[NNVM][FRONTEND] Tensorflow frontend support (#1188)

nnvm/python/nnvm/compiler/build_module.py

@@ -270,6 +270,10 @@ def build(graph, target=None, shape=None, dtype="float32",
     # Apply optimization
     with target:
         graph = optimize(graph, shape, dtype, layout)
+
+    # Clear extra params without nodes.
+    _remove_noref_params(params, graph)
+
     # Precompute prune
     if params and cfg.pass_enabled("PrecomputePrune"):
         graph, params = precompute_prune(graph, params)
@@ -296,6 +300,24 @@ def build(graph, target=None, shape=None, dtype="float32",
         params.update(init_var)
     return graph, libmod, params
 
+def _remove_noref_params(params, graph):
+    """ Helper to clear non referenced params
+
+    Parameters
+    ----------
+    graph : Graph
+        The input graph
+
+    params: dict of str to ndarray
+        The parameter dictionary
+    """
+    arg_list = set(graph.symbol.list_input_names())
+
+    if params:
+        param_keys = list(params.keys())
+        for key in param_keys:
+            if key not in arg_list:
+                params.pop(key)
 
 def _run_graph(graph, params):
     """Helper utility to build and run and get outputs, only use cpu mode.

nnvm/python/nnvm/frontend/__init__.py

@@ -5,3 +5,4 @@ from .onnx import from_onnx
 from .coreml import from_coreml
 from .keras import from_keras
 from .darknet import from_darknet
+from .tensorflow import from_tensorflow

nnvm/python/nnvm/frontend/tensorflow.py

+# pylint: disable=import-self, invalid-name, unused-argument
+"""TF: Tensorflow frontend."""
+from __future__ import absolute_import as _abs
+from __future__ import print_function
+
+# Numpy support
+import numpy as np
+
+import tvm
+from .. import symbol as _sym
+from .. import graph as _graph
+from .. compiler import graph_util
+from .common import get_nnvm_op, AttrConverter as AttrConvert
+
+__all__ = ['from_tensorflow']
+
+class AttrCvt(object):
+    """A Wrapper to handle some common jobs:
+    """
+    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):
+        self._ignores.append('_output_shapes')
+        self._ignores.append('_input_shapes')
+        self._ignores.append('T')
+        self._ignores.append('use_cudnn_on_gpu')
+        return AttrConvert(self._op_name, self._transforms, self._excludes,
+                           self._disables, self._ignores, self._extras,
+                           self._custom_check)(inputs, attrs, *args)
+
+def _get_pad_pair(input1d, kernel1d, stride1d):
+    if input1d % stride1d == 0:
+        pad = max(kernel1d - stride1d, 0)
+    else:
+        pad = max(kernel1d - (input1d % stride1d), 0)
+
+    pad_before = pad // 2
+    pad_after = pad - pad_before
+
+    return [pad_before, pad_after]
+
+def _math_name_picker(surfix):
+    def _impl(attr):
+        return 'broadcast_' + surfix
+    return _impl
+
+def _dimension_picker(prefix, surfix=''):
+    def _impl(attr):
+        kernel = attr['kernel_shape']
+        if len(kernel) == 2:
+            return prefix + '2d' + surfix
+        else:
+            raise NotImplementedError("Only 2d kernel supported.")
+    return _impl
+
+def _dimension_constraint():
+    def _dim_check(attrs):
+        if len(attrs['kernel_shape']) == 2:
+            return True
+        return False
+    return _dim_check, "Only 2d kernel supported."
+
+def _infer_channels(inputs, params, transpose=False):
+    """A hack for getting 'channles' or 'units' since tensorflow don't provide
+    these attributes. We check the shape of weights provided to get the number.
+    """
+    g = _graph.create(inputs)
+    shape_dict = {k: v.shape for k, v in params.items()}
+    _, out_shapes = graph_util.infer_shape(g, **shape_dict)
+    channels = out_shapes[0][0] if not transpose else out_shapes[0][1]
+    return channels
+
+def _rsqrt():
+    def _impl(inputs, attr, *args):
+        return AttrCvt(op_name="__pow_scalar__", extras={'scalar': -0.5})(inputs, attr)
+    return _impl
+
+def _elemwise(name):
+    def _impl(inputs, attr, *args):
+        assert len(inputs) == 2, "Math op take 2 inputs, {} given".format(len(inputs))
+        op_name = _math_name_picker(name)(attr)
+        axis = int(attr.get('axis', 0))
+        conv_ops = ["conv2d", "conv2d_transpose"]
+        if op_name == 'broadcast_add' and inputs[0].attr('op_name') in conv_ops:
+            # TODO: remove hard coded infershape
+            inputs[1] = _sym.expand_dims(inputs[1], axis=axis, num_newaxis=2)
+        return get_nnvm_op(op_name)(*inputs)
+    return _impl
+
+def _pooling(name):
+    def _impl(inputs, attr, params):
+
+        attr['data_format'] = attr['data_format'].decode("utf-8")
+
+        if attr['data_format'] == 'NHWC':
+            attr['kernel_shape'] = (attr['ksize'][1], attr['ksize'][2])
+        elif attr['data_format'] == 'NCHW':
+            attr['kernel_shape'] = (attr['ksize'][2], attr['ksize'][3])
+        else:
+            raise TypeError("Unsupported data_format type : {}".format(attr['data_format']))
+
+        # Fix strides
+        attr['strides'] = (attr['strides'][1], attr['strides'][2])
+
+        # Fix padding
+        input_shapes = attr['_input_shapes'][inputs[0]]
+        attr['padding'] = attr['padding'].decode("utf-8")
+
+        if attr['padding'] == 'VALID':
+            attr['padding'] = [0, 0]
+        elif attr['padding'] == 'SAME':
+            stride_h, stride_w = attr['strides']
+            kernel_h, kernel_w = attr['kernel_shape']
+            if attr['data_format'] == 'NHWC':
+                in_h = input_shapes[0][1]
+                in_w = input_shapes[0][2]
+            else:
+                in_h = input_shapes[0][2]
+                in_w = input_shapes[0][3]
+
+            pad_v = _get_pad_pair(in_h, kernel_h, stride_h)
+            pad_h = _get_pad_pair(in_w, kernel_w, stride_w)
+
+            if attr['data_format'] == 'NHWC':
+                inputs[0] = _sym.pad(data=inputs[0],
+                                     pad_width=((0, 0),
+                                                (pad_v[0], pad_v[1]),
+                                                (pad_h[0], pad_h[1]),
+                                                (0, 0)))
+            else:
+                inputs[0] = _sym.pad(data=inputs[0],
+                                     pad_width=((0, 0),
+                                                (0, 0),
+                                                (pad_v[0], pad_v[1]),
+                                                (pad_h[0], pad_h[1])))
+            attr['padding'] = [0, 0]
+        else:
+            raise TypeError("Unsupported padding type : {}".format(attr['padding']))
+
+        return AttrCvt(
+            op_name=_dimension_picker(name),
+            transforms={
+                'kernel_shape':'pool_size',
+                'data_format':'layout'},
+            ignores=['ksize'],
+            extras={'ceil_mode': False},
+            custom_check=_dimension_constraint())(inputs, attr)
+    return _impl
+
+def _conv():
+    def _impl(inputs, attr, params):
+        attr['data_format'] = attr['data_format'].decode("utf-8")
+
+        # Extract kernel shape from params
+        conv_param_weights = params[inputs[1].list_output_names()[0]]
+
+        if attr['data_format'] == 'NHWC':
+            attr['kernel_shape'] = (conv_param_weights.shape[0], conv_param_weights.shape[1])
+            attr['channels'] = conv_param_weights.shape[3]
+            if 'dilations' in attr:
+                attr['dilations'] = (attr['dilations'][0], attr['dilations'][1])
+        elif attr['data_format'] == 'NCHW':
+            attr['kernel_shape'] = (conv_param_weights.shape[2], conv_param_weights.shape[3])
+            attr['channels'] = conv_param_weights.shape[1]
+            if 'dilations' in attr:
+                attr['dilations'] = (attr['dilations'][2], attr['dilations'][3])
+        else:
+            raise TypeError("Unsupported data format type : {}".format(attr['data_format']))
+
+        # Fix strides
+        attr['strides'] = (attr['strides'][1], attr['strides'][2])
+
+        # Fix padding
+        input_shapes = attr['_input_shapes'][inputs[0]]
+        attr['padding'] = attr['padding'].decode("utf-8")
+
+        if attr['padding'] == 'VALID':
+            attr['padding'] = [0, 0]
+        elif attr['padding'] == 'SAME':
+            stride_h, stride_w = attr['strides']
+            kernel_h, kernel_w = attr['kernel_shape']
+            if attr['data_format'] == 'NHWC':
+                in_h = input_shapes[0][1]
+                in_w = input_shapes[0][2]
+            else:
+                in_h = input_shapes[0][2]
+                in_w = input_shapes[0][3]
+
+            pad_v = _get_pad_pair(in_h, kernel_h, stride_h)
+            pad_h = _get_pad_pair(in_w, kernel_w, stride_w)
+
+            if attr['data_format'] == 'NHWC':
+                inputs[0] = _sym.pad(data=inputs[0],
+                                     pad_width=((0, 0),
+                                                (pad_v[0], pad_v[1]),
+                                                (pad_h[0], pad_h[1]),
+                                                (0, 0)))
+            else:
+                inputs[0] = _sym.pad(data=inputs[0],
+                                     pad_width=((0, 0),
+                                                (0, 0),
+                                                (pad_v[0], pad_v[1]),
+                                                (pad_h[0], pad_h[1])))
+
+            attr['padding'] = [0, 0]
+
+        else:
+            raise TypeError("Unsupported padding type : {}".format(attr['padding']))
+
+        if 'kernel_layout' not in attr:
+            attr['kernel_layout'] = 'HWIO' if attr['data_format'] == 'NHWC' else 'OIHW'
+
+        return AttrCvt(
+            op_name=_dimension_picker('conv'),
+            transforms={
+                'kernel_shape': 'kernel_size',
+                'data_format': 'layout',
+                'dilations': ('dilation', (0, 0)),
+                'group': ('groups', 1)},
+            extras={'use_bias': len(inputs) == 3},
+            custom_check=_dimension_constraint())(inputs, attr)
+    return _impl
+
+def _decode_image():
+    def _impl(inputs, attr, params):
+        # Image decode wrapper: Expecting user to feed decoded input to next layer drop this layer.
+        print("DecodeJpeg: It's a pass through, please handle preprocessing before input")
+        return inputs[0]
+    return _impl
+
+def _cast():
+    def _impl(inputs, attr, params):
+        # Convert from tensorflow Dtype to str
+        attr['DstT'] = attr['DstT'].name
+        return AttrCvt(op_name='cast', transforms={'DstT': 'dtype'}, ignores=['SrcT'])(inputs, attr)
+    return _impl
+
+def _expand_dims():
+    def _impl(inputs, attr, params):
+        dim_input = inputs.pop(1)
+        axis = params[dim_input.list_output_names()[0]]
+        params.pop(dim_input.list_output_names()[0])
+        return AttrCvt(op_name="expand_dims", ignores=['Tdim'],
+                       extras={'axis': axis.asnumpy()[0]})(inputs, attr)
+    return _impl
+
+def _resize_bilinear():
+    def _impl(inputs, attr, params):
+        # Change this when we have corresponding resize bilinear operation.
+        print("ResizeBilinear:Only NN (nearest neighbor) supported in symetric mode of dimensions")
+        print("Change this when we have corresponding resize bilinear operation")
+
+        # NHWC
+        input_shape = attr['_input_shapes'][inputs[0]][0]
+        in_hw = (input_shape[1], input_shape[2])
+        out_hw = params[inputs[1].list_output_names()[0]]
+        inputs.pop(1)
+        attr['layout'] = 'NHWC'
+
+        if in_hw[0] < 0 or in_hw[1] < 0:
+            scale = 1
+        else:
+            # Considering height alone for scale
+            scale = out_hw[0] / in_hw[0]
+
+        return AttrCvt(op_name="upsampling",
+                       ignores=['Tdim', 'align_corners'],
+                       extras={'scale': scale})(inputs, attr)
+    return _impl
+
+def _check_numerics():
+    def _impl(inputs, attr, params):
+        # Making a copy node assuming no need to verify
+        return AttrCvt(op_name="copy", ignores=['message'])(inputs, attr)
+    return _impl
+
+
+def _matmul():
+    def _impl(inputs, attr, params):
+        channels = _infer_channels(inputs[1], params, not attr['transpose_b'])
+        if attr['transpose_a']:
+            inputs[0] = _sym.transpose(inputs[0], axis(1, 0))
+        if not attr['transpose_b']:
+            inputs[1] = _sym.transpose(inputs[1], axes=(1, 0))
+        return AttrCvt(op_name="dense",
+                       extras={'use_bias': False, 'units': channels},
+                       ignores=['transpose_a', 'transpose_b', 'T'])(inputs, attr)
+
+    return _impl
+
+def _identity():
+    def _impl(inputs, attr, params):
+        return inputs[0]
+    return _impl
+
+def _concatV2():
+    def _impl(inputs, attr, params):
+        pop_node = inputs.pop(len(inputs)-1)
+        axis = params[pop_node.list_output_names()[0]]
+        params.pop(pop_node.list_output_names()[0])
+        return AttrCvt(
+            op_name="concatenate", ignores=['T', 'N', 'Tidx'],
+            extras={'axis': axis.asnumpy()[0]})(inputs, attr)
+    return _impl
+
+def _concat():
+    def _impl(inputs, attr, params):
+        pop_node = inputs.pop(0)
+        axis = params[pop_node.list_output_names()[0]]
+        params.pop(pop_node.list_output_names()[0])
+        return AttrCvt(
+            op_name="concatenate", ignores=['N'],
+            extras={'axis': axis.asnumpy()[0]})(inputs, attr)
+    return _impl
+
+def _reshape():
+    def _impl(inputs, attr, params):
+        pop_node = inputs.pop(1)
+        shape_arg = params[pop_node.list_output_names()[0]]
+        params.pop(pop_node.list_output_names()[0])
+        return AttrCvt(
+            op_name="reshape",
+            extras={'shape':tuple(shape_arg.asnumpy())},
+            ignores=['Tshape'])(inputs, attr)
+    return _impl
+
+def _bias_add():
+    def _impl(inputs, attr, params):
+        return _sym.broadcast_add(inputs[0], inputs[1])
+    return _impl
+
+def _squeeze():
+    def _impl(inputs, attr, params):
+        return AttrCvt(
+            op_name="squeeze",
+            transforms={'squeeze_dims':'axis'},
+            ignores=['T'])(inputs, attr)
+    return _impl
+
+def _batch_norm():
+    def _impl(inputs, attr, params):
+        # Rearrange inputs from
+        # (data, moving_mean, moving_variance, beta, gamma)
+        #     to
+        # (data, gamma, beta, moving_mean, moving_var)
+        new_inputs = [inputs[0], inputs[4], inputs[3], inputs[1], inputs[2]]
+
+        return AttrCvt(
+            op_name='batch_norm',
+            transforms={'scale_after_normalization':'scale', 'variance_epsilon':'epsilon'},
+            extras={'axis': 3}, # Fix axis
+            disables=['momentum'])(new_inputs, attr)
+    return _impl
+
+# compatible operators that do NOT require any conversion.
+_identity_list = []
+
+# _convert_map defines maps of name to converter functor(callable)
+# for 1 to 1 mapping, use Renamer if nothing but name is different
+# use AttrCvt if attributes need to be converted
+# for 1 to N mapping(composed), use custom callable functions
+# for N to 1 mapping, currently not supported(?)
+_convert_map = {
+    'AvgPool'                           : _pooling('avg_pool'),
+    'BatchNormWithGlobalNormalization'  : _batch_norm(),
+    'BiasAdd'                           : _bias_add(),
+    'Cast'                              : _cast(),
+    'CheckNumerics'                     : _check_numerics(),
+    'Concat'                            : _concat(),
+    'ConcatV2'                          : _concatV2(),
+    'Conv2D'                            : _conv(),
+    'DecodeJpeg'                        : _decode_image(),
+    'ExpandDims'                        : _expand_dims(),
+    'Identity'                          : _identity(),
+    'MatMul'                            : _matmul(),
+    'MaxPool'                           : _pooling('max_pool'),
+    'Mul'                               : _elemwise('mul'),
+    'Relu'                              : AttrCvt('relu'),
+    'Reshape'                           : _reshape(),
+    'ResizeBilinear'                    : _resize_bilinear(),
+    'Softmax'                           : AttrCvt('softmax', {'axis': ('axis', 1)}),
+    'Sub'                               : _elemwise('sub'),
+    'Add'                               : _elemwise('add'),
+    'Rsqrt'                             : _rsqrt(),
+    'Squeeze'                           : _squeeze(),
+}
+
+
+class GraphProto(object):
+    """ A helper class for handling nnvm graph copying from Tensorflow GraphDef.
+    Definition:
+        https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/graph.proto
+    """
+    def __init__(self):
+        self._nodes = {}
+        self._params = {}
+        self._renames = {}
+        self._replacements = {}
+        self._output_shapes = {}
+        self._num_input = 0
+        self._num_param = 0
+        self._input_node = ''
+
+    def from_tensorflow(self, graph):
+        """Construct nnvm nodes from tensorflow  graph definition - GraphDef.
+
+        Follow the tensorflow graph definition to parse and convert it to NNVM.
+        Some of the assumptions listed below.
+
+            -> First Const or Placeholder node will be considered as graph input.
+            -> Rest all Const nodes are params.
+            -> Last node is assumed as graph output.
+            -> _output_shapes : Attribute should present in the tenserflow forzen graph.
+            -> DecodeJpeg, ResizeBilinear: These are dummy operators.
+                                           Hence user should handle preprocessing outside.
+            -> CheckNumerics: No implementation as of now for this.
+                              Just copies input to output.
+
+
+        Parameters
+        ----------
+        graph : tensorflow graph definition object
+            The loaded tensorflow GraphDef
+
+        Returns
+        -------
+        sym : nnvm.sym.Symbol
+            The returned nnvm symbol
+        params : dict
+            A dict of name: tvm.nd.array pairs, used as pretrained weights
+        """
+        # Parse throught all nodes and start extracting
+        # params aka Const nodes
+        # input nodes  : First const node
+        # normal nodes : other normal nodes
+
+        try:
+            from tensorflow.python.framework import tensor_util
+        except ImportError as e:
+            raise ImportError(
+                "Unable to import tensorflow which is required {}".format(e))
+
+        for node in graph.node:
+            # Tensorflow doesn't have seperate list for params extraction.
+            # Operator name 'Const' is treated as a parameter to build NNVM params dict.
+            if node.op == "Placeholder":
+                # Assuming only one input graph with type 'Placeholder'
+                self._input_node = node.name
+                self._num_input += 1
+                self._nodes[node.name] = _sym.Variable(name=node.name)
+
+                self._output_shapes[node.name] = \
+                     [tensor_util.TensorShapeProtoToList(shape) \
+                     for shape in self._parse_attr(node.attr)['_output_shapes']]
+            elif node.op == "Const":
+                # Assuming first Const node as Graph Input node
+                if self._input_node == '':
+                    self._input_node = node.name
+                    self._num_input += 1
+                    self._nodes[node.name] = _sym.Variable(name=node.name)
+                else:
+                    # Rest all nodes are Param nodes, lets parse
+                    self._num_param += 1
+                    for key, value in node.attr.items():
+                        self._parse_param(key, value, node.name)
+                    if node.name not in self._nodes:
+                        raise NotImplementedError( \
+                            "Const {} couldn't be converted to Param.".format(node.name))
+
+                self._output_shapes[node.name] = \
+                     [tensor_util.TensorShapeProtoToList(shape) \
+                     for shape in self._parse_attr(node.attr)['_output_shapes']]
+            else:
+                attr = self._parse_attr(node.attr)
+                self._output_shapes[node.name] = \
+                     [tensor_util.TensorShapeProtoToList(shape) for shape in attr['_output_shapes']]
+
+                # Pass the parsed shapes instead
+                attr["_output_shapes"] = self._output_shapes[node.name]
+
+                try:
+                    inputs = [self._nodes[i] for i in node.input]
+                    input_shapes = {}
+                    for i in node.input:
+                        if i not in self._params:
+                            input_shapes[self._nodes[i]] = self._output_shapes[i]
+                    attr['_input_shapes'] = input_shapes
+                except KeyError:
+                    # TODO: Need to find clean way to handle '^CheckNumerics'
+                    print("Some Exception while inputs list:", node.input, " ignoring...")
+
+                inputs = self._fix_extranodes(node.op, attr, inputs)
+
+                op = self._convert_operator(node.op, inputs, attr)
+                # Assuming only one output.
+                self._nodes[node.name] = op
+                node_output = op
+        # Assume the final node is the output node
+        out = node_output
+        return out, self._params
+
+    def _parse_param(self, key, value, name):
+        try:
+            from tensorflow.python.framework import tensor_util
+        except ImportError as e:
+            raise ImportError(
+                "Unable to import tensorflow which is required {}".format(e))
+
+        if key == 'value':
+            np_array = tensor_util.MakeNdarray(value.tensor)
+            array_ndim = len(np_array.shape)
+            if array_ndim == 0:
+                new_array = np.empty([1], dtype=np_array.dtype)
+                new_array[0] = np_array
+                self._params[name] = tvm.nd.array(new_array)
+            else:
+                self._params[name] = tvm.nd.array(np_array)
+            self._nodes[name] = _sym.Variable(name=name,
+                                              shape=self._params[name].shape)
+        else:
+            if key != 'dtype' and key != '_output_shapes':
+                raise NotImplementedError \
+                    ("Other attributes for a Const(param) Node {} ? .".format(key))
+
+    def _get_attr(self, buf):
+        """Returns the value of the attr of this buf with the given `name`.
+
+        Args:
+          buf: attrvalue protobuf.
+
+        Returns:
+          The value of the attr, as a Python object.
+
+        Raises:
+          ValueError: If this op does not have an attr with the given `name`.
+        """
+        fields = ["s", "i", "f", "b", "type", "shape", "tensor", "func"]
+
+        x = buf
+
+        ret = []
+
+        try:
+            from tensorflow.python.framework import dtypes
+        except ImportError as e:
+            raise ImportError(
+                "Unable to import tensorflow which is required {}".format(e))
+
+        # Treat an empty oneof value as an empty list.
+        if not x.WhichOneof("value"):
+            return ret
+        if x.HasField("list"):
+            for f in fields:
+                if getattr(x.list, f):
+                    if f == "type":
+                        ret = [dtypes.as_dtype(x) for x in list(getattr(x.list, f))]
+                    else:
+                        ret = list(getattr(x.list, f))
+        else:
+            for f in fields:
+                if x.HasField(f):
+                    if f == "type":
+                        ret = dtypes.as_dtype(getattr(x, f))
+                    else:
+                        ret = getattr(x, f)
+        return ret
+
+    def _parse_attr(self, attr_proto):
+        """Convert a list of AttributeProto to a dict, with names as keys."""
+        attrs = {}
+        for key, value in attr_proto.items():
+            attrs[key] = self._get_attr(value)
+
+        return attrs
+
+    def _convert_operator(self, op_name, inputs, attrs, identity_list=None, convert_map=None):
+        """Convert from Tensorflow operator to nnvm operator.
+        The converter must specify conversions explicity for incompatible name, and
+        apply handlers to operator attributes.
+
+        Parameters
+        ----------
+        op_name : str
+            Operator name, such as Conv2D, AvgPool
+        inputs : list of nnvm.Symbol
+            List of input symbols.
+        attrs : dict
+            Dict of operator attributes
+        identity_list : list
+            List of operators that don't require conversion
+        convert_map : dict
+            Dict of name : callable, where name is the op's name that
+            require conversion to nnvm, callable are functions which
+            take attrs and return (new_op_name, new_attrs)
+
+        Returns
+        -------
+        sym : nnvm.Symbol
+            Converted nnvm Symbol
+        """
+        identity_list = identity_list if identity_list else _identity_list
+        convert_map = convert_map if convert_map else _convert_map
+        if op_name in identity_list:
+            sym = get_nnvm_op(op_name)(*inputs, **attrs)
+        elif op_name in convert_map:
+            sym = convert_map[op_name](inputs, attrs, self._params)
+        else:
+            raise NotImplementedError("Operator {} not implemented.".format(op_name))
+        return sym
+
+    def _fix_extranodes(self, op_name, attr, inputs):
+        if op_name == "Softmax":
+            # Require some times flatten of data before it goes to softmax
+            # Need to relook into this with latest softmax axis support.
+            op = AttrCvt(op_name='flatten')(inputs, {})
+            node_output = op.list_output_names()
+            for k, i in zip(list(node_output), range(len(node_output))):
+                self._nodes[k] = op[i]
+            inputs = [op]
+
+        return inputs
+
+def from_tensorflow(graph):
+    """  Load tensorflow graph which is a python tensorflow graph object into nnvm graph.
+    The companion parameters will be handled automatically.
+
+    Parameters
+    ----------
+    graph : GraphDef object
+        Tensorflow GraphDef
+
+    Returns
+    -------
+    sym : nnvm.Symbol
+        Compatible nnvm symbol
+
+    params : dict of str to tvm.ndarray
+        Dict of converted parameters stored in tvm.ndarray format
+    """
+    g = GraphProto()
+    sym, params = g.from_tensorflow(graph)
+    return sym, params

nnvm/python/nnvm/testing/tf.py

+# pylint: disable=invalid-name, unused-variable, unused-argument, no-init
+"""
+Tensorflow Model Helpers
+========================
+Some helper definitions for tensorflow models.
+"""
+import re
+import os.path
+import numpy as np
+
+# Tensorflow imports
+import tensorflow as tf
+from tensorflow.core.framework import graph_pb2
+
+######################################################################
+# Some helper functions
+# ---------------------
+
+def ProcessGraphDefParam(graph_def):
+    """Type-checks and possibly canonicalizes `graph_def`.
+
+    Parameters
+    ----------
+    graph_def : Obj
+        tensorflow graph definition.
+
+    Returns
+    -------
+    graph_def : Obj
+        tensorflow graph devinition
+
+    """
+
+    if not isinstance(graph_def, graph_pb2.GraphDef):
+        # `graph_def` could be a dynamically-created message, so try a duck-typed
+        # approach
+        try:
+            old_graph_def = graph_def
+            graph_def = graph_pb2.GraphDef()
+            graph_def.MergeFrom(old_graph_def)
+        except TypeError:
+            raise TypeError('graph_def must be a GraphDef proto.')
+    return graph_def
+
+class NodeLookup(object):
+    """Converts integer node ID's to human readable labels."""
+
+    def __init__(self,
+                 label_lookup_path=None,
+                 uid_lookup_path=None):
+        self.node_lookup = self.load(label_lookup_path, uid_lookup_path)
+
+    def load(self, label_lookup_path, uid_lookup_path):
+        """Loads a human readable English name for each softmax node.
+
+        Parameters
+        ----------
+        label_lookup_path: String
+            File containing String UID to integer node ID mapping .
+
+        uid_lookup_path: String
+            File containing String UID to human-readable string mapping.
+
+        Returns
+        -------
+        node_id_to_name: dict
+            dict from integer node ID to human-readable string.
+
+        """
+        if not tf.gfile.Exists(uid_lookup_path):
+            tf.logging.fatal('File does not exist %s', uid_lookup_path)
+        if not tf.gfile.Exists(label_lookup_path):
+            tf.logging.fatal('File does not exist %s', label_lookup_path)
+
+        # Loads mapping from string UID to human-readable string
+        proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines()
+        uid_to_human = {}
+        p = re.compile(r'[n\d]*[ \S,]*')
+        for line in proto_as_ascii_lines:
+            parsed_items = p.findall(line)
+            uid = parsed_items[0]
+            human_string = parsed_items[2]
+            uid_to_human[uid] = human_string
+
+        # Loads mapping from string UID to integer node ID.
+        node_id_to_uid = {}
+        proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines()
+        for line in proto_as_ascii:
+            if line.startswith('  target_class:'):
+                target_class = int(line.split(': ')[1])
+            if line.startswith('  target_class_string:'):
+                target_class_string = line.split(': ')[1]
+                node_id_to_uid[target_class] = target_class_string[1:-2]
+
+        # Loads the final mapping of integer node ID to human-readable string
+        node_id_to_name = {}
+        for key, val in node_id_to_uid.items():
+            if val not in uid_to_human:
+                tf.logging.fatal('Failed to locate: %s', val)
+            name = uid_to_human[val]
+            node_id_to_name[key] = name
+
+        return node_id_to_name
+
+    def id_to_string(self, node_id):
+        if node_id not in self.node_lookup:
+            return ''
+        return self.node_lookup[node_id]
+
+def read_normalized_tensor_from_image_file(file_name,
+                                           input_height=299,
+                                           input_width=299,
+                                           input_mean=0,
+                                           input_std=255):
+    """ Preprocessing of image
+    Parameters
+    ----------
+
+    file_name: String
+        Image filename.
+
+    input_height: int
+        model input height.
+
+    input_width: int
+        model input width
+
+    input_mean: int
+        Mean to be substracted in normalization.
+
+    input_std: int
+        Standard deviation used in normalization.
+
+    Returns
+    -------
+
+    np_array: Numpy array
+        Normalized image data as a numpy array.
+
+    """
+
+    input_name = "file_reader"
+    output_name = "normalized"
+    file_reader = tf.read_file(file_name, input_name)
+
+    image_reader = tf.image.decode_jpeg(file_reader, channels=3,
+                                        name='jpeg_reader')
+    float_caster = tf.cast(image_reader, tf.float32)
+    dims_expander = tf.expand_dims(float_caster, 0)
+    resized = tf.image.resize_bilinear(dims_expander, [input_height, input_width])
+    normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
+    tf.InteractiveSession()
+    np_array = normalized.eval()
+    return np_array
+
+def get_workload_inception_v3():
+    """ Import Inception V3 workload from frozen protobuf
+
+    Parameters
+    ----------
+        Nothing.
+
+    Returns
+    -------
+    (normalized, graph_def) : Tuple
+        normalized is normalized input for graph testing.
+        graph_def is the tensorflow workload for Inception V3.
+    """
+
+    repo_base = 'https://github.com/dmlc/web-data/raw/master/tensorflow/models/InceptionV3/'
+    model_name = 'inception_v3_2016_08_28_frozen-with_shapes.pb'
+    model_url = os.path.join(repo_base, model_name)
+    image_name = 'elephant-299.jpg'
+    image_url = os.path.join(repo_base, image_name)
+
+    from mxnet.gluon.utils import download
+    download(model_url, model_name)
+    download(image_url, image_name)
+
+    normalized = read_normalized_tensor_from_image_file(os.path.join("./", image_name))
+
+    # Creates graph from saved graph_def.pb.
+    with tf.gfile.FastGFile(os.path.join("./", model_name), 'rb') as f:
+        graph_def = tf.GraphDef()
+        graph_def.ParseFromString(f.read())
+        graph = tf.import_graph_def(graph_def, name='')
+        return (normalized, graph_def)
+
+def get_workload_inception_v1():
+    """ Import Inception V1 workload from frozen protobuf
+
+    Parameters
+    ----------
+        Nothing.
+
+    Returns
+    -------
+    (image_data, tvm_data, graph_def) : Tuple
+        image_data is raw encoded image data for TF input.
+        tvm_data is the decoded image data for TVM input.
+        graph_def is the tensorflow workload for Inception V1.
+
+    """
+
+    repo_base = 'https://github.com/dmlc/web-data/raw/master/tensorflow/models/InceptionV1/'
+    model_name = 'classify_image_graph_def-with_shapes.pb'
+    model_url = os.path.join(repo_base, model_name)
+    image_name = 'elephant-299.jpg'
+    image_url = os.path.join(repo_base, image_name)
+
+    from mxnet.gluon.utils import download
+    download(model_url, model_name)
+    download(image_url, image_name)
+
+    if not tf.gfile.Exists(os.path.join("./", image_name)):
+        tf.logging.fatal('File does not exist %s', image)
+    image_data = tf.gfile.FastGFile(os.path.join("./", image_name), 'rb').read()
+
+    # TVM doesn't handle decode, hence decode it.
+    from PIL import Image
+    tvm_data = Image.open(os.path.join("./", image_name)).resize((299, 299))
+    tvm_data = np.array(tvm_data)
+
+    # Creates graph from saved graph_def.pb.
+    with tf.gfile.FastGFile(os.path.join("./", model_name), 'rb') as f:
+        graph_def = tf.GraphDef()
+        graph_def.ParseFromString(f.read())
+        graph = tf.import_graph_def(graph_def, name='')
+        return (image_data, tvm_data, graph_def)

nnvm/python/nnvm/top/tensor.py

@@ -52,6 +52,15 @@ reg.register_schedule("_assign", _fschedule_broadcast)
 reg.register_pattern("copy", OpPattern.ELEMWISE)
 reg.register_schedule("copy", _fschedule_broadcast)
 
+# cast
+@reg.register_compute("cast")
+def compute_cast(attrs, inputs, _):
+    """Compute definition of cast"""
+    dtype = attrs.get_string("dtype")
+    return topi.cast(inputs[0], dtype)
+reg.register_pattern("cast", OpPattern.ELEMWISE)
+reg.register_schedule("cast", _fschedule_broadcast)
+
 # exp
 reg.register_pattern("exp", OpPattern.ELEMWISE)
 reg.register_schedule("exp", _fschedule_broadcast)

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

+# pylint: disable=import-self, invalid-name, unused-argument
+"""
+Tensorflow testcases
+====================
+This article is a test script to test tensorflow operator with NNVM.
+"""
+from __future__ import print_function
+import numpy as np
+import nnvm.compiler
+import tvm
+import tensorflow as tf
+from tensorflow.python.framework import constant_op
+from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import gen_array_ops
+from tensorflow.core.framework import graph_pb2
+
+import nnvm.testing.tf
+
+#######################################################################
+# Generic run functions for TVM & tensorflow
+# ------------------------------------------
+def run_tvm_graph(graph_def, input_data, input_node, output_shape, output_dtype):
+    """ Generic function to compile on nnvm and execute on tvm """
+
+    sym, params = nnvm.frontend.from_tensorflow(graph_def)
+    target = 'llvm'
+    if isinstance(input_data, list):
+        shape_dict = {}
+        dtype_dict = {}
+        for i, e in enumerate(input_node):
+            shape_dict[e] = input_data[i].shape
+            dtype_dict[e] = input_data[i].dtype
+    else:
+        shape_dict = {input_node: input_data.shape}
+        dtype_dict = {input_node: input_data.dtype}
+
+    graph, lib, params = nnvm.compiler.build(sym, target, shape_dict,
+                                             dtype=dtype_dict, params=params)
+
+    ctx = tvm.cpu(0)
+    from tvm.contrib import graph_runtime
+    m = graph_runtime.create(graph, lib, ctx)
+    # set inputs
+    if isinstance(input_data, list):
+        for i, e in enumerate(input_node):
+            m.set_input(e, tvm.nd.array(input_data[i].astype(input_data[i].dtype)))
+    else:
+        m.set_input(input_node, tvm.nd.array(input_data.astype(input_data.dtype)))
+
+    m.set_input(**params)
+    # execute
+    m.run()
+    # get outputs
+    tvm_output = m.get_output(0, tvm.nd.empty((output_shape), output_dtype))
+    return tvm_output.asnumpy()
+
+def run_tf_graph(sess, input_data, input_node, output_node):
+    """ Generic function to execute tensorflow """
+
+    tensor = sess.graph.get_tensor_by_name(output_node)
+
+    if isinstance(input_data, list):
+        input_dict = {}
+        for i, e in enumerate(input_node):
+            input_dict[e] = input_data[i]
+    else:
+        input_dict = {input_node: input_data}
+
+    output_data = sess.run(tensor, input_dict)
+    return output_data
+
+#######################################################################
+# Pooling
+# -------
+def _test_pooling(input_shape, **kwargs):
+    """ One iteration of pool operation with given shapes and attributes """
+
+    x = -np.arange(
+        np.prod(input_shape), dtype=np.float32).reshape(input_shape) - 1
+
+    with tf.Graph().as_default():
+        in_data = constant_op.constant(x, shape=input_shape, dtype='float32')
+        # pylint: disable=unused-variable
+        pool = nn_ops.pool(in_data, **kwargs)
+        # pylint: enable=unused-variable
+
+        if kwargs['pooling_type'] == 'MAX':
+            out_node = 'max_pool'
+            out_name = 'max_pool:0'
+        else:
+            out_node = 'avg_pool'
+            out_name = 'avg_pool:0'
+
+        with tf.Session() as sess:
+            graph_def = tf.graph_util.convert_variables_to_constants(
+                sess,
+                sess.graph.as_graph_def(add_shapes=True),
+                [out_node],
+                )
+
+            tf_output = run_tf_graph(sess, x, 'Const:0', out_name)
+            tvm_output = run_tvm_graph(graph_def, x.astype('float32'),
+                                       "Const", tf_output.shape, 'float32')
+            np.testing.assert_allclose(tf_output, tvm_output, atol=1e-3, rtol=1e-3)
+
+            sess.close()
+
+def test_forward_pooling():
+    """ Pooling """
+
+    _test_pooling(input_shape=[2, 9, 10, 2],
+                 window_shape=[1, 1],
+                 padding='SAME',
+                 pooling_type='MAX',
+                 dilation_rate=[1, 1],
+                 strides=[1, 1])
+
+    _test_pooling(input_shape=[2, 9, 10, 2],
+                 window_shape=[1, 1],
+                 padding='SAME',
+                 pooling_type='AVG',
+                 dilation_rate=[1, 1],
+                 strides=[1, 1])
+    _test_pooling(input_shape=[2, 10, 9, 2],
+                 window_shape=[1, 1],
+                 padding='SAME',
+                 pooling_type='MAX',
+                 dilation_rate=[1, 1],
+                 strides=[1, 1])
+    _test_pooling(input_shape=[2, 10, 9, 2],
+                 window_shape=[1, 1],
+                 padding='SAME',
+                 pooling_type='AVG',
+                 dilation_rate=[1, 1],
+                 strides=[1, 1])
+
+#######################################################################
+# Convolution
+# -----------
+
+def _test_convolution(tensor_in_sizes, filter_in_sizes,
+                      dilations, strides, padding, data_format):
+    """ One iteration of convolution with given shapes and attributes """
+
+    total_size_1 = 1
+    total_size_2 = 1
+    for s in tensor_in_sizes:
+        total_size_1 *= s
+    for s in filter_in_sizes:
+        total_size_2 *= s
+    # Initializes the input tensor with array containing incrementing
+    # numbers from 1.
+    data_array = [f * 1.0 for f in range(1, total_size_1 + 1)]
+    filter_array = [f * 1.0 for f in range(1, total_size_2 + 1)]
+
+    with tf.Graph().as_default():
+        in_data = constant_op.constant(data_array, shape=tensor_in_sizes, dtype='float32')
+        in_filter = constant_op.constant(filter_array, shape=filter_in_sizes, dtype='float32')
+        strides = [1] + strides + [1]
+        dilations = [1] + dilations + [1]
+
+        # pylint: disable=unused-variable
+        conv = nn_ops.conv2d(in_data,
+                             in_filter,
+                             strides=strides,
+                             padding=padding,
+                             data_format=data_format)
+        # pylint: enable=unused-variable
+
+        with tf.Session() as sess:
+            graph_def = tf.graph_util.convert_variables_to_constants(
+                sess,
+                sess.graph.as_graph_def(add_shapes=True),
+                ['Conv2D'],
+                )
+
+            tf_output = run_tf_graph(sess, np.reshape(data_array, tensor_in_sizes),
+                                     'Const:0', 'Conv2D:0')
+            tvm_output = run_tvm_graph(graph_def,
+                                       np.reshape(data_array, tensor_in_sizes).astype('float32'),
+                                       "Const", tf_output.shape, 'float32')
+
+            np.testing.assert_allclose(tf_output, tvm_output, atol=1e-3, rtol=1e-3)
+
+            sess.close()
+
+def test_forward_convolution():
+    _test_convolution([4, 8, 8, 176], [1, 1, 176, 32], [1, 1], [1, 1], 'SAME', 'NHWC')
+    _test_convolution([4, 17, 17, 19], [3, 3, 19, 19], [1, 1], [2, 2], 'VALID', 'NHWC')
+    _test_convolution([4, 17, 17, 124], [1, 1, 124, 19], [1, 1], [1, 1], 'SAME', 'NHWC')
+    _test_convolution([4, 17, 17, 12], [3, 3, 12, 32], [1, 1], [2, 2], 'VALID', 'NHWC')
+
+#######################################################################
+# Reshape
+# -------
+
+def _test_reshape(data, out_shape):
+    """ One iteration of reshape operation with given data and out shape """
+
+    with tf.Graph().as_default():
+        in_data = constant_op.constant(data, shape=data.shape, dtype=data.dtype)
+
+        # pylint: disable=unused-variable
+        reshape_out = array_ops.reshape(in_data, out_shape)
+        # pylint: enable=unused-variable
+
+        with tf.Session() as sess:
+            graph_def = tf.graph_util.convert_variables_to_constants(
+                sess,
+                sess.graph.as_graph_def(add_shapes=True),
+                ['Reshape'],
+                )
+
+            tf_output = run_tf_graph(sess, data,
+                                     'Const:0', 'Reshape:0')
+            tvm_output = run_tvm_graph(graph_def,
+                                       data,
+                                       "Const", tf_output.shape, data.dtype)
+
+            np.testing.assert_allclose(tf_output, tvm_output)
+
+            sess.close()
+
+def test_forward_reshape():
+    _test_reshape(np.arange(6.0), [2, 3])
+    _test_reshape(np.arange(6), [-1, 2])
+    _test_reshape(np.arange(6), [3, -1])
+    _test_reshape(np.arange(6), [-1])
+
+#######################################################################
+# Squeeze
+# -------
+
+def _test_squeeze(data, squeeze_dims=None):
+    """ One iteration of squeeze """
+
+    if squeeze_dims is None:
+        squeeze_dims = []
+
+    with tf.Graph().as_default():
+        in_data = constant_op.constant(data, shape=data.shape, dtype=data.dtype)
+
+        # pylint: disable=unused-variable
+        if squeeze_dims:
+            squeeze_out = array_ops.squeeze(in_data, squeeze_dims)
+        else:
+            squeeze_out = array_ops.squeeze(in_data)
+        # pylint: enable=unused-variable
+
+        with tf.Session() as sess:
+            graph_def = tf.graph_util.convert_variables_to_constants(
+                sess,
+                sess.graph.as_graph_def(add_shapes=True),
+                ['Squeeze'],
+                )
+
+            tf_output = run_tf_graph(sess, data,
+                                     'Const:0', 'Squeeze:0')
+            tvm_output = run_tvm_graph(graph_def,
+                                       data,
+                                       "Const", tf_output.shape, data.dtype)
+
+            np.testing.assert_allclose(tf_output, tvm_output)
+
+            sess.close()
+
+def test_forward_squeeze():
+    """ Squeeze """
+
+    # Nothing to squeeze.
+    _test_squeeze(np.arange(2).reshape((2)))
+    _test_squeeze(np.arange(6).reshape((2, 3)))
+
+    # Squeeze the middle element away.
+    _test_squeeze(np.arange(4).reshape((2, 1, 2)))
+
+    # Squeeze on both ends.
+    _test_squeeze(np.arange(6).reshape((1, 2, 1, 3, 1)))
+
+    # Positive squeeze dim index.
+    _test_squeeze(np.arange(6).reshape((1, 2, 1, 3, 1)), [0])
+    _test_squeeze(np.arange(6).reshape((1, 2, 1, 3, 1)), [2, 4])
+    _test_squeeze(np.arange(6).reshape((1, 2, 1, 3, 1)), [0, 4, 2])
+
+    # Negative squeeze dim index.
+    _test_squeeze(np.arange(6).reshape((1, 2, 1, 3, 1)), [-1])
+    _test_squeeze(np.arange(6).reshape((1, 2, 1, 3, 1)), [-3, -5])
+    _test_squeeze(np.arange(6).reshape((1, 2, 1, 3, 1)), [-3, -5, -1])
+
+#######################################################################
+# ConcatV2
+# --------
+
+def _test_concat_v2(data, dim):
+    """ One iteration of ConcatV2 """
+
+    with tf.Graph().as_default():
+
+        # pylint: disable=unused-variable
+        concat_out = gen_array_ops._concat_v2(data, dim)
+        # pylint: enable=unused-variable
+
+        with tf.Session() as sess:
+            graph_def = tf.graph_util.convert_variables_to_constants(
+                sess,
+                sess.graph.as_graph_def(add_shapes=True),
+                ['ConcatV2'],
+                )
+
+            tf_output = run_tf_graph(sess, data,
+                                     ['ConcatV2/values_0:0', 'ConcatV2/values_1:0'], 'ConcatV2:0')
+            tvm_output = run_tvm_graph(graph_def,
+                                       data,
+                                       ["ConcatV2/values_0", 'ConcatV2/values_1'],
+                                       tf_output.shape, tf_output.dtype)
+
+            np.testing.assert_allclose(tf_output, tvm_output)
+
+            sess.close()
+
+def _test_forward_concat_v2():
+    t1 = np.array([])
+    t2 = np.array([])
+    test_concat_v2([t1, t2], 0)
+
+    t1 = np.array([[1, 2, 3], [4, 5, 6]])
+    t2 = np.array([[7, 8, 9], [10, 11, 12]])
+
+    _test_concat_v2([t1, t2], 1)
+
+#######################################################################
+# Multi Input to graph
+# --------------------
+
+def test_forward_multi_input():
+    with tf.Graph().as_default():
+        in1 = tf.placeholder(tf.int32, shape=[3, 3], name='in1')
+        in2 = tf.placeholder(tf.int32, shape=[3, 3], name='in2')
+        in3 = tf.placeholder(tf.int32, shape=[3, 3], name='in3')
+        in4 = tf.placeholder(tf.int32, shape=[3, 3], name='in4')
+
+        out1 = tf.add(in1, in2, name='out1')
+        out2 = tf.subtract(in3, in4, name='out2')
+
+        out = tf.multiply(out1, out2, name='out')
+
+        with tf.Session() as sess:
+            graph_def = tf.graph_util.convert_variables_to_constants(
+                sess,
+                sess.graph.as_graph_def(add_shapes=True),
+                ['out'],
+                )
+
+            in_data = np.arange(9, dtype='int32').reshape([3, 3])
+
+            tf_output = run_tf_graph(sess, [in_data, in_data, in_data, in_data ],
+                                     ['in1:0', 'in2:0', 'in3:0', 'in4:0'], 'out:0')
+            tvm_output = run_tvm_graph(graph_def,
+                                       [in_data, in_data, in_data, in_data ],
+                                       ['in1', 'in2', 'in3', 'in4'],
+                                       tf_output.shape, tf_output.dtype)
+
+            np.testing.assert_allclose(tf_output, tvm_output)
+
+            sess.close()
+
+#######################################################################
+# Inception V3
+# ------------
+def test_forward_inception_v3():
+    '''test inception V3 model'''
+    with tf.Graph().as_default():
+        (data, graph_def) = nnvm.testing.tf.get_workload_inception_v3()
+        # Call the utility to import the graph definition into default graph.
+        graph_def = nnvm.testing.tf.ProcessGraphDefParam(graph_def)
+
+        tvm_output = run_tvm_graph(graph_def, data, 'input', (1, 1001), 'float32')
+        with tf.Session() as sess:
+            tf_output = run_tf_graph(sess, data, 'input:0', 'InceptionV3/Predictions/Reshape_1:0')
+
+            top_tvm = np.squeeze(tvm_output).argsort()[-3:][::-1]
+            top_tf = np.squeeze(tf_output).argsort()[-3:][::-1]
+
+            # TVM implementation of SAME padding some times make a slight deviation.
+            # Hence check for top predictions.
+            top_tvm = np.sort(top_tvm)
+            top_tf = np.sort(top_tf)
+
+            np.testing.assert_allclose(top_tf, top_tvm)
+
+#######################################################################
+# Inception V1
+# ------------
+def test_forward_inception_v1():
+    '''test inception V1 model'''
+    with tf.Graph().as_default():
+        (data, tvm_data, graph_def) = nnvm.testing.tf.get_workload_inception_v1()
+        # Call the utility to import the graph definition into default graph.
+        graph_def = nnvm.testing.tf.ProcessGraphDefParam(graph_def)
+
+        tvm_output = run_tvm_graph(graph_def, tvm_data, 'DecodeJpeg/contents', (1, 1008), 'float32')
+
+        with tf.Session() as sess:
+            tf_output = run_tf_graph(sess, data, 'DecodeJpeg/contents:0', 'softmax:0')
+
+        np.testing.assert_allclose(tf_output, tvm_output, rtol=2e-2, atol=2e-2)
+
+#######################################################################
+# Main
+# ----
+if __name__ == '__main__':
+    test_forward_convolution()
+    test_forward_pooling()
+    test_forward_reshape()
+    test_forward_squeeze()
+    if tf.__version__ == '1.4.1':
+        _test_forward_concat_v2()
+    test_forward_multi_input()
+    test_forward_inception_v3()
+    test_forward_inception_v1()

tests/scripts/task_python_nnvm.sh

@@ -18,3 +18,6 @@ python3 -m nose -v nnvm/tests/python/frontend/mxnet || exit -1
 
 echo "Running Keras frontend test..."
 python3 -m nose -v nnvm/tests/python/frontend/keras || exit -1
+
+echo "Running Tensorflow frontend test..."
+python3 -m nose -v nnvm/tests/python/frontend/tensorflow || exit -1

tutorials/nnvm/from_tensorflow.py

+"""
+Compile Tensorflow Models
+=========================
+This article is an introductory tutorial to deploy tensorflow models with NNVM.
+
+For us to begin with, tensorflow module is required to be installed.
+
+A quick solution is to install tensorlfow from
+
+https://www.tensorflow.org/install/install_sources
+"""
+
+import nnvm
+import tvm
+import numpy as np
+import os.path
+
+# Tensorflow imports
+import tensorflow as tf
+from tensorflow.core.framework import graph_pb2
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import tensor_util
+
+import nnvm.testing.tf
+
+repo_base = 'https://github.com/dmlc/web-data/raw/master/tensorflow/models/InceptionV1/'
+img_name = 'elephant-299.jpg'
+image_url = os.path.join(repo_base, img_name)
+model_name = 'classify_image_graph_def-with_shapes.pb'
+model_url = os.path.join(repo_base, model_name)
+map_proto = 'imagenet_2012_challenge_label_map_proto.pbtxt'
+map_proto_url = os.path.join(repo_base, map_proto)
+lable_map = 'imagenet_synset_to_human_label_map.txt'
+lable_map_url = os.path.join(repo_base, lable_map)
+
+
+######################################################################
+# Download processed tensorflow model
+# -----------------------------------
+# In this section, we download a pretrained Tensorflow model and classify an image.
+from mxnet.gluon.utils import download
+
+download(image_url, img_name)
+download(model_url, model_name)
+download(map_proto_url, map_proto)
+download(lable_map_url, lable_map)
+
+
+######################################################################
+# Creates graph from saved graph_def.pb.
+# --------------------------------------
+
+with tf.gfile.FastGFile(os.path.join(
+        "./", model_name), 'rb') as f:
+    graph_def = tf.GraphDef()
+    graph_def.ParseFromString(f.read())
+    graph = tf.import_graph_def(graph_def, name='')
+    # Call the utility to import the graph definition into default graph.
+    graph_def = nnvm.testing.tf.ProcessGraphDefParam(graph_def)
+
+
+######################################################################
+# Decode image
+# ------------
+from PIL import Image
+image = Image.open(img_name).resize((299, 299))
+
+def transform_image(image):
+    image = np.array(image)
+    return image
+
+x = transform_image(image)
+
+######################################################################
+# Import the graph to NNVM
+# ------------------------
+sym, params = nnvm.frontend.from_tensorflow(graph_def)
+
+######################################################################
+# Now compile the graph through NNVM
+import nnvm.compiler
+target = 'llvm'
+shape_dict = {'DecodeJpeg/contents': x.shape}
+dtype_dict = {'DecodeJpeg/contents': 'uint8'}
+graph, lib, params = nnvm.compiler.build(sym, target, shape_dict, dtype=dtype_dict, params=params)
+
+######################################################################
+# Execute the portable graph on TVM
+# ---------------------------------
+# Now, we would like to reproduce the same forward computation using TVM.
+from tvm.contrib import graph_runtime
+ctx = tvm.cpu(0)
+dtype = 'uint8'
+m = graph_runtime.create(graph, lib, ctx)
+# set inputs
+m.set_input('DecodeJpeg/contents', tvm.nd.array(x.astype(dtype)))
+m.set_input(**params)
+# execute
+m.run()
+# get outputs
+tvm_output = m.get_output(0, tvm.nd.empty(((1, 1008)), 'float32'))
+
+
+######################################################################
+# Process the output to human readable
+# ------------------------------------
+predictions = tvm_output.asnumpy()
+predictions = np.squeeze(predictions)
+
+# Creates node ID --> English string lookup.
+node_lookup = nnvm.testing.tf.NodeLookup(label_lookup_path=os.path.join("./", map_proto),
+                                         uid_lookup_path=os.path.join("./", lable_map))
+
+top_k = predictions.argsort()[-5:][::-1]
+for node_id in top_k:
+    human_string = node_lookup.id_to_string(node_id)
+    score = predictions[node_id]
+    print('%s (score = %.5f)' % (human_string, score))
+
+######################################################################
+# Run the same graph with tensorflow and dump output.
+# ---------------------------------------------------
+
+def create_graph():
+    """Creates a graph from saved GraphDef file and returns a saver."""
+    # Creates graph from saved graph_def.pb.
+    with tf.gfile.FastGFile(model_name, 'rb') as f:
+        graph_def = tf.GraphDef()
+        graph_def.ParseFromString(f.read())
+        graph = tf.import_graph_def(graph_def, name='')
+        # Call the utility to import the graph definition into default graph.
+        graph_def = nnvm.testing.tf.ProcessGraphDefParam(graph_def)
+
+def run_inference_on_image(image):
+    """Runs inference on an image.
+
+    Parameters
+    ----------
+    image: String
+        Image file name.
+
+    Returns
+    -------
+        Nothing
+    """
+    if not tf.gfile.Exists(image):
+        tf.logging.fatal('File does not exist %s', image)
+    image_data = tf.gfile.FastGFile(image, 'rb').read()
+
+    # Creates graph from saved GraphDef.
+    create_graph()
+
+    with tf.Session() as sess:
+        softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
+        predictions = sess.run(softmax_tensor,
+                               {'DecodeJpeg/contents:0': image_data})
+
+        predictions = np.squeeze(predictions)
+
+        # Creates node ID --> English string lookup.
+        node_lookup = nnvm.testing.tf.NodeLookup(label_lookup_path=os.path.join("./", map_proto),
+                                                 uid_lookup_path=os.path.join("./", lable_map))
+
+        top_k = predictions.argsort()[-5:][::-1]
+        print ("===== TENSORFLOW RESULTS =======")
+        for node_id in top_k:
+            human_string = node_lookup.id_to_string(node_id)
+            score = predictions[node_id]
+            print('%s (score = %.5f)' % (human_string, score))
+
+run_inference_on_image (img_name)