From 5847ed3ac97af806e8cf3f8ba12c3479393dbc50 Mon Sep 17 00:00:00 2001
From: Tianqi Chen <tqchen@users.noreply.github.com>
Date: Mon, 11 Mar 2019 21:21:40 -0700
Subject: [PATCH] [DOCS] Cleanup the relay docs location (#2785)

---
 .../deploy_model_on_rasp.py                   |  0
 .../{relay => frontend}/using_external_lib.py |  0
 tutorials/relay/from_mxnet.py                 | 86 -------------------
 3 files changed, 86 deletions(-)
 rename tutorials/{relay => frontend}/deploy_model_on_rasp.py (100%)
 rename tutorials/{relay => frontend}/using_external_lib.py (100%)
 delete mode 100644 tutorials/relay/from_mxnet.py

diff --git a/tutorials/relay/deploy_model_on_rasp.py b/tutorials/frontend/deploy_model_on_rasp.py
similarity index 100%
rename from tutorials/relay/deploy_model_on_rasp.py
rename to tutorials/frontend/deploy_model_on_rasp.py
diff --git a/tutorials/relay/using_external_lib.py b/tutorials/frontend/using_external_lib.py
similarity index 100%
rename from tutorials/relay/using_external_lib.py
rename to tutorials/frontend/using_external_lib.py
diff --git a/tutorials/relay/from_mxnet.py b/tutorials/relay/from_mxnet.py
deleted file mode 100644
index dff921780314..000000000000
--- a/tutorials/relay/from_mxnet.py
+++ /dev/null
@@ -1,86 +0,0 @@
-"""
-.. _tutorial-from-mxnet:
-
-Compile MXNet Models
-====================
-**Author**: `Joshua Z. Zhang <https://zhreshold.github.io/>`_, `Eddie Yan <https://github.com/eqy>`_
-
-This article is an introductory tutorial to deploy mxnet models with Relay.
-
-For us to begin with, mxnet module is required to be installed.
-
-A quick solution is
-
-.. code-block:: bash
-
-    pip install mxnet --user
-
-or please refer to offical installation guide.
-https://mxnet.incubator.apache.org/versions/master/install/index.html
-"""
-# some standard imports
-import mxnet as mx
-from tvm import relay
-import tvm
-import numpy as np
-
-######################################################################
-# Download Resnet18 model from Gluon Model Zoo
-# ---------------------------------------------
-# In this section, we download a pretrained imagenet model and classify an image.
-from mxnet.gluon.model_zoo.vision import get_model
-from mxnet.gluon.utils import download
-from PIL import Image
-from matplotlib import pyplot as plt
-block = get_model('resnet18_v1', pretrained=True)
-
-img_name = 'cat.png'
-synset_url = ''.join(['https://gist.githubusercontent.com/zhreshold/',
-                      '4d0b62f3d01426887599d4f7ede23ee5/raw/',
-                      '596b27d23537e5a1b5751d2b0481ef172f58b539/',
-                      'imagenet1000_clsid_to_human.txt'])
-synset_name = 'synset.txt'
-download('https://github.com/dmlc/mxnet.js/blob/master/data/cat.png?raw=true', img_name)
-download(synset_url, synset_name)
-with open(synset_name) as f:
-    synset = eval(f.read())
-image = Image.open(img_name).resize((224, 224))
-plt.imshow(image)
-plt.show()
-
-def transform_image(image):
-    image = np.array(image) - np.array([123., 117., 104.])
-    image /= np.array([58.395, 57.12, 57.375])
-    image = image.transpose((2, 0, 1))
-    image = image[np.newaxis, :]
-    return image
-
-x = transform_image(image)
-print('x', x.shape)
-
-######################################################################
-# Compile the Graph
-# -----------------
-# Now we would like to port the Gluon model to a portable computational graph.
-# It's as easy as several lines.
-# We support MXNet static graph(symbol) and HybridBlock in mxnet.gluon
-input_shape = (1, 3, 224, 224)
-dtype = 'float32'
-net, params = relay.frontend.from_mxnet(block, shape={'data': input_shape}, dtype=dtype)
-# we want a probability so add a softmax operator
-net = relay.Function(net.params, relay.nn.softmax(net.body), None, net.type_params, net.attrs)
-
-######################################################################
-# now compile the graph
-target = 'cuda'
-shape_dict = {'data': x.shape}
-with relay.build_config(opt_level=3):
-    intrp = relay.build_module.create_executor('graph', net, tvm.gpu(0), target) 
-
-######################################################################
-# Execute the portable graph on TVM
-# ---------------------------------
-# Now, we would like to reproduce the same forward computation using TVM.
-tvm_output = intrp.evaluate(net)(tvm.nd.array(x.astype(dtype)), **params)
-top1 = np.argmax(tvm_output.asnumpy()[0])
-print('TVM prediction top-1:', top1, synset[top1])