# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import os import re import numpy as np import PIL import torch from timm import create_model from torch.optim.lr_scheduler import OneCycleLR from torch.utils.data import DataLoader, Dataset from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor from accelerate import Accelerator ######################################################################## # This is a fully working simple example to use Accelerate # # This example trains a ResNet50 on the Oxford-IIT Pet Dataset # in any of the following settings (with the same script): # - single CPU or single GPU # - multi GPUS (using PyTorch distributed mode) # - (multi) TPUs # - fp16 (mixed-precision) or fp32 (normal precision) # # To run it in each of these various modes, follow the instructions # in the readme for examples: # https://github.com/huggingface/accelerate/tree/main/examples # ######################################################################## # Function to get the label from the filename def extract_label(fname): stem = fname.split(os.path.sep)[-1] return re.search(r"^(.*)_\d+\.jpg$", stem).groups()[0] class PetsDataset(Dataset): def __init__(self, file_names, image_transform=None, label_to_id=None): self.file_names = file_names self.image_transform = image_transform self.label_to_id = label_to_id def __len__(self): return len(self.file_names) def __getitem__(self, idx): fname = self.file_names[idx] raw_image = PIL.Image.open(fname) image = raw_image.convert("RGB") if self.image_transform is not None: image = self.image_transform(image) label = extract_label(fname) if self.label_to_id is not None: label = self.label_to_id[label] return {"image": image, "label": label} def training_function(config, args): # Initialize accelerator accelerator = Accelerator(cpu=args.cpu, mixed_precision=args.mixed_precision) # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs lr = config["lr"] num_epochs = int(config["num_epochs"]) seed = int(config["seed"]) batch_size = int(config["batch_size"]) image_size = config["image_size"] if not isinstance(image_size, (list, tuple)): image_size = (image_size, image_size) # Grab all the image filenames file_names = [os.path.join(args.data_dir, fname) for fname in os.listdir(args.data_dir) if fname.endswith(".jpg")] # Build the label correspondences all_labels = [extract_label(fname) for fname in file_names] id_to_label = list(set(all_labels)) id_to_label.sort() label_to_id = {lbl: i for i, lbl in enumerate(id_to_label)} # Set the seed before splitting the data. np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) # Split our filenames between train and validation random_perm = np.random.permutation(len(file_names)) cut = int(0.8 * len(file_names)) train_split = random_perm[:cut] eval_split = random_perm[cut:] # For training we use a simple RandomResizedCrop train_tfm = Compose([RandomResizedCrop(image_size, scale=(0.5, 1.0)), ToTensor()]) train_dataset = PetsDataset( [file_names[i] for i in train_split], image_transform=train_tfm, label_to_id=label_to_id ) # For evaluation, we use a deterministic Resize eval_tfm = Compose([Resize(image_size), ToTensor()]) eval_dataset = PetsDataset([file_names[i] for i in eval_split], image_transform=eval_tfm, label_to_id=label_to_id) # Instantiate dataloaders. train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=batch_size, num_workers=4) eval_dataloader = DataLoader(eval_dataset, shuffle=False, batch_size=batch_size, num_workers=4) # Instantiate the model (we build the model here so that the seed also control new weights initialization) model = create_model("resnet50d", pretrained=True, num_classes=len(label_to_id)) # We could avoid this line since the accelerator is set with `device_placement=True` (default value). # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that). model = model.to(accelerator.device) # Freezing the base model for param in model.parameters(): param.requires_grad = False for param in model.get_classifier().parameters(): param.requires_grad = True # We normalize the batches of images to be a bit faster. mean = torch.tensor(model.default_cfg["mean"])[None, :, None, None].to(accelerator.device) std = torch.tensor(model.default_cfg["std"])[None, :, None, None].to(accelerator.device) # Instantiate optimizer optimizer = torch.optim.Adam(params=model.parameters(), lr=lr / 25) # Instantiate learning rate scheduler lr_scheduler = OneCycleLR(optimizer=optimizer, max_lr=lr, epochs=num_epochs, steps_per_epoch=len(train_dataloader)) # Prepare everything # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the # prepare method. model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) # Now we train the model for epoch in range(num_epochs): model.train() for step, batch in enumerate(train_dataloader): # We could avoid this line since we set the accelerator with `device_placement=True`. batch = {k: v.to(accelerator.device) for k, v in batch.items()} inputs = (batch["image"] - mean) / std outputs = model(inputs) loss = torch.nn.functional.cross_entropy(outputs, batch["label"]) accelerator.backward(loss) optimizer.step() lr_scheduler.step() optimizer.zero_grad() model.eval() accurate = 0 num_elems = 0 for _, batch in enumerate(eval_dataloader): # We could avoid this line since we set the accelerator with `device_placement=True`. batch = {k: v.to(accelerator.device) for k, v in batch.items()} inputs = (batch["image"] - mean) / std with torch.no_grad(): outputs = model(inputs) predictions = outputs.argmax(dim=-1) predictions, references = accelerator.gather_for_metrics((predictions, batch["label"])) accurate_preds = predictions == references num_elems += accurate_preds.shape[0] accurate += accurate_preds.long().sum() eval_metric = accurate.item() / num_elems # Use accelerator.print to print only on the main process. accelerator.print(f"epoch {epoch}: {100 * eval_metric:.2f}") def main(): parser = argparse.ArgumentParser(description="Simple example of training script.") parser.add_argument("--data_dir", required=True, help="The data folder on disk.") parser.add_argument( "--mixed_precision", type=str, default=None, choices=["no", "fp16", "bf16"], help="Whether to use mixed precision. Choose" "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10." "and an Nvidia Ampere GPU.", ) parser.add_argument( "--checkpointing_steps", type=str, default=None, help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.", ) parser.add_argument("--cpu", action="store_true", help="If passed, will train on the CPU.") args = parser.parse_args() config = {"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224} training_function(config, args) if __name__ == "__main__": main()