# Copyright (c) 2021 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 os import numpy as np import pickle import paddle import paddleslim import subprocess import time __all__ = [ "save_cls_model", "save_det_model", "save_seg_model", "nearest_interpolate", "opt_model", "load_predictor" ] def opt_model(opt="paddle_lite_opt", model_file='', param_file='', optimize_out_type='protobuf', valid_targets='arm', enable_fp16=False, sparse_ratio=0): assert os.path.exists(model_file) and os.path.exists( param_file), f'{model_file} or {param_file} does not exist.' save_dir = f'./opt_models_tmp/{os.getpid()}_{time.time()}' if not os.path.exists(save_dir): os.makedirs(save_dir) assert optimize_out_type in ['protobuf', 'naive_buffer'] if optimize_out_type == 'protobuf': model_out = os.path.join(save_dir, 'pbmodel') else: model_out = os.path.join(save_dir, 'model') enable_fp16 = str(enable_fp16).lower() sparse_model = True if sparse_ratio > 0 else False sparse_threshold = max(sparse_ratio - 0.1, 0.1) cmd = f'{opt} --model_file={model_file} --param_file={param_file} --optimize_out_type={optimize_out_type} --optimize_out={model_out} --valid_targets={valid_targets} --enable_fp16={enable_fp16} --sparse_model={sparse_model} --sparse_threshold={sparse_threshold}' print(f'commands:{cmd}') m = subprocess.Popen( cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True) out = m.communicate() print(out, 'opt done!') if optimize_out_type == 'protobuf': model_out = os.path.join(model_out, 'model') else: model_out = model_out + '.nb' return model_out def sample_generator(input_shape, batch_num): def __reader__(): for i in range(batch_num): image = np.random.random(input_shape).astype('float32') yield image return __reader__ def save_cls_model(model, input_shape, save_dir, data_type): paddle.jit.save( model, path=os.path.join(save_dir, 'fp32model'), input_spec=[ paddle.static.InputSpec( shape=input_shape, dtype='float32', name='x'), ]) model_file = os.path.join(save_dir, 'fp32model.pdmodel') param_file = os.path.join(save_dir, 'fp32model.pdiparams') if data_type == 'int8': paddle.enable_static() exe = paddle.fluid.Executor(paddle.fluid.CPUPlace()) save_dir = os.path.dirname(model_file) quantize_model_path = os.path.join(save_dir, 'int8model') if not os.path.exists(quantize_model_path): os.makedirs(quantize_model_path) paddleslim.quant.quant_post_static( executor=exe, model_dir=save_dir, quantize_model_path=quantize_model_path, sample_generator=sample_generator(input_shape, 1), model_filename=model_file.split('/')[-1], params_filename=param_file.split('/')[-1], batch_size=input_shape[0], batch_nums=1, weight_bits=8, activation_bits=8) model_file = os.path.join(quantize_model_path, '__model__') param_file = os.path.join(quantize_model_path, '__params__') return model_file, param_file def save_det_model(model, input_shape, save_dir, data_type, det_multi_input=False): model.eval() if det_multi_input: input_spec = [{ "image": paddle.static.InputSpec( shape=input_shape, name='image'), "im_shape": paddle.static.InputSpec( shape=[input_shape[0], 2], name='im_shape'), "scale_factor": paddle.static.InputSpec( shape=[input_shape[0], 2], name='scale_factor') }] data = { "image": paddle.randn( shape=input_shape, dtype='float32', name='image'), "im_shape": paddle.randn( shape=[input_shape[0], 2], dtype='float32', name='image'), "scale_factor": paddle.ones( shape=[input_shape[0], 2], dtype='float32', name='image') } else: input_spec = [{ "image": paddle.static.InputSpec( shape=input_shape, name='image'), }] data = { "image": paddle.randn( shape=input_shape, dtype='float32', name='image'), } if data_type == 'fp32': static_model = paddle.jit.to_static(model, input_spec=input_spec) paddle.jit.save( static_model, path=os.path.join(save_dir, 'fp32model'), input_spec=input_spec) model_file = os.path.join(save_dir, 'fp32model.pdmodel') param_file = os.path.join(save_dir, 'fp32model.pdiparams') else: ptq = paddleslim.dygraph.quant.PTQ() quant_model = ptq.quantize(model, fuse=True, fuse_list=None) quant_model(data) quantize_model_path = os.path.join(save_dir, 'int8model') if not os.path.exists(quantize_model_path): os.makedirs(quantize_model_path) ptq.save_quantized_model(quant_model, os.path.join(quantize_model_path, 'int8model'), input_spec) model_file = os.path.join(quantize_model_path, 'int8model.pdmodel') param_file = os.path.join(quantize_model_path, 'int8model.pdiparams') return model_file, param_file def save_seg_model(model, input_shape, save_dir, data_type): if data_type == 'fp32': paddle.jit.save( model, path=os.path.join(save_dir, 'fp32model'), input_spec=[ paddle.static.InputSpec( shape=input_shape, dtype='float32', name='x'), ]) model_file = os.path.join(save_dir, 'fp32model.pdmodel') param_file = os.path.join(save_dir, 'fp32model.pdiparams') else: save_dir = os.path.join(save_dir, 'int8model') quant_config = { 'weight_preprocess_type': None, 'activation_preprocess_type': None, 'weight_quantize_type': 'channel_wise_abs_max', 'activation_quantize_type': 'moving_average_abs_max', 'weight_bits': 8, 'activation_bits': 8, 'dtype': 'int8', 'window_size': 10000, 'moving_rate': 0.9, 'quantizable_layer_type': ['Conv2D', 'Linear'], } quantizer = paddleslim.QAT(config=quant_config) quantizer.quantize(model) quantizer.save_quantized_model( model, save_dir, input_spec=[ paddle.static.InputSpec( shape=input_shape, dtype='float32') ]) model_file = f'{save_dir}.pdmodel' param_file = f'{save_dir}.pdiparams' return model_file, param_file def nearest_interpolate(features, data): def distance(x, y): x = np.array(x) y = np.array(y) return np.sqrt(np.sum(np.square(x - y))) if len(data) <= 0: return None data_features = data[:, 0:-1] latency = data[:, -1] idx = 0 dist = distance(features, data_features[0]) for i in range(1, len(data_features)): cur_dist = distance(features, data_features[i]) if cur_dist < dist: idx = i dist = cur_dist return latency[idx] def download_predictor(op_dir, op): """Download op predictors' model file Args: op_dir(str): the path to op predictor. Actually, it's the hardware information. op(str): the op type. Returns: op_path: The path of the file. """ if not os.path.exists(op_dir): os.makedirs(op_dir) op_path = os.path.join(op_dir, op + '_predictor.pkl') if not os.path.exists(op_path): subprocess.call( f'wget -P {op_dir} https://paddlemodels.bj.bcebos.com/PaddleSlim/analysis/{op_path}', shell=True) return op_path def load_predictor(op_type, op_dir, data_type='fp32'): op = op_type if 'conv2d' in op_type: op = f'{op_type}_{data_type}' elif 'matmul' in op_type: op = 'matmul' op_path = download_predictor(op_dir, op) with open(op_path, 'rb') as f: model = pickle.load(f) return model