210 lines
8.1 KiB
Python
210 lines
8.1 KiB
Python
# 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 numpy as np
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from transformers.testing_utils import TestCasePlus, require_torch, require_vision
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from transformers.utils import is_torch_available, is_vision_available
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if is_torch_available():
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import torch
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if is_vision_available():
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from PIL import Image
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from transformers import (
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AutoProcessor,
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IdeficsImageProcessor,
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IdeficsProcessor,
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LlamaTokenizerFast,
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PreTrainedTokenizerFast,
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)
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@require_torch
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@require_vision
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class IdeficsProcessorTest(TestCasePlus):
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def setUp(self):
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super().setUp()
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self.checkpoint_path = self.get_auto_remove_tmp_dir()
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image_processor = IdeficsImageProcessor()
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tokenizer = LlamaTokenizerFast.from_pretrained("HuggingFaceM4/tiny-random-idefics")
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processor = IdeficsProcessor(image_processor, tokenizer)
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processor.save_pretrained(self.checkpoint_path)
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self.input_keys = ["pixel_values", "input_ids", "attention_mask", "image_attention_mask"]
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def get_tokenizer(self, **kwargs):
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return AutoProcessor.from_pretrained(self.checkpoint_path, **kwargs).tokenizer
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def get_image_processor(self, **kwargs):
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return AutoProcessor.from_pretrained(self.checkpoint_path, **kwargs).image_processor
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def prepare_prompts(self):
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"""This function prepares a list of PIL images"""
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num_images = 2
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images = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8) for x in range(num_images)]
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images = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in images]
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# print([type(x) for x in images])
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# die
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prompts = [
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# text and 1 image
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[
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"User:",
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images[0],
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"Describe this image.\nAssistant:",
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],
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# text and images
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[
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"User:",
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images[0],
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"Describe this image.\nAssistant: An image of two dogs.\n",
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"User:",
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images[1],
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"Describe this image.\nAssistant:",
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],
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# only text
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[
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"User:",
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"Describe this image.\nAssistant: An image of two kittens.\n",
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"User:",
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"Describe this image.\nAssistant:",
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],
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# only images
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[
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images[0],
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images[1],
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],
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]
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return prompts
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def test_save_load_pretrained_additional_features(self):
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processor = IdeficsProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor())
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processor.save_pretrained(self.checkpoint_path)
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tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
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image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0)
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processor = IdeficsProcessor.from_pretrained(
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self.checkpoint_path, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
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)
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self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
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self.assertIsInstance(processor.tokenizer, PreTrainedTokenizerFast)
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self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
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self.assertIsInstance(processor.image_processor, IdeficsImageProcessor)
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def test_processor(self):
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image_processor = self.get_image_processor()
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tokenizer = self.get_tokenizer()
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processor = IdeficsProcessor(tokenizer=tokenizer, image_processor=image_processor)
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prompts = self.prepare_prompts()
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# test that all prompts succeeded
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input_processor = processor(prompts, return_tensors="pt", padding="longest")
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for key in self.input_keys:
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assert torch.is_tensor(input_processor[key])
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def test_tokenizer_decode(self):
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image_processor = self.get_image_processor()
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tokenizer = self.get_tokenizer()
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processor = IdeficsProcessor(tokenizer=tokenizer, image_processor=image_processor)
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predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
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decoded_processor = processor.batch_decode(predicted_ids)
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decoded_tok = tokenizer.batch_decode(predicted_ids)
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self.assertListEqual(decoded_tok, decoded_processor)
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def test_tokenizer_padding(self):
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image_processor = self.get_image_processor()
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tokenizer = self.get_tokenizer(padding_side="right")
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processor = IdeficsProcessor(tokenizer=tokenizer, image_processor=image_processor)
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predicted_tokens = [
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"<s> Describe this image.\nAssistant:<unk><unk><unk><unk><unk><unk><unk><unk><unk>",
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"<s> Describe this image.\nAssistant:<unk><unk><unk><unk><unk><unk><unk><unk><unk><unk>",
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]
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predicted_attention_masks = [
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([1] * 10) + ([0] * 9),
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([1] * 10) + ([0] * 10),
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]
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prompts = [[prompt] for prompt in self.prepare_prompts()[2]]
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max_length = processor(prompts, padding="max_length", truncation=True, max_length=20)
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longest = processor(prompts, padding="longest", truncation=True, max_length=30)
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decoded_max_length = processor.tokenizer.decode(max_length["input_ids"][-1])
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decoded_longest = processor.tokenizer.decode(longest["input_ids"][-1])
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self.assertEqual(decoded_max_length, predicted_tokens[1])
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self.assertEqual(decoded_longest, predicted_tokens[0])
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self.assertListEqual(max_length["attention_mask"][-1].tolist(), predicted_attention_masks[1])
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self.assertListEqual(longest["attention_mask"][-1].tolist(), predicted_attention_masks[0])
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def test_tokenizer_left_padding(self):
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"""Identical to test_tokenizer_padding, but with padding_side not explicitly set."""
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image_processor = self.get_image_processor()
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tokenizer = self.get_tokenizer()
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processor = IdeficsProcessor(tokenizer=tokenizer, image_processor=image_processor)
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predicted_tokens = [
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"<unk><unk><unk><unk><unk><unk><unk><unk><unk><s> Describe this image.\nAssistant:",
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"<unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><s> Describe this image.\nAssistant:",
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]
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predicted_attention_masks = [
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([0] * 9) + ([1] * 10),
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([0] * 10) + ([1] * 10),
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]
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prompts = [[prompt] for prompt in self.prepare_prompts()[2]]
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max_length = processor(prompts, padding="max_length", truncation=True, max_length=20)
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longest = processor(prompts, padding="longest", truncation=True, max_length=30)
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decoded_max_length = processor.tokenizer.decode(max_length["input_ids"][-1])
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decoded_longest = processor.tokenizer.decode(longest["input_ids"][-1])
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self.assertEqual(decoded_max_length, predicted_tokens[1])
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self.assertEqual(decoded_longest, predicted_tokens[0])
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self.assertListEqual(max_length["attention_mask"][-1].tolist(), predicted_attention_masks[1])
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self.assertListEqual(longest["attention_mask"][-1].tolist(), predicted_attention_masks[0])
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def test_model_input_names(self):
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image_processor = self.get_image_processor()
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tokenizer = self.get_tokenizer()
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processor = IdeficsProcessor(tokenizer=tokenizer, image_processor=image_processor)
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prompts = self.prepare_prompts()
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inputs = processor(prompts, padding="longest")
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# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
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self.assertSetEqual(set(inputs.keys()), set(self.input_keys))
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