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spaCy
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cleanup

This commit is contained in:
svlandeg 2020-09-21 23:17:23 +02:00
parent fa5c416db6
commit 45b29c4a5b
1 changed files with 13 additions and 19 deletions
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28
spacy/cli/debug_model.py
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@ -78,7 +78,9 @@ def debug_model_cli(
debug_model(config, nlp, model, print_settings=print_settings) debug_model(config, nlp, model, print_settings=print_settings)
def debug_model(config, nlp, model: Model, *, print_settings: Optional[Dict[str, Any]] = None): def debug_model(
config, nlp, model: Model, *, print_settings: Optional[Dict[str, Any]] = None
):
if not isinstance(model, Model): if not isinstance(model, Model):
msg.fail( msg.fail(
f"Requires a Thinc Model to be analysed, but found {type(model)} instead.", f"Requires a Thinc Model to be analysed, but found {type(model)} instead.",
@ -97,7 +99,6 @@ def debug_model(config, nlp, model: Model, *, print_settings: Optional[Dict[str,
X = _get_docs() X = _get_docs()
# The output vector might differ from the official type of the output layer # The output vector might differ from the official type of the output layer
with data_validation(False): with data_validation(False):
# msg.info(f"Could not initialize the model with dummy data - using the train_corpus.")
try: try:
train_corpus = dot_to_object(config, config["training"]["train_corpus"]) train_corpus = dot_to_object(config, config["training"]["train_corpus"])
nlp.begin_training(lambda: train_corpus(nlp)) nlp.begin_training(lambda: train_corpus(nlp))
@ -108,7 +109,10 @@ def debug_model(config, nlp, model: Model, *, print_settings: Optional[Dict[str,
nlp.begin_training(lambda: [Example.from_dict(x, {}) for x in X]) nlp.begin_training(lambda: [Example.from_dict(x, {}) for x in X])
msg.info("Initialized the model with dummy data.") msg.info("Initialized the model with dummy data.")
except: except:
msg.fail("Could not initialize the model: you'll have to provide a valid train_corpus argument in the config file.", exits=1) msg.fail(
"Could not initialize the model: you'll have to provide a valid train_corpus argument in the config file.",
exits=1,
)
if print_settings.get("print_after_init"): if print_settings.get("print_after_init"):
msg.divider(f"STEP 1 - after initialization") msg.divider(f"STEP 1 - after initialization")
@ -121,7 +125,6 @@ def debug_model(config, nlp, model: Model, *, print_settings: Optional[Dict[str,
tok2vec = None tok2vec = None
if model.has_ref("tok2vec") and model.get_ref("tok2vec").name == "tok2vec-listener": if model.has_ref("tok2vec") and model.get_ref("tok2vec").name == "tok2vec-listener":
tok2vec = nlp.get_pipe("tok2vec") tok2vec = nlp.get_pipe("tok2vec")
tok2vec.model.initialize(X=X)
goldY = None goldY = None
for e in range(3): for e in range(3):
if tok2vec: if tok2vec:
@ -145,6 +148,10 @@ def debug_model(config, nlp, model: Model, *, print_settings: Optional[Dict[str,
msg.good(f"Succesfully ended analysis - model looks good.") msg.good(f"Succesfully ended analysis - model looks good.")
def get_gradient(goldY, Y, ops):
return ops.asarray(Y) - ops.asarray(goldY)
def _simulate_gold(element, counter=1): def _simulate_gold(element, counter=1):
if isinstance(element, Iterable): if isinstance(element, Iterable):
for i in range(len(element)): for i in range(len(element)):
@ -154,10 +161,6 @@ def _simulate_gold(element, counter=1):
return 1 / counter return 1 / counter
def get_gradient(goldY, Y, ops):
return ops.asarray(Y) - ops.asarray(goldY)
def _sentences(): def _sentences():
return [ return [
"Apple is looking at buying U.K. startup for $1 billion", "Apple is looking at buying U.K. startup for $1 billion",
@ -229,12 +232,3 @@ def _print_matrix(value):
sample_matrix = sample_matrix[0:5] sample_matrix = sample_matrix[0:5]
result = result + str(sample_matrix) result = result + str(sample_matrix)
return result return result
def _set_output_dim(model, nO):
# the dim inference doesn't always work 100%, we need this hack like we have it in pipe.pyx
if model.has_dim("nO") is None:
model.set_dim("nO", nO)
if model.has_ref("output_layer"):
if model.get_ref("output_layer").has_dim("nO") is None:
model.get_ref("output_layer").set_dim("nO", nO)