598 lines
25 KiB
Python
598 lines
25 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2022 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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"""
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Fine-tuning a 🤗 Transformers model for image classification.
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Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
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https://huggingface.co/models?filter=image-classification
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"""
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import json
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import logging
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import os
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import sys
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import warnings
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from dataclasses import dataclass, field
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from typing import Optional
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import evaluate
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import numpy as np
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import tensorflow as tf
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from datasets import load_dataset
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from PIL import Image
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import transformers
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from transformers import (
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TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
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AutoConfig,
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AutoImageProcessor,
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DefaultDataCollator,
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HfArgumentParser,
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PushToHubCallback,
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TFAutoModelForImageClassification,
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TFTrainingArguments,
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create_optimizer,
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set_seed,
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)
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from transformers.keras_callbacks import KerasMetricCallback
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from transformers.modeling_tf_utils import keras
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from transformers.trainer_utils import get_last_checkpoint, is_main_process
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from transformers.utils import check_min_version, send_example_telemetry
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from transformers.utils.versions import require_version
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logger = logging.getLogger(__name__)
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# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
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check_min_version("4.39.0.dev0")
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require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt")
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MODEL_CONFIG_CLASSES = list(TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
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MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
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def pil_loader(path: str):
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with open(path, "rb") as f:
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im = Image.open(f)
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return im.convert("RGB")
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@dataclass
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class DataTrainingArguments:
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"""
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Arguments pertaining to what data we are going to input our model for training and eval.
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Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify
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them on the command line.
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"""
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dataset_name: Optional[str] = field(
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default=None,
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metadata={
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"help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."
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},
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)
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dataset_config_name: Optional[str] = field(
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default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
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)
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train_dir: Optional[str] = field(default=None, metadata={"help": "A folder containing the training data."})
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validation_dir: Optional[str] = field(default=None, metadata={"help": "A folder containing the validation data."})
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train_val_split: Optional[float] = field(
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default=0.15, metadata={"help": "Percent to split off of train for validation."}
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)
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overwrite_cache: bool = field(
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default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
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)
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preprocessing_num_workers: Optional[int] = field(
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default=None,
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metadata={"help": "The number of processes to use for the preprocessing."},
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)
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max_train_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": (
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"For debugging purposes or quicker training, truncate the number of training examples to this "
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"value if set."
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)
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},
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)
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max_eval_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": (
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"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
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"value if set."
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)
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},
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)
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max_predict_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": (
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"For debugging purposes or quicker training, truncate the number of prediction examples to this "
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"value if set."
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)
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},
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)
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def __post_init__(self):
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if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
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raise ValueError(
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"You must specify either a dataset name from the hub or a train and/or validation directory."
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)
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@dataclass
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class ModelArguments:
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"""
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Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
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"""
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model_name_or_path: str = field(
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default="google/vit-base-patch16-224-in21k",
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metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"},
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)
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model_type: Optional[str] = field(
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default=None,
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metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
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)
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config_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
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)
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cache_dir: Optional[str] = field(
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default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
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)
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model_revision: str = field(
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default="main",
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metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
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)
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image_processor_name: str = field(default=None, metadata={"help": "Name or path of preprocessor config."})
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token: str = field(
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default=None,
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metadata={
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"help": (
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"The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
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"generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
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)
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},
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)
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use_auth_token: bool = field(
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default=None,
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metadata={
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"help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead."
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},
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)
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trust_remote_code: bool = field(
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default=False,
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metadata={
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"help": (
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"Whether or not to allow for custom models defined on the Hub in their own modeling files. This option "
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"should only be set to `True` for repositories you trust and in which you have read the code, as it will "
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"execute code present on the Hub on your local machine."
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)
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},
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)
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ignore_mismatched_sizes: bool = field(
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default=False,
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metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."},
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)
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def center_crop(image, size):
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size = (size, size) if isinstance(size, int) else size
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orig_height, orig_width, _ = image.shape
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crop_height, crop_width = size
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top = (orig_height - orig_width) // 2
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left = (orig_width - crop_width) // 2
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image = tf.image.crop_to_bounding_box(image, top, left, crop_height, crop_width)
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return image
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# Numpy and TensorFlow compatible version of PyTorch RandomResizedCrop. Code adapted from:
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# https://pytorch.org/vision/main/_modules/torchvision/transforms/transforms.html#RandomResizedCrop
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def random_crop(image, scale=(0.08, 1.0), ratio=(3.0 / 4.0, 4.0 / 3.0)):
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height, width, _ = image.shape
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area = height * width
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log_ratio = np.log(ratio)
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for _ in range(10):
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target_area = np.random.uniform(*scale) * area
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aspect_ratio = np.exp(np.random.uniform(*log_ratio))
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w = int(round(np.sqrt(target_area * aspect_ratio)))
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h = int(round(np.sqrt(target_area / aspect_ratio)))
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if 0 < w <= width and 0 < h <= height:
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i = np.random.randint(0, height - h + 1)
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j = np.random.randint(0, width - w + 1)
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return image[i : i + h, j : j + w, :]
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# Fallback to central crop
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in_ratio = float(width) / float(height)
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w = width if in_ratio < min(ratio) else int(round(height * max(ratio)))
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h = height if in_ratio > max(ratio) else int(round(width / min(ratio)))
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i = (height - h) // 2
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j = (width - w) // 2
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return image[i : i + h, j : j + w, :]
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def random_resized_crop(image, size, scale=(0.08, 1.0), ratio=(3.0 / 4.0, 4.0 / 3.0)):
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size = (size, size) if isinstance(size, int) else size
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image = random_crop(image, scale, ratio)
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image = tf.image.resize(image, size)
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return image
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def main():
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# See all possible arguments in src/transformers/training_args.py
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# or by passing the --help flag to this script.
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# We now keep distinct sets of args, for a cleaner separation of concerns.
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parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
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if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
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# If we pass only one argument to the script and it's the path to a json file,
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# let's parse it to get our arguments.
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model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
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else:
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model_args, data_args, training_args = parser.parse_args_into_dataclasses()
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if model_args.use_auth_token is not None:
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warnings.warn(
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"The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.",
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FutureWarning,
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)
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if model_args.token is not None:
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raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
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model_args.token = model_args.use_auth_token
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if not (training_args.do_train or training_args.do_eval or training_args.do_predict):
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exit("Must specify at least one of --do_train, --do_eval or --do_predict!")
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# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
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# information sent is the one passed as arguments along with your Python/TensorFlow versions.
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send_example_telemetry("run_image_classification", model_args, data_args, framework="tensorflow")
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# Checkpoints. Find the checkpoint the use when loading the model.
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checkpoint = None
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if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
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checkpoint = get_last_checkpoint(training_args.output_dir)
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if checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
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raise ValueError(
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f"Output directory ({training_args.output_dir}) already exists and is not empty. "
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"Use --overwrite_output_dir to overcome."
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)
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elif checkpoint is not None and training_args.resume_from_checkpoint is None:
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logger.info(
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f"Checkpoint detected, resuming training at {checkpoint}. To avoid this behavior, change "
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"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
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)
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# Setup logging
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logging.basicConfig(
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format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
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datefmt="%m/%d/%Y %H:%M:%S",
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handlers=[logging.StreamHandler(sys.stdout)],
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)
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log_level = training_args.get_process_log_level()
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logger.setLevel(log_level)
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# Set the verbosity to info of the Transformers logger (on main process only):
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if is_main_process(training_args.local_rank):
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transformers.utils.logging.set_verbosity_info()
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transformers.utils.logging.enable_default_handler()
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transformers.utils.logging.enable_explicit_format()
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logger.info(f"Training/evaluation parameters {training_args}")
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# region Dataset and labels
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# Set seed before initializing model.
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set_seed(training_args.seed)
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# Initialize our dataset and prepare it for the 'image-classification' task.
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if data_args.dataset_name is not None:
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dataset = load_dataset(
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data_args.dataset_name,
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data_args.dataset_config_name,
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cache_dir=model_args.cache_dir,
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task="image-classification",
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token=model_args.token,
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)
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else:
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data_files = {}
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if data_args.train_dir is not None:
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data_files["train"] = os.path.join(data_args.train_dir, "**")
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if data_args.validation_dir is not None:
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data_files["validation"] = os.path.join(data_args.validation_dir, "**")
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dataset = load_dataset(
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"imagefolder",
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data_files=data_files,
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cache_dir=model_args.cache_dir,
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task="image-classification",
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)
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# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
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# https://huggingface.co/docs/datasets/loading_datasets.
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# Prepare label mappings.
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# We'll include these in the model's config to get human readable labels in the Inference API.
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labels = dataset["train"].features["labels"].names
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label2id, id2label = {}, {}
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for i, label in enumerate(labels):
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label2id[label] = str(i)
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id2label[str(i)] = label
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# Load model image processor and configuration
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config = AutoConfig.from_pretrained(
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model_args.config_name or model_args.model_name_or_path,
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num_labels=len(labels),
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label2id=label2id,
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id2label=id2label,
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finetuning_task="image-classification",
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cache_dir=model_args.cache_dir,
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revision=model_args.model_revision,
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token=model_args.token,
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trust_remote_code=model_args.trust_remote_code,
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)
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image_processor = AutoImageProcessor.from_pretrained(
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model_args.image_processor_name or model_args.model_name_or_path,
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cache_dir=model_args.cache_dir,
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revision=model_args.model_revision,
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token=model_args.token,
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trust_remote_code=model_args.trust_remote_code,
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)
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# If we don't have a validation split, split off a percentage of train as validation.
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data_args.train_val_split = None if "validation" in dataset.keys() else data_args.train_val_split
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if isinstance(data_args.train_val_split, float) and data_args.train_val_split > 0.0:
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split = dataset["train"].train_test_split(data_args.train_val_split)
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dataset["train"] = split["train"]
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dataset["validation"] = split["test"]
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# Define our data preprocessing function. It takes an image file path as input and returns
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# Write a note describing the resizing behaviour.
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if "shortest_edge" in image_processor.size:
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# We instead set the target size as (shortest_edge, shortest_edge) to here to ensure all images are batchable.
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image_size = (image_processor.size["shortest_edge"], image_processor.size["shortest_edge"])
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else:
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image_size = (image_processor.size["height"], image_processor.size["width"])
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def _train_transforms(image):
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img_size = image_size
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image = keras.utils.img_to_array(image)
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image = random_resized_crop(image, size=img_size)
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image = tf.image.random_flip_left_right(image)
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image /= 255.0
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image = (image - image_processor.image_mean) / image_processor.image_std
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image = tf.transpose(image, perm=[2, 0, 1])
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return image
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def _val_transforms(image):
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image = keras.utils.img_to_array(image)
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image = tf.image.resize(image, size=image_size)
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# image = np.array(image) # FIXME - use tf.image function
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image = center_crop(image, size=image_size)
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image /= 255.0
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image = (image - image_processor.image_mean) / image_processor.image_std
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image = tf.transpose(image, perm=[2, 0, 1])
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return image
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def train_transforms(example_batch):
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"""Apply _train_transforms across a batch."""
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example_batch["pixel_values"] = [
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_train_transforms(pil_img.convert("RGB")) for pil_img in example_batch["image"]
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]
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return example_batch
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def val_transforms(example_batch):
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"""Apply _val_transforms across a batch."""
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example_batch["pixel_values"] = [_val_transforms(pil_img.convert("RGB")) for pil_img in example_batch["image"]]
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return example_batch
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train_dataset = None
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if training_args.do_train:
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if "train" not in dataset:
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raise ValueError("--do_train requires a train dataset")
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train_dataset = dataset["train"]
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if data_args.max_train_samples is not None:
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train_dataset = train_dataset.shuffle(seed=training_args.seed).select(range(data_args.max_train_samples))
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train_dataset = train_dataset.map(
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train_transforms,
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batched=True,
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num_proc=data_args.preprocessing_num_workers,
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load_from_cache_file=not data_args.overwrite_cache,
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)
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eval_dataset = None
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if training_args.do_eval:
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if "validation" not in dataset:
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raise ValueError("--do_eval requires a validation dataset")
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eval_dataset = dataset["validation"]
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if data_args.max_eval_samples is not None:
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eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
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# Set the validation transforms
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eval_dataset = eval_dataset.map(
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val_transforms,
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batched=True,
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num_proc=data_args.preprocessing_num_workers,
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load_from_cache_file=not data_args.overwrite_cache,
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)
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predict_dataset = None
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if training_args.do_predict:
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if "test" not in dataset:
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raise ValueError("--do_predict requires a test dataset")
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predict_dataset = dataset["test"]
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if data_args.max_predict_samples is not None:
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predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))
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# Set the test transforms
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predict_dataset = predict_dataset.map(
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val_transforms,
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batched=True,
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num_proc=data_args.preprocessing_num_workers,
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load_from_cache_file=not data_args.overwrite_cache,
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)
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collate_fn = DefaultDataCollator(return_tensors="np")
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# Load the accuracy metric from the datasets package
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metric = evaluate.load("accuracy", cache_dir=model_args.cache_dir)
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# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
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# predictions and label_ids field) and has to return a dictionary string to float.
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def compute_metrics(p):
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"""Computes accuracy on a batch of predictions"""
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logits, label_ids = p
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predictions = np.argmax(logits, axis=-1)
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metrics = metric.compute(predictions=predictions, references=label_ids)
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return metrics
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with training_args.strategy.scope():
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|
if checkpoint is None:
|
|
model_path = model_args.model_name_or_path
|
|
else:
|
|
model_path = checkpoint
|
|
|
|
model = TFAutoModelForImageClassification.from_pretrained(
|
|
model_path,
|
|
config=config,
|
|
from_pt=bool(".bin" in model_path),
|
|
cache_dir=model_args.cache_dir,
|
|
revision=model_args.model_revision,
|
|
token=model_args.token,
|
|
trust_remote_code=model_args.trust_remote_code,
|
|
ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
|
|
)
|
|
num_replicas = training_args.strategy.num_replicas_in_sync
|
|
total_train_batch_size = training_args.per_device_train_batch_size * num_replicas
|
|
total_eval_batch_size = training_args.per_device_eval_batch_size * num_replicas
|
|
|
|
dataset_options = tf.data.Options()
|
|
dataset_options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF
|
|
|
|
if training_args.do_train:
|
|
num_train_steps = int(len(train_dataset) * training_args.num_train_epochs)
|
|
if training_args.warmup_steps > 0:
|
|
num_warmpup_steps = int(training_args.warmup_steps)
|
|
elif training_args.warmup_ratio > 0:
|
|
num_warmpup_steps = int(training_args.warmup_ratio * num_train_steps)
|
|
else:
|
|
num_warmpup_steps = 0
|
|
|
|
optimizer, _ = create_optimizer(
|
|
init_lr=training_args.learning_rate,
|
|
num_train_steps=num_train_steps,
|
|
num_warmup_steps=num_warmpup_steps,
|
|
adam_beta1=training_args.adam_beta1,
|
|
adam_beta2=training_args.adam_beta2,
|
|
adam_epsilon=training_args.adam_epsilon,
|
|
weight_decay_rate=training_args.weight_decay,
|
|
adam_global_clipnorm=training_args.max_grad_norm,
|
|
)
|
|
# model.prepare_tf_dataset() wraps a Hugging Face dataset in a tf.data.Dataset which is ready to use in
|
|
# training. This is the recommended way to use a Hugging Face dataset when training with Keras. You can also
|
|
# use the lower-level dataset.to_tf_dataset() method, but you will have to specify things like column names
|
|
# yourself if you use this method, whereas they are automatically inferred from the model input names when
|
|
# using model.prepare_tf_dataset()
|
|
# For more info see the docs:
|
|
# https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.TFPreTrainedModel.prepare_tf_dataset
|
|
# https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.to_tf_dataset
|
|
train_dataset = model.prepare_tf_dataset(
|
|
train_dataset,
|
|
shuffle=True,
|
|
batch_size=total_train_batch_size,
|
|
collate_fn=collate_fn,
|
|
).with_options(dataset_options)
|
|
else:
|
|
optimizer = None
|
|
|
|
if training_args.do_eval:
|
|
eval_dataset = model.prepare_tf_dataset(
|
|
eval_dataset,
|
|
shuffle=False,
|
|
batch_size=total_eval_batch_size,
|
|
collate_fn=collate_fn,
|
|
).with_options(dataset_options)
|
|
|
|
if training_args.do_predict:
|
|
predict_dataset = model.prepare_tf_dataset(
|
|
predict_dataset,
|
|
shuffle=False,
|
|
batch_size=total_eval_batch_size,
|
|
collate_fn=collate_fn,
|
|
).with_options(dataset_options)
|
|
|
|
# Transformers models compute the right loss for their task by default when labels are passed, and will
|
|
# use this for training unless you specify your own loss function in compile().
|
|
model.compile(optimizer=optimizer, jit_compile=training_args.xla, metrics=["accuracy"])
|
|
|
|
push_to_hub_model_id = training_args.push_to_hub_model_id
|
|
if not push_to_hub_model_id:
|
|
model_name = model_args.model_name_or_path.split("/")[-1]
|
|
push_to_hub_model_id = f"{model_name}-finetuned-image-classification"
|
|
|
|
model_card_kwargs = {
|
|
"finetuned_from": model_args.model_name_or_path,
|
|
"tasks": "image-classification",
|
|
"dataset": data_args.dataset_name,
|
|
"tags": ["image-classification", "tensorflow", "vision"],
|
|
}
|
|
|
|
callbacks = []
|
|
if eval_dataset is not None:
|
|
callbacks.append(KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=eval_dataset))
|
|
if training_args.push_to_hub:
|
|
callbacks.append(
|
|
PushToHubCallback(
|
|
output_dir=training_args.output_dir,
|
|
hub_model_id=push_to_hub_model_id,
|
|
hub_token=training_args.push_to_hub_token,
|
|
tokenizer=image_processor,
|
|
**model_card_kwargs,
|
|
)
|
|
)
|
|
|
|
if training_args.do_train:
|
|
model.fit(
|
|
train_dataset,
|
|
validation_data=eval_dataset,
|
|
epochs=int(training_args.num_train_epochs),
|
|
callbacks=callbacks,
|
|
)
|
|
|
|
if training_args.do_eval:
|
|
n_eval_batches = len(eval_dataset)
|
|
eval_predictions = model.predict(eval_dataset, steps=n_eval_batches)
|
|
eval_labels = dataset["validation"]["labels"][: n_eval_batches * total_eval_batch_size]
|
|
eval_metrics = compute_metrics((eval_predictions.logits, eval_labels))
|
|
logging.info("Eval metrics:")
|
|
for metric_name, value in eval_metrics.items():
|
|
logging.info(f"{metric_name}: {value:.3f}")
|
|
|
|
if training_args.output_dir is not None:
|
|
os.makedirs(training_args.output_dir, exist_ok=True)
|
|
with open(os.path.join(training_args.output_dir, "all_results.json"), "w") as f:
|
|
f.write(json.dumps(eval_metrics))
|
|
|
|
if training_args.do_predict:
|
|
n_predict_batches = len(predict_dataset)
|
|
test_predictions = model.predict(predict_dataset, steps=n_predict_batches)
|
|
test_labels = dataset["validation"]["labels"][: n_predict_batches * total_eval_batch_size]
|
|
test_metrics = compute_metrics((test_predictions.logits, test_labels))
|
|
logging.info("Test metrics:")
|
|
for metric_name, value in test_metrics.items():
|
|
logging.info(f"{metric_name}: {value:.3f}")
|
|
|
|
if training_args.output_dir is not None and not training_args.push_to_hub:
|
|
# If we're not pushing to hub, at least save a local copy when we're done
|
|
model.save_pretrained(training_args.output_dir)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|