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@ -9,12 +9,44 @@ import os
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import time
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import time
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import argparse
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import argparse
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import logging
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import logging
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from contextlib import suppress
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from functools import partial
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import numpy as np
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import numpy as np
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import pandas as pd
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import torch
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import torch
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from timm.models import create_model, apply_test_time_pool
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from timm.models import create_model, apply_test_time_pool, load_checkpoint
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from timm.data import ImageDataset, create_loader, resolve_data_config
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from timm.data import create_dataset, create_loader, resolve_data_config
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from timm.utils import AverageMeter, setup_default_logging
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from timm.utils import AverageMeter, setup_default_logging, set_jit_fuser
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try:
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from apex import amp
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has_apex = True
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except ImportError:
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has_apex = False
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has_native_amp = False
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try:
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if getattr(torch.cuda.amp, 'autocast') is not None:
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has_native_amp = True
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except AttributeError:
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pass
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try:
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from functorch.compile import memory_efficient_fusion
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has_functorch = True
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except ImportError as e:
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has_functorch = False
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try:
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import torch._dynamo
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has_dynamo = True
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except ImportError:
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has_dynamo = False
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torch.backends.cudnn.benchmark = True
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torch.backends.cudnn.benchmark = True
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_logger = logging.getLogger('inference')
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_logger = logging.getLogger('inference')
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@ -23,8 +55,10 @@ _logger = logging.getLogger('inference')
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parser = argparse.ArgumentParser(description='PyTorch ImageNet Inference')
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parser = argparse.ArgumentParser(description='PyTorch ImageNet Inference')
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parser.add_argument('data', metavar='DIR',
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parser.add_argument('data', metavar='DIR',
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help='path to dataset')
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help='path to dataset')
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parser.add_argument('--output_dir', metavar='DIR', default='./',
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parser.add_argument('--dataset', '-d', metavar='NAME', default='',
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help='path to output files')
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help='dataset type (default: ImageFolder/ImageTar if empty)')
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parser.add_argument('--split', metavar='NAME', default='validation',
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help='dataset split (default: validation)')
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parser.add_argument('--model', '-m', metavar='MODEL', default='dpn92',
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parser.add_argument('--model', '-m', metavar='MODEL', default='dpn92',
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help='model architecture (default: dpn92)')
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help='model architecture (default: dpn92)')
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parser.add_argument('-j', '--workers', default=2, type=int, metavar='N',
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parser.add_argument('-j', '--workers', default=2, type=int, metavar='N',
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@ -32,17 +66,25 @@ parser.add_argument('-j', '--workers', default=2, type=int, metavar='N',
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parser.add_argument('-b', '--batch-size', default=256, type=int,
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parser.add_argument('-b', '--batch-size', default=256, type=int,
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metavar='N', help='mini-batch size (default: 256)')
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metavar='N', help='mini-batch size (default: 256)')
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parser.add_argument('--img-size', default=None, type=int,
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parser.add_argument('--img-size', default=None, type=int,
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metavar='N', help='Input image dimension')
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metavar='N', help='Input image dimension, uses model default if empty')
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parser.add_argument('--input-size', default=None, nargs=3, type=int,
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parser.add_argument('--input-size', default=None, nargs=3, type=int,
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metavar='N N N', help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty')
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metavar='N N N', help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty')
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parser.add_argument('--use-train-size', action='store_true', default=False,
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help='force use of train input size, even when test size is specified in pretrained cfg')
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parser.add_argument('--crop-pct', default=None, type=float,
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metavar='N', help='Input image center crop pct')
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parser.add_argument('--crop-mode', default=None, type=str,
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metavar='N', help='Input image crop mode (squash, border, center). Model default if None.')
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parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
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parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
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help='Override mean pixel value of dataset')
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help='Override mean pixel value of dataset')
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parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD',
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parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD',
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help='Override std deviation of of dataset')
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help='Override std deviation of of dataset')
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parser.add_argument('--interpolation', default='', type=str, metavar='NAME',
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parser.add_argument('--interpolation', default='', type=str, metavar='NAME',
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help='Image resize interpolation type (overrides model)')
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help='Image resize interpolation type (overrides model)')
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parser.add_argument('--num-classes', type=int, default=1000,
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parser.add_argument('--num-classes', type=int, default=None,
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help='Number classes in dataset')
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help='Number classes in dataset')
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parser.add_argument('--class-map', default='', type=str, metavar='FILENAME',
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help='path to class to idx mapping file (default: "")')
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parser.add_argument('--log-freq', default=10, type=int,
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parser.add_argument('--log-freq', default=10, type=int,
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metavar='N', help='batch logging frequency (default: 10)')
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metavar='N', help='batch logging frequency (default: 10)')
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parser.add_argument('--checkpoint', default='', type=str, metavar='PATH',
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parser.add_argument('--checkpoint', default='', type=str, metavar='PATH',
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@ -51,10 +93,51 @@ parser.add_argument('--pretrained', dest='pretrained', action='store_true',
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help='use pre-trained model')
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help='use pre-trained model')
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parser.add_argument('--num-gpu', type=int, default=1,
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parser.add_argument('--num-gpu', type=int, default=1,
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help='Number of GPUS to use')
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help='Number of GPUS to use')
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parser.add_argument('--no-test-pool', dest='no_test_pool', action='store_true',
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parser.add_argument('--test-pool', dest='test_pool', action='store_true',
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help='disable test time pool')
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help='enable test time pool')
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parser.add_argument('--topk', default=5, type=int,
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parser.add_argument('--channels-last', action='store_true', default=False,
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help='Use channels_last memory layout')
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parser.add_argument('--device', default='cuda', type=str,
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help="Device (accelerator) to use.")
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parser.add_argument('--amp', action='store_true', default=False,
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help='use Native AMP for mixed precision training')
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parser.add_argument('--amp-dtype', default='float16', type=str,
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help='lower precision AMP dtype (default: float16)')
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parser.add_argument('--use-ema', dest='use_ema', action='store_true',
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help='use ema version of weights if present')
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parser.add_argument('--fuser', default='', type=str,
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help="Select jit fuser. One of ('', 'te', 'old', 'nvfuser')")
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parser.add_argument('--dynamo-backend', default=None, type=str,
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help="Select dynamo backend. Default: None")
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scripting_group = parser.add_mutually_exclusive_group()
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scripting_group.add_argument('--torchscript', default=False, action='store_true',
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help='torch.jit.script the full model')
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scripting_group.add_argument('--aot-autograd', default=False, action='store_true',
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help="Enable AOT Autograd support.")
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scripting_group.add_argument('--dynamo', default=False, action='store_true',
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help="Enable Dynamo optimization.")
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parser.add_argument('--results-dir',type=str, default=None,
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help='folder for output results')
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parser.add_argument('--results-file', type=str, default=None,
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help='results filename (relative to results-dir)')
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parser.add_argument('--results-format', type=str, default='csv',
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help='results format (one of "csv", "json", "json-split", "parquet")')
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parser.add_argument('--topk', default=1, type=int,
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metavar='N', help='Top-k to output to CSV')
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metavar='N', help='Top-k to output to CSV')
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parser.add_argument('--fullname', action='store_true', default=False,
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help='use full sample name in output (not just basename).')
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parser.add_argument('--indices-name', default='index',
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help='name for output indices column(s)')
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parser.add_argument('--outputs-name', default=None,
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help='name for logit/probs output column(s)')
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parser.add_argument('--outputs-type', default='prob',
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help='output type colum ("prob" for probabilities, "logit" for raw logits)')
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parser.add_argument('--separate-columns', action='store_true', default=False,
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help='separate output columns per result index.')
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parser.add_argument('--exclude-outputs', action='store_true', default=False,
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help='exclude logits/probs from results, just indices. topk must be set !=0.')
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def main():
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def main():
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@ -63,48 +146,109 @@ def main():
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# might as well try to do something useful...
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# might as well try to do something useful...
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args.pretrained = args.pretrained or not args.checkpoint
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args.pretrained = args.pretrained or not args.checkpoint
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if torch.cuda.is_available():
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torch.backends.cuda.matmul.allow_tf32 = True
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torch.backends.cudnn.benchmark = True
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device = torch.device(args.device)
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# resolve AMP arguments based on PyTorch / Apex availability
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use_amp = None
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amp_autocast = suppress
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if args.amp:
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assert has_native_amp, 'Please update PyTorch to a version with native AMP (or use APEX).'
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assert args.amp_dtype in ('float16', 'bfloat16')
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amp_dtype = torch.bfloat16 if args.amp_dtype == 'bfloat16' else torch.float16
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amp_autocast = partial(torch.autocast, device_type=device.type, dtype=amp_dtype)
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_logger.info('Running inference in mixed precision with native PyTorch AMP.')
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else:
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_logger.info('Running inference in float32. AMP not enabled.')
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if args.fuser:
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set_jit_fuser(args.fuser)
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# create model
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# create model
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model = create_model(
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model = create_model(
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args.model,
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args.model,
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num_classes=args.num_classes,
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num_classes=args.num_classes,
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in_chans=3,
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in_chans=3,
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pretrained=args.pretrained,
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pretrained=args.pretrained,
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checkpoint_path=args.checkpoint)
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checkpoint_path=args.checkpoint,
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)
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if args.num_classes is None:
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assert hasattr(model, 'num_classes'), 'Model must have `num_classes` attr if not set on cmd line/config.'
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args.num_classes = model.num_classes
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_logger.info('Model %s created, param count: %d' %
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if args.checkpoint:
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(args.model, sum([m.numel() for m in model.parameters()])))
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load_checkpoint(model, args.checkpoint, args.use_ema)
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config = resolve_data_config(vars(args), model=model)
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_logger.info(
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model, test_time_pool = (model, False) if args.no_test_pool else apply_test_time_pool(model, config)
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f'Model {args.model} created, param count: {sum([m.numel() for m in model.parameters()])}')
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if args.num_gpu > 1:
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data_config = resolve_data_config(vars(args), model=model)
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model = torch.nn.DataParallel(model, device_ids=list(range(args.num_gpu))).cuda()
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test_time_pool = False
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if args.test_pool:
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model, test_time_pool = apply_test_time_pool(model, data_config)
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model = model.to(device)
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model.eval()
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if args.channels_last:
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model = model.to(memory_format=torch.channels_last)
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if args.torchscript:
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model = torch.jit.script(model)
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elif args.aot_autograd:
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assert has_functorch, "functorch is needed for --aot-autograd"
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model = memory_efficient_fusion(model)
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elif args.dynamo:
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assert has_dynamo, "torch._dynamo is needed for --dynamo"
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torch._dynamo.reset()
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if args.dynamo_backend is not None:
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model = torch._dynamo.optimize(args.dynamo_backend)(model)
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else:
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else:
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model = model.cuda()
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model = torch._dynamo.optimize()(model)
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if args.num_gpu > 1:
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model = torch.nn.DataParallel(model, device_ids=list(range(args.num_gpu)))
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dataset = create_dataset(
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root=args.data,
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name=args.dataset,
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split=args.split,
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class_map=args.class_map,
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)
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if test_time_pool:
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data_config['crop_pct'] = 1.0
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loader = create_loader(
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loader = create_loader(
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ImageDataset(args.data),
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dataset,
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input_size=config['input_size'],
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batch_size=args.batch_size,
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batch_size=args.batch_size,
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use_prefetcher=True,
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use_prefetcher=True,
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interpolation=config['interpolation'],
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mean=config['mean'],
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std=config['std'],
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num_workers=args.workers,
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num_workers=args.workers,
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crop_pct=1.0 if test_time_pool else config['crop_pct'])
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|
**data_config,
|
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|
|
|
|
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|
)
|
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model.eval()
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|
top_k = min(args.topk, args.num_classes)
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|
k = min(args.topk, args.num_classes)
|
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|
batch_time = AverageMeter()
|
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|
|
batch_time = AverageMeter()
|
|
|
|
end = time.time()
|
|
|
|
end = time.time()
|
|
|
|
topk_ids = []
|
|
|
|
all_indices = []
|
|
|
|
|
|
|
|
all_outputs = []
|
|
|
|
|
|
|
|
use_probs = args.outputs_type == 'prob'
|
|
|
|
with torch.no_grad():
|
|
|
|
with torch.no_grad():
|
|
|
|
for batch_idx, (input, _) in enumerate(loader):
|
|
|
|
for batch_idx, (input, _) in enumerate(loader):
|
|
|
|
input = input.cuda()
|
|
|
|
|
|
|
|
labels = model(input)
|
|
|
|
with amp_autocast():
|
|
|
|
topk = labels.topk(k)[1]
|
|
|
|
output = model(input)
|
|
|
|
topk_ids.append(topk.cpu().numpy())
|
|
|
|
|
|
|
|
|
|
|
|
if use_probs:
|
|
|
|
|
|
|
|
output = output.softmax(-1)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
if top_k:
|
|
|
|
|
|
|
|
output, indices = output.topk(top_k)
|
|
|
|
|
|
|
|
all_indices.append(indices.cpu().numpy())
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
all_outputs.append(output.cpu().numpy())
|
|
|
|
|
|
|
|
|
|
|
|
# measure elapsed time
|
|
|
|
# measure elapsed time
|
|
|
|
batch_time.update(time.time() - end)
|
|
|
|
batch_time.update(time.time() - end)
|
|
|
@ -114,13 +258,57 @@ def main():
|
|
|
|
_logger.info('Predict: [{0}/{1}] Time {batch_time.val:.3f} ({batch_time.avg:.3f})'.format(
|
|
|
|
_logger.info('Predict: [{0}/{1}] Time {batch_time.val:.3f} ({batch_time.avg:.3f})'.format(
|
|
|
|
batch_idx, len(loader), batch_time=batch_time))
|
|
|
|
batch_idx, len(loader), batch_time=batch_time))
|
|
|
|
|
|
|
|
|
|
|
|
topk_ids = np.concatenate(topk_ids, axis=0)
|
|
|
|
all_indices = np.concatenate(all_indices, axis=0) if all_indices else None
|
|
|
|
|
|
|
|
all_outputs = np.concatenate(all_outputs, axis=0).astype(np.float32)
|
|
|
|
|
|
|
|
filenames = loader.dataset.filenames(basename=not args.fullname)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
outputs_name = args.outputs_name or ('prob' if use_probs else 'logit')
|
|
|
|
|
|
|
|
data_dict = {'filename': filenames}
|
|
|
|
|
|
|
|
if args.separate_columns and all_outputs.shape[-1] > 1:
|
|
|
|
|
|
|
|
if all_indices is not None:
|
|
|
|
|
|
|
|
for i in range(all_indices.shape[-1]):
|
|
|
|
|
|
|
|
data_dict[f'{args.indices_name}_{i}'] = all_indices[:, i]
|
|
|
|
|
|
|
|
for i in range(all_outputs.shape[-1]):
|
|
|
|
|
|
|
|
data_dict[f'{outputs_name}_{i}'] = all_outputs[:, i]
|
|
|
|
|
|
|
|
else:
|
|
|
|
|
|
|
|
if all_indices is not None:
|
|
|
|
|
|
|
|
if all_indices.shape[-1] == 1:
|
|
|
|
|
|
|
|
all_indices = all_indices.squeeze(-1)
|
|
|
|
|
|
|
|
data_dict[args.indices_name] = list(all_indices)
|
|
|
|
|
|
|
|
if all_outputs.shape[-1] == 1:
|
|
|
|
|
|
|
|
all_outputs = all_outputs.squeeze(-1)
|
|
|
|
|
|
|
|
data_dict[outputs_name] = list(all_outputs)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
df = pd.DataFrame(data=data_dict)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
results_filename = args.results_file
|
|
|
|
|
|
|
|
needs_ext = False
|
|
|
|
|
|
|
|
if not results_filename:
|
|
|
|
|
|
|
|
# base default filename on model name + img-size
|
|
|
|
|
|
|
|
img_size = data_config["input_size"][1]
|
|
|
|
|
|
|
|
results_filename = f'{args.model}-{img_size}'
|
|
|
|
|
|
|
|
needs_ext = True
|
|
|
|
|
|
|
|
|
|
|
|
with open(os.path.join(args.output_dir, './topk_ids.csv'), 'w') as out_file:
|
|
|
|
if args.results_dir:
|
|
|
|
filenames = loader.dataset.filenames(basename=True)
|
|
|
|
results_filename = os.path.join(args.results_dir, results_filename)
|
|
|
|
for filename, label in zip(filenames, topk_ids):
|
|
|
|
|
|
|
|
out_file.write('{0},{1}\n'.format(
|
|
|
|
if args.results_format == 'parquet':
|
|
|
|
filename, ','.join([ str(v) for v in label])))
|
|
|
|
if needs_ext:
|
|
|
|
|
|
|
|
results_filename += '.parquet'
|
|
|
|
|
|
|
|
df = df.set_index('filename')
|
|
|
|
|
|
|
|
df.to_parquet(results_filename)
|
|
|
|
|
|
|
|
elif args.results_format == 'json':
|
|
|
|
|
|
|
|
if needs_ext:
|
|
|
|
|
|
|
|
results_filename += '.json'
|
|
|
|
|
|
|
|
df.to_json(results_filename, lines=True, orient='records')
|
|
|
|
|
|
|
|
elif args.results_format == 'json-split':
|
|
|
|
|
|
|
|
if needs_ext:
|
|
|
|
|
|
|
|
results_filename += '.json'
|
|
|
|
|
|
|
|
df.to_json(results_filename, indent=4, orient='split', index=False)
|
|
|
|
|
|
|
|
else:
|
|
|
|
|
|
|
|
if needs_ext:
|
|
|
|
|
|
|
|
results_filename += '.csv'
|
|
|
|
|
|
|
|
df.to_csv(results_filename, index=False)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
|
|
if __name__ == '__main__':
|
|
|
|