[RELAY][FRONTEND]Onnx to relay frontend (#2302)

docs/install/from_source.rst

@@ -35,7 +35,7 @@ Our goal is to build the shared libraries:
 .. code:: bash
 
     sudo apt-get update
-    sudo apt-get install -y python python-dev python-setuptools gcc libtinfo-dev zlib1g-dev
+    sudo apt-get install -y python python-dev python-setuptools gcc libtinfo-dev zlib1g-dev build-essential cmake
 
 The minimal building requirements are
 

nnvm/tests/python/frontend/onnx/test_forward.py

@@ -910,7 +910,7 @@ def test_single_ops():
         model = helper.make_model(graph, producer_name='_test')
         for target, ctx in ctx_list():
             tvm_out = get_tvm_output(model, [x], target, ctx)
-            tvm.testing.assert_allclose(out_np, tvm_out, rtol=1e-5, atol=1e-5)
+            tvm.testing.assert_allclose(out_np, tvm_out, rtol=rtol, atol=atol)
 
     x = np.random.uniform(size=in_shape).astype(dtype)
     verify_single_ops("Neg",x, -x)
@@ -918,13 +918,13 @@ def test_single_ops():
     verify_single_ops("Reciprocal",x, 1/x, rtol=1e-5, atol=1e-5)
     verify_single_ops("Sqrt",x, np.sqrt(x), rtol=1e-5, atol=1e-5)
     verify_single_ops("Relu",x, np.maximum(x, 0))
-    verify_single_ops("Exp",x, np.exp(x))
-    verify_single_ops("Log",x, np.log(x))
-    verify_single_ops("Log",x, np.log(x))
-    verify_single_ops("Tanh",x, np.tanh(x))
-    verify_single_ops("Sigmoid",x, 1 / (1 + np.exp(-x)))
-    verify_single_ops("Softsign",x, x / (1 + np.abs(x)))
-    verify_single_ops("SoftPlus",x, np.log(1 + np.exp(x)))
+    verify_single_ops("Exp",x, np.exp(x), rtol=1e-5, atol=1e-5)
+    verify_single_ops("Log",x, np.log(x), rtol=1e-5, atol=1e-5)
+    verify_single_ops("Log",x, np.log(x), rtol=1e-5, atol=1e-5)
+    verify_single_ops("Tanh",x, np.tanh(x), rtol=1e-5, atol=1e-5)
+    verify_single_ops("Sigmoid",x, 1 / (1 + np.exp(-x)), rtol=1e-5, atol=1e-5)
+    verify_single_ops("Softsign",x, x / (1 + np.abs(x)), rtol=1e-5, atol=1e-5)
+    verify_single_ops("SoftPlus",x, np.log(1 + np.exp(x)), rtol=1e-5, atol=1e-5)
 
 def test_leaky_relu():
     def leaky_relu_x(x, alpha):

python/tvm/relay/expr.py

@@ -465,6 +465,14 @@ def const(value, dtype=None):
     """
     if isinstance(value, (_base.numeric_types, (bool, list))):
         value = _np.array(value, dtype=dtype)
+    if not dtype:
+        # when dtype is None: int maps to "int32", float maps to "float32"
+        map_dtype = {
+            _np.dtype('int64'): _np.int32,
+            _np.dtype('float64'): _np.float32
+            }.get(value.dtype, None)
+        if map_dtype:
+            value = value.astype(map_dtype)
     if isinstance(value, (_np.ndarray, _np.generic)):
         value = _nd.array(value)
 

python/tvm/relay/frontend/__init__.py

@@ -9,3 +9,4 @@ from __future__ import absolute_import
 
 from .mxnet import from_mxnet
 from .keras import from_keras
+from .onnx import from_onnx

python/tvm/relay/frontend/common.py

@@ -1,6 +1,11 @@
 """Common utilities"""
 from __future__ import absolute_import as _abs
+import logging
+from topi.util import get_const_tuple
 from .. import expr as _expr
+from .. import expr as _expr
+from .. import ir_pass
+from .. import op as _op
 
 
 class RequiredAttr(object):
@@ -204,6 +209,30 @@ class StrAttrsDict(object):
             raise AttributeError("Required attribute {} not found.".format(key))
         return default
 
+def get_relay_op(op_name):
+    """Get the callable function from Relay based on operator name.
+    Parameters
+    ----------
+    op_name : str
+        The Relay operator name.
+    """
+    if '.' in op_name:
+        # explicit hierachical modules
+        op = _op
+        try:
+            for opn in op_name.split('.'):
+                op = getattr(op, opn)
+        except AttributeError:
+            op = None
+    else:
+        # try search op in various modules
+        for candidate in (_op, _op.nn, _op.image):
+            op = getattr(candidate, op_name, None)
+            if op is not None:
+                break
+    if not op:
+        raise RuntimeError("Unable to map op_name {} to relay".format(op_name))
+    return op
 
 class ExprTable(object):
     """Table storing Relay expressions by names."""
@@ -227,3 +256,156 @@ class ExprTable(object):
     def set_expr(self, name, expr):
         assert isinstance(expr, _expr.Expr)
         self.exprs[name] = expr
+
+
+class AttrCvt(object):
+    """Common attribute conveter. An AttrConverter instance is a callable:
+    ```
+    attr_converter = AttrConverter(op_name, transforms={'a':'b', 'c':('d', 1)})
+    new_op_name, new_attr = attr_converter(attrs)
+    ```
+
+    Parameters
+    ----------
+    op_name : str or callable
+        If set as str, returned operator name is the str.
+        If set as callable, returned operator is the str returned by calling:
+        `op_name = func(attr)`
+    transforms : dict of `new_name, or (new_name, default_value, transform function)`
+        If only a new_name is provided, it's like renaming the attribute name.
+        If default_value if provded, then the attribute is considered as optional.
+        If transform function is provided, the original attribute value is handled
+        by transform function.
+    excludes : list
+        A list of excluded attributes that should `NOT` appear.
+        Raise NotImplementedError if occured.
+    disables : list
+        A list of attributes that is disabled in relay. Log warnings.
+    ignores : list
+        A list of attributes that is ignored in relay. Debug level logging.
+    extras : dict
+        A series of additional attributes should be added anyway to the returned
+        attribute dict.
+    custom_check : callable
+        A custom function takes attribute, and return True/False.
+        Raise RuntimeError if not bool(True) returned.
+    """
+    def __init__(self, op_name, transforms=None,
+                 excludes=None, disables=None, ignores=None,
+                 extras=None, custom_check=None):
+        self._op_name = op_name
+        self._transforms = transforms if transforms else {}
+        self._excludes = excludes if excludes else []
+        self._disables = disables if disables else []
+        self._ignores = ignores if ignores else []
+        self._extras = extras if extras else {}
+        self._custom_check = custom_check
+
+    def __call__(self, inputs, attrs, *args):
+        # apply custom check
+        if self._custom_check:
+            func, msg = self._custom_check
+            if not func(attrs):
+                raise RuntimeError("Check failed: {}".format(msg))
+        # get new op_name
+        if isinstance(self._op_name, str):
+            op_name = self._op_name
+        else:
+            assert callable(self._op_name), "op_name can either be string or callable"
+            op_name = self._op_name(attrs)
+        # convert attributes
+        new_attrs = {}
+        for k in attrs.keys():
+            if k in self._excludes:
+                raise NotImplementedError("Attribute {} not supported yet.".format(k))
+            elif k in self._disables:
+                logging.warning("Attribute %s is disabled in relay.sym.%s", k, op_name)
+            elif k in self._ignores:
+                logging.debug("Attribute %s is ignored in relay.sym.%s", k, op_name)
+            elif k in self._transforms:
+                new_name, defaults, transform = self._parse_default(self._transforms[k])
+                if defaults is None:
+                    new_attr = self._required_attr(attrs, k)
+                else:
+                    new_attr = attrs.get(k, None)
+                if new_attr is None:
+                    new_attrs[new_name] = defaults
+                else:
+                    new_attrs[new_name] = transform(new_attr)
+            else:
+                # copy
+                new_attrs[k] = attrs[k]
+        # add extras
+        new_attrs.update(self._extras)
+        return get_relay_op(op_name)(*inputs, **new_attrs)
+
+    def _parse_default(self, target):
+        """Helper function to parse default values."""
+        if not isinstance(target, (list, tuple)):
+            k, v, t = target, None, lambda x: x
+        elif len(target) == 1:
+            k, v, t = target[0], None, lambda x: x
+        elif len(target) == 2:
+            k, v, t = target[0], target[1], lambda x: x
+        elif len(target) > 2:
+            k, v, t = target[0], target[1], target[2]
+        else:
+            k = None  # should raise
+        if not isinstance(k, str):
+            msg = "{} is not a valid target, (name, default) expected.".format(target)
+            raise ValueError(msg)
+        return k, v, t
+
+    def _parse_bool(self, value):
+        """Helper function to parse default boolean values."""
+        if isinstance(value, str):
+            return value.strip().lower() in ['true', '1', 't', 'y', 'yes']
+        return bool(value)
+
+    def _required_attr(self, attr, key):
+        """Wrapper for getting required attributes."""
+        assert isinstance(attr, dict)
+        if key not in attr:
+            raise AttributeError("Required attribute {} not found.".format(key))
+        return attr[key]
+
+def get_name(node):
+    name = ''
+    if hasattr(node, "name_hint"):
+        name = node.name_hint
+    return name
+
+def infer_shape(inputs):
+    """A method to get the output shape of an intermediate node in the graph."""
+    out_type = ir_pass.infer_type(inputs)
+    out_shapes = get_const_tuple(out_type.checked_type.shape)
+    return out_shapes
+
+def infer_channels(inputs, transpose=False):
+    """A hack for getting 'channels' or 'units' since caffe2 does not provide
+    these attributes. We check the shape of weights provided to get the number.
+    """
+    out_type = ir_pass.infer_type(inputs)
+    out_shapes = [get_const_tuple(out_type.checked_type.shape)]
+    channels = out_shapes[0][0] if not transpose else out_shapes[0][1]
+    return channels
+
+def new_var(name_hint,
+            type_annotation=None,
+            shape=None,
+            dtype="float32"):
+    return _expr.var(name_hint, type_annotation, shape, dtype)
+
+class Renamer(object):
+    """A simply renamer for operators.
+
+    Parameters
+    ----------
+    new_name : str
+        The new name for the operator
+    """
+    def __init__(self, new_name):
+        self._new_name = new_name
+
+    def __call__(self, inputs, attrs, *args):
+        return get_relay_op(self._new_name)(*inputs, **attrs)

python/tvm/relay/frontend/nnvm_common.py

@@ -4,15 +4,7 @@ from __future__ import absolute_import as _abs
 
 from .. import expr as _expr
 from .. import op as _op
-
-def _get_relay_op(op_name):
-    op = _op
-    for path in op_name.split("."):
-        op = getattr(op, path)
-    if not op:
-        raise RuntimeError("Unable to map op_name {} to relay".format(op_name))
-    return op
-
+from .common import get_relay_op
 
 def _warn_not_used(attr, op='nnvm'):
     import warnings
@@ -22,7 +14,7 @@ def _warn_not_used(attr, op='nnvm'):
 
 def _rename(new_op):
     if isinstance(new_op, str):
-        new_op = _get_relay_op(new_op)
+        new_op = get_relay_op(new_op)
     # attrs are ignored.
     def impl(inputs, _, _dtype='float32'):
         return new_op(*inputs)

tests/scripts/task_python_frontend.sh

@@ -32,3 +32,6 @@ python3 -m nose -v tests/python/frontend/mxnet || exit -1
 
 echo "Running relay Keras frontend test..."
 python3 -m nose -v tests/python/frontend/keras || exit -1
+
+echo "Running relay ONNX frondend test..."
+python3 -m nose -v tests/python/frontend/onnx || exit -1

tutorials/relay/from_onnx.py

@@ -0,0 +1,93 @@
+"""
+Compile ONNX Models
+===================
+**Author**: `Joshua Z. Zhang <https://zhreshold.github.io/>`_
+
+This article is an introductory tutorial to deploy ONNX models with Relay.
+
+For us to begin with, ONNX package must be installed.
+
+A quick solution is to install protobuf compiler, and
+
+.. code-block:: bash
+
+    pip install onnx --user
+
+or please refer to offical site.
+https://github.com/onnx/onnx
+"""
+import onnx
+import numpy as np
+import tvm
+import tvm.relay as relay
+
+def download(url, path, overwrite=False):
+    import os
+    if os.path.isfile(path) and not overwrite:
+        print('File {} existed, skip.'.format(path))
+        return
+    print('Downloading from url {} to {}'.format(url, path))
+    try:
+        import urllib.request
+        urllib.request.urlretrieve(url, path)
+    except:
+        import urllib
+        urllib.urlretrieve(url, path)
+
+######################################################################
+# Load pretrained ONNX model
+# ---------------------------------------------
+# The example super resolution model used here is exactly the same model in onnx tutorial
+# http://pytorch.org/tutorials/advanced/super_resolution_with_caffe2.html
+# we skip the pytorch model construction part, and download the saved onnx model
+model_url = ''.join(['https://gist.github.com/zhreshold/',
+                     'bcda4716699ac97ea44f791c24310193/raw/',
+                     '93672b029103648953c4e5ad3ac3aadf346a4cdc/',
+                     'super_resolution_0.2.onnx'])
+download(model_url, 'super_resolution.onnx', False)
+# now you have super_resolution.onnx on disk
+onnx_model = onnx.load('super_resolution.onnx')
+
+######################################################################
+# Load a test image
+# ---------------------------------------------
+# A single cat dominates the examples!
+from PIL import Image
+img_url = 'https://github.com/dmlc/mxnet.js/blob/master/data/cat.png?raw=true'
+download(img_url, 'cat.png')
+img = Image.open('cat.png').resize((224, 224))
+img_ycbcr = img.convert("YCbCr")  # convert to YCbCr
+img_y, img_cb, img_cr = img_ycbcr.split()
+x = np.array(img_y)[np.newaxis, np.newaxis, :, :]
+
+######################################################################
+# Compile the model with relay
+# ---------------------------------------------
+target = 'llvm'
+
+input_name = '1'
+shape_dict = {input_name: x.shape}
+sym, params = relay.frontend.from_onnx(onnx_model, shape_dict)
+
+with relay.build_config(opt_level=1):
+    intrp = relay.build_module.create_executor('graph', sym, tvm.cpu(0), target)
+
+######################################################################
+# Execute on TVM
+# ---------------------------------------------
+tvm_output = intrp.evaluate(sym)(tvm.nd.array(x.astype(dtype)), **params).asnumpy()
+
+######################################################################
+# Display results
+# ---------------------------------------------
+# We put input and output image neck to neck
+from matplotlib import pyplot as plt
+out_y = Image.fromarray(np.uint8((tvm_output[0, 0]).clip(0, 255)), mode='L')
+out_cb = img_cb.resize(out_y.size, Image.BICUBIC)
+out_cr = img_cr.resize(out_y.size, Image.BICUBIC)
+result = Image.merge('YCbCr', [out_y, out_cb, out_cr]).convert('RGB')
+canvas = np.full((672, 672*2, 3), 255)
+canvas[0:224, 0:224, :] = np.asarray(img)
+canvas[:, 672:, :] = np.asarray(result)
+plt.imshow(canvas.astype(np.uint8))
+plt.show()