timm_test.py

#!/usr/bin/env python
""" ImageNet Validation Script

This is intended to be a lean and easily modifiable ImageNet validation script for evaluating pretrained
models or training checkpoints against ImageNet or similarly organized image datasets. It prioritizes
canonical PyTorch, standard Python style, and good performance. Repurpose as you see fit.

Hacked together by Ross Wightman (https://github.com/rwightman)
"""
import argparse
import os
import csv
import glob
import time
import logging
import torch
import torch.nn as nn
import torch.nn.parallel
from collections import OrderedDict
from models.my_searched_model import my_specialized

try:
    from apex import amp
    has_apex = True
except ImportError:
    has_apex = False

from timm.models import create_model, apply_test_time_pool, load_checkpoint, is_model, list_models,\
    set_scriptable, set_no_jit
from timm.data import Dataset, DatasetTar, create_loader, resolve_data_config
from timm.utils import accuracy, AverageMeter, natural_key, setup_default_logging

torch.backends.cudnn.benchmark = True

parser = argparse.ArgumentParser(description='PyTorch ImageNet Validation')
parser.add_argument('data', metavar='DIR',
                    help='path to dataset')
parser.add_argument('--model', '-m', metavar='MODEL', default='dpn92',
                    help='model architecture (default: dpn92)')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                    help='number of data loading workers (default: 2)')
parser.add_argument('-b', '--batch-size', default=256, type=int,
                    metavar='N', help='mini-batch size (default: 256)')
parser.add_argument('--img-size', default=None, type=int,
                    metavar='N', help='Input image dimension, uses model default if empty')
parser.add_argument('--crop-pct', default=None, type=float,
                    metavar='N', help='Input image center crop pct')
parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
                    help='Override mean pixel value of dataset')
parser.add_argument('--std', type=float,  nargs='+', default=None, metavar='STD',
                    help='Override std deviation of of dataset')
parser.add_argument('--interpolation', default='', type=str, metavar='NAME',
                    help='Image resize interpolation type (overrides model)')
parser.add_argument('--num-classes', type=int, default=1000,
                    help='Number classes in dataset')
parser.add_argument('--class-map', default='', type=str, metavar='FILENAME',
                    help='path to class to idx mapping file (default: "")')
parser.add_argument('--log-freq', default=1, type=int,
                    metavar='N', help='batch logging frequency (default: 10)')
parser.add_argument('--checkpoint', default='', type=str, metavar='PATH',
                    help='path to latest checkpoint (default: none)')
parser.add_argument('--pretrained', type=str,
                    default=False, help='load pretrained module')
parser.add_argument('--num-gpu', type=int, default=1,
                    help='Number of GPUS to use')
parser.add_argument('--no-test-pool', dest='no_test_pool', action='store_true',
                    help='disable test time pool')
parser.add_argument('--no-prefetcher', action='store_true', default=False,
                    help='disable fast prefetcher')
parser.add_argument('--pin-mem', action='store_true', default=False,
                    help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
parser.add_argument('--amp', action='store_true', default=False,
                    help='Use AMP mixed precision')
parser.add_argument('--tf-preprocessing', action='store_true', default=False,
                    help='Use Tensorflow preprocessing pipeline (require CPU TF installed')
parser.add_argument('--use-ema', dest='use_ema', action='store_true',
                    help='use ema version of weights if present')
parser.add_argument('--torchscript', dest='torchscript', action='store_true',
                    help='convert model torchscript for inference')
parser.add_argument('--results-file', default='', type=str, metavar='FILENAME',
                    help='Output csv file for validation results (summary)')


def validate(args):
    # might as well try to validate something
    args.pretrained = args.pretrained or not args.checkpoint
    args.prefetcher = not args.no_prefetcher

    # create model
    model = my_specialized(num_classes=args.num_classes, net_config=args.model,
                           dropout_rate=0)
    ckpt = torch.load(args.pretrained)
    # for k in model:
    #     print(k)
    # return
    # set bn
    # model.set_bn_param(config.bn_momentum, config.bn_eps)
    for _key in list(ckpt['state_dict_ema'].keys()):
        if 'total_ops' in _key or 'total_params' in _key:
            del ckpt['state_dict_ema'][_key]
    model.load_state_dict(ckpt['state_dict_ema'])
    # model = create_model(
    #     args.model,
    #     pretrained=args.pretrained,
    #     num_classes=args.num_classes,
    #     in_chans=3,
    #     scriptable=args.torchscript)

    param_count = sum([m.numel() for m in model.parameters()])
    print('Model %s created, param count: %d' %
          (args.model, param_count))

    data_config = resolve_data_config(vars(args), model=model)
    model, test_time_pool = apply_test_time_pool(model, data_config, args)

    if args.torchscript:
        torch.jit.optimized_execution(True)
        model = torch.jit.script(model)

    if args.amp:
        model = amp.initialize(model.cuda(), opt_level='O1')
    else:
        model = model.cuda()

    if args.num_gpu > 1:
        model = torch.nn.DataParallel(
            model, device_ids=list(range(args.num_gpu)))

    criterion = nn.CrossEntropyLoss().cuda()

    # from torchvision.datasets import ImageNet
    # dataset = ImageNet(args.data, split='val')
    if os.path.splitext(args.data)[1] == '.tar' and os.path.isfile(args.data):
        dataset = DatasetTar(
            args.data, load_bytes=args.tf_preprocessing, class_map=args.class_map)
    else:
        dataset = Dataset(
            args.data, load_bytes=args.tf_preprocessing, class_map=args.class_map)

    crop_pct = 1.0 if test_time_pool else data_config['crop_pct']
    loader = create_loader(
        dataset,
        input_size=data_config['input_size'],
        batch_size=args.batch_size,
        use_prefetcher=args.prefetcher,
        interpolation=data_config['interpolation'],
        mean=data_config['mean'],
        std=data_config['std'],
        num_workers=args.workers,
        crop_pct=crop_pct,
        pin_memory=args.pin_mem,
        tf_preprocessing=args.tf_preprocessing)
    batch_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()

    model.eval()
    with torch.no_grad():
        # warmup, reduce variability of first batch time, especially for comparing torchscript vs non
        input = torch.randn((args.batch_size,) + data_config['input_size']).cuda()
        model(input)
        end = time.time()
        for i, (input, target) in enumerate(loader):
            if args.no_prefetcher:
                target = target.cuda()
                input = input.cuda()
                if args.fp16:
                    input = input.half()

            # compute output
            output = model(input)
            loss = criterion(output, target)

            # measure accuracy and record loss
            acc1, acc5 = accuracy(output.data, target, topk=(1, 5))
            losses.update(loss.item(), input.size(0))
            top1.update(acc1.item(), input.size(0))
            top5.update(acc5.item(), input.size(0))

            # measure elapsed time
            batch_time.update(time.time() - end)
            end = time.time()

            if i % args.log_freq == 0:
                print(
                    'Test: [{0:>4d}/{1}]  '
                    'Time: {batch_time.val:.3f}s ({batch_time.avg:.3f}s, {rate_avg:>7.2f}/s)  '
                    'Loss: {loss.val:>7.4f} ({loss.avg:>6.4f})  '
                    'Acc@1: {top1.val:>7.3f} ({top1.avg:>7.3f})  '
                    'Acc@5: {top5.val:>7.3f} ({top5.avg:>7.3f})'.format(
                        i, len(loader), batch_time=batch_time,
                        rate_avg=input.size(0) / batch_time.avg,
                        loss=losses, top1=top1, top5=top5))

    results = OrderedDict(
        top1=round(top1.avg, 4), top1_err=round(100 - top1.avg, 4),
        top5=round(top5.avg, 4), top5_err=round(100 - top5.avg, 4),
        param_count=round(param_count / 1e6, 2),
        img_size=data_config['input_size'][-1],
        cropt_pct=crop_pct,
        interpolation=data_config['interpolation'])

    print(' * Acc@1 {:.3f} ({:.3f}) Acc@5 {:.3f} ({:.3f})'.format(
        results['top1'], results['top1_err'], results['top5'], results['top5_err']))

    return results


def main():
    setup_default_logging()
    args = parser.parse_args()
    validate(args)
    # if os.path.isdir(args.checkpoint):
    #     # validate all checkpoints in a path with same model
    #     checkpoints = glob.glob(args.checkpoint + '/*.pth.tar')
    #     checkpoints += glob.glob(args.checkpoint + '/*.pth')
    #     model_names = list_models(args.model)
    #     model_cfgs = [(args.model, c)
    #                   for c in sorted(checkpoints, key=natural_key)]
    # else:
    #     if args.model == 'all':
    #         # validate all models in a list of names with pretrained checkpoints
    #         args.pretrained = True
    #         model_names = list_models(pretrained=True)
    #         model_cfgs = [(n, '') for n in model_names]
    #     elif not is_model(args.model):
    #         # model name doesn't exist, try as wildcard filter
    #         model_names = list_models(args.model)
    #         model_cfgs = [(n, '') for n in model_names]

    # if len(model_cfgs):
    #     results_file = args.results_file or './results-all.csv'
    #     logging.info('Running bulk validation on these pretrained models: {}'.format(
    #         ', '.join(model_names)))
    #     results = []
    #     try:
    #         start_batch_size = args.batch_size
    #         for m, c in model_cfgs:
    #             batch_size = start_batch_size
    #             args.model = m
    #             args.checkpoint = c
    #             result = OrderedDict(model=args.model)
    #             r = {}
    #             while not r and batch_size >= args.num_gpu:
    #                 try:
    #                     args.batch_size = batch_size
    #                     print('Validating with batch size: %d' %
    #                           args.batch_size)
    #                     r = validate(args)
    #                 except RuntimeError as e:
    #                     if batch_size <= args.num_gpu:
    #                         print(
    #                             "Validation failed with no ability to reduce batch size. Exiting.")
    #                         raise e
    #                     batch_size = max(batch_size // 2, args.num_gpu)
    #                     print("Validation failed, reducing batch size by 50%")
    #                     torch.cuda.empty_cache()
    #             result.update(r)
    #             if args.checkpoint:
    #                 result['checkpoint'] = args.checkpoint
    #             results.append(result)
    #     except KeyboardInterrupt:
    #         pass
    #     results = sorted(results, key=lambda x: x['top1'], reverse=True)
    #     if len(results):
    #         write_results(results_file, results)
    # else:
    #     validate(args)


def write_results(results_file, results):
    with open(results_file, mode='w') as cf:
        dw = csv.DictWriter(cf, fieldnames=results[0].keys())
        dw.writeheader()
        for r in results:
            dw.writerow(r)
        cf.flush()


if __name__ == '__main__':
    main()