From ed0708e86388dc92bcb74f5a20cd8e67cb2e7209 Mon Sep 17 00:00:00 2001 From: SunAhong1993 Date: Mon, 14 Dec 2020 12:20:38 +0800 Subject: [PATCH] fix the caffe --- x2paddle/core/program.py | 9 +- .../static/caffe2paddle/caffe_op_mapper.py | 26 +-- .../optimizer/fusion/static/bn_scale_fuser.py | 149 +++++++++++------- 3 files changed, 115 insertions(+), 69 deletions(-) diff --git a/x2paddle/core/program.py b/x2paddle/core/program.py index d3d3549..6dc2fc9 100644 --- a/x2paddle/core/program.py +++ b/x2paddle/core/program.py @@ -309,12 +309,19 @@ class PaddleGraph(object): if not osp.exists(code_dir): os.makedirs(code_dir) f = open(osp.join(code_dir, 'x2paddle_model.py'), 'w') + + if self.source_type == "caffe": + custom_import = "from x2paddle.op_mapper.static.caffe2paddle " + \ + "import caffe_custom_layer as x2paddle_nn" + else: + custom_import = "" write_code( f, [ "from paddle.fluid.initializer import Constant", "from paddle.fluid.param_attr import ParamAttr", "import paddle.fluid as fluid", + custom_import, "import paddle", "import math", "", ], @@ -347,7 +354,7 @@ class PaddleGraph(object): line += "{}, ".format(output) line = line.strip(", ") if layer.kernel.startswith("custom_layer"): - line += " = {}(".format(layer.kernel.split(":")[-1].lower() + "_layer") + line += "= x2paddle_nn.{}(".format(layer.kernel.split(":")[-1]) else: line += " = {}(".format(layer.kernel) for k, v in layer.inputs.items(): diff --git a/x2paddle/op_mapper/static/caffe2paddle/caffe_op_mapper.py b/x2paddle/op_mapper/static/caffe2paddle/caffe_op_mapper.py index 316d586..53e7222 100644 --- a/x2paddle/op_mapper/static/caffe2paddle/caffe_op_mapper.py +++ b/x2paddle/op_mapper/static/caffe2paddle/caffe_op_mapper.py @@ -212,7 +212,7 @@ class CaffeOpMapper(OpMapper): kernel_weight_name = node.name + '_weights' self.params[kernel_weight_name] = data[0] self.paddle_graph.add_layer( - kernel="paddle.static.nn.create_parameter", + kernel="paddle.static.create_parameter", inputs={}, outputs=[kernel_weight_name], shape=self.params[kernel_weight_name].shape, @@ -222,7 +222,7 @@ class CaffeOpMapper(OpMapper): kernel_bias_name = node.name + '_bias' self.params[kernel_bias_name] = data[1] self.paddle_graph.add_layer( - kernel="paddle.static.nn.create_parameter", + kernel="paddle.static.create_parameter", inputs={}, outputs=[kernel_bias_name], shape=self.params[kernel_bias_name].shape, @@ -268,7 +268,7 @@ class CaffeOpMapper(OpMapper): kernel_weight_name = node.name + '_weights' self.params[kernel_weight_name] = data[0] self.paddle_graph.add_layer( - kernel="paddle.static.nn.create_parameter", + kernel="paddle.static.create_parameter", inputs={}, outputs=[kernel_weight_name], shape=self.params[kernel_weight_name].shape, @@ -278,7 +278,7 @@ class CaffeOpMapper(OpMapper): kernel_bias_name = node.name + '_bias' self.params[kernel_bias_name] = data[1] self.paddle_graph.add_layer( - kernel="paddle.static.nn.create_parameter", + kernel="paddle.static.create_parameter", inputs={}, outputs=[kernel_bias_name], shape=self.params[kernel_bias_name].shape, @@ -330,7 +330,7 @@ class CaffeOpMapper(OpMapper): kernel_weight_name = node.name + '_weights' self.params[kernel_weight_name] = data[0] self.paddle_graph.add_layer( - kernel="paddle.static.nn.create_parameter", + kernel="paddle.static.create_parameter", inputs={}, outputs=[kernel_weight_name], shape=self.params[kernel_weight_name].shape, @@ -340,7 +340,7 @@ class CaffeOpMapper(OpMapper): kernel_bias_name = node.name + '_bias' self.params[kernel_bias_name] = data[1] self.paddle_graph.add_layer( - kernel="paddle.static.nn.create_parameter", + kernel="paddle.static.create_parameter", inputs={}, outputs=[kernel_bias_name], shape=self.params[kernel_bias_name].shape, @@ -467,7 +467,7 @@ class CaffeOpMapper(OpMapper): kernel_weight_name = node.name + '_weights' self.params[kernel_weight_name] = data[0] self.paddle_graph.add_layer( - kernel="paddle.static.nn.create_parameter", + kernel="paddle.static.create_parameter", inputs={}, outputs=[kernel_weight_name], shape=self.params[kernel_weight_name].shape, @@ -477,7 +477,7 @@ class CaffeOpMapper(OpMapper): kernel_bias_name = node.name + '_bias' self.params[kernel_bias_name] = data[1] self.paddle_graph.add_layer( - kernel="paddle.static.nn.create_parameter", + kernel="paddle.static.create_parameter", inputs={}, outputs=[kernel_bias_name], shape=self.params[kernel_bias_name].shape, @@ -614,7 +614,7 @@ class CaffeOpMapper(OpMapper): kernel_weight_name = node.name + '_weights' self.params[kernel_weight_name] = np.squeeze(data[0]) self.paddle_graph.add_layer( - kernel="paddle.static.nn.create_parameter", + kernel="paddle.static.create_parameter", inputs={}, outputs=[kernel_weight_name], shape=[num_parameters], @@ -727,7 +727,7 @@ class CaffeOpMapper(OpMapper): mean_name = node.name + '_mean' self.params[mean_name] = mean self.paddle_graph.add_layer( - kernel="paddle.static.nn.create_parameter", + kernel="paddle.static.create_parameter", inputs={}, outputs=[mean_name], shape=self.params[mean_name].shape, @@ -736,7 +736,7 @@ class CaffeOpMapper(OpMapper): variance_name = node.name + '_variance' self.params[variance_name] = variance self.paddle_graph.add_layer( - kernel="paddle.static.nn.create_parameter", + kernel="paddle.static.create_parameter", inputs={}, outputs=[variance_name], shape=self.params[variance_name].shape, @@ -790,7 +790,7 @@ class CaffeOpMapper(OpMapper): axis=1) else: self.paddle_graph.add_layer( - "paddle.static.nn.create_parameter", + "paddle.static.create_parameter", inputs={}, outputs=[node.name + "_cparam1"], shape=self.params[node.name + "_cparam1"].shape, @@ -807,7 +807,7 @@ class CaffeOpMapper(OpMapper): outputs=[node.name + "_mul"], axis=axis) self.paddle_graph.add_layer( - "paddle.static.nn.create_parameter", + "paddle.static.create_parameter", inputs={}, outputs=[node.name + "_cparam2"], shape=self.params[node.name + "_cparam2"].shape, diff --git a/x2paddle/optimizer/fusion/static/bn_scale_fuser.py b/x2paddle/optimizer/fusion/static/bn_scale_fuser.py index 05d1780..946e7fe 100644 --- a/x2paddle/optimizer/fusion/static/bn_scale_fuser.py +++ b/x2paddle/optimizer/fusion/static/bn_scale_fuser.py @@ -20,87 +20,126 @@ from x2paddle.core.util import * class Static_BNScaleFuser(FuseBase): def __init__(self): - super(Static_BNScaleFuser, self).__init__(graph_type="dygraph") + super(Static_BNScaleFuser, self).__init__(graph_type="static") + patterns = list() def build_pattern(self): """ 描述需要替换的batchnorm2d图结构。 batchnorm2d层模式python实现代码示例: - conv5_bn = fluid.layers.batch_norm(input=conv5, is_test=True, param_attr=None, bias_attr=None, moving_mean_name='conv5_bn_mean', moving_variance_name='conv5_bn_variance', epsilon=9.999999747378752e-06, name='conv5_bn') - conv5_scale_scale = fluid.ParamAttr(name='conv5_scale_scale') - conv5_scale_cparam1 = fluid.layers.create_parameter(attr=conv5_scale_scale, dtype=conv5_bn.dtype, shape=[256], name='conv5_scale_cparam1', is_bias=True, default_initializer=Constant(value=1.0)) - conv5_scale_mul = fluid.layers.elementwise_mul(x=conv5_bn, y=conv5_scale_cparam1, axis=1) - conv5_scale_offset = fluid.ParamAttr(name='conv5_scale_offset') - conv5_scale_cparam2 = fluid.layers.create_parameter(attr=conv5_scale_offset, dtype=conv5_bn.dtype, shape=[256], name='conv5_scale_cparam2', is_bias=True, default_initializer=Constant(value=1.0)) - conv5_scale = fluid.layers.elementwise_add(x=conv5_scale_mul, y=conv5_scale_cparam2, axis=1) + 模式一： + conv1_bn = paddle.nn.functional.batch_norm(x=conv1, weight=conv1_bn_weight, bias=conv1_bn_bias, running_mean=conv1_bn_mean, running_var=conv1_bn_variance, epsilon=9.999999747378752e-06, momentum=0.9990000128746033) + conv1_scale_cparam1 = paddle.static.create_parameter(shape=(32,), dtype='float32', name='conv1_scale_cparam1') + conv1_scale_mul = paddle.multiply(x=conv1_bn, y=conv1_scale_cparam1, axis=1) + conv1_scale_cparam2 = paddle.static.create_parameter(shape=(32,), dtype='float32', name='conv1_scale_cparam2') + conv1_scale_cparam2 = paddle.reshape(x=conv1_scale_cparam2, shape=[32, 1, 1]) + conv1_scale = paddle.add(x=conv1_scale_mul, y=conv1_scale_cparam2) + 模式二： + conv1_bn = paddle.nn.functional.batch_norm(x=conv1, weight=conv1_bn_weight, bias=conv1_bn_bias, running_mean=conv1_bn_mean, running_var=conv1_bn_variance, epsilon=9.999999747378752e-06, momentum=0.9990000128746033) + conv1_scale_cparam1 = paddle.static.create_parameter(shape=(32,), dtype='float32', name='conv1_scale_cparam1') + conv1_scale_mul = paddle.multiply(x=conv1_bn, y=conv1_scale_cparam1, axis=1) + conv1_scale_cparam2 = paddle.static.create_parameter(shape=(32,), dtype='float32', name='conv1_scale_cparam2') + conv1_scale = paddle.add(x=conv1_scale_mul, y=conv1_scale_cparam2) """ def gen_name(id): return "x" + str(id) - self.pattern.add_layer( - "fluid.layers.batch_norm", - inputs={"input": "bn-input-0"}, + pattern = PaddleGraph(graph_type="dygraph") + pattern.add_layer( + "paddle.nn.functional.batch_norm", + inputs={"input": "bn-input-0", + "weight": "bn-input-1", + "bias": "bn-input-2", + "running_mean": "bn-input-3", + "running_var": "bn-input-4",}, outputs=[gen_name(0)]) - self.pattern.add_layer( - "fluid.ParamAttr", + pattern.add_layer( + "paddle.static.create_parameter", inputs={}, outputs=[gen_name(1)]) - self.pattern.add_layer( - "fluid.layers.create_parameter", - inputs={"attr": gen_name(1)}, - outputs=[gen_name(2)]) inputs_dict = {} inputs_dict['x'] = gen_name(0) - inputs_dict['y'] = gen_name(2) - self.pattern.add_layer( - "fluid.layers.elementwise_mul", + inputs_dict['y'] = gen_name(1) + pattern.add_layer( + "paddle.multiply", inputs=inputs_dict, - outputs=[gen_name(3)]) - self.pattern.add_layer( - "fluid.ParamAttr", + outputs=[gen_name(2)]) + pattern.add_layer( + "paddle.static.create_parameter", inputs={}, + outputs=[gen_name(3)]) + pattern.add_layer( + "paddle.reshape", + inputs={"x": gen_name(3)}, outputs=[gen_name(4)]) - self.pattern.add_layer( - "fluid.layers.create_parameter", - inputs={"attr": gen_name(4)}, + inputs_dict = {} + inputs_dict['x'] = gen_name(2) + inputs_dict['y'] = gen_name(4) + pattern.add_layer( + "paddle.add", + inputs=inputs_dict, outputs=[gen_name(5)]) + pattern.build(inputs={"input-0": "bn-input-0", + "input-1": "bn-input-1", + "input-2": "bn-input-2", + "input-3": "bn-input-3", + "input-4": "bn-input-4"}) + self.patterns.append(pattern) + + pattern = PaddleGraph(graph_type="dygraph") + pattern.add_layer( + "paddle.nn.functional.batch_norm", + inputs={"input": "bn-input-0", + "weight": "bn-input-1", + "bias": "bn-input-2", + "running_mean": "bn-input-3", + "running_var": "bn-input-4",}, + outputs=[gen_name(0)]) + pattern.add_layer( + "paddle.static.create_parameter", + inputs={}, + outputs=[gen_name(1)]) inputs_dict = {} - inputs_dict['x'] = gen_name(3) - inputs_dict['y'] = gen_name(5) - self.pattern.add_layer( - "fluid.layers.elementwise_add", + inputs_dict['x'] = gen_name(0) + inputs_dict['y'] = gen_name(1) + pattern.add_layer( + "paddle.multiply", inputs=inputs_dict, - outputs=[gen_name(6)]) - self.pattern.build(inputs={"input-0": "bn-input-0"}) + outputs=[gen_name(2)]) + pattern.add_layer( + "paddle.static.create_parameter", + inputs={}, + outputs=[gen_name(3)]) + inputs_dict = {} + inputs_dict['x'] = gen_name(2) + inputs_dict['y'] = gen_name(3) + pattern.add_layer( + "paddle.add", + inputs=inputs_dict, + outputs=[gen_name(4)]) + pattern.build(inputs={"input-0": "bn-input-0", + "input-1": "bn-input-1", + "input-2": "bn-input-2", + "input-3": "bn-input-3", + "input-4": "bn-input-4"}) + self.patterns.append(pattern) def insert_new_layer(self, graph, parameters, matches): new_layer = self.gen_new_layer(parameters, matches) - new_layer_id = list(matches.keys())[0] + new_layer_id = list(matches.keys())[-1] graph.layers[new_layer_id] = new_layer + matches.pop(list(matches.keys())[1]) + matches.pop(list(matches.keys())[2]) matches.pop(new_layer_id) def gen_new_layer(self, parameters, matches): layers_id = list(matches.keys()) - layer = matches[layers_id[0]] - layer_inputs = layer.inputs - layer_name = layer.outputs[0] - layer_attrs = layer.attrs - layer_attrs["param_attr"] = string("{}_scale".format(layer_name)) - layer_attrs["bias_attr"] = string("{}_offset".format(layer_name)) - layer = matches[layers_id[-1]] - layer_outputs = layer.outputs + bn_layer = matches[layers_id[0]] layer = matches[layers_id[1]] - layer_name = layer.outputs[0] - scale_numpy = parameters.pop(layer_name) - parameters[layer_attrs["param_attr"][1: -1]] = scale_numpy - layer = matches[layers_id[4]] - layer_name = layer.outputs[0] - scale_numpy = parameters.pop(layer_name) - parameters[layer_attrs["bias_attr"][1: -1]] = scale_numpy - new_layer = PaddleLayer( - layers_id[0], - "fluid.layers.batch_norm", - inputs=layer_inputs, - outputs=layer_outputs, - **layer_attrs) - return new_layer \ No newline at end of file + bn_layer.inputs["weight"] = layer.outputs[0] + layer = matches[layers_id[3]] + bn_layer.inputs["bias"] = layer.outputs[0] + bn_layer.id = layers_id[-1] + layer = matches[layers_id[-1]] + bn_layer.outputs = layer.outputs + return bn_layer \ No newline at end of file -- GitLab