# 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))