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Revert "add return_tensor parameter for feature extraction (#19257)" (#19680)

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This reverts commit 35bd089a24.
This commit is contained in:
Sylvain Gugger 2022-10-17 11:56:29 -04:00 committed by GitHub
parent bf0addc56e
commit f2ecb9eec4
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2 changed files with 3 additions and 29 deletions
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14
src/transformers/pipelines/feature_extraction.py
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@ -31,8 +31,6 @@ class FeatureExtractionPipeline(Pipeline):
If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the `model`, or to PyTorch if no model is
provided.
return_tensor (`bool`, *optional*):
If `True`, returns a tensor according to the specified framework, otherwise returns a list.
task (`str`, defaults to `""`):
A task-identifier for the pipeline.
args_parser ([`~pipelines.ArgumentHandler`], *optional*):
@ -42,7 +40,7 @@ class FeatureExtractionPipeline(Pipeline):
the associated CUDA device id.
"""
def _sanitize_parameters(self, truncation=None, tokenize_kwargs=None, return_tensors=None, **kwargs):
def _sanitize_parameters(self, truncation=None, tokenize_kwargs=None, **kwargs):
if tokenize_kwargs is None:
tokenize_kwargs = {}
@ -55,11 +53,7 @@ class FeatureExtractionPipeline(Pipeline):
preprocess_params = tokenize_kwargs
postprocess_params = {}
if return_tensors is not None:
postprocess_params["return_tensors"] = return_tensors
return preprocess_params, {}, postprocess_params
return preprocess_params, {}, {}
def preprocess(self, inputs, **tokenize_kwargs) -> Dict[str, GenericTensor]:
return_tensors = self.framework
@ -70,10 +64,8 @@ class FeatureExtractionPipeline(Pipeline):
model_outputs = self.model(**model_inputs)
return model_outputs
def postprocess(self, model_outputs, return_tensors=False):
def postprocess(self, model_outputs):
# [0] is the first available tensor, logits or last_hidden_state.
if return_tensors:
return model_outputs[0]
if self.framework == "pt":
return model_outputs[0].tolist()
elif self.framework == "tf":

18
tests/pipelines/test_pipelines_feature_extraction.py
View File

@ -15,8 +15,6 @@
import unittest
import numpy as np
import tensorflow as tf
import torch
from transformers import (
FEATURE_EXTRACTOR_MAPPING,
@ -135,22 +133,6 @@ class FeatureExtractionPipelineTests(unittest.TestCase, metaclass=PipelineTestCa
tokenize_kwargs=tokenize_kwargs,
)
@require_torch
def test_return_tensors_pt(self):
feature_extractor = pipeline(
task="feature-extraction", model="hf-internal-testing/tiny-random-distilbert", framework="pt"
)
outputs = feature_extractor("This is a test" * 100, return_tensors=True)
self.assertTrue(torch.is_tensor(outputs))
@require_tf
def test_return_tensors_tf(self):
feature_extractor = pipeline(
task="feature-extraction", model="hf-internal-testing/tiny-random-distilbert", framework="tf"
)
outputs = feature_extractor("This is a test" * 100, return_tensors=True)
self.assertTrue(tf.is_tensor(outputs))
def get_shape(self, input_, shape=None):
if shape is None:
shape = []