esvit/eval_linear.py

# Modified by Chunyuan Li (chunyl@microsoft.com)
#
# 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 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
torchvision_archs = sorted(name for name in torchvision_models.__dict__
    if name.islower() and not name.startswith("__")
    and callable(torchvision_models.__dict__[name]))

import utils
import models.vision_transformer as vits
from models.vision_transformer import DINOHead
from models import build_model

from config import config
from config import update_config
from config import save_config

def eval_linear(args):
    utils.init_distributed_mode(args)
    print("git:\n  {}\n".format(utils.get_sha()))
    print("\n".join("%s: %s" % (k, str(v)) for k, v in sorted(dict(vars(args)).items())))
    cudnn.benchmark = True

    # ============ preparing data ... ============
    train_transform = pth_transforms.Compose([
        pth_transforms.RandomResizedCrop(224),
        pth_transforms.RandomHorizontalFlip(),
        pth_transforms.ToTensor(),
        pth_transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
    ])
    val_transform = pth_transforms.Compose([
        pth_transforms.Resize(256, interpolation=3),
        pth_transforms.CenterCrop(224),
        pth_transforms.ToTensor(),
        pth_transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
    ])

    if args.zip_mode:

        from .zipdata import ZipData

        datapath_train = os.path.join(config.DATA.DATA_PATH, 'train.zip')
        data_map_train = os.path.join(config.DATA.DATA_PATH, 'train_map.txt')

        datapath_val = os.path.join(config.DATA.DATA_PATH, 'val.zip')
        data_map_val = os.path.join(config.DATA.DATA_PATH, 'val_map.txt')
        
        dataset_train = ZipData(datapath_train, data_map_train, train_transform)
        dataset_val = ZipData(datapath_val, data_map_val, val_transform)
        
    else:
        dataset_train = datasets.ImageFolder(os.path.join(args.data_path, "train"), transform=train_transform)
        dataset_val = datasets.ImageFolder(os.path.join(args.data_path, "val"), transform=val_transform)

    sampler = torch.utils.data.distributed.DistributedSampler(dataset_train)
    train_loader = torch.utils.data.DataLoader(
        dataset_train,
        sampler=sampler,
        batch_size=args.batch_size_per_gpu,
        num_workers=args.num_workers,
        pin_memory=True,
    )
    val_loader = torch.utils.data.DataLoader(
        dataset_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.")

    # ============ building network ... ============
    # if the network is a 4-stage vision transformer (i.e. swin)
    if 'swin' in args.arch :
        update_config(config, args)
        model = build_model(config, is_teacher=True)

        swin_spec = config.MODEL.SPEC
        embed_dim=swin_spec['DIM_EMBED']
        depths=swin_spec['DEPTHS']
        num_heads=swin_spec['NUM_HEADS'] 

        num_features = []
        for i, d in enumerate(depths):
            num_features += [int(embed_dim * 2 ** i)] * d 
        
        print(num_features)
        num_features_linear = sum(num_features[-args.n_last_blocks:])

        print(f'num_features_linear {num_features_linear}')

        linear_classifier = LinearClassifier(num_features_linear, args.num_labels)

    # if the network is a 4-stage vision transformer (i.e. longformer)
    elif 'vil' in args.arch :
        update_config(config, args)
        model = build_model(config, is_teacher=True)

        msvit_spec = config.MODEL.SPEC
        arch = msvit_spec.MSVIT.ARCH

        layer_cfgs = model.layer_cfgs
        num_stages = len(model.layer_cfgs)
        depths = [cfg['n'] for cfg in model.layer_cfgs]
        dims = [cfg['d'] for cfg in model.layer_cfgs]
        out_planes = model.layer_cfgs[-1]['d']
        Nglos = [cfg['g'] for cfg in model.layer_cfgs]

        print(dims)

        num_features = []
        for i, d in enumerate(depths):
            num_features += [ dims[i] ] * d 
        
        print(num_features)
        num_features_linear = sum(num_features[-args.n_last_blocks:])

        print(f'num_features_linear {num_features_linear}')

        linear_classifier = LinearClassifier(num_features_linear, args.num_labels)


    # if the network is a 4-stage vision transformer (i.e. CvT)
    elif 'cvt' in args.arch :
        update_config(config, args)
        model = build_model(config, is_teacher=True)

        cvt_spec = config.MODEL.SPEC
        embed_dim=cvt_spec['DIM_EMBED']
        depths=cvt_spec['DEPTH']
        num_heads=cvt_spec['NUM_HEADS'] 


        print(f'embed_dim {embed_dim} depths {depths}')
        num_features = []
        for i, d in enumerate(depths):
            num_features += [int(embed_dim[i])] * int(d) 
        
        print(num_features)
        num_features_linear = sum(num_features[-args.n_last_blocks:])

        print(f'num_features_linear {num_features_linear}')

        linear_classifier = LinearClassifier(num_features_linear, args.num_labels)



    # if the network is a vanilla vision transformer (i.e. deit_tiny, deit_small, vit_base)
    elif args.arch in vits.__dict__.keys():
        depths = []
        model = vits.__dict__[args.arch](patch_size=args.patch_size, num_classes=0)
        linear_classifier = LinearClassifier(model.embed_dim * (args.n_last_blocks + int(args.avgpool_patchtokens)), args.num_labels)

    model.cuda()
    model.eval()
    print(f"Model {args.arch} {args.patch_size}x{args.patch_size} built.")
    # load weights to evaluate
    utils.load_pretrained_weights(model, args.pretrained_weights, args.checkpoint_key, args.arch, args.patch_size)

    
    linear_classifier = linear_classifier.cuda()
    linear_classifier = nn.parallel.DistributedDataParallel(linear_classifier, device_ids=[args.gpu])

    # 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=0, # we do not apply weight decay
    )
    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, args.epochs, eta_min=0)

    if args.output_dir and dist.get_rank() == 0:
        if not os.path.exists(args.output_dir):
            os.makedirs(args.output_dir)  
              

    # Optionally resume from a checkpoint
    to_restore = {"epoch": 0, "best_acc": 0.}
    utils.restart_from_checkpoint(
        os.path.join(args.output_dir, "checkpoint.pth.tar"),
        run_variables=to_restore,
        state_dict=linear_classifier,
        optimizer=optimizer,
        scheduler=scheduler,
    )
    start_epoch = to_restore["epoch"]
    best_acc = to_restore["best_acc"]

    for epoch in range(start_epoch, args.epochs):
        train_loader.sampler.set_epoch(epoch)

        train_stats = train(model, linear_classifier, optimizer, train_loader, epoch, args.n_last_blocks, args.avgpool_patchtokens, depths)
        scheduler.step()

        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, linear_classifier, args.n_last_blocks, args.avgpool_patchtokens, depths)
            print(f"Accuracy at epoch {epoch} of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%")
            best_acc = max(best_acc, test_stats["acc1"])
            print(f'Max accuracy so far: {best_acc:.2f}%')
            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,
                "state_dict": linear_classifier.state_dict(),
                "optimizer": optimizer.state_dict(),
                "scheduler": scheduler.state_dict(),
                "best_acc": best_acc,
            }
            torch.save(save_dict, os.path.join(args.output_dir, "checkpoint.pth.tar"))
    print("Training of the supervised linear classifier on frozen features completed.\n"
                "Top-1 test accuracy: {acc:.1f}".format(acc=max_accuracy))


def train(model, linear_classifier, optimizer, loader, epoch, n, avgpool, depths):
    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 (inp, target) in metric_logger.log_every(loader, 20, header):
        # move to gpu
        inp = inp.cuda(non_blocking=True)
        target = target.cuda(non_blocking=True)

        # forward
        with torch.no_grad():
            output = model.forward_return_n_last_blocks(inp, n, avgpool, depths)
        
        # print(f'output {output.shape}')
        output = linear_classifier(output)

        # compute cross entropy loss
        loss = nn.CrossEntropyLoss()(output, target)

        # compute the gradients
        optimizer.zero_grad()
        loss.backward()

        # step
        optimizer.step()

        # log 
        torch.cuda.synchronize()
        metric_logger.update(loss=loss.item())
        metric_logger.update(lr=optimizer.param_groups[0]["lr"])
    # 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, linear_classifier, n, avgpool, depths):
    linear_classifier.eval()
    metric_logger = utils.MetricLogger(delimiter="  ")
    header = 'Test:'
    for inp, target in metric_logger.log_every(val_loader, 20, header):
        # move to gpu
        inp = inp.cuda(non_blocking=True)
        target = target.cuda(non_blocking=True)

        # compute output
        output = model.forward_return_n_last_blocks(inp, n, avgpool, depths)
        output = linear_classifier(output)
        loss = nn.CrossEntropyLoss()(output, target)

        acc1, acc5 = utils.accuracy(output, target, topk=(1, 5))

        batch_size = inp.shape[0]
        metric_logger.update(loss=loss.item())
        metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
        metric_logger.meters['acc5'].update(acc5.item(), n=batch_size)
    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))
    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.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('--cfg',
                        help='experiment configure file name',
                        type=str)

    parser.add_argument('--arch', default='deit_small', type=str,
        choices=['cvt_tiny', 'swin_tiny','swin_small', 'swin_base', 'swin_large', 'swin', 'vil', 'vil_1281', 'vil_2262', 'deit_tiny', 'deit_small', 'vit_base'] + torchvision_archs,
        help="""Name of architecture to train. For quick experiments with ViTs,
        we recommend using deit_tiny or deit_small.""")

    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 DeiT-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 DeiT-Small and to True with ViT-Base.""")

    parser.add_argument('--patch_size', default=16, type=int, help='Patch resolution of the model.')
    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("--lr", default=0.001, type=float, 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('--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=".", help='Path to save logs and checkpoints')

    # Dataset
    parser.add_argument('--zip_mode', type=utils.bool_flag, default=False, help="""Whether or not
        to use zip file.""")

    parser.add_argument('--num_labels', default=1000, type=int, help='number of classes in a dataset')

    parser.add_argument('opts',
                        help="Modify config options using the command-line",
                        default=None,
                        nargs=argparse.REMAINDER)    

    args = parser.parse_args()


    eval_linear(args)