# copyright (c) 2018 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 unittest import os import time import sys import random import math import functools import contextlib import tempfile import numpy as np from PIL import Image, ImageEnhance import paddle import paddle.fluid as fluid from paddle.dataset.common import download from paddle.fluid.contrib.slim.quantization import PostTrainingQuantization paddle.enable_static() random.seed(0) np.random.seed(0) DATA_DIM = 224 THREAD = 1 BUF_SIZE = 102400 DATA_DIR = 'data/ILSVRC2012' img_mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1)) img_std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1)) def resize_short(img, target_size): percent = float(target_size) / min(img.size[0], img.size[1]) resized_width = int(round(img.size[0] * percent)) resized_height = int(round(img.size[1] * percent)) img = img.resize((resized_width, resized_height), Image.LANCZOS) return img def crop_image(img, target_size, center): width, height = img.size size = target_size if center == True: w_start = (width - size) / 2 h_start = (height - size) / 2 else: w_start = np.random.randint(0, width - size + 1) h_start = np.random.randint(0, height - size + 1) w_end = w_start + size h_end = h_start + size img = img.crop((w_start, h_start, w_end, h_end)) return img def process_image(sample, mode, color_jitter, rotate): img_path = sample[0] img = Image.open(img_path) img = resize_short(img, target_size=256) img = crop_image(img, target_size=DATA_DIM, center=True) if img.mode != 'RGB': img = img.convert('RGB') img = np.array(img).astype('float32').transpose((2, 0, 1)) / 255 img -= img_mean img /= img_std return img, sample[1] def _reader_creator(file_list, mode, shuffle=False, color_jitter=False, rotate=False, data_dir=DATA_DIR): def reader(): with open(file_list) as flist: full_lines = [line.strip() for line in flist] if shuffle: np.random.shuffle(full_lines) lines = full_lines for line in lines: img_path, label = line.split() img_path = os.path.join(data_dir, img_path) if not os.path.exists(img_path): continue yield img_path, int(label) mapper = functools.partial(process_image, mode=mode, color_jitter=color_jitter, rotate=rotate) return paddle.reader.xmap_readers(mapper, reader, THREAD, BUF_SIZE) def val(data_dir=DATA_DIR): file_list = os.path.join(data_dir, 'val_list.txt') return _reader_creator(file_list, 'val', shuffle=False, data_dir=data_dir) class TestPostTrainingQuantization(unittest.TestCase): def setUp(self): self.int8_download = 'int8/download' self.cache_folder = os.path.expanduser('~/.cache/paddle/dataset/' + self.int8_download) self.data_cache_folder = '' data_urls = [] data_md5s = [] if os.environ.get('DATASET') == 'full': data_urls.append( 'https://paddle-inference-dist.bj.bcebos.com/int8/ILSVRC2012_img_val.tar.gz.partaa' ) data_md5s.append('60f6525b0e1d127f345641d75d41f0a8') data_urls.append( 'https://paddle-inference-dist.bj.bcebos.com/int8/ILSVRC2012_img_val.tar.gz.partab' ) data_md5s.append('1e9f15f64e015e58d6f9ec3210ed18b5') self.data_cache_folder = self.download_data(data_urls, data_md5s, "full_data", False) else: data_urls.append( 'http://paddle-inference-dist.bj.bcebos.com/int8/calibration_test_data.tar.gz' ) data_md5s.append('1b6c1c434172cca1bf9ba1e4d7a3157d') self.data_cache_folder = self.download_data(data_urls, data_md5s, "small_data", False) # reader/decorator.py requires the relative path to the data folder if not os.path.exists("./data/ILSVRC2012"): cmd = 'rm -rf {0} && ln -s {1} {0}'.format("data", self.data_cache_folder) os.system(cmd) self.batch_size = 1 if os.environ.get('DATASET') == 'full' else 50 self.sample_iterations = 50 if os.environ.get( 'DATASET') == 'full' else 2 self.infer_iterations = 50000 if os.environ.get( 'DATASET') == 'full' else 2 self.root_path = tempfile.TemporaryDirectory() self.int8_model = os.path.join(self.root_path.name, "post_training_quantization") def tearDown(self): self.root_path.cleanup() def cache_unzipping(self, target_folder, zip_path): if not os.path.exists(target_folder): cmd = 'mkdir {0} && tar xf {1} -C {0}'.format( target_folder, zip_path) os.system(cmd) def download_data(self, data_urls, data_md5s, folder_name, is_model=True): data_cache_folder = os.path.join(self.cache_folder, folder_name) zip_path = '' if os.environ.get('DATASET') == 'full': file_names = [] for i in range(0, len(data_urls)): download(data_urls[i], self.int8_download, data_md5s[i]) file_names.append(data_urls[i].split('/')[-1]) zip_path = os.path.join(self.cache_folder, 'full_imagenet_val.tar.gz') if not os.path.exists(zip_path): cat_command = 'cat' for file_name in file_names: cat_command += ' ' + os.path.join(self.cache_folder, file_name) cat_command += ' > ' + zip_path os.system(cat_command) if os.environ.get('DATASET') != 'full' or is_model: download(data_urls[0], self.int8_download, data_md5s[0]) file_name = data_urls[0].split('/')[-1] zip_path = os.path.join(self.cache_folder, file_name) print('Data is downloaded at {0}'.format(zip_path)) self.cache_unzipping(data_cache_folder, zip_path) return data_cache_folder def download_model(self): pass def run_program(self, model_path, batch_size, infer_iterations): image_shape = [3, 224, 224] place = fluid.CPUPlace() exe = fluid.Executor(place) [infer_program, feed_dict, fetch_targets] = \ fluid.io.load_inference_model(model_path, exe) val_reader = paddle.batch(val(), batch_size) iterations = infer_iterations test_info = [] cnt = 0 periods = [] for batch_id, data in enumerate(val_reader()): image = np.array([x[0].reshape(image_shape) for x in data]).astype("float32") label = np.array([x[1] for x in data]).astype("int64") label = label.reshape([-1, 1]) t1 = time.time() _, acc1, _ = exe.run(infer_program, feed={ feed_dict[0]: image, feed_dict[1]: label }, fetch_list=fetch_targets) t2 = time.time() period = t2 - t1 periods.append(period) test_info.append(np.mean(acc1) * len(data)) cnt += len(data) if (batch_id + 1) % 100 == 0: print("{0} images,".format(batch_id + 1)) sys.stdout.flush() if (batch_id + 1) == iterations: break throughput = cnt / np.sum(periods) latency = np.average(periods) acc1 = np.sum(test_info) / cnt return (throughput, latency, acc1) def generate_quantized_model(self, model_path, quantizable_op_type, batch_size, batch_nums, algo="KL", round_type="round", is_full_quantize=False, is_use_cache_file=False, is_optimize_model=False, batch_nums=10, onnx_format=False): try: os.system("mkdir " + self.int8_model) except Exception as e: print("Failed to create {} due to {}".format( self.int8_model, str(e))) sys.exit(-1) place = fluid.CPUPlace() exe = fluid.Executor(place) scope = fluid.global_scope() val_reader = val() ptq = PostTrainingQuantization(executor=exe, sample_generator=val_reader, model_dir=model_path, batch_size=batch_size, batch_nums=batch_nums, algo=algo, batch_nums=batch_nums, quantizable_op_type=quantizable_op_type, round_type=round_type, is_full_quantize=is_full_quantize, optimize_model=is_optimize_model, onnx_format=onnx_format, is_use_cache_file=is_use_cache_file) ptq.quantize() ptq.save_quantized_model(self.int8_model) def run_test(self, model, algo, round_type, data_urls, data_md5s, quantizable_op_type, is_full_quantize, is_use_cache_file, is_optimize_model, diff_threshold, onnx_format=False, batch_nums=10): infer_iterations = self.infer_iterations batch_size = self.batch_size sample_iterations = self.sample_iterations model_cache_folder = self.download_data(data_urls, data_md5s, model) print("Start FP32 inference for {0} on {1} images ...".format( model, infer_iterations * batch_size)) (fp32_throughput, fp32_latency, fp32_acc1) = self.run_program( os.path.join(model_cache_folder, "model"), batch_size, infer_iterations) print("Start INT8 post training quantization for {0} on {1} images ...". format(model, sample_iterations * batch_size)) self.generate_quantized_model(os.path.join(model_cache_folder, "model"), quantizable_op_type, batch_size, sample_iterations, algo, round_type, is_full_quantize, is_use_cache_file, is_optimize_model, batch_nums, onnx_format) print("Start INT8 inference for {0} on {1} images ...".format( model, infer_iterations * batch_size)) (int8_throughput, int8_latency, int8_acc1) = self.run_program(self.int8_model, batch_size, infer_iterations) print("---Post training quantization of {} method---".format(algo)) print( "FP32 {0}: batch_size {1}, throughput {2} images/second, latency {3} second, accuracy {4}." .format(model, batch_size, fp32_throughput, fp32_latency, fp32_acc1)) print( "INT8 {0}: batch_size {1}, throughput {2} images/second, latency {3} second, accuracy {4}.\n" .format(model, batch_size, int8_throughput, int8_latency, int8_acc1)) sys.stdout.flush() delta_value = fp32_acc1 - int8_acc1 self.assertLess(delta_value, diff_threshold) class TestPostTrainingKLForMobilenetv1(TestPostTrainingQuantization): def test_post_training_kl_mobilenetv1(self): model = "MobileNet-V1" algo = "KL" round_type = "round" data_urls = [ 'http://paddle-inference-dist.bj.bcebos.com/int8/mobilenetv1_int8_model.tar.gz' ] data_md5s = ['13892b0716d26443a8cdea15b3c6438b'] quantizable_op_type = [ "conv2d", "depthwise_conv2d", "mul", "pool2d", ] is_full_quantize = False is_use_cache_file = False is_optimize_model = True diff_threshold = 0.025 self.run_test(model, algo, round_type, data_urls, data_md5s, quantizable_op_type, is_full_quantize, is_use_cache_file, is_optimize_model, diff_threshold) class TestPostTrainingavgForMobilenetv1(TestPostTrainingQuantization): def test_post_training_avg_mobilenetv1(self): model = "MobileNet-V1" algo = "avg" round_type = "round" data_urls = [ 'http://paddle-inference-dist.bj.bcebos.com/int8/mobilenetv1_int8_model.tar.gz' ] data_md5s = ['13892b0716d26443a8cdea15b3c6438b'] quantizable_op_type = [ "conv2d", "depthwise_conv2d", "mul", ] is_full_quantize = False is_use_cache_file = False is_optimize_model = True diff_threshold = 0.025 self.run_test(model, algo, round_type, data_urls, data_md5s, quantizable_op_type, is_full_quantize, is_use_cache_file, is_optimize_model, diff_threshold) class TestPostTraininghistForMobilenetv1(TestPostTrainingQuantization): def test_post_training_hist_mobilenetv1(self): model = "MobileNet-V1" algo = "hist" round_type = "round" data_urls = [ 'http://paddle-inference-dist.bj.bcebos.com/int8/mobilenetv1_int8_model.tar.gz' ] data_md5s = ['13892b0716d26443a8cdea15b3c6438b'] quantizable_op_type = [ "conv2d", "depthwise_conv2d", "mul", ] is_full_quantize = False is_use_cache_file = False is_optimize_model = True diff_threshold = 0.03 batch_nums = 3 self.run_test(model, algo, round_type, data_urls, data_md5s, quantizable_op_type, is_full_quantize, is_use_cache_file, is_optimize_model, diff_threshold, batch_nums=batch_nums) class TestPostTrainingAbsMaxForMobilenetv1(TestPostTrainingQuantization): def test_post_training_abs_max_mobilenetv1(self): model = "MobileNet-V1" algo = "abs_max" round_type = "round" data_urls = [ 'http://paddle-inference-dist.bj.bcebos.com/int8/mobilenetv1_int8_model.tar.gz' ] data_md5s = ['13892b0716d26443a8cdea15b3c6438b'] quantizable_op_type = [ "conv2d", "mul", ] is_full_quantize = False is_use_cache_file = False is_optimize_model = False # The accuracy diff of post-training quantization (abs_max) maybe bigger diff_threshold = 0.05 self.run_test(model, algo, round_type, data_urls, data_md5s, quantizable_op_type, is_full_quantize, is_use_cache_file, is_optimize_model, diff_threshold) class TestPostTrainingAvgONNXFormatForMobilenetv1(TestPostTrainingQuantization): def test_post_training_onnx_format_mobilenetv1(self): model = "MobileNet-V1" algo = "emd" round_type = "round" data_urls = [ 'http://paddle-inference-dist.bj.bcebos.com/int8/mobilenetv1_int8_model.tar.gz' ] data_md5s = ['13892b0716d26443a8cdea15b3c6438b'] quantizable_op_type = [ "conv2d", "depthwise_conv2d", "mul", ] is_full_quantize = False is_use_cache_file = False is_optimize_model = True onnx_format = True diff_threshold = 0.05 batch_nums = 3 self.run_test(model, algo, round_type, data_urls, data_md5s, quantizable_op_type, is_full_quantize, is_use_cache_file, is_optimize_model, diff_threshold, onnx_format=onnx_format, batch_nums=batch_nums) if __name__ == '__main__': unittest.main()