[Tutorial] Add a tutorial for PyTorch (#4936)

* Add a tutorial for PyTorch

* Fix sphinx formatting, add version support

* Remove space

* Remove version check

* Some refactoring

* Use no grad

* Rename input

* Update cat img source

tutorials/frontend/from_pytorch.py

+# 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.
+"""
+Compile PyTorch Models
+======================
+**Author**: `Alex Wong <https://github.com/alexwong/>`_
+
+This article is an introductory tutorial to deploy PyTorch models with Relay.
+
+For us to begin with, PyTorch should be installed.
+TorchVision is also required since we will be using it as our model zoo.
+
+A quick solution is to install via pip
+
+.. code-block:: bash
+
+    pip install torch==1.4.0
+    pip install torchvision==0.5.0
+
+or please refer to official site
+https://pytorch.org/get-started/locally/
+
+PyTorch versions should be backwards compatible but should be used
+with the proper TorchVision version.
+
+Currently, TVM supports PyTorch 1.4, 1.3, and 1.2. Other versions may
+be unstable.
+"""
+
+# tvm, relay
+import tvm
+from tvm import relay
+
+# numpy, packaging
+import numpy as np
+from packaging import version
+from tvm.contrib.download import download_testdata
+
+# PyTorch imports
+import torch
+import torchvision
+
+######################################################################
+# Load a pretrained PyTorch model
+# -------------------------------
+model_name = 'resnet18'
+model = getattr(torchvision.models, model_name)(pretrained=True)
+model = model.eval()
+
+# We grab the TorchScripted model via tracing
+input_shape = [1, 3, 224, 224]
+input_data = torch.randn(input_shape)
+scripted_model = torch.jit.trace(model, input_data).eval()
+
+######################################################################
+# Load a test image
+# -----------------
+# Classic cat example!
+from PIL import Image
+img_url = 'https://github.com/dmlc/mxnet.js/blob/master/data/cat.png?raw=true'
+img_path = download_testdata(img_url, 'cat.png', module='data')
+img = Image.open(img_path).resize((224, 224))
+
+# Preprocess the image and convert to tensor
+from torchvision import transforms
+my_preprocess = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                         std=[0.229, 0.224, 0.225])
+])
+img = my_preprocess(img)
+img = np.expand_dims(img, 0)
+
+######################################################################
+# Import the graph to Relay
+# -------------------------
+# Convert PyTorch graph to Relay graph.
+shape_dict = {'img': img.shape}
+mod, params = relay.frontend.from_pytorch(scripted_model,
+                                          shape_dict)
+
+######################################################################
+# Relay Build
+# -----------
+# Compile the graph to llvm target with given input specification.
+target = 'llvm'
+target_host = 'llvm'
+ctx = tvm.cpu(0)
+with relay.build_config(opt_level=3):
+    graph, lib, params = relay.build(mod,
+                                     target=target,
+                                     target_host=target_host,
+                                     params=params)
+
+######################################################################
+# Execute the portable graph on TVM
+# ---------------------------------
+# Now we can try deploying the compiled model on target.
+from tvm.contrib import graph_runtime
+dtype = 'float32'
+m = graph_runtime.create(graph, lib, ctx)
+# Set inputs
+m.set_input('img', tvm.nd.array(img.astype(dtype)))
+m.set_input(**params)
+# Execute
+m.run()
+# Get outputs
+tvm_output = m.get_output(0, tvm.nd.empty(((1, 1000)), 'float32'))
+
+#####################################################################
+# Look up synset name
+# -------------------
+# Look up prediction top 1 index in 1000 class synset.
+synset_url = ''.join(['https://raw.githubusercontent.com/Cadene/',
+                      'pretrained-models.pytorch/master/data/',
+                      'imagenet_synsets.txt'])
+synset_name = 'imagenet_synsets.txt'
+synset_path = download_testdata(synset_url, synset_name, module='data')
+with open(synset_path) as f:
+    synsets = f.readlines()
+
+synsets = [x.strip() for x in synsets]
+splits = [line.split(' ') for line in synsets]
+key_to_classname = {spl[0]:' '.join(spl[1:]) for spl in splits}
+
+class_url = ''.join(['https://raw.githubusercontent.com/Cadene/',
+                      'pretrained-models.pytorch/master/data/',
+                      'imagenet_classes.txt'])
+class_name = 'imagenet_classes.txt'
+class_path = download_testdata(class_url, class_name, module='data')
+with open(class_path) as f:
+    class_id_to_key = f.readlines()
+
+class_id_to_key = [x.strip() for x in class_id_to_key]
+
+# Get top-1 result for TVM
+top1_tvm = np.argmax(tvm_output.asnumpy()[0])
+tvm_class_key = class_id_to_key[top1_tvm]
+
+# Convert input to PyTorch variable and get PyTorch result for comparison
+with torch.no_grad():
+    torch_img = torch.from_numpy(img)
+    output = model(torch_img)
+
+    # Get top-1 result for PyTorch
+    top1_torch = np.argmax(output.numpy())
+    torch_class_key = class_id_to_key[top1_torch]
+
+print('Relay top-1 id: {}, class name: {}'.format(top1_tvm, key_to_classname[tvm_class_key]))
+print('Torch top-1 id: {}, class name: {}'.format(top1_torch, key_to_classname[torch_class_key]))
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