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c63fcabfe9
* Enabling dataset iteration on pipelines. Enabling dataset iteration on pipelines. Unifying parameters under `set_parameters` function. Small fix. Last fixes after rebase Remove print. Fixing text2text `generate_kwargs` No more `self.max_length`. Fixing tf only conversational. Consistency in start/stop index over TF/PT. Speeding up drastically on TF (nasty bug where max_length would increase a ton.) Adding test for support for non fast tokenizers. Fixign GPU usage on zero-shot. Fix working on Tf. Update src/transformers/pipelines/base.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Update src/transformers/pipelines/base.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Small cleanup. Remove all asserts + simple format. * Fixing audio-classification for large PR. * Overly explicity null checking. * Encapsulating GPU/CPU pytorch manipulation directly within `base.py`. * Removed internal state for parameters of the pipeline. Instead of overriding implicitly internal state, we moved to real named arguments on every `preprocess`, `_forward`, `postprocess` function. Instead `_sanitize_parameters` will be used to split all kwargs of both __init__ and __call__ into the 3 kinds of named parameters. * Move import warnings. * Small fixes. * Quality. * Another small fix, using the CI to debug faster. * Last fixes. * Last fix. * Small cleanup of tensor moving. * is not None. * Adding a bunch of docs + a iteration test. * Fixing doc style. * KeyDataset = None guard. * RRemoving the Cuda test for pipelines (was testing). * Even more simple iteration test. * Correct import . * Long day. * Fixes in docs. * [WIP] migrating object detection. * Fixed the target_size bug. * Fixup. * Bad variable name. * Fixing `ensure_on_device` respects original ModelOutput.
254 lines
11 KiB
Python
254 lines
11 KiB
Python
# Copyright 2021 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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from transformers import (
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MODEL_FOR_OBJECT_DETECTION_MAPPING,
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AutoFeatureExtractor,
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AutoModelForObjectDetection,
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ObjectDetectionPipeline,
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is_vision_available,
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pipeline,
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)
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from transformers.testing_utils import (
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is_pipeline_test,
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nested_simplify,
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require_datasets,
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require_tf,
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require_timm,
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require_torch,
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require_vision,
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slow,
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)
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from .test_pipelines_common import ANY, PipelineTestCaseMeta
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if is_vision_available():
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from PIL import Image
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else:
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class Image:
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@staticmethod
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def open(*args, **kwargs):
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pass
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@require_vision
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@require_timm
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@require_torch
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@is_pipeline_test
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class ObjectDetectionPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
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model_mapping = MODEL_FOR_OBJECT_DETECTION_MAPPING
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@require_datasets
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def run_pipeline_test(self, model, tokenizer, feature_extractor):
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object_detector = ObjectDetectionPipeline(model=model, feature_extractor=feature_extractor)
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outputs = object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png", threshold=0.0)
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self.assertGreater(len(outputs), 0)
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for detected_object in outputs:
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self.assertEqual(
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detected_object,
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{
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"score": ANY(float),
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"label": ANY(str),
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"box": {"xmin": ANY(int), "ymin": ANY(int), "xmax": ANY(int), "ymax": ANY(int)},
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},
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)
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import datasets
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dataset = datasets.load_dataset("Narsil/image_dummy", "image", split="test")
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batch = [
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Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
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"http://images.cocodataset.org/val2017/000000039769.jpg",
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# RGBA
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dataset[0]["file"],
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# LA
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dataset[1]["file"],
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# L
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dataset[2]["file"],
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]
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batch_outputs = object_detector(batch, threshold=0.0)
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self.assertEqual(len(batch), len(batch_outputs))
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for outputs in batch_outputs:
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self.assertGreater(len(outputs), 0)
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for detected_object in outputs:
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self.assertEqual(
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detected_object,
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{
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"score": ANY(float),
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"label": ANY(str),
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"box": {"xmin": ANY(int), "ymin": ANY(int), "xmax": ANY(int), "ymax": ANY(int)},
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},
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)
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@require_tf
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@unittest.skip("Object detection not implemented in TF")
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def test_small_model_tf(self):
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pass
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@require_torch
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def test_small_model_pt(self):
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model_id = "mishig/tiny-detr-mobilenetsv3"
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model = AutoModelForObjectDetection.from_pretrained(model_id)
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feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
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object_detector = ObjectDetectionPipeline(model=model, feature_extractor=feature_extractor)
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outputs = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg", threshold=0.0)
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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{"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 160, "ymin": 120, "xmax": 480, "ymax": 359}},
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{"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 160, "ymin": 120, "xmax": 480, "ymax": 359}},
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],
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)
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outputs = object_detector(
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[
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"http://images.cocodataset.org/val2017/000000039769.jpg",
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"http://images.cocodataset.org/val2017/000000039769.jpg",
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],
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threshold=0.0,
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)
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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[
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{"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 160, "ymin": 120, "xmax": 480, "ymax": 359}},
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{"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 160, "ymin": 120, "xmax": 480, "ymax": 359}},
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],
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[
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{"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 160, "ymin": 120, "xmax": 480, "ymax": 359}},
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{"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 160, "ymin": 120, "xmax": 480, "ymax": 359}},
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],
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],
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)
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@require_torch
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@slow
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def test_large_model_pt(self):
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model_id = "facebook/detr-resnet-50"
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model = AutoModelForObjectDetection.from_pretrained(model_id)
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feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
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object_detector = ObjectDetectionPipeline(model=model, feature_extractor=feature_extractor)
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outputs = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg")
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
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{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
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{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
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{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
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{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
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],
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)
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outputs = object_detector(
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[
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"http://images.cocodataset.org/val2017/000000039769.jpg",
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"http://images.cocodataset.org/val2017/000000039769.jpg",
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]
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)
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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[
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{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
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{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
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{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
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{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
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{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
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],
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[
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{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
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{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
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{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
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{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
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{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
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],
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],
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)
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@require_torch
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@slow
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def test_integration_torch_object_detection(self):
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model_id = "facebook/detr-resnet-50"
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object_detector = pipeline("object-detection", model=model_id)
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outputs = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg")
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
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{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
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{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
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{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
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{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
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],
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)
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outputs = object_detector(
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[
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"http://images.cocodataset.org/val2017/000000039769.jpg",
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"http://images.cocodataset.org/val2017/000000039769.jpg",
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]
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)
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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[
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{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
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{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
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{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
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{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
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{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
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],
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[
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{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
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{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
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{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
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{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
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{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
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],
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],
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)
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@require_torch
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@slow
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def test_threshold(self):
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threshold = 0.9985
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model_id = "facebook/detr-resnet-50"
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object_detector = pipeline("object-detection", model=model_id)
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outputs = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg", threshold=threshold)
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
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{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
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],
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)
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