340 lines
11 KiB
Python
340 lines
11 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Computer vision example on Transfer Learning.
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This computer vision example illustrates how one could fine-tune a pre-trained
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network (by default, a ResNet50 is used) using pytorch-lightning. For the sake
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of this example, the 'cats and dogs dataset' (~60MB, see `DATA_URL` below) and
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the proposed network (denoted by `TransferLearningModel`, see below) is
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trained for 15 epochs.
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The training consists of three stages.
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From epoch 0 to 4, the feature extractor (the pre-trained network) is frozen except
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maybe for the BatchNorm layers (depending on whether `train_bn = True`). The BatchNorm
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layers (if `train_bn = True`) and the parameters of the classifier are trained as a
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single parameters group with lr = 1e-2.
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From epoch 5 to 9, the last two layer groups of the pre-trained network are unfrozen
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and added to the optimizer as a new parameter group with lr = 1e-4 (while lr = 1e-3
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for the first parameter group in the optimizer).
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Eventually, from epoch 10, all the remaining layer groups of the pre-trained network
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are unfrozen and added to the optimizer as a third parameter group. From epoch 10,
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the parameters of the pre-trained network are trained with lr = 1e-5 while those of
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the classifier is trained with lr = 1e-4.
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Note:
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See: https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
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"""
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import argparse
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from pathlib import Path
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from tempfile import TemporaryDirectory
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from typing import Union
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import torch.nn.functional as F
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from torch import nn, optim
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from torch.optim.lr_scheduler import MultiStepLR
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from torch.optim.optimizer import Optimizer
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from torch.utils.data import DataLoader
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from torchvision import models, transforms
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from torchvision.datasets import ImageFolder
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from torchvision.datasets.utils import download_and_extract_archive
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import pytorch_lightning as pl
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from pl_examples import cli_lightning_logo
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from pytorch_lightning import _logger as log
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from pytorch_lightning.callbacks.finetuning import BaseFinetuning
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DATA_URL = "https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip"
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# --- Finetuning Callback ---
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class MilestonesFinetuningCallback(BaseFinetuning):
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def __init__(self, milestones: tuple = (5, 10), train_bn: bool = True):
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self.milestones = milestones
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self.train_bn = train_bn
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def freeze_before_training(self, pl_module: pl.LightningModule):
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self.freeze(module=pl_module.feature_extractor, train_bn=self.train_bn)
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def finetune_function(self, pl_module: pl.LightningModule, epoch: int, optimizer: Optimizer, opt_idx: int):
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if epoch == self.milestones[0]:
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# unfreeze 5 last layers
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self.unfreeze_and_add_param_group(
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module=pl_module.feature_extractor[-5:], optimizer=optimizer, train_bn=self.train_bn
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)
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elif epoch == self.milestones[1]:
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# unfreeze remaing layers
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self.unfreeze_and_add_param_group(
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module=pl_module.feature_extractor[:-5], optimizer=optimizer, train_bn=self.train_bn
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)
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# --- Pytorch-lightning module ---
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class TransferLearningModel(pl.LightningModule):
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"""Transfer Learning with pre-trained ResNet50.
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>>> with TemporaryDirectory(dir='.') as tmp_dir:
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... TransferLearningModel(tmp_dir) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
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TransferLearningModel(
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(feature_extractor): Sequential(...)
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(fc): Sequential(...)
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)
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"""
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def __init__(
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self,
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dl_path: Union[str, Path],
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backbone: str = "resnet50",
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train_bn: bool = True,
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milestones: tuple = (5, 10),
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batch_size: int = 32,
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lr: float = 1e-2,
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lr_scheduler_gamma: float = 1e-1,
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num_workers: int = 6,
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**kwargs,
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) -> None:
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"""
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Args:
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dl_path: Path where the data will be downloaded
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"""
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super().__init__()
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self.dl_path = dl_path
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self.backbone = backbone
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self.train_bn = train_bn
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self.milestones = milestones
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self.batch_size = batch_size
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self.lr = lr
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self.lr_scheduler_gamma = lr_scheduler_gamma
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self.num_workers = num_workers
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self.dl_path = dl_path
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self.__build_model()
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self.train_acc = pl.metrics.Accuracy()
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self.valid_acc = pl.metrics.Accuracy()
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self.save_hyperparameters()
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def __build_model(self):
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"""Define model layers & loss."""
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# 1. Load pre-trained network:
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model_func = getattr(models, self.backbone)
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backbone = model_func(pretrained=True)
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_layers = list(backbone.children())[:-1]
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self.feature_extractor = nn.Sequential(*_layers)
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# 2. Classifier:
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_fc_layers = [
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nn.Linear(2048, 256),
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nn.ReLU(),
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nn.Linear(256, 32),
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nn.Linear(32, 1),
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]
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self.fc = nn.Sequential(*_fc_layers)
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# 3. Loss:
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self.loss_func = F.binary_cross_entropy_with_logits
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def forward(self, x):
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"""Forward pass. Returns logits."""
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# 1. Feature extraction:
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x = self.feature_extractor(x)
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x = x.squeeze(-1).squeeze(-1)
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# 2. Classifier (returns logits):
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x = self.fc(x)
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return F.sigmoid(x)
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def loss(self, logits, labels):
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return self.loss_func(input=logits, target=labels)
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def training_step(self, batch, batch_idx):
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# 1. Forward pass:
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x, y = batch
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y_logits = self.forward(x)
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y_true = y.view((-1, 1)).type_as(x)
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# 2. Compute loss
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train_loss = self.loss(y_logits, y_true)
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# 3. Compute accuracy:
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self.log("train_acc", self.train_acc(y_logits, y_true.int()), prog_bar=True)
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return train_loss
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def validation_step(self, batch, batch_idx):
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# 1. Forward pass:
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x, y = batch
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y_logits = self.forward(x)
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y_true = y.view((-1, 1)).type_as(x)
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# 2. Compute loss
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self.log("val_loss", self.loss(y_logits, y_true), prog_bar=True)
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# 3. Compute accuracy:
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self.log("val_acc", self.valid_acc(y_logits, y_true.int()), prog_bar=True)
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def configure_optimizers(self):
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optimizer = optim.Adam(filter(lambda p: p.requires_grad, self.parameters()), lr=self.lr)
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scheduler = MultiStepLR(optimizer, milestones=self.milestones, gamma=self.lr_scheduler_gamma)
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return [optimizer], [scheduler]
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def prepare_data(self):
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"""Download images and prepare images datasets."""
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download_and_extract_archive(url=DATA_URL, download_root=self.dl_path, remove_finished=True)
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def setup(self, stage: str):
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data_path = Path(self.dl_path).joinpath("cats_and_dogs_filtered")
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# 2. Load the data + preprocessing & data augmentation
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normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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train_dataset = ImageFolder(
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root=data_path.joinpath("train"),
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transform=transforms.Compose([
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transforms.Resize((224, 224)),
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transforms.RandomHorizontalFlip(),
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transforms.ToTensor(),
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normalize,
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]),
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)
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valid_dataset = ImageFolder(
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root=data_path.joinpath("validation"),
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transform=transforms.Compose([
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transforms.Resize((224, 224)),
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transforms.ToTensor(),
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normalize,
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]),
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)
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self.train_dataset = train_dataset
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self.valid_dataset = valid_dataset
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def __dataloader(self, train: bool):
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"""Train/validation loaders."""
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_dataset = self.train_dataset if train else self.valid_dataset
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loader = DataLoader(dataset=_dataset, batch_size=self.batch_size, num_workers=self.num_workers, shuffle=train)
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return loader
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def train_dataloader(self):
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log.info("Training data loaded.")
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return self.__dataloader(train=True)
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def val_dataloader(self):
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log.info("Validation data loaded.")
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return self.__dataloader(train=False)
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@staticmethod
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def add_model_specific_args(parent_parser):
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parser = argparse.ArgumentParser(parents=[parent_parser])
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parser.add_argument(
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"--backbone",
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default="resnet50",
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type=str,
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metavar="BK",
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help="Name (as in ``torchvision.models``) of the feature extractor",
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)
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parser.add_argument(
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"--epochs", default=15, type=int, metavar="N", help="total number of epochs", dest="nb_epochs"
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)
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parser.add_argument("--batch-size", default=8, type=int, metavar="B", help="batch size", dest="batch_size")
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parser.add_argument("--gpus", type=int, default=1, help="number of gpus to use")
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parser.add_argument(
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"--lr", "--learning-rate", default=1e-3, type=float, metavar="LR", help="initial learning rate", dest="lr"
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)
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parser.add_argument(
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"--lr-scheduler-gamma",
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default=1e-1,
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type=float,
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metavar="LRG",
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help="Factor by which the learning rate is reduced at each milestone",
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dest="lr_scheduler_gamma",
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)
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parser.add_argument(
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"--num-workers", default=6, type=int, metavar="W", help="number of CPU workers", dest="num_workers"
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)
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parser.add_argument(
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"--train-bn",
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default=True,
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type=bool,
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metavar="TB",
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help="Whether the BatchNorm layers should be trainable",
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dest="train_bn",
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)
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parser.add_argument(
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"--milestones", default=[2, 4], type=list, metavar="M", help="List of two epochs milestones"
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)
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return parser
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def main(args: argparse.Namespace) -> None:
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"""Train the model.
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Args:
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args: Model hyper-parameters
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Note:
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For the sake of the example, the images dataset will be downloaded
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to a temporary directory.
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"""
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with TemporaryDirectory(dir=args.root_data_path) as tmp_dir:
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model = TransferLearningModel(dl_path=tmp_dir, **vars(args))
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finetuning_callback = MilestonesFinetuningCallback(milestones=args.milestones)
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trainer = pl.Trainer(
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weights_summary=None,
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progress_bar_refresh_rate=1,
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num_sanity_val_steps=0,
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gpus=args.gpus,
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max_epochs=args.nb_epochs,
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callbacks=[finetuning_callback]
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)
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trainer.fit(model)
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def get_args() -> argparse.Namespace:
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parent_parser = argparse.ArgumentParser(add_help=False)
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parent_parser.add_argument(
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"--root-data-path",
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metavar="DIR",
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type=str,
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default=Path.cwd().as_posix(),
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help="Root directory where to download the data",
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dest="root_data_path",
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)
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parser = TransferLearningModel.add_model_specific_args(parent_parser)
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return parser.parse_args()
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if __name__ == "__main__":
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cli_lightning_logo()
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main(get_args())
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