提交 dbdaf15c 编写于 作者: G guomingz 提交者: Tao Luo

[V1.3] Add the calibration tool code for int8 inference and focus test. (#15062)

* Add the calibration tool code for int8 inference and focus test.

* Fix the calibration tool per the review comments.

test=develop

* Update the calibrator doc and remove extra line.

* Fix the invalid is_negative_input attr set on Mobilenet.

* Add the comments and fix the format issue.

test=develop

* Update the CMakelist.txt for Calibration PR.Disable the Calibration UT if not enable MKLDNN.

test=develop

* Update the CMakeList.txt.

test=develop

* Disable the test_calibration case on WIN and MAC.

test=develop

* Add the missing brackets.

test=develop

* Remove the outdated map operator which not supported on Python3.

test=develop

* Fix the style issue.

test=develop

* 1.Update the CMakeList.txt to disable calibration tool ut when the WITH_MKL is not set;
2.Add the workaround to enable the FLAGS_use_mkldnn for PR_CI(PADDLE).

test=develop

* Fix the typo and format the License header.

test=develop

* 1.Add and Update TODOs per review comments.
2.Code clean.

test=develop
上级 b7b68f2a
# 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.
此差异已折叠。
file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py") file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}") string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
if(APPLE OR WIN32 OR NOT WITH_MKL)
list(REMOVE_ITEM TEST_OPS test_calibration)
endif()
foreach(src ${TEST_OPS}) foreach(src ${TEST_OPS})
py_test(${src} SRCS ${src}.py) py_test(${src} SRCS ${src}.py)
endforeach() endforeach()
# 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 numpy as np
import time
import sys
import random
import paddle
import paddle.fluid as fluid
import argparse
import functools
import contextlib
import paddle.fluid.profiler as profiler
from PIL import Image, ImageEnhance
import math
sys.path.append('..')
import int8_inference.utility as ut
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))
# TODO(guomingz): Remove duplicated code from line 45 ~ line 114
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 TestCalibration(unittest.TestCase):
def setUp(self):
# TODO(guomingz): Put the download process in the cmake.
# Download and unzip test data set
imagenet_dl_url = 'http://paddle-inference-dist.bj.bcebos.com/int8/calibration_test_data.tar.gz'
zip_file_name = imagenet_dl_url.split('/')[-1]
cmd = 'rm -rf data {} && mkdir data && wget {} && tar xvf {} -C data'.format(
zip_file_name, imagenet_dl_url, zip_file_name)
os.system(cmd)
# resnet50 fp32 data
resnet50_fp32_model_url = 'http://paddle-inference-dist.bj.bcebos.com/int8/resnet50_int8_model.tar.gz'
resnet50_zip_name = resnet50_fp32_model_url.split('/')[-1]
resnet50_unzip_folder_name = 'resnet50_fp32'
cmd = 'rm -rf {} {} && mkdir {} && wget {} && tar xvf {} -C {}'.format(
resnet50_unzip_folder_name, resnet50_zip_name,
resnet50_unzip_folder_name, resnet50_fp32_model_url,
resnet50_zip_name, resnet50_unzip_folder_name)
os.system(cmd)
self.iterations = 100
self.skip_batch_num = 5
def run_program(self, model_path, generate_int8=False, algo='direct'):
image_shape = [3, 224, 224]
os.environ['FLAGS_use_mkldnn'] = 'True'
fluid.memory_optimize(fluid.default_main_program())
exe = fluid.Executor(fluid.CPUPlace())
[infer_program, feed_dict,
fetch_targets] = fluid.io.load_inference_model(model_path, exe)
t = fluid.transpiler.InferenceTranspiler()
t.transpile(infer_program, fluid.CPUPlace())
val_reader = paddle.batch(val(), batch_size=1)
if generate_int8:
int8_model = os.path.join(os.getcwd(), "calibration_out")
if os.path.exists(int8_model):
os.system("rm -rf " + int8_model)
os.system("mkdir " + int8_model)
print("Start calibration ...")
calibrator = ut.Calibrator(
program=infer_program,
pretrained_model=model_path,
iterations=100,
debug=False,
algo=algo)
sampling_data = {}
calibrator.generate_sampling_program()
test_info = []
cnt = 0
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])
running_program = calibrator.sampling_program.clone(
) if generate_int8 else infer_program.clone()
for op in running_program.current_block().ops:
if op.has_attr("use_mkldnn"):
op._set_attr("use_mkldnn", True)
_, acc1, _ = exe.run(
running_program,
feed={feed_dict[0]: image,
feed_dict[1]: label},
fetch_list=fetch_targets)
if generate_int8:
for i in calibrator.sampling_program.list_vars():
if i.name in calibrator.sampling_vars:
np_data = np.array(fluid.global_scope().find_var(i.name)
.get_tensor())
if i.name not in sampling_data:
sampling_data[i.name] = []
sampling_data[i.name].append(np_data)
test_info.append(np.mean(acc1) * len(data))
cnt += len(data)
if batch_id != self.iterations - 1:
continue
break
if generate_int8:
calibrator.generate_quantized_data(sampling_data)
fluid.io.save_inference_model(int8_model, feed_dict, fetch_targets,
exe, calibrator.sampling_program)
print(
"Calibration is done and the corresponding files were generated at {}".
format(os.path.abspath("calibration_out")))
else:
return np.sum(test_info) / cnt
def test_calibration_for_resnet50(self):
fp32_acc1 = self.run_program("resnet50_fp32/model")
self.run_program("resnet50_fp32/model", True)
int8_acc1 = self.run_program("calibration_out")
delta_value = np.abs(fp32_acc1 - int8_acc1)
self.assertLess(delta_value, 0.01)
if __name__ == '__main__':
unittest.main()
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