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spaCy
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initialize through nlp object and with train_corpus

This commit is contained in:
svlandeg 2020-09-21 23:09:22 +02:00
parent 447b3e5787
commit fa5c416db6
1 changed files with 20 additions and 8 deletions
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28
spacy/cli/debug_model.py
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@ -1,5 +1,9 @@
import warnings
from typing import Dict, Any, Optional, Iterable from typing import Dict, Any, Optional, Iterable
from pathlib import Path from pathlib import Path
from spacy.training import Example
from spacy.util import dot_to_object
from wasabi import msg from wasabi import msg
from thinc.api import require_gpu, fix_random_seed, set_dropout_rate, Adam from thinc.api import require_gpu, fix_random_seed, set_dropout_rate, Adam
from thinc.api import Model, data_validation, set_gpu_allocator from thinc.api import Model, data_validation, set_gpu_allocator
@ -71,12 +75,10 @@ def debug_model_cli(
exits=1, exits=1,
) )
model = pipe.model model = pipe.model
# call _link_components directly as we won't call nlp.begin_training debug_model(config, nlp, model, print_settings=print_settings)
nlp._link_components()
debug_model(nlp, model, print_settings=print_settings)
def debug_model(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.",
@ -93,10 +95,21 @@ def debug_model(nlp, model: Model, *, print_settings: Optional[Dict[str, Any]] =
# STEP 1: Initializing the model and printing again # STEP 1: Initializing the model and printing again
X = _get_docs() X = _get_docs()
_set_output_dim(nO=7, model=model)
# 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):
model.initialize(X=X) # msg.info(f"Could not initialize the model with dummy data - using the train_corpus.")
try:
train_corpus = dot_to_object(config, config["training"]["train_corpus"])
nlp.begin_training(lambda: train_corpus(nlp))
msg.info("Initialized the model with the training corpus.")
except ValueError:
try:
_set_output_dim(nO=7, model=model)
nlp.begin_training(lambda: [Example.from_dict(x, {}) for x in X])
msg.info("Initialized the model with dummy data.")
except:
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")
_print_model(model, print_settings) _print_model(model, print_settings)
@ -114,8 +127,7 @@ def debug_model(nlp, model: Model, *, print_settings: Optional[Dict[str, Any]] =
if tok2vec: if tok2vec:
tok2vec.predict(X) tok2vec.predict(X)
Y, get_dX = model.begin_update(X) Y, get_dX = model.begin_update(X)
# simulate a goldY value if goldY is None:
if not goldY:
goldY = _simulate_gold(Y) goldY = _simulate_gold(Y)
dY = get_gradient(goldY, Y, model.ops) dY = get_gradient(goldY, Y, model.ops)
get_dX(dY) get_dX(dY)