From 354f31c83409967afb7b17c2b2f68cd3ee03f307 Mon Sep 17 00:00:00 2001
From: root <schilkunda@ambarella.com>
Date: Mon, 28 Jun 2021 21:19:09 +0000
Subject: [PATCH 01/12] [Relay to Onnx conversion]

* added support for Sigmoid op
* added unit test
---
 python/tvm/contrib/target/onnx.py |  1 +
 tests/python/contrib/test_onnx.py | 16 ++++++++++++++++
 2 files changed, 17 insertions(+)

diff --git a/python/tvm/contrib/target/onnx.py b/python/tvm/contrib/target/onnx.py
index b05265fa976a..cf53307ef503 100644
--- a/python/tvm/contrib/target/onnx.py
+++ b/python/tvm/contrib/target/onnx.py
@@ -667,6 +667,7 @@ def convert_attributes(cls, attrs):
     "clip": Clip,
     "expand_dims": Expand,
     "nn.lrn": LRN,
+    "sigmoid": rename("Sigmoid"),
 }
 
 
diff --git a/tests/python/contrib/test_onnx.py b/tests/python/contrib/test_onnx.py
index 3636409f8a06..2bbe23e1b896 100644
--- a/tests/python/contrib/test_onnx.py
+++ b/tests/python/contrib/test_onnx.py
@@ -530,6 +530,21 @@ def verify_lrn(xshape, size, dtype="float32"):
             verify_lrn(i, s)
 
 
+def test_sigmoid():
+    """Sigmoid unit test."""
+    def verify_sigmoid(dshape, dtype="float32"):
+        x = relay.var("x", relay.ty.TensorType(dshape, dtype))
+        y = relay.sigmoid(x)
+        func = relay.Function([x], y)
+        x_data = np.random.uniform(size=dshape).astype(dtype)
+        verify_results(func, [x_data], "test_sigmoid", rtol=1e-4, atol=1e-4)
+
+    isize = [(1,3,480,640), (1,3,224,224)]
+
+    for i in isize:
+        verify_sigmoid(i)
+
+
 if __name__ == "__main__":
     test_add()
     test_bias_add()
@@ -554,3 +569,4 @@ def verify_lrn(xshape, size, dtype="float32"):
     test_clip()
     test_expand_dims()
     test_lrn()
+    test_sigmoid()

From acb27e7d1bfdd0fb81c71ac34054332c0c424a58 Mon Sep 17 00:00:00 2001
From: root <schilkunda@ambarella.com>
Date: Mon, 28 Jun 2021 21:24:28 +0000
Subject: [PATCH 02/12] [Relay to Onnx conversion][Copy]

* added support for Copy op
* added unit test
---
 python/tvm/contrib/target/onnx.py |  1 +
 tests/python/contrib/test_onnx.py | 16 ++++++++++++++++
 2 files changed, 17 insertions(+)

diff --git a/python/tvm/contrib/target/onnx.py b/python/tvm/contrib/target/onnx.py
index cf53307ef503..1d1e13dd0516 100644
--- a/python/tvm/contrib/target/onnx.py
+++ b/python/tvm/contrib/target/onnx.py
@@ -668,6 +668,7 @@ def convert_attributes(cls, attrs):
     "expand_dims": Expand,
     "nn.lrn": LRN,
     "sigmoid": rename("Sigmoid"),
+    "copy": rename("Identity"),
 }
 
 
diff --git a/tests/python/contrib/test_onnx.py b/tests/python/contrib/test_onnx.py
index 2bbe23e1b896..332751d09e6d 100644
--- a/tests/python/contrib/test_onnx.py
+++ b/tests/python/contrib/test_onnx.py
@@ -545,6 +545,21 @@ def verify_sigmoid(dshape, dtype="float32"):
         verify_sigmoid(i)
 
 
+def test_copy():
+    """Copy unit test."""
+    def verify_copy(dshape, dtype="float32"):
+        x = relay.var("x", relay.ty.TensorType(dshape, dtype))
+        y = relay.copy(x)
+        func = relay.Function([x], y)
+        x_data = np.random.uniform(size=dshape).astype(dtype)
+        verify_results(func, [x_data], "test_copy", rtol=1e-4, atol=1e-4)
+
+    isize = [(1,3,480,640), (1,3,224,224)]
+
+    for i in isize:
+        verify_copy(i)
+
+
 if __name__ == "__main__":
     test_add()
     test_bias_add()
@@ -570,3 +585,4 @@ def verify_sigmoid(dshape, dtype="float32"):
     test_expand_dims()
     test_lrn()
     test_sigmoid()
+    test_copy()

From 2e323850e49c0edbcf3dcb0ee0aa6c5021b59519 Mon Sep 17 00:00:00 2001
From: root <schilkunda@ambarella.com>
Date: Mon, 28 Jun 2021 21:31:11 +0000
Subject: [PATCH 03/12] [Relay to Onnx conversion][Pool] * added missing
 ceil_mode in average pool and max pool conversion

---
 python/tvm/contrib/target/onnx.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/python/tvm/contrib/target/onnx.py b/python/tvm/contrib/target/onnx.py
index 1d1e13dd0516..89c575c5ffd8 100644
--- a/python/tvm/contrib/target/onnx.py
+++ b/python/tvm/contrib/target/onnx.py
@@ -147,6 +147,7 @@ def convert_attributes(cls, attrs):
             "pads": attrs.get_int_tuple("padding"),
             "strides": attrs.get_int_tuple("strides"),
             "kernel_shape": attrs.get_int_tuple("pool_size"),
+            "ceil_mode": 1 if attrs.ceil_mode else 0,
         }
 
 

From 88792dfbbcabf0402be2d7dbce0036137cc13ca6 Mon Sep 17 00:00:00 2001
From: root <schilkunda@ambarella.com>
Date: Mon, 28 Jun 2021 21:37:51 +0000
Subject: [PATCH 04/12] [Relay to Onnx conversion][Round]

* added support for Round op
* added unit test
---
 python/tvm/contrib/target/onnx.py |  1 +
 tests/python/contrib/test_onnx.py | 16 ++++++++++++++++
 2 files changed, 17 insertions(+)

diff --git a/python/tvm/contrib/target/onnx.py b/python/tvm/contrib/target/onnx.py
index 89c575c5ffd8..4eb8341e40e4 100644
--- a/python/tvm/contrib/target/onnx.py
+++ b/python/tvm/contrib/target/onnx.py
@@ -670,6 +670,7 @@ def convert_attributes(cls, attrs):
     "nn.lrn": LRN,
     "sigmoid": rename("Sigmoid"),
     "copy": rename("Identity"),
+    "round": rename("Round"),
 }
 
 
diff --git a/tests/python/contrib/test_onnx.py b/tests/python/contrib/test_onnx.py
index 332751d09e6d..b9151947d235 100644
--- a/tests/python/contrib/test_onnx.py
+++ b/tests/python/contrib/test_onnx.py
@@ -560,6 +560,21 @@ def verify_copy(dshape, dtype="float32"):
         verify_copy(i)
 
 
+def test_round():
+    """Round unit test."""
+    def verify_round(dshape, dtype="float32"):
+        x = relay.var("x", relay.ty.TensorType(dshape, dtype))
+        y = relay.round(x)
+        func = relay.Function([x], y)
+        x_data = np.random.uniform(size=dshape).astype(dtype)
+        verify_results(func, [x_data], "test_round", rtol=1e-4, atol=1e-4)
+
+    isize = [(1,3,480,640), (1,3,224,224)]
+
+    for i in isize:
+        verify_round(i)
+
+
 if __name__ == "__main__":
     test_add()
     test_bias_add()
@@ -586,3 +601,4 @@ def verify_copy(dshape, dtype="float32"):
     test_lrn()
     test_sigmoid()
     test_copy()
+    test_round()

From 38040fe75c7b391e61d86ac99bc4e4cb71cf4638 Mon Sep 17 00:00:00 2001
From: root <schilkunda@ambarella.com>
Date: Mon, 28 Jun 2021 21:48:22 +0000
Subject: [PATCH 05/12] [Relay to Onnx conversion][Cast]

* added support for Cast op
* added unit test
---
 python/tvm/contrib/target/onnx.py | 12 ++++++++++++
 tests/python/contrib/test_onnx.py | 18 ++++++++++++++++++
 2 files changed, 30 insertions(+)

diff --git a/python/tvm/contrib/target/onnx.py b/python/tvm/contrib/target/onnx.py
index 4eb8341e40e4..8264e4cc407e 100644
--- a/python/tvm/contrib/target/onnx.py
+++ b/python/tvm/contrib/target/onnx.py
@@ -24,6 +24,7 @@
 import onnx
 import onnx.utils
 from onnx import numpy_helper, OperatorSetIdProto, defs
+from onnx import TensorProto
 import tvm
 from tvm import relay
 import tvm._ffi
@@ -634,6 +635,16 @@ def convert_attributes(cls, attrs):
         return {"alpha": attrs.alpha, "beta": attrs.beta, "bias": attrs.bias, "size": attrs.size}
 
 
+class Cast(OpConverter):
+    """ Operator converter for Cast."""
+
+    @classmethod
+    def convert_attributes(cls, attrs):
+        return {
+            'to': getattr(TensorProto, attrs.dtype.upper())
+        }
+
+
 relay_to_onnx_op_mapping = {
     "reshape": Reshape,
     "nn.conv2d": Conv,
@@ -671,6 +682,7 @@ def convert_attributes(cls, attrs):
     "sigmoid": rename("Sigmoid"),
     "copy": rename("Identity"),
     "round": rename("Round"),
+    "cast": Cast,
 }
 
 
diff --git a/tests/python/contrib/test_onnx.py b/tests/python/contrib/test_onnx.py
index b9151947d235..cd79aaba54b0 100644
--- a/tests/python/contrib/test_onnx.py
+++ b/tests/python/contrib/test_onnx.py
@@ -575,6 +575,23 @@ def verify_round(dshape, dtype="float32"):
         verify_round(i)
 
 
+def test_cast():
+    """Cast unit test."""
+    def verify_cast(dshape, dtype):
+        x = relay.var("x", relay.ty.TensorType(dshape, "float32"))
+        y = relay.cast(x, dtype)
+        func = relay.Function([x], y)
+        x_data = np.random.uniform(size=dshape).astype("float32")
+        verify_results(func, [x_data], "test_cast", rtol=1e-4, atol=1e-4)
+
+    isize = [(1,3,480,640), (1,3,224,224)]
+    out_dtypes = ['int8', 'int16', 'uint8', 'uint16']
+
+    for i in isize:
+        for o_dtype in out_dtypes:
+            verify_cast(i, o_dtype)
+
+
 if __name__ == "__main__":
     test_add()
     test_bias_add()
@@ -602,3 +619,4 @@ def verify_round(dshape, dtype="float32"):
     test_sigmoid()
     test_copy()
     test_round()
+    test_cast()

From 86d44b83564aff8a720b9b8be99448ed255f1811 Mon Sep 17 00:00:00 2001
From: root <schilkunda@ambarella.com>
Date: Mon, 28 Jun 2021 21:50:09 +0000
Subject: [PATCH 06/12] [Relay to Onnx testing]

* fixed formatting
---
 tests/python/contrib/test_onnx.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tests/python/contrib/test_onnx.py b/tests/python/contrib/test_onnx.py
index cd79aaba54b0..32ee4de5f1f8 100644
--- a/tests/python/contrib/test_onnx.py
+++ b/tests/python/contrib/test_onnx.py
@@ -516,6 +516,7 @@ def verify_expand_dims(dshape, axis, num_newaxis, dtype="float32"):
 
 
 def test_lrn():
+    """LRN unit test."""
     def verify_lrn(xshape, size, dtype="float32"):
         x = relay.var("x", relay.ty.TensorType(xshape, dtype))
         y = relay.nn.lrn(x, size=size, axis=1, alpha=1.0, beta=1.0, bias=1.0)

From f9c70c56b7396a9a9b4b7e81dd11f5e38dcd93e3 Mon Sep 17 00:00:00 2001
From: root <schilkunda@ambarella.com>
Date: Mon, 28 Jun 2021 22:01:21 +0000
Subject: [PATCH 07/12] * fixed formatting issues

---
 tests/python/contrib/test_onnx.py | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

diff --git a/tests/python/contrib/test_onnx.py b/tests/python/contrib/test_onnx.py
index 32ee4de5f1f8..b90e63006e27 100644
--- a/tests/python/contrib/test_onnx.py
+++ b/tests/python/contrib/test_onnx.py
@@ -517,6 +517,7 @@ def verify_expand_dims(dshape, axis, num_newaxis, dtype="float32"):
 
 def test_lrn():
     """LRN unit test."""
+
     def verify_lrn(xshape, size, dtype="float32"):
         x = relay.var("x", relay.ty.TensorType(xshape, dtype))
         y = relay.nn.lrn(x, size=size, axis=1, alpha=1.0, beta=1.0, bias=1.0)
@@ -533,6 +534,7 @@ def verify_lrn(xshape, size, dtype="float32"):
 
 def test_sigmoid():
     """Sigmoid unit test."""
+
     def verify_sigmoid(dshape, dtype="float32"):
         x = relay.var("x", relay.ty.TensorType(dshape, dtype))
         y = relay.sigmoid(x)
@@ -540,7 +542,7 @@ def verify_sigmoid(dshape, dtype="float32"):
         x_data = np.random.uniform(size=dshape).astype(dtype)
         verify_results(func, [x_data], "test_sigmoid", rtol=1e-4, atol=1e-4)
 
-    isize = [(1,3,480,640), (1,3,224,224)]
+    isize = [(1, 3, 480, 640), (1, 3, 224, 224)]
 
     for i in isize:
         verify_sigmoid(i)
@@ -548,6 +550,7 @@ def verify_sigmoid(dshape, dtype="float32"):
 
 def test_copy():
     """Copy unit test."""
+
     def verify_copy(dshape, dtype="float32"):
         x = relay.var("x", relay.ty.TensorType(dshape, dtype))
         y = relay.copy(x)
@@ -555,7 +558,7 @@ def verify_copy(dshape, dtype="float32"):
         x_data = np.random.uniform(size=dshape).astype(dtype)
         verify_results(func, [x_data], "test_copy", rtol=1e-4, atol=1e-4)
 
-    isize = [(1,3,480,640), (1,3,224,224)]
+    isize = [(1, 3, 480, 640), (1, 3, 224, 224)]
 
     for i in isize:
         verify_copy(i)
@@ -563,6 +566,7 @@ def verify_copy(dshape, dtype="float32"):
 
 def test_round():
     """Round unit test."""
+
     def verify_round(dshape, dtype="float32"):
         x = relay.var("x", relay.ty.TensorType(dshape, dtype))
         y = relay.round(x)
@@ -570,7 +574,7 @@ def verify_round(dshape, dtype="float32"):
         x_data = np.random.uniform(size=dshape).astype(dtype)
         verify_results(func, [x_data], "test_round", rtol=1e-4, atol=1e-4)
 
-    isize = [(1,3,480,640), (1,3,224,224)]
+    isize = [(1, 3, 480, 640), (1, 3, 224, 224)]
 
     for i in isize:
         verify_round(i)
@@ -578,6 +582,7 @@ def verify_round(dshape, dtype="float32"):
 
 def test_cast():
     """Cast unit test."""
+
     def verify_cast(dshape, dtype):
         x = relay.var("x", relay.ty.TensorType(dshape, "float32"))
         y = relay.cast(x, dtype)
@@ -585,8 +590,8 @@ def verify_cast(dshape, dtype):
         x_data = np.random.uniform(size=dshape).astype("float32")
         verify_results(func, [x_data], "test_cast", rtol=1e-4, atol=1e-4)
 
-    isize = [(1,3,480,640), (1,3,224,224)]
-    out_dtypes = ['int8', 'int16', 'uint8', 'uint16']
+    isize = [(1, 3, 480, 640), (1, 3, 224, 224)]
+    out_dtypes = ["int8", "int16", "uint8", "uint16"]
 
     for i in isize:
         for o_dtype in out_dtypes:

From 02b6e858853e8e1250757274dfdf6f158c659003 Mon Sep 17 00:00:00 2001
From: root <schilkunda@ambarella.com>
Date: Mon, 28 Jun 2021 22:05:55 +0000
Subject: [PATCH 08/12] * fixed formatting issue in onnx.py

---
 python/tvm/contrib/target/onnx.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/python/tvm/contrib/target/onnx.py b/python/tvm/contrib/target/onnx.py
index 8264e4cc407e..e18448ad61e0 100644
--- a/python/tvm/contrib/target/onnx.py
+++ b/python/tvm/contrib/target/onnx.py
@@ -640,9 +640,7 @@ class Cast(OpConverter):
 
     @classmethod
     def convert_attributes(cls, attrs):
-        return {
-            'to': getattr(TensorProto, attrs.dtype.upper())
-        }
+        return {"to": getattr(TensorProto, attrs.dtype.upper())}
 
 
 relay_to_onnx_op_mapping = {

From ee528176a4d32063f360c10e514b85d740ec1fc1 Mon Sep 17 00:00:00 2001
From: root <schilkunda@ambarella.com>
Date: Tue, 29 Jun 2021 18:05:09 +0000
Subject: [PATCH 09/12] [Relay to Onnx conversion][Conv2d Transpose]

* Added support for conv2d transpose operator
* Added unit test case. Unit test is similar to the conv2d unit test.
---
 python/tvm/contrib/target/onnx.py | 15 +++++++++
 tests/python/contrib/test_onnx.py | 52 +++++++++++++++++++++++++++++++
 2 files changed, 67 insertions(+)

diff --git a/python/tvm/contrib/target/onnx.py b/python/tvm/contrib/target/onnx.py
index e18448ad61e0..8e8b24e2c798 100644
--- a/python/tvm/contrib/target/onnx.py
+++ b/python/tvm/contrib/target/onnx.py
@@ -139,6 +139,20 @@ def convert_attributes(cls, attrs):
         }
 
 
+class ConvTranspose(OpConverter):
+    """Operator converter for ConvTranspose."""
+
+    @classmethod
+    def convert_attributes(cls, attrs):
+        return {
+            "group": attrs.get_int("groups"),
+            "pads": attrs.get_int_tuple("padding"),
+            "strides": attrs.get_int_tuple("strides"),
+            "dilations": attrs.get_int_tuple("dilation"),
+            "kernel_shape": attrs.get_int_tuple("kernel_size"),
+            "output_padding": attrs.get_int_tuple("output_padding"),
+        }
+
 class MaxPool(OpConverter):
     """Operator converter for MaxPool."""
 
@@ -646,6 +660,7 @@ def convert_attributes(cls, attrs):
 relay_to_onnx_op_mapping = {
     "reshape": Reshape,
     "nn.conv2d": Conv,
+    "nn.conv2d_transpose": ConvTranspose,
     "add": rename("Add"),
     "nn.relu": rename("Relu"),
     "transpose": Transpose,
diff --git a/tests/python/contrib/test_onnx.py b/tests/python/contrib/test_onnx.py
index b90e63006e27..b85519aa3cca 100644
--- a/tests/python/contrib/test_onnx.py
+++ b/tests/python/contrib/test_onnx.py
@@ -174,6 +174,57 @@ def verify_conv2d(
     verify_conv2d("float32", 1, dshape, kshape, padding=(1, 1), channels=10, kernel_size=(4, 4))
 
 
+def test_conv2d_transpose():
+    """Conv2d_Transpose unit tests."""
+    def verify_conv2d_transpose(
+        dtype, scale, dshape, kshape,
+        padding=(1, 1), groups=1, dilation=(1, 1), **attrs
+    ):
+        x = relay.var("x", shape=dshape, dtype=dtype)
+        w = relay.var("w", shape=kshape, dtype=dtype)
+        y = relay.nn.conv2d_transpose(x, w, padding=padding,
+                dilation=dilation, groups=groups, **attrs)
+        func = relay.Function([x, w], y)
+        data = np.random.uniform(-scale, scale, size=dshape).astype(dtype)
+        kernel = np.random.uniform(-scale, scale, size=kshape).astype(dtype)
+        verify_results(func, [data, kernel], "test_conv2d_transpose", rtol=1e-5, atol=1e-5)
+
+    dshape = (1, 3, 224, 224)
+    kshape = (3, 10, 3, 3)
+    verify_conv2d_transpose(
+        "float32", 1, dshape, kshape, padding=(1, 1),
+        channels=10, kernel_size=(3, 3)
+    )
+
+    dshape = (1, 3, 224, 224)
+    kshape = (3, 10, 3, 3)
+    verify_conv2d_transpose(
+        "float32", 1, dshape, kshape, padding=(2, 2),
+        channels=10, kernel_size=(3, 3)
+    )
+
+    dshape = (1, 3, 18, 18)
+    kshape = (3, 10, 2, 2)
+    verify_conv2d_transpose(
+        "float32", 1, dshape, kshape, padding=(2, 2),
+        channels=10, kernel_size=(2, 2), dilation=(1, 1),
+    )
+
+    dshape = (1, 3, 18, 18)
+    kshape = (3, 10, 4, 4)
+    verify_conv2d_transpose(
+        "float32", 1, dshape, kshape, padding=(1, 1),
+        channels=10, kernel_size=(4, 4)
+    )
+
+    dshape = (1, 3, 18, 18)
+    kshape = (3, 10, 4, 4)
+    verify_conv2d_transpose(
+        "float32", 1, dshape, kshape, padding=(1, 1),
+        channels=10, kernel_size=(4, 4)
+    )
+
+
 def test_reshape():
     def verify_reshape(shape, newshape):
         x = relay.var("x", relay.TensorType(shape, "float32"))
@@ -602,6 +653,7 @@ def verify_cast(dshape, dtype):
     test_add()
     test_bias_add()
     test_conv2d()
+    test_conv2d_transpose()
     test_reshape()
     test_transpose()
     test_dense()

From 3e60da825ec551c9c5841da08f9d8f588ca1ac32 Mon Sep 17 00:00:00 2001
From: root <schilkunda@ambarella.com>
Date: Tue, 29 Jun 2021 19:00:19 +0000
Subject: [PATCH 10/12] * Fixed formatting errors

---
 python/tvm/contrib/target/onnx.py |  1 +
 tests/python/contrib/test_onnx.py | 28 ++++++++++++++++------------
 2 files changed, 17 insertions(+), 12 deletions(-)

diff --git a/python/tvm/contrib/target/onnx.py b/python/tvm/contrib/target/onnx.py
index 8e8b24e2c798..10a428120606 100644
--- a/python/tvm/contrib/target/onnx.py
+++ b/python/tvm/contrib/target/onnx.py
@@ -153,6 +153,7 @@ def convert_attributes(cls, attrs):
             "output_padding": attrs.get_int_tuple("output_padding"),
         }
 
+
 class MaxPool(OpConverter):
     """Operator converter for MaxPool."""
 
diff --git a/tests/python/contrib/test_onnx.py b/tests/python/contrib/test_onnx.py
index b85519aa3cca..1f95dae882a3 100644
--- a/tests/python/contrib/test_onnx.py
+++ b/tests/python/contrib/test_onnx.py
@@ -176,14 +176,16 @@ def verify_conv2d(
 
 def test_conv2d_transpose():
     """Conv2d_Transpose unit tests."""
+
     def verify_conv2d_transpose(
         dtype, scale, dshape, kshape,
         padding=(1, 1), groups=1, dilation=(1, 1), **attrs
     ):
         x = relay.var("x", shape=dshape, dtype=dtype)
         w = relay.var("w", shape=kshape, dtype=dtype)
-        y = relay.nn.conv2d_transpose(x, w, padding=padding,
-                dilation=dilation, groups=groups, **attrs)
+        y = relay.nn.conv2d_transpose(
+            x, w, padding=padding, dilation=dilation, groups=groups, **attrs
+        )
         func = relay.Function([x, w], y)
         data = np.random.uniform(-scale, scale, size=dshape).astype(dtype)
         kernel = np.random.uniform(-scale, scale, size=kshape).astype(dtype)
@@ -192,36 +194,38 @@ def verify_conv2d_transpose(
     dshape = (1, 3, 224, 224)
     kshape = (3, 10, 3, 3)
     verify_conv2d_transpose(
-        "float32", 1, dshape, kshape, padding=(1, 1),
-        channels=10, kernel_size=(3, 3)
+        "float32", 1, dshape, kshape, padding=(1, 1), channels=10, kernel_size=(3, 3)
     )
 
     dshape = (1, 3, 224, 224)
     kshape = (3, 10, 3, 3)
     verify_conv2d_transpose(
-        "float32", 1, dshape, kshape, padding=(2, 2),
-        channels=10, kernel_size=(3, 3)
+        "float32", 1, dshape, kshape, padding=(2, 2), channels=10, kernel_size=(3, 3)
     )
 
     dshape = (1, 3, 18, 18)
     kshape = (3, 10, 2, 2)
     verify_conv2d_transpose(
-        "float32", 1, dshape, kshape, padding=(2, 2),
-        channels=10, kernel_size=(2, 2), dilation=(1, 1),
+        "float32",
+        1,
+        dshape,
+        kshape,
+        padding=(2, 2),
+        channels=10,
+        kernel_size=(2, 2),
+        dilation=(1, 1),
     )
 
     dshape = (1, 3, 18, 18)
     kshape = (3, 10, 4, 4)
     verify_conv2d_transpose(
-        "float32", 1, dshape, kshape, padding=(1, 1),
-        channels=10, kernel_size=(4, 4)
+        "float32", 1, dshape, kshape, padding=(1, 1), channels=10, kernel_size=(4, 4)
     )
 
     dshape = (1, 3, 18, 18)
     kshape = (3, 10, 4, 4)
     verify_conv2d_transpose(
-        "float32", 1, dshape, kshape, padding=(1, 1),
-        channels=10, kernel_size=(4, 4)
+        "float32", 1, dshape, kshape, padding=(1, 1), channels=10, kernel_size=(4, 4)
     )
 
 

From beb305ef0f2e5aea0fd9b02b9699d965107e156e Mon Sep 17 00:00:00 2001
From: root <schilkunda@ambarella.com>
Date: Tue, 29 Jun 2021 19:37:32 +0000
Subject: [PATCH 11/12] [Relay to Onnx][Pad]

* Fixed issue in Pad conversion
* Updated unit test
* Known issue: Relay pad defn is missing pad_value arg
---
 python/tvm/contrib/target/onnx.py | 14 ++++++++++++--
 tests/python/contrib/test_onnx.py |  3 +++
 2 files changed, 15 insertions(+), 2 deletions(-)

diff --git a/python/tvm/contrib/target/onnx.py b/python/tvm/contrib/target/onnx.py
index 10a428120606..590148e9cbf8 100644
--- a/python/tvm/contrib/target/onnx.py
+++ b/python/tvm/contrib/target/onnx.py
@@ -347,7 +347,12 @@ def convert_attributes(cls, attrs):
             after.append(axis_pads[1])
         pads = before + after
         pads = numpy.asarray(pads, dtype=pads[0].dtype)
-        return {"pads": pads, "mode": attrs.get_str("pad_mode"), "constant_value": attrs.pad_value}
+        return {
+            "pads": pads,
+            "mode": attrs.get_str("pad_mode"),
+            #"constant_value": float(attrs.get_str("pad_value")),
+            "constant_value": 0,
+        }
 
     @classmethod
     def convert(cls, node_entry, model_container, node_dict):
@@ -367,7 +372,12 @@ def convert(cls, node_entry, model_container, node_dict):
             add_input(value, name, "value", model_container),
         ]
 
-        node = onnx.helper.make_node(cls.__name__, input_names, node_entry["output_names"])
+        node = onnx.helper.make_node(
+            cls.__name__,
+            input_names,
+            node_entry["output_names"],
+            mode=attrs["mode"]
+        )
         model_container.add_nodes([node])
 
 
diff --git a/tests/python/contrib/test_onnx.py b/tests/python/contrib/test_onnx.py
index 1f95dae882a3..12b44c758c00 100644
--- a/tests/python/contrib/test_onnx.py
+++ b/tests/python/contrib/test_onnx.py
@@ -27,6 +27,7 @@
 import tvm
 from tvm import relay
 from tvm.contrib.target.onnx import to_onnx
+from tvm.relay.testing import run_infer_type
 
 
 def func_to_onnx(func, name):
@@ -325,12 +326,14 @@ def verify_batch_norm(axis=1):
 
 
 def test_pad():
+    """Pad unit test."""
     def verify_pad():
         for dtype in ["float16", "float32"]:
             dshape = (4, 10, 7, 7)
             x = relay.var("x", shape=dshape, dtype=dtype)
             y = relay.nn.pad(x, ((1, 1), (2, 2), (3, 3), (4, 4)))
             func = relay.Function([x], y)
+            func = run_infer_type(func)
             x_data = np.random.uniform(size=dshape).astype(dtype)
             verify_results(func, [x_data], "test_pad", rtol=1e-5, atol=1e-5)
 

From f67214ac3c3bbcd01bdb4bb99822d08f74d20b37 Mon Sep 17 00:00:00 2001
From: root <schilkunda@ambarella.com>
Date: Tue, 29 Jun 2021 20:35:25 +0000
Subject: [PATCH 12/12] [Relay to Onnx conversion][Pad]

* Changed pad_value to input instead of attrs
* See PR: https://github.com/apache/tvm/pull/7860
* Fixed some formatting errors
---
 python/tvm/contrib/target/onnx.py | 14 ++++----------
 tests/python/contrib/test_onnx.py | 19 +++++++++----------
 2 files changed, 13 insertions(+), 20 deletions(-)

diff --git a/python/tvm/contrib/target/onnx.py b/python/tvm/contrib/target/onnx.py
index 590148e9cbf8..e442c806a2f3 100644
--- a/python/tvm/contrib/target/onnx.py
+++ b/python/tvm/contrib/target/onnx.py
@@ -350,8 +350,6 @@ def convert_attributes(cls, attrs):
         return {
             "pads": pads,
             "mode": attrs.get_str("pad_mode"),
-            #"constant_value": float(attrs.get_str("pad_value")),
-            "constant_value": 0,
         }
 
     @classmethod
@@ -363,20 +361,16 @@ def convert(cls, node_entry, model_container, node_dict):
         attrs = cls.convert_attributes(node_entry["relay_node"].attrs)
 
         name = node_entry["name"]
-        data = numpy.asarray(attrs["pads"], dtype=attrs["pads"][0].dtype).astype(numpy.int64)
-        value = numpy.dtype(node_entry["types"][0].dtype).type(attrs["constant_value"])
+        pad_data = numpy.asarray(attrs["pads"], dtype=attrs["pads"][0].dtype).astype(numpy.int64)
 
         input_names = [
             node_entry["input_names"][0],
-            add_input(data, name, "pads", model_container),
-            add_input(value, name, "value", model_container),
+            add_input(pad_data, name, "pads", model_container),
+            node_entry["input_names"][1],
         ]
 
         node = onnx.helper.make_node(
-            cls.__name__,
-            input_names,
-            node_entry["output_names"],
-            mode=attrs["mode"]
+            cls.__name__, input_names, node_entry["output_names"], mode=attrs["mode"]
         )
         model_container.add_nodes([node])
 
diff --git a/tests/python/contrib/test_onnx.py b/tests/python/contrib/test_onnx.py
index 12b44c758c00..8567f2c814cf 100644
--- a/tests/python/contrib/test_onnx.py
+++ b/tests/python/contrib/test_onnx.py
@@ -179,8 +179,7 @@ def test_conv2d_transpose():
     """Conv2d_Transpose unit tests."""
 
     def verify_conv2d_transpose(
-        dtype, scale, dshape, kshape,
-        padding=(1, 1), groups=1, dilation=(1, 1), **attrs
+        dtype, scale, dshape, kshape, padding=(1, 1), groups=1, dilation=(1, 1), **attrs
     ):
         x = relay.var("x", shape=dshape, dtype=dtype)
         w = relay.var("w", shape=kshape, dtype=dtype)
@@ -327,15 +326,15 @@ def verify_batch_norm(axis=1):
 
 def test_pad():
     """Pad unit test."""
+
     def verify_pad():
-        for dtype in ["float16", "float32"]:
-            dshape = (4, 10, 7, 7)
-            x = relay.var("x", shape=dshape, dtype=dtype)
-            y = relay.nn.pad(x, ((1, 1), (2, 2), (3, 3), (4, 4)))
-            func = relay.Function([x], y)
-            func = run_infer_type(func)
-            x_data = np.random.uniform(size=dshape).astype(dtype)
-            verify_results(func, [x_data], "test_pad", rtol=1e-5, atol=1e-5)
+        dshape = (4, 10, 7, 7)
+        x = relay.var("x", shape=dshape, dtype="int32")
+        y = relay.nn.pad(x, ((1, 1), (2, 2), (3, 3), (4, 4)))
+        func = relay.Function([x], y)
+        func = run_infer_type(func)
+        x_data = np.random.randint(low=-255, high=255, size=dshape).astype(np.int32)
+        verify_results(func, [x_data], "test_pad", rtol=1e-5, atol=1e-5)
 
     verify_pad()