Add crop_pct arg to validate, extra fields to csv output, 'all' filters pretrained

validate.py

@@ -30,6 +30,8 @@ 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',
@@ -81,6 +83,7 @@ def validate(args):
 
     criterion = nn.CrossEntropyLoss().cuda()
 
+    crop_pct = 1.0 if test_time_pool else data_config['crop_pct']
     loader = create_loader(
         Dataset(args.data, load_bytes=args.tf_preprocessing),
         input_size=data_config['input_size'],
@@ -90,7 +93,7 @@ def validate(args):
         mean=data_config['mean'],
         std=data_config['std'],
         num_workers=args.workers,
-        crop_pct=1.0 if test_time_pool else data_config['crop_pct'],
+        crop_pct=crop_pct,
         tf_preprocessing=args.tf_preprocessing)
 
     batch_time = AverageMeter()
@@ -124,16 +127,19 @@ def validate(args):
                     'Test: [{0:>4d}/{1}]  '
                     'Time: {batch_time.val:.3f} ({batch_time.avg:.3f})  '
                     'Loss: {loss.val:>7.4f} ({loss.avg:>6.4f})  '
-                    'Prec@1: {top1.val:>7.4f} ({top1.avg:>7.4f})  '
+                    'Prec@1: {top1.val:>7.3f} ({top1.avg:>7.3f})  '
-                    'Prec@5: {top5.val:>7.4f} ({top5.avg:>7.4f})'.format(
+                    'Prec@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, 3), top1_err=round(100 - top1.avg, 3),
+        top1=round(top1.avg, 4), top1_err=round(100 - top1.avg, 4),
-        top5=round(top5.avg, 3), top5_err=round(100 - top5.avg, 3),
+        top5=round(top5.avg, 4), top5_err=round(100 - top5.avg, 4),
-        param_count=round(param_count / 1e6, 2))
+        param_count=round(param_count / 1e6, 2),
+        img_size=data_config['input_size'][-1],
+        cropt_pct=crop_pct,
+        interpolation=data_config['interpolation'])
 
     logging.info(' * Prec@1 {:.3f} ({:.3f}) Prec@5 {:.3f} ({:.3f})'.format(
        results['top1'], results['top1_err'], results['top5'], results['top5_err']))
@@ -155,7 +161,7 @@ def main():
         if args.model == 'all':
             # validate all models in a list of names with pretrained checkpoints
             args.pretrained = True
-            model_names = list_models()
+            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
@@ -170,7 +176,8 @@ def main():
                 args.model = m
                 args.checkpoint = c
                 result = OrderedDict(model=args.model)
-                result.update(validate(args))
+                r = validate(args)
+                result.update(r)
                 if args.checkpoint:
                     result['checkpoint'] = args.checkpoint
                 dw = csv.DictWriter(cf, fieldnames=result.keys())