From d10f85b5131c9e3eb75277cdbb95b90fcc20dec4 Mon Sep 17 00:00:00 2001
From: Iman Tabrizian <itabrizian@nvidia.com>
Date: Wed, 20 Sep 2023 17:21:31 -0400
Subject: [PATCH 1/7] Refactor model generation scripts

---
 qa/common/gen_common.py                       | 144 ++++++++++++++++++
 qa/common/gen_ensemble_model_utils.py         |  28 +---
 .../gen_qa_dyna_sequence_implicit_models.py   |  67 +-------
 qa/common/gen_qa_dyna_sequence_models.py      | 125 +--------------
 qa/common/gen_qa_identity_models.py           | 124 +--------------
 qa/common/gen_qa_implicit_models.py           |  98 +-----------
 qa/common/gen_qa_models.py                    | 127 +--------------
 qa/common/gen_qa_noshape_models.py            |  54 +------
 qa/common/gen_qa_ragged_models.py             |  99 +-----------
 qa/common/gen_qa_reshape_models.py            | 126 +--------------
 qa/common/gen_qa_sequence_models.py           | 125 +--------------
 qa/common/gen_qa_trt_data_dependent_shape.py  |  40 +----
 qa/common/gen_qa_trt_format_models.py         |  42 +----
 qa/common/gen_qa_trt_plugin_models.py         |  42 +----
 14 files changed, 208 insertions(+), 1033 deletions(-)
 create mode 100644 qa/common/gen_common.py

diff --git a/qa/common/gen_common.py b/qa/common/gen_common.py
new file mode 100644
index 0000000000..9ef41a3a19
--- /dev/null
+++ b/qa/common/gen_common.py
@@ -0,0 +1,144 @@
+# Copyright 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#  * Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#  * Redistributions in binary form must reproduce the above copyright
+#    notice, this list of conditions and the following disclaimer in the
+#    documentation and/or other materials provided with the distribution.
+#  * Neither the name of NVIDIA CORPORATION nor the names of its
+#    contributors may be used to endorse or promote products derived
+#    from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+# PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+# Common utilities for model generation scripts
+
+
+def np_to_onnx_dtype(np_dtype):
+    if np_dtype == bool:
+        return onnx.TensorProto.BOOL
+    elif np_dtype == np.int8:
+        return onnx.TensorProto.INT8
+    elif np_dtype == np.int16:
+        return onnx.TensorProto.INT16
+    elif np_dtype == np.int32:
+        return onnx.TensorProto.INT32
+    elif np_dtype == np.int64:
+        return onnx.TensorProto.INT64
+    elif np_dtype == np.uint8:
+        return onnx.TensorProto.UINT8
+    elif np_dtype == np.uint16:
+        return onnx.TensorProto.UINT16
+    elif np_dtype == np.float16:
+        return onnx.TensorProto.FLOAT16
+    elif np_dtype == np.float32:
+        return onnx.TensorProto.FLOAT
+    elif np_dtype == np.float64:
+        return onnx.TensorProto.DOUBLE
+    elif np_dtype == np_dtype_string:
+        return onnx.TensorProto.STRING
+
+
+def np_to_model_dtype(np_dtype):
+    if np_dtype == bool:
+        return "TYPE_BOOL"
+    elif np_dtype == np.int8:
+        return "TYPE_INT8"
+    elif np_dtype == np.int16:
+        return "TYPE_INT16"
+    elif np_dtype == np.int32:
+        return "TYPE_INT32"
+    elif np_dtype == np.int64:
+        return "TYPE_INT64"
+    elif np_dtype == np.uint8:
+        return "TYPE_UINT8"
+    elif np_dtype == np.uint16:
+        return "TYPE_UINT16"
+    elif np_dtype == np.float16:
+        return "TYPE_FP16"
+    elif np_dtype == np.float32:
+        return "TYPE_FP32"
+    elif np_dtype == np.float64:
+        return "TYPE_FP64"
+    elif np_dtype == np_dtype_string:
+        return "TYPE_STRING"
+    return None
+
+
+def np_to_trt_dtype(np_dtype):
+    if np_dtype == bool:
+        return trt.bool
+    elif np_dtype == np.int8:
+        return trt.int8
+    elif np_dtype == np.int32:
+        return trt.int32
+    elif np_dtype == np.float16:
+        return trt.float16
+    elif np_dtype == np.float32:
+        return trt.float32
+    return None
+
+
+def np_to_tf_dtype(np_dtype):
+    if np_dtype == bool:
+        return tf.bool
+    elif np_dtype == np.int8:
+        return tf.int8
+    elif np_dtype == np.int16:
+        return tf.int16
+    elif np_dtype == np.int32:
+        return tf.int32
+    elif np_dtype == np.int64:
+        return tf.int64
+    elif np_dtype == np.uint8:
+        return tf.uint8
+    elif np_dtype == np.uint16:
+        return tf.uint16
+    elif np_dtype == np.float16:
+        return tf.float16
+    elif np_dtype == np.float32:
+        return tf.float32
+    elif np_dtype == np.float64:
+        return tf.float64
+    elif np_dtype == np_dtype_string:
+        return tf.string
+    return None
+
+
+def np_to_torch_dtype(np_dtype):
+    if np_dtype == bool:
+        return torch.bool
+    elif np_dtype == np.int8:
+        return torch.int8
+    elif np_dtype == np.int16:
+        return torch.int16
+    elif np_dtype == np.int32:
+        return torch.int
+    elif np_dtype == np.int64:
+        return torch.long
+    elif np_dtype == np.uint8:
+        return torch.uint8
+    elif np_dtype == np.uint16:
+        return None  # Not supported in Torch
+    elif np_dtype == np.float16:
+        return None
+    elif np_dtype == np.float32:
+        return torch.float
+    elif np_dtype == np.float64:
+        return torch.double
+    elif np_dtype == np_dtype_string:
+        return None  # Not supported in Torch
+    return None
diff --git a/qa/common/gen_ensemble_model_utils.py b/qa/common/gen_ensemble_model_utils.py
index ceac0340dc..6528b51464 100755
--- a/qa/common/gen_ensemble_model_utils.py
+++ b/qa/common/gen_ensemble_model_utils.py
@@ -31,37 +31,13 @@
 import numpy as np
 import test_util as tu
 
+from .gen_common import np_to_model_dtype
+
 BASIC_ENSEMBLE_TYPES = ["simple", "sequence", "fan"]
 
 np_dtype_string = np.dtype(object)
 
 
-def np_to_model_dtype(np_dtype):
-    if np_dtype == bool:
-        return "TYPE_BOOL"
-    elif np_dtype == np.int8:
-        return "TYPE_INT8"
-    elif np_dtype == np.int16:
-        return "TYPE_INT16"
-    elif np_dtype == np.int32:
-        return "TYPE_INT32"
-    elif np_dtype == np.int64:
-        return "TYPE_INT64"
-    elif np_dtype == np.uint8:
-        return "TYPE_UINT8"
-    elif np_dtype == np.uint16:
-        return "TYPE_UINT16"
-    elif np_dtype == np.float16:
-        return "TYPE_FP16"
-    elif np_dtype == np.float32:
-        return "TYPE_FP32"
-    elif np_dtype == np.float64:
-        return "TYPE_FP64"
-    elif np_dtype == np_dtype_string:
-        return "TYPE_STRING"
-    return None
-
-
 def fixed_to_variable_size(shape):
     return [-1] * len(shape)
 
diff --git a/qa/common/gen_qa_dyna_sequence_implicit_models.py b/qa/common/gen_qa_dyna_sequence_implicit_models.py
index 1c4815d5dd..b8cb4e59b3 100755
--- a/qa/common/gen_qa_dyna_sequence_implicit_models.py
+++ b/qa/common/gen_qa_dyna_sequence_implicit_models.py
@@ -31,75 +31,12 @@
 
 import numpy as np
 
+from .gen_common import np_to_model_dtype, np_to_onnx_dtype, np_to_trt_dtype
+
 FLAGS = None
 np_dtype_string = np.dtype(object)
 
 
-def np_to_onnx_dtype(np_dtype):
-    if np_dtype == bool:
-        return onnx.TensorProto.BOOL
-    elif np_dtype == np.int8:
-        return onnx.TensorProto.INT8
-    elif np_dtype == np.int16:
-        return onnx.TensorProto.INT16
-    elif np_dtype == np.int32:
-        return onnx.TensorProto.INT32
-    elif np_dtype == np.int64:
-        return onnx.TensorProto.INT64
-    elif np_dtype == np.uint8:
-        return onnx.TensorProto.UINT8
-    elif np_dtype == np.uint16:
-        return onnx.TensorProto.UINT16
-    elif np_dtype == np.float16:
-        return onnx.TensorProto.FLOAT16
-    elif np_dtype == np.float32:
-        return onnx.TensorProto.FLOAT
-    elif np_dtype == np.float64:
-        return onnx.TensorProto.DOUBLE
-    elif np_dtype == np_dtype_string:
-        return onnx.TensorProto.STRING
-
-
-def np_to_model_dtype(np_dtype):
-    if np_dtype == bool:
-        return "TYPE_BOOL"
-    elif np_dtype == np.int8:
-        return "TYPE_INT8"
-    elif np_dtype == np.int16:
-        return "TYPE_INT16"
-    elif np_dtype == np.int32:
-        return "TYPE_INT32"
-    elif np_dtype == np.int64:
-        return "TYPE_INT64"
-    elif np_dtype == np.uint8:
-        return "TYPE_UINT8"
-    elif np_dtype == np.uint16:
-        return "TYPE_UINT16"
-    elif np_dtype == np.float16:
-        return "TYPE_FP16"
-    elif np_dtype == np.float32:
-        return "TYPE_FP32"
-    elif np_dtype == np.float64:
-        return "TYPE_FP64"
-    elif np_dtype == np_dtype_string:
-        return "TYPE_STRING"
-    return None
-
-
-def np_to_trt_dtype(np_dtype):
-    if np_dtype == bool:
-        return trt.bool
-    elif np_dtype == np.int8:
-        return trt.int8
-    elif np_dtype == np.int32:
-        return trt.int32
-    elif np_dtype == np.float16:
-        return trt.float16
-    elif np_dtype == np.float32:
-        return trt.float32
-    return None
-
-
 def create_onnx_modelfile(models_dir, model_version, max_batch, dtype, shape):
     if not tu.validate_for_onnx_model(dtype, dtype, dtype, shape, shape, shape):
         return
diff --git a/qa/common/gen_qa_dyna_sequence_models.py b/qa/common/gen_qa_dyna_sequence_models.py
index 8a02497bfa..27aef7d861 100755
--- a/qa/common/gen_qa_dyna_sequence_models.py
+++ b/qa/common/gen_qa_dyna_sequence_models.py
@@ -31,127 +31,18 @@
 
 import numpy as np
 
+from .gen_common import (
+    np_to_model_dtype,
+    np_to_onnx_dtype,
+    np_to_tf_dtype,
+    np_to_torch_dtype,
+    np_to_trt_dtype,
+)
+
 FLAGS = None
 np_dtype_string = np.dtype(object)
 
 
-def np_to_model_dtype(np_dtype):
-    if np_dtype == bool:
-        return "TYPE_BOOL"
-    elif np_dtype == np.int8:
-        return "TYPE_INT8"
-    elif np_dtype == np.int16:
-        return "TYPE_INT16"
-    elif np_dtype == np.int32:
-        return "TYPE_INT32"
-    elif np_dtype == np.int64:
-        return "TYPE_INT64"
-    elif np_dtype == np.uint8:
-        return "TYPE_UINT8"
-    elif np_dtype == np.uint16:
-        return "TYPE_UINT16"
-    elif np_dtype == np.float16:
-        return "TYPE_FP16"
-    elif np_dtype == np.float32:
-        return "TYPE_FP32"
-    elif np_dtype == np.float64:
-        return "TYPE_FP64"
-    elif np_dtype == np_dtype_string:
-        return "TYPE_STRING"
-    return None
-
-
-def np_to_tf_dtype(np_dtype):
-    if np_dtype == bool:
-        return tf.bool
-    elif np_dtype == np.int8:
-        return tf.int8
-    elif np_dtype == np.int16:
-        return tf.int16
-    elif np_dtype == np.int32:
-        return tf.int32
-    elif np_dtype == np.int64:
-        return tf.int64
-    elif np_dtype == np.uint8:
-        return tf.uint8
-    elif np_dtype == np.uint16:
-        return tf.uint16
-    elif np_dtype == np.float16:
-        return tf.float16
-    elif np_dtype == np.float32:
-        return tf.float32
-    elif np_dtype == np.float64:
-        return tf.float64
-    elif np_dtype == np_dtype_string:
-        return tf.string
-    return None
-
-
-def np_to_trt_dtype(np_dtype):
-    if np_dtype == bool:
-        return trt.bool
-    elif np_dtype == np.int8:
-        return trt.int8
-    elif np_dtype == np.int32:
-        return trt.int32
-    elif np_dtype == np.float16:
-        return trt.float16
-    elif np_dtype == np.float32:
-        return trt.float32
-    return None
-
-
-def np_to_onnx_dtype(np_dtype):
-    if np_dtype == bool:
-        return onnx.TensorProto.BOOL
-    elif np_dtype == np.int8:
-        return onnx.TensorProto.INT8
-    elif np_dtype == np.int16:
-        return onnx.TensorProto.INT16
-    elif np_dtype == np.int32:
-        return onnx.TensorProto.INT32
-    elif np_dtype == np.int64:
-        return onnx.TensorProto.INT64
-    elif np_dtype == np.uint8:
-        return onnx.TensorProto.UINT8
-    elif np_dtype == np.uint16:
-        return onnx.TensorProto.UINT16
-    elif np_dtype == np.float16:
-        return onnx.TensorProto.FLOAT16
-    elif np_dtype == np.float32:
-        return onnx.TensorProto.FLOAT
-    elif np_dtype == np.float64:
-        return onnx.TensorProto.DOUBLE
-    elif np_dtype == np_dtype_string:
-        return onnx.TensorProto.STRING
-
-
-def np_to_torch_dtype(np_dtype):
-    if np_dtype == bool:
-        return torch.bool
-    elif np_dtype == np.int8:
-        return torch.int8
-    elif np_dtype == np.int16:
-        return torch.int16
-    elif np_dtype == np.int32:
-        return torch.int
-    elif np_dtype == np.int64:
-        return torch.long
-    elif np_dtype == np.uint8:
-        return torch.uint8
-    elif np_dtype == np.uint16:
-        return None  # Not supported in Torch
-    elif np_dtype == np.float16:
-        return None
-    elif np_dtype == np.float32:
-        return torch.float
-    elif np_dtype == np.float64:
-        return torch.double
-    elif np_dtype == np_dtype_string:
-        return None  # Not supported in Torch
-    return None
-
-
 def create_tf_modelfile(
     create_savedmodel, models_dir, model_version, max_batch, dtype, shape
 ):
diff --git a/qa/common/gen_qa_identity_models.py b/qa/common/gen_qa_identity_models.py
index 0aaac8d9e4..f864ab8b10 100755
--- a/qa/common/gen_qa_identity_models.py
+++ b/qa/common/gen_qa_identity_models.py
@@ -33,128 +33,18 @@
 import gen_ensemble_model_utils as emu
 import numpy as np
 
+from .gen_common import (
+    np_to_model_dtype,
+    np_to_onnx_dtype,
+    np_to_tf_dtype,
+    np_to_trt_dtype,
+)
+
 FLAGS = None
 np_dtype_string = np.dtype(object)
 from typing import List, Tuple
 
 
-def np_to_model_dtype(np_dtype):
-    if np_dtype == bool:
-        return "TYPE_BOOL"
-    elif np_dtype == np.int8:
-        return "TYPE_INT8"
-    elif np_dtype == np.int16:
-        return "TYPE_INT16"
-    elif np_dtype == np.int32:
-        return "TYPE_INT32"
-    elif np_dtype == np.int64:
-        return "TYPE_INT64"
-    elif np_dtype == np.uint8:
-        return "TYPE_UINT8"
-    elif np_dtype == np.uint16:
-        return "TYPE_UINT16"
-    elif np_dtype == np.float16:
-        return "TYPE_FP16"
-    elif np_dtype == np.float32:
-        return "TYPE_FP32"
-    elif np_dtype == np.float64:
-        return "TYPE_FP64"
-    elif np_dtype == np_dtype_string:
-        return "TYPE_STRING"
-    return None
-
-
-def np_to_tf_dtype(np_dtype):
-    if np_dtype == bool:
-        return tf.bool
-    elif np_dtype == np.int8:
-        return tf.int8
-    elif np_dtype == np.int16:
-        return tf.int16
-    elif np_dtype == np.int32:
-        return tf.int32
-    elif np_dtype == np.int64:
-        return tf.int64
-    elif np_dtype == np.uint8:
-        return tf.uint8
-    elif np_dtype == np.uint16:
-        return tf.uint16
-    elif np_dtype == np.float16:
-        return tf.float16
-    elif np_dtype == np.float32:
-        return tf.float32
-    elif np_dtype == np.float64:
-        return tf.float64
-    elif np_dtype == np_dtype_string:
-        return tf.string
-    return None
-
-
-def np_to_trt_dtype(np_dtype):
-    if np_dtype == bool:
-        return trt.bool
-    elif np_dtype == np.int8:
-        return trt.int8
-    elif np_dtype == np.int32:
-        return trt.int32
-    elif np_dtype == np.float16:
-        return trt.float16
-    elif np_dtype == np.float32:
-        return trt.float32
-    return None
-
-
-def np_to_onnx_dtype(np_dtype):
-    if np_dtype == bool:
-        return onnx.TensorProto.BOOL
-    elif np_dtype == np.int8:
-        return onnx.TensorProto.INT8
-    elif np_dtype == np.int16:
-        return onnx.TensorProto.INT16
-    elif np_dtype == np.int32:
-        return onnx.TensorProto.INT32
-    elif np_dtype == np.int64:
-        return onnx.TensorProto.INT64
-    elif np_dtype == np.uint8:
-        return onnx.TensorProto.UINT8
-    elif np_dtype == np.uint16:
-        return onnx.TensorProto.UINT16
-    elif np_dtype == np.float16:
-        return onnx.TensorProto.FLOAT16
-    elif np_dtype == np.float32:
-        return onnx.TensorProto.FLOAT
-    elif np_dtype == np.float64:
-        return onnx.TensorProto.DOUBLE
-    elif np_dtype == np_dtype_string:
-        return onnx.TensorProto.STRING
-    return None
-
-
-def np_to_torch_dtype(np_dtype):
-    if np_dtype == bool:
-        return torch.bool
-    elif np_dtype == np.int8:
-        return torch.int8
-    elif np_dtype == np.int16:
-        return torch.int16
-    elif np_dtype == np.int32:
-        return torch.int
-    elif np_dtype == np.int64:
-        return torch.long
-    elif np_dtype == np.uint8:
-        return torch.uint8
-    elif np_dtype == np.uint16:
-        return None  # Not supported in Torch
-    elif np_dtype == np.float16:
-        return None
-    elif np_dtype == np.float32:
-        return torch.float
-    elif np_dtype == np.float64:
-        return torch.double
-    elif np_dtype == np_dtype_string:
-        return List[str]
-
-
 def create_tf_modelfile(
     create_savedmodel, models_dir, model_version, io_cnt, max_batch, dtype, shape
 ):
diff --git a/qa/common/gen_qa_implicit_models.py b/qa/common/gen_qa_implicit_models.py
index 84ca98c47c..3e17df23c8 100755
--- a/qa/common/gen_qa_implicit_models.py
+++ b/qa/common/gen_qa_implicit_models.py
@@ -33,101 +33,17 @@
 import gen_ensemble_model_utils as emu
 import numpy as np
 
+from .gen_common import (
+    np_to_model_dtype,
+    np_to_onnx_dtype,
+    np_to_torch_dtype,
+    np_to_trt_dtype,
+)
+
 FLAGS = None
 np_dtype_string = np.dtype(object)
 
 
-def np_to_model_dtype(np_dtype):
-    if np_dtype == bool:
-        return "TYPE_BOOL"
-    elif np_dtype == np.int8:
-        return "TYPE_INT8"
-    elif np_dtype == np.int16:
-        return "TYPE_INT16"
-    elif np_dtype == np.int32:
-        return "TYPE_INT32"
-    elif np_dtype == np.int64:
-        return "TYPE_INT64"
-    elif np_dtype == np.uint8:
-        return "TYPE_UINT8"
-    elif np_dtype == np.uint16:
-        return "TYPE_UINT16"
-    elif np_dtype == np.float16:
-        return "TYPE_FP16"
-    elif np_dtype == np.float32:
-        return "TYPE_FP32"
-    elif np_dtype == np.float64:
-        return "TYPE_FP64"
-    elif np_dtype == np_dtype_string:
-        return "TYPE_STRING"
-    return None
-
-
-def np_to_onnx_dtype(np_dtype):
-    if np_dtype == bool:
-        return onnx.TensorProto.BOOL
-    elif np_dtype == np.int8:
-        return onnx.TensorProto.INT8
-    elif np_dtype == np.int16:
-        return onnx.TensorProto.INT16
-    elif np_dtype == np.int32:
-        return onnx.TensorProto.INT32
-    elif np_dtype == np.int64:
-        return onnx.TensorProto.INT64
-    elif np_dtype == np.uint8:
-        return onnx.TensorProto.UINT8
-    elif np_dtype == np.uint16:
-        return onnx.TensorProto.UINT16
-    elif np_dtype == np.float16:
-        return onnx.TensorProto.FLOAT16
-    elif np_dtype == np.float32:
-        return onnx.TensorProto.FLOAT
-    elif np_dtype == np.float64:
-        return onnx.TensorProto.DOUBLE
-    elif np_dtype == np_dtype_string:
-        return onnx.TensorProto.STRING
-
-
-def np_to_trt_dtype(np_dtype):
-    if np_dtype == bool:
-        return trt.bool
-    elif np_dtype == np.int8:
-        return trt.int8
-    elif np_dtype == np.int32:
-        return trt.int32
-    elif np_dtype == np.float16:
-        return trt.float16
-    elif np_dtype == np.float32:
-        return trt.float32
-    return None
-
-
-def np_to_torch_dtype(np_dtype):
-    if np_dtype == bool:
-        return torch.bool
-    elif np_dtype == np.int8:
-        return torch.int8
-    elif np_dtype == np.int16:
-        return torch.int16
-    elif np_dtype == np.int32:
-        return torch.int
-    elif np_dtype == np.int64:
-        return torch.long
-    elif np_dtype == np.uint8:
-        return torch.uint8
-    elif np_dtype == np.uint16:
-        return None  # Not supported in Torch
-    elif np_dtype == np.float16:
-        return None
-    elif np_dtype == np.float32:
-        return torch.float
-    elif np_dtype == np.float64:
-        return torch.double
-    elif np_dtype == np_dtype_string:
-        return List[str]
-    return None
-
-
 def create_onnx_modelfile_wo_initial_state(
     models_dir, model_version, max_batch, dtype, shape
 ):
diff --git a/qa/common/gen_qa_models.py b/qa/common/gen_qa_models.py
index f050861a62..961185fa37 100755
--- a/qa/common/gen_qa_models.py
+++ b/qa/common/gen_qa_models.py
@@ -33,130 +33,19 @@
 import gen_ensemble_model_utils as emu
 import numpy as np
 
+from .gen_common import (
+    np_to_model_dtype,
+    np_to_onnx_dtype,
+    np_to_tf_dtype,
+    np_to_torch_dtype,
+    np_to_trt_dtype,
+)
+
 FLAGS = None
 np_dtype_string = np.dtype(object)
 from typing import List, Tuple
 
 
-def np_to_model_dtype(np_dtype):
-    if np_dtype == bool:
-        return "TYPE_BOOL"
-    elif np_dtype == np.int8:
-        return "TYPE_INT8"
-    elif np_dtype == np.int16:
-        return "TYPE_INT16"
-    elif np_dtype == np.int32:
-        return "TYPE_INT32"
-    elif np_dtype == np.int64:
-        return "TYPE_INT64"
-    elif np_dtype == np.uint8:
-        return "TYPE_UINT8"
-    elif np_dtype == np.uint16:
-        return "TYPE_UINT16"
-    elif np_dtype == np.float16:
-        return "TYPE_FP16"
-    elif np_dtype == np.float32:
-        return "TYPE_FP32"
-    elif np_dtype == np.float64:
-        return "TYPE_FP64"
-    elif np_dtype == np_dtype_string:
-        return "TYPE_STRING"
-    return None
-
-
-def np_to_tf_dtype(np_dtype):
-    if np_dtype == bool:
-        return tf.bool
-    elif np_dtype == np.int8:
-        return tf.int8
-    elif np_dtype == np.int16:
-        return tf.int16
-    elif np_dtype == np.int32:
-        return tf.int32
-    elif np_dtype == np.int64:
-        return tf.int64
-    elif np_dtype == np.uint8:
-        return tf.uint8
-    elif np_dtype == np.uint16:
-        return tf.uint16
-    elif np_dtype == np.float16:
-        return tf.float16
-    elif np_dtype == np.float32:
-        return tf.float32
-    elif np_dtype == np.float64:
-        return tf.float64
-    elif np_dtype == np_dtype_string:
-        return tf.string
-    return None
-
-
-def np_to_trt_dtype(np_dtype):
-    if np_dtype == bool:
-        return trt.bool
-    elif np_dtype == np.int8:
-        return trt.int8
-    elif np_dtype == np.int32:
-        return trt.int32
-    elif np_dtype == np.uint8:
-        return trt.uint8
-    elif np_dtype == np.float16:
-        return trt.float16
-    elif np_dtype == np.float32:
-        return trt.float32
-    return None
-
-
-def np_to_onnx_dtype(np_dtype):
-    if np_dtype == bool:
-        return onnx.TensorProto.BOOL
-    elif np_dtype == np.int8:
-        return onnx.TensorProto.INT8
-    elif np_dtype == np.int16:
-        return onnx.TensorProto.INT16
-    elif np_dtype == np.int32:
-        return onnx.TensorProto.INT32
-    elif np_dtype == np.int64:
-        return onnx.TensorProto.INT64
-    elif np_dtype == np.uint8:
-        return onnx.TensorProto.UINT8
-    elif np_dtype == np.uint16:
-        return onnx.TensorProto.UINT16
-    elif np_dtype == np.float16:
-        return onnx.TensorProto.FLOAT16
-    elif np_dtype == np.float32:
-        return onnx.TensorProto.FLOAT
-    elif np_dtype == np.float64:
-        return onnx.TensorProto.DOUBLE
-    elif np_dtype == np_dtype_string:
-        return onnx.TensorProto.STRING
-    return None
-
-
-def np_to_torch_dtype(np_dtype):
-    if np_dtype == bool:
-        return torch.bool
-    elif np_dtype == np.int8:
-        return torch.int8
-    elif np_dtype == np.int16:
-        return torch.int16
-    elif np_dtype == np.int32:
-        return torch.int
-    elif np_dtype == np.int64:
-        return torch.long
-    elif np_dtype == np.uint8:
-        return torch.uint8
-    elif np_dtype == np.uint16:
-        return None  # Not supported in Torch
-    elif np_dtype == np.float16:
-        return None
-    elif np_dtype == np.float32:
-        return torch.float
-    elif np_dtype == np.float64:
-        return torch.double
-    elif np_dtype == np_dtype_string:
-        return List[str]
-
-
 def create_graphdef_modelfile(
     models_dir,
     max_batch,
diff --git a/qa/common/gen_qa_noshape_models.py b/qa/common/gen_qa_noshape_models.py
index b37f1c1bbb..75e6396504 100755
--- a/qa/common/gen_qa_noshape_models.py
+++ b/qa/common/gen_qa_noshape_models.py
@@ -32,62 +32,12 @@
 
 import numpy as np
 
+from .gen_common import np_to_model_dtype, np_to_tf_dtype
+
 FLAGS = None
 np_dtype_string = np.dtype(object)
 
 
-def np_to_model_dtype(np_dtype):
-    if np_dtype == bool:
-        return "TYPE_BOOL"
-    elif np_dtype == np.int8:
-        return "TYPE_INT8"
-    elif np_dtype == np.int16:
-        return "TYPE_INT16"
-    elif np_dtype == np.int32:
-        return "TYPE_INT32"
-    elif np_dtype == np.int64:
-        return "TYPE_INT64"
-    elif np_dtype == np.uint8:
-        return "TYPE_UINT8"
-    elif np_dtype == np.uint16:
-        return "TYPE_UINT16"
-    elif np_dtype == np.float16:
-        return "TYPE_FP16"
-    elif np_dtype == np.float32:
-        return "TYPE_FP32"
-    elif np_dtype == np.float64:
-        return "TYPE_FP64"
-    elif np_dtype == np_dtype_string:
-        return "TYPE_STRING"
-    return None
-
-
-def np_to_tf_dtype(np_dtype):
-    if np_dtype == bool:
-        return tf.bool
-    elif np_dtype == np.int8:
-        return tf.int8
-    elif np_dtype == np.int16:
-        return tf.int16
-    elif np_dtype == np.int32:
-        return tf.int32
-    elif np_dtype == np.int64:
-        return tf.int64
-    elif np_dtype == np.uint8:
-        return tf.uint8
-    elif np_dtype == np.uint16:
-        return tf.uint16
-    elif np_dtype == np.float16:
-        return tf.float16
-    elif np_dtype == np.float32:
-        return tf.float32
-    elif np_dtype == np.float64:
-        return tf.float64
-    elif np_dtype == np_dtype_string:
-        return tf.string
-    return None
-
-
 def create_savedmodel_modelfile(
     models_dir,
     max_batch,
diff --git a/qa/common/gen_qa_ragged_models.py b/qa/common/gen_qa_ragged_models.py
index 0ac70e80f6..c4292f9420 100755
--- a/qa/common/gen_qa_ragged_models.py
+++ b/qa/common/gen_qa_ragged_models.py
@@ -31,99 +31,14 @@
 
 import numpy as np
 
-np_dtype_string = np.dtype(object)
-
+from .gen_common import (
+    np_to_model_dtype,
+    np_to_onnx_dtype,
+    np_to_tf_dtype,
+    np_to_trt_dtype,
+)
 
-def np_to_model_dtype(np_dtype):
-    if np_dtype == bool:
-        return "TYPE_BOOL"
-    elif np_dtype == np.int8:
-        return "TYPE_INT8"
-    elif np_dtype == np.int16:
-        return "TYPE_INT16"
-    elif np_dtype == np.int32:
-        return "TYPE_INT32"
-    elif np_dtype == np.int64:
-        return "TYPE_INT64"
-    elif np_dtype == np.uint8:
-        return "TYPE_UINT8"
-    elif np_dtype == np.uint16:
-        return "TYPE_UINT16"
-    elif np_dtype == np.float16:
-        return "TYPE_FP16"
-    elif np_dtype == np.float32:
-        return "TYPE_FP32"
-    elif np_dtype == np.float64:
-        return "TYPE_FP64"
-    elif np_dtype == np_dtype_string:
-        return "TYPE_STRING"
-    return None
-
-
-def np_to_trt_dtype(np_dtype):
-    if np_dtype == bool:
-        return trt.bool
-    elif np_dtype == np.int8:
-        return trt.int8
-    elif np_dtype == np.int32:
-        return trt.int32
-    elif np_dtype == np.float16:
-        return trt.float16
-    elif np_dtype == np.float32:
-        return trt.float32
-    return None
-
-
-def np_to_tf_dtype(np_dtype):
-    if np_dtype == bool:
-        return tf.bool
-    elif np_dtype == np.int8:
-        return tf.int8
-    elif np_dtype == np.int16:
-        return tf.int16
-    elif np_dtype == np.int32:
-        return tf.int32
-    elif np_dtype == np.int64:
-        return tf.int64
-    elif np_dtype == np.uint8:
-        return tf.uint8
-    elif np_dtype == np.uint16:
-        return tf.uint16
-    elif np_dtype == np.float16:
-        return tf.float16
-    elif np_dtype == np.float32:
-        return tf.float32
-    elif np_dtype == np.float64:
-        return tf.float64
-    elif np_dtype == np_dtype_string:
-        return tf.string
-    return None
-
-
-def np_to_onnx_dtype(np_dtype):
-    if np_dtype == bool:
-        return onnx.TensorProto.BOOL
-    elif np_dtype == np.int8:
-        return onnx.TensorProto.INT8
-    elif np_dtype == np.int16:
-        return onnx.TensorProto.INT16
-    elif np_dtype == np.int32:
-        return onnx.TensorProto.INT32
-    elif np_dtype == np.int64:
-        return onnx.TensorProto.INT64
-    elif np_dtype == np.uint8:
-        return onnx.TensorProto.UINT8
-    elif np_dtype == np.uint16:
-        return onnx.TensorProto.UINT16
-    elif np_dtype == np.float16:
-        return onnx.TensorProto.FLOAT16
-    elif np_dtype == np.float32:
-        return onnx.TensorProto.FLOAT
-    elif np_dtype == np.float64:
-        return onnx.TensorProto.DOUBLE
-    elif np_dtype == np_dtype_string:
-        return onnx.TensorProto.STRING
-    return None
+np_dtype_string = np.dtype(object)
 
 
 def create_savedmodel_modelfile(models_dir, model_version, dtype):
diff --git a/qa/common/gen_qa_reshape_models.py b/qa/common/gen_qa_reshape_models.py
index 983f06c1d0..fe34446786 100755
--- a/qa/common/gen_qa_reshape_models.py
+++ b/qa/common/gen_qa_reshape_models.py
@@ -33,129 +33,19 @@
 import gen_ensemble_model_utils as emu
 import numpy as np
 
+from .gen_common import (
+    np_to_model_dtype,
+    np_to_onnx_dtype,
+    np_to_tf_dtype,
+    np_to_torch_dtype,
+    np_to_trt_dtype,
+)
+
 FLAGS = None
 np_dtype_string = np.dtype(object)
 from typing import List
 
 
-def np_to_model_dtype(np_dtype):
-    if np_dtype == bool:
-        return "TYPE_BOOL"
-    elif np_dtype == np.int8:
-        return "TYPE_INT8"
-    elif np_dtype == np.int16:
-        return "TYPE_INT16"
-    elif np_dtype == np.int32:
-        return "TYPE_INT32"
-    elif np_dtype == np.int64:
-        return "TYPE_INT64"
-    elif np_dtype == np.uint8:
-        return "TYPE_UINT8"
-    elif np_dtype == np.uint16:
-        return "TYPE_UINT16"
-    elif np_dtype == np.float16:
-        return "TYPE_FP16"
-    elif np_dtype == np.float32:
-        return "TYPE_FP32"
-    elif np_dtype == np.float64:
-        return "TYPE_FP64"
-    elif np_dtype == np_dtype_string:
-        return "TYPE_STRING"
-    return None
-
-
-def np_to_tf_dtype(np_dtype):
-    if np_dtype == bool:
-        return tf.bool
-    elif np_dtype == np.int8:
-        return tf.int8
-    elif np_dtype == np.int16:
-        return tf.int16
-    elif np_dtype == np.int32:
-        return tf.int32
-    elif np_dtype == np.int64:
-        return tf.int64
-    elif np_dtype == np.uint8:
-        return tf.uint8
-    elif np_dtype == np.uint16:
-        return tf.uint16
-    elif np_dtype == np.float16:
-        return tf.float16
-    elif np_dtype == np.float32:
-        return tf.float32
-    elif np_dtype == np.float64:
-        return tf.float64
-    elif np_dtype == np_dtype_string:
-        return tf.string
-    return None
-
-
-def np_to_trt_dtype(np_dtype):
-    if np_dtype == bool:
-        return trt.bool
-    elif np_dtype == np.int8:
-        return trt.int8
-    elif np_dtype == np.int32:
-        return trt.int32
-    elif np_dtype == np.float16:
-        return trt.float16
-    elif np_dtype == np.float32:
-        return trt.float32
-    return None
-
-
-def np_to_onnx_dtype(np_dtype):
-    if np_dtype == bool:
-        return onnx.TensorProto.BOOL
-    elif np_dtype == np.int8:
-        return onnx.TensorProto.INT8
-    elif np_dtype == np.int16:
-        return onnx.TensorProto.INT16
-    elif np_dtype == np.int32:
-        return onnx.TensorProto.INT32
-    elif np_dtype == np.int64:
-        return onnx.TensorProto.INT64
-    elif np_dtype == np.uint8:
-        return onnx.TensorProto.UINT8
-    elif np_dtype == np.uint16:
-        return onnx.TensorProto.UINT16
-    elif np_dtype == np.float16:
-        return onnx.TensorProto.FLOAT16
-    elif np_dtype == np.float32:
-        return onnx.TensorProto.FLOAT
-    elif np_dtype == np.float64:
-        return onnx.TensorProto.DOUBLE
-    elif np_dtype == np_dtype_string:
-        return onnx.TensorProto.STRING
-    return None
-
-
-def np_to_torch_dtype(np_dtype):
-    if np_dtype == bool:
-        return torch.bool
-    elif np_dtype == np.int8:
-        return torch.int8
-    elif np_dtype == np.int16:
-        return torch.int16
-    elif np_dtype == np.int32:
-        return torch.int
-    elif np_dtype == np.int64:
-        return torch.long
-    elif np_dtype == np.uint8:
-        return torch.uint8
-    elif np_dtype == np.uint16:
-        return None  # Not supported in Torch
-    elif np_dtype == np.float16:
-        return None
-    elif np_dtype == np.float32:
-        return torch.float
-    elif np_dtype == np.float64:
-        return torch.double
-    elif np_dtype == np_dtype_string:
-        return List[str]
-    return None
-
-
 def create_tf_modelfile(
     create_savedmodel,
     models_dir,
diff --git a/qa/common/gen_qa_sequence_models.py b/qa/common/gen_qa_sequence_models.py
index 01cd515c15..c23c078f9c 100755
--- a/qa/common/gen_qa_sequence_models.py
+++ b/qa/common/gen_qa_sequence_models.py
@@ -32,127 +32,18 @@
 import gen_ensemble_model_utils as emu
 import numpy as np
 
+from .gen_common import (
+    np_to_model_dtype,
+    np_to_onnx_dtype,
+    np_to_tf_dtype,
+    np_to_torch_dtype,
+    np_to_trt_dtype,
+)
+
 FLAGS = None
 np_dtype_string = np.dtype(object)
 
 
-def np_to_model_dtype(np_dtype):
-    if np_dtype == bool:
-        return "TYPE_BOOL"
-    elif np_dtype == np.int8:
-        return "TYPE_INT8"
-    elif np_dtype == np.int16:
-        return "TYPE_INT16"
-    elif np_dtype == np.int32:
-        return "TYPE_INT32"
-    elif np_dtype == np.int64:
-        return "TYPE_INT64"
-    elif np_dtype == np.uint8:
-        return "TYPE_UINT8"
-    elif np_dtype == np.uint16:
-        return "TYPE_UINT16"
-    elif np_dtype == np.float16:
-        return "TYPE_FP16"
-    elif np_dtype == np.float32:
-        return "TYPE_FP32"
-    elif np_dtype == np.float64:
-        return "TYPE_FP64"
-    elif np_dtype == np_dtype_string:
-        return "TYPE_STRING"
-    return None
-
-
-def np_to_tf_dtype(np_dtype):
-    if np_dtype == bool:
-        return tf.bool
-    elif np_dtype == np.int8:
-        return tf.int8
-    elif np_dtype == np.int16:
-        return tf.int16
-    elif np_dtype == np.int32:
-        return tf.int32
-    elif np_dtype == np.int64:
-        return tf.int64
-    elif np_dtype == np.uint8:
-        return tf.uint8
-    elif np_dtype == np.uint16:
-        return tf.uint16
-    elif np_dtype == np.float16:
-        return tf.float16
-    elif np_dtype == np.float32:
-        return tf.float32
-    elif np_dtype == np.float64:
-        return tf.float64
-    elif np_dtype == np_dtype_string:
-        return tf.string
-    return None
-
-
-def np_to_trt_dtype(np_dtype):
-    if np_dtype == bool:
-        return trt.bool
-    elif np_dtype == np.int8:
-        return trt.int8
-    elif np_dtype == np.int32:
-        return trt.int32
-    elif np_dtype == np.float16:
-        return trt.float16
-    elif np_dtype == np.float32:
-        return trt.float32
-    return None
-
-
-def np_to_onnx_dtype(np_dtype):
-    if np_dtype == bool:
-        return onnx.TensorProto.BOOL
-    elif np_dtype == np.int8:
-        return onnx.TensorProto.INT8
-    elif np_dtype == np.int16:
-        return onnx.TensorProto.INT16
-    elif np_dtype == np.int32:
-        return onnx.TensorProto.INT32
-    elif np_dtype == np.int64:
-        return onnx.TensorProto.INT64
-    elif np_dtype == np.uint8:
-        return onnx.TensorProto.UINT8
-    elif np_dtype == np.uint16:
-        return onnx.TensorProto.UINT16
-    elif np_dtype == np.float16:
-        return onnx.TensorProto.FLOAT16
-    elif np_dtype == np.float32:
-        return onnx.TensorProto.FLOAT
-    elif np_dtype == np.float64:
-        return onnx.TensorProto.DOUBLE
-    elif np_dtype == np_dtype_string:
-        return onnx.TensorProto.STRING
-
-
-def np_to_torch_dtype(np_dtype):
-    if np_dtype == bool:
-        return torch.bool
-    elif np_dtype == np.int8:
-        return torch.int8
-    elif np_dtype == np.int16:
-        return torch.int16
-    elif np_dtype == np.int32:
-        return torch.int
-    elif np_dtype == np.int64:
-        return torch.long
-    elif np_dtype == np.uint8:
-        return torch.uint8
-    elif np_dtype == np.uint16:
-        return None  # Not supported in Torch
-    elif np_dtype == np.float16:
-        return None
-    elif np_dtype == np.float32:
-        return torch.float
-    elif np_dtype == np.float64:
-        return torch.double
-    elif np_dtype == np_dtype_string:
-        return None  # Not supported in Torch
-    return None
-
-
 def create_tf_modelfile(
     create_savedmodel, models_dir, model_version, max_batch, dtype, shape
 ):
diff --git a/qa/common/gen_qa_trt_data_dependent_shape.py b/qa/common/gen_qa_trt_data_dependent_shape.py
index 1b40455fd6..a0dc38083a 100755
--- a/qa/common/gen_qa_trt_data_dependent_shape.py
+++ b/qa/common/gen_qa_trt_data_dependent_shape.py
@@ -33,45 +33,7 @@
 import tensorrt as trt
 import test_util as tu
 
-
-def np_to_model_dtype(np_dtype):
-    if np_dtype == bool:
-        return "TYPE_BOOL"
-    elif np_dtype == np.int8:
-        return "TYPE_INT8"
-    elif np_dtype == np.int16:
-        return "TYPE_INT16"
-    elif np_dtype == np.int32:
-        return "TYPE_INT32"
-    elif np_dtype == np.int64:
-        return "TYPE_INT64"
-    elif np_dtype == np.uint8:
-        return "TYPE_UINT8"
-    elif np_dtype == np.uint16:
-        return "TYPE_UINT16"
-    elif np_dtype == np.float16:
-        return "TYPE_FP16"
-    elif np_dtype == np.float32:
-        return "TYPE_FP32"
-    elif np_dtype == np.float64:
-        return "TYPE_FP64"
-    elif np_dtype == np_dtype_string:
-        return "TYPE_STRING"
-    return None
-
-
-def np_to_trt_dtype(np_dtype):
-    if np_dtype == bool:
-        return trt.bool
-    elif np_dtype == np.int8:
-        return trt.int8
-    elif np_dtype == np.int32:
-        return trt.int32
-    elif np_dtype == np.float16:
-        return trt.float16
-    elif np_dtype == np.float32:
-        return trt.float32
-    return None
+from .gen_common import np_to_model_dtype, np_to_trt_dtype
 
 
 # The 'nonzero' model that we use for data dependent shape is naturally
diff --git a/qa/common/gen_qa_trt_format_models.py b/qa/common/gen_qa_trt_format_models.py
index 9502cdb972..518f42cc61 100755
--- a/qa/common/gen_qa_trt_format_models.py
+++ b/qa/common/gen_qa_trt_format_models.py
@@ -33,47 +33,9 @@
 import tensorrt as trt
 import test_util as tu
 
-np_dtype_string = np.dtype(object)
-
+from .gen_common import np_to_model_dtype, np_to_trt_dtype
 
-def np_to_model_dtype(np_dtype):
-    if np_dtype == bool:
-        return "TYPE_BOOL"
-    elif np_dtype == np.int8:
-        return "TYPE_INT8"
-    elif np_dtype == np.int16:
-        return "TYPE_INT16"
-    elif np_dtype == np.int32:
-        return "TYPE_INT32"
-    elif np_dtype == np.int64:
-        return "TYPE_INT64"
-    elif np_dtype == np.uint8:
-        return "TYPE_UINT8"
-    elif np_dtype == np.uint16:
-        return "TYPE_UINT16"
-    elif np_dtype == np.float16:
-        return "TYPE_FP16"
-    elif np_dtype == np.float32:
-        return "TYPE_FP32"
-    elif np_dtype == np.float64:
-        return "TYPE_FP64"
-    elif np_dtype == np_dtype_string:
-        return "TYPE_STRING"
-    return None
-
-
-def np_to_trt_dtype(np_dtype):
-    if np_dtype == bool:
-        return trt.bool
-    elif np_dtype == np.int8:
-        return trt.int8
-    elif np_dtype == np.int32:
-        return trt.int32
-    elif np_dtype == np.float16:
-        return trt.float16
-    elif np_dtype == np.float32:
-        return trt.float32
-    return None
+np_dtype_string = np.dtype(object)
 
 
 def trt_format_to_string(trt_format):
diff --git a/qa/common/gen_qa_trt_plugin_models.py b/qa/common/gen_qa_trt_plugin_models.py
index ce6b309f3d..bf4f2288e5 100755
--- a/qa/common/gen_qa_trt_plugin_models.py
+++ b/qa/common/gen_qa_trt_plugin_models.py
@@ -32,6 +32,8 @@
 import numpy as np
 import tensorrt as trt
 
+from .gen_common import np_to_model_dtype, np_to_trt_dtype
+
 np_dtype_string = np.dtype(object)
 
 TRT_LOGGER = trt.Logger()
@@ -40,46 +42,6 @@
 PLUGIN_CREATORS = trt.get_plugin_registry().plugin_creator_list
 
 
-def np_to_model_dtype(np_dtype):
-    if np_dtype == bool:
-        return "TYPE_BOOL"
-    elif np_dtype == np.int8:
-        return "TYPE_INT8"
-    elif np_dtype == np.int16:
-        return "TYPE_INT16"
-    elif np_dtype == np.int32:
-        return "TYPE_INT32"
-    elif np_dtype == np.int64:
-        return "TYPE_INT64"
-    elif np_dtype == np.uint8:
-        return "TYPE_UINT8"
-    elif np_dtype == np.uint16:
-        return "TYPE_UINT16"
-    elif np_dtype == np.float16:
-        return "TYPE_FP16"
-    elif np_dtype == np.float32:
-        return "TYPE_FP32"
-    elif np_dtype == np.float64:
-        return "TYPE_FP64"
-    elif np_dtype == np_dtype_string:
-        return "TYPE_STRING"
-    return None
-
-
-def np_to_trt_dtype(np_dtype):
-    if np_dtype == bool:
-        return trt.bool
-    elif np_dtype == np.int8:
-        return trt.int8
-    elif np_dtype == np.int32:
-        return trt.int32
-    elif np_dtype == np.float16:
-        return trt.float16
-    elif np_dtype == np.float32:
-        return trt.float32
-    return None
-
-
 def get_trt_plugin(plugin_name):
     plugin = None
     field_collection = None

From c6c8a8be33c0bdd29288afd786e6f70f634a6e57 Mon Sep 17 00:00:00 2001
From: Iman Tabrizian <itabrizian@nvidia.com>
Date: Wed, 20 Sep 2023 17:51:56 -0400
Subject: [PATCH 2/7] Fix codeql

---
 qa/common/gen_common.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/qa/common/gen_common.py b/qa/common/gen_common.py
index 9ef41a3a19..8d4ac2cbec 100644
--- a/qa/common/gen_common.py
+++ b/qa/common/gen_common.py
@@ -50,6 +50,7 @@ def np_to_onnx_dtype(np_dtype):
         return onnx.TensorProto.DOUBLE
     elif np_dtype == np_dtype_string:
         return onnx.TensorProto.STRING
+    return None
 
 
 def np_to_model_dtype(np_dtype):

From 5d44a31c5435b985e6eb78606d01eb13f156bee6 Mon Sep 17 00:00:00 2001
From: Iman Tabrizian <itabrizian@nvidia.com>
Date: Thu, 21 Sep 2023 11:29:53 -0400
Subject: [PATCH 3/7] Fix relative path import

---
 qa/common/gen_ensemble_model_utils.py             | 3 +--
 qa/common/gen_qa_dyna_sequence_implicit_models.py | 3 +--
 qa/common/gen_qa_dyna_sequence_models.py          | 3 +--
 qa/common/gen_qa_identity_models.py               | 3 +--
 qa/common/gen_qa_implicit_models.py               | 3 +--
 qa/common/gen_qa_models.py                        | 3 +--
 qa/common/gen_qa_noshape_models.py                | 3 +--
 qa/common/gen_qa_ragged_models.py                 | 3 +--
 qa/common/gen_qa_reshape_models.py                | 3 +--
 qa/common/gen_qa_sequence_models.py               | 3 +--
 qa/common/gen_qa_trt_data_dependent_shape.py      | 3 +--
 qa/common/gen_qa_trt_format_models.py             | 3 +--
 qa/common/gen_qa_trt_plugin_models.py             | 3 +--
 13 files changed, 13 insertions(+), 26 deletions(-)

diff --git a/qa/common/gen_ensemble_model_utils.py b/qa/common/gen_ensemble_model_utils.py
index 6528b51464..dd4f6e326a 100755
--- a/qa/common/gen_ensemble_model_utils.py
+++ b/qa/common/gen_ensemble_model_utils.py
@@ -30,8 +30,7 @@
 
 import numpy as np
 import test_util as tu
-
-from .gen_common import np_to_model_dtype
+from gen_common import np_to_model_dtype
 
 BASIC_ENSEMBLE_TYPES = ["simple", "sequence", "fan"]
 
diff --git a/qa/common/gen_qa_dyna_sequence_implicit_models.py b/qa/common/gen_qa_dyna_sequence_implicit_models.py
index b8cb4e59b3..ffa3f48ede 100755
--- a/qa/common/gen_qa_dyna_sequence_implicit_models.py
+++ b/qa/common/gen_qa_dyna_sequence_implicit_models.py
@@ -30,8 +30,7 @@
 import os
 
 import numpy as np
-
-from .gen_common import np_to_model_dtype, np_to_onnx_dtype, np_to_trt_dtype
+from gen_common import np_to_model_dtype, np_to_onnx_dtype, np_to_trt_dtype
 
 FLAGS = None
 np_dtype_string = np.dtype(object)
diff --git a/qa/common/gen_qa_dyna_sequence_models.py b/qa/common/gen_qa_dyna_sequence_models.py
index 27aef7d861..469d524ffb 100755
--- a/qa/common/gen_qa_dyna_sequence_models.py
+++ b/qa/common/gen_qa_dyna_sequence_models.py
@@ -30,8 +30,7 @@
 import os
 
 import numpy as np
-
-from .gen_common import (
+from gen_common import (
     np_to_model_dtype,
     np_to_onnx_dtype,
     np_to_tf_dtype,
diff --git a/qa/common/gen_qa_identity_models.py b/qa/common/gen_qa_identity_models.py
index f864ab8b10..60b045a09c 100755
--- a/qa/common/gen_qa_identity_models.py
+++ b/qa/common/gen_qa_identity_models.py
@@ -32,8 +32,7 @@
 
 import gen_ensemble_model_utils as emu
 import numpy as np
-
-from .gen_common import (
+from gen_common import (
     np_to_model_dtype,
     np_to_onnx_dtype,
     np_to_tf_dtype,
diff --git a/qa/common/gen_qa_implicit_models.py b/qa/common/gen_qa_implicit_models.py
index 3e17df23c8..89872c3b92 100755
--- a/qa/common/gen_qa_implicit_models.py
+++ b/qa/common/gen_qa_implicit_models.py
@@ -32,8 +32,7 @@
 
 import gen_ensemble_model_utils as emu
 import numpy as np
-
-from .gen_common import (
+from gen_common import (
     np_to_model_dtype,
     np_to_onnx_dtype,
     np_to_torch_dtype,
diff --git a/qa/common/gen_qa_models.py b/qa/common/gen_qa_models.py
index 961185fa37..82d241f470 100755
--- a/qa/common/gen_qa_models.py
+++ b/qa/common/gen_qa_models.py
@@ -32,8 +32,7 @@
 
 import gen_ensemble_model_utils as emu
 import numpy as np
-
-from .gen_common import (
+from gen_common import (
     np_to_model_dtype,
     np_to_onnx_dtype,
     np_to_tf_dtype,
diff --git a/qa/common/gen_qa_noshape_models.py b/qa/common/gen_qa_noshape_models.py
index 75e6396504..af26017495 100755
--- a/qa/common/gen_qa_noshape_models.py
+++ b/qa/common/gen_qa_noshape_models.py
@@ -31,8 +31,7 @@
 from builtins import range
 
 import numpy as np
-
-from .gen_common import np_to_model_dtype, np_to_tf_dtype
+from gen_common import np_to_model_dtype, np_to_tf_dtype
 
 FLAGS = None
 np_dtype_string = np.dtype(object)
diff --git a/qa/common/gen_qa_ragged_models.py b/qa/common/gen_qa_ragged_models.py
index c4292f9420..18d465dc94 100755
--- a/qa/common/gen_qa_ragged_models.py
+++ b/qa/common/gen_qa_ragged_models.py
@@ -30,8 +30,7 @@
 import os
 
 import numpy as np
-
-from .gen_common import (
+from gen_common import (
     np_to_model_dtype,
     np_to_onnx_dtype,
     np_to_tf_dtype,
diff --git a/qa/common/gen_qa_reshape_models.py b/qa/common/gen_qa_reshape_models.py
index fe34446786..4ac5347a79 100755
--- a/qa/common/gen_qa_reshape_models.py
+++ b/qa/common/gen_qa_reshape_models.py
@@ -32,8 +32,7 @@
 
 import gen_ensemble_model_utils as emu
 import numpy as np
-
-from .gen_common import (
+from gen_common import (
     np_to_model_dtype,
     np_to_onnx_dtype,
     np_to_tf_dtype,
diff --git a/qa/common/gen_qa_sequence_models.py b/qa/common/gen_qa_sequence_models.py
index c23c078f9c..4c9ca9d8e5 100755
--- a/qa/common/gen_qa_sequence_models.py
+++ b/qa/common/gen_qa_sequence_models.py
@@ -31,8 +31,7 @@
 
 import gen_ensemble_model_utils as emu
 import numpy as np
-
-from .gen_common import (
+from gen_common import (
     np_to_model_dtype,
     np_to_onnx_dtype,
     np_to_tf_dtype,
diff --git a/qa/common/gen_qa_trt_data_dependent_shape.py b/qa/common/gen_qa_trt_data_dependent_shape.py
index a0dc38083a..c6600ed919 100755
--- a/qa/common/gen_qa_trt_data_dependent_shape.py
+++ b/qa/common/gen_qa_trt_data_dependent_shape.py
@@ -32,8 +32,7 @@
 import numpy as np
 import tensorrt as trt
 import test_util as tu
-
-from .gen_common import np_to_model_dtype, np_to_trt_dtype
+from gen_common import np_to_model_dtype, np_to_trt_dtype
 
 
 # The 'nonzero' model that we use for data dependent shape is naturally
diff --git a/qa/common/gen_qa_trt_format_models.py b/qa/common/gen_qa_trt_format_models.py
index 518f42cc61..e077139aec 100755
--- a/qa/common/gen_qa_trt_format_models.py
+++ b/qa/common/gen_qa_trt_format_models.py
@@ -32,8 +32,7 @@
 import numpy as np
 import tensorrt as trt
 import test_util as tu
-
-from .gen_common import np_to_model_dtype, np_to_trt_dtype
+from gen_common import np_to_model_dtype, np_to_trt_dtype
 
 np_dtype_string = np.dtype(object)
 
diff --git a/qa/common/gen_qa_trt_plugin_models.py b/qa/common/gen_qa_trt_plugin_models.py
index bf4f2288e5..10c9d5284a 100755
--- a/qa/common/gen_qa_trt_plugin_models.py
+++ b/qa/common/gen_qa_trt_plugin_models.py
@@ -31,8 +31,7 @@
 
 import numpy as np
 import tensorrt as trt
-
-from .gen_common import np_to_model_dtype, np_to_trt_dtype
+from gen_common import np_to_model_dtype, np_to_trt_dtype
 
 np_dtype_string = np.dtype(object)
 

From 5726204bda6edb0646090bad98a20a57d9a74b5c Mon Sep 17 00:00:00 2001
From: Iman Tabrizian <itabrizian@nvidia.com>
Date: Thu, 21 Sep 2023 11:43:37 -0400
Subject: [PATCH 4/7] Fix package structure

---
 qa/common/__init__.py   | 25 +++++++++++++++++++++++++
 qa/common/gen_common.py | 13 +++++++++++++
 2 files changed, 38 insertions(+)
 create mode 100644 qa/common/__init__.py

diff --git a/qa/common/__init__.py b/qa/common/__init__.py
new file mode 100644
index 0000000000..f3a6dc3f00
--- /dev/null
+++ b/qa/common/__init__.py
@@ -0,0 +1,25 @@
+# Copyright 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#  * Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#  * Redistributions in binary form must reproduce the above copyright
+#    notice, this list of conditions and the following disclaimer in the
+#    documentation and/or other materials provided with the distribution.
+#  * Neither the name of NVIDIA CORPORATION nor the names of its
+#    contributors may be used to endorse or promote products derived
+#    from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+# PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/qa/common/gen_common.py b/qa/common/gen_common.py
index 8d4ac2cbec..d2968ed178 100644
--- a/qa/common/gen_common.py
+++ b/qa/common/gen_common.py
@@ -25,9 +25,14 @@
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 # Common utilities for model generation scripts
+import numpy as np
+
+np_dtype_string = np.dtype(object)
 
 
 def np_to_onnx_dtype(np_dtype):
+    import onnx
+
     if np_dtype == bool:
         return onnx.TensorProto.BOOL
     elif np_dtype == np.int8:
@@ -54,6 +59,8 @@ def np_to_onnx_dtype(np_dtype):
 
 
 def np_to_model_dtype(np_dtype):
+    import numpy as np
+
     if np_dtype == bool:
         return "TYPE_BOOL"
     elif np_dtype == np.int8:
@@ -80,6 +87,8 @@ def np_to_model_dtype(np_dtype):
 
 
 def np_to_trt_dtype(np_dtype):
+    import tensorrt as trt
+
     if np_dtype == bool:
         return trt.bool
     elif np_dtype == np.int8:
@@ -94,6 +103,8 @@ def np_to_trt_dtype(np_dtype):
 
 
 def np_to_tf_dtype(np_dtype):
+    import tensorflow as tf
+
     if np_dtype == bool:
         return tf.bool
     elif np_dtype == np.int8:
@@ -120,6 +131,8 @@ def np_to_tf_dtype(np_dtype):
 
 
 def np_to_torch_dtype(np_dtype):
+    import torch
+
     if np_dtype == bool:
         return torch.bool
     elif np_dtype == np.int8:

From 56a22ac1ec6bfb56f29308837b0f03a69f56e613 Mon Sep 17 00:00:00 2001
From: Iman Tabrizian <itabrizian@nvidia.com>
Date: Thu, 21 Sep 2023 11:46:21 -0400
Subject: [PATCH 5/7] Copy the gen_common file

---
 qa/common/__init__.py             | 25 -------------------------
 qa/common/gen_qa_model_repository |  1 +
 2 files changed, 1 insertion(+), 25 deletions(-)
 delete mode 100644 qa/common/__init__.py

diff --git a/qa/common/__init__.py b/qa/common/__init__.py
deleted file mode 100644
index f3a6dc3f00..0000000000
--- a/qa/common/__init__.py
+++ /dev/null
@@ -1,25 +0,0 @@
-# Copyright 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions
-# are met:
-#  * Redistributions of source code must retain the above copyright
-#    notice, this list of conditions and the following disclaimer.
-#  * Redistributions in binary form must reproduce the above copyright
-#    notice, this list of conditions and the following disclaimer in the
-#    documentation and/or other materials provided with the distribution.
-#  * Neither the name of NVIDIA CORPORATION nor the names of its
-#    contributors may be used to endorse or promote products derived
-#    from this software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
-# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
-# PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
-# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
-# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
-# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
-# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
-# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/qa/common/gen_qa_model_repository b/qa/common/gen_qa_model_repository
index 85db0730b2..55bef11c46 100755
--- a/qa/common/gen_qa_model_repository
+++ b/qa/common/gen_qa_model_repository
@@ -154,6 +154,7 @@ cp ./gen_qa_ragged_models.py $HOST_SRCDIR/.
 cp ./test_util.py $HOST_SRCDIR/.
 cp ./gen_tag_sigdef.py $HOST_SRCDIR/.
 cp ./gen_qa_tf_parameters.py $HOST_SRCDIR/.
+cp ./gen_common.py $HOST_SRCDIR/.
 
 ONNXSCRIPT=onnx_gen.cmds
 OPENVINOSCRIPT=openvino_gen.cmds

From 250f4f698c870d6f36be52e5c6e3ad98a1dcbe5c Mon Sep 17 00:00:00 2001
From: Iman Tabrizian <itabrizian@nvidia.com>
Date: Thu, 21 Sep 2023 11:55:15 -0400
Subject: [PATCH 6/7] Add missing uint8

---
 qa/common/gen_common.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/qa/common/gen_common.py b/qa/common/gen_common.py
index d2968ed178..0ac87d5fd5 100644
--- a/qa/common/gen_common.py
+++ b/qa/common/gen_common.py
@@ -95,6 +95,8 @@ def np_to_trt_dtype(np_dtype):
         return trt.int8
     elif np_dtype == np.int32:
         return trt.int32
+    elif np_dtype == np.uint8:
+        return trt.uint8
     elif np_dtype == np.float16:
         return trt.float16
     elif np_dtype == np.float32:

From 760da92390cf27aacab205b5d17edcb1c2f7e0e4 Mon Sep 17 00:00:00 2001
From: Iman Tabrizian <itabrizian@nvidia.com>
Date: Mon, 25 Sep 2023 10:13:22 -0400
Subject: [PATCH 7/7] Remove duplicate import

---
 qa/common/gen_common.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/qa/common/gen_common.py b/qa/common/gen_common.py
index 0ac87d5fd5..8bd97720b5 100644
--- a/qa/common/gen_common.py
+++ b/qa/common/gen_common.py
@@ -59,8 +59,6 @@ def np_to_onnx_dtype(np_dtype):
 
 
 def np_to_model_dtype(np_dtype):
-    import numpy as np
-
     if np_dtype == bool:
         return "TYPE_BOOL"
     elif np_dtype == np.int8: