feat: Support Densenet and MobilenetV2 conversion from PyTorch

mace/core/types.h

@@ -69,6 +69,8 @@ enum FrameworkType {
   TENSORFLOW = 0,
   CAFFE = 1,
   ONNX = 2,
+  MEGENGINE = 3,
+  PYTORCH = 4
 };
 
 template <typename T>

tools/converter.py

@@ -61,6 +61,7 @@ PlatformTypeStrs = [
     "caffe",
     "onnx",
     "megengine",
+    "pytorch",
 ]
 PlatformType = Enum('PlatformType', [(ele, ele) for ele in PlatformTypeStrs],
                     type=str)
@@ -520,6 +521,13 @@ def format_model_config(flags):
                 if not isinstance(value, list):
                     subgraph[key] = [value]
                 subgraph[key] = [str(v) for v in subgraph[key]]
+# --inputs_shapes will be passed to ELF file `mace_run_static', if input_shapes
+# contains spaces, such as: '1, 3, 224, 224', because mace_run.cc use gflags to
+# parse command line arguments, --input_shapes 1, 3, 224, 224 will be passed as
+# `--input_shapes 1,'. So we strip out spaces here.
+                if key in [YAMLKeyword.input_shapes,
+                           YAMLKeyword.output_shapes]:
+                    subgraph[key] = [e.replace(' ', '') for e in subgraph[key]]
             input_size = len(subgraph[YAMLKeyword.input_tensors])
             output_size = len(subgraph[YAMLKeyword.output_tensors])
 

tools/device.py

@@ -632,6 +632,9 @@ class DeviceWrapper:
                     'Run model {} on {}'.format(model_name, self.device_name)))
 
             model_config = configs[YAMLKeyword.models][model_name]
+            if model_config[YAMLKeyword.platform] == 'pytorch':
+                mace_check(flags.layers == "-1", "Device",
+                           'extracting intermediate layer output is not supported in pytorch JIT yet')  # noqa
             model_runtime = model_config[YAMLKeyword.runtime]
             subgraphs = model_config[YAMLKeyword.subgraphs]
 

tools/python/convert.py

@@ -190,6 +190,10 @@ def convert_model(conf, quantize_stat):
         from transform import megengine_converter
         converter = megengine_converter.MegengineConverter(
             option, conf["model_file_path"])
+    elif platform == Platform.PYTORCH:
+        from transform import pytorch_converter
+        converter = pytorch_converter.PytorchConverter(
+            option, conf["model_file_path"])
     else:
         mace_check(False, "Mace do not support platorm %s yet." % platform)
 

tools/python/transform/base_converter.py

@@ -88,6 +88,7 @@ class FrameworkType(Enum):
     CAFFE = 1
     ONNX = 2
     MEGENGINE = 3
+    PYTORCH = 4
 
 
 MaceSupportedOps = [

tools/python/transform/pytorch_converter.py

+# Copyright 2020 The MACE Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+import torch
+import sys
+import re
+import numpy as np
+import six
+
+from torch._C import _propagate_and_assign_input_shapes
+# extract node attribute such as node['value']
+from torch.onnx.utils import _node_getitem
+
+from py_proto import mace_pb2
+from transform import base_converter
+from transform.transformer import Transformer
+from transform.base_converter import PoolingType
+from transform.base_converter import ActivationType
+from transform.base_converter import EltwiseType
+from transform.base_converter import FrameworkType
+from transform.base_converter import MaceOp
+from transform.base_converter import MaceKeyword
+from transform.base_converter import ConverterUtil
+from transform.base_converter import RoundMode
+from transform.base_converter import DataFormat
+from utils.util import mace_check
+
+
+def _model_to_graph(model, args):
+    propagate = False
+    if isinstance(args, torch.Tensor):
+        args = (args, )
+
+    graph = model.forward.graph
+    method_graph, params = torch._C._jit_pass_lower_graph(graph, model._c)
+    in_vars, in_desc = torch.jit._flatten(tuple(args) + tuple(params))
+    graph = _propagate_and_assign_input_shapes(
+        method_graph, tuple(in_vars), False, propagate)
+    return graph
+
+
+class ValueType(object):
+    BoolType = torch._C.BoolType
+    FloatType = torch._C.FloatType
+    IntType = torch._C.IntType
+    ListType = torch._C.ListType
+    NoneType = torch._C.NoneType
+    TensorType = torch._C.TensorType
+    TupleType = torch._C.TupleType
+
+
+class NodeKind(object):
+    AdaptiveAvgPool2D = 'aten::adaptive_avg_pool2d'
+    Add_ = 'aten::add_'
+    Add = 'aten::add'
+    Addmm = 'aten::addmm'
+    AvgPool2D = 'aten::avg_pool2d'
+    BatchNorm = 'aten::batch_norm'
+    Cat = 'aten::cat'
+    Constant = 'prim::Constant'
+    Convolution = 'aten::_convolution'
+    Dropout = 'aten::dropout'
+    Flatten = 'aten::flatten'
+    HardTanh_ = 'aten::hardtanh_'  # ReLU6(inplace=True)
+    HardTanh = 'aten::hardtanh'  # ReLU6(inplace=False)
+    Int = 'aten::Int'
+    List = 'prim::ListConstruct'
+    Matmul = 'aten::matmul'
+    MaxPool2D = 'aten::max_pool2d'
+    NumToTensor = 'prim::NumToTensor'
+    Param = 'prim::Param'
+    Relu_ = 'aten::relu_'
+    Relu = 'aten::relu'
+    Reshape = 'aten::reshape'
+    Size = 'aten::size'  # pytorch can get Tensor size dynamically
+    T = 'aten::t'
+    Tuple = 'prim::TupleConstruct'
+
+
+class ConvParamIdx(object):
+    input_tensor_idx = 0
+    weight_idx = 1
+    bias_idx = 2
+    stride_idx = 3
+    pad_idx = 4
+    dilation_idx = 5
+    transposed_idx = 6
+    output_pad_idx = 7
+    groups_idx = 8
+    benchmark_idx = 9
+    determinstic_idx = 10
+    cudnn_enabled_idx = 11
+
+
+class AvgPool2DParamIdx(object):
+    input_tensor_idx = 0
+    kernel_size_idx = 1
+    stride_idx = 2
+    pad_idx = 3
+    ceil_mode_idx = 4
+    count_include_pad_idx = 5
+    divisor_override_idx = 6
+
+
+class MaxPool2DParamIdx(object):
+    input_tensor_idx = 0
+    kernel_size_idx = 1
+    stride_idx = 2
+    pad_idx = 3
+    dilation_idx = 4
+    ceil_mode_idx = 5
+
+
+class BNParamIdx(object):
+    input_tensor_idx = 0
+    weight_idx = 1
+    bias_idx = 2
+    running_mean_idx = 3
+    running_var_idx = 4
+    training_idx = 5
+    momentum_idx = 6
+    eps_idx = 7
+    cudnn_enabled_idx = 8
+
+
+class AddmmParamIdx(object):
+    bias_idx = 0
+    input_idx = 1
+    weight_idx = 2
+    beta_idx = 3
+    alpha_idx = 4
+
+
+class PytorchConverter(base_converter.ConverterInterface):
+    '''A class for convert pytorch model to MACE model.'''
+    activation_type = {
+        'ReLU': ActivationType.RELU,
+        'ReLU6': ActivationType.RELUX,
+    }
+    pooling_type_mode = {
+        NodeKind.AvgPool2D: PoolingType.AVG,
+        NodeKind.AdaptiveAvgPool2D: PoolingType.AVG,
+        NodeKind.MaxPool2D: PoolingType.MAX,
+    }
+    eltwise_type = {
+        NodeKind.Add: EltwiseType.SUM,
+        NodeKind.Add_: EltwiseType.SUM,
+    }
+
+    def model_to_graph(self):
+        dummy_input = ()
+        for in_node in self._option.input_nodes.values():
+            if len(in_node.shape) == 4:
+                in_data_format = in_node.data_format
+                if in_data_format == DataFormat.NHWC:
+                    N, H, W, C = in_node.shape
+                elif in_data_format == DataFormat.NCHW:
+                    N, C, H, W = in_node.shape
+                dummy_input = dummy_input + (torch.randn([N, C, H, W]),)
+            else:
+                dummy_input = dummy_input + (torch.randn(in_node.shape),)
+
+        graph = _model_to_graph(self._loaded_model, dummy_input)
+        state_dict = self._loaded_model.state_dict()
+        '''
+        num_batches_tracked in state_dict for BN layer is not used by any node,
+        delete them to avoid mistake name.
+        Maybe there are more unused keys in the future.
+        '''
+        unneeded_keys = []
+        for key in state_dict.keys():
+            if re.match(r'.*\.num_batches_tracked', key):
+                unneeded_keys.append(key)
+        for key in unneeded_keys:
+            del state_dict[key]
+        graph_inputs = list(graph.inputs())
+        user_input_num = len(graph_inputs) - len(state_dict)
+        param_names = list(state_dict.keys())
+        for i, inp in enumerate(graph.inputs()):
+            if i >= user_input_num:
+                inp.setDebugName(param_names[i - user_input_num])
+        return graph
+
+    def init_output_shape_cache(self):
+        self._output_shape_cache = {}
+        for input_node in self._option.input_nodes.values():
+            # input_shape is assigned in .yml file
+            input_shape = input_node.shape[:]
+            # transpose input from NHWC to NCHW
+            if len(input_shape) == 4 and \
+                    input_node.data_format == DataFormat.NHWC:
+                Transformer.transpose_shape(input_shape, [0, 3, 1, 2])
+            self._output_shape_cache[input_node.name] = input_shape
+
+    def __init__(self, option, src_model_file):
+        torch._C.Node.__getitem__ = _node_getitem
+        self._param_converts = (
+            NodeKind.Constant,
+            NodeKind.List,
+            NodeKind.Size,
+            NodeKind.NumToTensor,
+            NodeKind.Int,
+        )
+
+        self._option = option
+        self._mace_net_def = mace_pb2.NetDef()
+        ConverterUtil.set_filter_format(self._mace_net_def, DataFormat.OIHW)
+        ConverterUtil.add_data_format_arg(self._mace_net_def, DataFormat.NCHW)
+        self._op_converters = {
+            NodeKind.AdaptiveAvgPool2D: self.convert_pool,
+            NodeKind.Add: self.convert_add,
+            NodeKind.Add_: self.convert_add,
+            NodeKind.Addmm: self.convert_addmm,
+            NodeKind.AvgPool2D: self.convert_pool,
+            NodeKind.BatchNorm: self.convert_batch_norm,
+            NodeKind.Cat: self.convert_cat,
+            NodeKind.Convolution: self.convert_conv2d,
+            NodeKind.Dropout: self.convert_dropout,
+            NodeKind.Flatten: self.convert_flatten,
+            NodeKind.HardTanh_: self.convert_hardtanh,
+            NodeKind.HardTanh: self.convert_hardtanh,
+            NodeKind.Matmul: self.convert_matmul,
+            NodeKind.MaxPool2D: self.convert_pool,
+            NodeKind.Relu: self.convert_relu,
+            NodeKind.Relu_: self.convert_relu,
+            NodeKind.Reshape: self.convert_reshape,
+            NodeKind.T: self.convert_t,
+        }
+        self._loaded_model = torch.jit.load(src_model_file)
+        self._loaded_model.eval()
+        self._graph = self.model_to_graph()
+        self._output_node_name = list(self._graph.outputs())[0].debugName()
+        self._output_value_type = list(self._graph.outputs())[0].type()
+        if not isinstance(
+                self._output_value_type,
+                (ValueType.TensorType, ValueType.ListType,
+                 ValueType.TupleType)):
+            print('return type {} not supported'.format(
+                self._output_value_type))
+            sys.exit(1)
+        self._node_map = {}
+        self.init_output_shape_cache()
+
+    def run(self):
+        self.convert_ops()
+        return self._mace_net_def
+
+    def init_ignore_t(self, all_nodes):
+        # ignore_t saves aten::t() which is ignored
+        self.ignore_t = set()
+        for node in all_nodes:
+            node_kind = node.kind()
+            if node_kind == NodeKind.T and self.is_trans_fc_w(node):
+                self.ignore_t.add(node.output().debugName())
+
+    def convert_ops(self):
+        all_nodes = list(self._graph.nodes())
+        self.init_ignore_t(all_nodes)
+        for node in all_nodes:
+            if isinstance(
+                self._output_value_type,
+                (ValueType.TupleType, ValueType.ListType)) and \
+                    node.output().debugName() == self._output_node_name:
+                print('pytorch return type is {},'
+                      ' skipping adding it into MACE graph'.format(
+                       self._output_value_type))
+                continue
+
+            inputs_vals = list(node.inputs())  # list of Value
+            outputs_vals = list(node.outputs())
+            mace_check(len(outputs_vals) == 1,
+                       'pytorch converter supports nodes with single output,'
+                       ' {} outputs found'.format(len(outputs_vals)))
+            node_kind = node.kind()
+            # This means this node is parameter for Ops
+            if node_kind in self._param_converts:
+                continue
+            self._node_map[outputs_vals[0].debugName()] = node
+            mace_check(node_kind in self._op_converters,
+                       'MACE does not support pytorch node {} yet'.format(
+                        node_kind))
+            self._op_converters[node_kind](node, inputs_vals, outputs_vals)
+
+    def convert_general_op(self, outputs_vals):
+        op = self._mace_net_def.op.add()
+        op.name = outputs_vals[0].debugName()
+
+        data_type_arg = op.arg.add()
+        data_type_arg.name = 'T'
+        data_type_arg.i = self._option.data_type
+
+        framework_type_arg = op.arg.add()
+        framework_type_arg.name = MaceKeyword.mace_framework_type_str
+        framework_type_arg.i = FrameworkType.PYTORCH.value
+
+        ConverterUtil.add_data_format_arg(op, DataFormat.NCHW)
+
+        return op
+
+    def add_output_shape(self, op, shapes):
+        mace_check(len(op.output) == len(shapes),
+                   'Op {} ({}) output count is different from output shape count'.format(  # noqa
+                    op.name, op.type))
+        for i in range(len(shapes)):
+            output_name = op.output[i]
+            output_shape = op.output_shape.add()
+            output_shape.dims.extend(shapes[i])
+            self._output_shape_cache[output_name] = shapes[i]
+
+    def infer_shape_general(self, op):
+        if len(op.input) > 0:
+            mace_check(op.input[0] in self._output_shape_cache,
+                       "Op {} input {} does not exist".format(
+                       op.name, op.input[0]))
+            # initial values of _output_shape_cache come from:
+            # 1: input_shape in .yml file(may be transposed to NCHW)
+            # 2: tensor.dims. tensor is added by add_tensor_and_shape
+            input_shape = self._output_shape_cache[op.input[0]]
+            # pytorch BatchNorm 1/2/3D version use same function, the only way
+            # to check  dimension of BatchNorm is to checkout input dimension
+            if op.type == MaceOp.BatchNorm.name:
+                mace_check(len(input_shape) == 4,
+                           'only 2D BatchNorm is supported,'
+                           ' but {}D input found'.format(len(input_shape)))
+            self.add_output_shape(op, [input_shape])
+
+    def infer_shape_conv2d_pool(self, op):
+        input_shape = self._output_shape_cache[op.input[0]]
+        output_shape = np.zeros_like(input_shape)
+        if not op.type == MaceOp.Pooling:
+            filter_shape = self._output_shape_cache[op.input[1]]
+        paddings = ConverterUtil.get_arg(op,
+                                         MaceKeyword.mace_padding_values_str).ints  # noqa
+        strides = ConverterUtil.get_arg(op, MaceKeyword.mace_strides_str).ints
+        dilations_arg = ConverterUtil.get_arg(op,
+                                              MaceKeyword.mace_dilations_str)
+        if dilations_arg is not None:
+            dilations = dilations_arg.ints
+        else:
+            dilations = [1, 1]  # MACE pooling has no dilation
+        if op.type == MaceOp.Pooling.name:
+            kernels = ConverterUtil.get_arg(
+                op, MaceKeyword.mace_kernel_str).ints
+            if ConverterUtil.get_arg(
+                    op, MaceKeyword.mace_global_pooling_str) is not None:
+                kernels[0] = input_shape[2]
+                kernels[1] = input_shape[3]
+
+        round_func = math.floor
+        round_mode_arg = ConverterUtil.get_arg(
+            op, MaceKeyword.mace_round_mode_str)
+        if round_mode_arg is not None and \
+                round_mode_arg.i == RoundMode.CEIL.value:
+            round_func = math.ceil
+
+        # N_o = N_i
+        output_shape[0] = input_shape[0]
+        mace_check(ConverterUtil.data_format(op) == DataFormat.NCHW and
+                   ConverterUtil.filter_format(self._mace_net_def) ==
+                   DataFormat.OIHW, "MACE can only infer shape for NCHW input and OIHW filter")  # noqa
+        # get C_{out}
+        if op.type == MaceOp.DepthwiseConv2d.name:
+            # 1CHW for depthwise, here C=C_{out}
+            output_shape[1] = filter_shape[1]
+        elif op.type == MaceOp.Conv2D.name:
+            # filter format: OIHW
+            output_shape[1] = filter_shape[0]
+        else:
+            output_shape[1] = input_shape[1]
+        # H_{out}
+        p, d, s = paddings[0], dilations[0], strides[0]
+        k = kernels[0] if op.type == MaceOp.Pooling.name \
+            else filter_shape[2]
+        # https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html
+        output_shape[2] = int(
+            round_func(float(input_shape[2] + p - d * (k - 1) - 1) /
+                       float(s) + 1))
+        # W_{out}
+        p, d, s = paddings[1], dilations[1], strides[1]
+        k = kernels[1] if op.type == MaceOp.Pooling.name \
+            else filter_shape[3]
+        output_shape[3] = int(
+            round_func(float(input_shape[3] + p - d * (k - 1) - 1) /
+                       float(s) + 1))
+
+        self.add_output_shape(op, [output_shape])
+
+    def convert_conv2d(self, node, inputs_vals, outputs_vals):
+        op = self.convert_general_op(outputs_vals)
+
+        op.input.extend([inputs_vals[i].debugName() for i in range(2)])
+        op.output.extend([outputs_vals[0].debugName()])
+
+        # OIHW
+        key = inputs_vals[1].debugName()
+        filter_shape = self._loaded_model.state_dict()[key].shape
+        filter_shape = [int(elem) for elem in filter_shape]  # Size -> list
+        mace_check(len(filter_shape) == 4,
+                   'MACE only supports 2D Conv, current Conv is {}D'.format(
+                   len(filter_shape)-2))
+        filter_cin = filter_shape[1]
+        filter_cout = filter_shape[0]
+        group_node = inputs_vals[ConvParamIdx.groups_idx].node()
+        ngroups = group_node['value']
+        mace_check(ngroups == 1 or ngroups == filter_cout,
+                   'MACE only support conv without group or depthwise conv,'
+                   ' but group number of {} found'.format(ngroups))
+        is_depthwise = (ngroups != 1 and ngroups == filter_cout and
+                        filter_cin == 1)
+        if is_depthwise:
+            op.type = MaceOp.DepthwiseConv2d.name
+        else:
+            op.type = MaceOp.Conv2D.name
+
+        strides_node = inputs_vals[ConvParamIdx.stride_idx].node()
+        strides_vals = list(strides_node.inputs())
+        mace_strides = [strides_vals[i].node()['value'] for i in range(2)]
+        strides_arg = op.arg.add()
+        strides_arg.name = MaceKeyword.mace_strides_str
+        strides_arg.ints.extend(mace_strides)
+
+        pads_node = inputs_vals[ConvParamIdx.pad_idx].node()
+        pads_vals = list(pads_node.inputs())
+        mace_pads = [2 * pads_vals[i].node()['value'] for i in range(2)]
+        pads_arg = op.arg.add()
+        pads_arg.name = MaceKeyword.mace_padding_values_str
+        pads_arg.ints.extend(mace_pads)
+
+        dilations_node = inputs_vals[ConvParamIdx.dilation_idx].node()
+        dilations_vals = list(dilations_node.inputs())
+        mace_dilations = [dilations_vals[i].node()['value'] for i in range(2)]
+        dilation_arg = op.arg.add()
+        dilation_arg.name = MaceKeyword.mace_dilations_str
+        dilation_arg.ints.extend(mace_dilations)
+
+        filter_tensor_name = inputs_vals[ConvParamIdx.weight_idx].debugName()
+        filter_data = self._loaded_model.state_dict()[filter_tensor_name]
+        if is_depthwise:
+            # C1HW => 1CHW
+            filter_data = filter_data.permute((1, 0, 2, 3))
+        filter_data = filter_data.numpy()
+        self.add_tensor_and_shape(filter_tensor_name, filter_data.shape,
+                                  mace_pb2.DT_FLOAT, filter_data)
+
+        bias_val = inputs_vals[ConvParamIdx.bias_idx]
+        has_bias = (not isinstance(bias_val.type(), ValueType.NoneType))
+        if has_bias:
+            bias_tensor_name = inputs_vals[ConvParamIdx.bias_idx].debugName()
+            bias_data = self._loaded_model.state_dict()[bias_tensor_name]
+            bias_data = bias_data.numpy()
+            self.add_tensor_and_shape(bias_tensor_name, bias_data.shape,
+                                      mace_pb2.DT_FLOAT, bias_data)
+            op.input.extend([bias_tensor_name])
+        self.infer_shape_conv2d_pool(op)
+
+    def convert_batch_norm(self, node, inputs_vals, outputs_vals):
+        op = self.convert_general_op(outputs_vals)
+
+        op.input.extend([inputs_vals[0].debugName()])
+        op.output.extend([outputs_vals[0].debugName()])
+        op.type = MaceOp.BatchNorm.name
+
+        is_training = int(inputs_vals[BNParamIdx.training_idx].node()['value'])
+        mace_check(is_training == 0,
+                   "Only support batch normalization with is_training = 0,"
+                   " but got {}".format(is_training))
+        state_dict = self._loaded_model.state_dict()
+        gamma_key = inputs_vals[BNParamIdx.weight_idx].debugName()
+        gamma_value = state_dict[gamma_key].numpy().astype(np.float32)
+        beta_key = inputs_vals[BNParamIdx.bias_idx].debugName()
+        beta_value = state_dict[beta_key].numpy().astype(np.float32)
+        mean_name = inputs_vals[BNParamIdx.running_mean_idx].debugName()
+        mean_value = state_dict[mean_name].numpy().astype(np.float32)
+        var_name = inputs_vals[BNParamIdx.running_var_idx].debugName()
+        var_value = state_dict[var_name].numpy().astype(np.float32)
+        epsilon_value = inputs_vals[BNParamIdx.eps_idx].node()['value']
+
+        scale_name = gamma_key + '_scale'
+        offset_name = beta_key + '_offset'
+
+        scale_value = (
+            (1.0 / np.vectorize(math.sqrt)(
+             var_value + epsilon_value)) * gamma_value)
+        offset_value = (-mean_value * scale_value) + beta_value
+
+        self.add_tensor_and_shape(scale_name, scale_value.shape,
+                                  mace_pb2.DT_FLOAT, scale_value)
+        self.add_tensor_and_shape(offset_name, offset_value.shape,
+                                  mace_pb2.DT_FLOAT, offset_value)
+        op.input.extend([scale_name, offset_name])
+        self.infer_shape_general(op)
+
+    def convert_hardtanh(self, node, inputs_vals, outputs_vals):
+        op = self.convert_general_op(outputs_vals)
+        op.type = MaceOp.Activation.name
+
+        min_val = inputs_vals[1].node()['value']
+        max_val = inputs_vals[2].node()['value']
+        mace_check(abs(min_val) < 1e-8, "MACE only supports min == 0 Clip op")
+
+        op.input.extend([inputs_vals[0].debugName()])
+        op.output.extend([outputs_vals[0].debugName()])
+
+        type_arg = op.arg.add()
+        type_arg.name = MaceKeyword.mace_activation_type_str
+        if (abs(max_val - 6.) < 1e-8):
+            type_arg.s = six.b(self.activation_type['ReLU6'].name)
+
+            limit_arg = op.arg.add()
+            limit_arg.name = MaceKeyword.mace_activation_max_limit_str
+            limit_arg.f = 6.0
+        else:
+            print('only support converting hardtanh_ to ReLU6 yet')
+            sys.exit(1)
+        self.infer_shape_general(op)
+
+    def convert_add(self, node, inputs_vals, outputs_vals):
+        op = self.convert_general_op(outputs_vals)
+        op.type = MaceOp.Eltwise.name
+        type_arg = op.arg.add()
+        type_arg.name = MaceKeyword.mace_element_type_str
+        node_kind = node.kind()
+        type_arg.i = self.eltwise_type[node_kind].value
+        alpha = inputs_vals[2].node()['value']
+        mace_check(alpha == 1,
+                   'MACE only support alpha value of 1 for Add Op,'
+                   ' {} found'.format(alpha))
+        op.input.extend([inputs_vals[i].debugName() for i in range(2)])
+        op.output.extend([outputs_vals[0].debugName()])
+        lhs_kind = inputs_vals[0].node().kind()
+        rhs_kind = inputs_vals[1].node().kind()
+        if lhs_kind != NodeKind.Constant and rhs_kind == NodeKind.Constant:
+            const_value = inputs_vals[1].node()['value']
+            value_arg = op.arg.add()
+            value_arg.name = MaceKeyword.mace_scalar_input_str
+            value_arg.f = float(const_value)
+            value_index_arg = op.arg.add()
+            value_index_arg.name = MaceKeyword.mace_scalar_input_index_str
+            value_index_arg.i = 1
+            del op.input[1]
+        elif lhs_kind == NodeKind.Constant and rhs_kind != NodeKind.Constant:
+            const_value = inputs_vals[0].node()['value']
+            value_arg = op.arg.add()
+            value_arg.name = MaceKeyword.mace_scalar_input_str
+            value_arg.f = float(const_value)
+            value_index_arg = op.arg.add()
+            value_index_arg.name = MaceKeyword.mace_scalar_input_index_str
+            value_index_arg.i = 0
+            del op.input[0]
+        self.infer_shape_general(op)
+
+    def convert_relu(self, node, inputs_vals, outputs_vals):
+        op = self.convert_general_op(outputs_vals)
+        op.type = MaceOp.Activation.name
+
+        op.input.extend([inputs_vals[0].debugName()])
+        op.output.extend([outputs_vals[0].debugName()])
+
+        type_arg = op.arg.add()
+        type_arg.name = MaceKeyword.mace_activation_type_str
+        type_arg.s = six.b(self.activation_type['ReLU'].name)
+
+        self.infer_shape_general(op)
+
+    def infer_shape_cat(self, op):
+        output_shape = list(self._output_shape_cache[op.input[0]])
+        axis = ConverterUtil.get_arg(op, MaceKeyword.mace_axis_str).i
+        if axis < 0:
+            axis = len(output_shape) + axis
+        output_shape[axis] = 0
+        for input_node in op.input:
+            input_shape = list(self._output_shape_cache[input_node])
+            output_shape[axis] = output_shape[axis] + input_shape[axis]
+        self.add_output_shape(op, [output_shape])
+
+    def convert_cat(self, node, inputs_vals, outputs_vals):
+        op = self.convert_general_op(outputs_vals)
+        op.type = MaceOp.Concat.name
+        in_vals = list(inputs_vals[0].node().inputs())
+        in_names = [in_vals[i].debugName() for i in range(len(in_vals))]
+        op.input.extend(in_names)
+        op.output.extend([outputs_vals[0].debugName()])
+
+        axis_int = inputs_vals[1].node()['value']
+        axis_arg = op.arg.add()
+        axis_arg.name = MaceKeyword.mace_axis_str
+        axis_arg.i = axis_int
+
+        self.infer_shape_cat(op)
+
+    def convert_flatten(self, node, inputs_vals, outputs_vals):
+        op = self.convert_general_op(outputs_vals)
+        op.type = MaceOp.Reshape.name
+        op.input.extend([inputs_vals[0].debugName()])
+        op.output.extend([outputs_vals[0].debugName()])
+
+        input_shape = list(self._output_shape_cache[op.input[0]])
+        ndim = len(input_shape)
+        start_dim = inputs_vals[1].node()['value']
+        if start_dim < 0:
+            start_dim += ndim
+        end_dim = inputs_vals[2].node()['value']
+        if end_dim < 0:
+            end_dim += ndim
+        reshape_dims = []
+        for i in range(0, start_dim):
+            reshape_dims.append(input_shape[i])
+        mid_shape = 1
+        for i in range(start_dim, end_dim+1):
+            mid_shape *= input_shape[i]
+        reshape_dims.append(mid_shape)
+        for i in range(end_dim + 1, ndim):
+            reshape_dims.append(input_shape[i])
+
+        dim_arg = op.arg.add()
+        dim_arg.name = MaceKeyword.mace_dim_str
+        dim_arg.ints.extend(reshape_dims)
+        self.infer_shape_reshape(op)
+
+    def get_weight_from_node(self, node):
+        input_list = list(node.inputs())
+        key = input_list[0].debugName()
+        return self._loaded_model.state_dict()[key]
+
+    def is_trans_fc_w(self, node):
+        in_vals = list(node.inputs())
+        mace_check(len(in_vals) == 1, 't() must have 1 input')
+        in_name = in_vals[0].debugName()
+        if in_name in self._loaded_model.state_dict() and \
+                len(self._loaded_model.state_dict()[in_name].shape) == 2:
+            return True
+        return False
+
+    def infer_shape_fully_connected(self, op):
+        input_shape = self._output_shape_cache[op.input[0]]
+        weight_shape = self._output_shape_cache[op.input[1]]
+        data_format = ConverterUtil.data_format(op)
+        mace_check(data_format == DataFormat.NCHW,
+                   "format {} is not supported".format(data_format))
+        output_shape = [input_shape[0], weight_shape[0], 1, 1]
+        self.add_output_shape(op, [output_shape])
+
+    def convert_addmm(self, node, inputs_vals, outputs_vals):
+        op = self.convert_general_op(outputs_vals)
+
+        weight_in_node = inputs_vals[AddmmParamIdx.weight_idx].node()
+        is_mat2_w = weight_in_node.kind() == NodeKind.T and self.is_trans_fc_w(
+            weight_in_node)
+        alpha = inputs_vals[AddmmParamIdx.alpha_idx].node()['value']
+        alpha_type = inputs_vals[AddmmParamIdx.alpha_idx].type()
+        is_alpha_fc = isinstance(alpha_type, ValueType.IntType) and alpha == 1
+        is_bias_w = inputs_vals[AddmmParamIdx.bias_idx].debugName() in \
+            self._loaded_model.state_dict()
+        beta = inputs_vals[AddmmParamIdx.beta_idx].node()['value']
+        beta_type = inputs_vals[AddmmParamIdx.beta_idx].type()
+        is_beta_fc = isinstance(beta_type, ValueType.IntType) and beta == 1
+
+        # when mat2 is from state_dict and alpha=1 and bias is from state_dict
+        # and beta =1, it is fc
+        is_fc = is_mat2_w and is_alpha_fc and is_bias_w and is_beta_fc
+
+        mace_check(is_fc, 'addmm can only be converted into FC yet')
+        # pytorch usually prepend a reshape/flatten before fc, convert fc
+        # into matmul followed by biasadd, thus reshape/flatten and matmul
+        # will be merged. see transform_matmul_to_fc for detail.
+        name_back = op.name
+        matmul_op_name = op.name + '_matmul'
+        op.name = matmul_op_name
+        op.type = MaceOp.MatMul.name
+        fc_upstream_name = inputs_vals[AddmmParamIdx.input_idx].debugName()
+        op.input.extend([fc_upstream_name])
+        op.output.extend([matmul_op_name])
+
+        weight_tensor_name = op.name + '_weight'
+        weight_tensor = self.get_weight_from_node(weight_in_node)
+        weight_data = weight_tensor.numpy()
+        self.add_tensor_and_shape(weight_tensor_name, weight_data.shape,
+                                  mace_pb2.DT_FLOAT, weight_data)
+        op.input.extend([weight_tensor_name])
+
+        transpose_a_arg = op.arg.add()
+        transpose_a_arg.name = MaceKeyword.mace_transpose_a_str
+        transpose_a_arg.i = 0
+        transpose_b_arg = op.arg.add()
+        transpose_b_arg.name = MaceKeyword.mace_transpose_b_str
+        transpose_b_arg.i = 1  # OxI, trans_b needed
+
+        self.infer_shape_matmul(op)
+
+        opb = self.convert_general_op(outputs_vals)
+        opb.type = MaceOp.BiasAdd.name
+        bias_tensor_name = opb.name + '_bias'
+        key = inputs_vals[AddmmParamIdx.bias_idx].debugName()
+        bias_data = self._loaded_model.state_dict()[key]
+        bias_data = bias_data.numpy()
+        self.add_tensor_and_shape(bias_tensor_name, bias_data.reshape(
+            -1).shape, mace_pb2.DT_FLOAT, bias_data)
+        opb.input.extend([matmul_op_name, bias_tensor_name])
+        opb.output.extend([name_back])
+        self.infer_shape_general(opb)
+
+    def infer_shape_matmul(self, op):
+        lhs_shape = self._output_shape_cache[op.input[0]]
+        lhs_rank = len(lhs_shape)
+        lhs_rows = lhs_shape[-2]
+        lhs_cols = lhs_shape[-1]
+        rhs_shape = self._output_shape_cache[op.input[1]]
+        rhs_rank = len(rhs_shape)
+        rhs_rows = rhs_shape[-2]
+        rhs_cols = rhs_shape[-1]
+        transpose_a_ = ConverterUtil.get_arg(
+            op, MaceKeyword.mace_transpose_a_str).i
+        transpose_b_ = ConverterUtil.get_arg(
+            op, MaceKeyword.mace_transpose_b_str).i
+
+        rows = lhs_cols if transpose_a_ else lhs_rows
+        cols = rhs_rows if transpose_b_ else rhs_cols
+
+        if lhs_rank >= rhs_rank:
+            if lhs_rank > rhs_rank:
+                mace_check(rhs_rank == 2,
+                           'The rhs rank of non-batched MatMul must be 2')  # noqa
+            output_shape = lhs_shape.copy()
+            output_shape[lhs_rank - 2] = rows
+            output_shape[lhs_rank - 1] = cols
+        else:
+            output_shape = rhs_shape.copy()
+            output_shape[rhs_rank - 2] = rows
+            output_shape[rhs_rank - 1] = cols
+        self.add_output_shape(op, [output_shape])
+
+    def convert_matmul(self, node, inputs_vals, outputs_vals):
+        op = self.convert_general_op(outputs_vals)
+
+        weight_in_node = inputs_vals[1].node()
+        is_weight = weight_in_node.kind() == NodeKind.T and self.is_trans_fc_w(
+            weight_in_node)
+
+        op.type = MaceOp.MatMul.name
+        op.input.extend([inputs_vals[i].debugName() for i in range(2)])
+        op.output.extend([outputs_vals[0].debugName()])
+        if is_weight:
+            weight_tensor_name = op.input[1]
+            weight_val = inputs_vals[1]
+            weight_tensor = self.get_weight_from_node(weight_in_node)
+            weight_data = weight_tensor.numpy()
+            self.add_tensor_and_shape(weight_tensor_name, weight_data.shape,
+                                      mace_pb2.DT_FLOAT, weight_data)
+
+        lhs_shape = self._output_shape_cache[op.input[0]]
+        rhs_shape = self._output_shape_cache[op.input[1]]
+        lhs_rank = len(lhs_shape)
+        rhs_rank = len(rhs_shape)
+        mace_check(lhs_rank >= 2 and rhs_rank >= 2,
+                   "The rank of MatMul must be >= 2,"
+                   " but lhs_rank = {} and rhs_rank = {} found".format(
+                    lhs_rank, rhs_rank))
+
+        transpose_a_arg = op.arg.add()
+        transpose_a_arg.name = MaceKeyword.mace_transpose_a_str
+        transpose_a_arg.i = 0
+        transpose_b_arg = op.arg.add()
+        transpose_b_arg.name = MaceKeyword.mace_transpose_b_str
+        if is_weight:
+            transpose_b_arg.i = 1
+        else:
+            transpose_b_arg.i = 0
+
+        self.infer_shape_matmul(op)
+
+    def convert_pool(self, node, inputs_vals, outputs_vals):
+        op = self.convert_general_op(outputs_vals)
+        op.type = MaceOp.Pooling.name
+
+        op.input.extend([inputs_vals[0].debugName()])
+        op.output.extend([outputs_vals[0].debugName()])
+
+        node_kind = node.kind()
+        idx_map = {NodeKind.AvgPool2D: AvgPool2DParamIdx,
+                   NodeKind.MaxPool2D: MaxPool2DParamIdx}
+
+        if node_kind == NodeKind.AdaptiveAvgPool2D:
+            output_shape_node = inputs_vals[1].node()
+            output_shape_vals = list(output_shape_node.inputs())
+            target_output_shape = [
+                output_shape_vals[i].node()['value'] for i in range(2)]
+            mace_check(target_output_shape[0] == 1 and
+                       target_output_shape[1] == 1,
+                       'only support output shape of [1, 1] for AdaptiveAvgPool2D')  # noqa
+
+            strides_arg = op.arg.add()
+            strides_arg.name = MaceKeyword.mace_strides_str
+            strides_arg.ints.extend([1, 1])
+
+            pads_arg = op.arg.add()
+            pads_arg.name = MaceKeyword.mace_padding_values_str
+            pads_arg.ints.extend([0, 0])
+
+            kernels_arg = op.arg.add()
+            kernels_arg.name = MaceKeyword.mace_kernel_str
+            kernels_arg.ints.extend([0, 0])
+
+            global_pooling_arg = op.arg.add()
+            global_pooling_arg.name = MaceKeyword.mace_global_pooling_str
+            global_pooling_arg.i = 1
+        else:
+            pad_node = inputs_vals[idx_map[node_kind].pad_idx].node()
+            pad_vals = list(pad_node.inputs())
+            mace_check(len(pad_vals) == 2,
+                       "only support 2D pooling,"
+                       " but {}D padding value found".format(len(pad_vals)))
+            # MACE pads include both sides, pytorch pads include single side
+            pads = [2 * pad_vals[i].node()['value'] for i in range(2)]
+            pads_arg = op.arg.add()
+            pads_arg.name = MaceKeyword.mace_padding_values_str
+            pads_arg.ints.extend(pads)
+
+            if node_kind == NodeKind.MaxPool2D:
+                dilation_node = inputs_vals[
+                    idx_map[node_kind].dilation_idx].node()
+                dilation_vals = list(dilation_node.inputs())
+                dilations = [dilation_vals[i].node()['value']
+                             for i in range(2)]
+                mace_check(dilations[0] == 1 and dilations[1] == 1,
+                           "MACE pooling does not support dilation")
+
+            kernel_node = inputs_vals[
+                idx_map[node_kind].kernel_size_idx].node()
+            kernel_vals = list(kernel_node.inputs())
+            kernels = [kernel_vals[i].node()['value'] for i in range(2)]
+            kernels_arg = op.arg.add()
+            kernels_arg.name = MaceKeyword.mace_kernel_str
+            kernels_arg.ints.extend(kernels)
+
+            stride_node = inputs_vals[idx_map[node_kind].stride_idx].node()
+            stride_vals = list(stride_node.inputs())
+            strides = [stride_vals[i].node()['value'] for i in range(2)]
+            strides_arg = op.arg.add()
+            strides_arg.name = MaceKeyword.mace_strides_str
+            strides_arg.ints.extend(strides)
+
+            ceil_node = inputs_vals[idx_map[node_kind].ceil_mode_idx].node()
+            ceil_mode = bool(ceil_node['value'])
+            round_mode_arg = op.arg.add()
+            round_mode_arg.name = MaceKeyword.mace_round_mode_str
+            round_mode_arg.i = RoundMode.FLOOR.value
+            if ceil_mode:
+                round_mode_arg.i = RoundMode.CEIL.value
+
+            if node_kind == NodeKind.AvgPool2D:
+                count_include_pad_node = inputs_vals[
+                    AvgPool2DParamIdx.count_include_pad_idx].node()
+                count_include_pad = bool(count_include_pad_node['value'])
+                # if there is no padding, count_include_pad has no effect;
+                # otherwise, MACE does not support count_include_pad.
+                if count_include_pad:
+                    mace_check(pads[0] == 0 and pads[1] == 0,
+                               "if count_include_pad is set, pad must be zero."
+                               " pad values ({},{}) found.".format(
+                               pads[0], pads[1]))
+                # divisor
+                divisor_override_node = inputs_vals[
+                    AvgPool2DParamIdx.divisor_override_idx].node()
+                mace_check(isinstance(divisor_override_node.output().type(),
+                           ValueType.NoneType),
+                           "MACE does not support divisor_override parameter for AvgPool2D")  # noqa
+
+        pooling_type_arg = op.arg.add()
+        pooling_type_arg.name = MaceKeyword.mace_pooling_type_str
+        pooling_type_arg.i = self.pooling_type_mode[node_kind].value
+
+        self.infer_shape_conv2d_pool(op)
+
+    def node_to_int(self, shape_node):
+        if shape_node.kind() == NodeKind.Constant:
+            return shape_node['value']
+        elif shape_node.kind() == NodeKind.Size:
+            input_node = list(shape_node.inputs())[0].node()
+            axis_node = list(shape_node.inputs())[1].node()
+            axis_int = self.node_to_int(axis_node)
+            input_tensor_shape = self._output_shape_cache[
+                input_node.output().debugName()]
+            return input_tensor_shape[axis_int]
+        else:
+            input_node = list(shape_node.inputs())[0].node()
+            return self.node_to_int(input_node)
+
+    def infer_shape_reshape(self, op):
+        input_shape = self._output_shape_cache[op.input[0]]
+        dim_arg = ConverterUtil.get_arg(op, MaceKeyword.mace_dim_str)
+        output_shape = list(dim_arg.ints)  # reshape_dims
+        product = input_size = 1
+        idx = -1
+        num_minus1 = 0
+
+        for dim in input_shape:
+            input_size *= dim
+        reshape_dims = list(dim_arg.ints)
+        for i in range(len(reshape_dims)):
+            if reshape_dims[i] == -1:
+                idx = i
+                output_shape[i] = 1
+                num_minus1 += 1
+            else:
+                output_shape[i] = reshape_dims[i]
+                product *= reshape_dims[i]
+        mace_check(num_minus1 <= 1, 'only 0 or 1 negative shape supported')
+        if idx != -1:
+            output_shape[idx] = int(input_size / product)
+
+        self.add_output_shape(op, [output_shape])
+
+    def convert_reshape(self, node, inputs_vals, outputs_vals):
+        op = self.convert_general_op(outputs_vals)
+        op.type = MaceOp.Reshape.name
+        op.input.extend([inputs_vals[0].debugName()])
+        op.output.extend([outputs_vals[0].debugName()])
+
+        # sometimes pytorch's reshape parameter is infered from input Tensor,
+        # e.g. y = y.reshape(x.size(0), -1)
+        # so static parameter may be unable to get here.
+        dim_arg = op.arg.add()
+        dim_arg.name = MaceKeyword.mace_dim_str
+        shape_list_node = list(node.inputs())[1].node()
+        reshape_dims = []
+        for shape_val in shape_list_node.inputs():
+            shape_node = shape_val.node()
+            _kind = shape_node.kind()
+            if _kind == NodeKind.Constant:
+                reshape_dims.append(shape_node['value'])
+            elif _kind == NodeKind.Int:
+                _dim = int(self.node_to_int(shape_node))
+                reshape_dims.append(_dim)
+            else:
+                print('unsupported shape node kind {}'.format(_kind))
+        dim_arg.ints.extend(reshape_dims)
+        self.infer_shape_reshape(op)
+
+    def infer_shape_identity(self, op):
+        input_shape = self._output_shape_cache[op.input[0]]
+        output_shape = input_shape
+        self.add_output_shape(op, [output_shape])
+
+    def convert_dropout(self, node, inputs_vals, outputs_vals):
+        op = self.convert_general_op(outputs_vals)
+        training = int(inputs_vals[2].node()['value'])
+        mace_check(training == 0, 'for inference, dropout must be disabled')
+        op.type = MaceOp.Identity.name
+        op.input.extend([inputs_vals[0].debugName()])
+        op.output.extend([outputs_vals[0].debugName()])
+        self.infer_shape_identity(op)
+
+    def infer_shape_transpose(self, op):
+        input_shape = self._output_shape_cache[op.input[0]]
+        output_shape = np.zeros(len(input_shape), dtype=np.int32)
+        dims_arg = ConverterUtil.get_arg(op, MaceKeyword.mace_dims_str)
+        dims_ints = dims_arg.ints
+        for idx in range(len(dims_ints)):
+            output_shape[idx] = input_shape[dims_ints[idx]]
+        self.add_output_shape(op, [output_shape])
+
+    def convert_t(self, node, inputs_vals, outputs_vals):
+        if node.output().debugName() in self.ignore_t:
+            return
+        op = self.convert_general_op(outputs_vals)
+
+        op.input.extend([inputs_vals[0].debugName()])
+        op.output.extend([outputs_vals[0].debugName()])
+
+        # MACE only supports transpose 2/3/4D tensor.
+        input_shape = self._output_shape_cache[op.input[0]]
+        if len(input_shape) <= 1:
+            op.type = MaceOp.Identity.name
+            self.infer_shape_general(op)
+        else:
+            op.type = MaceOp.Transpose.name
+            dims_arg = op.arg.add()
+            dims_arg.name = MaceKeyword.mace_dims_str
+            dims_arg.ints.extend([1, 0])
+            self.infer_shape_transpose(op)
+
+    def add_tensor_and_shape(self, name, shape, data_type, value):
+        tensor = self._mace_net_def.tensors.add()
+        tensor.name = name
+        tensor.dims.extend(list(shape))
+        tensor.data_type = data_type
+        tensor.float_data.extend(value.flat)
+        self._output_shape_cache[name] = np.array(shape)

tools/python/transform/transformer.py

@@ -345,6 +345,7 @@ class Transformer(base_converter.ConverterInterface):
             input_info.dims.extend(input_node.shape)
             input_info.data_type = input_node.data_type
 
+        # tools/python/convert.py sets option.check_nodes
         output_nodes = self._option.check_nodes.values()
         for output_node in output_nodes:
             output_info = net.output_info.add()
@@ -1312,12 +1313,18 @@ class Transformer(base_converter.ConverterInterface):
         for op in net.op:
             # transform `input(4D) -> reshape(2D) -> matmul` to `fc(2D)`
             # fc output is 2D in transformer, using as 4D in op kernel
-            # work for TensorFlow
+            # work for TensorFlow/PyTorch/ONNX
+            framework = ConverterUtil.get_arg(
+                op, MaceKeyword.mace_framework_type_str).i
+            is_torch = framework == FrameworkType.PYTORCH.value
+            is_tf = framework == FrameworkType.TENSORFLOW.value
+            is_onnx = framework == FrameworkType.ONNX.value
+
             if op.type == MaceOp.Reshape.name and \
                     len(op.input) == 2 and \
                     op.input[1] in self._consts and \
                     len(op.output_shape[0].dims) == 2 and \
-                    filter_format == DataFormat.HWIO and \
+                    (is_tf or is_torch or is_onnx) and \
                     op.input[0] in self._producer:
                 input_op = self._producer[op.input[0]]
                 input_shape = input_op.output_shape[0].dims
@@ -1332,8 +1339,13 @@ class Transformer(base_converter.ConverterInterface):
                             is_fc = False
                         else:
                             weight = self._consts[matmul_op.input[1]]
-                            if len(weight.dims) != 2 or \
-                               weight.dims[0] != op.output_shape[0].dims[1]:
+                            od = op.output_shape[0].dims
+                            wd = weight.dims
+                            if len(wd) != 2:
+                                is_fc = False
+                            # tf fc weight: IO; onnx/pytorch fc weight: OI
+                            if (is_tf and wd[0] != od[1]) or \
+                                    ((is_torch or is_onnx) and wd[1] != od[1]):
                                 is_fc = False
                     if is_fc:
                         print('convert reshape and matmul to fc')
@@ -1344,24 +1356,40 @@ class Transformer(base_converter.ConverterInterface):
                             matmul_op.type = MaceOp.FullyConnected.name
                             weight_data = np.array(weight.float_data).reshape(
                                 weight.dims)
-                            weight.dims[:] = input_shape[1:] + \
-                                [weight_data.shape[1]]
+                            if is_tf:
+                                weight.dims[:] = input_shape[1:] + \
+                                    [weight_data.shape[1]]
+                            if is_torch or is_onnx:
+                                in_data_format = ConverterUtil.data_format(
+                                    input_op)
+                                # OI+NCHW[2:]=OIHW
+                                if in_data_format == DataFormat.NCHW:
+                                    weight.dims.extend(input_shape[2:])
+                                # OI+NHWC[1:3]=OIHW
+                                else:
+                                    weight.dims.extend(input_shape[1:3])
                         return True
 
             # transform `fc1(2D) -> matmul` to `fc1(2D) -> fc1(2D)`
             if op.type == MaceOp.MatMul.name and \
-                    filter_format == DataFormat.HWIO and \
+                    (is_tf or is_torch or is_onnx) and \
                     op.input[1] in self._consts:
                 producer = self._producer[op.input[0]]
                 weight = self._consts[op.input[1]]
                 if len(weight.dims) == 2 and self.is_after_fc(op) and \
                         len(producer.output_shape[0].dims) == 2 and \
-                        weight.dims[0] == producer.output_shape[0].dims[1]:
+                        ((is_tf and weight.dims[0] == producer.output_shape[0].dims[1]) or  # noqa
+                         (is_torch and weight.dims[1] == producer.output_shape[0].dims[1]) or  # noqa
+                         (is_onnx and weight.dims[1] == producer.output_shape[0].dims[1])):  # noqa
                     six.print_('convert matmul to fc')
                     op.type = MaceOp.FullyConnected.name
                     weight_data = np.array(weight.float_data).reshape(
                         weight.dims)
-                    weight.dims[:] = [1, 1] + list(weight_data.shape)
+                    # only 1 of the 2 branches can be executed
+                    if is_tf:
+                        weight.dims[:] = [1, 1] + list(weight_data.shape)
+                    if is_torch or is_onnx:
+                        weight.dims.extend([1, 1])
                     return True
 
             if self._option.device == DeviceType.APU.value:
@@ -2257,7 +2285,7 @@ class Transformer(base_converter.ConverterInterface):
             dim_arg = ConverterUtil.get_arg(op, MaceKeyword.mace_dim_str)
             shape_tensor = None
             if len(op.input) == 1:
-                print("Transform Caffe Reshape")
+                print("Transform Caffe or PyTorch Reshape")
                 dims = []
                 axis_arg = ConverterUtil.get_arg(op, MaceKeyword.mace_axis_str)
                 # transform caffe reshape op

tools/python/utils/config_parser.py

@@ -151,6 +151,7 @@ class Platform(Enum):
     CAFFE = 1
     ONNX = 2
     MEGENGINE = 3
+    PYTORCH = 4
 
 
 def parse_platform(str):

tools/python/utils/device.py

@@ -51,8 +51,8 @@ def execute(cmd, verbose=True):
             print(line)
         buf.append(line)
 
-    for l in p.stdout:
-        line = l.strip()
+    for li in p.stdout:
+        line = li.strip()
         if verbose:
             print(line)
         buf.append(line)

tools/python/validate.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import os
+import sys
 import os.path
 import numpy as np
 import six
@@ -204,6 +205,48 @@ def validate_tf_model(model_file,
                                    validation_threshold, log_file)
 
 
+def validate_pytorch_model(model_file,
+                           input_file, mace_out_file,
+                           input_names, input_shapes, input_data_formats,
+                           output_names, output_shapes, output_data_formats,
+                           validation_threshold, input_data_types, log_file):
+    import torch
+    loaded_model = torch.jit.load(model_file)
+    pytorch_inputs = []
+    for i in range(len(input_names)):
+        input_value = load_data(
+            util.formatted_file_name(input_file, input_names[i]),
+            input_data_types[i])
+        input_value = input_value.reshape(input_shapes[i])
+        if input_data_formats[i] == DataFormat.NHWC and \
+                len(input_shapes[i]) == 4:
+            input_value = input_value.transpose((0, 3, 1, 2))
+        input_value = torch.from_numpy(input_value)
+        pytorch_inputs.append(input_value)
+    with torch.no_grad():
+        pytorch_outputs = loaded_model(*pytorch_inputs)
+
+    if isinstance(pytorch_outputs, torch.Tensor):
+        pytorch_outputs = [pytorch_outputs]
+    else:
+        if not isinstance(pytorch_outputs, (list, tuple)):
+            print('return type {} unsupported'.format(type(pytorch_outputs)))
+            sys.exit(1)
+    for i in range(len(output_names)):
+        value = pytorch_outputs[i].numpy()
+        output_file_name = util.formatted_file_name(
+            mace_out_file, output_names[i])
+        mace_out_value = load_data(output_file_name)
+        # MACE: always returns tensor of dim 1
+        # pytorch: NCHW, conversion is needed
+        if output_data_formats[i] == DataFormat.NHWC and \
+                len(output_shapes[i]) == 4:
+            mace_out_value = mace_out_value.reshape(output_shapes[i])\
+                .transpose((0, 3, 1, 2))
+        compare_output(output_names[i], mace_out_value,
+                       value, validation_threshold, log_file)
+
+
 def validate_caffe_model(model_file, input_file,
                          mace_out_file, weight_file,
                          input_names, input_shapes, input_data_formats,
@@ -387,6 +430,12 @@ def validate(platform, model_file, weight_file, input_file, mace_out_file,
                           output_node, output_shape, output_data_format,
                           validation_threshold, input_data_type,
                           log_file)
+    elif platform == Platform.PYTORCH:
+        validate_pytorch_model(model_file, input_file, mace_out_file,
+                               input_node, input_shape, input_data_format,
+                               output_node, output_shape, output_data_format,
+                               validation_threshold, input_data_type,
+                               log_file)
     elif platform == Platform.CAFFE:
         validate_caffe_model(model_file,
                              input_file, mace_out_file, weight_file,

tools/sh_commands.py

@@ -53,7 +53,8 @@ def strip_invalid_utf8(str):
 def split_stdout(stdout_str):
     stdout_str = strip_invalid_utf8(stdout_str)
     # Filter out last empty line
-    return [l.strip() for l in stdout_str.split('\n') if len(l.strip()) > 0]
+    return [line.strip() for line in stdout_str.split('\n') if
+            len(line.strip()) > 0]
 
 
 def make_output_processor(buff):
@@ -659,7 +660,7 @@ def validate_model(abi,
                 sh.rm("-rf", "%s/%s" % (model_output_dir, formatted_name))
             device.pull_from_data_dir(formatted_name, model_output_dir)
 
-    if platform == "tensorflow" or platform == "onnx":
+    if platform == "tensorflow" or platform == "onnx" or platform == "pytorch":
         validate(platform, model_file_path, "",
                  "%s/%s" % (model_output_dir, input_file_name),
                  "%s/%s" % (model_output_dir, output_file_name), device_type,

tools/validate.py

@@ -216,6 +216,48 @@ def validate_tf_model(platform, device_type, model_file,
                                    validation_threshold, log_file)
 
 
+def validate_pytorch_model(platform, device_type, model_file,
+                           input_file, mace_out_file,
+                           input_names, input_shapes, input_data_formats,
+                           output_names, output_shapes, output_data_formats,
+                           validation_threshold, input_data_types, log_file):
+    import torch
+    loaded_model = torch.jit.load(model_file)
+    pytorch_inputs = []
+    for i in range(len(input_names)):
+        input_value = load_data(
+            common.formatted_file_name(input_file, input_names[i]),
+            input_data_types[i])
+        input_value = input_value.reshape(input_shapes[i])
+        if input_data_formats[i] == common.DataFormat.NHWC and \
+                len(input_shapes[i]) == 4:
+            input_value = input_value.transpose((0, 3, 1, 2))
+        input_value = torch.from_numpy(input_value)
+        pytorch_inputs.append(input_value)
+    with torch.no_grad():
+        pytorch_outputs = loaded_model(*pytorch_inputs)
+
+    if isinstance(pytorch_outputs, torch.Tensor):
+        pytorch_outputs = [pytorch_outputs]
+    else:
+        if not isinstance(pytorch_outputs, (list, tuple)):
+            print('return type {} unsupported yet'.format(
+                type(pytorch_outputs)))
+            sys.exit(1)
+    for i in range(len(output_names)):
+        value = pytorch_outputs[i].numpy()
+        output_file_name = common.formatted_file_name(
+            mace_out_file, output_names[i])
+        mace_out_value = load_data(output_file_name)
+        # MACE: NHWC, pytorch: NCHW, conversion is needed
+        if output_data_formats[i] == common.DataFormat.NHWC and \
+                len(output_shapes[i]) == 4:
+            mace_out_value = mace_out_value.reshape(output_shapes[i])\
+                .transpose((0, 3, 1, 2))
+        compare_output(platform, device_type, output_names[i], mace_out_value,
+                       value, validation_threshold, log_file)
+
+
 def validate_caffe_model(platform, device_type, model_file, input_file,
                          mace_out_file, weight_file,
                          input_names, input_shapes, input_data_formats,
@@ -418,6 +460,13 @@ def validate(platform, model_file, weight_file, input_file, mace_out_file,
                           output_names, output_shapes, output_data_formats,
                           validation_threshold, input_data_types,
                           log_file)
+    elif platform == 'pytorch':
+        validate_pytorch_model(platform, device_type,
+                               model_file, input_file, mace_out_file,
+                               input_names, input_shapes, input_data_formats,
+                               output_names, output_shapes,
+                               output_data_formats, validation_threshold,
+                               input_data_types, log_file)
     elif platform == 'caffe':
         validate_caffe_model(platform, device_type, model_file,
                              input_file, mace_out_file, weight_file,