From badcb6302b41733f817e6e9b557cb7f7082b5f67 Mon Sep 17 00:00:00 2001
From: Qiao Longfei <qiaolongfei@baidu.com>
Date: Tue, 10 Jul 2018 00:54:53 +0800
Subject: [PATCH] change line seperator of tensorflow/test_forward from CRLF to
LF (#1405)
---
.../frontend/tensorflow/test_forward.py | 1092 ++++++++---------
1 file changed, 546 insertions(+), 546 deletions(-)
diff --git a/nnvm/tests/python/frontend/tensorflow/test_forward.py b/nnvm/tests/python/frontend/tensorflow/test_forward.py
index 5a37918f1..9fcf33bdf 100644
--- a/nnvm/tests/python/frontend/tensorflow/test_forward.py
+++ b/nnvm/tests/python/frontend/tensorflow/test_forward.py
@@ -1,546 +1,546 @@
-# 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.python.ops import math_ops
-from tensorflow.python.ops import variable_scope
-from tensorflow.python.ops import variables
-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])
-
- _test_pooling(input_shape=[2, 9, 10, 2],
- window_shape=[2, 1],
- padding='SAME',
- pooling_type='MAX',
- dilation_rate=[1, 1],
- strides=[1, 1])
- _test_pooling(input_shape=[2, 9, 10, 2],
- window_shape=[2, 1],
- padding='SAME',
- pooling_type='AVG',
- dilation_rate=[1, 1],
- strides=[2, 1])
-
- _test_pooling(input_shape=[2, 10, 9, 2],
- window_shape=[2, 3],
- padding='SAME',
- pooling_type='MAX',
- dilation_rate=[1, 1],
- strides=[2, 1])
- _test_pooling(input_shape=[2, 10, 9, 2],
- window_shape=[2, 3],
- padding='SAME',
- pooling_type='AVG',
- dilation_rate=[1, 1],
- strides=[1, 2])
-
-
-#######################################################################
-# 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)
-
-#######################################################################
-# Sigmoid
-# -------
-
-def _test_sigmoid(data):
- """ One iteration of sigmoid """
-
- with tf.Graph().as_default():
- in_data = constant_op.constant(data, shape=data.shape, dtype=data.dtype)
-
- # pylint: disable=unused-variable
- sigmoid_out = math_ops.sigmoid(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),
- ['Sigmoid'],
- )
-
- tf_output = run_tf_graph(sess, data,
- 'Const:0', 'Sigmoid:0')
- tvm_output = run_tvm_graph(graph_def,
- data,
- "Const", tf_output.shape, data.dtype)
-
- np.testing.assert_allclose(tf_output, tvm_output, atol=1e-5, rtol=1e-5)
-
- sess.close()
-
-def test_forward_sigmoid():
- """ Sigmoid """
-
- _test_sigmoid(np.random.uniform(size=(3, 4, 4, 3)).astype('float32'))
-
-
-#######################################################################
-# Variable
-# --------
-
-def _test_variable(data):
- tf.reset_default_graph()
- input_op = array_ops.placeholder(shape=data.shape, dtype=data.dtype)
- input_tensor = array_ops.reshape(input_op, data.shape)
-
- size = input_tensor.shape.dims[1]
- with variable_scope.variable_scope("linear", reuse=None):
- w = variable_scope.get_variable(
- "w", shape=[size, size], dtype=input_tensor.dtype)
- # pylint: disable=unused-variable
- output_op = math_ops.matmul(input_tensor, w)
- # pylint: enable=unused-variable
-
- with tf.Session() as sess:
- sess.run(variables.global_variables_initializer())
- final_graph_def = tf.graph_util.convert_variables_to_constants(
- sess,
- sess.graph.as_graph_def(add_shapes=True),
- ['MatMul'],
- )
-
- tf_output = run_tf_graph(sess, data, 'Placeholder:0', 'MatMul:0')
- tvm_output = run_tvm_graph(final_graph_def, data,
- "Placeholder", tf_output.shape, data.dtype)
-
- np.testing.assert_allclose(tf_output, tvm_output, atol=1e-5, rtol=1e-5)
- sess.close()
-
-def test_forward_variable():
- """Variable type op test"""
- _test_variable(np.random.uniform(size=(32, 100)).astype('float32'))
-
-
-#######################################################################
-# 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]
-
- np.testing.assert_allclose(top_tf, top_tvm, rtol=1e-5, atol=1e-5)
-
-#######################################################################
-# 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)
-
-#######################################################################
-# Mobilenet
-# ---------
-def test_forward_mobilenet():
- '''test mobilenet model'''
- with tf.Graph().as_default():
- graph_def = nnvm.testing.tf.get_workload_mobilenet()
- # Call the utility to import the graph definition into default graph.
- graph_def = nnvm.testing.tf.ProcessGraphDefParam(graph_def)
-
- data = np.random.uniform(size=(1, 224, 224, 3)).astype('float32')
- out_node = 'MobilenetV1/Predictions/Reshape_1'
-
- with tf.Session() as sess:
- tf_output = run_tf_graph(sess, data, 'input:0', out_node + ':0')
-
- out_shape = tf_output.shape
- tvm_output = run_tvm_graph(graph_def, data, 'input', out_shape, 'float32')
- top_tvm = np.squeeze(tvm_output).argsort()[-10:][::-1]
- top_tf = np.squeeze(tf_output).argsort()[-10:][::-1]
-
- np.testing.assert_allclose(np.squeeze(tvm_output), np.squeeze(tf_output), rtol=1e-5, atol=1e-5)
-
-#######################################################################
-# Main
-# ----
-if __name__ == '__main__':
- test_forward_convolution()
- test_forward_pooling()
- test_forward_reshape()
- test_forward_squeeze()
- test_forward_sigmoid()
- if tf.__version__ == '1.4.1':
- _test_forward_concat_v2()
- test_forward_multi_input()
- test_forward_inception_v3()
- test_forward_inception_v1()
- test_forward_mobilenet()
- test_forward_variable()
+# 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.python.ops import math_ops
+from tensorflow.python.ops import variable_scope
+from tensorflow.python.ops import variables
+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])
+
+ _test_pooling(input_shape=[2, 9, 10, 2],
+ window_shape=[2, 1],
+ padding='SAME',
+ pooling_type='MAX',
+ dilation_rate=[1, 1],
+ strides=[1, 1])
+ _test_pooling(input_shape=[2, 9, 10, 2],
+ window_shape=[2, 1],
+ padding='SAME',
+ pooling_type='AVG',
+ dilation_rate=[1, 1],
+ strides=[2, 1])
+
+ _test_pooling(input_shape=[2, 10, 9, 2],
+ window_shape=[2, 3],
+ padding='SAME',
+ pooling_type='MAX',
+ dilation_rate=[1, 1],
+ strides=[2, 1])
+ _test_pooling(input_shape=[2, 10, 9, 2],
+ window_shape=[2, 3],
+ padding='SAME',
+ pooling_type='AVG',
+ dilation_rate=[1, 1],
+ strides=[1, 2])
+
+
+#######################################################################
+# 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)
+
+#######################################################################
+# Sigmoid
+# -------
+
+def _test_sigmoid(data):
+ """ One iteration of sigmoid """
+
+ with tf.Graph().as_default():
+ in_data = constant_op.constant(data, shape=data.shape, dtype=data.dtype)
+
+ # pylint: disable=unused-variable
+ sigmoid_out = math_ops.sigmoid(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),
+ ['Sigmoid'],
+ )
+
+ tf_output = run_tf_graph(sess, data,
+ 'Const:0', 'Sigmoid:0')
+ tvm_output = run_tvm_graph(graph_def,
+ data,
+ "Const", tf_output.shape, data.dtype)
+
+ np.testing.assert_allclose(tf_output, tvm_output, atol=1e-5, rtol=1e-5)
+
+ sess.close()
+
+def test_forward_sigmoid():
+ """ Sigmoid """
+
+ _test_sigmoid(np.random.uniform(size=(3, 4, 4, 3)).astype('float32'))
+
+
+#######################################################################
+# Variable
+# --------
+
+def _test_variable(data):
+ tf.reset_default_graph()
+ input_op = array_ops.placeholder(shape=data.shape, dtype=data.dtype)
+ input_tensor = array_ops.reshape(input_op, data.shape)
+
+ size = input_tensor.shape.dims[1]
+ with variable_scope.variable_scope("linear", reuse=None):
+ w = variable_scope.get_variable(
+ "w", shape=[size, size], dtype=input_tensor.dtype)
+ # pylint: disable=unused-variable
+ output_op = math_ops.matmul(input_tensor, w)
+ # pylint: enable=unused-variable
+
+ with tf.Session() as sess:
+ sess.run(variables.global_variables_initializer())
+ final_graph_def = tf.graph_util.convert_variables_to_constants(
+ sess,
+ sess.graph.as_graph_def(add_shapes=True),
+ ['MatMul'],
+ )
+
+ tf_output = run_tf_graph(sess, data, 'Placeholder:0', 'MatMul:0')
+ tvm_output = run_tvm_graph(final_graph_def, data,
+ "Placeholder", tf_output.shape, data.dtype)
+
+ np.testing.assert_allclose(tf_output, tvm_output, atol=1e-5, rtol=1e-5)
+ sess.close()
+
+def test_forward_variable():
+ """Variable type op test"""
+ _test_variable(np.random.uniform(size=(32, 100)).astype('float32'))
+
+
+#######################################################################
+# 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]
+
+ np.testing.assert_allclose(top_tf, top_tvm, rtol=1e-5, atol=1e-5)
+
+#######################################################################
+# 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)
+
+#######################################################################
+# Mobilenet
+# ---------
+def test_forward_mobilenet():
+ '''test mobilenet model'''
+ with tf.Graph().as_default():
+ graph_def = nnvm.testing.tf.get_workload_mobilenet()
+ # Call the utility to import the graph definition into default graph.
+ graph_def = nnvm.testing.tf.ProcessGraphDefParam(graph_def)
+
+ data = np.random.uniform(size=(1, 224, 224, 3)).astype('float32')
+ out_node = 'MobilenetV1/Predictions/Reshape_1'
+
+ with tf.Session() as sess:
+ tf_output = run_tf_graph(sess, data, 'input:0', out_node + ':0')
+
+ out_shape = tf_output.shape
+ tvm_output = run_tvm_graph(graph_def, data, 'input', out_shape, 'float32')
+ top_tvm = np.squeeze(tvm_output).argsort()[-10:][::-1]
+ top_tf = np.squeeze(tf_output).argsort()[-10:][::-1]
+
+ np.testing.assert_allclose(np.squeeze(tvm_output), np.squeeze(tf_output), rtol=1e-5, atol=1e-5)
+
+#######################################################################
+# Main
+# ----
+if __name__ == '__main__':
+ test_forward_convolution()
+ test_forward_pooling()
+ test_forward_reshape()
+ test_forward_squeeze()
+ test_forward_sigmoid()
+ if tf.__version__ == '1.4.1':
+ _test_forward_concat_v2()
+ test_forward_multi_input()
+ test_forward_inception_v3()
+ test_forward_inception_v1()
+ test_forward_mobilenet()
+ test_forward_variable()
--
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