From a75d8915e756e56347a5f751890c62a3bb8e2935 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Mon, 9 Mar 2020 10:52:09 +0800
Subject: [PATCH 1/7] [DLMED] add arbitrary format support for all
 event-handlers

---
 ...ference_3d.py => unet_inference_3d_array.py} | 17 +++++++++--------
 monai/handlers/segmentation_saver.py            | 15 +++++++++------
 monai/utils/sliding_window_inference.py         |  4 ++--
 tests/test_sliding_window_inference.py          |  2 +-
 4 files changed, 21 insertions(+), 17 deletions(-)
 rename examples/{unet_inference_3d.py => unet_inference_3d_array.py} (83%)

diff --git a/examples/unet_inference_3d.py b/examples/unet_inference_3d_array.py
similarity index 83%
rename from examples/unet_inference_3d.py
rename to examples/unet_inference_3d_array.py
index aa84a6560d..a4d0b7ffec 100644
--- a/examples/unet_inference_3d.py
+++ b/examples/unet_inference_3d_array.py
@@ -21,6 +21,9 @@
 from ignite.engine import Engine
 from torch.utils.data import DataLoader
 
+# assumes the framework is found here, change as necessary
+sys.path.append("..")
+
 from monai import config
 from monai.handlers.checkpoint_loader import CheckpointLoader
 from monai.handlers.segmentation_saver import SegmentationSaver
@@ -31,7 +34,6 @@
 from monai.data.synthetic import create_test_image_3d
 from monai.utils.sliding_window_inference import sliding_window_inference
 
-sys.path.append("..")  # assumes the framework is found here, change as necessary
 config.print_config()
 
 tempdir = tempfile.mkdtemp()
@@ -51,7 +53,7 @@
 segtrans = transforms.Compose([AddChannel(), ToTensor()])
 ds = NiftiDataset(images, segs, transform=imtrans, seg_transform=segtrans, image_only=False)
 
-device = torch.device("cpu:0")
+device = torch.device("cuda:0")
 roi_size = (64, 64, 64)
 sw_batch_size = 4
 net = UNet(
@@ -65,7 +67,7 @@
 net.to(device)
 
 
-def _sliding_window_processor(_engine, batch):
+def _sliding_window_processor(engine, batch):
     net.eval()
     img, seg, meta_data = batch
     with torch.no_grad():
@@ -75,11 +77,10 @@ def _sliding_window_processor(_engine, batch):
 
 infer_engine = Engine(_sliding_window_processor)
 
-# checkpoint_handler = ModelCheckpoint('./', 'net', n_saved=10, save_interval=3, require_empty=False)
-# infer_engine.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=checkpoint_handler, to_save={'net': net})
-
-SegmentationSaver(output_path='tempdir', output_ext='.nii.gz', output_postfix='seg').attach(infer_engine)
-CheckpointLoader(load_path='./net_checkpoint_9.pth', load_dict={'net': net}).attach(infer_engine)
+# for the arrary data format, assume the 3rd item of batch data is the meta_data
+SegmentationSaver(output_path='tempdir', output_ext='.nii.gz', output_postfix='seg',
+                  batch_transform=lambda x: x[2]).attach(infer_engine)
+CheckpointLoader(load_path='./runs/net_checkpoint_120.pth', load_dict={'net': net}).attach(infer_engine)
 
 loader = DataLoader(ds, batch_size=1, num_workers=1, pin_memory=torch.cuda.is_available())
 state = infer_engine.run(loader)
diff --git a/monai/handlers/segmentation_saver.py b/monai/handlers/segmentation_saver.py
index a87e517f81..65b18158e1 100644
--- a/monai/handlers/segmentation_saver.py
+++ b/monai/handlers/segmentation_saver.py
@@ -23,13 +23,15 @@ class SegmentationSaver:
     """
 
     def __init__(self, output_path='./', dtype='float32', output_postfix='seg', output_ext='.nii.gz',
-                 output_transform=lambda x: x, name=None):
+                 batch_transform=lambda x: x, output_transform=lambda x: x, name=None):
         """
         Args:
             output_path (str): output image directory.
             dtype (str): to convert the image to save to this datatype.
             output_postfix (str): a string appended to all output file names.
             output_ext (str): output file extension name.
+            batch_transform (Callable): a callable that is used to transform the
+                ignite.engine.batch into expected format to extract the meta_data dictionary.
             output_transform (Callable): a callable that is used to transform the
                 ignite.engine.output into the form expected nifti image data.
                 The first dimension of this transform's output will be treated as the
@@ -40,6 +42,7 @@ def __init__(self, output_path='./', dtype='float32', output_postfix='seg', outp
         self.dtype = dtype
         self.output_postfix = output_postfix
         self.output_ext = output_ext
+        self.batch_transform = batch_transform
         self.output_transform = output_transform
 
         self.logger = None if name is None else logging.getLogger(name)
@@ -88,12 +91,12 @@ def _create_file_basename(postfix, input_file_name, folder_path, data_root_dir="
 
     def __call__(self, engine):
         """
-        This method assumes:
-            - 3rd output of engine.state.batch is a meta data dict, and have the keys:
+        This method assumes to have the keys:
             'filename_or_obj' -- for output file name creation
             and optionally 'original_affine', 'affine' for data orientation handling.
             - output file datatype from `engine.state.output.dtype`.
         """
+        meta_data = self.batch_transform(engine.state.batch)
         meta_data = engine.state.batch[2]  # assuming 3rd output of input dataset is a meta data dict
         filenames = meta_data['filename_or_obj']
         original_affine = meta_data.get('original_affine', None)
@@ -101,11 +104,11 @@ def __call__(self, engine):
         engine_output = self.output_transform(engine.state.output)
         for batch_id, filename in enumerate(filenames):  # save a batch of files
             seg_output = engine_output[batch_id]
-            _affine = affine[batch_id]
-            _original_affine = original_affine[batch_id]
+            affine_ = affine[batch_id]
+            original_affine_ = original_affine[batch_id]
             if isinstance(seg_output, torch.Tensor):
                 seg_output = seg_output.detach().cpu().numpy()
             output_filename = self._create_file_basename(self.output_postfix, filename, self.output_path)
             output_filename = '{}{}'.format(output_filename, self.output_ext)
-            write_nifti(seg_output, _affine, output_filename, _original_affine, dtype=seg_output.dtype)
+            write_nifti(seg_output, affine_, output_filename, original_affine_, dtype=seg_output.dtype)
             self.logger.info('saved: {}'.format(output_filename))
diff --git a/monai/utils/sliding_window_inference.py b/monai/utils/sliding_window_inference.py
index 2efc7a7481..708a0f284e 100644
--- a/monai/utils/sliding_window_inference.py
+++ b/monai/utils/sliding_window_inference.py
@@ -10,7 +10,7 @@
 # limitations under the License.
 
 import torch
-
+from ignite.utils import convert_tensor
 from monai.transforms.transforms import ImageEndPadder
 from monai.transforms.transforms import ToTensor
 from monai.data.utils import dense_patch_slices
@@ -49,7 +49,7 @@ def sliding_window_inference(inputs, roi_size, sw_batch_size, predictor, device)
     # in case that image size is smaller than roi size
     image_size = tuple(max(image_size[i], roi_size[i]) for i in range(num_spatial_dims))
     inputs = ImageEndPadder(roi_size, 'constant')(inputs)  # in np array
-    inputs = ToTensor()(inputs)
+    inputs = convert_tensor(ToTensor()(inputs), device, False)
 
     # TODO: interval from user's specification
     scan_interval = _get_scan_interval(image_size, roi_size, num_spatial_dims)
diff --git a/tests/test_sliding_window_inference.py b/tests/test_sliding_window_inference.py
index e0d727c407..142d3e31e5 100644
--- a/tests/test_sliding_window_inference.py
+++ b/tests/test_sliding_window_inference.py
@@ -34,7 +34,7 @@ def test_sliding_window_default(self, image_shape, roi_shape, sw_batch_size):
         device = torch.device("cpu:0")
 
         def compute(data):
-            return data.to(device) + 1
+            return data + 1
 
         result = sliding_window_inference(inputs, roi_shape, sw_batch_size, compute, device)
         expected_val = np.ones(image_shape, dtype=np.float32) + 1

From c7b3168ee7db9e6b8f2a66a89f03c65cda05ec16 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Tue, 10 Mar 2020 01:25:25 +0800
Subject: [PATCH 2/7] [DLMED] update StatsHandler and fix comments

---
 examples/unet_inference_3d_array.py     |  3 +-
 examples/unet_segmentation_3d_array.py  |  4 +--
 monai/handlers/stats_handler.py         | 28 +++++++++++-------
 monai/utils/sliding_window_inference.py |  3 +-
 tests/test_handler_stats.py             | 38 +++++++++++++++++++++----
 5 files changed, 55 insertions(+), 21 deletions(-)

diff --git a/examples/unet_inference_3d_array.py b/examples/unet_inference_3d_array.py
index a4d0b7ffec..07cf243b37 100644
--- a/examples/unet_inference_3d_array.py
+++ b/examples/unet_inference_3d_array.py
@@ -17,7 +17,6 @@
 import nibabel as nib
 import numpy as np
 import torch
-import torchvision.transforms as transforms
 from ignite.engine import Engine
 from torch.utils.data import DataLoader
 
@@ -27,6 +26,7 @@
 from monai import config
 from monai.handlers.checkpoint_loader import CheckpointLoader
 from monai.handlers.segmentation_saver import SegmentationSaver
+import monai.transforms.compose as transforms
 from monai.data.nifti_reader import NiftiDataset
 from monai.transforms import AddChannel, Rescale, ToTensor
 from monai.networks.nets.unet import UNet
@@ -80,6 +80,7 @@ def _sliding_window_processor(engine, batch):
 # for the arrary data format, assume the 3rd item of batch data is the meta_data
 SegmentationSaver(output_path='tempdir', output_ext='.nii.gz', output_postfix='seg',
                   batch_transform=lambda x: x[2]).attach(infer_engine)
+# the model was trained by "unet_segmentation_3d_array" exmple
 CheckpointLoader(load_path='./runs/net_checkpoint_120.pth', load_dict={'net': net}).attach(infer_engine)
 
 loader = DataLoader(ds, batch_size=1, num_workers=1, pin_memory=torch.cuda.is_available())
diff --git a/examples/unet_segmentation_3d_array.py b/examples/unet_segmentation_3d_array.py
index 0e68eb67c8..11a4565a3a 100644
--- a/examples/unet_segmentation_3d_array.py
+++ b/examples/unet_segmentation_3d_array.py
@@ -30,7 +30,7 @@
 import monai.transforms.compose as transforms
 
 from monai.data.nifti_reader import NiftiDataset
-from monai.transforms import (AddChannel, Rescale, ToTensor, UniformRandomPatch)
+from monai.transforms import AddChannel, Rescale, ToTensor, UniformRandomPatch
 from monai.handlers.stats_handler import StatsHandler
 from monai.handlers.mean_dice import MeanDice
 from monai.visualize import img2tensorboard
@@ -148,7 +148,7 @@ def log_training_loss(engine):
 
 # Add stats event handler to print validation stats via evaluator
 logging.basicConfig(stream=sys.stdout, level=logging.INFO)
-val_stats_handler = StatsHandler()
+val_stats_handler = StatsHandler(output_transform=lambda output: (None, None))
 val_stats_handler.attach(evaluator)
 
 # Add early stopping handler to evaluator.
diff --git a/monai/handlers/stats_handler.py b/monai/handlers/stats_handler.py
index b1ba3563d5..ba0f1633ec 100644
--- a/monai/handlers/stats_handler.py
+++ b/monai/handlers/stats_handler.py
@@ -19,26 +19,35 @@
 class StatsHandler(object):
     """StatsHandler defines a set of Ignite Event-handlers for all the log printing logics.
     It's can be used for any Ignite Engine(trainer, validator and evaluator).
-    And it can support logging for epoch level and iteration level with pre-defined StatsLoggers.
-    By default, this class logs the dictionary of `engine.state.metrics`.
+    And it can support logging for epoch level and iteration level with pre-defined loggers.
+    By default:
+    (1) epoch_print_logger logs `engine.state.metrics`.
+    (2) iteration_print_logger logs loss value, expected output format is (y_pred, loss).
     """
 
     def __init__(self,
                  epoch_print_logger=None,
                  iteration_print_logger=None,
+                 batch_transform=lambda x: x,
+                 output_transform=lambda x: x,
                  name=None):
         """
         Args:
             epoch_print_logger (Callable): customized callable printer for epoch level logging.
-                                            must accept parameter "engine", use default printer if None.
+                must accept parameter "engine", use default printer if None.
             iteration_print_logger (Callable): custimized callable printer for iteration level logging.
-                                            must accept parameter "engine", use default printer if None.
+                must accept parameter "engine", use default printer if None.
+            batch_transform (Callable): a callable that is used to transform the
+                ignite.engine.batch into expected format to extract input data.
+            output_transform (Callable): a callable that is used to transform the
+                ignite.engine.output into expected format to extract several output data.
             name (str): identifier of logging.logger to use, defaulting to `engine.logger`.
         """
 
         self.epoch_print_logger = epoch_print_logger
         self.iteration_print_logger = iteration_print_logger
-
+        self.batch_transform = batch_transform
+        self.output_transform = output_transform
         self.logger = None if name is None else logging.getLogger(name)
 
 
@@ -125,8 +134,8 @@ def _default_iteration_print(self, engine: Engine):
             engine (ignite.engine): Ignite Engine, it can be a trainer, validator or evaluator.
 
         """
-        prints_dict = engine.state.metrics
-        if not prints_dict:
+        loss = self.output_transform(engine.state.output)[1]
+        if loss is None:
             return
         num_iterations = engine.state.epoch_length
         current_iteration = (engine.state.iteration - 1) % num_iterations + 1
@@ -138,9 +147,6 @@ def _default_iteration_print(self, engine: Engine):
             num_epochs,
             current_iteration,
             num_iterations)
-
-        for name in sorted(prints_dict):
-            value = prints_dict[name]
-            out_str += KEY_VAL_FORMAT.format(name, value)
+        out_str += KEY_VAL_FORMAT.format('Loss', loss)
 
         self.logger.info(out_str)
diff --git a/monai/utils/sliding_window_inference.py b/monai/utils/sliding_window_inference.py
index 708a0f284e..0125a19d06 100644
--- a/monai/utils/sliding_window_inference.py
+++ b/monai/utils/sliding_window_inference.py
@@ -12,7 +12,6 @@
 import torch
 from ignite.utils import convert_tensor
 from monai.transforms.transforms import ImageEndPadder
-from monai.transforms.transforms import ToTensor
 from monai.data.utils import dense_patch_slices
 
 
@@ -49,7 +48,7 @@ def sliding_window_inference(inputs, roi_size, sw_batch_size, predictor, device)
     # in case that image size is smaller than roi size
     image_size = tuple(max(image_size[i], roi_size[i]) for i in range(num_spatial_dims))
     inputs = ImageEndPadder(roi_size, 'constant')(inputs)  # in np array
-    inputs = convert_tensor(ToTensor()(inputs), device, False)
+    inputs = convert_tensor(torch.from_numpy(inputs), device, False)
 
     # TODO: interval from user's specification
     scan_interval = _get_scan_interval(image_size, roi_size, num_spatial_dims)
diff --git a/tests/test_handler_stats.py b/tests/test_handler_stats.py
index 5bbe17d1c2..58a62133d2 100644
--- a/tests/test_handler_stats.py
+++ b/tests/test_handler_stats.py
@@ -9,6 +9,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import torch
 import logging
 import re
 import unittest
@@ -29,12 +30,12 @@ def test_metrics_print(self):
 
         # set up engine
         def _train_func(engine, batch):
-            pass
+            return None, torch.tensor(0.0)
 
         engine = Engine(_train_func)
 
         # set up dummy metric
-        @engine.on(Events.ITERATION_COMPLETED)
+        @engine.on(Events.EPOCH_COMPLETED)
         def _update_metric(engine):
             current_metric = engine.state.metrics.get(key_to_print, 0.1)
             engine.state.metrics[key_to_print] = current_metric + 0.1
@@ -52,9 +53,36 @@ def _update_metric(engine):
         matched = []
         for idx, line in enumerate(output_str.split('\n')):
             if grep.match(line):
-                self.assertTrue(has_key_word.match(line))
-                matched.append(idx)
-        self.assertEqual(matched, [1, 2, 3, 5, 6, 7, 8, 10])
+                if idx in [5, 10]:
+                    self.assertTrue(has_key_word.match(line))
+
+    def test_loss_print(self):
+        log_stream = StringIO()
+        logging.basicConfig(stream=log_stream, level=logging.INFO)
+        key_to_handler = 'test_logging'
+        key_to_print = 'Loss'
+
+        # set up engine
+        def _train_func(engine, batch):
+            return None, torch.tensor(0.0)
+
+        engine = Engine(_train_func)
+
+        # set up testing handler
+        stats_handler = StatsHandler(name=key_to_handler)
+        stats_handler.attach(engine)
+
+        engine.run(range(3), max_epochs=2)
+
+        # check logging output
+        output_str = log_stream.getvalue()
+        grep = re.compile('.*{}.*'.format(key_to_handler))
+        has_key_word = re.compile('.*{}.*'.format(key_to_print))
+        matched = []
+        for idx, line in enumerate(output_str.split('\n')):
+            if grep.match(line):
+                if idx in [1, 2, 3, 6, 7, 8]:
+                    self.assertTrue(has_key_word.match(line))
 
 
 if __name__ == '__main__':

From df66cbaa58ae948c16a2eb9561af4636cfdee933 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Tue, 10 Mar 2020 11:26:08 +0800
Subject: [PATCH 3/7] [DLMED] update according to comments

---
 examples/unet_segmentation_3d_array.py | 2 +-
 monai/handlers/segmentation_saver.py   | 2 +-
 monai/handlers/stats_handler.py        | 7 ++++---
 3 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/examples/unet_segmentation_3d_array.py b/examples/unet_segmentation_3d_array.py
index 11a4565a3a..f431b47faf 100644
--- a/examples/unet_segmentation_3d_array.py
+++ b/examples/unet_segmentation_3d_array.py
@@ -148,7 +148,7 @@ def log_training_loss(engine):
 
 # Add stats event handler to print validation stats via evaluator
 logging.basicConfig(stream=sys.stdout, level=logging.INFO)
-val_stats_handler = StatsHandler(output_transform=lambda output: (None, None))
+val_stats_handler = StatsHandler()
 val_stats_handler.attach(evaluator)
 
 # Add early stopping handler to evaluator.
diff --git a/monai/handlers/segmentation_saver.py b/monai/handlers/segmentation_saver.py
index 65b18158e1..0983f4b135 100644
--- a/monai/handlers/segmentation_saver.py
+++ b/monai/handlers/segmentation_saver.py
@@ -95,9 +95,9 @@ def __call__(self, engine):
             'filename_or_obj' -- for output file name creation
             and optionally 'original_affine', 'affine' for data orientation handling.
             - output file datatype from `engine.state.output.dtype`.
+        And this method assumes self.batch_transform will extract meta data from the input batch.
         """
         meta_data = self.batch_transform(engine.state.batch)
-        meta_data = engine.state.batch[2]  # assuming 3rd output of input dataset is a meta data dict
         filenames = meta_data['filename_or_obj']
         original_affine = meta_data.get('original_affine', None)
         affine = meta_data.get('affine', None)
diff --git a/monai/handlers/stats_handler.py b/monai/handlers/stats_handler.py
index ba0f1633ec..3dba0fca22 100644
--- a/monai/handlers/stats_handler.py
+++ b/monai/handlers/stats_handler.py
@@ -10,7 +10,7 @@
 # limitations under the License.
 
 import logging
-
+import torch
 from ignite.engine import Engine, Events
 
 KEY_VAL_FORMAT = '{}: {:.4f} '
@@ -128,14 +128,15 @@ def _default_epoch_print(self, engine: Engine):
 
     def _default_iteration_print(self, engine: Engine):
         """Execute iteration log operation based on Ignite engine.state data.
-        print the values from ignite state.logs dict.
+        Print the values from ignite state.logs dict.
+        Default behaivor is to print loss from output[1], skip if output[1] is not loss.
 
         Args:
             engine (ignite.engine): Ignite Engine, it can be a trainer, validator or evaluator.
 
         """
         loss = self.output_transform(engine.state.output)[1]
-        if loss is None:
+        if loss is None or (isinstance(loss, torch.Tensor) is True and len(loss.shape) > 0):
             return
         num_iterations = engine.state.epoch_length
         current_iteration = (engine.state.iteration - 1) % num_iterations + 1

From 2d13d1de3134211712437410e4e5352a936c1781 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Tue, 10 Mar 2020 13:17:54 +0800
Subject: [PATCH 4/7] [DLMED] fix integration test error

---
 tests/integration_sliding_window.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tests/integration_sliding_window.py b/tests/integration_sliding_window.py
index 32abbfff5c..db10d7cc49 100644
--- a/tests/integration_sliding_window.py
+++ b/tests/integration_sliding_window.py
@@ -58,7 +58,8 @@ def _sliding_window_processor(_engine, batch):
     infer_engine = Engine(_sliding_window_processor)
 
     with tempfile.TemporaryDirectory() as temp_dir:
-        SegmentationSaver(output_path=temp_dir, output_ext='.nii.gz', output_postfix='seg').attach(infer_engine)
+        SegmentationSaver(output_path=temp_dir, output_ext='.nii.gz', output_postfix='seg',
+                          batch_transform=lambda x: x[2]).attach(infer_engine)
 
         infer_engine.run(loader)
 

From 476e5336120973f7720c10d9e533ad8008299ee3 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Tue, 10 Mar 2020 14:27:40 +0800
Subject: [PATCH 5/7] [DLMED] support both Tensor and Float loss value

---
 monai/handlers/stats_handler.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/monai/handlers/stats_handler.py b/monai/handlers/stats_handler.py
index 3dba0fca22..69bdc9379d 100644
--- a/monai/handlers/stats_handler.py
+++ b/monai/handlers/stats_handler.py
@@ -148,6 +148,6 @@ def _default_iteration_print(self, engine: Engine):
             num_epochs,
             current_iteration,
             num_iterations)
-        out_str += KEY_VAL_FORMAT.format('Loss', loss)
+        out_str += KEY_VAL_FORMAT.format('Loss', loss.item() if isinstance(loss, torch.Tensor) else loss)
 
         self.logger.info(out_str)

From 50dadecb097c21d453177fb991973b4c14e81614 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Tue, 10 Mar 2020 18:09:11 +0800
Subject: [PATCH 6/7] [DLMED] add dict based UNet inference example

---
 examples/unet_inference_3d_dict.py    | 94 +++++++++++++++++++++++++++
 examples/unet_segmentation_3d_dict.py |  4 +-
 2 files changed, 96 insertions(+), 2 deletions(-)
 create mode 100644 examples/unet_inference_3d_dict.py

diff --git a/examples/unet_inference_3d_dict.py b/examples/unet_inference_3d_dict.py
new file mode 100644
index 0000000000..c0d27c3c20
--- /dev/null
+++ b/examples/unet_inference_3d_dict.py
@@ -0,0 +1,94 @@
+# Copyright 2020 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import sys
+import tempfile
+from glob import glob
+
+import nibabel as nib
+import numpy as np
+import torch
+from ignite.engine import Engine
+from torch.utils.data import DataLoader
+
+# assumes the framework is found here, change as necessary
+sys.path.append("..")
+
+import monai
+from monai.data.utils import list_data_collate
+from monai.utils.sliding_window_inference import sliding_window_inference
+from monai.data.synthetic import create_test_image_3d
+from monai.networks.utils import predict_segmentation
+from monai.networks.nets.unet import UNet
+from monai.transforms.composables import LoadNiftid, AsChannelFirstd
+import monai.transforms.compose as transforms
+from monai.handlers.segmentation_saver import SegmentationSaver
+from monai.handlers.checkpoint_loader import CheckpointLoader
+from monai import config
+
+config.print_config()
+
+tempdir = tempfile.mkdtemp()
+# tempdir = './temp'
+for i in range(50):
+    im, seg = create_test_image_3d(256, 256, 256, channel_dim=-1)
+
+    n = nib.Nifti1Image(im, np.eye(4))
+    nib.save(n, os.path.join(tempdir, 'im%i.nii.gz' % i))
+
+    n = nib.Nifti1Image(seg, np.eye(4))
+    nib.save(n, os.path.join(tempdir, 'seg%i.nii.gz' % i))
+
+images = sorted(glob(os.path.join(tempdir, 'im*.nii.gz')))
+segs = sorted(glob(os.path.join(tempdir, 'seg*.nii.gz')))
+val_files = [{'img': img, 'seg': seg} for img, seg in zip(images, segs)]
+val_transforms = transforms.Compose([
+    LoadNiftid(keys=['img', 'seg']),
+    AsChannelFirstd(keys=['img', 'seg'], channel_dim=-1)
+])
+val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
+
+device = torch.device("cuda:0")
+roi_size = (64, 64, 64)
+sw_batch_size = 4
+net = UNet(
+    dimensions=3,
+    in_channels=1,
+    num_classes=1,
+    channels=(16, 32, 64, 128, 256),
+    strides=(2, 2, 2, 2),
+    num_res_units=2,
+)
+net.to(device)
+
+
+def _sliding_window_processor(engine, batch):
+    net.eval()
+    with torch.no_grad():
+        seg_probs = sliding_window_inference(batch['img'], roi_size, sw_batch_size, lambda x: net(x)[0], device)
+        return predict_segmentation(seg_probs)
+
+
+infer_engine = Engine(_sliding_window_processor)
+
+# for the arrary data format, assume the 3rd item of batch data is the meta_data
+SegmentationSaver(output_path='tempdir', output_ext='.nii.gz', output_postfix='seg',
+                  batch_transform=lambda batch: {'filename_or_obj': batch['img.filename_or_obj'],
+                                                 'original_affine': batch['img.original_affine'],
+                                                 'affine': batch['img.affine'],
+                                                 }).attach(infer_engine)
+# the model was trained by "unet_segmentation_3d_array" exmple
+CheckpointLoader(load_path='./runs/net_checkpoint_120.pth', load_dict={'net': net}).attach(infer_engine)
+
+val_loader = DataLoader(val_ds, batch_size=1, num_workers=4, collate_fn=list_data_collate,
+                        pin_memory=torch.cuda.is_available())
+state = infer_engine.run(val_loader)
diff --git a/examples/unet_segmentation_3d_dict.py b/examples/unet_segmentation_3d_dict.py
index d7ea3795ea..5eb4dec27c 100644
--- a/examples/unet_segmentation_3d_dict.py
+++ b/examples/unet_segmentation_3d_dict.py
@@ -70,7 +70,7 @@
 
 # Define nifti dataset, dataloader.
 ds = monai.data.Dataset(data=train_files, transform=train_transforms)
-loader = DataLoader(ds, batch_size=2, num_workers=2, collate_fn=list_data_collate,
+loader = DataLoader(ds, batch_size=2, num_workers=4, collate_fn=list_data_collate,
                     pin_memory=torch.cuda.is_available())
 check_data = monai.utils.misc.first(loader)
 print(check_data['img'].shape, check_data['seg'].shape)
@@ -190,7 +190,7 @@ def log_metrics_to_tensorboard(engine):
 logging.basicConfig(stream=sys.stdout, level=logging.INFO)
 
 train_ds = monai.data.Dataset(data=train_files, transform=train_transforms)
-train_loader = DataLoader(train_ds, batch_size=2, num_workers=8, collate_fn=list_data_collate,
+train_loader = DataLoader(train_ds, batch_size=2, num_workers=4, collate_fn=list_data_collate,
                           pin_memory=torch.cuda.is_available())
 
 train_epochs = 30

From 2a85d9da1adaacdba6dc2529ee33ba512e949e4c Mon Sep 17 00:00:00 2001
From: Wenqi Li <wenqil@nvidia.com>
Date: Tue, 10 Mar 2020 11:46:03 +0000
Subject: [PATCH 7/7] update docstring

---
 examples/unet_inference_3d_array.py    |  1 +
 examples/unet_inference_3d_dict.py     |  1 +
 examples/unet_segmentation_3d_array.py |  2 +-
 examples/unet_segmentation_3d_dict.py  |  2 +-
 monai/handlers/segmentation_saver.py   | 14 +++++++++-----
 monai/handlers/stats_handler.py        |  7 +++----
 6 files changed, 16 insertions(+), 11 deletions(-)

diff --git a/examples/unet_inference_3d_array.py b/examples/unet_inference_3d_array.py
index 07cf243b37..8fe417c7dd 100644
--- a/examples/unet_inference_3d_array.py
+++ b/examples/unet_inference_3d_array.py
@@ -38,6 +38,7 @@
 
 tempdir = tempfile.mkdtemp()
 # tempdir = './temp'
+print('generating synthetic data to {} (this may take a while)'.format(tempdir))
 for i in range(50):
     im, seg = create_test_image_3d(256, 256, 256)
 
diff --git a/examples/unet_inference_3d_dict.py b/examples/unet_inference_3d_dict.py
index c0d27c3c20..405b49aa8d 100644
--- a/examples/unet_inference_3d_dict.py
+++ b/examples/unet_inference_3d_dict.py
@@ -39,6 +39,7 @@
 
 tempdir = tempfile.mkdtemp()
 # tempdir = './temp'
+print('generating synthetic data to {} (this may take a while)'.format(tempdir))
 for i in range(50):
     im, seg = create_test_image_3d(256, 256, 256, channel_dim=-1)
 
diff --git a/examples/unet_segmentation_3d_array.py b/examples/unet_segmentation_3d_array.py
index f431b47faf..de5996470d 100644
--- a/examples/unet_segmentation_3d_array.py
+++ b/examples/unet_segmentation_3d_array.py
@@ -41,7 +41,7 @@
 
 # Create a temporary directory and 50 random image, mask paris
 tempdir = tempfile.mkdtemp()
-
+print('generating synthetic data to {} (this may take a while)'.format(tempdir))
 for i in range(50):
     im, seg = create_test_image_3d(128, 128, 128)
 
diff --git a/examples/unet_segmentation_3d_dict.py b/examples/unet_segmentation_3d_dict.py
index 5eb4dec27c..640bed21c0 100644
--- a/examples/unet_segmentation_3d_dict.py
+++ b/examples/unet_segmentation_3d_dict.py
@@ -41,7 +41,7 @@
 
 # Create a temporary directory and 50 random image, mask paris
 tempdir = tempfile.mkdtemp()
-
+print('generating synthetic data to {} (this may take a while)'.format(tempdir))
 for i in range(50):
     im, seg = create_test_image_3d(128, 128, 128, channel_dim=-1)
 
diff --git a/monai/handlers/segmentation_saver.py b/monai/handlers/segmentation_saver.py
index 0983f4b135..e0fa50310a 100644
--- a/monai/handlers/segmentation_saver.py
+++ b/monai/handlers/segmentation_saver.py
@@ -91,16 +91,20 @@ def _create_file_basename(postfix, input_file_name, folder_path, data_root_dir="
 
     def __call__(self, engine):
         """
-        This method assumes to have the keys:
-            'filename_or_obj' -- for output file name creation
-            and optionally 'original_affine', 'affine' for data orientation handling.
-            - output file datatype from `engine.state.output.dtype`.
-        And this method assumes self.batch_transform will extract meta data from the input batch.
+        This method assumes self.batch_transform will extract Metadata from the input batch.
+        Metadata should have the following keys:
+
+            - ``'filename_or_obj'`` -- for output file name creation
+            - ``'original_affine'`` (optional) for data orientation handling
+            - ``'affine'`` (optional) for data output affine.
+
+        output file datatype is determined from ``engine.state.output.dtype``.
         """
         meta_data = self.batch_transform(engine.state.batch)
         filenames = meta_data['filename_or_obj']
         original_affine = meta_data.get('original_affine', None)
         affine = meta_data.get('affine', None)
+
         engine_output = self.output_transform(engine.state.output)
         for batch_id, filename in enumerate(filenames):  # save a batch of files
             seg_output = engine_output[batch_id]
diff --git a/monai/handlers/stats_handler.py b/monai/handlers/stats_handler.py
index 69bdc9379d..47709543fe 100644
--- a/monai/handlers/stats_handler.py
+++ b/monai/handlers/stats_handler.py
@@ -50,7 +50,6 @@ def __init__(self,
         self.output_transform = output_transform
         self.logger = None if name is None else logging.getLogger(name)
 
-
     def attach(self, engine: Engine):
         """Register a set of Ignite Event-Handlers to a specified Ignite engine.
 
@@ -136,8 +135,8 @@ def _default_iteration_print(self, engine: Engine):
 
         """
         loss = self.output_transform(engine.state.output)[1]
-        if loss is None or (isinstance(loss, torch.Tensor) is True and len(loss.shape) > 0):
-            return
+        if loss is None or (torch.is_tensor(loss) and len(loss.shape) > 0):
+            return  # not printing multi dimensional output
         num_iterations = engine.state.epoch_length
         current_iteration = (engine.state.iteration - 1) % num_iterations + 1
         current_epoch = engine.state.epoch
@@ -148,6 +147,6 @@ def _default_iteration_print(self, engine: Engine):
             num_epochs,
             current_iteration,
             num_iterations)
-        out_str += KEY_VAL_FORMAT.format('Loss', loss.item() if isinstance(loss, torch.Tensor) else loss)
+        out_str += KEY_VAL_FORMAT.format('Loss', loss.item() if torch.is_tensor(loss) else loss)
 
         self.logger.info(out_str)