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[Large PR] Entire rework of pipelines. (#13308)

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* 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.
This commit is contained in:
Nicolas Patry
2021-09-10 14:47:48 +02:00
committed by GitHub
parent 09549aa18c
commit c63fcabfe9
28 changed files with 1559 additions and 1290 deletions
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76
tests/test_pipelines_object_detection.py
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@@ -116,8 +116,8 @@ class ObjectDetectionPipelineTests(unittest.TestCase, metaclass=PipelineTestCase
self.assertEqual(
nested_simplify(outputs, decimals=4),
[
{"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 266, "ymin": 200, "xmax": 799, "ymax": 599}},
{"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 266, "ymin": 200, "xmax": 799, "ymax": 599}},
{"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 160, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 160, "ymin": 120, "xmax": 480, "ymax": 359}},
],
)
@@ -133,12 +133,12 @@ class ObjectDetectionPipelineTests(unittest.TestCase, metaclass=PipelineTestCase
nested_simplify(outputs, decimals=4),
[
[
{"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 266, "ymin": 200, "xmax": 799, "ymax": 599}},
{"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 266, "ymin": 200, "xmax": 799, "ymax": 599}},
{"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 160, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 160, "ymin": 120, "xmax": 480, "ymax": 359}},
],
[
{"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 266, "ymin": 200, "xmax": 799, "ymax": 599}},
{"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 266, "ymin": 200, "xmax": 799, "ymax": 599}},
{"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 160, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 160, "ymin": 120, "xmax": 480, "ymax": 359}},
],
],
)
@@ -156,11 +156,11 @@ class ObjectDetectionPipelineTests(unittest.TestCase, metaclass=PipelineTestCase
self.assertEqual(
nested_simplify(outputs, decimals=4),
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 66, "ymin": 118, "xmax": 292, "ymax": 196}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 555, "ymin": 120, "xmax": 613, "ymax": 312}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 1065, "ymax": 789}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 22, "ymin": 86, "xmax": 523, "ymax": 784}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 575, "ymin": 39, "xmax": 1066, "ymax": 614}},
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
)
@@ -174,18 +174,18 @@ class ObjectDetectionPipelineTests(unittest.TestCase, metaclass=PipelineTestCase
nested_simplify(outputs, decimals=4),
[
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 66, "ymin": 118, "xmax": 292, "ymax": 196}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 555, "ymin": 120, "xmax": 613, "ymax": 312}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 1065, "ymax": 789}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 22, "ymin": 86, "xmax": 523, "ymax": 784}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 575, "ymin": 39, "xmax": 1066, "ymax": 614}},
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 66, "ymin": 118, "xmax": 292, "ymax": 196}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 555, "ymin": 120, "xmax": 613, "ymax": 312}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 1065, "ymax": 789}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 22, "ymin": 86, "xmax": 523, "ymax": 784}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 575, "ymin": 39, "xmax": 1066, "ymax": 614}},
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
],
)
@@ -201,11 +201,11 @@ class ObjectDetectionPipelineTests(unittest.TestCase, metaclass=PipelineTestCase
self.assertEqual(
nested_simplify(outputs, decimals=4),
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 66, "ymin": 118, "xmax": 292, "ymax": 196}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 555, "ymin": 120, "xmax": 613, "ymax": 312}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 1065, "ymax": 789}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 22, "ymin": 86, "xmax": 523, "ymax": 784}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 575, "ymin": 39, "xmax": 1066, "ymax": 614}},
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
)
@@ -219,18 +219,18 @@ class ObjectDetectionPipelineTests(unittest.TestCase, metaclass=PipelineTestCase
nested_simplify(outputs, decimals=4),
[
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 66, "ymin": 118, "xmax": 292, "ymax": 196}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 555, "ymin": 120, "xmax": 613, "ymax": 312}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 1065, "ymax": 789}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 22, "ymin": 86, "xmax": 523, "ymax": 784}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 575, "ymin": 39, "xmax": 1066, "ymax": 614}},
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 66, "ymin": 118, "xmax": 292, "ymax": 196}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 555, "ymin": 120, "xmax": 613, "ymax": 312}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 1065, "ymax": 789}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 22, "ymin": 86, "xmax": 523, "ymax": 784}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 575, "ymin": 39, "xmax": 1066, "ymax": 614}},
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
],
)
@@ -247,7 +247,7 @@ class ObjectDetectionPipelineTests(unittest.TestCase, metaclass=PipelineTestCase
self.assertEqual(
nested_simplify(outputs, decimals=4),
[
{"score": 0.9988, "label": "cat", "box": {"xmin": 22, "ymin": 86, "xmax": 523, "ymax": 784}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 575, "ymin": 39, "xmax": 1066, "ymax": 614}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
)