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93 lines
4.4 KiB
Python
93 lines
4.4 KiB
Python
# Copyright 2024 The HuggingFace Inc. 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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"""Testing suite for the PyTorch emu3 model."""
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import tempfile
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import unittest
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import numpy as np
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from transformers import Emu3Processor, GPT2TokenizerFast
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from transformers.utils import is_vision_available
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from ...test_processing_common import ProcessorTesterMixin
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if is_vision_available():
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from transformers import Emu3ImageProcessor
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class Emu3ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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processor_class = Emu3Processor
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@classmethod
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def setUpClass(cls):
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cls.tmpdirname = tempfile.mkdtemp()
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image_processor = Emu3ImageProcessor(min_pixels=28 * 28, max_pixels=56 * 56)
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extra_special_tokens = extra_special_tokens = {
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"image_token": "<image>",
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"boi_token": "<|image start|>",
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"eoi_token": "<|image end|>",
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"image_wrapper_token": "<|image token|>",
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"eof_token": "<|extra_201|>",
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}
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tokenizer = GPT2TokenizerFast.from_pretrained(
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"openai-community/gpt2", extra_special_tokens=extra_special_tokens
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)
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tokenizer.pad_token_id = 0
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tokenizer.sep_token_id = 1
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processor = cls.processor_class(
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image_processor=image_processor, tokenizer=tokenizer, chat_template="dummy_template"
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)
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processor.save_pretrained(cls.tmpdirname)
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cls.image_token = processor.image_token
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@staticmethod
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def prepare_processor_dict():
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return {
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"chat_template": "{% for message in messages %}{% if message['role'] != 'system' %}{{ message['role'].upper() + ': '}}{% endif %}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}{{ '<image>' }}{% endfor %}{# Render all text next #}{% if message['role'] != 'assistant' %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{{ content['text'] + ' '}}{% endfor %}{% else %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ content['text'] + ' '}}{% endgeneration %}{% endfor %}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'ASSISTANT:' }}{% endif %}",
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} # fmt: skip
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def test_processor_for_generation(self):
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processor_components = self.prepare_components()
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processor = self.processor_class(**processor_components)
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# we don't need an image as input because the model will generate one
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input_str = "lower newer"
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image_input = self.prepare_image_inputs()
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inputs = processor(text=input_str, return_for_image_generation=True, return_tensors="pt")
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self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask", "image_sizes"])
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self.assertEqual(inputs[self.text_input_name].shape[-1], 8)
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# when `return_for_image_generation` is set, we raise an error that image should not be provided
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with self.assertRaises(ValueError):
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inputs = processor(
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text=input_str, images=image_input, return_for_image_generation=True, return_tensors="pt"
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)
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def test_processor_postprocess(self):
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processor_components = self.prepare_components()
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processor = self.processor_class(**processor_components)
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input_str = "lower newer"
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orig_image_input = self.prepare_image_inputs()
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orig_image = np.array(orig_image_input).transpose(2, 0, 1)
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inputs = processor(text=input_str, images=orig_image, do_resize=False, return_tensors="np")
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normalized_image_input = inputs.pixel_values
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unnormalized_images = processor.postprocess(normalized_image_input, return_tensors="np")["pixel_values"]
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# For an image where pixels go from 0 to 255 the diff can be 1 due to some numerical precision errors when scaling and unscaling
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self.assertTrue(np.abs(orig_image - unnormalized_images).max() >= 1)
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