Significant ugprade to inference.py, support for different formats, formatting, etc.

inference.py

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