# Copyright (c) 2020 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 copy import numpy as np from x2paddle.optimizer.pattern_matcher import FuseBase from x2paddle.core.program import PaddleGraph, PaddleLayer from x2paddle.core.util import * class InterpolateBilinearFuser(FuseBase): def __init__(self): super(InterpolateBilinearFuser, self).__init__() self.pattenrs = list() def build_pattern(self): """ 描述需要替换的双线性插值图结构。 interpolate_bilinear层模式python实现代码示例: x2195 = x2181.shape x2195 = len(x2195) x2196 = x2195 - 2 x2197 = [] for _x2199 in range(x2196): x2197.append(None) x2200 = (x2181, x8, None, None) ... x2267 = x2266 == 3 if x2267 : raise RaiseException('Exception') x2268 = None else: x2270 = x2181.shape x2270 = len(x2270) x2271 = x2270 == 4 if x2271 : x2274 = x2197[0] x2275 = x2197[1] x2233_isinstance = isinstance(x2233, paddle.static.Variable) if x2233_isinstance : x2233 = x2233.numpy().tolist() x2276 = paddle.nn.functional.interpolate(x=x2181, size=x2233, scale_factor=x2274, align_corners=False, align_mode=0, mode='bilinear') x2272 = x2276 else: x2277 = x2181.shape x2277 = len(x2277) x2278 = x2277 == 5 if x2278 : raise RaiseException('Exception') else: raise RaiseException('Exception') x2272 = None x2268 = x2272 """ def gen_name(id): return "x" + str(id) pattern = PaddleGraph() pattern.add_layer( "prim.shape", inputs={"input": "interpolate-input-0"}, outputs=[gen_name(9)]) pattern.add_layer( "prim.len", inputs={"input": gen_name(9)}, outputs=[gen_name(9)]) pattern.add_layer( "prim.sub", inputs={"x": gen_name(9)}, outputs=[gen_name(10)], y=2) pattern.add_layer("prim.list", inputs={}, outputs=[gen_name(11)]) pattern.add_layer( "prim.loop", inputs={"input": gen_name(10)}, outputs=[gen_name(12.1), gen_name(12.2)]) loop_layer = pattern.layers[list(pattern.layers.keys())[-1]] pattern_block = PaddleGraph(loop_layer) pattern_block.add_layer( "prim.append", inputs={"list": gen_name(11)}, outputs=[], element=None) loop_layer.inputs["input-0"] = gen_name(11) loop_layer.add_block(pattern_block) pattern.add_layer( "prim.tuple", inputs={ "input0": "interpolate-input-0", "input1": "interpolate-input-4", }, outputs=[gen_name(12)], input2=None, input3=None) pattern.add_layer( "prim.eq", inputs={"x": "interpolate-input-2"}, outputs=[gen_name(10.1)], y=3) pattern.add_layer( "prim.if", inputs={"input": gen_name(10.1)}, outputs=[gen_name(14)]) if_layer1 = pattern.layers[list(pattern.layers.keys())[-1]] pattern_block = PaddleGraph(parent_layer=if_layer1) pattern_block.add_layer( "prim.exception", inputs={}, outputs=[gen_name(15)], input="Exception") pattern_block.add_layer( "prim.equal", inputs={}, outputs=[gen_name(14)], input=None) if_layer1.add_block(pattern_block) pattern_block = PaddleGraph(parent_layer=if_layer1) pattern_block.add_layer( "prim.shape", inputs={"input": "interpolate-input-0"}, outputs=[gen_name(18)]) pattern_block.add_layer( "prim.len", inputs={"input": gen_name(18)}, outputs=[gen_name(18)]) pattern_block.add_layer( "prim.eq", inputs={"x": gen_name(18)}, outputs=[gen_name(19)], y=4) pattern_block.add_layer( "prim.if", inputs={"input": gen_name(19)}, outputs=[gen_name(20)]) if_layer2 = pattern_block.layers[list(pattern_block.layers.keys())[-1]] pattern_block_block = PaddleGraph(parent_layer=if_layer2) pattern_block_block.add_layer( "prim.getitem", inputs={"list": gen_name(11)}, outputs=[gen_name(21)], element=0) pattern_block_block.add_layer( "prim.getitem", inputs={"list": gen_name(11)}, outputs=[gen_name(22)], element=1) pattern_block_block.add_layer( "prim.isinstance", inputs={"input": "interpolate-input-3"}, outputs=["interpolate-input-0_isinstance"], cls="paddle.static.Variable") pattern_block_block.add_layer( "prim.if", {"input": "interpolate-input-0_isinstance"}, outputs=["interpolate-input-0_if1"]) if_layer_isinstance = pattern_block_block.layers[list( pattern_block_block.layers.keys())[-1]] pattern_block_block_block = PaddleGraph(if_layer_isinstance) pattern_block_block_block.add_layer( "prim.var2list", inputs={"input": "interpolate-input-3"}, outputs=["interpolate-input-3"]) if_layer_isinstance.add_block(pattern_block_block_block) pattern_block_block_block = PaddleGraph(if_layer_isinstance) if_layer_isinstance.add_block(pattern_block_block_block) if_layer_isinstance.inputs["input-0"] = "interpolate-input-3" pattern_block_block.add_layer( "paddle.nn.functional.interpolate", inputs={ "input": "interpolate-input-0", "size": "interpolate-input-3", }, outputs=[gen_name(23)]) pattern_block_block.add_layer( "prim.equal", inputs={"input": gen_name(23)}, outputs=[gen_name(20)]) if_layer2.add_block(pattern_block_block) pattern_block_block = PaddleGraph(if_layer2) pattern_block_block.add_layer( "prim.shape", inputs={"input": "interpolate-input-0"}, outputs=[gen_name(24)]) pattern_block_block.add_layer( "prim.len", inputs={"input": gen_name(24)}, outputs=[gen_name(24)]) pattern_block_block.add_layer( "prim.eq", inputs={"x": gen_name(24)}, outputs=[gen_name(25)], y=5) pattern_block_block.add_layer( "prim.if", inputs={"input": gen_name(25)}, outputs=[gen_name(26)]) if_layer3 = pattern_block_block.layers[list( pattern_block_block.layers.keys())[-1]] pattern_block_block_block = PaddleGraph(parent_layer=if_layer3) pattern_block_block_block.add_layer( "prim.exception", inputs={}, outputs=[gen_name(27)], input="Exception") if_layer3.add_block(pattern_block_block_block) pattern_block_block_block = PaddleGraph(parent_layer=if_layer3) pattern_block_block_block.add_layer( "prim.exception", inputs={}, outputs=[gen_name(28)], input="Exception") if_layer3.add_block(pattern_block_block_block) pattern_block_block.add_layer( "prim.equal", inputs={}, outputs=[gen_name(20)], input=None) if_layer2.add_block(pattern_block_block) if_layer2.inputs.update({ "input-0": "interpolate-input-0", "input-1": "interpolate-input-3", "input-2": "interpolate-input-3", "input-3": gen_name(11), "input-5": gen_name(11), }) pattern_block.add_layer( "prim.equal", inputs={"input": gen_name(20)}, outputs=[gen_name(14)]) if_layer1.add_block(pattern_block) if_layer1.inputs.update({ 'input-2': 'interpolate-input-0', 'input-4': gen_name(11), 'input-6': gen_name(11), 'input-8': 'interpolate-input-0', 'input-9': 'interpolate-input-3', 'input-10': 'interpolate-input-0' }) pattern.build(inputs={ "input-0": "interpolate-input-0", "input-1": "interpolate-input-1", "input-2": "interpolate-input-2", "input-3": "interpolate-input-3", "input-4": "interpolate-input-4" }) self.patterns.append(pattern) def insert_new_layer(self, graph, parameters, matches): new_layer = self.gen_new_layer(parameters, matches) global_layers = graph.get_global_layers() new_matches = dict() is_match = False for layer_id, layer in global_layers.items(): if layer_id == list(matches.keys())[0] and not is_match: new_matches[layer_id] = layer is_match = True if is_match: new_matches[layer_id] = layer if layer_id == list(matches.keys())[-1]: break new_layer_id = new_layer.layer_id graph.layers[new_layer_id] = new_layer new_matches.pop(new_layer_id) matches.clear() for layer_id, layer in new_matches.items(): matches[layer_id] = layer def gen_new_layer(self, parameters, matches): layers = list() layers_id = list(matches.keys()) layer = matches[layers_id[6]] size = layer.inputs["input1"] layer = matches[layers_id[19]] new_layer = copy.deepcopy(layer) layer = matches[layers_id[9]] new_layer.outputs[0] = layer.outputs[0] new_layer.layer_id = layers_id[7] new_layer.inputs["size"] = size return new_layer