Add semantic script, trainer (#16834)
* Add first draft * Improve script and README * Improve README * Apply suggestions from code review * Improve script, add link to resulting model * Add corresponding test * Adjust learning rate
This commit is contained in:
NielsRogge
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#!/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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import json
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import logging
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import os
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import random
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import sys
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from dataclasses import dataclass, field
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from typing import Optional
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import datasets
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import numpy as np
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import torch
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from datasets import load_dataset
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from PIL import Image
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from torch import nn
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from torchvision import transforms
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from torchvision.transforms import functional
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import transformers
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from huggingface_hub import hf_hub_download
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from transformers import (
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AutoConfig,
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AutoFeatureExtractor,
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AutoModelForSemanticSegmentation,
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HfArgumentParser,
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Trainer,
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TrainingArguments,
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default_data_collator,
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)
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from transformers.trainer_utils import get_last_checkpoint
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from transformers.utils import check_min_version
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from transformers.utils.versions import require_version
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""" Finetuning any 🤗 Transformers model supported by AutoModelForSemanticSegmentation for semantic segmentation leveraging the Trainer API."""
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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("4.19.0.dev0")
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require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/semantic-segmentation/requirements.txt")
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def pad_if_smaller(img, size, fill=0):
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min_size = min(img.size)
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if min_size < size:
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original_width, original_height = img.size
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pad_height = size - original_height if original_height < size else 0
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pad_width = size - original_width if original_width < size else 0
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img = functional.pad(img, (0, 0, pad_width, pad_height), fill=fill)
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return img
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class Compose:
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def __init__(self, transforms):
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self.transforms = transforms
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def __call__(self, image, target):
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for t in self.transforms:
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image, target = t(image, target)
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return image, target
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class Identity:
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def __init__(self):
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pass
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def __call__(self, image, target):
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return image, target
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class Resize:
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def __init__(self, size):
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self.size = size
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def __call__(self, image, target):
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image = functional.resize(image, self.size)
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target = functional.resize(target, self.size, interpolation=transforms.InterpolationMode.NEAREST)
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return image, target
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class RandomResize:
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def __init__(self, min_size, max_size=None):
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self.min_size = min_size
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if max_size is None:
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max_size = min_size
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self.max_size = max_size
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def __call__(self, image, target):
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size = random.randint(self.min_size, self.max_size)
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image = functional.resize(image, size)
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target = functional.resize(target, size, interpolation=transforms.InterpolationMode.NEAREST)
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return image, target
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class RandomCrop:
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def __init__(self, size):
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self.size = size
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def __call__(self, image, target):
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image = pad_if_smaller(image, self.size)
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target = pad_if_smaller(target, self.size, fill=255)
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crop_params = transforms.RandomCrop.get_params(image, (self.size, self.size))
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image = functional.crop(image, *crop_params)
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target = functional.crop(target, *crop_params)
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return image, target
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class RandomHorizontalFlip:
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def __init__(self, flip_prob):
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self.flip_prob = flip_prob
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def __call__(self, image, target):
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if random.random() < self.flip_prob:
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image = functional.hflip(image)
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target = functional.hflip(target)
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return image, target
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class PILToTensor:
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def __call__(self, image, target):
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image = functional.pil_to_tensor(image)
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target = torch.as_tensor(np.array(target), dtype=torch.int64)
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return image, target
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class ConvertImageDtype:
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def __init__(self, dtype):
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self.dtype = dtype
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def __call__(self, image, target):
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image = functional.convert_image_dtype(image, self.dtype)
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return image, target
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class Normalize:
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def __init__(self, mean, std):
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self.mean = mean
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self.std = std
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def __call__(self, image, target):
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image = functional.normalize(image, mean=self.mean, std=self.std)
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return image, target
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class ReduceLabels:
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def __call__(self, image, target):
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if not isinstance(target, np.ndarray):
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target = np.array(target).astype(np.uint8)
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# avoid using underflow conversion
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target[target == 0] = 255
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target = target - 1
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target[target == 254] = 255
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target = Image.fromarray(target)
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return image, target
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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="segments/sidewalk-semantic",
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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_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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max_train_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "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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max_eval_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "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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reduce_labels: Optional[bool] = field(
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default=False,
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metadata={"help": "Whether or not to reduce all labels by 1 and replace background by 255."},
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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="nvidia/mit-b0",
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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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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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feature_extractor_name: str = field(default=None, metadata={"help": "Name or path of preprocessor config."})
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use_auth_token: bool = field(
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default=False,
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metadata={
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"help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
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"with private models)."
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},
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)
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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, TrainingArguments))
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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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# 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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transformers.utils.logging.set_verbosity(log_level)
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transformers.utils.logging.enable_default_handler()
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transformers.utils.logging.enable_explicit_format()
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# Log on each process the small summary:
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logger.warning(
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f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
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+ f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
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)
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logger.info(f"Training/evaluation parameters {training_args}")
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# Detecting last checkpoint.
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last_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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last_checkpoint = get_last_checkpoint(training_args.output_dir)
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if last_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 last_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 {last_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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# Load dataset
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# In distributed training, the load_dataset function guarantees that only one local process can concurrently
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# download the dataset.
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# TODO support datasets from local folders
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dataset = load_dataset(data_args.dataset_name, cache_dir=model_args.cache_dir)
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# Rename column names to standardized names (only "image" and "label" need to be present)
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if "pixel_values" in dataset["train"].column_names:
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dataset = dataset.rename_columns({"pixel_values": "image"})
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if "annotation" in dataset["train"].column_names:
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dataset = dataset.rename_columns({"annotation": "label"})
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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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# 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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if data_args.dataset_name == "scene_parse_150":
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repo_id = "datasets/huggingface/label-files"
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filename = "ade20k-id2label.json"
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else:
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repo_id = f"datasets/{data_args.dataset_name}"
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filename = "id2label.json"
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id2label = json.load(open(hf_hub_download(repo_id, filename), "r"))
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id2label = {int(k): v for k, v in id2label.items()}
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label2id = {v: str(k) for k, v in id2label.items()}
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# Load the mean IoU metric from the datasets package
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metric = datasets.load_metric("mean_iou")
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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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@torch.no_grad()
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def compute_metrics(eval_pred):
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logits, labels = eval_pred
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logits_tensor = torch.from_numpy(logits)
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# scale the logits to the size of the label
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logits_tensor = nn.functional.interpolate(
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logits_tensor,
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size=labels.shape[-2:],
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mode="bilinear",
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align_corners=False,
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).argmax(dim=1)
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pred_labels = logits_tensor.detach().cpu().numpy()
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metrics = metric.compute(
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predictions=pred_labels,
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references=labels,
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num_labels=len(id2label),
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ignore_index=0,
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reduce_labels=feature_extractor.reduce_labels,
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)
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# add per category metrics as individual key-value pairs
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per_category_accuracy = metrics.pop("per_category_accuracy").tolist()
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per_category_iou = metrics.pop("per_category_iou").tolist()
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metrics.update({f"accuracy_{id2label[i]}": v for i, v in enumerate(per_category_accuracy)})
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metrics.update({f"iou_{id2label[i]}": v for i, v in enumerate(per_category_iou)})
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return metrics
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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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label2id=label2id,
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id2label=id2label,
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cache_dir=model_args.cache_dir,
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revision=model_args.model_revision,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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model = AutoModelForSemanticSegmentation.from_pretrained(
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model_args.model_name_or_path,
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from_tf=bool(".ckpt" in model_args.model_name_or_path),
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config=config,
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cache_dir=model_args.cache_dir,
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revision=model_args.model_revision,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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feature_extractor = AutoFeatureExtractor.from_pretrained(
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model_args.feature_extractor_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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use_auth_token=True if model_args.use_auth_token else None,
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)
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# Define torchvision transforms to be applied to each image + target.
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# Not that straightforward in torchvision: https://github.com/pytorch/vision/issues/9
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# Currently based on official torchvision references: https://github.com/pytorch/vision/blob/main/references/segmentation/transforms.py
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train_transforms = Compose(
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[
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ReduceLabels() if data_args.reduce_labels else Identity(),
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RandomCrop(size=feature_extractor.size),
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RandomHorizontalFlip(flip_prob=0.5),
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PILToTensor(),
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ConvertImageDtype(torch.float),
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Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std),
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]
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)
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# Define torchvision transform to be applied to each image.
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# jitter = ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.1)
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val_transforms = Compose(
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[
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ReduceLabels() if data_args.reduce_labels else Identity(),
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Resize(size=(feature_extractor.size, feature_extractor.size)),
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PILToTensor(),
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ConvertImageDtype(torch.float),
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Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std),
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]
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)
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def preprocess_train(example_batch):
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pixel_values = []
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labels = []
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for image, target in zip(example_batch["image"], example_batch["label"]):
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image, target = train_transforms(image.convert("RGB"), target)
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pixel_values.append(image)
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labels.append(target)
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encoding = dict()
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encoding["pixel_values"] = torch.stack(pixel_values)
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encoding["labels"] = torch.stack(labels)
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return encoding
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def preprocess_val(example_batch):
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pixel_values = []
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labels = []
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for image, target in zip(example_batch["image"], example_batch["label"]):
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image, target = val_transforms(image.convert("RGB"), target)
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pixel_values.append(image)
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labels.append(target)
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encoding = dict()
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encoding["pixel_values"] = torch.stack(pixel_values)
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encoding["labels"] = torch.stack(labels)
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return encoding
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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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if data_args.max_train_samples is not None:
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dataset["train"] = (
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dataset["train"].shuffle(seed=training_args.seed).select(range(data_args.max_train_samples))
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)
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# Set the training transforms
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dataset["train"].set_transform(preprocess_train)
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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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if data_args.max_eval_samples is not None:
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dataset["validation"] = (
|
||||
dataset["validation"].shuffle(seed=training_args.seed).select(range(data_args.max_eval_samples))
|
||||
)
|
||||
# Set the validation transforms
|
||||
dataset["validation"].set_transform(preprocess_val)
|
||||
|
||||
# Initalize our trainer
|
||||
trainer = Trainer(
|
||||
model=model,
|
||||
args=training_args,
|
||||
train_dataset=dataset["train"] if training_args.do_train else None,
|
||||
eval_dataset=dataset["validation"] if training_args.do_eval else None,
|
||||
compute_metrics=compute_metrics,
|
||||
tokenizer=feature_extractor,
|
||||
data_collator=default_data_collator,
|
||||
)
|
||||
|
||||
# Training
|
||||
if training_args.do_train:
|
||||
checkpoint = None
|
||||
if training_args.resume_from_checkpoint is not None:
|
||||
checkpoint = training_args.resume_from_checkpoint
|
||||
elif last_checkpoint is not None:
|
||||
checkpoint = last_checkpoint
|
||||
train_result = trainer.train(resume_from_checkpoint=checkpoint)
|
||||
trainer.save_model()
|
||||
trainer.log_metrics("train", train_result.metrics)
|
||||
trainer.save_metrics("train", train_result.metrics)
|
||||
trainer.save_state()
|
||||
|
||||
# Evaluation
|
||||
if training_args.do_eval:
|
||||
metrics = trainer.evaluate()
|
||||
trainer.log_metrics("eval", metrics)
|
||||
trainer.save_metrics("eval", metrics)
|
||||
|
||||
# Write model card and (optionally) push to hub
|
||||
kwargs = {
|
||||
"finetuned_from": model_args.model_name_or_path,
|
||||
"dataset": data_args.dataset_name,
|
||||
"tags": ["image-segmentation", "vision"],
|
||||
}
|
||||
if training_args.push_to_hub:
|
||||
trainer.push_to_hub(**kwargs)
|
||||
else:
|
||||
trainer.create_model_card(**kwargs)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user