add caffe emitter for alexnet

x2paddle/emitter/caffe_emitter.py

@@ -11,3 +11,283 @@
 # 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 numbers
+from x2paddle.parser.caffe_parser import CaffeGraph
+from x2paddle.core.emitter import Emitter
+from x2paddle.core.util import *
+
+
+class CaffeEmitter(Emitter):
+    def __init__(self, parser):
+        super(CaffeEmitter, self).__init__()
+        self.parser = parser
+        self.graph = parser.caffe_graph
+        self.weights = dict()
+        self.resolver = parser.resolver
+
+    def run(self):
+        print("Total nodes: {}".format(len(self.graph.topo_sort)))
+        for node_name in self.graph.topo_sort:
+            node = self.graph.get_node(node_name)
+            op = node.layer_type
+            if hasattr(self, op):
+                emit_func = getattr(self, op)
+                emit_func(node)
+
+        for i in range(len(self.graph.topo_sort)):
+            node_name = self.graph.topo_sort[i]
+            node = self.graph.get_node(node_name)
+            for layer in node.fluid_code.layers:
+                print(layer.get_code())
+
+        for name, param in self.weights.items():
+            export_paddle_param(param, name.replace('/', '_'), "params1")
+
+    @staticmethod
+    def get_kernel_value(scalar, repeated, idx, default=None):
+        if scalar:
+            return scalar
+        if repeated:
+            if isinstance(repeated, numbers.Number):
+                return repeated
+            if len(repeated) == 1:
+                # Same value applies to all spatial dimensions
+                return int(repeated[0])
+            assert idx < len(repeated)
+            # Extract the value for the given spatial dimension
+            return repeated[idx]
+        if default is None:
+            raise ValueError('Unable to determine kernel parameter!')
+        return default
+
+    def get_kernel_parameters(self, kind, params):
+        assert kind in ['Convolution', 'Pooling', 'Deconvolution']
+
+        k_h = self.get_kernel_value(params.kernel_h, params.kernel_size, 0)
+        k_w = self.get_kernel_value(params.kernel_w, params.kernel_size, 1)
+        s_h = self.get_kernel_value(params.stride_h,
+                                    params.stride,
+                                    0,
+                                    default=1)
+        s_w = self.get_kernel_value(params.stride_w,
+                                    params.stride,
+                                    1,
+                                    default=1)
+        p_h = self.get_kernel_value(params.pad_h, params.pad, 0, default=0)
+        p_w = self.get_kernel_value(params.pad_w, params.pad, 1, default=0)
+        dila_h = dila_w = 1
+        group = 1
+        c_o = 1
+        if kind in ['Convolution', 'Deconvolution']:
+            c_o = params.num_output
+            group = params.group
+            dila_len = len(params.dilation)
+            if dila_len == 2:
+                dila_h = params.dilation[0]
+                dila_w = params.dilation[1]
+            elif dila_len == 1:
+                dila_h = dila_w = params.dilation[0]
+            else:
+                assert dila_len == 0, "invalid length[%s] of dilation in convolution" % (
+                    dila_len)
+
+        kernel = [k_h, k_w]
+        stride = [s_h, s_w]
+        pad = [p_h, p_w]
+        dilation = [dila_h, dila_w]
+
+        return c_o, kernel, stride, pad, dilation, group
+
+    def Input(self, node):
+        shape = list(node.layer.input_param.shape[0].dim)[1:]
+        dtype = 'float32'
+        attr = {
+            'dtype': string(dtype),
+            'shape': shape,
+            'name': string(node.layer_name)
+        }
+        node.fluid_code.add_layer("data",
+                                  inputs=None,
+                                  output=node,
+                                  param_attr=attr)
+
+    def Convolution(self, node):
+        data = node.data
+        self.weights[node.layer_name + '_weights'] = data[0]
+        if len(data) == 2:
+            self.weights[node.layer_name + '_bias'] = data[1]
+        params = node.layer.convolution_param
+        channel, kernel, stride, pad, dilation, group = self.get_kernel_parameters(
+            node.layer_type, params)
+        assert len(node.inputs
+                   ) == 1, 'The count of Convolution node\'s input is not 1.'
+        input = self.graph.get_bottom_node(node, idx=0)
+        attr = {
+            'filter_size': kernel,
+            'num_filters': channel,
+            'stride': stride,
+            'padding': pad,
+            'dilation': dilation,
+            'groups': group,
+            'name': string(node.layer_name),
+            'param_attr': string(node.layer_name + '_weights'),
+            'bias_attr': string(node.layer_name + '_bias'),
+        }
+        node.fluid_code.add_layer("conv2d",
+                                  inputs=input,
+                                  output=node,
+                                  param_attr=attr)
+
+    def Deconvolution(self, node):
+        data = node.data
+        self.weights[node.layer_name + '_weights'] = data[0]
+        if len(data) == 2:
+            self.weights[node.layer_name + '_bias'] = data[1]
+        params = node.layer.convolution_param
+        channel, kernel, stride, pad, dilation, group = self.get_kernel_parameters(
+            node.layer_type, params)
+        assert len(node.inputs
+                   ) == 1, 'The count of Deconvolution node\'s input is not 1.'
+        input = self.graph.get_bottom_node(node, idx=0)
+        attr = {
+            'output_size': None,
+            'filter_size': kernel,
+            'num_filters': channel,
+            'stride': stride,
+            'padding': pad,
+            'dilation': dilation,
+            'groups': group,
+            'name': string(node.layer_name),
+            'param_attr': string(node.layer_name + '_weights'),
+            'bias_attr': string(node.layer_name + '_bias')
+        }
+        node.fluid_code.add_layer("conv2d_transpose",
+                                  inputs=input,
+                                  output=node,
+                                  param_attr=attr)
+
+    def Pooling(self, node):
+        params = node.layer.pooling_param
+        channel, kernel, stride, pad, dilation, group = self.get_kernel_parameters(
+            node.layer_type, params)
+        if params.pool == 0:
+            pool_type = 'max'
+        else:
+            pool_type = 'avg'
+        assert len(
+            node.inputs) == 1, 'The count of Pooling node\'s input is not 1.'
+        input = self.graph.get_bottom_node(node, idx=0)
+        attr = {
+            'pool_size': kernel,
+            'pool_stride': stride,
+            'pool_padding': pad,
+            'ceil_mode': True,
+            'pool_type': string(pool_type),
+            'exclusive': True,
+            'name': string(node.layer_name)
+        }
+        node.fluid_code.add_layer("pool2d",
+                                  inputs=input,
+                                  output=node,
+                                  param_attr=attr)
+
+    def ReLU(self, node):
+        assert len(
+            node.inputs) == 1, 'The count of ReLU node\'s input is not 1.'
+        input = self.graph.get_bottom_node(node, idx=0)
+        attr = {'name': string(node.layer_name)}
+        node.fluid_code.add_layer("relu",
+                                  inputs=input,
+                                  output=node,
+                                  param_attr=attr)
+
+    def LRN(self, node):
+        assert len(node.inputs) == 1, 'The count of LRN node\'s input is not 1.'
+        params = node.layer.lrn_param
+        # The window size must be an odd value. For a window
+        # size of (2*n+1), Paddle defines depth_radius = n.
+        assert params.local_size % 2 == 1
+        # Caffe scales by (alpha/(2*n+1)), whereas Paddle
+        # just scales by alpha (as does Krizhevsky's paper).
+        # We'll account for that here.
+        alpha = params.alpha / float(params.local_size)
+        input = self.graph.get_bottom_node(node, idx=0)
+        attr = {
+            'n': params.local_size,
+            'k': 1.0,
+            'alpha': alpha,
+            'beta': params.beta,
+            'name': string(node.layer_name)
+        }
+        node.fluid_code.add_layer("lrn",
+                                  inputs=input,
+                                  output=node,
+                                  param_attr=attr)
+
+    def InnerProduct(self, node):
+        data = node.data
+        self.weights[node.layer_name + '_weights'] = data[0]
+        if len(data) == 2:
+            self.weights[node.layer_name + '_bias'] = data[1]
+        assert len(node.inputs
+                   ) == 1, 'The count of InnerProduct node\'s input is not 1.'
+        params = node.layer.inner_product_param
+        assert params.axis == 1
+        assert params.bias_term == True
+        input = self.graph.get_bottom_node(node, idx=0)
+        attr = {
+            'size': params.num_output,
+            'name': string(node.layer_name),
+            'act': None,
+            'param_attr': string(node.layer_name + '_weights'),
+            'bias_attr': string(node.layer_name + '_bias')
+        }
+        node.fluid_code.add_layer("fc",
+                                  inputs=input,
+                                  output=node,
+                                  param_attr=attr)
+
+    def Softmax(self, node):
+        assert len(
+            node.inputs) == 1, 'The count of Softmax node\'s input is not 1.'
+        input = self.graph.get_bottom_node(node, idx=0)
+        params = node.layer.softmax_param
+        axis = params.axis
+        shape = node.input_shape[0]
+        dims = len(shape)
+        axis = axis + dims if axis < 0 else axis
+        need_transpose = False
+        if axis + 1 != dims:
+            need_transpose = True
+        if need_transpose:
+            in_order = list(range(dims))
+            in_order.remove(axis)
+            in_order.append(axis)
+            attr = {
+                'perm': in_order,
+                'name': string(node.layer_name + '_transpose_in')
+            }
+            node.fluid_code.add_layer("transpose",
+                                      inputs=input,
+                                      output=node,
+                                      param_attr=attr)
+        attr = {'name': string(node.layer_name + '_softmax')}
+        node.fluid_code.add_layer("softmax",
+                                  inputs=node if need_transpose else input,
+                                  output=node,
+                                  param_attr=attr)
+        if need_transpose:
+            out_order = [
+                0,
+            ] * dims
+            for id, v in enumerate(in_order):
+                out_order[v] = id
+            attr = {
+                'perm': out_order,
+                'name': string(node.layer_name + '_transpose_out')
+            }
+            node.fluid_code.add_layer("transpose",
+                                      inputs=node,
+                                      output=node,
+                                      param_attr=attr)

x2paddle/parser/caffe_parser.py

@@ -17,6 +17,8 @@ import sys
 from google.protobuf import text_format
 import numpy as np
 from x2paddle.core.graph import GraphNode, Graph
+from x2paddle.core.fluid_code import FluidCode
+from x2paddle.parser import caffe_shape
 
 
 class CaffeResolver(object):
@@ -62,10 +64,19 @@ class CaffeGraphNode(GraphNode):
         else:
             super(CaffeGraphNode, self).__init__(layer, layer_name)
         self.layer_type = layer.type
+        self.fluid_code = FluidCode()
 
     def set_params(self, params):
         self.data = params
 
+    def set_output_shape(self, input_shape):
+        func_name = 'shape_' + self.layer_type.lower()
+        self.output_shape = getattr(caffe_shape, func_name)(self.layer,
+                                                            input_shape)
+
+    def set_input_shape(self, input_shape):
+        self.input_shape = input_shape
+
 
 class CaffeGraph(Graph):
     def __init__(self, model, params):
@@ -148,8 +159,34 @@ class CaffeGraph(Graph):
             else:
                 notice('Ignoring parameters for non-existent layer: %s' % \
                         layer_name)
+        for layer_name in self.node_map:
+            node = self.node_map[layer_name]
+            inputs = node.inputs
+            i = 0
+            input_shape = []
+            for nm in inputs:
+                last_node = self.get_node(nm)
+                tmp = node.layer.bottom[i]
+                i = i + 1
+                idx = list(last_node.layer.top).index(tmp)
+                input_shape.append(last_node.output_shape[idx])
+            node.set_output_shape(input_shape)
+            node.set_input_shape(input_shape)
+
         super(CaffeGraph, self).build()
 
+    def get_bottom_node(self, node, idx=0, copy=False):
+        input_node_name = node.inputs[idx]
+        assert input_node_name in self.node_map, 'The {} isn\'t a valid node'.format(
+            name)
+        input_node = self.node_map[input_node_name]
+        if len(input_node.layer.top) > 1:
+            idx = list(input_node.layer.top).index(node.layer.bottom[need])
+            name = input_node_name + ':' + str(idx)
+        else:
+            name = input_node_name
+        return self.get_node(name, copy=copy)
+
 
 class CaffeParser(object):
     def __init__(self, proto_path, model_path, use_caffe=True):

x2paddle/parser/caffe_shape.py

+#   Copyright (c) 2019  PaddlePaddle 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
+
+
+def get_params_w_h(params):
+    if hasattr(params, 'dilation'):
+        if len(params.dilation) == 0:
+            dila_h = 1
+            dila_w = 1
+        elif len(params.dilation) == 1:
+            dila_h = params.dilation[0]
+            dila_w = params.dilation[0]
+        else:
+            dila_h = params.dilation[0]
+            dila_w = params.dilation[1]
+    else:
+        dila_h = 1
+        dila_w = 1
+
+    if not isinstance(getattr(params, 'pad'), int):
+        if len(params.pad) == 0:
+            pad_h = 1
+            pad_w = 1
+        elif len(params.pad) == 1:
+            pad_h = params.pad[0]
+            pad_w = params.pad[0]
+        else:
+            pad_h, pad_w, = params.pad[0]
+            pad_w = params.pad[1]
+        if params.pad_h != 0 or params.pad_w != 0:
+            pad_h = params.pad_h
+            pad_w = params.pad_w
+    else:
+        if params.pad_h != 0 or params.pad_w != 0:
+            pad_h = params.pad_h
+            pad_w = params.pad_w
+        else:
+            pad_h = getattr(params, 'pad')
+            pad_w = getattr(params, 'pad')
+
+    if not isinstance(getattr(params, 'kernel_size'), int):
+        if len(params.kernel_size) == 0:
+            kernel_h = 1
+            kernel_w = 1
+        elif len(params.kernel_size) == 1:
+            kernel_h = params.kernel_size[0]
+            kernel_w = params.kernel_size[0]
+        else:
+            kernel_h = params.kernel_size[0]
+            kernel_w = params.kernel_size[1]
+        if params.kernel_h != 0 or params.kernel_w != 0:
+            kernel_h = params.kernel_h
+            kernel_w = params.kernel_w
+    else:
+        if params.kernel_h != 0 or params.kernel_w != 0:
+            kernel_h = params.kernel_h
+            kernel_w = params.kernel_w
+        else:
+            kernel_h = getattr(params, 'kernel_size')
+            kernel_w = getattr(params, 'kernel_size')
+    if not isinstance(getattr(params, 'stride'), int):
+        if len(params.stride) == 0:
+            stride_h = 1
+            stride_w = 1
+        elif len(params.stride) == 1:
+            stride_h = params.stride[0]
+            stride_w = params.stride[0]
+        else:
+            stride_h = params.stride[0]
+            stride_w = params.stride[1]
+        if params.stride_h != 0 or params.stride_w != 0:
+            stride_h = params.stride_h
+            stride_w = params.stride_w
+    else:
+        if params.stride_h != 0 or params.stride_w != 0:
+            stride_h = params.stride_h
+            stride_w = params.stride_w
+        else:
+            stride_h = getattr(params, 'stride')
+            stride_w = getattr(params, 'stride')
+    return dila_h, dila_w, pad_h, pad_w, kernel_h, kernel_w, stride_h, stride_w
+
+
+def get_filter_output_shape(i_h, i_w, params, round_func):
+    dila_h, dila_w, pad_h, pad_w, kernel_h, kernel_w, stride_h, stride_w = get_params_w_h(
+        params)
+    o_h = (i_h + 2 * pad_h - (dila_h *
+                              (kernel_h - 1) + 1)) / float(stride_h) + 1
+    o_w = (i_w + 2 * pad_w - (dila_w *
+                              (kernel_w - 1) + 1)) / float(stride_w) + 1
+
+    return (int(round_func(o_h)), int(round_func(o_w)))
+
+
+def get_strided_kernel_output_shape(params, input_shape, round_func):
+
+    o_h, o_w = get_filter_output_shape(input_shape[2], input_shape[3], params,
+                                       round_func)
+    has_c_o = hasattr(params, 'num_output')
+    c = params.num_output if has_c_o else input_shape[1]
+    return [[input_shape[0], c, o_h, o_w]]
+
+
+def shape_convolution(layer, input_shape):
+    params = layer.convolution_param
+    return get_strided_kernel_output_shape(params, input_shape[0], math.floor)
+
+
+def shape_deconvolution(layer, input_shape):
+    h_i = input_shape[2]
+    w_i = input_shape[3]
+
+    params = layer.convolution_param
+    dila_h, dila_w, pad_h, pad_w, kernel_h, kernel_w, stride_h, stride_w = get_params_w_h(
+        params)
+
+    h_o = (h_i - 1) * stride_h - 2 * pad_h + dila_h * (kernel_h - 1) + 1
+    w_o = (w_i - 1) * stride_w - 2 * pad_w + dila_w * (kernel_w - 1) + 1
+
+    has_c_o = hasattr(params, 'num_output')
+    c = params.num_output if has_c_o else input_shape.channels
+    return [[input_shape[0][0], c, h_o, w_o]]
+
+
+def shape_pooling(layer, input_shape):
+    params = layer.pooling_param
+    global_pool = getattr(params, 'global_pooling', False)
+    if global_pool:
+        return [[input_shape[0][0], input_shape[0][1], 1, 1]]
+
+    ceil_mode = getattr(params, 'ceil_mode', True)
+    if ceil_mode is True:
+        method = math.ceil
+    else:
+        method = math.floor
+    return get_strided_kernel_output_shape(params, input_shape[0], method)
+
+
+def shape_innerproduct(layer, input_shape):
+    params = layer.inner_product_param
+    return [[input_shape[0][0], params.num_output]]
+
+
+def shape_lrn(layer, input_shape):
+    return input_shape
+
+
+def shape_relu(layer, input_shape):
+    return input_shape
+
+
+def shape_softmax(layer, input_shape):
+    return input_shape
+
+
+def shape_input(layer, input_shape):
+    return [list(layer.input_param.shape[0].dim)]