ydshieh
commited on
Commit
•
a99072f
1
Parent(s):
66d7526
update model.py and add coco files
Browse files- coco_dataset_script.py +142 -0
- model.py +41 -9
- run_summarization_coco.py +826 -0
- test_coco_dataset_script.py +27 -0
coco_dataset_script.py
ADDED
@@ -0,0 +1,142 @@
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1 |
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import csv
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2 |
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import json
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3 |
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import os
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4 |
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import datasets
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6 |
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import pandas as pd
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import numpy as np
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8 |
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# TODO: Add BibTeX citation
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# Find for instance the citation on arxiv or on the dataset repo/website
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+
_CITATION = """\
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@InProceedings{huggingface:dataset,
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title = {A great new dataset},
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author={huggingface, Inc.
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},
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year={2020}
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}
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"""
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# TODO: Add description of the dataset here
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# You can copy an official description
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_DESCRIPTION = """\
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This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
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"""
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# TODO: Add a link to an official homepage for the dataset here
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_HOMEPAGE = ""
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# TODO: Add the licence for the dataset here if you can find it
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_LICENSE = ""
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# TODO: Add link to the official dataset URLs here
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# The HuggingFace dataset library don't host the datasets but only point to the original files
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# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
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_URLs = {
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}
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# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
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class COCODataset(datasets.GeneratorBasedBuilder):
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"""TODO: Short description of my dataset."""
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VERSION = datasets.Version("1.1.0")
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DEFAULT_CONFIG_NAME = "en"
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def _info(self):
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# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
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features = datasets.Features(
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{
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"id": datasets.Value("int64"),
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"en": datasets.Value("string"),
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"fr": datasets.Value("string"),
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"image_id": datasets.Value("int64"),
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"image_file": datasets.Value("string")
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# These are the features of your dataset like images, labels ...
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}
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)
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return datasets.DatasetInfo(
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# This is the description that will appear on the datasets page.
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description=_DESCRIPTION,
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# This defines the different columns of the dataset and their types
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features=features, # Here we define them above because they are different between the two configurations
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# If there's a common (input, target) tuple from the features,
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# specify them here. They'll be used if as_supervised=True in
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# builder.as_dataset.
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supervised_keys=None,
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# Homepage of the dataset for documentation
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homepage=_HOMEPAGE,
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# License for the dataset if available
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license=_LICENSE,
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# Citation for the dataset
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
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# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
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data_dir = self.config.data_dir
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"data_dir": data_dir,
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"split": "train",
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"data_dir": data_dir,
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"split": "test"
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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# These kwargs will be passed to _generate_examples
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106 |
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gen_kwargs={
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"data_dir": data_dir,
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"split": "val",
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},
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),
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]
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def _generate_examples(
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self, data_dir, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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):
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""" Yields examples as (key, example) tuples. """
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# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
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# The `key` is here for legacy reason (tfds) and is not important in itself.
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# /home/33611/caption/
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# train2014
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if split == 'dev':
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split == 'val'
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with open(os.path.join(data_dir, f'{split}.json')) as fp:
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examples = json.load(fp)
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for id_, ex in enumerate(examples):
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image_id = ex["image_id"]
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fn = f'COCO_{split}2014_{str(image_id).zfill(12)}.jpg'
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image_file = os.path.join(data_dir, f'{split}2014', fn)
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yield id_, {
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"id": ex["id"],
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"en": ex["caption"],
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"fr": ex["fr"],
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"image_id": ex["image_id"],
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"image_file": image_file
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}
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model.py
CHANGED
@@ -3,6 +3,9 @@ import sys, os
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3 |
current_path = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(current_path)
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# Main model - ViTGPT2LM
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from vit_gpt2.modeling_flax_vit_gpt2_lm import FlaxViTGPT2LMForConditionalGeneration
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@@ -24,21 +27,27 @@ feature_extractor = ViTFeatureExtractor.from_pretrained(vit_model_name)
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gpt2_model_name = 'asi/gpt-fr-cased-small'
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tokenizer = GPT2Tokenizer.from_pretrained(gpt2_model_name)
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-
max_length =
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num_beams =
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gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
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image = Image.open(requests.get(url, stream=True).raw)
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# batch dim is added automatically
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encoder_inputs = feature_extractor(images=image, return_tensors="jax")
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pixel_values = encoder_inputs.pixel_values
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# generation
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-
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generation = flax_vit_gpt2_lm.generate(batch['pixel_values'], **gen_kwargs)
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token_ids = np.array(generation.sequences)[0]
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caption = tokenizer.decode(token_ids)
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@@ -48,10 +57,33 @@ def predict(image):
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if __name__ == '__main__':
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-
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-
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-
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print(f'token_ids: {token_ids}')
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print(f'caption: {caption}')
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current_path = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(current_path)
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# jax
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import jax
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# Main model - ViTGPT2LM
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from vit_gpt2.modeling_flax_vit_gpt2_lm import FlaxViTGPT2LMForConditionalGeneration
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gpt2_model_name = 'asi/gpt-fr-cased-small'
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tokenizer = GPT2Tokenizer.from_pretrained(gpt2_model_name)
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max_length = 64
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num_beams = 16
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gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
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@jax.jit
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def predict_fn(pixel_values):
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return flax_vit_gpt2_lm.generate(pixel_values, **gen_kwargs)
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def predict(image, pxs=None):
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# batch dim is added automatically
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encoder_inputs = feature_extractor(images=image, return_tensors="jax")
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pixel_values = encoder_inputs.pixel_values
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if pxs is not None:
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pixel_values = pxs
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# generation
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generation = predict_fn(pixel_values)
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token_ids = np.array(generation.sequences)[0]
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caption = tokenizer.decode(token_ids)
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58 |
if __name__ == '__main__':
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from datetime import datetime
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idx = 11
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url = f'./wit_data_dir/train/images/{idx}.jpg'
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image = Image.open(url)
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encoder_inputs = feature_extractor(images=image, return_tensors="np")
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pv1 = encoder_inputs.pixel_values
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pv2 = np.load(f'./wit_data_dir/train/numpy/{idx}.npy')
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print(np.sum(np.abs(pv1 - pv2)))
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s = datetime.now()
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caption, token_ids = predict(image, pxs=pv2)
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e = datetime.now()
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e = (e - s).total_seconds()
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print(e)
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print(f'token_ids: {token_ids}')
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print(f'caption: {caption}')
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for _ in range(1):
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s = datetime.now()
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caption, token_ids = predict(image, pxs=None)
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e = datetime.now()
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e = (e - s).total_seconds()
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print(e)
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print('-' * 20)
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print(f'token_ids: {token_ids}')
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print(f'caption: {caption}')
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run_summarization_coco.py
ADDED
@@ -0,0 +1,826 @@
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|
1 |
+
#!/usr/bin/env python
|
2 |
+
# coding=utf-8
|
3 |
+
# Copyright 2021 The HuggingFace Team All rights reserved.
|
4 |
+
#
|
5 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
6 |
+
# you may not use this file except in compliance with the License.
|
7 |
+
# You may obtain a copy of the License at
|
8 |
+
#
|
9 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
10 |
+
#
|
11 |
+
# Unless required by applicable law or agreed to in writing, software
|
12 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
13 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
14 |
+
# See the License for the specific language governing permissions and
|
15 |
+
# limitations under the License.
|
16 |
+
"""
|
17 |
+
Fine-tuning the library models for summarization.
|
18 |
+
"""
|
19 |
+
# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.
|
20 |
+
|
21 |
+
import sys, os
|
22 |
+
|
23 |
+
current_path = os.path.dirname(os.path.abspath(__file__))
|
24 |
+
sys.path.append(current_path)
|
25 |
+
|
26 |
+
import logging
|
27 |
+
import os
|
28 |
+
import sys
|
29 |
+
import time
|
30 |
+
from dataclasses import dataclass, field
|
31 |
+
from functools import partial
|
32 |
+
from pathlib import Path
|
33 |
+
from typing import Callable, Optional
|
34 |
+
|
35 |
+
import datasets
|
36 |
+
import nltk # Here to have a nice missing dependency error message early on
|
37 |
+
import numpy as np
|
38 |
+
from datasets import Dataset, load_dataset, load_metric
|
39 |
+
from tqdm import tqdm
|
40 |
+
|
41 |
+
import jax
|
42 |
+
import jax.numpy as jnp
|
43 |
+
import optax
|
44 |
+
import transformers
|
45 |
+
from filelock import FileLock
|
46 |
+
from flax import jax_utils, traverse_util
|
47 |
+
from flax.jax_utils import unreplicate
|
48 |
+
from flax.training import train_state
|
49 |
+
from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
|
50 |
+
from transformers import (
|
51 |
+
CONFIG_MAPPING,
|
52 |
+
FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
|
53 |
+
AutoConfig,
|
54 |
+
AutoTokenizer,
|
55 |
+
FlaxAutoModelForSeq2SeqLM,
|
56 |
+
HfArgumentParser,
|
57 |
+
TrainingArguments,
|
58 |
+
is_tensorboard_available,
|
59 |
+
)
|
60 |
+
from transformers.file_utils import is_offline_mode
|
61 |
+
|
62 |
+
from transformers import ViTFeatureExtractor, GPT2Tokenizer, GPT2Config
|
63 |
+
from vit_gpt2.modeling_flax_vit_gpt2_lm import FlaxViTGPT2LMForConditionalGeneration
|
64 |
+
|
65 |
+
logger = logging.getLogger(__name__)
|
66 |
+
|
67 |
+
try:
|
68 |
+
nltk.data.find("tokenizers/punkt")
|
69 |
+
except (LookupError, OSError):
|
70 |
+
if is_offline_mode():
|
71 |
+
raise LookupError(
|
72 |
+
"Offline mode: run this script without TRANSFORMERS_OFFLINE first to download nltk data files"
|
73 |
+
)
|
74 |
+
with FileLock(".lock") as lock:
|
75 |
+
nltk.download("punkt", quiet=True)
|
76 |
+
|
77 |
+
|
78 |
+
MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.keys())
|
79 |
+
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
|
80 |
+
|
81 |
+
|
82 |
+
@dataclass
|
83 |
+
class ModelArguments:
|
84 |
+
"""
|
85 |
+
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
|
86 |
+
"""
|
87 |
+
|
88 |
+
model_name_or_path: Optional[str] = field(
|
89 |
+
default=None,
|
90 |
+
metadata={
|
91 |
+
"help": "The model checkpoint for weights initialization."
|
92 |
+
"Don't set if you want to train a model from scratch."
|
93 |
+
},
|
94 |
+
)
|
95 |
+
model_type: Optional[str] = field(
|
96 |
+
default=None,
|
97 |
+
metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
|
98 |
+
)
|
99 |
+
config_name: Optional[str] = field(
|
100 |
+
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
|
101 |
+
)
|
102 |
+
tokenizer_name: Optional[str] = field(
|
103 |
+
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
|
104 |
+
)
|
105 |
+
cache_dir: Optional[str] = field(
|
106 |
+
default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
|
107 |
+
)
|
108 |
+
use_fast_tokenizer: bool = field(
|
109 |
+
default=True,
|
110 |
+
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
|
111 |
+
)
|
112 |
+
dtype: Optional[str] = field(
|
113 |
+
default="float32",
|
114 |
+
metadata={
|
115 |
+
"help": "Floating-point format in which the model weights should be initialized and trained. Choose one of `[float32, float16, bfloat16]`."
|
116 |
+
},
|
117 |
+
)
|
118 |
+
|
119 |
+
|
120 |
+
@dataclass
|
121 |
+
class DataTrainingArguments:
|
122 |
+
"""
|
123 |
+
Arguments pertaining to what data we are going to input our model for training and eval.
|
124 |
+
"""
|
125 |
+
|
126 |
+
dataset_name: Optional[str] = field(
|
127 |
+
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
|
128 |
+
)
|
129 |
+
dataset_config_name: Optional[str] = field(
|
130 |
+
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
|
131 |
+
)
|
132 |
+
text_column: Optional[str] = field(
|
133 |
+
default=None,
|
134 |
+
metadata={"help": "The name of the column in the datasets containing the full texts (for summarization)."},
|
135 |
+
)
|
136 |
+
summary_column: Optional[str] = field(
|
137 |
+
default=None,
|
138 |
+
metadata={"help": "The name of the column in the datasets containing the summaries (for summarization)."},
|
139 |
+
)
|
140 |
+
train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
|
141 |
+
validation_file: Optional[str] = field(
|
142 |
+
default=None,
|
143 |
+
metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
|
144 |
+
)
|
145 |
+
max_source_length: Optional[int] = field(
|
146 |
+
default=1024,
|
147 |
+
metadata={
|
148 |
+
"help": "The maximum total input sequence length after tokenization. Sequences longer "
|
149 |
+
"than this will be truncated, sequences shorter will be padded."
|
150 |
+
},
|
151 |
+
)
|
152 |
+
max_target_length: Optional[int] = field(
|
153 |
+
default=128,
|
154 |
+
metadata={
|
155 |
+
"help": "The maximum total sequence length for target text after tokenization. Sequences longer "
|
156 |
+
"than this will be truncated, sequences shorter will be padded."
|
157 |
+
},
|
158 |
+
)
|
159 |
+
val_max_target_length: Optional[int] = field(
|
160 |
+
default=None,
|
161 |
+
metadata={
|
162 |
+
"help": "The maximum total sequence length for validation target text after tokenization. Sequences longer "
|
163 |
+
"than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`."
|
164 |
+
"This argument is also used to override the `max_length` param of `model.generate`, which is used "
|
165 |
+
"during evaluation."
|
166 |
+
},
|
167 |
+
)
|
168 |
+
max_train_samples: Optional[int] = field(
|
169 |
+
default=None,
|
170 |
+
metadata={
|
171 |
+
"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
|
172 |
+
"value if set."
|
173 |
+
},
|
174 |
+
)
|
175 |
+
max_eval_samples: Optional[int] = field(
|
176 |
+
default=None,
|
177 |
+
metadata={
|
178 |
+
"help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
|
179 |
+
"value if set."
|
180 |
+
},
|
181 |
+
)
|
182 |
+
max_predict_samples: Optional[int] = field(
|
183 |
+
default=None,
|
184 |
+
metadata={
|
185 |
+
"help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
|
186 |
+
"value if set."
|
187 |
+
},
|
188 |
+
)
|
189 |
+
preprocessing_num_workers: Optional[int] = field(
|
190 |
+
default=None,
|
191 |
+
metadata={"help": "The number of processes to use for the preprocessing."},
|
192 |
+
)
|
193 |
+
source_prefix: Optional[str] = field(
|
194 |
+
default=None, metadata={"help": "A prefix to add before every source text (useful for T5 models)."}
|
195 |
+
)
|
196 |
+
predict_with_generate: bool = field(
|
197 |
+
default=False, metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}
|
198 |
+
)
|
199 |
+
num_beams: Optional[int] = field(
|
200 |
+
default=None,
|
201 |
+
metadata={
|
202 |
+
"help": "Number of beams to use for evaluation. This argument will be passed to `model.generate`, "
|
203 |
+
"which is used during evaluation."
|
204 |
+
},
|
205 |
+
)
|
206 |
+
overwrite_cache: bool = field(
|
207 |
+
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
|
208 |
+
)
|
209 |
+
|
210 |
+
def __post_init__(self):
|
211 |
+
if self.dataset_name is None and self.train_file is None and self.validation_file is None:
|
212 |
+
raise ValueError("Need either a dataset name or a training/validation file.")
|
213 |
+
else:
|
214 |
+
if self.train_file is not None:
|
215 |
+
extension = self.train_file.split(".")[-1]
|
216 |
+
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
|
217 |
+
if self.validation_file is not None:
|
218 |
+
extension = self.validation_file.split(".")[-1]
|
219 |
+
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
|
220 |
+
if self.val_max_target_length is None:
|
221 |
+
self.val_max_target_length = self.max_target_length
|
222 |
+
|
223 |
+
|
224 |
+
summarization_name_mapping = {
|
225 |
+
"amazon_reviews_multi": ("review_body", "review_title"),
|
226 |
+
"big_patent": ("description", "abstract"),
|
227 |
+
"cnn_dailymail": ("article", "highlights"),
|
228 |
+
"orange_sum": ("text", "summary"),
|
229 |
+
"pn_summary": ("article", "summary"),
|
230 |
+
"psc": ("extract_text", "summary_text"),
|
231 |
+
"samsum": ("dialogue", "summary"),
|
232 |
+
"thaisum": ("body", "summary"),
|
233 |
+
"xglue": ("news_body", "news_title"),
|
234 |
+
"xsum": ("document", "summary"),
|
235 |
+
"wiki_summary": ("article", "highlights"),
|
236 |
+
}
|
237 |
+
|
238 |
+
|
239 |
+
class TrainState(train_state.TrainState):
|
240 |
+
dropout_rng: jnp.ndarray
|
241 |
+
|
242 |
+
def replicate(self):
|
243 |
+
return jax_utils.replicate(self).replace(dropout_rng=shard_prng_key(self.dropout_rng))
|
244 |
+
|
245 |
+
|
246 |
+
def data_loader(rng: jax.random.PRNGKey, dataset: Dataset, batch_size: int, shuffle: bool = False):
|
247 |
+
"""
|
248 |
+
Returns batches of size `batch_size` from truncated `dataset`, sharded over all local devices.
|
249 |
+
Shuffle batches if `shuffle` is `True`.
|
250 |
+
"""
|
251 |
+
steps_per_epoch = len(dataset) // batch_size
|
252 |
+
|
253 |
+
if shuffle:
|
254 |
+
batch_idx = jax.random.permutation(rng, len(dataset))
|
255 |
+
else:
|
256 |
+
batch_idx = jnp.arange(len(dataset))
|
257 |
+
|
258 |
+
batch_idx = batch_idx[: steps_per_epoch * batch_size] # Skip incomplete batch.
|
259 |
+
batch_idx = batch_idx.reshape((steps_per_epoch, batch_size))
|
260 |
+
|
261 |
+
for idx in batch_idx:
|
262 |
+
batch = dataset[idx]
|
263 |
+
batch = {k: jnp.array(v) for k, v in batch.items()}
|
264 |
+
|
265 |
+
batch = shard(batch)
|
266 |
+
|
267 |
+
yield batch
|
268 |
+
|
269 |
+
|
270 |
+
def write_metric(summary_writer, train_metrics, eval_metrics, train_time, step):
|
271 |
+
summary_writer.scalar("train_time", train_time, step)
|
272 |
+
|
273 |
+
train_metrics = get_metrics(train_metrics)
|
274 |
+
for key, vals in train_metrics.items():
|
275 |
+
tag = f"train_{key}"
|
276 |
+
for i, val in enumerate(vals):
|
277 |
+
summary_writer.scalar(tag, val, step - len(vals) + i + 1)
|
278 |
+
|
279 |
+
for metric_name, value in eval_metrics.items():
|
280 |
+
summary_writer.scalar(f"eval_{metric_name}", value, step)
|
281 |
+
|
282 |
+
|
283 |
+
def create_learning_rate_fn(
|
284 |
+
train_ds_size: int, train_batch_size: int, num_train_epochs: int, num_warmup_steps: int, learning_rate: float
|
285 |
+
) -> Callable[[int], jnp.array]:
|
286 |
+
"""Returns a linear warmup, linear_decay learning rate function."""
|
287 |
+
steps_per_epoch = train_ds_size // train_batch_size
|
288 |
+
num_train_steps = steps_per_epoch * num_train_epochs
|
289 |
+
warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
|
290 |
+
decay_fn = optax.linear_schedule(
|
291 |
+
init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps
|
292 |
+
)
|
293 |
+
schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
|
294 |
+
return schedule_fn
|
295 |
+
|
296 |
+
|
297 |
+
def main():
|
298 |
+
# See all possible arguments in src/transformers/training_args.py
|
299 |
+
# or by passing the --help flag to this script.
|
300 |
+
# We now keep distinct sets of args, for a cleaner separation of concerns.
|
301 |
+
|
302 |
+
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
|
303 |
+
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
|
304 |
+
# If we pass only one argument to the script and it's the path to a json file,
|
305 |
+
# let's parse it to get our arguments.
|
306 |
+
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
|
307 |
+
else:
|
308 |
+
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
|
309 |
+
|
310 |
+
if (
|
311 |
+
os.path.exists(training_args.output_dir)
|
312 |
+
and os.listdir(training_args.output_dir)
|
313 |
+
and training_args.do_train
|
314 |
+
and not training_args.overwrite_output_dir
|
315 |
+
):
|
316 |
+
raise ValueError(
|
317 |
+
f"Output directory ({training_args.output_dir}) already exists and is not empty."
|
318 |
+
"Use --overwrite_output_dir to overcome."
|
319 |
+
)
|
320 |
+
|
321 |
+
# Make one log on every process with the configuration for debugging.
|
322 |
+
logging.basicConfig(
|
323 |
+
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
324 |
+
datefmt="%m/%d/%Y %H:%M:%S",
|
325 |
+
level=logging.INFO,
|
326 |
+
)
|
327 |
+
# Setup logging, we only want one process per machine to log things on the screen.
|
328 |
+
logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
|
329 |
+
if jax.process_index() == 0:
|
330 |
+
datasets.utils.logging.set_verbosity_warning()
|
331 |
+
transformers.utils.logging.set_verbosity_info()
|
332 |
+
else:
|
333 |
+
datasets.utils.logging.set_verbosity_error()
|
334 |
+
transformers.utils.logging.set_verbosity_error()
|
335 |
+
|
336 |
+
# Set the verbosity to info of the Transformers logger (on main process only):
|
337 |
+
logger.info(f"Training/evaluation parameters {training_args}")
|
338 |
+
|
339 |
+
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
|
340 |
+
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
|
341 |
+
# (the dataset will be downloaded automatically from the datasets Hub).
|
342 |
+
#
|
343 |
+
# For CSV/JSON files this script will use the first column for the full texts and the second column for the
|
344 |
+
# summaries (unless you specify column names for this with the `text_column` and `summary_column` arguments).
|
345 |
+
#
|
346 |
+
if data_args.dataset_name is not None:
|
347 |
+
# Downloading and loading a dataset from the hub.
|
348 |
+
dataset = load_dataset(
|
349 |
+
data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, keep_in_memory=False, data_dir='/home/33611/caption/'
|
350 |
+
)
|
351 |
+
else:
|
352 |
+
data_files = {}
|
353 |
+
if data_args.train_file is not None:
|
354 |
+
data_files["train"] = data_args.train_file
|
355 |
+
extension = data_args.train_file.split(".")[-1]
|
356 |
+
if data_args.validation_file is not None:
|
357 |
+
data_files["validation"] = data_args.validation_file
|
358 |
+
extension = data_args.validation_file.split(".")[-1]
|
359 |
+
if data_args.test_file is not None:
|
360 |
+
data_files["test"] = data_args.test_file
|
361 |
+
extension = data_args.test_file.split(".")[-1]
|
362 |
+
dataset = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
|
363 |
+
|
364 |
+
vit_name_path = 'google/vit-base-patch16-224-in21k'
|
365 |
+
gpt2_name_path = 'asi/gpt-fr-cased-small'
|
366 |
+
|
367 |
+
gpt2_config = GPT2Config.from_pretrained(gpt2_name_path)
|
368 |
+
gpt2_config.add_cross_attention = True
|
369 |
+
|
370 |
+
|
371 |
+
vit_gpt2_name_path = ''
|
372 |
+
|
373 |
+
feature_extractor = ViTFeatureExtractor.from_pretrained(vit_name_path)
|
374 |
+
|
375 |
+
tokenizer = GPT2Tokenizer.from_pretrained(gpt2_name_path)
|
376 |
+
|
377 |
+
if not vit_gpt2_name_path:
|
378 |
+
assert vit_name_path
|
379 |
+
assert gpt2_name_path
|
380 |
+
vit_gpt2_model = FlaxViTGPT2LMForConditionalGeneration.from_vit_gpt2_pretrained(
|
381 |
+
vit_name_path, gpt2_name_path
|
382 |
+
)
|
383 |
+
else:
|
384 |
+
vit_gpt2_model = FlaxViTGPT2LMForConditionalGeneration.from_pretrained(
|
385 |
+
vit_gpt2_name_path
|
386 |
+
)
|
387 |
+
|
388 |
+
model = vit_gpt2_model
|
389 |
+
model.config.is_encoder_decoder = True
|
390 |
+
model.config.decoder_start_token_id = gpt2_config.bos_token_id
|
391 |
+
model.config.bos_token_id = gpt2_config.bos_token_id
|
392 |
+
model.config.eos_token_id = gpt2_config.eos_token_id
|
393 |
+
model.config.pad_token_id = gpt2_config.pad_token_id
|
394 |
+
|
395 |
+
# Preprocessing the datasets.
|
396 |
+
# We need to tokenize inputs and targets.
|
397 |
+
if training_args.do_train:
|
398 |
+
column_names = dataset["train"].column_names
|
399 |
+
elif training_args.do_eval:
|
400 |
+
column_names = dataset["validation"].column_names
|
401 |
+
elif training_args.do_predict:
|
402 |
+
column_names = dataset["test"].column_names
|
403 |
+
else:
|
404 |
+
logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
|
405 |
+
return
|
406 |
+
|
407 |
+
image_file_column = 'image_file'
|
408 |
+
caption_column = 'fr'
|
409 |
+
|
410 |
+
# Temporarily set max_target_length for training.
|
411 |
+
max_target_length = data_args.max_target_length
|
412 |
+
|
413 |
+
# In Flax, for seq2seq models we need to pass `decoder_input_ids`
|
414 |
+
# as the Flax models don't accept `labels`, we need to prepare the decoder_input_ids here
|
415 |
+
# for that dynamically import the `shift_tokens_right` function from the model file
|
416 |
+
model_module = __import__(vit_gpt2_model.__module__, fromlist=["shift_tokens_right"])
|
417 |
+
shift_tokens_right_fn = getattr(model_module, "shift_tokens_right")
|
418 |
+
|
419 |
+
# Setting padding="max_length" as we need fixed length inputs for jitted functions
|
420 |
+
def preprocess_function(examples):
|
421 |
+
|
422 |
+
_pixel_values = []
|
423 |
+
for y in examples[image_file_column]:
|
424 |
+
with Image.open(y) as image:
|
425 |
+
encoder_inputs = feature_extractor(images=image, return_tensors="np")
|
426 |
+
x = encoder_inputs.pixel_values
|
427 |
+
_pixel_values.append(x)
|
428 |
+
pixel_values = np.concatenate(_pixel_values)
|
429 |
+
|
430 |
+
targets = examples[caption_column]
|
431 |
+
|
432 |
+
# Add eos_token!!
|
433 |
+
targets = [x.lower() + ' ' + tokenizer.eos_token for x in targets]
|
434 |
+
|
435 |
+
model_inputs = {}
|
436 |
+
model_inputs['pixel_values'] = pixel_values
|
437 |
+
|
438 |
+
# Setup the tokenizer for targets
|
439 |
+
with tokenizer.as_target_tokenizer():
|
440 |
+
labels = tokenizer(
|
441 |
+
targets, max_length=max_target_length, padding="max_length", truncation=True, return_tensors="np"
|
442 |
+
)
|
443 |
+
|
444 |
+
model_inputs["labels"] = labels["input_ids"]
|
445 |
+
|
446 |
+
#print(labels["input_ids"])
|
447 |
+
#print(gpt2_config.pad_token_id)
|
448 |
+
#rint(gpt2_config.bos_token_id)
|
449 |
+
|
450 |
+
decoder_input_ids = shift_tokens_right_fn(
|
451 |
+
jnp.array(labels["input_ids"]), gpt2_config.pad_token_id, gpt2_config.bos_token_id
|
452 |
+
)
|
453 |
+
model_inputs["input_ids"] = np.asarray(decoder_input_ids)
|
454 |
+
|
455 |
+
# We need decoder_attention_mask so we can ignore pad tokens from loss
|
456 |
+
model_inputs["attention_mask"] = labels["attention_mask"]
|
457 |
+
|
458 |
+
return model_inputs
|
459 |
+
|
460 |
+
if training_args.do_train:
|
461 |
+
if "train" not in dataset:
|
462 |
+
raise ValueError("--do_train requires a train dataset")
|
463 |
+
train_dataset = dataset["train"]
|
464 |
+
if data_args.max_train_samples is not None:
|
465 |
+
train_dataset = train_dataset.select(range(data_args.max_train_samples))
|
466 |
+
|
467 |
+
train_dataset = train_dataset.map(
|
468 |
+
preprocess_function,
|
469 |
+
batched=True,
|
470 |
+
num_proc=data_args.preprocessing_num_workers,
|
471 |
+
remove_columns=column_names,
|
472 |
+
load_from_cache_file=not data_args.overwrite_cache,
|
473 |
+
desc="Running tokenizer on train dataset",
|
474 |
+
)
|
475 |
+
|
476 |
+
if training_args.do_eval:
|
477 |
+
max_target_length = data_args.val_max_target_length
|
478 |
+
if "validation" not in dataset:
|
479 |
+
raise ValueError("--do_eval requires a validation dataset")
|
480 |
+
eval_dataset = dataset["validation"]
|
481 |
+
if data_args.max_eval_samples is not None:
|
482 |
+
eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
|
483 |
+
eval_dataset = eval_dataset.map(
|
484 |
+
preprocess_function,
|
485 |
+
batched=True,
|
486 |
+
num_proc=data_args.preprocessing_num_workers,
|
487 |
+
remove_columns=column_names,
|
488 |
+
load_from_cache_file=not data_args.overwrite_cache,
|
489 |
+
desc="Running tokenizer on validation dataset",
|
490 |
+
)
|
491 |
+
|
492 |
+
if training_args.do_predict:
|
493 |
+
max_target_length = data_args.val_max_target_length
|
494 |
+
if "test" not in dataset:
|
495 |
+
raise ValueError("--do_predict requires a test dataset")
|
496 |
+
predict_dataset = dataset["test"]
|
497 |
+
if data_args.max_predict_samples is not None:
|
498 |
+
predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))
|
499 |
+
predict_dataset = predict_dataset.map(
|
500 |
+
preprocess_function,
|
501 |
+
batched=True,
|
502 |
+
num_proc=data_args.preprocessing_num_workers,
|
503 |
+
remove_columns=column_names,
|
504 |
+
load_from_cache_file=not data_args.overwrite_cache,
|
505 |
+
desc="Running tokenizer on prediction dataset",
|
506 |
+
)
|
507 |
+
|
508 |
+
# Metric
|
509 |
+
metric = load_metric("rouge")
|
510 |
+
|
511 |
+
def postprocess_text(preds, labels):
|
512 |
+
preds = [pred.strip() for pred in preds]
|
513 |
+
labels = [label.strip() for label in labels]
|
514 |
+
|
515 |
+
# rougeLSum expects newline after each sentence
|
516 |
+
preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
|
517 |
+
labels = ["\n".join(nltk.sent_tokenize(label)) for label in labels]
|
518 |
+
|
519 |
+
return preds, labels
|
520 |
+
|
521 |
+
def compute_metrics(preds, labels):
|
522 |
+
decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
|
523 |
+
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
|
524 |
+
|
525 |
+
# Some simple post-processing
|
526 |
+
decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
|
527 |
+
|
528 |
+
result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
|
529 |
+
# Extract a few results from ROUGE
|
530 |
+
result = {key: value.mid.fmeasure * 100 for key, value in result.items()}
|
531 |
+
|
532 |
+
prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
|
533 |
+
result["gen_len"] = np.mean(prediction_lens)
|
534 |
+
result = {k: round(v, 4) for k, v in result.items()}
|
535 |
+
return result
|
536 |
+
|
537 |
+
# Enable tensorboard only on the master node
|
538 |
+
has_tensorboard = is_tensorboard_available()
|
539 |
+
if has_tensorboard and jax.process_index() == 0:
|
540 |
+
try:
|
541 |
+
from flax.metrics.tensorboard import SummaryWriter
|
542 |
+
|
543 |
+
summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir))
|
544 |
+
except ImportError as ie:
|
545 |
+
has_tensorboard = False
|
546 |
+
logger.warning(
|
547 |
+
f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
|
548 |
+
)
|
549 |
+
else:
|
550 |
+
logger.warning(
|
551 |
+
"Unable to display metrics through TensorBoard because the package is not installed: "
|
552 |
+
"Please run pip install tensorboard to enable."
|
553 |
+
)
|
554 |
+
|
555 |
+
# Initialize our training
|
556 |
+
rng = jax.random.PRNGKey(training_args.seed)
|
557 |
+
rng, dropout_rng = jax.random.split(rng)
|
558 |
+
|
559 |
+
# Store some constant
|
560 |
+
num_epochs = int(training_args.num_train_epochs)
|
561 |
+
train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count()
|
562 |
+
eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count()
|
563 |
+
steps_per_epoch = len(train_dataset) // train_batch_size
|
564 |
+
total_train_steps = steps_per_epoch * num_epochs
|
565 |
+
|
566 |
+
# Create learning rate schedule
|
567 |
+
linear_decay_lr_schedule_fn = create_learning_rate_fn(
|
568 |
+
len(train_dataset),
|
569 |
+
train_batch_size,
|
570 |
+
training_args.num_train_epochs,
|
571 |
+
training_args.warmup_steps,
|
572 |
+
training_args.learning_rate,
|
573 |
+
)
|
574 |
+
|
575 |
+
# We use Optax's "masking" functionality to not apply weight decay
|
576 |
+
# to bias and LayerNorm scale parameters. decay_mask_fn returns a
|
577 |
+
# mask boolean with the same structure as the parameters.
|
578 |
+
# The mask is True for parameters that should be decayed.
|
579 |
+
# Note that this mask is specifically adapted for FlaxBart.
|
580 |
+
# For FlaxT5, one should correct the layer norm parameter naming
|
581 |
+
# accordingly - see `run_t5_mlm_flax.py` e.g.
|
582 |
+
def decay_mask_fn(params):
|
583 |
+
flat_params = traverse_util.flatten_dict(params)
|
584 |
+
layer_norm_params = [
|
585 |
+
(name, "scale") for name in ["self_attn_layer_norm", "layernorm_embedding", "final_layer_norm"]
|
586 |
+
]
|
587 |
+
flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_params) for path in flat_params}
|
588 |
+
return traverse_util.unflatten_dict(flat_mask)
|
589 |
+
|
590 |
+
# create adam optimizer
|
591 |
+
adamw = optax.adamw(
|
592 |
+
learning_rate=linear_decay_lr_schedule_fn,
|
593 |
+
b1=training_args.adam_beta1,
|
594 |
+
b2=training_args.adam_beta2,
|
595 |
+
eps=training_args.adam_epsilon,
|
596 |
+
weight_decay=training_args.weight_decay,
|
597 |
+
mask=decay_mask_fn,
|
598 |
+
)
|
599 |
+
|
600 |
+
# Setup train state
|
601 |
+
state = TrainState.create(apply_fn=vit_gpt2_model.__call__, params=vit_gpt2_model.params, tx=adamw, dropout_rng=dropout_rng)
|
602 |
+
|
603 |
+
# label smoothed cross entropy
|
604 |
+
def loss_fn(logits, labels, padding_mask, label_smoothing_factor=0.0):
|
605 |
+
"""
|
606 |
+
The label smoothing implementation is adapted from Flax's official example:
|
607 |
+
https://github.com/google/flax/blob/87a211135c6a377c8f29048a1cac3840e38b9da4/examples/wmt/train.py#L104
|
608 |
+
"""
|
609 |
+
vocab_size = logits.shape[-1]
|
610 |
+
confidence = 1.0 - label_smoothing_factor
|
611 |
+
low_confidence = (1.0 - confidence) / (vocab_size - 1)
|
612 |
+
normalizing_constant = -(
|
613 |
+
confidence * jnp.log(confidence) + (vocab_size - 1) * low_confidence * jnp.log(low_confidence + 1e-20)
|
614 |
+
)
|
615 |
+
soft_labels = onehot(labels, vocab_size, on_value=confidence, off_value=low_confidence)
|
616 |
+
|
617 |
+
loss = optax.softmax_cross_entropy(logits, soft_labels)
|
618 |
+
loss = loss - normalizing_constant
|
619 |
+
|
620 |
+
# ignore padded tokens from loss
|
621 |
+
loss = loss * padding_mask
|
622 |
+
loss = loss.sum() / padding_mask.sum()
|
623 |
+
return loss
|
624 |
+
|
625 |
+
# Define gradient update step fn
|
626 |
+
def train_step(state, batch, label_smoothing_factor=0.0):
|
627 |
+
dropout_rng, new_dropout_rng = jax.random.split(state.dropout_rng)
|
628 |
+
|
629 |
+
def compute_loss(params):
|
630 |
+
labels = batch.pop("labels")
|
631 |
+
logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
|
632 |
+
loss = loss_fn(logits, labels, batch["attention_mask"], label_smoothing_factor)
|
633 |
+
return loss
|
634 |
+
|
635 |
+
grad_fn = jax.value_and_grad(compute_loss)
|
636 |
+
loss, grad = grad_fn(state.params)
|
637 |
+
grad = jax.lax.pmean(grad, "batch")
|
638 |
+
|
639 |
+
new_state = state.apply_gradients(grads=grad, dropout_rng=new_dropout_rng)
|
640 |
+
|
641 |
+
metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}
|
642 |
+
metrics = jax.lax.pmean(metrics, axis_name="batch")
|
643 |
+
|
644 |
+
return new_state, metrics
|
645 |
+
|
646 |
+
# Define eval fn
|
647 |
+
def eval_step(params, batch, label_smoothing_factor=0.0):
|
648 |
+
labels = batch.pop("labels")
|
649 |
+
logits = model(**batch, params=params, train=False)[0]
|
650 |
+
loss = loss_fn(logits, labels, batch["attention_mask"], label_smoothing_factor)
|
651 |
+
|
652 |
+
# summarize metrics
|
653 |
+
metrics = {"loss": loss}
|
654 |
+
metrics = jax.lax.pmean(metrics, axis_name="batch")
|
655 |
+
return metrics
|
656 |
+
|
657 |
+
# Define generation function
|
658 |
+
max_length = (
|
659 |
+
data_args.val_max_target_length if data_args.val_max_target_length is not None else model.config.max_length
|
660 |
+
)
|
661 |
+
num_beams = data_args.num_beams if data_args.num_beams is not None else model.config.num_beams
|
662 |
+
gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
|
663 |
+
|
664 |
+
def generate_step(params, batch):
|
665 |
+
model.params = params
|
666 |
+
# output_ids = model.generate(batch["pixel_values"], **gen_kwargs)
|
667 |
+
|
668 |
+
#encoder_outputs = model.encode(pixel_values=batch['pixel_values'])
|
669 |
+
#output_ids = model.generate(batch["input_ids"], attention_mask=batch["attention_mask"], encoder_outputs=encoder_outputs, **gen_kwargs)
|
670 |
+
|
671 |
+
# encoder_outputs = model.encode(pixel_values=batch['pixel_values'], params=params, train=False)
|
672 |
+
output_ids = model.generate(batch['pixel_values'], **gen_kwargs)
|
673 |
+
|
674 |
+
|
675 |
+
return output_ids.sequences
|
676 |
+
|
677 |
+
# Create parallel version of the train and eval step
|
678 |
+
p_train_step = jax.pmap(
|
679 |
+
partial(train_step, label_smoothing_factor=training_args.label_smoothing_factor), "batch", donate_argnums=(0,)
|
680 |
+
)
|
681 |
+
p_eval_step = jax.pmap(partial(eval_step, label_smoothing_factor=training_args.label_smoothing_factor), "batch")
|
682 |
+
p_generate_step = jax.pmap(generate_step, "batch")
|
683 |
+
|
684 |
+
# Replicate the train state on each device
|
685 |
+
state = state.replicate()
|
686 |
+
|
687 |
+
logger.info("***** Running training *****")
|
688 |
+
logger.info(f" Num examples = {len(train_dataset)}")
|
689 |
+
logger.info(f" Num Epochs = {num_epochs}")
|
690 |
+
logger.info(f" Instantaneous batch size per device = {training_args.per_device_train_batch_size}")
|
691 |
+
logger.info(f" Total train batch size (w. parallel & distributed) = {train_batch_size}")
|
692 |
+
logger.info(f" Total optimization steps = {total_train_steps}")
|
693 |
+
|
694 |
+
train_time = 0
|
695 |
+
epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0)
|
696 |
+
for epoch in epochs:
|
697 |
+
# ======================== Training ================================
|
698 |
+
train_start = time.time()
|
699 |
+
|
700 |
+
# Create sampling rng
|
701 |
+
rng, input_rng = jax.random.split(rng)
|
702 |
+
train_metrics = []
|
703 |
+
|
704 |
+
# Generate an epoch by shuffling sampling indices from the train dataset
|
705 |
+
train_loader = data_loader(input_rng, train_dataset, train_batch_size, shuffle=True)
|
706 |
+
steps_per_epoch = len(train_dataset) // train_batch_size
|
707 |
+
# train
|
708 |
+
for _ in tqdm(range(steps_per_epoch), desc="Training...", position=1, leave=False):
|
709 |
+
batch = next(train_loader)
|
710 |
+
state, train_metric = p_train_step(state, batch)
|
711 |
+
train_metrics.append(train_metric)
|
712 |
+
|
713 |
+
train_time += time.time() - train_start
|
714 |
+
|
715 |
+
train_metric = unreplicate(train_metric)
|
716 |
+
|
717 |
+
desc = f"Epoch... ({epoch + 1}/{num_epochs} | Loss: {train_metric['loss']}, Learning Rate: {train_metric['learning_rate']})"
|
718 |
+
epochs.write(desc)
|
719 |
+
epochs.desc = desc
|
720 |
+
logger.info(desc)
|
721 |
+
with open(os.path.join(training_args.output_dir, f'report.txt'), 'a', encoding='UTF-8') as fp:
|
722 |
+
fp.write(desc + '\n')
|
723 |
+
|
724 |
+
|
725 |
+
# ======================== Evaluating ==============================
|
726 |
+
eval_metrics = []
|
727 |
+
eval_preds = []
|
728 |
+
eval_labels = []
|
729 |
+
|
730 |
+
eval_loader = data_loader(input_rng, eval_dataset, eval_batch_size)
|
731 |
+
eval_steps = len(eval_dataset) // eval_batch_size
|
732 |
+
for _ in tqdm(range(eval_steps), desc="Evaluating...", position=2, leave=False):
|
733 |
+
# Model forward
|
734 |
+
batch = next(eval_loader)
|
735 |
+
labels = batch["labels"]
|
736 |
+
|
737 |
+
metrics = p_eval_step(state.params, batch)
|
738 |
+
eval_metrics.append(metrics)
|
739 |
+
|
740 |
+
# generation
|
741 |
+
if data_args.predict_with_generate:
|
742 |
+
generated_ids = p_generate_step(state.params, batch)
|
743 |
+
eval_preds.extend(jax.device_get(generated_ids.reshape(-1, gen_kwargs["max_length"])))
|
744 |
+
eval_labels.extend(jax.device_get(labels.reshape(-1, labels.shape[-1])))
|
745 |
+
|
746 |
+
# normalize eval metrics
|
747 |
+
eval_metrics = get_metrics(eval_metrics)
|
748 |
+
eval_metrics = jax.tree_map(jnp.mean, eval_metrics)
|
749 |
+
|
750 |
+
# compute ROUGE metrics
|
751 |
+
rouge_desc = ""
|
752 |
+
if data_args.predict_with_generate:
|
753 |
+
rouge_metrics = compute_metrics(eval_preds, eval_labels)
|
754 |
+
eval_metrics.update(rouge_metrics)
|
755 |
+
rouge_desc = " ".join([f"Eval {key}: {value} |" for key, value in rouge_metrics.items()])
|
756 |
+
|
757 |
+
# Print metrics and update progress bar
|
758 |
+
desc = f"Epoch... ({epoch + 1}/{num_epochs} | Eval Loss: {eval_metrics['loss']} | {rouge_desc})"
|
759 |
+
epochs.write(desc)
|
760 |
+
epochs.desc = desc
|
761 |
+
logger.info(desc)
|
762 |
+
with open(os.path.join(training_args.output_dir, f'report.txt'), 'a', encoding='UTF-8') as fp:
|
763 |
+
fp.write(desc + '\n')
|
764 |
+
|
765 |
+
|
766 |
+
# Save metrics
|
767 |
+
if has_tensorboard and jax.process_index() == 0:
|
768 |
+
cur_step = epoch * (len(train_dataset) // train_batch_size)
|
769 |
+
write_metric(summary_writer, train_metrics, eval_metrics, train_time, cur_step)
|
770 |
+
|
771 |
+
# ======================== Prediction loop ==============================
|
772 |
+
if training_args.do_predict:
|
773 |
+
logger.info("*** Predict ***")
|
774 |
+
|
775 |
+
pred_metrics = []
|
776 |
+
pred_generations = []
|
777 |
+
pred_labels = []
|
778 |
+
|
779 |
+
pred_loader = data_loader(input_rng, predict_dataset, eval_batch_size)
|
780 |
+
pred_steps = len(predict_dataset) // eval_batch_size
|
781 |
+
for _ in tqdm(range(pred_steps), desc="Predicting...", position=2, leave=False):
|
782 |
+
# Model forward
|
783 |
+
batch = next(pred_loader)
|
784 |
+
labels = batch["labels"]
|
785 |
+
|
786 |
+
metrics = p_eval_step(state.params, batch)
|
787 |
+
pred_metrics.append(metrics)
|
788 |
+
|
789 |
+
# generation
|
790 |
+
if data_args.predict_with_generate:
|
791 |
+
generated_ids = p_generate_step(state.params, batch)
|
792 |
+
pred_generations.extend(jax.device_get(generated_ids.reshape(-1, gen_kwargs["max_length"])))
|
793 |
+
pred_labels.extend(jax.device_get(labels.reshape(-1, labels.shape[-1])))
|
794 |
+
|
795 |
+
# normalize prediction metrics
|
796 |
+
pred_metrics = get_metrics(pred_metrics)
|
797 |
+
pred_metrics = jax.tree_map(jnp.mean, pred_metrics)
|
798 |
+
|
799 |
+
# compute ROUGE metrics
|
800 |
+
rouge_desc = ""
|
801 |
+
if data_args.predict_with_generate:
|
802 |
+
rouge_metrics = compute_metrics(pred_generations, pred_labels)
|
803 |
+
pred_metrics.update(rouge_metrics)
|
804 |
+
rouge_desc = " ".join([f"Predict {key}: {value} |" for key, value in rouge_metrics.items()])
|
805 |
+
|
806 |
+
# Print metrics
|
807 |
+
desc = f"Predict Loss: {pred_metrics['loss']} | {rouge_desc})"
|
808 |
+
epochs.write(desc)
|
809 |
+
epochs.desc = desc
|
810 |
+
logger.info(desc)
|
811 |
+
with open(os.path.join(training_args.output_dir, f'report.txt'), 'a', encoding='UTF-8') as fp:
|
812 |
+
fp.write(desc + '\n')
|
813 |
+
|
814 |
+
|
815 |
+
# save checkpoint after each epoch and push checkpoint to the hub
|
816 |
+
if jax.process_index() == 0:
|
817 |
+
params = jax.device_get(jax.tree_map(lambda x: x[0], state.params))
|
818 |
+
model.save_pretrained(
|
819 |
+
os.path.join(training_args.output_dir, f'ckpt_{epoch+1}'),
|
820 |
+
params=params,
|
821 |
+
push_to_hub=training_args.push_to_hub,
|
822 |
+
commit_message=f"Saving weights and logs of epoch {epoch+1}",
|
823 |
+
)
|
824 |
+
|
825 |
+
if __name__ == "__main__":
|
826 |
+
main()
|
test_coco_dataset_script.py
ADDED
@@ -0,0 +1,27 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import csv
|
2 |
+
import json
|
3 |
+
import os
|
4 |
+
|
5 |
+
import datasets
|
6 |
+
import pandas as pd
|
7 |
+
import numpy as np
|
8 |
+
|
9 |
+
ds = datasets.load_dataset('./coco_dataset_script.py', data_dir='/home/33611/caption/')
|
10 |
+
ds = ds['train']
|
11 |
+
|
12 |
+
|
13 |
+
def transform(example):
|
14 |
+
|
15 |
+
example['pixel_values'] = np.load(example['pixels_file'])
|
16 |
+
return example
|
17 |
+
|
18 |
+
|
19 |
+
# ds = ds.map(transform)
|
20 |
+
|
21 |
+
n = 0
|
22 |
+
for x in ds:
|
23 |
+
n += 1
|
24 |
+
assert os.path.isfile(x['image_file'])
|
25 |
+
if n == 10:
|
26 |
+
print(x)
|
27 |
+
break
|