Add oneflow fronted tutorials (apache#11036)

* add relay.f.frontend.fm_oneflow support cnns

* support cuda

* fix mobilenetv2 and reviews

* fix: model without meta info

* support eager and yolo, add test

* fix: license

* add: tutorials

* fix: support new graph

* fix some comments

* refine

* fix concat op convert bug

* refine

* refine

* change cuda to cpu

* fix bug

* fix ci error in tvm

* fix pylint check

* delete useless file

* add skimage package in docker

* fix ci error

* fix bug

* add oneflow fronted test in ci

* merge conflict

* fix tutorial

* try to find error in ci

* revert

* merge conflict

* black oneflow

* Delete from_oneflow.py

* fix bug when upgrade oneflow to 0.7.0

* add tutorials

* add tutorials

* try to fix

* fix bug

* add test

* fix bug

* fix flowvision bug

* Update test_forward.py

* Update test_forward.py

Co-authored-by: hhhfccz <[email protected]>

gallery/how_to/compile_models/from_oneflow.py

@@ -0,0 +1,177 @@
+# 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 OneFlow Models
+======================
+**Author**: `Xiaoyu Zhang <https://github.com/BBuf/>`_
+
+This article is an introductory tutorial to deploy OneFlow models with Relay.
+
+For us to begin with, OneFlow package should be installed.
+
+A quick solution is to install via pip
+
+.. code-block:: bash
+
+    pip install flowvision==0.1.0
+    python3 -m pip install -f https://release.oneflow.info oneflow==0.7.0+cpu
+
+or please refer to official site:
+https://github.com/Oneflow-Inc/oneflow
+
+Currently, TVM supports OneFlow 0.7.0. Other versions may be unstable.
+"""
+import os, math
+from matplotlib import pyplot as plt
+import numpy as np
+from PIL import Image
+
+# oneflow imports
+import flowvision
+import oneflow as flow
+import oneflow.nn as nn
+
+import tvm
+from tvm import relay
+from tvm.contrib.download import download_testdata
+
+######################################################################
+# Load a pretrained OneFlow model and save model
+# ----------------------------------------------
+model_name = "resnet18"
+model = getattr(flowvision.models, model_name)(pretrained=True)
+model = model.eval()
+
+model_dir = "resnet18_model"
+if not os.path.exists(model_dir):
+    flow.save(model.state_dict(), model_dir)
+
+######################################################################
+# Load a test image
+# -----------------
+# Classic cat example!
+from PIL import Image
+
+img_url = "https://github.com/dmlc/mxnet.js/blob/main/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 flowvision 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.numpy(), 0)
+
+######################################################################
+# Import the graph to Relay
+# -------------------------
+# Convert OneFlow graph to Relay graph. The input name can be arbitrary.
+class Graph(flow.nn.Graph):
+    def __init__(self, module):
+        super().__init__()
+        self.m = module
+
+    def build(self, x):
+        out = self.m(x)
+        return out
+
+
+graph = Graph(model)
+_ = graph._compile(flow.randn(1, 3, 224, 224))
+
+mod, params = relay.frontend.from_oneflow(graph, model_dir)
+
+######################################################################
+# Relay Build
+# -----------
+# Compile the graph to llvm target with given input specification.
+target = tvm.target.Target("llvm", host="llvm")
+dev = tvm.cpu(0)
+with tvm.transform.PassContext(opt_level=3):
+    lib = relay.build(mod, target=target, params=params)
+
+######################################################################
+# Execute the portable graph on TVM
+# ---------------------------------
+# Now we can try deploying the compiled model on target.
+target = "cuda"
+with tvm.transform.PassContext(opt_level=10):
+    intrp = relay.build_module.create_executor("graph", mod, tvm.cuda(0), target)
+
+print(type(img))
+print(img.shape)
+tvm_output = intrp.evaluate()(tvm.nd.array(img.astype("float32")), **params)
+
+#####################################################################
+# 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.numpy()[0])
+tvm_class_key = class_id_to_key[top1_tvm]
+
+# Convert input to OneFlow variable and get OneFlow result for comparison
+with flow.no_grad():
+    torch_img = flow.from_numpy(img)
+    output = model(torch_img)
+
+    # Get top-1 result for OneFlow
+    top_oneflow = np.argmax(output.numpy())
+    oneflow_class_key = class_id_to_key[top_oneflow]
+
+print("Relay top-1 id: {}, class name: {}".format(top1_tvm, key_to_classname[tvm_class_key]))
+print(
+    "OneFlow top-1 id: {}, class name: {}".format(top_oneflow, key_to_classname[oneflow_class_key])
+)

tests/python/frontend/oneflow/test_forward.py

@@ -710,14 +710,14 @@ def forward(self, x1, x2, x3):
         verify_concat(model, device=device)
 
 
-# if __name__ == "__main__":
-# test_conv2d()
-# test_pool2d()
-# test_normalization()
-# test_upsample()
-# test_convtran()
-# test_activation()
-# test_math()
-# test_slice()
-# test_concat()
-# rmdir("log")
+if __name__ == "__main__":
+    test_conv2d()
+    test_pool2d()
+    test_normalization()
+    test_upsample()
+    test_convtran()
+    test_activation()
+    test_math()
+    test_slice()
+    test_concat()
+    rmdir("log")