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Move image-classification pipeline to new testing (#13272)

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- Enforce `test_small_models_{tf,pt}` methods to exist (enforce checking
actual values in small tests)
- Add support for non RGB image for the pipeline.
This commit is contained in:
Nicolas Patry
2021-08-26 11:52:49 +02:00
committed by GitHub
parent 401377e679
commit 6b586ed18c
6 changed files with 199 additions and 157 deletions
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51
tests/test_pipelines_common.py
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@@ -15,6 +15,7 @@
import importlib
import logging
import string
from abc import abstractmethod
from functools import lru_cache
from typing import List, Optional
from unittest import mock, skipIf
@@ -123,15 +124,18 @@ class PipelineTestCaseMeta(type):
model = ModelClass(tiny_config)
if hasattr(model, "eval"):
model = model.eval()
try:
tokenizer = get_tiny_tokenizer_from_checkpoint(checkpoint)
if hasattr(model.config, "max_position_embeddings"):
tokenizer.model_max_length = model.config.max_position_embeddings
# Rust Panic exception are NOT Exception subclass
# Some test tokenizer contain broken vocabs or custom PreTokenizer, so we
# provide some default tokenizer and hope for the best.
except: # noqa: E722
self.skipTest(f"Ignoring {ModelClass}, cannot create a simple tokenizer")
if tokenizer_class is not None:
try:
tokenizer = get_tiny_tokenizer_from_checkpoint(checkpoint)
if hasattr(model.config, "max_position_embeddings"):
tokenizer.model_max_length = model.config.max_position_embeddings
# Rust Panic exception are NOT Exception subclass
# Some test tokenizer contain broken vocabs or custom PreTokenizer, so we
# provide some default tokenizer and hope for the best.
except: # noqa: E722
self.skipTest(f"Ignoring {ModelClass}, cannot create a simple tokenizer")
else:
tokenizer = None
feature_extractor = get_tiny_feature_extractor_from_checkpoint(checkpoint, tiny_config)
self.run_pipeline_test(model, tokenizer, feature_extractor)
@@ -149,16 +153,21 @@ class PipelineTestCaseMeta(type):
tiny_config = get_tiny_config_from_class(configuration)
tokenizer_classes = TOKENIZER_MAPPING.get(configuration, [])
feature_extractor_class = FEATURE_EXTRACTOR_MAPPING.get(configuration, None)
feature_extractor_name = (
feature_extractor_class.__name__ if feature_extractor_class else "nofeature_extractor"
)
if not tokenizer_classes:
# We need to test even if there are no tokenizers.
tokenizer_classes = [None]
for tokenizer_class in tokenizer_classes:
if tokenizer_class is not None and tokenizer_class.__name__.endswith("Fast"):
if tokenizer_class is not None:
tokenizer_name = tokenizer_class.__name__
else:
tokenizer_name = "notokenizer"
tokenizer_name = tokenizer_class.__name__ if tokenizer_class else "notokenizer"
feature_extractor_name = (
feature_extractor_class.__name__
if feature_extractor_class
else "nofeature_extractor"
)
test_name = f"test_{prefix}_{configuration.__name__}_{model_architecture.__name__}_{tokenizer_name}_{feature_extractor_name}"
test_name = f"test_{prefix}_{configuration.__name__}_{model_architecture.__name__}_{tokenizer_name}_{feature_extractor_name}"
if tokenizer_class is not None or feature_extractor_class is not None:
dct[test_name] = gen_test(
model_architecture,
checkpoint,
@@ -167,6 +176,14 @@ class PipelineTestCaseMeta(type):
feature_extractor_class,
)
@abstractmethod
def inner(self):
raise NotImplementedError("Not implemented test")
# Force these 2 methods to exist
dct["test_small_model_pt"] = dct.get("test_small_model_pt", inner)
dct["test_small_model_tf"] = dct.get("test_small_model_tf", inner)
return type.__new__(mcs, name, bases, dct)