def _get_transformer_image():
scale, mean_, std_ = _get_scalemeanstd()
transformers = []
if scale > 0:
transformers.append(ts.ColorScale(np.single(scale)))
transformers.append(ts.ColorNormalize(mean_, std_))
return transformers
def _val_impl(args, model_specs, logger):
assert args.prefetch_threads == 1
assert args.weights is not None
net_args, net_auxs = util.load_params_from_file(args.weights)
mod = _get_module(model_specs)
has_gt = args.split in (
'train',
'val',
)
scale_, mean_, std_ = _get_scalemeanstd()
if args.test_scales is None:
crop_sizes = [model_specs['crop_size']]
else:
crop_sizes = sorted([int(_)
for _ in args.test_scales.split(',')])[::-1]
batch_images = args.batch_images
if has_gt:
gt_labels = np.array(
parse_split_file(model_specs['split_filename'], args.data_root)[1])
save_dir = os.path.join(args.output, os.path.splitext(args.log_file)[0])
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
preds = []
for crop_size in crop_sizes:
save_path = os.path.join(save_dir, 'preds_sz{}'.format(crop_size))
if os.path.isfile(save_path):
logger.info('File %s exists, skipped crop size %d', save_path,
crop_size)
with open(save_path) as f:
preds.append(cPickle.load(f))
continue
ts_list = [
ts.Scale(crop_size),
ts.ThreeCrops(crop_size)
if args.test_3crops else ts.CenterCrop(crop_size),
]
if scale_ > 0:
ts_list.append(ts.ListInput(ts.ColorScale(np.single(scale_))))
ts_list += [ts.ListInput(ts.ColorNormalize(mean_, std_))]
transformer = ts.Compose(ts_list)
dataiter = FileIter(
split_filename=model_specs['split_filename'],
data_root=args.data_root,
has_gt=has_gt,
batch_images=batch_images,
transformer=transformer,
prefetch_threads=args.prefetch_threads,
prefetcher_type=args.prefetcher,
)
dataiter.reset()
mod.bind(dataiter.provide_data,
dataiter.provide_label,
for_training=False,
force_rebind=True)
if not mod.params_initialized:
mod.init_params(arg_params=net_args, aux_params=net_auxs)
this_call_preds = []
start = time.time()
counter = [0, 0]
for nbatch, batch in enumerate(dataiter):
mod.forward(batch, is_train=False)
outputs = mod.get_outputs()[0].asnumpy()
outputs = outputs.reshape(
(batch_images, -1, model_specs['classes'])).mean(1)
this_call_preds.append(outputs)
if args.test_flipping:
batch.data[0] = mx.nd.flip(batch.data[0], axis=3)
mod.forward(batch, is_train=False)
outputs = mod.get_outputs()[0].asnumpy()
outputs = outputs.reshape(
(batch_images, -1, model_specs['classes'])).mean(1)
this_call_preds[-1] = (this_call_preds[-1] + outputs) / 2
score_str = ''
if has_gt:
counter[0] += batch_images
counter[1] += (this_call_preds[-1].argmax(1) ==
gt_labels[nbatch * batch_images:(nbatch + 1) *
batch_images]).sum()
score_str = ', Top1 {:.4f}%'.format(100.0 * counter[1] /
counter[0])
logger.info('Crop size {}, done {}/{} at speed: {:.2f}/s{}'.\
format(crop_size, nbatch+1, dataiter.batches_per_epoch, 1.*(nbatch+1)*batch_images / (time.time()-start), score_str))
logger.info('Done crop size {} in {:.4f}s.'.format(
crop_size,
time.time() - start))
this_call_preds = np.vstack(this_call_preds)
with open(save_path, 'wb') as f:
cPickle.dump(this_call_preds, f)
preds.append(this_call_preds)
for num_sizes in set((
1,
len(crop_sizes),
)):
for this_pred_inds in itertools.combinations(xrange(len(crop_sizes)),
num_sizes):
this_pred = np.mean([preds[_] for _ in this_pred_inds], axis=0)
this_pred_label = this_pred.argsort(1)[:, -1 - np.arange(5)]
logger.info('Done testing crop_size %s',
[crop_sizes[_] for _ in this_pred_inds])
if has_gt:
top1 = 100. * (this_pred_label[:, 0]
== gt_labels).sum() / gt_labels.size
top5 = 100. * sum(
map(lambda x, y: y in x.tolist(), this_pred_label,
gt_labels)) / gt_labels.size
logger.info('Top1 %.4f%%, Top5 %.4f%%', top1, top5)
else:
# TODO: Save predictions for submission
raise NotImplementedError('Save predictions for submission')
