Copy ben's changes
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Trevor Morris
parent
7d57d32e44
commit
1051c37705
@ -62,14 +62,14 @@ limitations under the License.
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#define TFTRT_RETURN_ERROR_IF_FALSE(status, node) \
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do { \
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if (status == false) { \
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if ((status) == false) { \
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TFTRT_INTERNAL_ERROR_AT_NODE(node); \
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} \
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} while (0)
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#define TFTRT_RETURN_ERROR_IF_NULLPTR(ptr, node) \
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do { \
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if (ptr == nullptr) { \
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if ((ptr) == nullptr) { \
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TFTRT_INTERNAL_ERROR_AT_NODE(node); \
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} \
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} while (0)
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@ -1577,12 +1577,14 @@ tensorflow::Status ConvertConv2DHelper(OpConverterParams* params, int group) {
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const nvinfer1::ITensor* tensor = inputs.at(0).tensor();
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TFAttrs attrs(node_def);
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int c_index = 1;
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int h_index = 2;
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int w_index = 3;
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auto data_format = attrs.get<string>("data_format");
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if (data_format == "NHWC") {
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TF_RETURN_IF_ERROR(params->converter->TransposeTensor(
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const_cast<nvinfer1::ITensor*>(tensor), {0, 3, 1, 2}, &tensor));
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c_index = 3;
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h_index = 1;
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w_index = 2;
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// TODO(jie): transpose it
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@ -1618,14 +1620,30 @@ tensorflow::Status ConvertConv2DHelper(OpConverterParams* params, int group) {
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<< tf_stride[3];
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const nvinfer1::DimsHW stride(tf_stride[h_index], tf_stride[w_index]);
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auto tf_dilations = attrs.get<std::vector<int>>("dilations");
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if ((int)tf_dilations.size() != 4) {
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return tensorflow::errors::InvalidArgument(
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"Convolution dilations field must specify 4 dimensions " +
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node_def.name());
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}
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if (tf_dilations[0] != 1 || tf_dilations[c_index] != 1) {
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return tensorflow::errors::Unimplemented(
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"Dilation rate must be 1 for batch and channel dimensions, at ",
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node_def.name());
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}
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nvinfer1::DimsHW dilation(tf_dilations[h_index], tf_dilations[w_index]);
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std::vector<std::pair<int, int>> padding;
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// TODO(jie): padding.
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if (attrs.get<string>("padding") == "SAME") {
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// This is NCHW tensor with no batch dimension.
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// 1 -> h
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// 2 -> w
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nvinfer1::DimsHW effective_kernel_size = kernel_size;
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effective_kernel_size.h() += (kernel_size.h() - 1) * (dilation.h() - 1);
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effective_kernel_size.w() += (kernel_size.w() - 1) * (dilation.w() - 1);
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padding = CreateSamePadding(
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stride, kernel_size,
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stride, effective_kernel_size,
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{static_cast<int>(tensor_dim.d[1]), static_cast<int>(tensor_dim.d[2])});
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} else {
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padding = {{0, 0}, {0, 0}};
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@ -1659,6 +1677,7 @@ tensorflow::Status ConvertConv2DHelper(OpConverterParams* params, int group) {
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layer->setPadding({padding[0].first, padding[1].first});
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layer->setName(node_def.name().c_str());
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layer->setNbGroups(num_groups);
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layer->setDilation(dilation);
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const nvinfer1::ITensor* output_tensor = layer->getOutput(0);
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VLOG(2) << "TENSOR out: " << DebugString(output_tensor->getDimensions());
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VLOG(2) << "data_format: " << data_format;
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172
tensorflow/contrib/tensorrt/test/conv2d_test.py
Normal file
172
tensorflow/contrib/tensorrt/test/conv2d_test.py
Normal file
@ -0,0 +1,172 @@
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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Model script to test TF-TensorRT integration."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt_test
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import gen_array_ops
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from tensorflow.python.ops import gen_nn_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import nn_ops
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from tensorflow.python.platform import test
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def conv2d_layer(inputs, filters, kernel_size, strides=(1, 1), padding='valid',
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data_format='channels_last', dilation_rate=(1, 1), name=None):
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dtype = inputs.dtype
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c_axis = -1 if data_format == 'channels_last' else 1
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nchan = inputs.shape[c_axis]
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weights_shape = (kernel_size[0], kernel_size[1], nchan, filters)
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weights = constant_op.constant(np.random.randn(*weights_shape), dtype=dtype)
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padding = padding.upper()
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if data_format == 'channels_last':
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strides = [1] + list(strides) + [1]
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dilations = [1] + list(dilation_rate) + [1]
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data_format = 'NHWC'
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else:
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strides = [1, 1] + list(strides)
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dilations = [1, 1] + list(dilation_rate)
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data_format = 'NCHW'
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return gen_nn_ops.conv2d(inputs, weights, strides=strides, padding=padding,
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dilations=dilations, data_format=data_format)
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def div_round_up(n, d):
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return (n - 1) // d + 1
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class Conv2DNCHWTest(trt_test.TfTrtIntegrationTestBase):
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def GetParams(self):
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"""Testing conversion of Conv2D (data_format=NCHW) in TF-TRT conversion."""
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np.random.seed(1234)
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dtype = dtypes.float32
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input_name = "input"
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n, c, h, w = 13, 3, 7, 11
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num_filters = 5
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input_dims = [n, c, h, w]
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output_name = "output"
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g = ops.Graph()
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with g.as_default():
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inp = array_ops.placeholder(
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dtype=dtype, shape=[None] + input_dims[1:], name=input_name)
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with g.device("/GPU:0"):
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results = []
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for kernel_size in [(3, 3), (3, 2)]:
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for dilation_rate in [(1, 1), (2, 3)]:
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result = conv2d_layer(inp, num_filters, kernel_size,
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dilation_rate=dilation_rate, padding='same',
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data_format='channels_first')
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results.append(result)
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output = sum(results)
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output = array_ops.identity(output, name=output_name)
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return trt_test.TfTrtIntegrationTestParams(
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gdef=g.as_graph_def(),
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input_names=[input_name],
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input_dims=[input_dims],
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output_names=[output_name],
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expected_output_dims=[(n, num_filters, h, w)])
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def ExpectedEnginesToBuild(self, run_params):
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"""Return the expected engines to build."""
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return ["my_trt_op_0"]
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class Conv2DStridedNCHWTest(trt_test.TfTrtIntegrationTestBase):
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def GetParams(self):
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"""Testing conversion of strided Conv2D (data_format=NCHW) in TF-TRT
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conversion."""
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np.random.seed(1234)
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dtype = dtypes.float32
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input_name = "input"
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n, c, h, w = 13, 3, 7, 11
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num_filters = 5
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input_dims = [n, c, h, w]
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output_name = "output"
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g = ops.Graph()
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with g.as_default():
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inp = array_ops.placeholder(
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dtype=dtype, shape=[None] + input_dims[1:], name=input_name)
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with g.device("/GPU:0"):
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output = inp
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output = conv2d_layer(output, num_filters, (3, 2), strides=(2, 2),
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padding='same', data_format='channels_first')
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h = div_round_up(h, 2)
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w = div_round_up(w, 2)
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output = conv2d_layer(output, num_filters, (3, 3), strides=(2, 2),
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dilation_rate=(2, 3), padding='same',
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data_format='channels_first')
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h = div_round_up(h, 2)
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w = div_round_up(w, 2)
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output = array_ops.identity(output, name=output_name)
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return trt_test.TfTrtIntegrationTestParams(
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gdef=g.as_graph_def(),
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input_names=[input_name],
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input_dims=[input_dims],
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output_names=[output_name],
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expected_output_dims=[(n, num_filters, h, w)])
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def ExpectedEnginesToBuild(self, run_params):
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"""Return the expected engines to build."""
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return ["my_trt_op_0"]
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class Conv2DNHWCTest(trt_test.TfTrtIntegrationTestBase):
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def GetParams(self):
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"""Testing conversion of Conv2D (data_format=NHWC) in TF-TRT conversion."""
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np.random.seed(1234)
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dtype = dtypes.float32
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input_name = "input"
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n, h, w, c = 13, 7, 11, 3
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num_filters = 5
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input_dims = [n, h, w, c]
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output_name = "output"
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g = ops.Graph()
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with g.as_default():
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inp = array_ops.placeholder(
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dtype=dtype, shape=[None] + input_dims[1:], name=input_name)
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with g.device("/GPU:0"):
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results = []
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for kernel_size in [(3, 3), (3, 2)]:
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for dilation_rate in [(1, 1), (2, 3)]:
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result = conv2d_layer(inp, num_filters, kernel_size,
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dilation_rate=dilation_rate, padding='same',
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data_format='channels_last')
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results.append(result)
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output = sum(results)
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output = array_ops.identity(output, name=output_name)
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return trt_test.TfTrtIntegrationTestParams(
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gdef=g.as_graph_def(),
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input_names=[input_name],
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input_dims=[input_dims],
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output_names=[output_name],
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expected_output_dims=[(n, h, w, num_filters)])
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def ExpectedEnginesToBuild(self, run_params):
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"""Return the expected engines to build."""
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return ["my_trt_op_0"]
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if __name__ == "__main__":
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test.main()
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