third_party/weakly_supvervised_detection/main.py

# Copyright (c) Facebook, Inc. and its affiliates.
# 
# 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 os
import argparse
import json
from pathlib import Path
import time
import datetime

import torch
from torch import nn
import torch.distributed as dist
import torch.backends.cudnn as cudnn
from torchvision import datasets
from torchvision import transforms as pth_transforms
from torchvision import models as torchvision_models

import utils
import vision_transformer as vits

from torch.utils.tensorboard import SummaryWriter
import shutil
import itertools
import numpy as np

from timm.scheduler import create_scheduler
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from timm.data import create_transform
from timm.data import Mixup

from samplers import RASampler
from datasets import build_dataset

def main(args):
    if args.device != 'cuda':
        args.distributed = False
    else:
        utils.init_distributed_mode(args)

    print(args)

    # ========fix seeds ========
    seed = args.seed + utils.get_rank()
    torch.manual_seed(seed)
    np.random.seed(seed)

    device = torch.device(args.device)
    cudnn.benchmark = True

    # ============ building network ... ============
    # if the network is a Vision Transformer (i.e. vit_tiny, vit_small, vit_base)
    if args.arch in vits.__dict__.keys():
        model = vits.__dict__[args.arch](patch_size=args.patch_size, num_classes=args.num_labels, adjacency_bp=args.adjacency_bp, temperature=args.temperature)
        embed_dim = model.embed_dim * (args.n_last_blocks + int(args.avgpool_patchtokens))
    else:
        print(f"Unknow architecture: {args.arch}")
        sys.exit(1)

    model.to(device)
    model.eval()

    model_without_ddp = model
    #if args.distributed:
    #    model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) # TODO: where has unused params?
    #    #model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True)
    #    model_without_ddp = model.module
    n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
    print('number of params:', n_parameters)
    # load weights to evaluate
    utils.load_pretrained_weights(model_without_ddp, args.pretrained_weights, args.checkpoint_key, args.arch, args.patch_size)
    print(f"Model {args.arch} built.")

    linear_classifier = LinearClassifier(embed_dim, num_labels=args.num_labels)
    linear_classifier = linear_classifier.cuda()
    classifier_without_ddp = linear_classifier
    linear_classifier = nn.parallel.DistributedDataParallel(linear_classifier, device_ids=[args.gpu])

    # ============ Build dataset ============
    dataset_train, args.num_labels = build_dataset(is_train = True, args=args)
    dataset_val, _ = build_dataset(is_train=False, args=args)
    
    num_tasks = utils.get_world_size()
    global_rank = utils.get_rank()

    if args.distributed:
        if args.data_aug and args.repeated_aug:
            sampler_train = RASampler(
                    dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True
                )
        else:
            sampler_train = torch.utils.data.distributed.DistributedSampler(dataset_train)
        sampler_val = torch.utils.data.distributed.DistributedSampler(dataset_val, shuffle=False)
    else:
        sampler = torch.utils.data.RandomSampler(dataset_train)
        sampler_val = torch.utils.data.SequentialSampler(dataset_val)

    train_loader = torch.utils.data.DataLoader(
        dataset_train,
        sampler=sampler_train,
        batch_size=args.batch_size_per_gpu,
        num_workers=args.num_workers,
        pin_memory=True,
    )
    val_loader = torch.utils.data.DataLoader(
        dataset_val,
        sampler=sampler_val,
        batch_size=args.batch_size_per_gpu,
        num_workers=args.num_workers,
        pin_memory=True,
    )
    print(f"Data loaded with {len(dataset_train)} train and {len(dataset_val)} val imgs.")

    if args.evaluate:
        checkpoint = torch.load(args.checkpoint, map_location='cpu')
        model_without_ddp.load_state_dict(checkpoint['model'])
        test_stats = validate_network(val_loader, model_without_ddp, device)
        print(f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%")
        return
    
    # set optimizer
    optimizer = torch.optim.SGD(
        linear_classifier.parameters(),
        args.lr * (args.batch_size_per_gpu * utils.get_world_size()) / 256., # linear scaling rule
        momentum=0.9,
        weight_decay=args.weight_decay, # we do not apply weight decay
    )
    scheduler, _ = create_scheduler(args, optimizer)

    criterion = nn.CrossEntropyLoss()
    # ----Mixup -----
    mixup_fn = None
    smoothing = None
    if args.data_aug:
        print('Data augmentation: Mixup CutMix enable')
        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.num_labels)
        criterion = SoftTargetCrossEntropy()

    if utils.is_main_process():
        writer = SummaryWriter(args.output_dir + '/log')
    start_epoch = 0
    best_acc = 0
    print("Starting training")
    start_time = time.time()
    for epoch in range(start_epoch, args.epochs):
        if args.distributed:
            train_loader.sampler.set_epoch(epoch)

        train_stats = train(model_without_ddp, device, optimizer, train_loader, epoch, criterion, linear_classifier, args.n_last_blocks, args.avgpool_patchtokens, mixup_fn)

        scheduler.step(epoch)

        log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
                     'epoch': epoch}
        if epoch % args.val_freq == 0 or epoch == args.epochs - 1:
            test_stats = validate_network(val_loader, model, device, linear_classifier, args.n_last_blocks, args.avgpool_patchtokens)
            print(f"Accuracy at epoch {epoch} of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%")
            log_stats = {**{k: v for k, v in log_stats.items()},
                         **{f'test_{k}': v for k, v in test_stats.items()}}
        if utils.is_main_process():
            with (Path(args.output_dir) / "log.txt").open("a") as f:
                f.write(json.dumps(log_stats) + "\n")
            save_dict = {
                "epoch": epoch + 1,
                "classifier": classifier_without_ddp.state_dict(),
                "model": model_without_ddp.state_dict(),
                "optimizer": optimizer.state_dict(),
                "scheduler": scheduler.state_dict(),
                "best_acc": best_acc,
            }

            writer.add_scalar('Train_loss', train_stats['loss'], global_step=epoch)
            writer.add_scalar('Learning_rate', train_stats['lr'], global_step=epoch)
            writer.add_scalar('Train Acc_1', train_stats['acc1'], global_step=epoch)
            writer.add_scalar('Acc_1', test_stats['acc1'], global_step=epoch)
            writer.add_scalar('Acc_5', test_stats['acc5'], global_step=epoch)
           
            checkpoint_path = os.path.join(args.output_dir, "checkpoint.pth")
            torch.save(save_dict, checkpoint_path)
            
            if best_acc < float(test_stats['acc1']):
                  best_acc = float(test_stats['acc1'])
                  shutil.copyfile(checkpoint_path, args.output_dir + '/model_best.pth')
            print(f'Max accuracy so far: {best_acc:.2f}%')
    print("Training of the TokenCut completed.\n"
                "Top-1 test accuracy: {acc:.1f}".format(acc=best_acc))
    total_time = time.time() - start_time
    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
    print(f'Training time {total_time_str}')
    

def train(model, device, optimizer, loader, epoch, criterion, linear_classifier, n, avgpool, mixup_fn=None,):
    linear_classifier.train()
    metric_logger = utils.MetricLogger(delimiter="  ")
    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
    header = 'Epoch: [{}]'.format(epoch)
    for batch in metric_logger.log_every(loader, 20, header):
        inp, target = batch[:2]
        # move to gpu
        inp = inp.to(device, non_blocking=True)
        target = target.to(device, non_blocking=True)
        hard_target = target.clone()
        if args.data_aug:
            inp, target = mixup_fn(inp, target)
        # forward
        with torch.no_grad():
            intermediate_output,_ = model.get_intermediate_layers(inp, n)
            output = torch.cat([x[:, 0] for x in intermediate_output], dim=-1)
            if avgpool:
                output = torch.cat((output.unsqueeze(-1), torch.mean(intermediate_output[-1][:, 1:], dim=1).unsqueeze(-1)), dim=-1)
                output = output.reshape(output.shape[0], -1)
        output = linear_classifier(output)

        # compute cross entropy loss
        loss = criterion(output, target)
        
        acc1, = utils.accuracy(output, hard_target, topk=(1,))
        # compute the gradients
        optimizer.zero_grad()
        loss.backward()

        # step
        optimizer.step()

        # log 
        torch.cuda.synchronize()
        batch_size = inp.shape[0]
        metric_logger.update(loss=loss.item())
        metric_logger.update(lr=optimizer.param_groups[0]["lr"])
        metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
    # gather the stats from all processes
    metric_logger.synchronize_between_processes()
    print("Averaged stats:", metric_logger)
    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}


@torch.no_grad()
def validate_network(val_loader, model, device, linear_classifier, n, avgpool):
    linear_classifier.eval()
    metric_logger = utils.MetricLogger(delimiter="  ")
    header = 'Test:'
   # for inp, target, _ in metric_logger.log_every(val_loader, 20, header):
    for batch in metric_logger.log_every(val_loader, 20, header):
        inp, target = batch[:2]
        # move to gpu
        inp = inp.to(device, non_blocking=True)
        target = target.to(device, non_blocking=True)

        # forward
        with torch.no_grad():
            intermediate_output,_ = model.get_intermediate_layers(inp, n)
            output = torch.cat([x[:, 0] for x in intermediate_output], dim=-1)
            if avgpool:
                output = torch.cat((output.unsqueeze(-1), torch.mean(intermediate_output[-1][:, 1:], dim=1).unsqueeze(-1)), dim=-1)
                output = output.reshape(output.shape[0], -1)
        output = linear_classifier(output)
        loss = nn.CrossEntropyLoss()(output, target)

        if linear_classifier.module.num_labels >= 5:
            acc1, acc5 = utils.accuracy(output, target, topk=(1, 5))
        else:
            acc1, = utils.accuracy(output, target, topk=(1,))

        batch_size = inp.shape[0]
        metric_logger.update(loss=loss.item())
        metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
        if linear_classifier.module.num_labels >= 5:
            metric_logger.meters['acc5'].update(acc5.item(), n=batch_size)
    if linear_classifier.module.num_labels >= 5:
        print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}'
          .format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss))
    else:
        print('* Acc@1 {top1.global_avg:.3f} loss {losses.global_avg:.3f}'
          .format(top1=metric_logger.acc1, losses=metric_logger.loss))

    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}

class LinearClassifier(nn.Module):
    """Linear layer to train on top of frozen features"""
    def __init__(self, dim, num_labels=1000):
        super(LinearClassifier, self).__init__()
        self.num_labels = num_labels
        self.linear = nn.Linear(dim, num_labels)
        self.linear.weight.data.normal_(mean=0.0, std=0.01)
        self.linear.bias.data.zero_()

    def forward(self, x):
        # flatten
        x = x.view(x.size(0), -1)

        # linear layer
        return self.linear(x)

if __name__ == '__main__':
    parser = argparse.ArgumentParser('Evaluation with linear classification on ImageNet')
    parser.add_argument('--n_last_blocks', default=4, type=int, help="""Concatenate [CLS] tokens
        for the `n` last blocks. We use `n=4` when evaluating ViT-Small and `n=1` with ViT-Base.""")
    parser.add_argument('--avgpool_patchtokens', default=False, type=utils.bool_flag,
        help="""Whether ot not to concatenate the global average pooled features to the [CLS] token.
        We typically set this to False for ViT-Small and to True with ViT-Base.""")
    parser.add_argument('--arch', default='vit_small', choices=['vit_small', 'vit_base'], type=str, help='Architecture')
    parser.add_argument('--dataset', default='cub', type=str, choices=['cub', 'imagenet'], help='Architecture')
    parser.add_argument('--patch_size', default=16, type=int, help='Patch resolution of the model.')
    parser.add_argument('--input_size', default=224, type=int, help='Input image size, default(224).')
    parser.add_argument('--pretrained_weights', default='', type=str, help="Path to pretrained weights to evaluate.")
    parser.add_argument("--checkpoint_key", default="teacher", type=str, help='Key to use in the checkpoint (example: "teacher")')
    parser.add_argument('--epochs', default=100, type=int, help='Number of epochs of training.')
    parser.add_argument('--batch_size_per_gpu', default=128, type=int, help='Per-GPU batch-size')
    parser.add_argument("--dist_url", default="env://", type=str, help="""url used to set up
        distributed training; see https://pytorch.org/docs/stable/distributed.html""")
    parser.add_argument("--local_rank", default=0, type=int, help="Please ignore and do not set this argument.")
    parser.add_argument('--data_path', default='/path/to/imagenet/', type=str)
    parser.add_argument('--num_workers', default=10, type=int, help='Number of data loading workers per GPU.')
    parser.add_argument('--val_freq', default=1, type=int, help="Epoch frequency for validation.")
    parser.add_argument('--output_dir', default="./checkpoints", help='Path to save logs and checkpoints')
    parser.add_argument('--num_labels', default=1000, type=int, help='Number of labels for linear classifier')
    parser.add_argument('--evaluate', dest='evaluate', action='store_true', help='evaluate model on validation set')
    parser.add_argument('--weight_decay', default=0.1, type=float, help="weight_decay, default 0.1")
    parser.add_argument('--device', default='cuda', help='device to use for training / testing')
    parser.add_argument('--distributed', default=False, action='store_true', help='device to use for training / testing')
    parser.add_argument('--adjacency_bp', default=False, action='store_true', help='whether backprop from adjacency matrix')
    parser.add_argument('--temperature', default=1, type=int,  help='Temperature for mask')
    parser.add_argument('--seed', default=0, type=int)

    # ------ lr scheduler ------
    parser.add_argument('--sched', default='cosine', type=str, metavar='SCHEDULER',
                        help='LR scheduler (default: "cosine"')
    parser.add_argument('--lr', type=float, default=1e-4, metavar='LR',
                        help="""Learning rate at the beginning of
        training (highest LR used during training). The learning rate is linearly scaled
        with the batch size, and specified here for a reference batch size of 256.
        We recommend tweaking the LR depending on the checkpoint evaluated.""")
    parser.add_argument('--lr-noise', type=float, nargs='+', default=None, metavar='pct, pct',
                        help='learning rate noise on/off epoch percentages')
    parser.add_argument('--lr-noise-pct', type=float, default=0.67, metavar='PERCENT',
                        help='learning rate noise limit percent (default: 0.67)')
    parser.add_argument('--lr-noise-std', type=float, default=1.0, metavar='STDDEV',
                        help='learning rate noise std-dev (default: 1.0)')
    parser.add_argument('--warmup-lr', type=float, default=1e-6, metavar='LR',
                        help='warmup learning rate (default: 1e-6)')
    parser.add_argument('--min-lr', type=float, default=1e-5, metavar='LR',
                        help='lower lr bound for cyclic schedulers that hit 0 (1e-5)')
    parser.add_argument('--decay-epochs', type=float, default=5, metavar='N',
                        help='epoch interval to decay LR')
    parser.add_argument('--warmup-epochs', type=int, default=5, metavar='N',
                        help='epochs to warmup LR, if scheduler supports')
    parser.add_argument('--cooldown-epochs', type=int, default=10, metavar='N',
                        help='epochs to cooldown LR at min_lr, after cyclic schedule ends')
    parser.add_argument('--patience-epochs', type=int, default=10, metavar='N',
                        help='patience epochs for Plateau LR scheduler (default: 10')
    parser.add_argument('--decay-rate', '--dr', type=float, default=0.1, metavar='RATE',
                        help='LR decay rate (default: 0.1)')
    # --------data aug---------
    parser.add_argument('--label-smooth-loss', default=False, action='store_true', help='use label smooth')
    # * Random Erase params
    parser.add_argument('--reprob', type=float, default=0.25, metavar='PCT',
                        help='Random erase prob (default: 0.25)')
    parser.add_argument('--remode', type=str, default='pixel',
                        help='Random erase mode (default: "pixel")')
    parser.add_argument('--recount', type=int, default=1,
                        help='Random erase count (default: 1)')
    parser.add_argument('--resplit', action='store_true', default=False,
                        help='Do not random erase first (clean) augmentation split')

    # * Mixup params
    parser.add_argument('--mixup', type=float, default=0.8,
                        help='mixup alpha, mixup enabled if > 0. (default: 0.8)')
    parser.add_argument('--cutmix', type=float, default=1.0,
                        help='cutmix alpha, cutmix enabled if > 0. (default: 1.0)')
    parser.add_argument('--cutmix-minmax', type=float, nargs='+', default=None,
                        help='cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)')
    parser.add_argument('--mixup-prob', type=float, default=1.0,
                        help='Probability of performing mixup or cutmix when either/both is enabled')
    parser.add_argument('--mixup-switch-prob', type=float, default=0.5,
                        help='Probability of switching to cutmix when both mixup and cutmix enabled')
    parser.add_argument('--mixup-mode', type=str, default='batch',
                        help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"')

    # Augmentation parameters
    parser.add_argument('--color-jitter', type=float, default=0.4, metavar='PCT',
                        help='Color jitter factor (default: 0.4)')
    parser.add_argument('--aa', type=str, default='rand-m9-mstd0.5-inc1', metavar='NAME',
                        help='Use AutoAugment policy. "v0" or "original". " + \
                             "(default: rand-m9-mstd0.5-inc1)'),
    parser.add_argument('--smoothing', type=float, default=0.1, help='Label smoothing (default: 0.1)')
    parser.add_argument('--train-interpolation', type=str, default='bicubic',
                        help='Training interpolation (random, bilinear, bicubic default: "bicubic")')

    parser.add_argument('--repeated-aug', action='store_true')
    parser.add_argument('--no-repeated-aug', action='store_false', dest='repeated_aug')
    parser.set_defaults(repeated_aug=True)

    parser.add_argument('--no_center_crop', default=False, action='store_true', help='Center crop input image')
    parser.add_argument('--data-aug', action='store_true', default=False, help='disable the data augmentations.')
    parser.add_argument('--ori_size', default=False, action='store_true', help='Evaluate on image raw size')
    args = parser.parse_args()
    main(args)