| /external/tensorflow/tensorflow/tools/graph_transforms/ |
| flatten_atrous.cc | 56 const NodeDef& conv_node = match.inputs[0].node;
110 flattened_conv_node.set_op(conv_node.op());
111 flattened_conv_node.set_device(conv_node.device());
116 CopyNodeAttr(conv_node, "T", "T", &flattened_conv_node);
117 CopyNodeAttr(conv_node, "strides", "strides", &flattened_conv_node);
119 CopyNodeAttr(conv_node, "data_format", "data_format",
122 if (conv_node.op() == "Conv2D") {
123 CopyNodeAttr(conv_node, "use_cudnn_on_gpu", "use_cudnn_on_gpu",
|
| fuse_convolutions.cc | 51 const NodeDef& conv_node = match.node;
68 AddNodeInput(conv_node.input(1), &fused_conv);
72 CopyNodeAttr(conv_node, "T", "T", &fused_conv);
73 CopyNodeAttr(conv_node, "padding", "padding", &fused_conv);
74 CopyNodeAttr(conv_node, "strides", "strides", &fused_conv);
100 const NodeDef& conv_node = match.node;
110 pad_dims_node.set_name(conv_node.name() + "_dummy_paddings");
123 AddNodeInput(conv_node.input(1), &fused_conv);
127 CopyNodeAttr(conv_node, "T", "T", &fused_conv);
128 CopyNodeAttr(conv_node, "padding", "padding", &fused_conv)
[all...] |
| fold_batch_norms.cc | 41 {"Conv2D|MatMul", // conv_node
55 const NodeDef& conv_node = match.inputs[0].node;
61 for (const auto& node : {conv_node, weights_node, mul_values_node}) {
65 {mul_node, conv_node, input_node, weights_node,
76 const int weights_cols_index = conv_node.op() == "Conv2D" ? 3 : 1;
107 new_conv_node = conv_node;
|
| fold_old_batch_norms.cc | 112 const NodeDef& conv_node = conv_node_match.node; local
113 CHECK_EQ("Conv2D", conv_node.op());
150 new_nodes->push_back(conv_node);
154 bias_offset_node.set_name(conv_node.name() + "_bn_offset");
162 CopyNodeAttr(conv_node, "T", "T", &bias_add_node);
163 AddNodeInput(conv_node.name(), &bias_add_node);
263 {"Conv2D", // conv_node
297 {"Conv2D", // conv_node
303 {"Conv2D", // conv_node
|
| /external/tensorflow/tensorflow/core/grappler/optimizers/ |
| layout_optimizer_test.cc | 375 auto conv_node = node_map.GetNode("Conv2D"); local
376 EXPECT_EQ(conv_node->attr().at({"data_format"}).s(), "NHWC");
389 auto conv_node = node_map.GetNode("Conv2D"); local
390 EXPECT_EQ(conv_node->attr().at({"data_format"}).s(), "NCHW");
404 auto conv_node = node_map.GetNode("Conv2D"); local
405 EXPECT_EQ(conv_node->attr().at({"data_format"}).s(), "NCHW");
419 auto conv_node = node_map.GetNode("Conv2D"); local
420 EXPECT_EQ(conv_node->attr().at({"data_format"}).s(), "NHWC");
433 auto conv_node = node_map.GetNode("Conv2D"); local
434 EXPECT_EQ(conv_node->attr().at({"data_format"}).s(), "NHWC")
447 auto conv_node = node_map.GetNode("FusedBatchNormGrad"); local
461 auto conv_node = node_map.GetNode("FusedBatchNormGrad"); local
[all...] |
| arithmetic_optimizer_test.cc | 1053 const NodeDef* conv_node = CHECK_NOTNULL(node_map.GetNode("Conv2D")); local
1097 const NodeDef* conv_node = CHECK_NOTNULL(node_map.GetNode("Conv2D")); local
[all...] |
| /external/tensorflow/tensorflow/tools/quantization/ |
| quantize_graph_test.py | 93 conv_node = quantize_graph.create_node(
95 quantize_graph.set_attr_dtype(conv_node, "T", dtypes.float32)
96 quantize_graph.set_attr_int_list(conv_node, "strides", [1, stride, stride, 1])
97 quantize_graph.set_attr_string(conv_node, "padding", padding)
98 float_graph_def.node.extend([conv_node])
[all...] |
