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Subject [GitHub] [incubator-tvm] anijain2305 commented on a change in pull request #4977: [Torch, QNN] Add support for quantized models via QNN
Date Mon, 02 Mar 2020 18:25:25 GMT
anijain2305 commented on a change in pull request #4977: [Torch, QNN] Add support for quantized
models via QNN
URL: https://github.com/apache/incubator-tvm/pull/4977#discussion_r386565439
 
 

 ##########
 File path: python/tvm/relay/frontend/qnn_torch.py
 ##########
 @@ -0,0 +1,675 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name, import-outside-toplevel
+""" Functions to convert quantized torch models to QNN """
+
+import numpy as np
+
+import tvm
+from tvm import relay
+from tvm.relay import expr as _expr
+from tvm.relay import op as _op
+from tvm.relay.frontend.common import infer_shape
+
+
+class QuantParam:
 
 Review comment:
   For consistency, I would suggest using QNN instead of quant wherever possible. We tried
to do the same in TFLite and MXNet parser.

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