mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 21:30:07 +06:00
3b44d2f042
* Update many similar visual pipelines * Add input tests * Add ImageToText as well * Add output tests * Add output tests * Add output tests * OutputElement -> Output * Correctly test elements * make fixup * fix typo in the task list * Fix VQA testing * Add copyright to image_classification.py * Revert changes to VQA pipeline because outputs have differences - will move to another PR * make fixup * Remove deprecation warnings
307 lines
11 KiB
Python
307 lines
11 KiB
Python
# Copyright 2021 The HuggingFace Team. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import unittest
|
|
|
|
from huggingface_hub import ImageClassificationOutputElement
|
|
|
|
from transformers import (
|
|
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
|
|
TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
|
|
PreTrainedTokenizerBase,
|
|
is_torch_available,
|
|
is_vision_available,
|
|
)
|
|
from transformers.pipelines import ImageClassificationPipeline, pipeline
|
|
from transformers.testing_utils import (
|
|
compare_pipeline_output_to_hub_spec,
|
|
is_pipeline_test,
|
|
nested_simplify,
|
|
require_tf,
|
|
require_torch,
|
|
require_torch_or_tf,
|
|
require_vision,
|
|
slow,
|
|
)
|
|
|
|
from .test_pipelines_common import ANY
|
|
|
|
|
|
if is_torch_available():
|
|
import torch
|
|
|
|
if is_vision_available():
|
|
from PIL import Image
|
|
else:
|
|
|
|
class Image:
|
|
@staticmethod
|
|
def open(*args, **kwargs):
|
|
pass
|
|
|
|
|
|
@is_pipeline_test
|
|
@require_torch_or_tf
|
|
@require_vision
|
|
class ImageClassificationPipelineTests(unittest.TestCase):
|
|
model_mapping = MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
|
|
tf_model_mapping = TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
|
|
|
|
def get_test_pipeline(self, model, tokenizer, processor, torch_dtype="float32"):
|
|
image_classifier = ImageClassificationPipeline(
|
|
model=model, image_processor=processor, top_k=2, torch_dtype=torch_dtype
|
|
)
|
|
examples = [
|
|
Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
|
|
"http://images.cocodataset.org/val2017/000000039769.jpg",
|
|
]
|
|
return image_classifier, examples
|
|
|
|
def run_pipeline_test(self, image_classifier, examples):
|
|
outputs = image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png")
|
|
|
|
self.assertEqual(
|
|
outputs,
|
|
[
|
|
{"score": ANY(float), "label": ANY(str)},
|
|
{"score": ANY(float), "label": ANY(str)},
|
|
],
|
|
)
|
|
|
|
import datasets
|
|
|
|
# we use revision="refs/pr/1" until the PR is merged
|
|
# https://hf.co/datasets/hf-internal-testing/fixtures_image_utils/discussions/1
|
|
dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", split="test", revision="refs/pr/1")
|
|
|
|
# Accepts URL + PIL.Image + lists
|
|
outputs = image_classifier(
|
|
[
|
|
Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
|
|
"http://images.cocodataset.org/val2017/000000039769.jpg",
|
|
# RGBA
|
|
dataset[0]["image"],
|
|
# LA
|
|
dataset[1]["image"],
|
|
# L
|
|
dataset[2]["image"],
|
|
]
|
|
)
|
|
self.assertEqual(
|
|
outputs,
|
|
[
|
|
[
|
|
{"score": ANY(float), "label": ANY(str)},
|
|
{"score": ANY(float), "label": ANY(str)},
|
|
],
|
|
[
|
|
{"score": ANY(float), "label": ANY(str)},
|
|
{"score": ANY(float), "label": ANY(str)},
|
|
],
|
|
[
|
|
{"score": ANY(float), "label": ANY(str)},
|
|
{"score": ANY(float), "label": ANY(str)},
|
|
],
|
|
[
|
|
{"score": ANY(float), "label": ANY(str)},
|
|
{"score": ANY(float), "label": ANY(str)},
|
|
],
|
|
[
|
|
{"score": ANY(float), "label": ANY(str)},
|
|
{"score": ANY(float), "label": ANY(str)},
|
|
],
|
|
],
|
|
)
|
|
|
|
for single_output in outputs:
|
|
for output_element in single_output:
|
|
compare_pipeline_output_to_hub_spec(output_element, ImageClassificationOutputElement)
|
|
|
|
@require_torch
|
|
def test_small_model_pt(self):
|
|
small_model = "hf-internal-testing/tiny-random-vit"
|
|
image_classifier = pipeline("image-classification", model=small_model)
|
|
|
|
outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
|
|
)
|
|
|
|
outputs = image_classifier(
|
|
[
|
|
"http://images.cocodataset.org/val2017/000000039769.jpg",
|
|
"http://images.cocodataset.org/val2017/000000039769.jpg",
|
|
],
|
|
top_k=2,
|
|
)
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[
|
|
[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
|
|
[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
|
|
],
|
|
)
|
|
|
|
@require_tf
|
|
def test_small_model_tf(self):
|
|
small_model = "hf-internal-testing/tiny-random-vit"
|
|
image_classifier = pipeline("image-classification", model=small_model, framework="tf")
|
|
|
|
outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
|
|
)
|
|
|
|
outputs = image_classifier(
|
|
[
|
|
"http://images.cocodataset.org/val2017/000000039769.jpg",
|
|
"http://images.cocodataset.org/val2017/000000039769.jpg",
|
|
],
|
|
top_k=2,
|
|
)
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[
|
|
[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
|
|
[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
|
|
],
|
|
)
|
|
|
|
def test_custom_tokenizer(self):
|
|
tokenizer = PreTrainedTokenizerBase()
|
|
|
|
# Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
|
|
image_classifier = pipeline(
|
|
"image-classification", model="hf-internal-testing/tiny-random-vit", tokenizer=tokenizer
|
|
)
|
|
|
|
self.assertIs(image_classifier.tokenizer, tokenizer)
|
|
|
|
@require_torch
|
|
def test_torch_float16_pipeline(self):
|
|
image_classifier = pipeline(
|
|
"image-classification", model="hf-internal-testing/tiny-random-vit", torch_dtype=torch.float16
|
|
)
|
|
outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
|
|
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=3),
|
|
[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
|
|
)
|
|
|
|
@require_torch
|
|
def test_torch_bfloat16_pipeline(self):
|
|
image_classifier = pipeline(
|
|
"image-classification", model="hf-internal-testing/tiny-random-vit", torch_dtype=torch.bfloat16
|
|
)
|
|
outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
|
|
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=3),
|
|
[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
|
|
)
|
|
|
|
@slow
|
|
@require_torch
|
|
def test_perceiver(self):
|
|
# Perceiver is not tested by `run_pipeline_test` properly.
|
|
# That is because the type of feature_extractor and model preprocessor need to be kept
|
|
# in sync, which is not the case in the current design
|
|
image_classifier = pipeline("image-classification", model="deepmind/vision-perceiver-conv")
|
|
outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[
|
|
{"score": 0.4385, "label": "tabby, tabby cat"},
|
|
{"score": 0.321, "label": "tiger cat"},
|
|
{"score": 0.0502, "label": "Egyptian cat"},
|
|
{"score": 0.0137, "label": "crib, cot"},
|
|
{"score": 0.007, "label": "radiator"},
|
|
],
|
|
)
|
|
|
|
image_classifier = pipeline("image-classification", model="deepmind/vision-perceiver-fourier")
|
|
outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[
|
|
{"score": 0.5658, "label": "tabby, tabby cat"},
|
|
{"score": 0.1309, "label": "tiger cat"},
|
|
{"score": 0.0722, "label": "Egyptian cat"},
|
|
{"score": 0.0707, "label": "remote control, remote"},
|
|
{"score": 0.0082, "label": "computer keyboard, keypad"},
|
|
],
|
|
)
|
|
|
|
image_classifier = pipeline("image-classification", model="deepmind/vision-perceiver-learned")
|
|
outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[
|
|
{"score": 0.3022, "label": "tabby, tabby cat"},
|
|
{"score": 0.2362, "label": "Egyptian cat"},
|
|
{"score": 0.1856, "label": "tiger cat"},
|
|
{"score": 0.0324, "label": "remote control, remote"},
|
|
{"score": 0.0096, "label": "quilt, comforter, comfort, puff"},
|
|
],
|
|
)
|
|
|
|
@slow
|
|
@require_torch
|
|
def test_multilabel_classification(self):
|
|
small_model = "hf-internal-testing/tiny-random-vit"
|
|
|
|
# Sigmoid is applied for multi-label classification
|
|
image_classifier = pipeline("image-classification", model=small_model)
|
|
image_classifier.model.config.problem_type = "multi_label_classification"
|
|
|
|
outputs = image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[{"label": "LABEL_1", "score": 0.5356}, {"label": "LABEL_0", "score": 0.4612}],
|
|
)
|
|
|
|
outputs = image_classifier(
|
|
[
|
|
"http://images.cocodataset.org/val2017/000000039769.jpg",
|
|
"http://images.cocodataset.org/val2017/000000039769.jpg",
|
|
]
|
|
)
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[
|
|
[{"label": "LABEL_1", "score": 0.5356}, {"label": "LABEL_0", "score": 0.4612}],
|
|
[{"label": "LABEL_1", "score": 0.5356}, {"label": "LABEL_0", "score": 0.4612}],
|
|
],
|
|
)
|
|
|
|
@slow
|
|
@require_torch
|
|
def test_function_to_apply(self):
|
|
small_model = "hf-internal-testing/tiny-random-vit"
|
|
|
|
# Sigmoid is applied for multi-label classification
|
|
image_classifier = pipeline("image-classification", model=small_model)
|
|
|
|
outputs = image_classifier(
|
|
"http://images.cocodataset.org/val2017/000000039769.jpg",
|
|
function_to_apply="sigmoid",
|
|
)
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[{"label": "LABEL_1", "score": 0.5356}, {"label": "LABEL_0", "score": 0.4612}],
|
|
)
|