From 88528b27986a1c7e335f2501efa27014116903a4 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Tue, 6 Jul 2021 17:42:40 +0800
Subject: [PATCH 01/11] [DLMED] add decollate logic to DeepGrow

Signed-off-by: Nic Ma <nma@nvidia.com>
---
 monai/apps/deepgrow/interaction.py |  6 ++-
 monai/apps/deepgrow/transforms.py  | 63 +++++++-----------------------
 tests/test_deepgrow_interaction.py | 32 +++++++++++----
 tests/test_deepgrow_transforms.py  | 34 +++++++---------
 4 files changed, 59 insertions(+), 76 deletions(-)

diff --git a/monai/apps/deepgrow/interaction.py b/monai/apps/deepgrow/interaction.py
index 473ad5bac2..6092c70d31 100644
--- a/monai/apps/deepgrow/interaction.py
+++ b/monai/apps/deepgrow/interaction.py
@@ -12,6 +12,7 @@
 
 import torch
 
+from monai.data import decollate_batch, list_data_collate
 from monai.engines import SupervisedEvaluator, SupervisedTrainer
 from monai.engines.utils import IterationEvents
 from monai.transforms import Compose
@@ -74,6 +75,9 @@ def __call__(self, engine: Union[SupervisedTrainer, SupervisedEvaluator], batchd
             batchdata[self.key_probability] = torch.as_tensor(
                 ([1.0 - ((1.0 / self.max_interactions) * j)] if self.train else [1.0]) * len(inputs)
             )
-            batchdata = self.transforms(batchdata)
+            # decollate batch data to execute click transforms
+            batchdata = [self.transforms(i) for i in decollate_batch(batchdata, detach=True)]
+            # collate list into a batch for next round interaction
+            batchdata = list_data_collate(batchdata)
 
         return engine._iteration(engine, batchdata)
diff --git a/monai/apps/deepgrow/transforms.py b/monai/apps/deepgrow/transforms.py
index cfdeb5c87f..86fe23ccd4 100644
--- a/monai/apps/deepgrow/transforms.py
+++ b/monai/apps/deepgrow/transforms.py
@@ -159,7 +159,7 @@ class AddGuidanceSignald(Transform):
         guidance: key to store guidance.
         sigma: standard deviation for Gaussian kernel.
         number_intensity_ch: channel index.
-        batched: whether input is batched or not.
+
     """
 
     def __init__(
@@ -168,13 +168,11 @@ def __init__(
         guidance: str = "guidance",
         sigma: int = 2,
         number_intensity_ch: int = 1,
-        batched: bool = False,
     ):
         self.image = image
         self.guidance = guidance
         self.sigma = sigma
         self.number_intensity_ch = number_intensity_ch
-        self.batched = batched
 
     def _get_signal(self, image, guidance):
         dimensions = 3 if len(image.shape) > 3 else 2
@@ -210,16 +208,8 @@ def _get_signal(self, image, guidance):
         return signal
 
     def _apply(self, image, guidance):
-        if not self.batched:
-            signal = self._get_signal(image, guidance)
-            return np.concatenate([image, signal], axis=0)
-
-        images = []
-        for i, g in zip(image, guidance):
-            i = i[0 : 0 + self.number_intensity_ch, ...]
-            signal = self._get_signal(i, g)
-            images.append(np.concatenate([i, signal], axis=0))
-        return images
+        signal = self._get_signal(image, guidance)
+        return np.concatenate([image, signal], axis=0)
 
     def __call__(self, data):
         d = dict(data)
@@ -244,7 +234,7 @@ class FindDiscrepancyRegionsd(Transform):
         label: key to label source.
         pred: key to prediction source.
         discrepancy: key to store discrepancies found between label and prediction.
-        batched: whether input is batched or not.
+
     """
 
     def __init__(
@@ -253,7 +243,6 @@ def __init__(
         self.label = label
         self.pred = pred
         self.discrepancy = discrepancy
-        self.batched = batched
 
     @staticmethod
     def disparity(label, pred):
@@ -266,13 +255,7 @@ def disparity(label, pred):
         return [pos_disparity, neg_disparity]
 
     def _apply(self, label, pred):
-        if not self.batched:
-            return self.disparity(label, pred)
-
-        disparity = []
-        for la, pr in zip(label, pred):
-            disparity.append(self.disparity(la, pr))
-        return disparity
+        return self.disparity(label, pred)
 
     def __call__(self, data):
         d = dict(data)
@@ -309,7 +292,7 @@ class AddRandomGuidanced(Randomizable, Transform):
         guidance: key to guidance source.
         discrepancy: key that represents discrepancies found between label and prediction.
         probability: key that represents click/interaction probability.
-        batched: whether input is batched or not.
+
     """
 
     def __init__(
@@ -317,22 +300,15 @@ def __init__(
         guidance: str = "guidance",
         discrepancy: str = "discrepancy",
         probability: str = "probability",
-        batched: bool = True,
     ):
         self.guidance = guidance
         self.discrepancy = discrepancy
         self.probability = probability
-        self.batched = batched
         self._will_interact = None
 
     def randomize(self, data=None):
         probability = data[self.probability]
-        if not self.batched:
-            self._will_interact = self.R.choice([True, False], p=[probability, 1.0 - probability])
-        else:
-            self._will_interact = []
-            for p in probability:
-                self._will_interact.append(self.R.choice([True, False], p=[p, 1.0 - p]))
+        self._will_interact = self.R.choice([True, False], p=[probability, 1.0 - probability])
 
     def find_guidance(self, discrepancy):
         distance = distance_transform_cdt(discrepancy).flatten()
@@ -368,23 +344,14 @@ def add_guidance(self, discrepancy, will_interact):
 
     def _apply(self, guidance, discrepancy):
         guidance = guidance.tolist() if isinstance(guidance, np.ndarray) else guidance
-        if not self.batched:
-            pos, neg = self.add_guidance(discrepancy, self._will_interact)
-            if pos:
-                guidance[0].append(pos)
-                guidance[1].append([-1] * len(pos))
-            if neg:
-                guidance[0].append([-1] * len(neg))
-                guidance[1].append(neg)
-        else:
-            for g, d, w in zip(guidance, discrepancy, self._will_interact):
-                pos, neg = self.add_guidance(d, w)
-                if pos:
-                    g[0].append(pos)
-                    g[1].append([-1] * len(pos))
-                if neg:
-                    g[0].append([-1] * len(neg))
-                    g[1].append(neg)
+        pos, neg = self.add_guidance(discrepancy, self._will_interact)
+        if pos:
+            guidance[0].append(pos)
+            guidance[1].append([-1] * len(pos))
+        if neg:
+            guidance[0].append([-1] * len(neg))
+            guidance[1].append(neg)
+
         return np.asarray(guidance)
 
     def __call__(self, data):
diff --git a/tests/test_deepgrow_interaction.py b/tests/test_deepgrow_interaction.py
index 77c37bf5f3..4e9547b0e1 100644
--- a/tests/test_deepgrow_interaction.py
+++ b/tests/test_deepgrow_interaction.py
@@ -12,16 +12,23 @@
 import unittest
 
 import torch
+import numpy as np
 
 from monai.apps.deepgrow.interaction import Interaction
+from monai.apps.deepgrow.transforms import (
+    AddInitialSeedPointd,
+    AddGuidanceSignald,
+    FindAllValidSlicesd,
+    FindDiscrepancyRegionsd,
+)
 from monai.data import Dataset
 from monai.engines import SupervisedTrainer
 from monai.engines.utils import IterationEvents
-from monai.transforms import Activationsd, Compose, ToNumpyd
+from monai.transforms import Activationsd, Compose, ToNumpyd, ToTensord
 
 
 def add_one(engine):
-    if engine.state.best_metric is -1:
+    if engine.state.best_metric == -1:
         engine.state.best_metric = 0
     else:
         engine.state.best_metric = engine.state.best_metric + 1
@@ -31,19 +38,30 @@ class TestInteractions(unittest.TestCase):
     def run_interaction(self, train, compose):
         data = []
         for i in range(5):
-            data.append({"image": torch.tensor([float(i)]), "label": torch.tensor([float(i)])})
-        network = torch.nn.Linear(1, 1)
+            data.append({"image": np.ones((1, 2, 2, 2)).astype(np.float32), "label": np.ones((1, 2, 2, 2))})
+        network = torch.nn.PReLU()
         lr = 1e-3
         opt = torch.optim.SGD(network.parameters(), lr)
         loss = torch.nn.L1Loss()
-        dataset = Dataset(data, transform=None)
+        train_transforms = Compose([
+            FindAllValidSlicesd(label='label', sids='sids'),
+            AddInitialSeedPointd(label='label', guidance='guidance', sids='sids'),
+            AddGuidanceSignald(image='image', guidance='guidance'),
+            ToTensord(keys=('image', 'label')),
+        ])
+        dataset = Dataset(data, transform=train_transforms)
         data_loader = torch.utils.data.DataLoader(dataset, batch_size=5)
 
-        iteration_transforms = [Activationsd(keys="pred", sigmoid=True), ToNumpyd(keys="pred")]
+        iteration_transforms = [
+            Activationsd(keys="pred", sigmoid=True),
+            ToNumpyd(keys=['image', 'label', 'pred', 'guidance']),
+            FindDiscrepancyRegionsd(label='label', pred='pred', discrepancy='discrepancy'),
+            AddGuidanceSignald(image='image', guidance='guidance'),
+        ]
         iteration_transforms = Compose(iteration_transforms) if compose else iteration_transforms
 
         i = Interaction(transforms=iteration_transforms, train=train, max_interactions=5)
-        self.assertEqual(len(i.transforms.transforms), 2, "Mismatch in expected transforms")
+        self.assertEqual(len(i.transforms.transforms), 4, "Mismatch in expected transforms")
 
         # set up engine
         engine = SupervisedTrainer(
diff --git a/tests/test_deepgrow_transforms.py b/tests/test_deepgrow_transforms.py
index 83bb5ebaa4..eb1d5967d4 100644
--- a/tests/test_deepgrow_transforms.py
+++ b/tests/test_deepgrow_transforms.py
@@ -30,8 +30,6 @@
 
 IMAGE = np.array([[[[1, 0, 2, 0, 1], [0, 1, 2, 1, 0], [2, 2, 3, 2, 2], [0, 1, 2, 1, 0], [1, 0, 2, 0, 1]]]])
 LABEL = np.array([[[[0, 0, 0, 0, 0], [0, 1, 0, 1, 0], [0, 0, 1, 0, 0], [0, 1, 0, 1, 0], [0, 0, 0, 0, 0]]]])
-BATCH_IMAGE = np.array([[[[[1, 0, 2, 0, 1], [0, 1, 2, 1, 0], [2, 2, 3, 2, 2], [0, 1, 2, 1, 0], [1, 0, 2, 0, 1]]]]])
-BATCH_LABEL = np.array([[[[[0, 0, 0, 0, 0], [0, 1, 0, 1, 0], [0, 0, 1, 0, 0], [0, 1, 0, 1, 0], [0, 0, 0, 0, 0]]]]])
 
 DATA_1 = {
     "image": IMAGE,
@@ -61,24 +59,22 @@
 }
 
 DATA_3 = {
-    "image": BATCH_IMAGE,
-    "label": BATCH_LABEL,
-    "pred": np.array([[[[[0, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 1, 1, 0, 0], [0, 1, 0, 1, 0], [0, 0, 0, 0, 0]]]]]),
+    "image": IMAGE,
+    "label": LABEL,
+    "pred": np.array([[[[0, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 1, 1, 0, 0], [0, 1, 0, 1, 0], [0, 0, 0, 0, 0]]]]),
 }
 
 DATA_4 = {
-    "image": BATCH_IMAGE,
-    "label": BATCH_LABEL,
-    "guidance": np.array([[[[1, 0, 2, 2]], [[-1, -1, -1, -1]]]]),
+    "image": IMAGE,
+    "label": LABEL,
+    "guidance": np.array([[[1, 0, 2, 2]], [[-1, -1, -1, -1]]]),
     "discrepancy": np.array(
         [
-            [
-                [[[[0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]]],
-                [[[[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]]],
-            ]
+            [[[[0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]]],
+            [[[[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]]],
         ]
     ),
-    "probability": [1.0],
+    "probability": 1.0,
 }
 
 DATA_5 = {
@@ -196,7 +192,7 @@
 ]
 
 ADD_GUIDANCE_TEST_CASE_1 = [
-    {"image": "image", "guidance": "guidance", "batched": False},
+    {"image": "image", "guidance": "guidance"},
     DATA_2,
     np.array(
         [
@@ -233,18 +229,16 @@
     DATA_3,
     np.array(
         [
-            [
-                [[[[0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]]],
-                [[[[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]]],
-            ]
+            [[[[0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]]],
+            [[[[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]]],
         ]
     ),
 ]
 
 ADD_RANDOM_GUIDANCE_TEST_CASE_1 = [
-    {"guidance": "guidance", "discrepancy": "discrepancy", "probability": "probability", "batched": True},
+    {"guidance": "guidance", "discrepancy": "discrepancy", "probability": "probability"},
     DATA_4,
-    np.array([[[[1, 0, 2, 2], [1, 0, 1, 3]], [[-1, -1, -1, -1], [-1, -1, -1, -1]]]]),
+    np.array([[[1, 0, 2, 2], [1, 0, 1, 3]], [[-1, -1, -1, -1], [-1, -1, -1, -1]]]),
 ]
 
 ADD_GUIDANCE_FROM_POINTS_TEST_CASE_1 = [

From 5cee4b8ec1bd94c615841e9c37b49483d9b92f11 Mon Sep 17 00:00:00 2001
From: monai-bot <monai.miccai2019@gmail.com>
Date: Tue, 6 Jul 2021 09:58:25 +0000
Subject: [PATCH 02/11] [MONAI] python code formatting

Signed-off-by: monai-bot <monai.miccai2019@gmail.com>
---
 tests/test_deepgrow_interaction.py | 24 +++++++++++++-----------
 1 file changed, 13 insertions(+), 11 deletions(-)

diff --git a/tests/test_deepgrow_interaction.py b/tests/test_deepgrow_interaction.py
index 4e9547b0e1..95076253c1 100644
--- a/tests/test_deepgrow_interaction.py
+++ b/tests/test_deepgrow_interaction.py
@@ -11,13 +11,13 @@
 
 import unittest
 
-import torch
 import numpy as np
+import torch
 
 from monai.apps.deepgrow.interaction import Interaction
 from monai.apps.deepgrow.transforms import (
-    AddInitialSeedPointd,
     AddGuidanceSignald,
+    AddInitialSeedPointd,
     FindAllValidSlicesd,
     FindDiscrepancyRegionsd,
 )
@@ -43,20 +43,22 @@ def run_interaction(self, train, compose):
         lr = 1e-3
         opt = torch.optim.SGD(network.parameters(), lr)
         loss = torch.nn.L1Loss()
-        train_transforms = Compose([
-            FindAllValidSlicesd(label='label', sids='sids'),
-            AddInitialSeedPointd(label='label', guidance='guidance', sids='sids'),
-            AddGuidanceSignald(image='image', guidance='guidance'),
-            ToTensord(keys=('image', 'label')),
-        ])
+        train_transforms = Compose(
+            [
+                FindAllValidSlicesd(label="label", sids="sids"),
+                AddInitialSeedPointd(label="label", guidance="guidance", sids="sids"),
+                AddGuidanceSignald(image="image", guidance="guidance"),
+                ToTensord(keys=("image", "label")),
+            ]
+        )
         dataset = Dataset(data, transform=train_transforms)
         data_loader = torch.utils.data.DataLoader(dataset, batch_size=5)
 
         iteration_transforms = [
             Activationsd(keys="pred", sigmoid=True),
-            ToNumpyd(keys=['image', 'label', 'pred', 'guidance']),
-            FindDiscrepancyRegionsd(label='label', pred='pred', discrepancy='discrepancy'),
-            AddGuidanceSignald(image='image', guidance='guidance'),
+            ToNumpyd(keys=["image", "label", "pred", "guidance"]),
+            FindDiscrepancyRegionsd(label="label", pred="pred", discrepancy="discrepancy"),
+            AddGuidanceSignald(image="image", guidance="guidance"),
         ]
         iteration_transforms = Compose(iteration_transforms) if compose else iteration_transforms
 

From 770db627f2c0464ef4d9ab4cb626f930a0c02b53 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Tue, 6 Jul 2021 21:47:44 +0800
Subject: [PATCH 03/11] [DLMED] fix flake8 issue

Signed-off-by: Nic Ma <nma@nvidia.com>
---
 tests/test_deepgrow_interaction.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/tests/test_deepgrow_interaction.py b/tests/test_deepgrow_interaction.py
index 95076253c1..3d3c9fa01e 100644
--- a/tests/test_deepgrow_interaction.py
+++ b/tests/test_deepgrow_interaction.py
@@ -36,9 +36,7 @@ def add_one(engine):
 
 class TestInteractions(unittest.TestCase):
     def run_interaction(self, train, compose):
-        data = []
-        for i in range(5):
-            data.append({"image": np.ones((1, 2, 2, 2)).astype(np.float32), "label": np.ones((1, 2, 2, 2))})
+        data = [{"image": np.ones((1, 2, 2, 2)).astype(np.float32), "label": np.ones((1, 2, 2, 2))} for _ in range(5)]
         network = torch.nn.PReLU()
         lr = 1e-3
         opt = torch.optim.SGD(network.parameters(), lr)

From 98528603fe4d9b7055b7a531e7870d46cf8d3bd5 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Tue, 6 Jul 2021 23:35:33 +0800
Subject: [PATCH 04/11] [DLMED] add AddRandomGuidanced to test

Signed-off-by: Nic Ma <nma@nvidia.com>
---
 monai/apps/deepgrow/transforms.py  | 22 +---------------------
 tests/test_deepgrow_interaction.py |  4 +++-
 2 files changed, 4 insertions(+), 22 deletions(-)

diff --git a/monai/apps/deepgrow/transforms.py b/monai/apps/deepgrow/transforms.py
index 86fe23ccd4..790b4f8919 100644
--- a/monai/apps/deepgrow/transforms.py
+++ b/monai/apps/deepgrow/transforms.py
@@ -224,12 +224,6 @@ class FindDiscrepancyRegionsd(Transform):
     """
     Find discrepancy between prediction and actual during click interactions during training.
 
-    If batched is true:
-
-        label is in shape (B, C, D, H, W) or (B, C, H, W)
-        pred has same shape as label
-        discrepancy will have shape (B, 2, C, D, H, W) or (B, 2, C, H, W)
-
     Args:
         label: key to label source.
         pred: key to prediction source.
@@ -269,23 +263,9 @@ def __call__(self, data):
 class AddRandomGuidanced(Randomizable, Transform):
     """
     Add random guidance based on discrepancies that were found between label and prediction.
-
-    If batched is True, input shape is as below:
-
-        Guidance is of shape (B, 2, N, # of dim) where B is batch size, 2 means positive and negative,
-        N means how many guidance points, # of dim is the total number of dimensions of the image
-        (for example if the image is CDHW, then # of dim would be 4).
-
-        Discrepancy is of shape (B, 2, C, D, H, W) or (B, 2, C, H, W)
-
-        Probability is of shape (B, 1)
-
-    else:
-
+    input shape is as below:
         Guidance is of shape (2, N, # of dim)
-
         Discrepancy is of shape (2, C, D, H, W) or (2, C, H, W)
-
         Probability is of shape (1)
 
     Args:
diff --git a/tests/test_deepgrow_interaction.py b/tests/test_deepgrow_interaction.py
index 3d3c9fa01e..20aa7448a9 100644
--- a/tests/test_deepgrow_interaction.py
+++ b/tests/test_deepgrow_interaction.py
@@ -18,6 +18,7 @@
 from monai.apps.deepgrow.transforms import (
     AddGuidanceSignald,
     AddInitialSeedPointd,
+    AddRandomGuidanced,
     FindAllValidSlicesd,
     FindDiscrepancyRegionsd,
 )
@@ -56,12 +57,13 @@ def run_interaction(self, train, compose):
             Activationsd(keys="pred", sigmoid=True),
             ToNumpyd(keys=["image", "label", "pred", "guidance"]),
             FindDiscrepancyRegionsd(label="label", pred="pred", discrepancy="discrepancy"),
+            AddRandomGuidanced(guidance='guidance', discrepancy='discrepancy', probability='probability'),
             AddGuidanceSignald(image="image", guidance="guidance"),
         ]
         iteration_transforms = Compose(iteration_transforms) if compose else iteration_transforms
 
         i = Interaction(transforms=iteration_transforms, train=train, max_interactions=5)
-        self.assertEqual(len(i.transforms.transforms), 4, "Mismatch in expected transforms")
+        self.assertEqual(len(i.transforms.transforms), 5, "Mismatch in expected transforms")
 
         # set up engine
         engine = SupervisedTrainer(

From 69018473e0f808417085639646442c50d12318e2 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Wed, 7 Jul 2021 00:06:06 +0800
Subject: [PATCH 05/11] [DLMED] update network

Signed-off-by: Nic Ma <nma@nvidia.com>
---
 tests/test_deepgrow_interaction.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_deepgrow_interaction.py b/tests/test_deepgrow_interaction.py
index 20aa7448a9..83552f5e71 100644
--- a/tests/test_deepgrow_interaction.py
+++ b/tests/test_deepgrow_interaction.py
@@ -38,7 +38,7 @@ def add_one(engine):
 class TestInteractions(unittest.TestCase):
     def run_interaction(self, train, compose):
         data = [{"image": np.ones((1, 2, 2, 2)).astype(np.float32), "label": np.ones((1, 2, 2, 2))} for _ in range(5)]
-        network = torch.nn.PReLU()
+        network = torch.nn.Linear(2, 2)
         lr = 1e-3
         opt = torch.optim.SGD(network.parameters(), lr)
         loss = torch.nn.L1Loss()

From 4ce590349a5b078d601a27a3b8a2d4e395ae3405 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Wed, 7 Jul 2021 00:25:22 +0800
Subject: [PATCH 06/11] [DLMED] fix typo

Signed-off-by: Nic Ma <nma@nvidia.com>
---
 monai/apps/deepgrow/transforms.py  | 4 +---
 tests/test_deepgrow_interaction.py | 3 ++-
 2 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/monai/apps/deepgrow/transforms.py b/monai/apps/deepgrow/transforms.py
index 790b4f8919..0f042ca38c 100644
--- a/monai/apps/deepgrow/transforms.py
+++ b/monai/apps/deepgrow/transforms.py
@@ -231,9 +231,7 @@ class FindDiscrepancyRegionsd(Transform):
 
     """
 
-    def __init__(
-        self, label: str = "label", pred: str = "pred", discrepancy: str = "discrepancy", batched: bool = True
-    ):
+    def __init__(self, label: str = "label", pred: str = "pred", discrepancy: str = "discrepancy"):
         self.label = label
         self.pred = pred
         self.discrepancy = discrepancy
diff --git a/tests/test_deepgrow_interaction.py b/tests/test_deepgrow_interaction.py
index 83552f5e71..f466dc8499 100644
--- a/tests/test_deepgrow_interaction.py
+++ b/tests/test_deepgrow_interaction.py
@@ -59,11 +59,12 @@ def run_interaction(self, train, compose):
             FindDiscrepancyRegionsd(label="label", pred="pred", discrepancy="discrepancy"),
             AddRandomGuidanced(guidance='guidance', discrepancy='discrepancy', probability='probability'),
             AddGuidanceSignald(image="image", guidance="guidance"),
+            ToTensord(keys=('image', 'label')),
         ]
         iteration_transforms = Compose(iteration_transforms) if compose else iteration_transforms
 
         i = Interaction(transforms=iteration_transforms, train=train, max_interactions=5)
-        self.assertEqual(len(i.transforms.transforms), 5, "Mismatch in expected transforms")
+        self.assertEqual(len(i.transforms.transforms), 6, "Mismatch in expected transforms")
 
         # set up engine
         engine = SupervisedTrainer(

From d317d00c1cc9b9fb02dbc42d6c34100223ba62cd Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Wed, 7 Jul 2021 01:12:02 +0800
Subject: [PATCH 07/11] [DLMED] fix issue in addsignal

Signed-off-by: Nic Ma <nma@nvidia.com>
---
 monai/apps/deepgrow/transforms.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/monai/apps/deepgrow/transforms.py b/monai/apps/deepgrow/transforms.py
index 0f042ca38c..6d44eba5e9 100644
--- a/monai/apps/deepgrow/transforms.py
+++ b/monai/apps/deepgrow/transforms.py
@@ -209,6 +209,7 @@ def _get_signal(self, image, guidance):
 
     def _apply(self, image, guidance):
         signal = self._get_signal(image, guidance)
+        image = image[0 : 0 + self.number_intensity_ch, ...]
         return np.concatenate([image, signal], axis=0)
 
     def __call__(self, data):

From c9a41f8b9cfd4f27f63843bd6ad983ae02cbd006 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Wed, 7 Jul 2021 07:43:59 +0800
Subject: [PATCH 08/11] [DLMED] fix guidance issue

Signed-off-by: Nic Ma <nma@nvidia.com>
---
 monai/apps/deepgrow/transforms.py  | 8 +++++---
 tests/test_deepgrow_interaction.py | 2 +-
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/monai/apps/deepgrow/transforms.py b/monai/apps/deepgrow/transforms.py
index 6d44eba5e9..3389025210 100644
--- a/monai/apps/deepgrow/transforms.py
+++ b/monai/apps/deepgrow/transforms.py
@@ -12,7 +12,7 @@
 
 import numpy as np
 import torch
-
+import json
 from monai.config import IndexSelection, KeysCollection
 from monai.networks.layers import GaussianFilter
 from monai.transforms import Resize, SpatialCrop
@@ -144,7 +144,7 @@ def _apply(self, label, sid):
     def __call__(self, data):
         d = dict(data)
         self.randomize(data)
-        d[self.guidance] = self._apply(d[self.label], self.sid)
+        d[self.guidance] = json.dumps(self._apply(d[self.label], self.sid).astype(int).tolist())
         return d
 
 
@@ -177,6 +177,7 @@ def __init__(
     def _get_signal(self, image, guidance):
         dimensions = 3 if len(image.shape) > 3 else 2
         guidance = guidance.tolist() if isinstance(guidance, np.ndarray) else guidance
+        guidance = json.loads(guidance) if isinstance(guidance, str) else guidance
         if dimensions == 3:
             signal = np.zeros((len(guidance), image.shape[-3], image.shape[-2], image.shape[-1]), dtype=np.float32)
         else:
@@ -323,6 +324,7 @@ def add_guidance(self, discrepancy, will_interact):
 
     def _apply(self, guidance, discrepancy):
         guidance = guidance.tolist() if isinstance(guidance, np.ndarray) else guidance
+        guidance = json.loads(guidance) if isinstance(guidance, str) else guidance
         pos, neg = self.add_guidance(discrepancy, self._will_interact)
         if pos:
             guidance[0].append(pos)
@@ -331,7 +333,7 @@ def _apply(self, guidance, discrepancy):
             guidance[0].append([-1] * len(neg))
             guidance[1].append(neg)
 
-        return np.asarray(guidance)
+        return json.dumps(np.asarray(guidance).astype(int).tolist())
 
     def __call__(self, data):
         d = dict(data)
diff --git a/tests/test_deepgrow_interaction.py b/tests/test_deepgrow_interaction.py
index f466dc8499..2458b1e7d4 100644
--- a/tests/test_deepgrow_interaction.py
+++ b/tests/test_deepgrow_interaction.py
@@ -55,7 +55,7 @@ def run_interaction(self, train, compose):
 
         iteration_transforms = [
             Activationsd(keys="pred", sigmoid=True),
-            ToNumpyd(keys=["image", "label", "pred", "guidance"]),
+            ToNumpyd(keys=["image", "label", "pred"]),
             FindDiscrepancyRegionsd(label="label", pred="pred", discrepancy="discrepancy"),
             AddRandomGuidanced(guidance='guidance', discrepancy='discrepancy', probability='probability'),
             AddGuidanceSignald(image="image", guidance="guidance"),

From 42ac148bdbc52d7bba7d5b8de49d682d431a870d Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Wed, 7 Jul 2021 07:52:18 +0800
Subject: [PATCH 09/11] [DLMED] fix flake8

Signed-off-by: Nic Ma <nma@nvidia.com>
---
 monai/apps/deepgrow/transforms.py  | 3 ++-
 tests/test_deepgrow_interaction.py | 4 ++--
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/monai/apps/deepgrow/transforms.py b/monai/apps/deepgrow/transforms.py
index 3389025210..138593c172 100644
--- a/monai/apps/deepgrow/transforms.py
+++ b/monai/apps/deepgrow/transforms.py
@@ -8,11 +8,12 @@
 # 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 json
 from typing import Callable, Dict, Optional, Sequence, Union
 
 import numpy as np
 import torch
-import json
+
 from monai.config import IndexSelection, KeysCollection
 from monai.networks.layers import GaussianFilter
 from monai.transforms import Resize, SpatialCrop
diff --git a/tests/test_deepgrow_interaction.py b/tests/test_deepgrow_interaction.py
index 2458b1e7d4..e5c7bf9051 100644
--- a/tests/test_deepgrow_interaction.py
+++ b/tests/test_deepgrow_interaction.py
@@ -57,9 +57,9 @@ def run_interaction(self, train, compose):
             Activationsd(keys="pred", sigmoid=True),
             ToNumpyd(keys=["image", "label", "pred"]),
             FindDiscrepancyRegionsd(label="label", pred="pred", discrepancy="discrepancy"),
-            AddRandomGuidanced(guidance='guidance', discrepancy='discrepancy', probability='probability'),
+            AddRandomGuidanced(guidance="guidance", discrepancy="discrepancy", probability="probability"),
             AddGuidanceSignald(image="image", guidance="guidance"),
-            ToTensord(keys=('image', 'label')),
+            ToTensord(keys=("image", "label")),
         ]
         iteration_transforms = Compose(iteration_transforms) if compose else iteration_transforms
 

From df99a095876568fc41da669a7ed346186761b5db Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Wed, 7 Jul 2021 08:00:09 +0800
Subject: [PATCH 10/11] [DLMED] fix mypy and docs

Signed-off-by: Nic Ma <nma@nvidia.com>
---
 monai/apps/deepgrow/interaction.py | 4 ++--
 monai/apps/deepgrow/transforms.py  | 6 +++---
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/monai/apps/deepgrow/interaction.py b/monai/apps/deepgrow/interaction.py
index 6092c70d31..11b56086df 100644
--- a/monai/apps/deepgrow/interaction.py
+++ b/monai/apps/deepgrow/interaction.py
@@ -76,8 +76,8 @@ def __call__(self, engine: Union[SupervisedTrainer, SupervisedEvaluator], batchd
                 ([1.0 - ((1.0 / self.max_interactions) * j)] if self.train else [1.0]) * len(inputs)
             )
             # decollate batch data to execute click transforms
-            batchdata = [self.transforms(i) for i in decollate_batch(batchdata, detach=True)]
+            batchdata_list = [self.transforms(i) for i in decollate_batch(batchdata, detach=True)]
             # collate list into a batch for next round interaction
-            batchdata = list_data_collate(batchdata)
+            batchdata = list_data_collate(batchdata_list)
 
         return engine._iteration(engine, batchdata)
diff --git a/monai/apps/deepgrow/transforms.py b/monai/apps/deepgrow/transforms.py
index 138593c172..db450792b0 100644
--- a/monai/apps/deepgrow/transforms.py
+++ b/monai/apps/deepgrow/transforms.py
@@ -265,9 +265,9 @@ class AddRandomGuidanced(Randomizable, Transform):
     """
     Add random guidance based on discrepancies that were found between label and prediction.
     input shape is as below:
-        Guidance is of shape (2, N, # of dim)
-        Discrepancy is of shape (2, C, D, H, W) or (2, C, H, W)
-        Probability is of shape (1)
+    Guidance is of shape (2, N, # of dim)
+    Discrepancy is of shape (2, C, D, H, W) or (2, C, H, W)
+    Probability is of shape (1)
 
     Args:
         guidance: key to guidance source.

From 7b2e9b9dd14d96e82ba5cb15efe125531eec4882 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Wed, 7 Jul 2021 09:51:00 +0800
Subject: [PATCH 11/11] [DLMED] fix CI tests

Signed-off-by: Nic Ma <nma@nvidia.com>
---
 tests/test_deepgrow_transforms.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tests/test_deepgrow_transforms.py b/tests/test_deepgrow_transforms.py
index eb1d5967d4..f50e92d146 100644
--- a/tests/test_deepgrow_transforms.py
+++ b/tests/test_deepgrow_transforms.py
@@ -188,7 +188,7 @@
 ADD_INITIAL_POINT_TEST_CASE_1 = [
     {"label": "label", "guidance": "guidance", "sids": "sids"},
     DATA_1,
-    np.array([[[1, 0, 2, 2]], [[-1, -1, -1, -1]]]),
+    "[[[1, 0, 2, 2]], [[-1, -1, -1, -1]]]",
 ]
 
 ADD_GUIDANCE_TEST_CASE_1 = [
@@ -238,7 +238,7 @@
 ADD_RANDOM_GUIDANCE_TEST_CASE_1 = [
     {"guidance": "guidance", "discrepancy": "discrepancy", "probability": "probability"},
     DATA_4,
-    np.array([[[1, 0, 2, 2], [1, 0, 1, 3]], [[-1, -1, -1, -1], [-1, -1, -1, -1]]]),
+    "[[[1, 0, 2, 2], [1, 0, 1, 3]], [[-1, -1, -1, -1], [-1, -1, -1, -1]]]",
 ]
 
 ADD_GUIDANCE_FROM_POINTS_TEST_CASE_1 = [
@@ -392,7 +392,7 @@ def test_correct_results(self, arguments, input_data, expected_result):
         add_fn = AddInitialSeedPointd(**arguments)
         add_fn.set_random_state(seed)
         result = add_fn(input_data)
-        np.testing.assert_allclose(result[arguments["guidance"]], expected_result)
+        self.assertEqual(result[arguments["guidance"]], expected_result)
 
 
 class TestAddGuidanceSignald(unittest.TestCase):
@@ -416,7 +416,7 @@ def test_correct_results(self, arguments, input_data, expected_result):
         add_fn = AddRandomGuidanced(**arguments)
         add_fn.set_random_state(seed)
         result = add_fn(input_data)
-        np.testing.assert_allclose(result[arguments["guidance"]], expected_result, rtol=1e-5)
+        self.assertEqual(result[arguments["guidance"]], expected_result)
 
 
 class TestAddGuidanceFromPointsd(unittest.TestCase):