v1.0
This commit is contained in:
rmaphoh
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# RETFound_MAE
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RETFound - A foundation model for retinal image
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## RETFound - A foundation model for retinal image
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This is official repo for RETFound, which heavily bases on [MAE](https://github.com/facebookresearch/mae):
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### Key features
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- RETFound was trained on 1.6 million retinal images
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- RETFound has been validated in multiple disease detection tasks
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- RETFound can be efficiently adapted to customised task
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### Install enviroment
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Create enviroment with conda:
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```
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conda create -n retfound python=3.6.15 -y
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```
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Install Pytorch 1.81 (cuda 11.1)
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```
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pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html
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```
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Install others
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```
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pip install -r requirement.txt
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```
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### Fine-tuning with RETFound weights
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- RETFound pre-trained weights
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<table><tbody>
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<!-- START TABLE -->
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<!-- TABLE HEADER -->
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<th valign="bottom"></th>
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<th valign="bottom">ViT-Large</th>
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<!-- TABLE BODY -->
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<tr><td align="left">Colour fundus image</td>
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<td align="center"><a href="https://dl.fbaipublicfiles.com/mae/pretrain/mae_pretrain_vit_large.pth">download</a></td>
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</tr>
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<!-- TABLE BODY -->
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<tr><td align="left">OCT</td>
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<td align="center"><a href="https://dl.fbaipublicfiles.com/mae/pretrain/mae_pretrain_vit_large.pth">download</a></td>
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</tr>
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</tbody></table>
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- Organise data (use IDRiD as example)
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<p align="left">
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<img src="https://user-images.githubusercontent.com/11435359/146857310-f258c86c-fde6-48e8-9cee-badd2b21bd2c.png" width="480">
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</p>
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- Start fine-tuning (use IDRiD as example). A fine-tuned checkpoint will be saved during training. Evaluation will be run after training.
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```
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python -m torch.distributed.launch --nproc_per_node=1 --master_port=48798 main_finetune.py
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--batch_size 16 \
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--world_size 1 \
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--model vit_large_patch16 \
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--epochs 50 \
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--blr 5e-3 --layer_decay 0.65 \
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--weight_decay 0.05 --drop_path 0.2 \
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--nb_classes 5 \
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--data_path ./IDRiD_data/ \
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--task ./finetune_IDRiD/ \
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--finetune ./RETFound_cfp_weights.pth
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```
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- For evaluation only
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```
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python -m torch.distributed.launch --nproc_per_node=1 --master_port=48798 main_finetune.py
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--eval --batch_size 16 \
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--world_size 1 \
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--model vit_large_patch16 \
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--epochs 40 \
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--blr 5e-3 --layer_decay 0.65 \
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--weight_decay 0.05 --drop_path 0.2 \
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--nb_classes 5 \
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--data_path ./IDRiD_data/ \
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--task ./internal_IDRiD/ \
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--resume ./finetune_IDRiD/checkpoint-best.pth
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```
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@@ -0,0 +1,214 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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# --------------------------------------------------------
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# References:
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# DeiT: https://github.com/facebookresearch/deit
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# BEiT: https://github.com/microsoft/unilm/tree/master/beit
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# --------------------------------------------------------
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import math
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import sys
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import csv
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import os
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from timm.data import Mixup
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from timm.utils import accuracy
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from typing import Iterable, Optional
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import util.misc as misc
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import util.lr_sched as lr_sched
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from sklearn.metrics import accuracy_score, roc_auc_score, f1_score, average_precision_score,multilabel_confusion_matrix
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from pycm import *
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import matplotlib.pyplot as plt
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import numpy as np
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def misc_measures(confusion_matrix):
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acc = []
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sensitivity = []
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specificity = []
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precision = []
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G = []
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F1_score_2 = []
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mcc_ = []
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for i in range(1, confusion_matrix.shape[0]):
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cm1=confusion_matrix[i]
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acc.append(1.*(cm1[0,0]+cm1[1,1])/np.sum(cm1))
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sensitivity_ = 1.*cm1[1,1]/(cm1[1,0]+cm1[1,1])
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sensitivity.append(sensitivity_)
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specificity_ = 1.*cm1[0,0]/(cm1[0,1]+cm1[0,0])
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specificity.append(specificity_)
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precision_ = 1.*cm1[1,1]/(cm1[1,1]+cm1[0,1])
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precision.append(precision_)
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G.append(np.sqrt(sensitivity_*specificity_))
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F1_score_2.append(2*precision_*sensitivity_/(precision_+sensitivity_))
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mcc = (cm1[0,0]*cm1[1,1]-cm1[0,1]*cm1[1,0])/np.sqrt((cm1[0,0]+cm1[0,1])*(cm1[0,0]+cm1[1,0])*(cm1[1,1]+cm1[1,0])*(cm1[1,1]+cm1[0,1]))
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mcc_.append(mcc)
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acc = np.array(acc).mean()
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sensitivity = np.array(sensitivity).mean()
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specificity = np.array(specificity).mean()
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precision = np.array(precision).mean()
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G = np.array(G).mean()
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F1_score_2 = np.array(F1_score_2).mean()
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mcc_ = np.array(mcc_).mean()
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return acc, sensitivity, specificity, precision, G, F1_score_2, mcc_
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def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
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data_loader: Iterable, optimizer: torch.optim.Optimizer,
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device: torch.device, epoch: int, loss_scaler, max_norm: float = 0,
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mixup_fn: Optional[Mixup] = None, log_writer=None,
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args=None):
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model.train(True)
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metric_logger = misc.MetricLogger(delimiter=" ")
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metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}'))
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header = 'Epoch: [{}]'.format(epoch)
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print_freq = 20
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accum_iter = args.accum_iter
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optimizer.zero_grad()
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if log_writer is not None:
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print('log_dir: {}'.format(log_writer.log_dir))
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for data_iter_step, (samples, targets) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
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# we use a per iteration (instead of per epoch) lr scheduler
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if data_iter_step % accum_iter == 0:
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lr_sched.adjust_learning_rate(optimizer, data_iter_step / len(data_loader) + epoch, args)
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samples = samples.to(device, non_blocking=True)
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targets = targets.to(device, non_blocking=True)
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if mixup_fn is not None:
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samples, targets = mixup_fn(samples, targets)
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with torch.cuda.amp.autocast():
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outputs = model(samples)
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loss = criterion(outputs, targets)
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loss_value = loss.item()
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if not math.isfinite(loss_value):
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print("Loss is {}, stopping training".format(loss_value))
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sys.exit(1)
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loss /= accum_iter
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loss_scaler(loss, optimizer, clip_grad=max_norm,
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parameters=model.parameters(), create_graph=False,
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update_grad=(data_iter_step + 1) % accum_iter == 0)
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if (data_iter_step + 1) % accum_iter == 0:
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optimizer.zero_grad()
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torch.cuda.synchronize()
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metric_logger.update(loss=loss_value)
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min_lr = 10.
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max_lr = 0.
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for group in optimizer.param_groups:
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min_lr = min(min_lr, group["lr"])
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max_lr = max(max_lr, group["lr"])
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metric_logger.update(lr=max_lr)
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loss_value_reduce = misc.all_reduce_mean(loss_value)
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if log_writer is not None and (data_iter_step + 1) % accum_iter == 0:
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""" We use epoch_1000x as the x-axis in tensorboard.
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This calibrates different curves when batch size changes.
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"""
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epoch_1000x = int((data_iter_step / len(data_loader) + epoch) * 1000)
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log_writer.add_scalar('loss', loss_value_reduce, epoch_1000x)
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log_writer.add_scalar('lr', max_lr, epoch_1000x)
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# gather the stats from all processes
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metric_logger.synchronize_between_processes()
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print("Averaged stats:", metric_logger)
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return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
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@torch.no_grad()
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def evaluate(data_loader, model, device, task, epoch, mode, num_class):
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criterion = torch.nn.CrossEntropyLoss()
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metric_logger = misc.MetricLogger(delimiter=" ")
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header = 'Test:'
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if not os.path.exists(task):
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os.makedirs(task)
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prediction_decode_list = []
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prediction_list = []
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true_label_decode_list = []
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true_label_onehot_list = []
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# switch to evaluation mode
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model.eval()
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for batch in metric_logger.log_every(data_loader, 10, header):
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images = batch[0]
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target = batch[-1]
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images = images.to(device, non_blocking=True)
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target = target.to(device, non_blocking=True)
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true_label=F.one_hot(target.to(torch.int64), num_classes=num_class)
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# compute output
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with torch.cuda.amp.autocast():
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output = model(images)
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loss = criterion(output, target)
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prediction_softmax = nn.Softmax(dim=1)(output)
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_,prediction_decode = torch.max(prediction_softmax, 1)
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_,true_label_decode = torch.max(true_label, 1)
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prediction_decode_list.extend(prediction_decode.cpu().detach().numpy())
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true_label_decode_list.extend(true_label_decode.cpu().detach().numpy())
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true_label_onehot_list.extend(true_label.cpu().detach().numpy())
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prediction_list.extend(prediction_softmax.cpu().detach().numpy())
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acc1,_ = accuracy(output, target, topk=(1,2))
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batch_size = images.shape[0]
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metric_logger.update(loss=loss.item())
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metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
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# gather the stats from all processes
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true_label_decode_list = np.array(true_label_decode_list)
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prediction_decode_list = np.array(prediction_decode_list)
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confusion_matrix = multilabel_confusion_matrix(true_label_decode_list, prediction_decode_list,labels=[i for i in range(num_class)])
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acc, sensitivity, specificity, precision, G, F1, mcc = misc_measures(confusion_matrix)
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auc_roc = roc_auc_score(true_label_onehot_list, prediction_list,multi_class='ovr',average='macro')
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auc_pr = average_precision_score(true_label_onehot_list, prediction_list,average='macro')
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metric_logger.synchronize_between_processes()
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print('Sklearn Metrics - Acc: {:.4f} AUC-roc: {:.4f} AUC-pr: {:.4f} F1-score: {:.4f} MCC: {:.4f}'.format(acc, auc_roc, auc_pr, F1, mcc))
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results_path = task+'_metrics_{}.csv'.format(mode)
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with open(results_path,mode='a',newline='',encoding='utf8') as cfa:
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wf = csv.writer(cfa)
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data2=[[acc,sensitivity,specificity,precision,auc_roc,auc_pr,F1,mcc,metric_logger.loss]]
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for i in data2:
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wf.writerow(i)
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if mode=='test':
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cm = ConfusionMatrix(actual_vector=true_label_decode_list, predict_vector=prediction_decode_list)
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cm.plot(cmap=plt.cm.Blues,number_label=True,normalized=True,plot_lib="matplotlib")
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plt.savefig(task+'confusion_matrix_test.jpg',dpi=600,bbox_inches ='tight')
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return {k: meter.global_avg for k, meter in metric_logger.meters.items()},auc_roc
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@@ -0,0 +1,387 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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# --------------------------------------------------------
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# References:
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# DeiT: https://github.com/facebookresearch/deit
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# BEiT: https://github.com/microsoft/unilm/tree/master/beit
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# --------------------------------------------------------
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import argparse
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import datetime
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import json
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import numpy as np
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import os
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import time
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from pathlib import Path
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import torch
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import torch.backends.cudnn as cudnn
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from torch.utils.tensorboard import SummaryWriter
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import timm
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assert timm.__version__ == "0.3.2" # version check
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from timm.models.layers import trunc_normal_
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from timm.data.mixup import Mixup
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from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
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import util.lr_decay as lrd
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import util.misc as misc
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from util.datasets import build_dataset
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from util.pos_embed import interpolate_pos_embed
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from util.misc import NativeScalerWithGradNormCount as NativeScaler
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import models_vit
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from engine_finetune import train_one_epoch, evaluate
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def get_args_parser():
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parser = argparse.ArgumentParser('MAE fine-tuning for image classification', add_help=False)
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parser.add_argument('--batch_size', default=64, type=int,
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help='Batch size per GPU (effective batch size is batch_size * accum_iter * # gpus')
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parser.add_argument('--epochs', default=50, type=int)
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parser.add_argument('--accum_iter', default=1, type=int,
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help='Accumulate gradient iterations (for increasing the effective batch size under memory constraints)')
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# Model parameters
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parser.add_argument('--model', default='vit_large_patch16', type=str, metavar='MODEL',
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help='Name of model to train')
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parser.add_argument('--input_size', default=224, type=int,
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help='images input size')
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parser.add_argument('--drop_path', type=float, default=0.1, metavar='PCT',
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help='Drop path rate (default: 0.1)')
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# Optimizer parameters
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parser.add_argument('--clip_grad', type=float, default=None, metavar='NORM',
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help='Clip gradient norm (default: None, no clipping)')
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parser.add_argument('--weight_decay', type=float, default=0.05,
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help='weight decay (default: 0.05)')
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parser.add_argument('--lr', type=float, default=None, metavar='LR',
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help='learning rate (absolute lr)')
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parser.add_argument('--blr', type=float, default=1e-3, metavar='LR',
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help='base learning rate: absolute_lr = base_lr * total_batch_size / 256')
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parser.add_argument('--layer_decay', type=float, default=0.75,
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help='layer-wise lr decay from ELECTRA/BEiT')
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parser.add_argument('--min_lr', type=float, default=1e-6, metavar='LR',
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help='lower lr bound for cyclic schedulers that hit 0')
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parser.add_argument('--warmup_epochs', type=int, default=10, metavar='N',
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help='epochs to warmup LR')
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# Augmentation parameters
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parser.add_argument('--color_jitter', type=float, default=None, metavar='PCT',
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help='Color jitter factor (enabled only when not using Auto/RandAug)')
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parser.add_argument('--aa', type=str, default='rand-m9-mstd0.5-inc1', metavar='NAME',
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help='Use AutoAugment policy. "v0" or "original". " + "(default: rand-m9-mstd0.5-inc1)'),
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parser.add_argument('--smoothing', type=float, default=0.1,
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help='Label smoothing (default: 0.1)')
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# * Random Erase params
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parser.add_argument('--reprob', type=float, default=0.25, metavar='PCT',
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help='Random erase prob (default: 0.25)')
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parser.add_argument('--remode', type=str, default='pixel',
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help='Random erase mode (default: "pixel")')
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parser.add_argument('--recount', type=int, default=1,
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help='Random erase count (default: 1)')
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parser.add_argument('--resplit', action='store_true', default=False,
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help='Do not random erase first (clean) augmentation split')
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# * Mixup params
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parser.add_argument('--mixup', type=float, default=0,
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help='mixup alpha, mixup enabled if > 0.')
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parser.add_argument('--cutmix', type=float, default=0,
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help='cutmix alpha, cutmix enabled if > 0.')
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parser.add_argument('--cutmix_minmax', type=float, nargs='+', default=None,
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help='cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)')
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parser.add_argument('--mixup_prob', type=float, default=1.0,
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help='Probability of performing mixup or cutmix when either/both is enabled')
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parser.add_argument('--mixup_switch_prob', type=float, default=0.5,
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help='Probability of switching to cutmix when both mixup and cutmix enabled')
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parser.add_argument('--mixup_mode', type=str, default='batch',
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help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"')
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# * Finetuning params
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parser.add_argument('--finetune', default='',type=str,
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help='finetune from checkpoint')
|
||||
parser.add_argument('--task', default='',type=str,
|
||||
help='finetune from checkpoint')
|
||||
parser.add_argument('--global_pool', action='store_true')
|
||||
parser.set_defaults(global_pool=True)
|
||||
parser.add_argument('--cls_token', action='store_false', dest='global_pool',
|
||||
help='Use class token instead of global pool for classification')
|
||||
|
||||
# Dataset parameters
|
||||
parser.add_argument('--data_path', default='/home/jupyter/Mor_DR_data/data/data/IDRID/Disease_Grading/', type=str,
|
||||
help='dataset path')
|
||||
parser.add_argument('--nb_classes', default=1000, type=int,
|
||||
help='number of the classification types')
|
||||
|
||||
parser.add_argument('--output_dir', default='./output_dir',
|
||||
help='path where to save, empty for no saving')
|
||||
parser.add_argument('--log_dir', default='./output_dir',
|
||||
help='path where to tensorboard log')
|
||||
parser.add_argument('--device', default='cuda',
|
||||
help='device to use for training / testing')
|
||||
parser.add_argument('--seed', default=0, type=int)
|
||||
parser.add_argument('--resume', default='',
|
||||
help='resume from checkpoint')
|
||||
|
||||
parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
|
||||
help='start epoch')
|
||||
parser.add_argument('--eval', action='store_true',
|
||||
help='Perform evaluation only')
|
||||
parser.add_argument('--dist_eval', action='store_true', default=False,
|
||||
help='Enabling distributed evaluation (recommended during training for faster monitor')
|
||||
parser.add_argument('--num_workers', default=10, type=int)
|
||||
parser.add_argument('--pin_mem', action='store_true',
|
||||
help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
|
||||
parser.add_argument('--no_pin_mem', action='store_false', dest='pin_mem')
|
||||
parser.set_defaults(pin_mem=True)
|
||||
|
||||
# distributed training parameters
|
||||
parser.add_argument('--world_size', default=1, type=int,
|
||||
help='number of distributed processes')
|
||||
parser.add_argument('--local_rank', default=-1, type=int)
|
||||
parser.add_argument('--dist_on_itp', action='store_true')
|
||||
parser.add_argument('--dist_url', default='env://',
|
||||
help='url used to set up distributed training')
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
def main(args):
|
||||
misc.init_distributed_mode(args)
|
||||
|
||||
print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__))))
|
||||
print("{}".format(args).replace(', ', ',\n'))
|
||||
|
||||
device = torch.device(args.device)
|
||||
|
||||
# fix the seed for reproducibility
|
||||
seed = args.seed + misc.get_rank()
|
||||
torch.manual_seed(seed)
|
||||
np.random.seed(seed)
|
||||
|
||||
cudnn.benchmark = True
|
||||
|
||||
dataset_train = build_dataset(is_train='train', args=args)
|
||||
dataset_val = build_dataset(is_train='val', args=args)
|
||||
dataset_test = build_dataset(is_train='test', args=args)
|
||||
|
||||
if True: # args.distributed:
|
||||
num_tasks = misc.get_world_size()
|
||||
global_rank = misc.get_rank()
|
||||
sampler_train = torch.utils.data.DistributedSampler(
|
||||
dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True
|
||||
)
|
||||
print("Sampler_train = %s" % str(sampler_train))
|
||||
if args.dist_eval:
|
||||
if len(dataset_val) % num_tasks != 0:
|
||||
print('Warning: Enabling distributed evaluation with an eval dataset not divisible by process number. '
|
||||
'This will slightly alter validation results as extra duplicate entries are added to achieve '
|
||||
'equal num of samples per-process.')
|
||||
sampler_val = torch.utils.data.DistributedSampler(
|
||||
dataset_val, num_replicas=num_tasks, rank=global_rank, shuffle=True) # shuffle=True to reduce monitor bias
|
||||
else:
|
||||
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
|
||||
|
||||
if args.dist_eval:
|
||||
if len(dataset_test) % num_tasks != 0:
|
||||
print('Warning: Enabling distributed evaluation with an eval dataset not divisible by process number. '
|
||||
'This will slightly alter validation results as extra duplicate entries are added to achieve '
|
||||
'equal num of samples per-process.')
|
||||
sampler_test = torch.utils.data.DistributedSampler(
|
||||
dataset_test, num_replicas=num_tasks, rank=global_rank, shuffle=True) # shuffle=True to reduce monitor bias
|
||||
else:
|
||||
sampler_test = torch.utils.data.SequentialSampler(dataset_test)
|
||||
|
||||
|
||||
else:
|
||||
sampler_train = torch.utils.data.RandomSampler(dataset_train)
|
||||
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
|
||||
sampler_test = torch.utils.data.SequentialSampler(dataset_test)
|
||||
|
||||
if global_rank == 0 and args.log_dir is not None and not args.eval:
|
||||
os.makedirs(args.log_dir, exist_ok=True)
|
||||
log_writer = SummaryWriter(log_dir=args.log_dir+args.task)
|
||||
else:
|
||||
log_writer = None
|
||||
|
||||
data_loader_train = torch.utils.data.DataLoader(
|
||||
dataset_train, sampler=sampler_train,
|
||||
batch_size=args.batch_size,
|
||||
num_workers=args.num_workers,
|
||||
pin_memory=args.pin_mem,
|
||||
drop_last=True,
|
||||
)
|
||||
|
||||
data_loader_val = torch.utils.data.DataLoader(
|
||||
dataset_val, sampler=sampler_val,
|
||||
batch_size=args.batch_size,
|
||||
num_workers=args.num_workers,
|
||||
pin_memory=args.pin_mem,
|
||||
drop_last=False
|
||||
)
|
||||
|
||||
data_loader_test = torch.utils.data.DataLoader(
|
||||
dataset_test, sampler=sampler_test,
|
||||
batch_size=args.batch_size,
|
||||
num_workers=args.num_workers,
|
||||
pin_memory=args.pin_mem,
|
||||
drop_last=False
|
||||
)
|
||||
|
||||
|
||||
mixup_fn = None
|
||||
mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
|
||||
if mixup_active:
|
||||
print("Mixup is activated!")
|
||||
mixup_fn = Mixup(
|
||||
mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, cutmix_minmax=args.cutmix_minmax,
|
||||
prob=args.mixup_prob, switch_prob=args.mixup_switch_prob, mode=args.mixup_mode,
|
||||
label_smoothing=args.smoothing, num_classes=args.nb_classes)
|
||||
|
||||
model = models_vit.__dict__[args.model](
|
||||
num_classes=args.nb_classes,
|
||||
drop_path_rate=args.drop_path,
|
||||
global_pool=args.global_pool,
|
||||
)
|
||||
|
||||
if args.finetune and not args.eval:
|
||||
checkpoint = torch.load(args.finetune, map_location='cpu')
|
||||
|
||||
print("Load pre-trained checkpoint from: %s" % args.finetune)
|
||||
checkpoint_model = checkpoint['model']
|
||||
state_dict = model.state_dict()
|
||||
for k in ['head.weight', 'head.bias']:
|
||||
if k in checkpoint_model and checkpoint_model[k].shape != state_dict[k].shape:
|
||||
print(f"Removing key {k} from pretrained checkpoint")
|
||||
del checkpoint_model[k]
|
||||
|
||||
# interpolate position embedding
|
||||
interpolate_pos_embed(model, checkpoint_model)
|
||||
|
||||
# load pre-trained model
|
||||
msg = model.load_state_dict(checkpoint_model, strict=False)
|
||||
print(msg)
|
||||
|
||||
if args.global_pool:
|
||||
assert set(msg.missing_keys) == {'head.weight', 'head.bias', 'fc_norm.weight', 'fc_norm.bias'}
|
||||
else:
|
||||
assert set(msg.missing_keys) == {'head.weight', 'head.bias'}
|
||||
|
||||
# manually initialize fc layer
|
||||
trunc_normal_(model.head.weight, std=2e-5)
|
||||
|
||||
model.to(device)
|
||||
|
||||
model_without_ddp = model
|
||||
n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
|
||||
|
||||
print("Model = %s" % str(model_without_ddp))
|
||||
print('number of params (M): %.2f' % (n_parameters / 1.e6))
|
||||
|
||||
eff_batch_size = args.batch_size * args.accum_iter * misc.get_world_size()
|
||||
|
||||
if args.lr is None: # only base_lr is specified
|
||||
args.lr = args.blr * eff_batch_size / 256
|
||||
|
||||
print("base lr: %.2e" % (args.lr * 256 / eff_batch_size))
|
||||
print("actual lr: %.2e" % args.lr)
|
||||
|
||||
print("accumulate grad iterations: %d" % args.accum_iter)
|
||||
print("effective batch size: %d" % eff_batch_size)
|
||||
|
||||
if args.distributed:
|
||||
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
|
||||
model_without_ddp = model.module
|
||||
|
||||
# build optimizer with layer-wise lr decay (lrd)
|
||||
param_groups = lrd.param_groups_lrd(model_without_ddp, args.weight_decay,
|
||||
no_weight_decay_list=model_without_ddp.no_weight_decay(),
|
||||
layer_decay=args.layer_decay
|
||||
)
|
||||
optimizer = torch.optim.AdamW(param_groups, lr=args.lr)
|
||||
loss_scaler = NativeScaler()
|
||||
|
||||
if mixup_fn is not None:
|
||||
# smoothing is handled with mixup label transform
|
||||
criterion = SoftTargetCrossEntropy()
|
||||
elif args.smoothing > 0.:
|
||||
criterion = LabelSmoothingCrossEntropy(smoothing=args.smoothing)
|
||||
else:
|
||||
criterion = torch.nn.CrossEntropyLoss()
|
||||
|
||||
print("criterion = %s" % str(criterion))
|
||||
|
||||
misc.load_model(args=args, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler)
|
||||
|
||||
if args.eval:
|
||||
test_stats,auc_roc = evaluate(data_loader_test, model, device, args.task, epoch=0, mode='test',num_class=args.nb_classes)
|
||||
exit(0)
|
||||
|
||||
print(f"Start training for {args.epochs} epochs")
|
||||
start_time = time.time()
|
||||
max_accuracy = 0.0
|
||||
max_auc = 0.0
|
||||
for epoch in range(args.start_epoch, args.epochs):
|
||||
if args.distributed:
|
||||
data_loader_train.sampler.set_epoch(epoch)
|
||||
train_stats = train_one_epoch(
|
||||
model, criterion, data_loader_train,
|
||||
optimizer, device, epoch, loss_scaler,
|
||||
args.clip_grad, mixup_fn,
|
||||
log_writer=log_writer,
|
||||
args=args
|
||||
)
|
||||
|
||||
val_stats,val_auc_roc = evaluate(data_loader_val, model, device,args.task,epoch, mode='val',num_class=args.nb_classes)
|
||||
if max_auc<val_auc_roc:
|
||||
max_auc = val_auc_roc
|
||||
|
||||
if args.output_dir:
|
||||
misc.save_model(
|
||||
args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
|
||||
loss_scaler=loss_scaler, epoch=epoch)
|
||||
|
||||
|
||||
if epoch==(args.epochs-1):
|
||||
test_stats,auc_roc = evaluate(data_loader_test, model, device,args.task,epoch, mode='test',num_class=args.nb_classes)
|
||||
|
||||
|
||||
if log_writer is not None:
|
||||
log_writer.add_scalar('perf/val_acc1', val_stats['acc1'], epoch)
|
||||
log_writer.add_scalar('perf/val_auc', val_auc_roc, epoch)
|
||||
log_writer.add_scalar('perf/val_loss', val_stats['loss'], epoch)
|
||||
|
||||
log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
|
||||
'epoch': epoch,
|
||||
'n_parameters': n_parameters}
|
||||
|
||||
if args.output_dir and misc.is_main_process():
|
||||
if log_writer is not None:
|
||||
log_writer.flush()
|
||||
with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") as f:
|
||||
f.write(json.dumps(log_stats) + "\n")
|
||||
|
||||
|
||||
total_time = time.time() - start_time
|
||||
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
|
||||
print('Training time {}'.format(total_time_str))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
args = get_args_parser()
|
||||
args = args.parse_args()
|
||||
|
||||
if args.output_dir:
|
||||
Path(args.output_dir).mkdir(parents=True, exist_ok=True)
|
||||
main(args)
|
||||
@@ -0,0 +1,61 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
# --------------------------------------------------------
|
||||
# References:
|
||||
# timm: https://github.com/rwightman/pytorch-image-models/tree/master/timm
|
||||
# DeiT: https://github.com/facebookresearch/deit
|
||||
# --------------------------------------------------------
|
||||
|
||||
from functools import partial
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
import timm.models.vision_transformer
|
||||
|
||||
|
||||
class VisionTransformer(timm.models.vision_transformer.VisionTransformer):
|
||||
""" Vision Transformer with support for global average pooling
|
||||
"""
|
||||
def __init__(self, global_pool=False, **kwargs):
|
||||
super(VisionTransformer, self).__init__(**kwargs)
|
||||
|
||||
self.global_pool = global_pool
|
||||
if self.global_pool:
|
||||
norm_layer = kwargs['norm_layer']
|
||||
embed_dim = kwargs['embed_dim']
|
||||
self.fc_norm = norm_layer(embed_dim)
|
||||
|
||||
del self.norm # remove the original norm
|
||||
|
||||
def forward_features(self, x):
|
||||
B = x.shape[0]
|
||||
x = self.patch_embed(x)
|
||||
|
||||
cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
|
||||
x = torch.cat((cls_tokens, x), dim=1)
|
||||
x = x + self.pos_embed
|
||||
x = self.pos_drop(x)
|
||||
|
||||
for blk in self.blocks:
|
||||
x = blk(x)
|
||||
|
||||
if self.global_pool:
|
||||
x = x[:, 1:, :].mean(dim=1) # global pool without cls token
|
||||
outcome = self.fc_norm(x)
|
||||
else:
|
||||
x = self.norm(x)
|
||||
outcome = x[:, 0]
|
||||
|
||||
return outcome
|
||||
|
||||
|
||||
def vit_large_patch16(**kwargs):
|
||||
model = VisionTransformer(
|
||||
img_size=224,patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
|
||||
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
|
||||
return model
|
||||
|
||||
@@ -0,0 +1,53 @@
|
||||
absl-py==0.13.0
|
||||
art==5.2
|
||||
cached-property==1.5.2
|
||||
cachetools==4.2.2
|
||||
charset-normalizer==2.0.4
|
||||
cycler==0.10.0
|
||||
dataclasses==0.8
|
||||
decorator==4.4.2
|
||||
future==0.18.2
|
||||
google-auth==1.35.0
|
||||
google-auth-oauthlib==0.4.5
|
||||
grpcio==1.39.0
|
||||
h5py==3.1.0
|
||||
idna==3.2
|
||||
imageio==2.9.0
|
||||
importlib-metadata==4.6.4
|
||||
joblib==1.0.1
|
||||
kiwisolver==1.3.1
|
||||
Markdown==3.3.4
|
||||
matplotlib==3.3.4
|
||||
networkx==2.5.1
|
||||
numpy==1.19.5
|
||||
oauthlib==3.1.1
|
||||
opencv-python==4.5.3.56
|
||||
pandas==0.25.3
|
||||
Pillow==8.3.1
|
||||
protobuf==3.17.3
|
||||
pyasn1==0.4.8
|
||||
pyasn1-modules==0.2.8
|
||||
pycm==3.2
|
||||
pydicom==2.3.0
|
||||
pyparsing==2.4.7
|
||||
python-dateutil==2.8.2
|
||||
pytz==2021.1
|
||||
PyWavelets==1.1.1
|
||||
requests==2.26.0
|
||||
requests-oauthlib==1.3.0
|
||||
rsa==4.7.2
|
||||
scikit-image==0.17.2
|
||||
scikit-learn==0.24.2
|
||||
scipy==1.5.4
|
||||
six==1.16.0
|
||||
tensorboard==2.6.0
|
||||
tensorboard-data-server==0.6.1
|
||||
tensorboard-plugin-wit==1.8.0
|
||||
threadpoolctl==2.2.0
|
||||
tifffile==2020.9.3
|
||||
timm==0.3.2
|
||||
tqdm==4.62.1
|
||||
typing-extensions==3.10.0.0
|
||||
urllib3==1.26.6
|
||||
Werkzeug==2.0.1
|
||||
zipp==3.5.0
|
||||
@@ -0,0 +1,60 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
# --------------------------------------------------------
|
||||
# References:
|
||||
# DeiT: https://github.com/facebookresearch/deit
|
||||
# --------------------------------------------------------
|
||||
|
||||
import os
|
||||
import PIL
|
||||
from torchvision import datasets, transforms
|
||||
from timm.data import create_transform
|
||||
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
|
||||
|
||||
|
||||
def build_dataset(is_train, args):
|
||||
|
||||
transform = build_transform(is_train, args)
|
||||
root = os.path.join(args.data_path, is_train)
|
||||
dataset = datasets.ImageFolder(root, transform=transform)
|
||||
|
||||
return dataset
|
||||
|
||||
|
||||
def build_transform(is_train, args):
|
||||
mean = IMAGENET_DEFAULT_MEAN
|
||||
std = IMAGENET_DEFAULT_STD
|
||||
# train transform
|
||||
if is_train=='train':
|
||||
# this should always dispatch to transforms_imagenet_train
|
||||
transform = create_transform(
|
||||
input_size=args.input_size,
|
||||
is_training=True,
|
||||
color_jitter=args.color_jitter,
|
||||
auto_augment=args.aa,
|
||||
interpolation='bicubic',
|
||||
re_prob=args.reprob,
|
||||
re_mode=args.remode,
|
||||
re_count=args.recount,
|
||||
mean=mean,
|
||||
std=std,
|
||||
)
|
||||
return transform
|
||||
|
||||
# eval transform
|
||||
t = []
|
||||
if args.input_size <= 224:
|
||||
crop_pct = 224 / 256
|
||||
else:
|
||||
crop_pct = 1.0
|
||||
size = int(args.input_size / crop_pct)
|
||||
t.append(
|
||||
transforms.Resize(size, interpolation=transforms.InterpolationMode.BICUBIC),
|
||||
)
|
||||
t.append(transforms.CenterCrop(args.input_size))
|
||||
t.append(transforms.ToTensor())
|
||||
t.append(transforms.Normalize(mean, std))
|
||||
return transforms.Compose(t)
|
||||
@@ -0,0 +1,76 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
# --------------------------------------------------------
|
||||
# References:
|
||||
# ELECTRA https://github.com/google-research/electra
|
||||
# BEiT: https://github.com/microsoft/unilm/tree/master/beit
|
||||
# --------------------------------------------------------
|
||||
|
||||
import json
|
||||
|
||||
|
||||
def param_groups_lrd(model, weight_decay=0.05, no_weight_decay_list=[], layer_decay=.75):
|
||||
"""
|
||||
Parameter groups for layer-wise lr decay
|
||||
Following BEiT: https://github.com/microsoft/unilm/blob/master/beit/optim_factory.py#L58
|
||||
"""
|
||||
param_group_names = {}
|
||||
param_groups = {}
|
||||
|
||||
num_layers = len(model.blocks) + 1
|
||||
|
||||
layer_scales = list(layer_decay ** (num_layers - i) for i in range(num_layers + 1))
|
||||
|
||||
for n, p in model.named_parameters():
|
||||
if not p.requires_grad:
|
||||
continue
|
||||
|
||||
# no decay: all 1D parameters and model specific ones
|
||||
if p.ndim == 1 or n in no_weight_decay_list:
|
||||
g_decay = "no_decay"
|
||||
this_decay = 0.
|
||||
else:
|
||||
g_decay = "decay"
|
||||
this_decay = weight_decay
|
||||
|
||||
layer_id = get_layer_id_for_vit(n, num_layers)
|
||||
group_name = "layer_%d_%s" % (layer_id, g_decay)
|
||||
|
||||
if group_name not in param_group_names:
|
||||
this_scale = layer_scales[layer_id]
|
||||
|
||||
param_group_names[group_name] = {
|
||||
"lr_scale": this_scale,
|
||||
"weight_decay": this_decay,
|
||||
"params": [],
|
||||
}
|
||||
param_groups[group_name] = {
|
||||
"lr_scale": this_scale,
|
||||
"weight_decay": this_decay,
|
||||
"params": [],
|
||||
}
|
||||
|
||||
param_group_names[group_name]["params"].append(n)
|
||||
param_groups[group_name]["params"].append(p)
|
||||
|
||||
# print("parameter groups: \n%s" % json.dumps(param_group_names, indent=2))
|
||||
|
||||
return list(param_groups.values())
|
||||
|
||||
|
||||
def get_layer_id_for_vit(name, num_layers):
|
||||
"""
|
||||
Assign a parameter with its layer id
|
||||
Following BEiT: https://github.com/microsoft/unilm/blob/master/beit/optim_factory.py#L33
|
||||
"""
|
||||
if name in ['cls_token', 'pos_embed']:
|
||||
return 0
|
||||
elif name.startswith('patch_embed'):
|
||||
return 0
|
||||
elif name.startswith('blocks'):
|
||||
return int(name.split('.')[1]) + 1
|
||||
else:
|
||||
return num_layers
|
||||
@@ -0,0 +1,21 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
import math
|
||||
|
||||
def adjust_learning_rate(optimizer, epoch, args):
|
||||
"""Decay the learning rate with half-cycle cosine after warmup"""
|
||||
if epoch < args.warmup_epochs:
|
||||
lr = args.lr * epoch / args.warmup_epochs
|
||||
else:
|
||||
lr = args.min_lr + (args.lr - args.min_lr) * 0.5 * \
|
||||
(1. + math.cos(math.pi * (epoch - args.warmup_epochs) / (args.epochs - args.warmup_epochs)))
|
||||
for param_group in optimizer.param_groups:
|
||||
if "lr_scale" in param_group:
|
||||
param_group["lr"] = lr * param_group["lr_scale"]
|
||||
else:
|
||||
param_group["lr"] = lr
|
||||
return lr
|
||||
+341
@@ -0,0 +1,341 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
# --------------------------------------------------------
|
||||
# References:
|
||||
# DeiT: https://github.com/facebookresearch/deit
|
||||
# BEiT: https://github.com/microsoft/unilm/tree/master/beit
|
||||
# --------------------------------------------------------
|
||||
|
||||
import builtins
|
||||
import datetime
|
||||
import os
|
||||
import time
|
||||
from collections import defaultdict, deque
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
from torch._six import inf
|
||||
|
||||
|
||||
class SmoothedValue(object):
|
||||
"""Track a series of values and provide access to smoothed values over a
|
||||
window or the global series average.
|
||||
"""
|
||||
|
||||
def __init__(self, window_size=20, fmt=None):
|
||||
if fmt is None:
|
||||
fmt = "{median:.4f} ({global_avg:.4f})"
|
||||
self.deque = deque(maxlen=window_size)
|
||||
self.total = 0.0
|
||||
self.count = 0
|
||||
self.fmt = fmt
|
||||
|
||||
def update(self, value, n=1):
|
||||
self.deque.append(value)
|
||||
self.count += n
|
||||
self.total += value * n
|
||||
|
||||
def synchronize_between_processes(self):
|
||||
"""
|
||||
Warning: does not synchronize the deque!
|
||||
"""
|
||||
if not is_dist_avail_and_initialized():
|
||||
return
|
||||
t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda')
|
||||
dist.barrier()
|
||||
dist.all_reduce(t)
|
||||
t = t.tolist()
|
||||
self.count = int(t[0])
|
||||
self.total = t[1]
|
||||
|
||||
@property
|
||||
def median(self):
|
||||
d = torch.tensor(list(self.deque))
|
||||
return d.median().item()
|
||||
|
||||
@property
|
||||
def avg(self):
|
||||
d = torch.tensor(list(self.deque), dtype=torch.float32)
|
||||
return d.mean().item()
|
||||
|
||||
@property
|
||||
def global_avg(self):
|
||||
return self.total / self.count
|
||||
|
||||
@property
|
||||
def max(self):
|
||||
return max(self.deque)
|
||||
|
||||
@property
|
||||
def value(self):
|
||||
return self.deque[-1]
|
||||
|
||||
def __str__(self):
|
||||
return self.fmt.format(
|
||||
median=self.median,
|
||||
avg=self.avg,
|
||||
global_avg=self.global_avg,
|
||||
max=self.max,
|
||||
value=self.value)
|
||||
|
||||
|
||||
class MetricLogger(object):
|
||||
def __init__(self, delimiter="\t"):
|
||||
self.meters = defaultdict(SmoothedValue)
|
||||
self.delimiter = delimiter
|
||||
|
||||
def update(self, **kwargs):
|
||||
for k, v in kwargs.items():
|
||||
if v is None:
|
||||
continue
|
||||
if isinstance(v, torch.Tensor):
|
||||
v = v.item()
|
||||
assert isinstance(v, (float, int))
|
||||
self.meters[k].update(v)
|
||||
|
||||
def __getattr__(self, attr):
|
||||
if attr in self.meters:
|
||||
return self.meters[attr]
|
||||
if attr in self.__dict__:
|
||||
return self.__dict__[attr]
|
||||
raise AttributeError("'{}' object has no attribute '{}'".format(
|
||||
type(self).__name__, attr))
|
||||
|
||||
def __str__(self):
|
||||
loss_str = []
|
||||
for name, meter in self.meters.items():
|
||||
loss_str.append(
|
||||
"{}: {}".format(name, str(meter))
|
||||
)
|
||||
return self.delimiter.join(loss_str)
|
||||
|
||||
def synchronize_between_processes(self):
|
||||
for meter in self.meters.values():
|
||||
meter.synchronize_between_processes()
|
||||
|
||||
def add_meter(self, name, meter):
|
||||
self.meters[name] = meter
|
||||
|
||||
def log_every(self, iterable, print_freq, header=None):
|
||||
i = 0
|
||||
if not header:
|
||||
header = ''
|
||||
start_time = time.time()
|
||||
end = time.time()
|
||||
iter_time = SmoothedValue(fmt='{avg:.4f}')
|
||||
data_time = SmoothedValue(fmt='{avg:.4f}')
|
||||
space_fmt = ':' + str(len(str(len(iterable)))) + 'd'
|
||||
log_msg = [
|
||||
header,
|
||||
'[{0' + space_fmt + '}/{1}]',
|
||||
'eta: {eta}',
|
||||
'{meters}',
|
||||
'time: {time}',
|
||||
'data: {data}'
|
||||
]
|
||||
if torch.cuda.is_available():
|
||||
log_msg.append('max mem: {memory:.0f}')
|
||||
log_msg = self.delimiter.join(log_msg)
|
||||
MB = 1024.0 * 1024.0
|
||||
for obj in iterable:
|
||||
data_time.update(time.time() - end)
|
||||
yield obj
|
||||
iter_time.update(time.time() - end)
|
||||
if i % print_freq == 0 or i == len(iterable) - 1:
|
||||
eta_seconds = iter_time.global_avg * (len(iterable) - i)
|
||||
eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
|
||||
if torch.cuda.is_available():
|
||||
print(log_msg.format(
|
||||
i, len(iterable), eta=eta_string,
|
||||
meters=str(self),
|
||||
time=str(iter_time), data=str(data_time),
|
||||
memory=torch.cuda.max_memory_allocated() / MB))
|
||||
else:
|
||||
print(log_msg.format(
|
||||
i, len(iterable), eta=eta_string,
|
||||
meters=str(self),
|
||||
time=str(iter_time), data=str(data_time)))
|
||||
i += 1
|
||||
end = time.time()
|
||||
total_time = time.time() - start_time
|
||||
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
|
||||
print('{} Total time: {} ({:.4f} s / it)'.format(
|
||||
header, total_time_str, total_time / len(iterable)))
|
||||
|
||||
|
||||
def setup_for_distributed(is_master):
|
||||
"""
|
||||
This function disables printing when not in master process
|
||||
"""
|
||||
builtin_print = builtins.print
|
||||
|
||||
def print(*args, **kwargs):
|
||||
force = kwargs.pop('force', False)
|
||||
force = force or (get_world_size() > 8)
|
||||
if is_master or force:
|
||||
now = datetime.datetime.now().time()
|
||||
builtin_print('[{}] '.format(now), end='') # print with time stamp
|
||||
builtin_print(*args, **kwargs)
|
||||
|
||||
builtins.print = print
|
||||
|
||||
|
||||
def is_dist_avail_and_initialized():
|
||||
if not dist.is_available():
|
||||
return False
|
||||
if not dist.is_initialized():
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def get_world_size():
|
||||
if not is_dist_avail_and_initialized():
|
||||
return 1
|
||||
return dist.get_world_size()
|
||||
|
||||
|
||||
def get_rank():
|
||||
if not is_dist_avail_and_initialized():
|
||||
return 0
|
||||
return dist.get_rank()
|
||||
|
||||
|
||||
def is_main_process():
|
||||
return get_rank() == 0
|
||||
|
||||
|
||||
def save_on_master(*args, **kwargs):
|
||||
if is_main_process():
|
||||
torch.save(*args, **kwargs)
|
||||
|
||||
|
||||
def init_distributed_mode(args):
|
||||
if args.dist_on_itp:
|
||||
args.rank = int(os.environ['OMPI_COMM_WORLD_RANK'])
|
||||
args.world_size = int(os.environ['OMPI_COMM_WORLD_SIZE'])
|
||||
args.gpu = int(os.environ['OMPI_COMM_WORLD_LOCAL_RANK'])
|
||||
args.dist_url = "tcp://%s:%s" % (os.environ['MASTER_ADDR'], os.environ['MASTER_PORT'])
|
||||
os.environ['LOCAL_RANK'] = str(args.gpu)
|
||||
os.environ['RANK'] = str(args.rank)
|
||||
os.environ['WORLD_SIZE'] = str(args.world_size)
|
||||
# ["RANK", "WORLD_SIZE", "MASTER_ADDR", "MASTER_PORT", "LOCAL_RANK"]
|
||||
elif 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
|
||||
args.rank = int(os.environ["RANK"])
|
||||
args.world_size = int(os.environ['WORLD_SIZE'])
|
||||
args.gpu = int(os.environ['LOCAL_RANK'])
|
||||
elif 'SLURM_PROCID' in os.environ:
|
||||
args.rank = int(os.environ['SLURM_PROCID'])
|
||||
args.gpu = args.rank % torch.cuda.device_count()
|
||||
else:
|
||||
print('Not using distributed mode')
|
||||
setup_for_distributed(is_master=True) # hack
|
||||
args.distributed = False
|
||||
return
|
||||
|
||||
args.distributed = True
|
||||
|
||||
torch.cuda.set_device(args.gpu)
|
||||
args.dist_backend = 'nccl'
|
||||
print('| distributed init (rank {}): {}, gpu {}'.format(
|
||||
args.rank, args.dist_url, args.gpu), flush=True)
|
||||
torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
|
||||
world_size=args.world_size, rank=args.rank)
|
||||
torch.distributed.barrier()
|
||||
setup_for_distributed(args.rank == 0)
|
||||
|
||||
|
||||
class NativeScalerWithGradNormCount:
|
||||
state_dict_key = "amp_scaler"
|
||||
|
||||
def __init__(self):
|
||||
self._scaler = torch.cuda.amp.GradScaler()
|
||||
|
||||
def __call__(self, loss, optimizer, clip_grad=None, parameters=None, create_graph=False, update_grad=True):
|
||||
self._scaler.scale(loss).backward(create_graph=create_graph)
|
||||
if update_grad:
|
||||
if clip_grad is not None:
|
||||
assert parameters is not None
|
||||
self._scaler.unscale_(optimizer) # unscale the gradients of optimizer's assigned params in-place
|
||||
norm = torch.nn.utils.clip_grad_norm_(parameters, clip_grad)
|
||||
else:
|
||||
self._scaler.unscale_(optimizer)
|
||||
norm = get_grad_norm_(parameters)
|
||||
self._scaler.step(optimizer)
|
||||
self._scaler.update()
|
||||
else:
|
||||
norm = None
|
||||
return norm
|
||||
|
||||
def state_dict(self):
|
||||
return self._scaler.state_dict()
|
||||
|
||||
def load_state_dict(self, state_dict):
|
||||
self._scaler.load_state_dict(state_dict)
|
||||
|
||||
|
||||
def get_grad_norm_(parameters, norm_type: float = 2.0) -> torch.Tensor:
|
||||
if isinstance(parameters, torch.Tensor):
|
||||
parameters = [parameters]
|
||||
parameters = [p for p in parameters if p.grad is not None]
|
||||
norm_type = float(norm_type)
|
||||
if len(parameters) == 0:
|
||||
return torch.tensor(0.)
|
||||
device = parameters[0].grad.device
|
||||
if norm_type == inf:
|
||||
total_norm = max(p.grad.detach().abs().max().to(device) for p in parameters)
|
||||
else:
|
||||
total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]), norm_type)
|
||||
return total_norm
|
||||
|
||||
|
||||
def save_model(args, epoch, model, model_without_ddp, optimizer, loss_scaler):
|
||||
output_dir = Path(args.output_dir)
|
||||
epoch_name = str(epoch)
|
||||
if loss_scaler is not None:
|
||||
checkpoint_paths = [args.task+'checkpoint-best.pth']
|
||||
for checkpoint_path in checkpoint_paths:
|
||||
to_save = {
|
||||
'model': model_without_ddp.state_dict(),
|
||||
'optimizer': optimizer.state_dict(),
|
||||
'epoch': epoch,
|
||||
'scaler': loss_scaler.state_dict(),
|
||||
'args': args,
|
||||
}
|
||||
|
||||
save_on_master(to_save, checkpoint_path)
|
||||
else:
|
||||
client_state = {'epoch': epoch}
|
||||
model.save_checkpoint(save_dir=args.task, tag="checkpoint-best", client_state=client_state)
|
||||
|
||||
|
||||
def load_model(args, model_without_ddp, optimizer, loss_scaler):
|
||||
if args.resume:
|
||||
if args.resume.startswith('https'):
|
||||
checkpoint = torch.hub.load_state_dict_from_url(
|
||||
args.resume, map_location='cpu', check_hash=True)
|
||||
else:
|
||||
checkpoint = torch.load(args.resume, map_location='cpu')
|
||||
model_without_ddp.load_state_dict(checkpoint['model'])
|
||||
print("Resume checkpoint %s" % args.resume)
|
||||
if 'optimizer' in checkpoint and 'epoch' in checkpoint and not (hasattr(args, 'eval') and args.eval):
|
||||
optimizer.load_state_dict(checkpoint['optimizer'])
|
||||
args.start_epoch = checkpoint['epoch'] + 1
|
||||
if 'scaler' in checkpoint:
|
||||
loss_scaler.load_state_dict(checkpoint['scaler'])
|
||||
print("With optim & sched!")
|
||||
|
||||
|
||||
def all_reduce_mean(x):
|
||||
world_size = get_world_size()
|
||||
if world_size > 1:
|
||||
x_reduce = torch.tensor(x).cuda()
|
||||
dist.all_reduce(x_reduce)
|
||||
x_reduce /= world_size
|
||||
return x_reduce.item()
|
||||
else:
|
||||
return x
|
||||
|
||||
@@ -0,0 +1,96 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
# --------------------------------------------------------
|
||||
# Position embedding utils
|
||||
# --------------------------------------------------------
|
||||
|
||||
import numpy as np
|
||||
|
||||
import torch
|
||||
|
||||
# --------------------------------------------------------
|
||||
# 2D sine-cosine position embedding
|
||||
# References:
|
||||
# Transformer: https://github.com/tensorflow/models/blob/master/official/nlp/transformer/model_utils.py
|
||||
# MoCo v3: https://github.com/facebookresearch/moco-v3
|
||||
# --------------------------------------------------------
|
||||
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
|
||||
"""
|
||||
grid_size: int of the grid height and width
|
||||
return:
|
||||
pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
|
||||
"""
|
||||
grid_h = np.arange(grid_size, dtype=np.float32)
|
||||
grid_w = np.arange(grid_size, dtype=np.float32)
|
||||
grid = np.meshgrid(grid_w, grid_h) # here w goes first
|
||||
grid = np.stack(grid, axis=0)
|
||||
|
||||
grid = grid.reshape([2, 1, grid_size, grid_size])
|
||||
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
|
||||
if cls_token:
|
||||
pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
|
||||
return pos_embed
|
||||
|
||||
|
||||
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
|
||||
assert embed_dim % 2 == 0
|
||||
|
||||
# use half of dimensions to encode grid_h
|
||||
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
|
||||
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
|
||||
|
||||
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
|
||||
return emb
|
||||
|
||||
|
||||
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
|
||||
"""
|
||||
embed_dim: output dimension for each position
|
||||
pos: a list of positions to be encoded: size (M,)
|
||||
out: (M, D)
|
||||
"""
|
||||
assert embed_dim % 2 == 0
|
||||
omega = np.arange(embed_dim // 2, dtype=np.float)
|
||||
omega /= embed_dim / 2.
|
||||
omega = 1. / 10000**omega # (D/2,)
|
||||
|
||||
pos = pos.reshape(-1) # (M,)
|
||||
out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product
|
||||
|
||||
emb_sin = np.sin(out) # (M, D/2)
|
||||
emb_cos = np.cos(out) # (M, D/2)
|
||||
|
||||
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
|
||||
return emb
|
||||
|
||||
|
||||
# --------------------------------------------------------
|
||||
# Interpolate position embeddings for high-resolution
|
||||
# References:
|
||||
# DeiT: https://github.com/facebookresearch/deit
|
||||
# --------------------------------------------------------
|
||||
def interpolate_pos_embed(model, checkpoint_model):
|
||||
if 'pos_embed' in checkpoint_model:
|
||||
pos_embed_checkpoint = checkpoint_model['pos_embed']
|
||||
embedding_size = pos_embed_checkpoint.shape[-1]
|
||||
num_patches = model.patch_embed.num_patches
|
||||
num_extra_tokens = model.pos_embed.shape[-2] - num_patches
|
||||
# height (== width) for the checkpoint position embedding
|
||||
orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
|
||||
# height (== width) for the new position embedding
|
||||
new_size = int(num_patches ** 0.5)
|
||||
# class_token and dist_token are kept unchanged
|
||||
if orig_size != new_size:
|
||||
print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size))
|
||||
extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
|
||||
# only the position tokens are interpolated
|
||||
pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
|
||||
pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
|
||||
pos_tokens = torch.nn.functional.interpolate(
|
||||
pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
|
||||
pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
|
||||
new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
|
||||
checkpoint_model['pos_embed'] = new_pos_embed
|
||||
Reference in New Issue
Block a user