Test cases

        Inception v1
        Convolution

python/nnvm/frontend/tensorflow.py

@@ -1,6 +1,7 @@
 # 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
@@ -227,6 +228,7 @@ def _impl(inputs, attr, params):
 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
 
@@ -248,8 +250,9 @@ def _impl(inputs, attr, params):
 
 def _resize_bilinear():
     def _impl(inputs, attr, params):
-        # Making a copy node assuming the input image shape is 299x299
+        # Making a copy node assuming the preprocessing already done.
         # Change this when we have corresponding resize bilinear operation.
+        print ("ResizeBilinear: It's a pass through, please handle preprocessing before input")
         pop_node = inputs.pop(1)
         params.pop(pop_node.list_output_names()[0])
         return AttrCvt(op_name="copy", ignores=['align_corners'])(inputs, attr)
@@ -427,6 +430,10 @@ def from_tensorflow(self, graph):
                     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] = \

tests/python/frontend/tensorflow/test_forward.py

@@ -0,0 +1,315 @@
+# pylint: disable=import-self, invalid-name, unused-argument
+"""
+Tensorflow testcases
+=====================
+This article is a test script to test tensorflow models with NNVM.
+All the required dependency files will be downloaded from the internet
+by the script.
+"""
+from __future__ import print_function
+import os
+import sys
+import urllib
+import requests
+import numpy as np
+import nnvm.compiler
+import tvm
+import tensorflow as tf
+from tensorflow.core.framework import graph_pb2
+from tensorflow.python.framework import constant_op
+from tensorflow.python.ops import nn_ops
+
+
+if sys.version_info >= (3,):
+    import urllib.request as urllib2
+else:
+    import urllib2
+
+#######################################################################
+# Some tensorflow helper functions to handle models
+# -------------------------------------------------
+def process_graph_default(graph_def):
+    """Type-checks and possibly canonicalizes `graph_def`."""
+    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
+
+
+def load_graph(model_name):
+    with tf.gfile.FastGFile(model_name, 'rb') as f:
+        graph_def = tf.GraphDef()
+        graph_def.ParseFromString(f.read())
+        # pylint: disable=unused-variable
+        graph = tf.import_graph_def(graph_def, name='')
+        # pylint: enable=unused-variable
+        graph_def = process_graph_default(graph_def)
+    return graph_def
+
+#######################################################################
+# File download helper function
+# -----------------------------
+def _download(url, path, overwrite=False, sizecompare=False):
+    ''' Download from internet'''
+    if os.path.isfile(path) and not overwrite:
+        if sizecompare:
+            file_size = os.path.getsize(path)
+            res_head = requests.head(url)
+            res_get = requests.get(url, stream=True)
+            if 'Content-Length' not in res_head.headers:
+                res_get = urllib2.urlopen(url)
+            urlfile_size = int(res_get.headers['Content-Length'])
+            if urlfile_size != file_size:
+                print("exist file got corrupted, downloading", path, " file freshly")
+                _download(url, path, True, False)
+                return
+        print('File {} exists, skip.'.format(path))
+        return
+    print('Downloading from url {} to {}'.format(url, path))
+    # pylint: disable=bare-except
+    try:
+        urllib.request.urlretrieve(url, path)
+        print('')
+    except:
+        urllib.urlretrieve(url, path)
+    # pylint: enable=bare-except
+
+#######################################################################
+# 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'
+    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
+    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):
+    tensor = sess.graph.get_tensor_by_name(output_node)
+    output_data = sess.run(tensor, {input_node: input_data})
+    return output_data
+
+#######################################################################
+# Inception V1
+# ------------
+def inception_v1_tvm(graph_def, image_name):
+    from PIL import Image
+    image = Image.open(image_name).resize((299, 299))
+    image = np.array(image)
+
+    output = run_tvm_graph(graph_def, image, 'DecodeJpeg/contents', (1, 1008), 'float32')
+    return np.squeeze(output)
+
+
+def inception_v1_tf(graph_def, image_name):
+    if not tf.gfile.Exists(image_name):
+        tf.logging.fatal('File does not exist %s', image)
+    image_data = tf.gfile.FastGFile(image_name, 'rb').read()
+
+    with tf.Session() as sess:
+        output = run_tf_graph(sess, image_data, 'DecodeJpeg/contents:0', 'softmax:0')
+        return np.squeeze(output)
+
+def test_forward_inception_v1():
+    '''test inception V1 model'''
+    model_name = 'inception_v1'
+
+    repo = 'https://github.com/srkreddy1238/dmlc_data/raw/master/models/tensorflow/InceptionV1/'
+    model_name = 'classify_image_graph_def-with_shapes.pb'
+
+    model_url = repo + model_name
+    _download(model_url, model_name)
+
+    graph_def = load_graph(model_name)
+
+    image_name = 'elephant-299.jpg'
+    image_url = repo + image_name
+    _download(image_url, image_name)
+
+    tf_output = inception_v1_tf(graph_def, image_name)
+    tvm_output = inception_v1_tvm(graph_def, image_name)
+
+    np.testing.assert_allclose(tf_output, tvm_output, rtol=2e-2, atol=2e-2)
+
+#######################################################################
+# Convolution
+# -----------
+
+# Borrowed from tensorflow for test cases.
+def get_shrunk_inception_shapes(shrink=10):
+    """Iterator for smaller versions of convolution shapes in 2015 Inception.
+
+    Relative to inception, each depth value is `depth // shrink`.
+
+    Args:
+        shrink: Factor to shrink each depth value by relative to Inception.
+
+    Yields:
+        Tuple (input_size, filter_size, out_size, stride, padding), the convolution
+        parameters of Inception layers.
+    """
+    input_sizes = [[4, 5, 5, 1248], [4, 8, 8, 384], [4, 8, 8, 384],
+                   [4, 8, 8, 2048], [4, 8, 8, 448], [4, 8, 8, 2048],
+                   [4, 8, 8, 2048], [4, 8, 8, 2048], [4, 8, 8, 1760],
+                   [4, 8, 8, 1760], [4, 8, 8, 1760], [4, 8, 8, 1760],
+                   [4, 17, 17, 192], [4, 17, 17, 192], [4, 17, 17, 1248],
+                   [4, 17, 17, 128], [4, 17, 17, 1248], [4, 17, 17, 224],
+                   [4, 17, 17, 192], [4, 17, 17, 192], [4, 17, 17, 1216],
+                   [4, 17, 17, 1216], [4, 17, 17, 224], [4, 17, 17, 192],
+                   [4, 17, 17, 192], [4, 17, 17, 1152], [4, 17, 17, 1152],
+                   [4, 17, 17, 192], [4, 17, 17, 160], [4, 17, 17, 1152],
+                   [4, 17, 17, 1024], [4, 17, 17, 128], [4, 17, 17, 1024],
+                   [4, 17, 17, 128], [4, 17, 17, 1024], [4, 17, 17, 128],
+                   [4, 17, 17, 768], [4, 17, 17, 128], [4, 17, 17, 128],
+                   [4, 17, 17, 768], [4, 17, 17, 768], [4, 35, 35, 96],
+                   [4, 35, 35, 288], [4, 35, 35, 64], [4, 35, 35, 288],
+                   [4, 35, 35, 256], [4, 35, 35, 48], [4, 35, 35, 256],
+                   [4, 35, 35, 96], [4, 35, 35, 192], [4, 35, 35, 192],
+                   [4, 35, 35, 192], [4, 73, 73, 64], [4, 73, 73, 64],
+                   [4, 147, 147, 24]]
+    filter_sizes = [[1, 1, 1248, 128], [1, 3, 384, 384], [3, 1, 384, 384],
+                    [1, 1, 2048, 192], [3, 3, 448, 384], [1, 1, 2048, 320],
+                    [1, 1, 2048, 448], [1, 1, 2048, 384], [1, 1, 1760, 384],
+                    [1, 1, 1760, 192], [1, 1, 1760, 448], [1, 1, 1760, 320],
+                    [3, 3, 192, 192], [3, 3, 192, 192], [1, 1, 1248, 192],
+                    [3, 3, 128, 320], [1, 1, 1248, 128], [1, 3, 224, 224],
+                    [3, 1, 192, 256], [1, 3, 192, 256], [1, 1, 1216, 192],
+                    [1, 1, 1216, 96], [3, 1, 224, 224], [3, 3, 192, 224],
+                    [1, 3, 192, 192], [1, 1, 1152, 192], [1, 1, 1152, 128],
+                    [3, 1, 192, 192], [3, 3, 160, 192], [1, 1, 1152, 160],
+                    [1, 1, 1024, 128], [1, 3, 128, 192], [1, 1, 1024, 160],
+                    [3, 1, 128, 192], [1, 1, 1024, 256], [3, 1, 128, 128],
+                    [1, 1, 768, 192], [1, 3, 128, 128], [3, 3, 128, 128],
+                    [1, 1, 768, 128], [1, 1, 768, 320], [3, 3, 96, 96],
+                    [3, 3, 288, 384], [3, 3, 64, 96], [1, 1, 288, 64],
+                    [1, 1, 256, 64], [5, 5, 48, 64], [1, 1, 256, 48],
+                    [3, 3, 96, 96], [1, 1, 192, 32], [1, 1, 192, 64],
+                    [1, 1, 192, 48], [3, 3, 64, 192], [1, 1, 64, 64],
+                    [1, 1, 24, 64]]
+    out_sizes = [[4, 5, 5, 128], [4, 8, 8, 384], [4, 8, 8, 384],
+                 [4, 8, 8, 192], [4, 8, 8, 384], [4, 8, 8, 320],
+                 [4, 8, 8, 448], [4, 8, 8, 384], [4, 8, 8, 384],
+                 [4, 8, 8, 192], [4, 8, 8, 448], [4, 8, 8, 320],
+                 [4, 8, 8, 192], [4, 17, 17, 192], [4, 17, 17, 192],
+                 [4, 8, 8, 320], [4, 17, 17, 128], [4, 17, 17, 224],
+                 [4, 17, 17, 256], [4, 17, 17, 256], [4, 17, 17, 192],
+                 [4, 17, 17, 96], [4, 17, 17, 224], [4, 17, 17, 224],
+                 [4, 17, 17, 192], [4, 17, 17, 192], [4, 17, 17, 128],
+                 [4, 17, 17, 192], [4, 17, 17, 192], [4, 17, 17, 160],
+                 [4, 17, 17, 128], [4, 17, 17, 192], [4, 17, 17, 160],
+                 [4, 17, 17, 192], [4, 17, 17, 256], [4, 17, 17, 128],
+                 [4, 17, 17, 192], [4, 17, 17, 128], [4, 17, 17, 128],
+                 [4, 17, 17, 128], [4, 17, 17, 320], [4, 17, 17, 96],
+                 [4, 17, 17, 384], [4, 35, 35, 96], [4, 35, 35, 64],
+                 [4, 35, 35, 64], [4, 35, 35, 64], [4, 35, 35, 48],
+                 [4, 35, 35, 96], [4, 35, 35, 32], [4, 35, 35, 64],
+                 [4, 35, 35, 48], [4, 71, 71, 192], [4, 73, 73, 64],
+                 [4, 147, 147, 64]]
+    strides = [
+        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1,
+        1, 1, 1, 1, 1
+    ]
+    # Shrink sizes to make the test faster
+    # pylint: disable=invalid-name
+    for i in input_sizes:
+        i[3] //= shrink
+    for f in filter_sizes:
+        f[2] //= shrink
+        f[3] //= shrink
+    for o in out_sizes:
+        o[3] //= shrink
+
+    VALID = "VALID"
+    SAME = "SAME"
+    paddings = [
+        SAME, SAME, SAME, SAME, SAME, SAME, SAME, SAME, SAME, SAME, SAME, SAME,
+        VALID, SAME, SAME, VALID, SAME, SAME, SAME, SAME, SAME, SAME, SAME, SAME,
+        SAME, SAME, SAME, SAME, SAME, SAME, SAME, SAME, SAME, SAME, SAME, SAME,
+        SAME, SAME, SAME, SAME, SAME, VALID, VALID, SAME, SAME, SAME, SAME, SAME,
+        SAME, SAME, SAME, SAME, VALID, VALID, VALID
+    ]
+    for i, f, o, s, p in zip(input_sizes, filter_sizes, out_sizes, strides,
+                             paddings):
+        yield i, f, o, s, p
+    # pylint: enable=invalid-name
+
+def test_convolution_iteration(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)]
+
+    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():
+    # pylint: disable=unused-variable
+    for index, (input_size_, filter_size_, output_size_, stride_,
+                padding_) in enumerate(get_shrunk_inception_shapes()):
+        with tf.Graph().as_default():
+            test_convolution_iteration(input_size_, filter_size_, [1, 1],
+                                       [stride_, stride_], padding_, 'NHWC')
+    # pylint: enable=unused-variable
+
+#######################################################################
+# Main
+# ----
+if __name__ == '__main__':
+    test_forward_inception_v1()
+    test_forward_convolution()