mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 05:10:06 +06:00
0835119bf3
* Add conversion script * Improve script * Fix bug * Add option to push to hub * Add support for classification models * Update model name * Upload feature extractor files first * Remove hash checking * Fix config * Add id2label * Add import * Fix id2label file name * Fix expected shape * Add model to README * Improve docs * Add integration test and fix CI * Fix code style * Add missing init * Add model to SPECIAL_MODULE_TO_TEST_MAP Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
62 lines
2.0 KiB
Python
62 lines
2.0 KiB
Python
# coding=utf-8
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# Copyright 2022 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 is_torch_available, is_vision_available
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from transformers.testing_utils import require_torch, require_vision, slow, torch_device
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if is_torch_available():
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import torch
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from transformers import AutoModelForImageClassification
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if is_vision_available():
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from transformers import AutoFeatureExtractor
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@require_torch
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@require_vision
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class DiTIntegrationTest(unittest.TestCase):
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@slow
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def test_for_image_classification(self):
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feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/dit-base-finetuned-rvlcdip")
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model = AutoModelForImageClassification.from_pretrained("microsoft/dit-base-finetuned-rvlcdip")
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model.to(torch_device)
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from datasets import load_dataset
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dataset = load_dataset("nielsr/rvlcdip-demo")
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image = dataset["train"][0]["image"].convert("RGB")
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inputs = feature_extractor(image, return_tensors="pt")
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# forward pass
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with torch.no_grad():
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outputs = model(**inputs)
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logits = outputs.logits
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expected_shape = torch.Size((1, 16))
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self.assertEqual(logits.shape, expected_shape)
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expected_slice = torch.tensor(
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[-0.4158, -0.4092, -0.4347],
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device=torch_device,
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dtype=torch.float,
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)
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self.assertTrue(torch.allclose(logits[0, :3], expected_slice, atol=1e-4))
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