Add data format explanation document and fix bugs in NetDefAdapter.

docs/development/data_format.md

+Data Format
+===========
+
+As we all know, input/output tensors in CNN model have data format like
+`NHWC`(tensorflow) or `NCHW`(caffe), but there is no data format for non-CNN model.
+
+However, in MACE, CNN model run on `CPU` with `float` type using `NCHW` data format,
+while the others using `NHWC` data format.
+
+To support all models, so there are some concepts in `MACE` you should know.
+
+Source Data Format
+-----------------------
+Source Data Format(`src_df` for short) stands for the original data format where 
+the model come from. For example, if you use caffe, the `src_df` is `NCHW`.
+We need this data format because some operators(Reshape etc.) are 
+ related to the data format.
+
+
+Operators Partition
+--------------------
+Generally, operators could be divided into 2 categories
+based on whether the operator needs inputs with fixed data format(`NHWC` or `NCHW`),
+one is the operators whose inputs have fixed data format(like `convolution`),
+the other is the operators whose inputs should be the same with source framework.
+
+Since the data format the operators need in MACE may be inconsistent with the original framework,
+ we need to add `Transpose` operator to transpose the input tensors if necessary. 
+ 
+However, for some operators like `concat`,
+we could transpose their arguments to eliminate `Transpose` op for acceleration.
+
+Based on these conditions, We partition the ops into 3 categories.
+1. Ops with fixed inputs' data format(`FixedDataFormatOps`): `Convolution`, `Depthwise Convolution`, etc.
+2. Ops could eliminate `Transpose` by transposing their arguments(`TransposableDataFormatOps`): `Concat`, `Element-wise`, etc.
+3. Ops keeping consistent with source platform(`SourceDataFormatOps`): `Reshape`, `ExpandDims`, etc.
+
+By default, the operators not in either `FixedDataFormatOps` or `TransposableDataFormatOps`
+are listed in `SourceDataFormatOps`.
+
+For detailed information, you could refer to [code](https://github.com/XiaoMi/mace/blob/master/mace/python/tools/converter_tool/base_converter.py).
+
+
+Data Format in Operator
+------------------------
+Based on the operator partition strategy, every operator in `MACE` has 
+data format argument which stands for the wanted inputs' data format,
+the values could be one of the [`NHWC`, `NCHW`, `AUTO`].
+1. `NHWC` or `NCHW` represent `src_df`.
+2. `AUTO` represents the operator's inputs must have fixed data format,
+and the real data format will be determined at runtime.
+the data format of operators in `FixedDataFormatOps` must be `AUTO`,
+while the data format of operators in `TransposableDataFormatOps` 
+is determined based on their inputs' ops data format.
+ 
+MACE will transpose the input tensors based on the data format information automatically at runtime.
+
+
+Data Format of Model's Inputs/Outputs
+-------------------------------------
+1. If the model's inputs/outputs have data format, MACE supports the data format 
+`NHWC` and `NCHW`.
+2. If the model's inputs/outputs do not have data format, just set `NONE` for
+ model's inputs and outputs at `model deployment file` and `MaceTensor`.

docs/development/dynamic_lstm.md → docs/development/dynamic_lstm.rst

 Dynamic LSTM
-==================
+============
 
 
 The DynamicLSTM in MACE is implemented for Kaldi's time delay RNN models.
@@ -8,17 +8,16 @@ The following pictures explain how to fuse components into a DynamicLSTMCell.
 
 Before fusing:
 
-<div  align="left">
-<img src="imgs/FuseLSTM.png" width = "320" height = "960" alt="how to fuse lstm" />
-</div>
+.. image:: imgs/FuseLSTM.png
+   :scale: 100 %
+   :align: center
 
 
 After fusing:
 
-<div  align="left">
-<img src="imgs/DynamicLSTM.png" width = "358" height = "391" alt="DynamicLSTM" />
-</div>
-
+.. image:: imgs/DynamicLSTM.png
+   :scale: 100 %
+   :align: center
 
 For more details about LSTMNonlinear in Kaldi,
 please refer to [LstmNonlinearComponent](http://kaldi-asr.org/doc/nnet-simple-component_8h_source.html#l02164)
\ No newline at end of file

docs/index.rst

@@ -41,6 +41,8 @@ The main documentation is organized into the following sections:
    development/how_to_run_tests
    development/how_to_debug
    development/memory_layout
+   development/data_format
+   development/dynamic_lstm
 
 .. toctree::
    :maxdepth: 1

mace/core/net_def_adapter.cc

@@ -113,8 +113,8 @@ MaceStatus NetDefAdapter::AdaptNetDef(
 
   // quantize model flag
   bool is_quantized_model = IsQuantizedModel(*net_def);
-  // Const tensors(filter) -> shape
-  std::unordered_map<std::string, std::vector<index_t>> tensor_shape_map;
+  // tensor -> shape
+  TensorShapeMap tensor_shape_map;
   // Output tensors -> information
   TensorInfoMap output_map;
   // output tensor : related information
@@ -135,13 +135,13 @@ MaceStatus NetDefAdapter::AdaptNetDef(
                << target_device->device_type();
   }
 
+  DataFormat expected_data_format = GetDefaultDataFormat(
+      target_device->device_type(), is_quantized_model);
   int input_size = target_net_def->input_info_size();
   for (int i = 0; i < input_size; ++i) {
     auto input_info = target_net_def->mutable_input_info(i);
     auto input_data_format = static_cast<DataFormat>(
         input_info->data_format());
-    DataFormat expected_data_format = GetDefaultDataFormat(
-        target_device->device_type(), is_quantized_model);
     std::vector<index_t> input_shape(input_info->dims().begin(),
                                      input_info->dims().end());
     if (input_data_format != DataFormat::NONE
@@ -192,12 +192,14 @@ MaceStatus NetDefAdapter::AdaptNetDef(
                                                  &op_def,
                                                  is_quantized_model,
                                                  &output_map,
+                                                 &tensor_shape_map,
                                                  &transformed_set,
                                                  &op_output_data_format,
                                                  target_net_def));
       MACE_RETURN_IF_ERROR(this->AdaptMemoryType(&context,
                                                  &op_def,
                                                  &output_map,
+                                                 &tensor_shape_map,
                                                  &transformed_set,
                                                  &op_output_mem_type,
                                                  target_net_def));
@@ -205,6 +207,7 @@ MaceStatus NetDefAdapter::AdaptNetDef(
       MACE_RETURN_IF_ERROR(this->AdaptMemoryType(&context,
                                                  &op_def,
                                                  &output_map,
+                                                 &tensor_shape_map,
                                                  &transformed_set,
                                                  &op_output_mem_type,
                                                  target_net_def));
@@ -212,6 +215,7 @@ MaceStatus NetDefAdapter::AdaptNetDef(
                                                  &op_def,
                                                  is_quantized_model,
                                                  &output_map,
+                                                 &tensor_shape_map,
                                                  &transformed_set,
                                                  &op_output_data_format,
                                                  target_net_def));
@@ -227,18 +231,20 @@ MaceStatus NetDefAdapter::AdaptNetDef(
             ProtoArgHelper::GetOptionalArg<OperatorDef, int>(
                 op_def, "T", static_cast<int>(DataType::DT_FLOAT)));
       }
+      auto output_shape = op_def.output_shape().empty() ?
+                          std::vector<index_t>() :
+                          std::vector<index_t>(
+                              op_def.output_shape(out_idx).dims().begin(),
+                              op_def.output_shape(out_idx).dims().end());
       output_map.emplace(
           op_def.output(out_idx),
           InternalOutputInfo(
               op_output_mem_type,
               dt,
               op_output_data_format,
-              op_def.output_shape().empty() ?
-              std::vector<index_t>() :
-              std::vector<index_t>(
-                  op_def.output_shape(out_idx).dims().begin(),
-                  op_def.output_shape(out_idx).dims().end()),
+              output_shape,
               target_net_def->op_size()));
+      tensor_shape_map.emplace(op_def.output(out_idx), output_shape);
     }
     // Add op to target net
     target_net_def->add_op()->CopyFrom(op_def);
@@ -357,6 +363,7 @@ MaceStatus NetDefAdapter::AdaptDataFormat(
     OperatorDef *op_def,
     bool is_quantized_model,
     TensorInfoMap *output_map,
+    TensorShapeMap *tensor_shape_map,
     std::unordered_set<std::string> *transformed_set,
     DataFormat *op_output_df,
     NetDef *target_net_def) {
@@ -465,6 +472,8 @@ MaceStatus NetDefAdapter::AdaptDataFormat(
                 dst_df,
                 output_shape,
                 target_net_def->op_size() - 1));
+        // update tensor shape map
+        tensor_shape_map->emplace(transformed_name, output_shape);
         // record transformed tensors
         transformed_set->insert(transformed_name);
       }
@@ -479,6 +488,7 @@ MaceStatus NetDefAdapter::AdaptMemoryType(
     OpConditionContext *context,
     OperatorDef *op_def,
     NetDefAdapter::TensorInfoMap *output_map,
+    TensorShapeMap *tensor_shape_map,
     std::unordered_set<std::string> *transformed_set,
     MemoryType *op_output_mem_types,
     NetDef *target_net_def) {
@@ -545,6 +555,8 @@ MaceStatus NetDefAdapter::AdaptMemoryType(
                   input_info.data_format,
                   input_info.shape,
                   target_net_def->op_size() - 1));
+          // update tensor shape map
+          tensor_shape_map->emplace(transformed_name, input_info.shape);
           // record transformed tensors
           transformed_set->insert(transformed_name);
         }
@@ -555,6 +567,7 @@ MaceStatus NetDefAdapter::AdaptMemoryType(
   }
 #else
   MACE_UNUSED(output_map);
+  MACE_UNUSED(tensor_shape_map);
   MACE_UNUSED(transformed_set);
   MACE_UNUSED(target_net_def);
 #endif  // MACE_ENABLE_OPENCL

mace/core/net_def_adapter.h

@@ -32,10 +32,21 @@ class OpRegistryBase;
 class Workspace;
 class Device;
 
-/// Conventions:
-/// 1. DataFormat::AUTO stands for formatted (NHWC or NCHW)
-/// 2. if Op with DataFormat::AUTO, the arguments of this op
-///    is formatted to NHWC
+///////////////////////////////////////////////////////////////////////////////
+///                               Conventions
+///
+/// 1. For the Ops ran with data format(like Conv2D),
+///    The inputs and outputs are DataFormat::NCHW if ran on CPU
+///    with float data type.
+///    while the inputs and outputs are DataFormat::NHWC for
+///    other situation(ran on GPU, quantization, DSP)
+///
+/// 2. Op with DataFormat::AUTO stands for inputs must have
+///    fixed format (NHWC or NCHW), determined at runtime.
+///
+/// 3. if Op with DataFormat::AUTO, the arguments of this op
+///    is formatted to NHWC.
+///////////////////////////////////////////////////////////////////////////////
 class NetDefAdapter {
  public:
   NetDefAdapter(const OpRegistryBase *op_registry,
@@ -77,6 +88,7 @@ class NetDefAdapter {
   };
 
   typedef std::unordered_map<std::string, InternalOutputInfo> TensorInfoMap;
+  typedef std::unordered_map<std::string, std::vector<index_t>> TensorShapeMap;
 
  private:
   MaceStatus AdaptDevice(OpConditionContext *context,
@@ -92,6 +104,7 @@ class NetDefAdapter {
       OperatorDef *op,
       bool is_quantized_model,
       TensorInfoMap *output_map,
+      TensorShapeMap *tensor_shape_map,
       std::unordered_set<std::string> *transformed_set,
       DataFormat *op_output_df,
       NetDef *target_net_def);
@@ -100,6 +113,7 @@ class NetDefAdapter {
       OpConditionContext *context,
       OperatorDef *op_def,
       TensorInfoMap *output_map,
+      TensorShapeMap *tensor_shape_map,
       std::unordered_set<std::string> *transformed_set,
       MemoryType *op_output_mem_types,
       NetDef *target_net_def);

mace/python/tools/converter_tool/base_converter.py

@@ -167,7 +167,7 @@ MaceSupportedOps = [
 
 MaceOp = Enum('MaceOp', [(op, op) for op in MaceSupportedOps], type=str)
 
-MaceHasDataFormatOps = [MaceOp.BatchNorm,
+MaceFixedDataFormatOps = [MaceOp.BatchNorm,
                           MaceOp.BatchToSpaceND,
                           MaceOp.Conv2D,
                           MaceOp.Deconv2D,
@@ -182,7 +182,7 @@ MaceHasDataFormatOps = [MaceOp.BatchNorm,
                           MaceOp.SpaceToBatchND,
                           MaceOp.SpaceToDepth]
 
-MaceMayHasDataFormatOps = [MaceOp.Activation,
+MaceTransposableDataFormatOps = [MaceOp.Activation,
                                  MaceOp.AddN,
                                  MaceOp.BiasAdd,
                                  MaceOp.ChannelShuffle,

mace/python/tools/converter_tool/transformer.py

@@ -27,8 +27,8 @@ from mace.python.tools.converter_tool.base_converter import EltwiseType
 from mace.python.tools.converter_tool.base_converter import FrameworkType
 from mace.python.tools.converter_tool.base_converter import MaceKeyword
 from mace.python.tools.converter_tool.base_converter import MaceOp
-from mace.python.tools.converter_tool.base_converter import MaceHasDataFormatOps  # noqa
-from mace.python.tools.converter_tool.base_converter import MaceMayHasDataFormatOps  # noqa
+from mace.python.tools.converter_tool.base_converter import MaceFixedDataFormatOps  # noqa
+from mace.python.tools.converter_tool.base_converter import MaceTransposableDataFormatOps  # noqa
 from mace.python.tools.converter_tool.base_converter import PaddingMode
 from mace.python.tools.converter_tool.base_converter import ReduceType
 from mace.python.tools.converter_tool.base_converter import TransformerRule
@@ -1348,9 +1348,9 @@ class Transformer(base_converter.ConverterInterface):
             if not df_arg:
                 df_arg = op.arg.add()
                 df_arg.name = MaceKeyword.mace_data_format_str
-            if op.type in MaceHasDataFormatOps:
+            if op.type in MaceFixedDataFormatOps:
                 df_arg.i = DataFormat.AUTO.value
-            elif op.type in MaceMayHasDataFormatOps:
+            elif op.type in MaceTransposableDataFormatOps:
                 input_df = DataFormat.AUTO.value
                 for input_tensor in op.input:
                     if input_tensor in self._consts:

mace/python/tools/operator.jinja2

@@ -96,10 +96,9 @@ void CreateOperator{{i}}(mace::OperatorDef *op) {
 
   {% if net.op[i].output_shape|length > 0 %}
   op->mutable_output_shape()->Reserve({{ net.op[i].output_shape|length }});
-  mace::OutputShape * output_shape = nullptr;
   {% for shape in net.op[i].output_shape %}
   {% if shape.dims|length > 0 %}
-  output_shape = op->add_output_shape();
+  mace::OutputShape * output_shape = op->add_output_shape();
 
   output_shape->mutable_dims()->Reserve({{ shape.dims|length }});
   {% for dim in shape.dims %}