[GitHub] [incubator-tvm] masahi commented on a change in pull request #4667: [Tutorial] Deploy Quantized Model on CUDA

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masahi commented on a change in pull request #4667: [Tutorial] Deploy Quantized Model on CUDA
URL: https://github.com/apache/incubator-tvm/pull/4667#discussion_r364966229
 
 

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 File path: tutorials/frontend/deploy_quantized.py
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+# 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.
+"""
+Deploy a Quantized Model on Cuda
+================================
+**Author**: `Wuwei Lin <https://github.com/vinx13>`_
+
+This article is an introductory tutorial of automatic quantization with TVM.
+We will import a GluonCV pre-trained model on ImageNet to Relay, quantize the Relay model
+and then perform the inference.
+"""
+
+import tvm
+from tvm import relay
+from tvm.relay import quantize as qtz
+import mxnet as mx
+from tvm.contrib.download import download_testdata
+from mxnet import gluon
+import logging
+import os
+
+batch_size = 1
+model_name = "resnet18_v1"
+target = 'cuda'
+ctx = tvm.context(target)
+
+###############################################################################
+# Prepare the Dataset
+# -------------------
+# We will demonstrate how to prepare the calibration dataset for quantization.
+# We first download the validate set of ImageNet and pre-process the dataset.
+calibration_rec = download_testdata(
+    'http://data.mxnet.io.s3-website-us-west-1.amazonaws.com/data/val_256_q90.rec',
+    'val_256_q90.rec')
+
+def get_val_data(num_workers=4):
+    mean_rgb = [123.68, 116.779, 103.939]
+    std_rgb = [58.393, 57.12, 57.375]
+
+    def batch_fn(batch):
+        return batch.data[0].asnumpy(), batch.label[0].asnumpy()
+
+    img_size = 299 if model_name == 'inceptionv3' else 224
+    val_data = mx.io.ImageRecordIter(
+        path_imgrec=calibration_rec,
+        preprocess_threads=num_workers,
+        shuffle=False,
+        batch_size=batch_size,
+        resize=256,
+        data_shape=(3, img_size, img_size),
+        mean_r=mean_rgb[0],
+        mean_g=mean_rgb[1],
+        mean_b=mean_rgb[2],
+        std_r=std_rgb[0],
+        std_g=std_rgb[1],
+        std_b=std_rgb[2],
+    )
+    return val_data, batch_fn
+
+
+###############################################################################
+# The calibration dataset should be a iterable object. We define the
+# calibration dataset as a generator object in Python. In this tutorials, we
+# only use a few samples for calibration.
+
+calibration_samples = 10
+
+def calibrate_dataset():
+    val_data, batch_fn = get_val_data()
+    val_data.reset()
+    for i, batch in enumerate(val_data):
+        if i * batch_size >= calibration_samples:
+            break
+        data, _ = batch_fn(batch)
+        yield {'data': data}
+
+
+###############################################################################
+# Import the model
+# ----------------
+# We use the Relay MxNet frontent to import a model from the Gluon model zoo.
 
 Review comment:
   frontend 
   

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