' '.join(correct_ans[ix]),list_of_choices[correct_ids[ix]]]
}
#pdb.set_trace()
with open(args.official_output, "w") as f:
json.dump(data_to_dump , f)
def main():
parser = argparse.ArgumentParser(
description='Evaluate a model on document-level SQuAD')
parser.add_argument('model', help='model to use')
parser.add_argument(
'output',
type=str,
help="Store the per-paragraph results in csv format in this file")
parser.add_argument('-n',
'--n_sample',
type=int,
default=None,
help="(for testing) sample documents")
parser.add_argument(
'-s',
'--async',
type=int,
default=10,
help="Encoding batch asynchronously, queueing up to this many")
parser.add_argument('-a',
'--answer_bound',
type=int,
default=17,
help="Max answer span length")
parser.add_argument('-p',
'--n_paragraphs',
type=int,
default=None,
help="Max number of paragraphs to use")
parser.add_argument(
'-b',
'--batch_size',
type=int,
default=200,
help="Batch size, larger sizes can be faster but uses more memory")
parser.add_argument('-c',
'--corpus',
choices=["dev", "train", "doc-rd-dev"],
default="dev")
parser.add_argument('--no_ema',
action="store_true",
help="Don't use EMA weights even if they exist")
args = parser.parse_args()
model_dir = ModelDir(args.model)
print("Loading data")
questions = []
ranker = SquadTfIdfRanker(NltkPlusStopWords(True),
args.n_paragraphs,
force_answer=False)
if args.corpus == "doc-rd-dev":
docs = SquadCorpus().get_dev()
if args.n_sample is not None:
docs.sort(key=lambda x: x.doc_id)
np.random.RandomState(0).shuffle(docs)
docs = docs[:args.n_sample]
print("Fetching document reader docs...")
doc_rd_versions = get_doc_rd_doc(docs)
print("Ranking and matching with questions...")
for doc in tqdm(docs):
doc_questions = flatten_iterable(x.questions
for x in doc.paragraphs)
paragraphs = doc_rd_versions[doc.title]
ranks = ranker.rank([x.words for x in doc_questions],
[x.text for x in paragraphs])
for i, question in enumerate(doc_questions):
para_ranks = np.argsort(ranks[i])
for para_rank, para_num in enumerate(
para_ranks[:args.n_paragraphs]):
# Just use dummy answers spans for these pairs
questions.append(
RankedParagraphQuestion(
question.words,
TokenSpans(question.answer.answer_text,
np.zeros((0, 2), dtype=np.int32)),
question.question_id, paragraphs[para_num],
para_rank, para_num))
rl = ResourceLoader()
else:
if args.corpus == "dev":
docs = SquadCorpus().get_dev()
else:
docs = SquadCorpus().get_train()
rl = SquadCorpus().get_resource_loader()
if args.n_sample is not None:
docs.sort(key=lambda x: x.doc_id)
np.random.RandomState(0).shuffle(docs)
docs = docs[:args.n_sample]
for q in ranker.ranked_questions(docs):
for i, p in enumerate(q.paragraphs):
questions.append(
RankedParagraphQuestion(
q.question, TokenSpans(q.answer_text, p.answer_spans),
q.question_id,
ParagraphWithInverse([p.text], p.original_text,
p.spans), i, p.paragraph_num))
print("Split %d docs into %d paragraphs" % (len(docs), len(questions)))
questions = sorted(questions,
key=lambda x: (x.n_context_words, len(x.question)),
reverse=True)
for q in questions:
if len(q.answer.answer_spans.shape) != 2:
raise ValueError()
checkpoint = model_dir.get_best_weights()
if checkpoint is not None:
print("Using best weights")
else:
print("Using latest checkpoint")
checkpoint = model_dir.get_latest_checkpoint()
if checkpoint is None:
raise ValueError("No checkpoints found")
data = ParagraphAndQuestionDataset(
questions, FixedOrderBatcher(args.batch_size, True))
model = model_dir.get_model()
evaluation = trainer.test(
model, [RecordParagraphSpanPrediction(args.answer_bound, True)],
{args.corpus: data}, rl, checkpoint, not args.no_ema,
args. async)[args.corpus]
print("Saving result")
output_file = args.output
df = pd.DataFrame(evaluation.per_sample)
df.sort_values(["question_id", "rank"], inplace=True, ascending=True)
group_by = ["question_id"]
f1 = compute_ranked_scores(df, "predicted_score", "text_f1", group_by)
em = compute_ranked_scores(df, "predicted_score", "text_em", group_by)
table = [["N Paragraphs", "EM", "F1"]]
table += list([str(i + 1), "%.4f" % e, "%.4f" % f]
for i, (e, f) in enumerate(zip(em, f1)))
print_table(table)
df.to_csv(output_file, index=False)
def main():
print('Starting...')
model_dir = ModelDir(OPTS.model)
model = model_dir.get_model()
if OPTS.elmo:
# Fix absolute path names from other codalab runs
lm = model.lm_model
if lm.lm_vocab_file.startswith('/0x'):
lm.lm_vocab_file = os.sep.join(lm.lm_vocab_file.split(os.sep)[2:])
if lm.options_file.startswith('/0x'):
lm.options_file = os.sep.join(lm.options_file.split(os.sep)[2:])
if lm.weight_file.startswith('/0x'):
lm.weight_file = os.sep.join(lm.weight_file.split(os.sep)[2:])
if lm.weight_file.startswith('/0x'):
lm.embed_weights_file = os.sep.join(lm.embed_weights_file.split(os.sep)[2:])
lm.embed_weights_file = None
#if not isinstance(model, ParagraphQuestionModel):
# raise ValueError("This script is built to work for ParagraphQuestionModel models only")
input_data, vocab = read_input_data(model)
print('Loading word vectors...')
model.set_input_spec(ParagraphAndQuestionSpec(batch_size=None), vocab)
print('Starting Tensorflow session...')
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
with sess.as_default():
prediction = model.get_prediction()
# Take 0-th here because we know we only truncate to one paragraph
start_logits_tf = prediction.start_logits[0]
end_logits_tf = prediction.end_logits[0]
none_logit_tf = prediction.none_logit[0]
if OPTS.elmo:
# See elmo/run_on_user_text.py
all_vars = tf.global_variables() + tf.get_collection(tf.GraphKeys.SAVEABLE_OBJECTS)
lm_var_names = {x.name for x in all_vars if x.name.startswith("bilm")}
vars = [x for x in all_vars if x.name not in lm_var_names]
model_dir.restore_checkpoint(sess, vars)
sess.run(tf.variables_initializer([x for x in all_vars if x.name in lm_var_names]))
else:
model_dir.restore_checkpoint(sess)
pred_obj = {}
na_prob_obj = {}
pred_always_ans_obj = {}
analysis_obj = {}
for context_raw, context_toks, ex in tqdm(input_data):
encoded = model.encode(ex, is_train=False)
start_logits, end_logits, none_logit = sess.run(
[start_logits_tf, end_logits_tf, none_logit_tf],
feed_dict=encoded)
# beam, p_na = logits_to_probs(
# context_raw, context_toks, start_logits, end_logits, none_logit,
# beam_size=DEFAULT_BEAM_SIZE)
beam, p_na = logits_to_probs(
context_raw, context_toks, start_logits, end_logits, none_logit,
beam_size=10)
# print(beam[0][0])
ans = beam[0][0]
# start, end = beam[0][2],beam[0][3]
non_empty_ans = [x[0] for x in beam if x[0]][0]
qid = ex[0].question_id
pred_obj[qid] = ans
na_prob_obj[qid] = p_na
pred_always_ans_obj[qid] = non_empty_ans
analysis_obj[qid] = [{'answer': b[0], 'span':[b[2], b[3]], 'prob':b[1]} for b in beam]
# print(analysis_obj[qid])
with open(OPTS.output_file, 'w') as f:
json.dump(pred_obj, f)
if OPTS.na_prob_file:
with open(OPTS.na_prob_file, 'w') as f:
json.dump(na_prob_obj, f)
if OPTS.always_answer_file:
with open(OPTS.always_answer_file, 'w') as f:
json.dump(pred_always_ans_obj, f)
if OPTS.analysis_file:
with open(OPTS.analysis_file, 'w') as f:
json.dump(analysis_obj, f, indent=2)
def main():
parser = argparse.ArgumentParser(
description="Run an ELMo model on user input")
parser.add_argument("model", help="Model directory")
parser.add_argument("question", help="Question to answer")
parser.add_argument("context", help="Context to answer the question with")
args = parser.parse_args()
# Tokenize the input, the models expected data to be tokenized using `NltkAndPunctTokenizer`
# Note the model expects case-sensitive input
tokenizer = NltkAndPunctTokenizer()
question = tokenizer.tokenize_paragraph_flat(args.question)
context = tokenizer.tokenize_paragraph_flat(args.context)
print("Loading model")
model_dir = ModelDir(args.model)
model = model_dir.get_model()
if not isinstance(model, ElmoQaModel):
raise ValueError(
"This script is build to work for ElmoQaModel models only")
# Important! This tells the language model not to use the pre-computed word vectors,
# which are only applicable for the SQuAD dev/train sets.
# Instead the language model will use its character-level CNN to compute
# the word vectors dynamically.
model.lm_model.embed_weights_file = None
# Tell the model the batch size and vocab to expect, This will load the needed
# word vectors and fix the batch size when building the graph / encoding the input
print("Setting up model")
voc = set(question)
voc.update(context)
model.set_input_spec(ParagraphAndQuestionSpec(batch_size=1), voc)
# Now we build the actual tensorflow graph, `best_span` and `conf` are
# tensors holding the predicted span (inclusive) and confidence scores for each
# element in the input batch
print("Build tf graph")
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
with sess.as_default():
# 17 means to limit the span to size 17 or less
best_spans, conf = model.get_prediction().get_best_span(17)
# Now restore the weights, this is a bit fiddly since we need to avoid restoring the
# bilm weights, and instead load them from the pre-computed data
all_vars = tf.global_variables() + tf.get_collection(
tf.GraphKeys.SAVEABLE_OBJECTS)
lm_var_names = {x.name for x in all_vars if x.name.startswith("bilm")}
vars = [x for x in all_vars if x.name not in lm_var_names]
model_dir.restore_checkpoint(sess, vars)
# Run the initializer of the lm weights, which will load them from the lm directory
sess.run(
tf.variables_initializer(
[x for x in all_vars if x.name in lm_var_names]))
# Now the model is ready to run
# The model takes input in the form of `ContextAndQuestion` objects, for example:
data = [ParagraphAndQuestion(context, question, None, "user-question1")]
print("Starting run")
# The model is run in two steps, first it "encodes" the paragraph/context pairs
# into numpy arrays, then to use `sess` to run the actual model get the predictions
encoded = model.encode(
data, is_train=False) # batch of `ContextAndQuestion` -> feed_dict
best_spans, conf = sess.run([best_spans, conf],
feed_dict=encoded) # feed_dict -> predictions
print("Best span: " + str(best_spans[0]))
print("Answer text: " +
" ".join(context[best_spans[0][0]:best_spans[0][1] + 1]))
print("Confidence: " + str(conf[0]))
import warnings
warnings.filterwarnings("ignore")
import tensorflow as tf
import re
from docqa.data_processing.document_splitter import MergeParagraphs, TopTfIdf, ShallowOpenWebRanker, PreserveParagraphs
from docqa.data_processing.qa_training_data import ParagraphAndQuestion, ParagraphAndQuestionSpec
from docqa.data_processing.text_utils import NltkAndPunctTokenizer, NltkPlusStopWords
from docqa.doc_qa_models import ParagraphQuestionModel
from docqa.model_dir import ModelDir
from docqa.utils import flatten_iterable
# load model
model_dir = ModelDir(os.path.join('.', 'qa-model'))
model = model_dir.get_model()
print('Generating Vocab...', end='')
# preload all the words
vocab = set()
with open(os.path.join('.', 'qa-model', 'vocab.txt'),
encoding='utf-8') as vocfile:
for line in vocfile:
word = line.strip()
if len(word) > 0:
vocab.add(word)
print('done')
print('Loading Model...', end='')
def perform_evaluation(model_name: str,
dataset_names: List[str],
tokens_per_paragraph: int,
filter_type: str,
n_processes: int,
n_paragraphs: int,
batch_size: int,
checkpoint: str,
no_ema: bool,
max_answer_len: int,
official_output_path: str,
paragraph_output_path: str,
aggregated_output_path: str,
elmo_char_cnn: bool,
n_samples: Union[int, None],
per_document: bool = False):
"""Perform an evaluation using cape's answer decoder
A file will be created listing the answers per question ID for each dataset
:param model_name: path to the model to evaluate
:param dataset_names: list of strings of datasets to evaluate
:param tokens_per_paragraph: how big to make paragraph chunks
:param filter_type: how to select the paragraphs to read
:param n_processes: how many processes to use when multiprocessing
:param n_paragraphs: how many paragraphs to read per question
:param batch_size: how many datapoints to evaluate at once
:param checkpoint: string, checkpoint to load
:param no_ema: if true, dont use EMA weights
:param max_answer_len: the maximum allowable length of an answer in tokens
:param official_output_path: path to write official output to
:param paragraph_output_path: path to write paragraph output to
:param aggregated_output_path: path to write aggregated output to
:param elmo_char_cnn: if true, uses the elmo CNN to make token embeddings, less OOV but
requires much more memory
:param per_document: if false, return best scoring answer to a question, if true,
the best scoring answer from each document is used instead.
"""
async = True
corpus_name = 'all'
print('Setting Up:')
model_dir = ModelDir(model_name)
model = model_dir.get_model()
dataset = get_multidataset(dataset_names)
splitter = MergeParagraphs(tokens_per_paragraph)
para_filter = get_para_filter(filter_type, per_document, n_paragraphs)
test_questions, n_questions = get_questions(per_document, dataset,
splitter, para_filter,
model.preprocessor,
n_processes, batch_size)
print("Starting eval")
checkpoint = get_checkpoint(checkpoint, model_dir)
evaluation = test(model,
[RecordParagraphSpanPrediction(max_answer_len, True)],
{corpus_name: test_questions}, ResourceLoader(),
checkpoint, not no_ema, async, n_samples,
elmo_char_cnn)[corpus_name]
print('Exporting and Post-processing')
if not all(len(x) == n_questions for x in evaluation.per_sample.values()):
raise RuntimeError()
df = pd.DataFrame(evaluation.per_sample)
compute_and_dump_official_output(df,
official_output_path,
per_document=per_document)
print("Saving paragraph result")
df.to_csv(paragraph_output_path, index=False)
print("Computing scores")
agg_df = get_aggregated_df(df, per_document)
agg_df.to_csv(aggregated_output_path, index=False)
def convert(model_dir, output_dir, best_weights=False):
print("Load model")
md = ModelDir(model_dir)
model = md.get_model()
dim = model.embed_mapper.layers[1].n_units
global_step = tf.get_variable('global_step',
shape=[],
dtype='int32',
initializer=tf.constant_initializer(0),
trainable=False)
print("Setting up cudnn version")
#global_step = tf.get_variable('global_step', shape=[], dtype='int32', trainable=False)
sess = tf.Session()
sess.run(global_step.assign(0))
with sess.as_default():
model.set_input_spec(
ParagraphAndQuestionSpec(1, None, None, 14), {"the"},
ResourceLoader(lambda a, b: {"the": np.zeros(300, np.float32)}))
print("Buiding graph")
pred = model.get_prediction()
test_questions = ParagraphAndQuestion(
["Harry", "Potter", "was", "written", "by", "JK"],
["Who", "wrote", "Harry", "Potter", "?"], None, "test_questions")
print("Load vars")
md.restore_checkpoint(sess)
print("Restore finished")
feed = model.encode([test_questions], False)
cuddn_out = sess.run([pred.start_logits, pred.end_logits], feed_dict=feed)
print("Done, copying files...")
if not exists(output_dir):
mkdir(output_dir)
for file in listdir(model_dir):
if isfile(file) and file != "model.npy":
copyfile(join(model_dir, file), join(output_dir, file))
print("Done, mapping tensors...")
to_save = []
to_init = []
for x in tf.trainable_variables():
if x.name.endswith("/gru_parameters:0"):
key = x.name[:-len("/gru_parameters:0")]
fw_params = x
if "map_embed" in x.name:
c = cudnn_rnn_ops.CudnnGRU(1, dim, 400)
elif "chained-out" in x.name:
c = cudnn_rnn_ops.CudnnGRU(1, dim, dim * 4)
else:
c = cudnn_rnn_ops.CudnnGRU(1, dim, dim * 2)
params_saveable = cudnn_rnn_ops.RNNParamsSaveable(
c, c.params_to_canonical, c.canonical_to_params, [fw_params],
key)
for spec in params_saveable.specs:
if spec.name.endswith("bias_cudnn 0") or \
spec.name.endswith("bias_cudnn 1"):
# ??? What do these even do?
continue
name = spec.name.split("/")
name.remove("cell_0")
if "forward" in name:
ix = name.index("forward")
name.insert(ix + 2, "fw")
else:
ix = name.index("backward")
name.insert(ix + 2, "bw")
del name[ix]
ix = name.index("multi_rnn_cell")
name[ix] = "bidirectional_rnn"
name = "/".join(name)
v = tf.Variable(sess.run(spec.tensor), name=name)
to_init.append(v)
to_save.append(v)
else:
to_save.append(x)
other = [
x for x in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
if x not in tf.trainable_variables()
]
print(other)
sess.run(tf.initialize_variables(to_init))
saver = tf.train.Saver(to_save + other)
save_dir = join(output_dir, "save")
if not exists(save_dir):
mkdir(save_dir)
saver.save(sess, join(save_dir, "checkpoint"), sess.run(global_step))
sess.close()
tf.reset_default_graph()
print("Updating model...")
model.embed_mapper.layers = [
model.embed_mapper.layers[0],
BiRecurrentMapper(CompatGruCellSpec(dim))
]
model.match_encoder.layers = list(model.match_encoder.layers)
other = model.match_encoder.layers[1].other
other.layers = list(other.layers)
other.layers[1] = BiRecurrentMapper(CompatGruCellSpec(dim))
pred = model.predictor.predictor
pred.first_layer = BiRecurrentMapper(CompatGruCellSpec(dim))
pred.second_layer = BiRecurrentMapper(CompatGruCellSpec(dim))
with open(join(output_dir, "model.pkl"), "wb") as f:
pickle.dump(model, f)
print("Testing...")
with open(join(output_dir, "model.pkl"), "rb") as f:
model = pickle.load(f)
sess = tf.Session()
model.set_input_spec(
ParagraphAndQuestionSpec(1, None, None, 14), {"the"},
ResourceLoader(lambda a, b: {"the": np.zeros(300, np.float32)}))
pred = model.get_prediction()
print("Rebuilding")
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(save_dir))
feed = model.encode([test_questions], False)
cpu_out = sess.run([pred.start_logits, pred.end_logits], feed_dict=feed)
print("These should be close:")
print([np.allclose(a, b) for a, b in zip(cpu_out, cuddn_out)])
print(cpu_out)
print(cuddn_out)
def main():
print('Starting...')
model_dir = ModelDir(OPTS.model)
model = model_dir.get_model()
tokenizer = NltkAndPunctTokenizer()
if not isinstance(model, ParagraphQuestionModel):
raise ValueError(
"This script is built to work for ParagraphQuestionModel models only"
)
if OPTS.reload_vocab:
loader = ResourceLoader()
else:
loader = CachingResourceLoader()
print('Loading word vectors...')
model.set_input_spec(ParagraphAndQuestionSpec(batch_size=None),
set([',']),
word_vec_loader=loader,
allow_update=True)
print('Starting Tensorflow session...')
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
with sess.as_default():
prediction = model.get_prediction()
# Take 0-th here because we know we only truncate to one paragraph
start_logits_tf = prediction.start_logits[0]
end_logits_tf = prediction.end_logits[0]
none_logit_tf = prediction.none_logit[0]
#best_spans_tf, conf_tf = prediction.get_best_span(MAX_SPAN_LENGTH)
model_dir.restore_checkpoint(sess)
splitter = Truncate(400) # NOTE: we truncate past 400 tokens
selector = TopTfIdf(NltkPlusStopWords(True), n_to_select=5)
app = bottle.Bottle()
@app.route('/')
def index():
return bottle.template('index')
@app.route('/post_query', method='post')
def post_query():
document_raw = bottle.request.forms.getunicode('document').strip()
question_raw = bottle.request.forms.getunicode('question').strip()
document = re.split("\s*\n\s*", document_raw)
question = tokenizer.tokenize_paragraph_flat(question_raw)
doc_toks = [tokenizer.tokenize_paragraph(p) for p in document]
split_doc = splitter.split(doc_toks)
context = selector.prune(question, split_doc)
if model.preprocessor is not None:
context = [
model.preprocessor.encode_text(question, x) for x in context
]
else:
context = [flatten_iterable(x.text) for x in context]
vocab = set(question)
for txt in context:
vocab.update(txt)
data = [
ParagraphAndQuestion(x, question, None, "user-question%d" % i)
for i, x in enumerate(context)
]
model.word_embed.update(loader, vocab)
encoded = model.encode(data, is_train=False)
start_logits, end_logits, none_logit = sess.run(
[start_logits_tf, end_logits_tf, none_logit_tf], feed_dict=encoded)
beam, p_na = logits_to_probs(document_raw,
context[0],
start_logits,
end_logits,
none_logit,
beam_size=BEAM_SIZE)
return bottle.template('results',
document=document_raw,
question=question_raw,
beam=beam,
p_na=p_na)
cur_dir = os.path.abspath(os.path.dirname(__file__))
bottle.TEMPLATE_PATH.insert(0, os.path.join(cur_dir, 'views'))
bottle.run(app, host=OPTS.hostname, port=OPTS.port, debug=OPTS.debug)
def main():
parser = argparse.ArgumentParser(
description="Run an ELMo model on user input")
parser.add_argument("model", help="Model directory")
parser.add_argument("ja_filepath", help="File path to japanese questions")
parser.add_argument("result_file",
help="File path to predicted result json")
args = parser.parse_args()
print(args)
print("Preprocessing...")
paragraphs, questions = read_squad_style_database(args.ja_filepath)
# Load the model
model_dir = ModelDir(args.model)
model = model_dir.get_model()
if not isinstance(model, ParagraphQuestionModel):
raise ValueError(
"This script is built to work for ParagraphQuestionModel models only"
)
paragraphs, questions = read_squad_style_database(args.ja_filepath)
predictions = {}
predictions["conf"] = {}
for qa in questions:
print(qa["id"])
title = qa["title"]
para_idx = qa["para_idx"]
context = paragraphs[title][para_idx]
question = qa["question"]
print(context)
print(question)
if model.preprocessor is not None:
context = [
model.preprocessor.encode_text(question, x) for x in context
]
print("Setting up model")
voc = set(question)
for txt in context:
voc.update(txt)
model.set_input_spec(ParagraphAndQuestionSpec(batch_size=len(context)),
voc)
print("Build tf graph")
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
with sess.as_default():
best_spans, conf = model.get_prediction().get_best_span(8)
# Loads the saved weights
model_dir.restore_checkpoint(sess)
data = [
ParagraphAndQuestion(x, question, None, "user-question%d" % i)
for i, x in enumerate(context)
]
print("Starting run")
encoded = model.encode(
data, is_train=False) # batch of `ContextAndQuestion` -> feed_dict
best_spans, conf = sess.run(
[best_spans, conf], feed_dict=encoded) # feed_dict -> predictions
print(best_spans)
predictions[qa["id"]] = best_spans
predictions["conf"][qa["id"]] = conf
print(predictions)
result_f = open(args.result_file, "w")
json.dump(predictions, result_f)
exit()
official_evaluator = OfficialEvaluator(args.ja_filepath, args.result_file)
evaluation = official_evaluator.evaluate()
print(evaluation)
def run():
parser = argparse.ArgumentParser()
parser.add_argument("input_data")
parser.add_argument("output_data")
parser.add_argument("--plot_dir", type=str, default=None)
parser.add_argument("--model_dir", type=str, default="/tmp/model/document-qa")
parser.add_argument("--lm_dir", type=str, default="/home/castle/data/lm/squad-context-concat-skip")
parser.add_argument("--glove_dir", type=str, default="/home/castle/data/glove")
parser.add_argument("--n", type=int, default=None)
parser.add_argument("-b", "--batch_size", type=int, default=30)
parser.add_argument("--ema", action="store_true")
args = parser.parse_args()
input_data = args.input_data
output_path = args.output_data
model_dir = ModelDir(args.model_dir)
nltk.data.path.append("nltk_data")
print("Loading data")
docs = parse_squad_data(input_data, "", NltkAndPunctTokenizer(), False)
pairs = split_docs(docs)
dataset = ParagraphAndQuestionDataset(pairs, ClusteredBatcher(args.batch_size, ContextLenKey(), False, True))
print("Done, init model")
model = model_dir.get_model()
# small hack, just load the vector file at its expected location rather then using the config location
loader = ResourceLoader(lambda a, b: load_word_vector_file(join(args.glove_dir, "glove.840B.300d.txt"), b))
lm_model = model.lm_model
basedir = args.lm_dir
plotdir = args.plot_dir
lm_model.lm_vocab_file = join(basedir, "squad_train_dev_all_unique_tokens.txt")
lm_model.options_file = join(basedir, "options_squad_lm_2x4096_512_2048cnn_2xhighway_skip.json")
lm_model.weight_file = join(basedir, "squad_context_concat_lm_2x4096_512_2048cnn_2xhighway_skip.hdf5")
lm_model.embed_weights_file = None
model.set_inputs([dataset], loader)
print("Done, building graph")
sess = tf.Session()
with sess.as_default():
pred = model.get_prediction()
best_span = pred.get_best_span(17)[0]
if plotdir != None:
start_logits_op, end_logits_op = pred.get_logits()
all_vars = tf.global_variables() + tf.get_collection(tf.GraphKeys.SAVEABLE_OBJECTS)
dont_restore_names = {x.name for x in all_vars if x.name.startswith("bilm")}
print(sorted(dont_restore_names))
vars = [x for x in all_vars if x.name not in dont_restore_names]
print("Done, loading weights")
checkpoint = model_dir.get_best_weights()
if checkpoint is None:
print("Loading most recent checkpoint")
checkpoint = model_dir.get_latest_checkpoint()
else:
print("Loading best weights")
saver = tf.train.Saver(vars)
saver.restore(sess, checkpoint)
if args.ema:
ema = tf.train.ExponentialMovingAverage(0)
saver = tf.train.Saver({ema.average_name(x): x for x in tf.trainable_variables()})
saver.restore(sess, checkpoint)
sess.run(tf.variables_initializer([x for x in all_vars if x.name in dont_restore_names]))
print("Done, starting evaluation")
out = {}
for i, batch in enumerate(dataset.get_epoch()):
if args.n is not None and i == args.n:
break
print("On batch size [%d], now in %d th batch" % (args.batch_size, i +1))
enc = model.encode(batch, False)
if plotdir != None:
spans, start_logits, end_logits = sess.run([best_span, start_logits_op, end_logits_op], feed_dict=enc)
for bi, point in enumerate(batch):
q = ' '.join(point.question)
c = point.paragraph.get_context()
gt = ' | '.join(point.answer.answer_text)
s, e = spans[bi]
pred = point.get_original_text(s, e)
start_dist = start_logits[bi]
end_dist = end_logits[bi]
c_interval = np.arange(0.0, start_dist.shape[0], 1)
c_label = c
plt.figure(1)
plt.subplot(211)
plt.plot(c_interval, start_dist, color='r')
plt.title("Q : " + q + " // A : " + gt, fontsize=9)
plt.text(0, 0, r'Predict : %s [%d:%d]' % (pred, s, e), color='b')
axes = plt.gca()
axes.set_ylim([-20, 20])
plt.subplot(212)
plt.plot(c_interval, end_dist, color='g')
plt.xticks(c_interval, c_label, rotation=90, fontsize=5)
axes = plt.gca()
axes.set_ylim([-20, 20])
plt.show()
break
else:
spans = sess.run(best_span, feed_dict=enc)
for (s, e), point in zip(spans, batch):
out[point.question_id] = point.get_original_text(s, e)
sess.close()
print("Done, saving")
with open(output_path, "w") as f:
json.dump(out, f)
print("Mission accomplished!")
def main():
parser = argparse.ArgumentParser(description='Evaluate a model on TriviaQA data')
parser.add_argument('model', help='model directory')
parser.add_argument('-p', '--paragraph_output', type=str,
help="Save fine grained results for each paragraph in csv format")
parser.add_argument('-o', '--official_output', type=str, help="Build an offical output file with the model's"
" most confident span for each (question, doc) pair")
parser.add_argument('--no_ema', action="store_true", help="Don't use EMA weights even if they exist")
parser.add_argument('--n_processes', type=int, default=None,
help="Number of processes to do the preprocessing (selecting paragraphs+loading context) with")
parser.add_argument('-i', '--step', type=int, default=None, help="checkpoint to load, default to latest")
parser.add_argument('-n', '--n_sample', type=int, default=None, help="Number of questions to evaluate on")
parser.add_argument('-a', '--async', type=int, default=10)
parser.add_argument('-t', '--tokens', type=int, default=400,
help="Max tokens per a paragraph")
parser.add_argument('-g', '--n_paragraphs', type=int, default=15,
help="Number of paragraphs to run the model on")
parser.add_argument('-f', '--filter', type=str, default=None, choices=["tfidf", "truncate", "linear"],
help="How to select paragraphs")
parser.add_argument('-b', '--batch_size', type=int, default=200,
help="Batch size, larger sizes might be faster but wll take more memory")
parser.add_argument('--max_answer_len', type=int, default=8,
help="Max answer span to select")
parser.add_argument('-c', '--corpus',
choices=["web-dev", "web-test", "web-verified-dev", "web-train",
"open-dev", "open-train"],
default="web-verified-dev")
args = parser.parse_args()
model_dir = ModelDir(args.model)
model = model_dir.get_model()
if args.corpus.startswith('web'):
dataset = TriviaQaWebDataset()
corpus = dataset.evidence
if args.corpus == "web-dev":
test_questions = dataset.get_dev()
elif args.corpus == "web-test":
test_questions = dataset.get_test()
elif args.corpus == "web-verified-dev":
test_questions = dataset.get_verified()
elif args.corpus == "web-train":
test_questions = dataset.get_train()
else:
raise RuntimeError()
else:
dataset = TriviaQaOpenDataset()
corpus = dataset.evidence
if args.corpus == "open-dev":
test_questions = dataset.get_dev()
elif args.corpus == "open-train":
test_questions = dataset.get_train()
else:
raise RuntimeError()
splitter = MergeParagraphs(args.tokens)
per_document = not args.corpus.startswith("open")
filter_name = args.filter
if filter_name is None:
if args.corpus.startswith("open"):
filter_name = "linear"
else:
filter_name = "tfidf"
print("Selecting %d paragraphs using %s method per %s" % (args.n_paragraphs, filter_name,
("question-document pair" if per_document else "question")))
if filter_name == "tfidf":
para_filter = TopTfIdf(NltkPlusStopWords(punctuation=True), args.n_paragraphs)
elif filter_name == "truncate":
para_filter = FirstN(args.n_paragraphs)
elif filter_name == "linear":
para_filter = ShallowOpenWebRanker(args.n_paragraphs)
else:
raise ValueError()
n_questions = args.n_sample
if n_questions is not None:
test_questions.sort(key=lambda x:x.question_id)
np.random.RandomState(0).shuffle(test_questions)
test_questions = test_questions[:n_questions]
print("Building question/paragraph pairs...")
# Loads the relevant questions/documents, selects the right paragraphs, and runs the model's preprocessor
if per_document:
prep = ExtractMultiParagraphs(splitter, para_filter, model.preprocessor, require_an_answer=False)
else:
prep = ExtractMultiParagraphsPerQuestion(splitter, para_filter, model.preprocessor, require_an_answer=False)
prepped_data = preprocess_par(test_questions, corpus, prep, args.n_processes, 1000)
data = []
for q in prepped_data.data:
for i, p in enumerate(q.paragraphs):
if q.answer_text is None:
ans = None
else:
ans = TokenSpans(q.answer_text, p.answer_spans)
data.append(DocumentParagraphQuestion(q.question_id, p.doc_id,
(p.start, p.end), q.question, p.text,
ans, i))
# Reverse so our first batch will be the largest (so OOMs happen early)
questions = sorted(data, key=lambda x: (x.n_context_words, len(x.question)), reverse=True)
print("Done, starting eval")
if args.step is not None:
if args.step == "latest":
checkpoint = model_dir.get_latest_checkpoint()
else:
checkpoint = model_dir.get_checkpoint(int(args.step))
else:
checkpoint = model_dir.get_best_weights()
if checkpoint is not None:
print("Using best weights")
else:
print("Using latest checkpoint")
checkpoint = model_dir.get_latest_checkpoint()
test_questions = ParagraphAndQuestionDataset(questions, FixedOrderBatcher(args.batch_size, True))
evaluation = trainer.test(model,
[RecordParagraphSpanPrediction(args.max_answer_len, True)],
{args.corpus:test_questions}, ResourceLoader(), checkpoint, not args.no_ema, args.async)[args.corpus]
if not all(len(x) == len(data) for x in evaluation.per_sample.values()):
raise RuntimeError()
df = pd.DataFrame(evaluation.per_sample)
if args.official_output is not None:
print("Saving question result")
# I didn't store the unormalized filenames exactly, so unfortunately we have to reload
# the source data to get exact filename to output an official test script
fns = {}
print("Loading proper filenames")
if args.corpus == 'web-test':
source = join(TRIVIA_QA, "qa", "web-test-without-answers.json")
elif args.corpus == "web-dev":
source = join(TRIVIA_QA, "qa", "web-dev.json")
else:
raise NotImplementedError()
with open(join(source)) as f:
data = json.load(f)["Data"]
for point in data:
for doc in point["EntityPages"]:
filename = doc["Filename"]
fn = join("wikipedia", filename[:filename.rfind(".")])
fn = normalize_wiki_filename(fn)
fns[(point["QuestionId"], fn)] = filename
answers = {}
scores = {}
for q_id, doc_id, start, end, txt, score in df[["question_id", "doc_id", "para_start", "para_end",
"text_answer", "predicted_score"]].itertuples(index=False):
filename = dataset.evidence.file_id_map[doc_id]
if filename.startswith("web"):
true_name = filename[4:] + ".txt"
else:
true_name = fns[(q_id, filename)]
key = q_id + "--" + true_name
prev_score = scores.get(key)
if prev_score is None or prev_score < score:
scores[key] = score
answers[key] = txt
with open(args.official_output, "w") as f:
json.dump(answers, f)
if per_document:
group_by = ["question_id", "doc_id"]
else:
group_by = ["question_id"]
# Print a table of scores as more paragraphs are used
df.sort_values(group_by + ["rank"], inplace=True)
f1 = compute_model_scores(df, "predicted_score", "text_f1", group_by)
em = compute_model_scores(df, "predicted_score", "text_em", group_by)
table = [["N Paragraphs", "EM", "F1"]]
table += list([str(i+1), "%.4f" % e, "%.4f" % f] for i, (e, f) in enumerate(zip(em, f1)))
print_table(table)
output_file = args.paragraph_output
if output_file is not None:
print("Saving paragraph result")
if output_file.endswith("json"):
with open(output_file, "w") as f:
json.dump(evaluation.per_sample, f)
elif output_file.endswith("pkl"):
with open(output_file, "wb") as f:
pickle.dump(evaluation.per_sample, f)
elif output_file.endswith("csv"):
df.to_csv(output_file, index=False)
else:
raise ValueError("Unrecognized file format")
def main():
parser = argparse.ArgumentParser(
description='Evaluate a model on TriviaQA data')
parser.add_argument('model', help='model directory')
parser.add_argument(
'-p',
'--paragraph_output',
type=str,
help="Save fine grained results for each paragraph in csv format")
parser.add_argument('-o',
'--official_output',
type=str,
help="Build an offical output file with the model's"
" most confident span for each (question, doc) pair")
parser.add_argument('--no_ema',
action="store_true",
help="Don't use EMA weights even if they exist")
parser.add_argument(
'--n_processes',
type=int,
default=None,
help=
"Number of processes to do the preprocessing (selecting paragraphs+loading context) with"
)
parser.add_argument('-i',
'--step',
type=int,
default=None,
help="checkpoint to load, default to latest")
parser.add_argument('-n',
'--n_sample',
type=int,
default=None,
help="Number of questions to evaluate on")
parser.add_argument('-a', '--async', type=int, default=10)
parser.add_argument('-t',
'--tokens',
type=int,
default=400,
help="Max tokens per a paragraph")
parser.add_argument('-g',
'--n_paragraphs',
type=int,
default=15,
help="Number of paragraphs to run the model on")
parser.add_argument('-f',
'--filter',
type=str,
default=None,
choices=["tfidf", "truncate", "linear"],
help="How to select paragraphs")
parser.add_argument(
'-b',
'--batch_size',
type=int,
default=200,
help="Batch size, larger sizes might be faster but wll take more memory"
)
parser.add_argument('--max_answer_len',
type=int,
default=8,
help="Max answer span to select")
parser.add_argument('-c',
'--corpus',
choices=[
"web-dev", "web-test", "web-verified-dev",
"web-train", "open-dev", "open-train", "wiki-dev",
"wiki-test"
],
default="web-verified-dev")
parser.add_argument("-s",
"--source_dir",
type=str,
default=None,
help="where to take input files")
parser.add_argument("--n_span_per_q",
type=int,
default=1,
help="where to take input files")
args = parser.parse_args()
dataset_name = args.source_dir.split('/')[-1]
model_name = args.model.split('/')[-1]
ElasticLogger().write_log('INFO',
'Start Evaluation',
context_dict={
'model': model_name,
'dataset': dataset_name
})
model_dir = ModelDir(args.model)
model = model_dir.get_model()
if args.corpus.startswith('web'):
dataset = TriviaQaWebDataset()
if args.corpus == "web-dev":
test_questions = dataset.get_dev()
elif args.corpus == "web-test":
test_questions = dataset.get_test()
elif args.corpus == "web-verified-dev":
test_questions = dataset.get_verified()
elif args.corpus == "web-train":
test_questions = dataset.get_train()
else:
raise AssertionError()
elif args.corpus.startswith("wiki"):
dataset = TriviaQaWikiDataset()
if args.corpus == "wiki-dev":
test_questions = dataset.get_dev()
elif args.corpus == "wiki-test":
test_questions = dataset.get_test()
else:
raise AssertionError()
else:
dataset = TriviaQaOpenDataset(args.source_dir)
if args.corpus == "open-dev":
# just loading the pkl that was saved in build_span_corpus
test_questions = dataset.get_dev()
elif args.corpus == "open-train":
test_questions = dataset.get_train()
else:
raise AssertionError()
### ALON debuging
#test_questions = test_questions[0:5]
corpus = dataset.evidence
splitter = MergeParagraphs(args.tokens)
per_document = args.corpus.startswith(
"web") # wiki and web are both multi-document
#per_document = True
filter_name = args.filter
if filter_name is None:
# Pick default depending on the kind of data we are using
if per_document:
filter_name = "tfidf"
else:
filter_name = "linear"
print("Selecting %d paragraphs using method \"%s\" per %s" %
(args.n_paragraphs, filter_name,
("question-document pair" if per_document else "question")))
if filter_name == "tfidf":
para_filter = TopTfIdf(NltkPlusStopWords(punctuation=True),
args.n_paragraphs)
elif filter_name == "truncate":
para_filter = FirstN(args.n_paragraphs)
elif filter_name == "linear":
para_filter = ShallowOpenWebRanker(args.n_paragraphs)
else:
raise ValueError()
n_questions = args.n_sample
docqa.config.SPANS_PER_QUESTION = args.n_span_per_q
#n_questions = 1
if n_questions is not None:
test_questions.sort(key=lambda x: x.question_id)
np.random.RandomState(0).shuffle(test_questions)
test_questions = test_questions[:n_questions]
print("Building question/paragraph pairs...")
# Loads the relevant questions/documents, selects the right paragraphs, and runs the model's preprocessor
if per_document:
prep = ExtractMultiParagraphs(splitter,
para_filter,
model.preprocessor,
require_an_answer=False)
else:
prep = ExtractMultiParagraphsPerQuestion(splitter,
para_filter,
model.preprocessor,
require_an_answer=False)
prepped_data = preprocess_par(test_questions, corpus, prep,
args.n_processes, 1000)
data = []
for q in prepped_data.data:
for i, p in enumerate(q.paragraphs):
if q.answer_text is None:
ans = None
else:
ans = TokenSpans(q.answer_text, p.answer_spans)
data.append(
DocumentParagraphQuestion(q.question_id, p.doc_id,
(p.start, p.end), q.question, p.text,
ans, i))
# Reverse so our first batch will be the largest (so OOMs happen early)
questions = sorted(data,
key=lambda x: (x.n_context_words, len(x.question)),
reverse=True)
print("Done, starting eval")
if args.step is not None:
if args.step == "latest":
checkpoint = model_dir.get_latest_checkpoint()
else:
checkpoint = model_dir.get_checkpoint(int(args.step))
else:
checkpoint = model_dir.get_best_weights()
if checkpoint is not None:
print("Using best weights")
else:
print("Using latest checkpoint")
checkpoint = model_dir.get_latest_checkpoint()
test_questions = ParagraphAndQuestionDataset(
questions, FixedOrderBatcher(args.batch_size, True))
evaluation = trainer.test(
model, [RecordParagraphSpanPrediction(args.max_answer_len, True)],
{args.corpus: test_questions}, ResourceLoader(), checkpoint,
not args.no_ema, args. async)[args.corpus]
if not all(len(x) == len(data) for x in evaluation.per_sample.values()):
raise RuntimeError()
df = pd.DataFrame(evaluation.per_sample)
if args.official_output is not None:
print("Saving question result")
fns = {}
if per_document:
# I didn't store the unormalized filenames exactly, so unfortunately we have to reload
# the source data to get exact filename to output an official test script
print("Loading proper filenames")
if args.corpus == 'web-test':
source = join(TRIVIA_QA, "qa", "web-test-without-answers.json")
elif args.corpus == "web-dev":
source = join(TRIVIA_QA, "qa", "web-dev.json")
else:
raise AssertionError()
with open(join(source)) as f:
data = json.load(f)["Data"]
for point in data:
for doc in point["EntityPages"]:
filename = doc["Filename"]
fn = join("wikipedia", filename[:filename.rfind(".")])
fn = normalize_wiki_filename(fn)
fns[(point["QuestionId"], fn)] = filename
answers = {}
scores = {}
for q_id, doc_id, start, end, txt, score in df[[
"question_id", "doc_id", "para_start", "para_end",
"text_answer", "predicted_score"
]].itertuples(index=False):
filename = dataset.evidence.file_id_map[doc_id]
if per_document:
if filename.startswith("web"):
true_name = filename[4:] + ".txt"
else:
true_name = fns[(q_id, filename)]
# Alon Patch for triviaqa test results
true_name = true_name.replace('TriviaQA_Org/', '')
key = q_id + "--" + true_name
else:
key = q_id
prev_score = scores.get(key)
if prev_score is None or prev_score < score:
scores[key] = score
answers[key] = txt
with open(args.official_output, "w") as f:
json.dump(answers, f)
output_file = args.paragraph_output
if output_file is not None:
print("Saving paragraph result")
df.to_csv(output_file, index=False)
print("Computing scores")
if per_document:
group_by = ["question_id", "doc_id"]
else:
group_by = ["question_id"]
# Print a table of scores as more paragraphs are used
df.sort_values(group_by + ["rank"], inplace=True)
df_scores = df.copy(deep=True)
df_scores['predicted_score'] = df_scores['predicted_score'].apply(
lambda x: pd.Series(x).max())
em = compute_ranked_scores(df_scores, "predicted_score", "text_em",
group_by)
f1 = compute_ranked_scores(df_scores, "predicted_score", "text_f1",
group_by)
table = [["N Paragraphs", "EM", "F1"]]
table += list([str(i + 1), "%.4f" % e, "%.4f" % f]
for i, (e, f) in enumerate(zip(em, f1)))
table_df = pd.DataFrame(table[1:], columns=table[0]).drop(['N Paragraphs'],
axis=1)
ElasticLogger().write_log('INFO', 'Results', context_dict={'model': model_name, 'dataset': dataset_name, \
'max_EM':table_df.max().ix['EM'], \
'max_F1':table_df.max().ix['F1'], \
'result_table': str(table_df)})
df_flat = []
for id, question in df.iterrows():
for text_answer, predicted_span, predicted_score in zip(
question['text_answer'], question['predicted_span'],
question['predicted_score']):
new_question = dict(question.copy())
new_question.update({
'text_answer': text_answer,
'predicted_span': predicted_span,
'predicted_score': predicted_score
})
df_flat.append(new_question)
results_df = pd.DataFrame(df_flat)
#Alon: outputing the estimates for all the
#results_df = results_df.groupby(['question_id', 'text_answer']).apply(lambda df: df.ix[df['predicted_score'].argmax()]).reset_index(drop=True)
results_df.sort_values(by=['question_id', 'predicted_score'],
ascending=False).set_index([
'question_id', 'text_answer'
])[['question', 'predicted_score',
'text_em']].to_csv('results.csv')
print_table(table)
def convert_saved_graph(model_dir, output_dir):
print("Load model")
md = ModelDir(model_dir)
model = md.get_model()
# remove the lm models word embeddings - cpu model will use Char-CNN
model.lm_model.embed_weights_file = None
dim = model.embed_mapper.layers[1].n_units
print("Setting up cudnn version")
sess = tf.Session()
with sess.as_default():
model.set_input_spec(
ParagraphAndQuestionSpec(1, None, None, 14), {"the"},
ResourceLoader(lambda a, b: {"the": np.zeros(300, np.float32)}))
print("Buiding graph")
pred = model.get_prediction()
test_questions = get_test_questions()
print("Load vars:")
all_vars = tf.global_variables() + tf.get_collection(
tf.GraphKeys.SAVEABLE_OBJECTS)
lm_var_names = {x.name for x in all_vars if x.name.startswith("bilm")}
vars = [x for x in all_vars if x.name not in lm_var_names]
md.restore_checkpoint(sess, vars)
sess.run(
tf.variables_initializer(
[x for x in all_vars if x.name in lm_var_names]))
feed = model.encode([test_questions], False)
cuddn_out = sess.run([pred.start_logits, pred.end_logits], feed_dict=feed)
print("Done, copying files...")
if not exists(output_dir):
mkdir(output_dir)
for file in listdir(model_dir):
if isfile(file) and file != "model.npy":
copyfile(join(model_dir, file), join(output_dir, file))
print("Done, mapping tensors...")
to_save, to_init = [], []
for x in tf.trainable_variables():
if x.name.endswith("/gru_parameters:0"):
key = x.name[:-len("/gru_parameters:0")]
indim, outdim = get_dims(x, dim)
c = cudnn_rnn_ops.CudnnGRUSaveable(x, 1, outdim, indim, scope=key)
for spec in c.specs:
if spec.name.endswith("bias_cudnn 0") or \
spec.name.endswith("bias_cudnn 1"):
print('Unsupported spec: ' + spec.name)
continue
if 'forward' in spec.name:
new_name = spec.name.replace(
'forward/rnn/multi_rnn_cell/cell_0/',
'bidirectional_rnn/fw/')
else:
new_name = spec.name.replace(
'backward/rnn/multi_rnn_cell/cell_0/',
'bidirectional_rnn/bw/')
v = tf.Variable(sess.run(spec.tensor), name=new_name)
to_init.append(v)
to_save.append(v)
else:
to_save.append(x)
save_dir = join(output_dir, "save")
if not exists(save_dir):
mkdir(save_dir)
# save:
all_vars = tf.global_variables() + tf.get_collection(
tf.GraphKeys.SAVEABLE_OBJECTS)
vars_to_save = [x for x in all_vars if not x.name.startswith("bilm")]
sess.run(tf.initialize_variables(to_init))
saver = tf.train.Saver(vars_to_save)
saver.save(
sess,
join(save_dir, 'checkpoint'),
global_step=123456789,
write_meta_graph=False,
)
sess.close()
tf.reset_default_graph()
return cuddn_out
def predict():
json_data = {"success": False, "predictions": []}
print("Preprocessing...")
# Load the model
model_dir = ModelDir(
"/home/antriv/conversation_ai/Transfer_Learning/ALLENAI_DocumentQA/document-qa/pretrained_models/models/triviaqa-unfiltered-shared-norm"
)
model = model_dir.get_model()
if not isinstance(model, ParagraphQuestionModel):
raise ValueError(
"This script is built to work for ParagraphQuestionModel models only"
)
# Load the question
question = (flask.request.data).decode("utf-8")
# Read the documents
documents = []
doclist = ["/home/antriv/data/The-Future-Computed.txt"]
for doc in doclist:
if not isfile(doc):
raise ValueError(doc + " does not exist")
with open(doc, "r") as f:
documents.append(f.read())
print("Loaded %d documents" % len(documents))
# Split documents into lists of paragraphs
documents = [re.split("\s*\n\s*", doc) for doc in documents]
# Tokenize the input, the models expects data to be tokenized using `NltkAndPunctTokenizer`
# Note the model expects case-sensitive input
tokenizer = NltkAndPunctTokenizer()
question = tokenizer.tokenize_paragraph_flat(question) # List of words
# Now list of document->paragraph->sentence->word
documents = [[tokenizer.tokenize_paragraph(p) for p in doc]
for doc in documents]
# Now group the document into paragraphs, this returns `ExtractedParagraph` objects
# that additionally remember the start/end token of the paragraph within the source document
splitter = MergeParagraphs(400)
#splitter = PreserveParagraphs() # Uncomment to use the natural paragraph grouping
documents = [splitter.split(doc) for doc in documents]
# Now select the top paragraphs using a `ParagraphFilter`
if len(documents) == 1:
# Use TF-IDF to select top paragraphs from the document
selector = TopTfIdf(NltkPlusStopWords(True), n_to_select=1000)
context = selector.prune(question, documents[0])
else:
# Use a linear classifier to select top paragraphs among all the documents
selector = ShallowOpenWebRanker(n_to_select=1000)
context = selector.prune(question, flatten_iterable(documents))
print("Select %d paragraph" % len(context))
if model.preprocessor is not None:
# Models are allowed to define an additional pre-processing step
# This will turn the `ExtractedParagraph` objects back into simple lists of tokens
context = [
model.preprocessor.encode_text(question, x) for x in context
]
else:
# Otherwise just use flattened text
context = [flatten_iterable(x.text) for x in context]
print("Setting up model")
# Tell the model the batch size (can be None) and vocab to expect, This will load the
# needed word vectors and fix the batch size to use when building the graph / encoding the input
voc = set(question)
for txt in context:
voc.update(txt)
model.set_input_spec(ParagraphAndQuestionSpec(batch_size=len(context)),
voc)
# Now we build the actual tensorflow graph, `best_span` and `conf` are
# tensors holding the predicted span (inclusive) and confidence scores for each
# element in the input batch, confidence scores being the pre-softmax logit for the span
print("Build tf graph")
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
# We need to use sess.as_default when working with the cuNND stuff, since we need an active
# session to figure out the # of parameters needed for each layer. The cpu-compatible models don't need this.
with sess.as_default():
# 8 means to limit the span to size 8 or less
best_spans, conf = model.get_prediction().get_best_span(8)
# Loads the saved weights
model_dir.restore_checkpoint(sess)
# Now the model is ready to run
# The model takes input in the form of `ContextAndQuestion` objects, for example:
data = [
ParagraphAndQuestion(x, question, None, "user-question%d" % i)
for i, x in enumerate(context)
]
print("Starting run")
# The model is run in two steps, first it "encodes" a batch of paragraph/context pairs
# into numpy arrays, then we use `sess` to run the actual model get the predictions
encoded = model.encode(
data, is_train=False) # batch of `ContextAndQuestion` -> feed_dict
best_spans, conf = sess.run([best_spans, conf],
feed_dict=encoded) # feed_dict -> predictions
best_para = np.argmax(
conf
) # We get output for each paragraph, select the most-confident one to print
print("Best Paragraph: " + str(best_para))
print("Best span: " + str(best_spans[best_para]))
print("Answer text: " +
" ".join(context[best_para]
[best_spans[best_para][0]:best_spans[best_para][1] + 1]))
print("Confidence: " + str(conf[best_para]))
y_output = " ".join(
context[best_para][best_spans[best_para][0]:best_spans[best_para][1] +
1])
print(y_output)
json_data["predictions"].append(str(y_output))
#indicate that the request was a success
json_data["success"] = True
#return the data dictionary as a JSON response
return flask.jsonify(json_data)
def getAnswer(self):
#parser = argparse.ArgumentParser(description="Run an ELMo model on user input")
#parser.add_argument("model", help="Model directory")
#parser.add_argument("question", help="Question to answer")
#parser.add_argument("documents", help="List of text documents to answer the question with", nargs='+')
#args = parser.parse_args()
#print("Preprocessing...")
# Load the model
model_dir = ModelDir(MODEL_DIR)
model = model_dir.get_model()
if not isinstance(model, ParagraphQuestionModel):
raise ValueError(
"This script is built to work for ParagraphQuestionModel models only"
)
conn = pyodbc.connect(DB_CONN)
cursor = conn.cursor()
#(23211,28690,33214,25638,25837,26454,28693,26137,31428,32087)
query="select cast(filetext as varchar(max)) as filetext, name, type from dbo.UserworkspaceData where objectmasterid= "+\
str(self.ObjectMasterId)+\
" order by id asc"
#query="select cast(filetext as varchar(max)) as filetext from kpl_tmp"
documents = []
document = ""
name = ""
filetype = 0
for doc in cursor.execute(query):
document = document + doc[0]
name = doc[1]
filetype = doc[2]
#open("E:/kpl.txt","w+").write(document)
documents.append(document)
#documents.replace("\n\n","\n")
#r.sub("",documents)
#documents=" ".join(documents.split())
#open("E:\kpl_test.txt","w+").write(document)
#doc="D:\Document QnA\document-qa-master\Data\Drug_Delivery_Surveying_Global_Competitive_Landscape_BMI.txt"
# =============================================================================
# if not isfile(doc):
# raise ValueError(doc + " does not exist")
# with open(doc, "r") as f:
# documents.append(f.read())
# =============================================================================
#print("Loaded %d documents" % len(documents))
#temp=documents[0].split()
# Split documents into lists of paragraphs
#documents=[" ".join(temp[i:(i+400)]) for i in range(1,len(temp),400)]
documents = [re.split("\s*\n\s*", doc) for doc in documents]
# Tokenize the input, the models expects data to be tokenized using `NltkAndPunctTokenizer`
# Note the model expects case-sensitive input
tokenizer = NltkAndPunctTokenizer()
question = tokenizer.tokenize_paragraph_flat(
self.Question) # List of words
# Now list of document->paragraph->sentence->word
documents = [[tokenizer.tokenize_paragraph(p) for p in doc]
for doc in documents]
# Now group the document into paragraphs, this returns `ExtractedParagraph` objects
# that additionally remember the start/end token of the paragraph within the source document
splitter = MergeParagraphs(400)
#splitter = PreserveParagraphs() # Uncomment to use the natural paragraph grouping
documents = [splitter.split(doc) for doc in documents]
#print(str(len(documents))+" kpl") #kpl
# Now select the top paragraphs using a `ParagraphFilter`
if len(documents) == 1:
# Use TF-IDF to select top paragraphs from the document
selector = TopTfIdf(NltkPlusStopWords(True), n_to_select=5)
context = selector.prune(question, documents[0])
else:
# Use a linear classifier to select top paragraphs among all the documents
selector = ShallowOpenWebRanker(n_to_select=10)
context = selector.prune(question, flatten_iterable(documents))
#print("Select %d paragraph" % len(context))
if model.preprocessor is not None:
# Models are allowed to define an additional pre-processing step
# This will turn the `ExtractedParagraph` objects back into simple lists of tokens
context = [
model.preprocessor.encode_text(question, x) for x in context
]
else:
# Otherwise just use flattened text
context = [flatten_iterable(x.text) for x in context]
#x=open("E:\context.txt","a+")
#[x.write(" ".join(cont)) for cont in context]
#x.write("\n.......................................................\n")
#print("Setting up model")
# Tell the model the batch size (can be None) and vocab to expect, This will load the
# needed word vectors and fix the batch size to use when building the graph / encoding the input
voc = set(question)
for txt in context:
voc.update(txt)
model.set_input_spec(self.nlp,
ParagraphAndQuestionSpec(batch_size=len(context)),
voc)
# Now we build the actual tensorflow graph, `best_span` and `conf` are
# tensors holding the predicted span (inclusive) and confidence scores for each
# element in the input batch, confidence scores being the pre-softmax logit for the span
#print("Build tf graph") #kpl
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
# We need to use sess.as_default when working with the cuNND stuff, since we need an active
# session to figure out the # of parameters needed for each layer. The cpu-compatible models don't need this.
with sess.as_default():
# 8 means to limit the span to size 8 or less
best_spans, conf = model.get_prediction().get_best_span(8)
# Loads the saved weights
model_dir.restore_checkpoint(sess)
# Now the model is ready to run
# The model takes input in the form of `ContextAndQuestion` objects, for example:
data = [
ParagraphAndQuestion(x, question, None, "user-question%d" % i)
for i, x in enumerate(context)
]
#print("Starting run")
# The model is run in two steps, first it "encodes" a batch of paragraph/context pairs
# into numpy arrays, then we use `sess` to run the actual model get the predictions
encoded = model.encode(
data, is_train=True) # batch of `ContextAndQuestion` -> feed_dict
best_spans, conf = sess.run(
[best_spans, conf], feed_dict=encoded) # feed_dict -> predictions
best_para = np.argmax(
conf
) # We get output for each paragraph, select the most-confident one to print
#print("Best Paragraph: " + str(best_para))
#print("Best span: " + str(best_spans[best_para]))
#print("Answer text: " + " ".join(context[best_para][best_spans[best_para][0]:best_spans[best_para][1]+1]))
#print("Confidence: " + str(conf[best_para]))
Answer = " ".join(context[best_para]
[best_spans[best_para][0]:best_spans[best_para][1] +
1])
print("Confidence: " + str(conf[best_para]))
print("Best Paragraph: " + str(best_para))
print("Best span: " + str(best_spans[best_para]))
print("Answer text: " + Answer)
print(" ".join(context[best_para]))
context[best_para][best_spans[best_para][
0]] = r"<em>" + context[best_para][best_spans[best_para][0]]
context[best_para][best_spans[best_para][1]] = context[best_para][
best_spans[best_para][1]] + r"</em>"
start = 0
end = len(context[best_para])
positions = [
x for x, n in enumerate(context[best_para]
[0:best_spans[best_para][0]]) if n == "."
]
if len(positions) >= 2: start = positions[len(positions) - 2] + 1
positions = [
x
for x, n in enumerate(context[best_para][best_spans[best_para][1] +
1:]) if n == "."
]
if len(positions) > 1:
end = best_spans[best_para][1] + 1 + positions[1]
d = dict()
if conf[best_para] > 10:
d["answer"] = Answer
else:
d["answer"] = ""
d["name"] = name
d["filetype"] = filetype
d["paragraph"] = re.sub(r' (?=\W)', '',
" ".join(context[best_para][start:end]))
d["ObjectMasterId"] = self.ObjectMasterId
return d
#if __name__ == "__main__":
# main()
def main():
parser = argparse.ArgumentParser(
description='Train a model on document-level SQuAD')
parser.add_argument(
'mode',
choices=["paragraph", "confidence", "shared-norm", "merge", "sigmoid"])
parser.add_argument("name", help="Output directory")
args = parser.parse_args()
mode = args.mode
out = args.name + "-" + datetime.now().strftime("%m%d-%H%M%S")
corpus = SquadCorpus()
if mode == "merge":
# Adds paragraph start tokens, since we will be concatenating paragraphs together
pre = WithIndicators(True, para_tokens=False, doc_start_token=False)
else:
pre = None
# model = get_model(50, 100, args.mode, pre)
tmp = ModelDir("models/squad-shared-norm")
model = tmp.get_model()
if mode == "paragraph":
# Run in the "standard" known-paragraph setting
if model.preprocessor is not None:
raise NotImplementedError()
n_epochs = 26
train_batching = ClusteredBatcher(45, ContextLenBucketedKey(3), True,
False)
eval_batching = ClusteredBatcher(45, ContextLenKey(), False, False)
data = DocumentQaTrainingData(corpus, None, train_batching,
eval_batching)
eval = [LossEvaluator(), SpanEvaluator(bound=[17], text_eval="squad")]
else:
eval_set_mode = {
"confidence": "flatten",
"sigmoid": "flatten",
"shared-norm": "group",
"merge": "merge"
}[mode]
eval_dataset = RandomParagraphSetDatasetBuilder(
100, eval_set_mode, True, 0)
if mode == "confidence" or mode == "sigmoid":
if mode == "sigmoid":
# needs to be trained for a really long time for reasons unknown, even this might be too small
n_epochs = 100
else:
n_epochs = 50 # more epochs since we only "see" the label very other epoch-osh
train_batching = ClusteredBatcher(45, ContextLenBucketedKey(3),
True, False)
data = PreprocessedData(
SquadCorpus(),
SquadTfIdfRanker(NltkPlusStopWords(True), 4, True,
model.preprocessor),
StratifyParagraphsBuilder(train_batching, 1),
eval_dataset,
eval_on_verified=False,
)
else:
n_epochs = 26
data = PreprocessedData(
SquadCorpus(),
SquadTfIdfRanker(NltkPlusStopWords(True), 4, True,
model.preprocessor),
StratifyParagraphSetsBuilder(25, args.mode == "merge", True,
1),
eval_dataset,
eval_on_verified=False,
)
eval = [LossEvaluator(), MultiParagraphSpanEvaluator(17, "squad")]
data.preprocess(1)
with open(__file__, "r") as f:
notes = f.read()
notes = args.mode + "\n" + notes
params = train_params(n_epochs)
if mode == "paragraph":
params.best_weights = ("dev", "b17/text-f1")
trainer.start_training(data,
model,
params,
eval,
model_dir.ModelDir(out),
notes,
initialize_from=tmp.get_best_weights())