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# coding=utf-8
# Copyright 2022 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 transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transformers import AutoFeatureExtractor
@require_torch
@require_vision
class DiTIntegrationTest(unittest.TestCase):
@slow
def test_for_image_classification(self):
feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/dit-base-finetuned-rvlcdip")
model = AutoModelForImageClassification.from_pretrained("microsoft/dit-base-finetuned-rvlcdip")
model.to(torch_device)
from datasets import load_dataset
dataset = load_dataset("nielsr/rvlcdip-demo")
image = dataset["train"][0]["image"].convert("RGB")
inputs = feature_extractor(image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
expected_shape = torch.Size((1, 16))
self.assertEqual(logits.shape, expected_shape)
expected_slice = torch.tensor(
[-0.4158, -0.4092, -0.4347],
device=torch_device,
dtype=torch.float,
)
self.assertTrue(torch.allclose(logits[0, :3], expected_slice, atol=1e-4))
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