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Update QAT INT8 related code (#23104)

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python/paddle/fluid/contrib/slim/tests/CMakeLists.txt
浏览文件 @ c524b930
......@@ -37,12 +37,18 @@ function(download_qat_model install_dir data_file)
endif()
endfunction()
function(inference_qat_int8_image_classification_test target model_dir dataset_path)
function(download_qat_fp32_model install_dir data_file)
if (NOT EXISTS ${install_dir}/${data_file})
inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/int8/QAT_models/fp32 ${data_file})
endif()
endfunction()
function(inference_qat_int8_image_classification_test target qat_model_dir dataset_path)
py_test(${target} SRCS "${CMAKE_CURRENT_SOURCE_DIR}/qat_int8_image_classification_comparison.py"
ENVS FLAGS_OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
FLAGS_use_mkldnn=true
ARGS --qat_model ${model_dir}/model
ARGS --qat_model ${qat_model_dir}
--infer_data ${dataset_path}
--batch_size 25
--batch_num 2
......@@ -51,44 +57,45 @@ endfunction()
# set batch_size 10 for UT only (avoid OOM). For whole dataset, use batch_size 25
function(inference_qat2_int8_image_classification_test target model_dir data_path quantized_ops)
py_test(${target} SRCS "${CMAKE_CURRENT_SOURCE_DIR}/qat_int8_image_classification_comparison.py"
function(inference_qat2_int8_image_classification_test target qat_model_dir fp32_model_dir dataset_path quantized_ops)
py_test(${target} SRCS "${CMAKE_CURRENT_SOURCE_DIR}/qat2_int8_image_classification_comparison.py"
ENVS FLAGS_OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
FLAGS_use_mkldnn=true
ARGS --qat_model ${model_dir}/float
--infer_data ${data_path}
ARGS --qat_model ${qat_model_dir}
--fp32_model ${fp32_model_dir}
--infer_data ${dataset_path}
--batch_size 10
--batch_num 2
--acc_diff_threshold 0.1
--quantized_ops ${quantized_ops}
--qat2)
--quantized_ops ${quantized_ops})
endfunction()
# set batch_size 10 for UT only (avoid OOM). For whole dataset, use batch_size 20
function(inference_qat2_int8_nlp_test target model_dir data_path labels_path quantized_ops)
py_test(${target} SRCS "${CMAKE_CURRENT_SOURCE_DIR}/qat_int8_nlp_comparison.py"
function(inference_qat2_int8_nlp_test target qat_model_dir fp32_model_dir dataset_path labels_path quantized_ops)
py_test(${target} SRCS "${CMAKE_CURRENT_SOURCE_DIR}/qat2_int8_nlp_comparison.py"
ENVS FLAGS_OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
FLAGS_use_mkldnn=true
ARGS --qat_model ${model_dir}/float
--infer_data ${data_path}
ARGS --qat_model ${qat_model_dir}
--fp32_model ${fp32_model_dir}
--infer_data ${dataset_path}
--labels ${labels_path}
--batch_size 10
--batch_num 2
--quantized_ops ${quantized_ops}
--acc_diff_threshold 0.1)
--acc_diff_threshold 0.1
--quantized_ops ${quantized_ops})
endfunction()
function(download_qat_data install_dir data_file)
if (NOT EXISTS ${install_dir}/${data_file})
inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/int8 ${data_file})
inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/int8 ${data_file})
endif()
endfunction()
function(download_qat_model install_dir data_file)
if (NOT EXISTS ${install_dir}/${data_file})
inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/int8/QAT_models ${data_file})
inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/int8/QAT_models ${data_file})
endif()
endfunction()
......@@ -163,43 +170,43 @@ if(LINUX AND WITH_MKLDNN)
set(QAT_RESNET50_MODEL_DIR "${QAT_INSTALL_DIR}/ResNet50_QAT")
set(QAT_RESNET50_MODEL_ARCHIVE "ResNet50_qat_model.tar.gz")
download_qat_model(${QAT_RESNET50_MODEL_DIR} ${QAT_RESNET50_MODEL_ARCHIVE})
inference_qat_int8_image_classification_test(test_qat_int8_resnet50_mkldnn ${QAT_RESNET50_MODEL_DIR} ${IMAGENET_DATA_PATH})
inference_qat_int8_image_classification_test(test_qat_int8_resnet50_mkldnn ${QAT_RESNET50_MODEL_DIR}/model ${IMAGENET_DATA_PATH})
# QAT ResNet101
set(QAT_RESNET101_MODEL_DIR "${QAT_INSTALL_DIR}/ResNet101_QAT")
set(QAT_RESNET101_MODEL_ARCHIVE "ResNet101_qat_model.tar.gz")
download_qat_model(${QAT_RESNET101_MODEL_DIR} ${QAT_RESNET101_MODEL_ARCHIVE})
# inference_qat_int8_image_classification_test(test_qat_int8_resnet101_mkldnn ${QAT_RESNET101_MODEL_DIR} ${IMAGENET_DATA_PATH})
# inference_qat_int8_image_classification_test(test_qat_int8_resnet101_mkldnn ${QAT_RESNET101_MODEL_DIR}/model ${IMAGENET_DATA_PATH})
# QAT GoogleNet
set(QAT_GOOGLENET_MODEL_DIR "${QAT_INSTALL_DIR}/GoogleNet_QAT")
set(QAT_GOOGLENET_MODEL_ARCHIVE "GoogleNet_qat_model.tar.gz")
download_qat_model(${QAT_GOOGLENET_MODEL_DIR} ${QAT_GOOGLENET_MODEL_ARCHIVE})
inference_qat_int8_image_classification_test(test_qat_int8_googlenet_mkldnn ${QAT_GOOGLENET_MODEL_DIR} ${IMAGENET_DATA_PATH})
inference_qat_int8_image_classification_test(test_qat_int8_googlenet_mkldnn ${QAT_GOOGLENET_MODEL_DIR}/model ${IMAGENET_DATA_PATH})
# QAT MobileNetV1
set(QAT_MOBILENETV1_MODEL_DIR "${QAT_INSTALL_DIR}/MobileNetV1_QAT")
set(QAT_MOBILENETV1_MODEL_ARCHIVE "MobileNetV1_qat_model.tar.gz")
download_qat_model(${QAT_MOBILENETV1_MODEL_DIR} ${QAT_MOBILENETV1_MODEL_ARCHIVE})
inference_qat_int8_image_classification_test(test_qat_int8_mobilenetv1_mkldnn ${QAT_MOBILENETV1_MODEL_DIR} ${IMAGENET_DATA_PATH})
inference_qat_int8_image_classification_test(test_qat_int8_mobilenetv1_mkldnn ${QAT_MOBILENETV1_MODEL_DIR}/model ${IMAGENET_DATA_PATH})
# QAT MobileNetV2
set(QAT_MOBILENETV2_MODEL_DIR "${QAT_INSTALL_DIR}/MobileNetV2_QAT")
set(QAT_MOBILENETV2_MODEL_ARCHIVE "MobileNetV2_qat_model.tar.gz")
download_qat_model(${QAT_MOBILENETV2_MODEL_DIR} ${QAT_MOBILENETV2_MODEL_ARCHIVE})
inference_qat_int8_image_classification_test(test_qat_int8_mobilenetv2_mkldnn ${QAT_MOBILENETV2_MODEL_DIR} ${IMAGENET_DATA_PATH})
inference_qat_int8_image_classification_test(test_qat_int8_mobilenetv2_mkldnn ${QAT_MOBILENETV2_MODEL_DIR}/model ${IMAGENET_DATA_PATH})
# QAT VGG16
set(QAT_VGG16_MODEL_DIR "${QAT_INSTALL_DIR}/VGG16_QAT")
set(QAT_VGG16_MODEL_ARCHIVE "VGG16_qat_model.tar.gz")
download_qat_model(${QAT_VGG16_MODEL_DIR} ${QAT_VGG16_MODEL_ARCHIVE})
# inference_qat_int8_image_classification_test(test_qat_int8_vgg16_mkldnn ${QAT_VGG16_MODEL_DIR} ${IMAGENET_DATA_PATH})
# inference_qat_int8_image_classification_test(test_qat_int8_vgg16_mkldnn ${QAT_VGG16_MODEL_DIR}/model ${IMAGENET_DATA_PATH})
# QAT VGG19
set(QAT_VGG19_MODEL_DIR "${QAT_INSTALL_DIR}/VGG19_QAT")
set(QAT_VGG19_MODEL_ARCHIVE "VGG19_qat_model.tar.gz")
download_qat_model(${QAT_VGG19_MODEL_DIR} ${QAT_VGG19_MODEL_ARCHIVE})
# inference_qat_int8_image_classification_test(test_qat_int8_vgg19_mkldnn ${QAT_VGG19_MODEL_DIR} ${IMAGENET_DATA_PATH})
# inference_qat_int8_image_classification_test(test_qat_int8_vgg19_mkldnn ${QAT_VGG19_MODEL_DIR}/model ${IMAGENET_DATA_PATH})
### QATv2 for image classification
......@@ -207,15 +214,17 @@ if(LINUX AND WITH_MKLDNN)
# QAT2 ResNet50
set(QAT2_RESNET50_MODEL_DIR "${QAT_INSTALL_DIR}/ResNet50_qat_perf")
set(FP32_RESNET50_MODEL_DIR "${INT8_INSTALL_DIR}/resnet50")
set(QAT2_RESNET50_MODEL_ARCHIVE "ResNet50_qat_perf.tar.gz")
download_qat_model(${QAT2_RESNET50_MODEL_DIR} ${QAT2_RESNET50_MODEL_ARCHIVE})
inference_qat2_int8_image_classification_test(test_qat2_int8_resnet50_mkldnn ${QAT2_RESNET50_MODEL_DIR}/ResNet50_qat_perf ${IMAGENET_DATA_PATH} ${QAT2_IC_QUANTIZED_OPS})
inference_qat2_int8_image_classification_test(test_qat2_int8_resnet50_mkldnn ${QAT2_RESNET50_MODEL_DIR}/ResNet50_qat_perf/float ${FP32_RESNET50_MODEL_DIR}/model ${IMAGENET_DATA_PATH} ${QAT2_IC_QUANTIZED_OPS})
# QAT2 MobileNetV1
set(QAT2_MOBILENETV1_MODEL_DIR "${QAT_INSTALL_DIR}/MobileNet_qat_perf")
set(FP32_MOBILENETV1_MODEL_DIR "${INT8_INSTALL_DIR}/mobilenetv1")
set(QAT2_MOBILENETV1_MODEL_ARCHIVE "MobileNet_qat_perf.tar.gz")
download_qat_model(${QAT2_MOBILENETV1_MODEL_DIR} ${QAT2_MOBILENETV1_MODEL_ARCHIVE})
inference_qat2_int8_image_classification_test(test_qat2_int8_mobilenetv1_mkldnn ${QAT2_MOBILENETV1_MODEL_DIR}/MobileNet_qat_perf ${IMAGENET_DATA_PATH} ${QAT2_IC_QUANTIZED_OPS})
inference_qat2_int8_image_classification_test(test_qat2_int8_mobilenetv1_mkldnn ${QAT2_MOBILENETV1_MODEL_DIR}/MobileNet_qat_perf/float ${FP32_MOBILENETV1_MODEL_DIR}/model ${IMAGENET_DATA_PATH} ${QAT2_IC_QUANTIZED_OPS})
### QATv2 for NLP
......@@ -231,7 +240,10 @@ if(LINUX AND WITH_MKLDNN)
set(QAT2_ERNIE_MODEL_ARCHIVE "ernie_qat.tar.gz")
set(QAT2_ERNIE_MODEL_DIR "${QAT_INSTALL_DIR}/Ernie_qat")
download_qat_model(${QAT2_ERNIE_MODEL_DIR} ${QAT2_ERNIE_MODEL_ARCHIVE})
inference_qat2_int8_nlp_test(test_qat2_int8_ernie_mkldnn ${QAT2_ERNIE_MODEL_DIR}/Ernie_qat ${NLP_DATA_PATH} ${NLP_LABLES_PATH} ${QAT2_NLP_QUANTIZED_OPS})
set(FP32_ERNIE_MODEL_ARCHIVE "ernie_fp32_model.tar.gz")
set(FP32_ERNIE_MODEL_DIR "${QAT_INSTALL_DIR}/Ernie_float")
download_qat_fp32_model(${FP32_ERNIE_MODEL_DIR} ${FP32_ERNIE_MODEL_ARCHIVE})
inference_qat2_int8_nlp_test(test_qat2_int8_ernie_mkldnn ${QAT2_ERNIE_MODEL_DIR}/Ernie_qat/float ${FP32_ERNIE_MODEL_DIR}/ernie_fp32_model ${NLP_DATA_PATH} ${NLP_LABLES_PATH} ${QAT2_NLP_QUANTIZED_OPS})
### Save QAT2 FP32 model or QAT2 INT8 model
......
python/paddle/fluid/contrib/slim/tests/qat2_int8_image_classification_comparison.py 0 → 100644
浏览文件 @ c524b930
# copyright (c) 2019 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 sys
import argparse
import logging
import struct
import six
import numpy as np
import time
import paddle
import paddle.fluid as fluid
from paddle.fluid.framework import IrGraph
from paddle.fluid.contrib.slim.quantization import Qat2Int8MkldnnPass
from paddle.fluid import core
logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s')
_logger = logging.getLogger(__name__)
_logger.setLevel(logging.INFO)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', type=int, default=1, help='Batch size.')
parser.add_argument(
'--skip_batch_num',
type=int,
default=0,
help='Number of the first minibatches to skip in performance statistics.'
)
parser.add_argument(
'--debug',
action='store_true',
help='If used, the graph of QAT model is drawn.')
parser.add_argument(
'--qat_model', type=str, default='', help='A path to a QAT model.')
parser.add_argument(
'--fp32_model', type=str, default='', help='A path to an FP32 model.')
parser.add_argument('--infer_data', type=str, default='', help='Data file.')
parser.add_argument(
'--batch_num',
type=int,
default=0,
help='Number of batches to process. 0 or less means whole dataset. Default: 0.'
)
parser.add_argument(
'--acc_diff_threshold',
type=float,
default=0.01,
help='Accepted accuracy difference threshold.')
parser.add_argument(
'--quantized_ops',
type=str,
default='',
help='A comma separated list of quantized operators.')
test_args, args = parser.parse_known_args(namespace=unittest)
return test_args, sys.argv[:1] + args
class Qat2Int8ImageClassificationComparisonTest(unittest.TestCase):
"""
Test for accuracy comparison of FP32 and QAT2 INT8 Image Classification inference.
"""
def _reader_creator(self, data_file='data.bin'):
def reader():
with open(data_file, 'rb') as fp:
num = fp.read(8)
num = struct.unpack('q', num)[0]
imgs_offset = 8
img_ch = 3
img_w = 224
img_h = 224
img_pixel_size = 4
img_size = img_ch * img_h * img_w * img_pixel_size
label_size = 8
labels_offset = imgs_offset + num * img_size
step = 0
while step < num:
fp.seek(imgs_offset + img_size * step)
img = fp.read(img_size)
img = struct.unpack_from(
'{}f'.format(img_ch * img_w * img_h), img)
img = np.array(img)
img.shape = (img_ch, img_w, img_h)
fp.seek(labels_offset + label_size * step)
label = fp.read(label_size)
label = struct.unpack('q', label)[0]
yield img, int(label)
step += 1
return reader
def _get_batch_accuracy(self, batch_output=None, labels=None):
total = 0
correct = 0
correct_5 = 0
for n, result in enumerate(batch_output):
index = result.argsort()
top_1_index = index[-1]
top_5_index = index[-5:]
total += 1
if top_1_index == labels[n]:
correct += 1
if labels[n] in top_5_index:
correct_5 += 1
acc1 = float(correct) / float(total)
acc5 = float(correct_5) / float(total)
return acc1, acc5
def _prepare_for_fp32_mkldnn(self, graph):
ops = graph.all_op_nodes()
for op_node in ops:
name = op_node.name()
if name in ['depthwise_conv2d']:
input_var_node = graph._find_node_by_name(
op_node.inputs, op_node.input("Input")[0])
weight_var_node = graph._find_node_by_name(
op_node.inputs, op_node.input("Filter")[0])
output_var_node = graph._find_node_by_name(
graph.all_var_nodes(), op_node.output("Output")[0])
attrs = {
name: op_node.op().attr(name)
for name in op_node.op().attr_names()
}
conv_op_node = graph.create_op_node(
op_type='conv2d',
attrs=attrs,
inputs={
'Input': input_var_node,
'Filter': weight_var_node
},
outputs={'Output': output_var_node})
graph.link_to(input_var_node, conv_op_node)
graph.link_to(weight_var_node, conv_op_node)
graph.link_to(conv_op_node, output_var_node)
graph.safe_remove_nodes(op_node)
return graph
def _predict(self,
test_reader=None,
model_path=None,
batch_size=1,
batch_num=1,
skip_batch_num=0,
transform_to_int8=False):
place = fluid.CPUPlace()
exe = fluid.Executor(place)
inference_scope = fluid.executor.global_scope()
with fluid.scope_guard(inference_scope):
if os.path.exists(os.path.join(model_path, '__model__')):
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(model_path, exe)
else:
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(
model_path, exe, 'model', 'params')
graph = IrGraph(core.Graph(inference_program.desc), for_test=True)
if (self._debug):
graph.draw('.', 'qat_orig', graph.all_op_nodes())
if (transform_to_int8):
transform_to_mkldnn_int8_pass = Qat2Int8MkldnnPass(
self._quantized_ops,
_scope=inference_scope,
_place=place,
_core=core,
_debug=self._debug)
graph = transform_to_mkldnn_int8_pass.apply(graph)
else:
graph = self._prepare_for_fp32_mkldnn(graph)
inference_program = graph.to_program()
dshape = [3, 224, 224]
outputs = []
infer_accs1 = []
infer_accs5 = []
batch_acc1 = 0.0
batch_acc5 = 0.0
fpses = []
batch_times = []
batch_time = 0.0
total_samples = 0
iters = 0
infer_start_time = time.time()
for data in test_reader():
if batch_num > 0 and iters >= batch_num:
break
if iters == skip_batch_num:
total_samples = 0
infer_start_time = time.time()
if six.PY2:
images = map(lambda x: x[0].reshape(dshape), data)
if six.PY3:
images = list(map(lambda x: x[0].reshape(dshape), data))
images = np.array(images).astype('float32')
labels = np.array([x[1] for x in data]).astype('int64')
if (transform_to_int8 == True):
# QAT INT8 models do not have accuracy measuring layers
start = time.time()
out = exe.run(inference_program,
feed={feed_target_names[0]: images},
fetch_list=fetch_targets)
batch_time = (time.time() - start) * 1000 # in miliseconds
outputs.append(out[0])
# Calculate accuracy result
batch_acc1, batch_acc5 = self._get_batch_accuracy(out[0],
labels)
else:
# FP32 models have accuracy measuring layers
labels = labels.reshape([-1, 1])
start = time.time()
out = exe.run(inference_program,
feed={
feed_target_names[0]: images,
feed_target_names[1]: labels
},
fetch_list=fetch_targets)
batch_time = (time.time() - start) * 1000 # in miliseconds
batch_acc1, batch_acc5 = out[1][0], out[2][0]
outputs.append(batch_acc1)
infer_accs1.append(batch_acc1)
infer_accs5.append(batch_acc5)
samples = len(data)
total_samples += samples
batch_times.append(batch_time)
fps = samples / batch_time * 1000
fpses.append(fps)
iters += 1
appx = ' (warm-up)' if iters <= skip_batch_num else ''
_logger.info('batch {0}{5}, acc1: {1:.4f}, acc5: {2:.4f}, '
'latency: {3:.4f} ms, fps: {4:.2f}'.format(
iters, batch_acc1, batch_acc5, batch_time /
batch_size, fps, appx))
# Postprocess benchmark data
batch_latencies = batch_times[skip_batch_num:]
batch_latency_avg = np.average(batch_latencies)
latency_avg = batch_latency_avg / batch_size
fpses = fpses[skip_batch_num:]
fps_avg = np.average(fpses)
infer_total_time = time.time() - infer_start_time
acc1_avg = np.mean(infer_accs1)
acc5_avg = np.mean(infer_accs5)
_logger.info('Total inference run time: {:.2f} s'.format(
infer_total_time))
return outputs, acc1_avg, acc5_avg, fps_avg, latency_avg
def _summarize_performance(self, fp32_fps, fp32_lat, int8_fps, int8_lat):
_logger.info('--- Performance summary ---')
_logger.info('FP32: avg fps: {0:.2f}, avg latency: {1:.4f} ms'.format(
fp32_fps, fp32_lat))
_logger.info('INT8: avg fps: {0:.2f}, avg latency: {1:.4f} ms'.format(
int8_fps, int8_lat))
def _compare_accuracy(self, fp32_acc1, fp32_acc5, int8_acc1, int8_acc5,
threshold):
_logger.info('--- Accuracy summary ---')
_logger.info(
'Accepted top1 accuracy drop threshold: {0}. (condition: (FP32_top1_acc - IN8_top1_acc) <= threshold)'
.format(threshold))
_logger.info(
'FP32: avg top1 accuracy: {0:.4f}, avg top5 accuracy: {1:.4f}'.
format(fp32_acc1, fp32_acc5))
_logger.info(
'INT8: avg top1 accuracy: {0:.4f}, avg top5 accuracy: {1:.4f}'.
format(int8_acc1, int8_acc5))
assert fp32_acc1 > 0.0
assert int8_acc1 > 0.0
assert fp32_acc1 - int8_acc1 <= threshold
def test_graph_transformation(self):
if not fluid.core.is_compiled_with_mkldnn():
return
qat_model_path = test_case_args.qat_model
assert qat_model_path, 'The QAT model path cannot be empty. Please, use the --qat_model option.'
fp32_model_path = test_case_args.fp32_model
assert fp32_model_path, 'The FP32 model path cannot be empty. Please, use the --fp32_model option.'
data_path = test_case_args.infer_data
assert data_path, 'The dataset path cannot be empty. Please, use the --infer_data option.'
batch_size = test_case_args.batch_size
batch_num = test_case_args.batch_num
skip_batch_num = test_case_args.skip_batch_num
acc_diff_threshold = test_case_args.acc_diff_threshold
self._debug = test_case_args.debug
self._quantized_ops = set(test_case_args.quantized_ops.split(','))
_logger.info('FP32 & QAT INT8 prediction run.')
_logger.info('QAT model: {0}'.format(qat_model_path))
_logger.info('FP32 model: {0}'.format(fp32_model_path))
_logger.info('Dataset: {0}'.format(data_path))
_logger.info('Batch size: {0}'.format(batch_size))
_logger.info('Batch number: {0}'.format(batch_num))
_logger.info('Accuracy drop threshold: {0}.'.format(acc_diff_threshold))
_logger.info('Quantized ops: {0}.'.format(self._quantized_ops))
_logger.info('--- FP32 prediction start ---')
val_reader = paddle.batch(
self._reader_creator(data_path), batch_size=batch_size)
fp32_output, fp32_acc1, fp32_acc5, fp32_fps, fp32_lat = self._predict(
val_reader,
fp32_model_path,
batch_size,
batch_num,
skip_batch_num,
transform_to_int8=False)
_logger.info('--- QAT INT8 prediction start ---')
val_reader = paddle.batch(
self._reader_creator(data_path), batch_size=batch_size)
int8_output, int8_acc1, int8_acc5, int8_fps, int8_lat = self._predict(
val_reader,
qat_model_path,
batch_size,
batch_num,
skip_batch_num,
transform_to_int8=True)
self._summarize_performance(fp32_fps, fp32_lat, int8_fps, int8_lat)
self._compare_accuracy(fp32_acc1, fp32_acc5, int8_acc1, int8_acc5,
acc_diff_threshold)
if __name__ == '__main__':
global test_case_args
test_case_args, remaining_args = parse_args()
unittest.main(argv=remaining_args)
python/paddle/fluid/contrib/slim/tests/qat_int8_nlp_comparison.py → python/paddle/fluid/contrib/slim/tests/qat2_int8_nlp_comparison.py
浏览文件 @ c524b930
......@@ -254,7 +254,7 @@ class QatInt8NLPComparisonTest(unittest.TestCase):
self._debug = test_case_args.debug
self._quantized_ops = set(test_case_args.quantized_ops.split(','))
_logger.info('QAT FP32 & INT8 prediction run.')
_logger.info('FP32 & QAT INT8 prediction run.')
_logger.info('QAT model: {0}'.format(qat_model_path))
_logger.info('FP32 model: {0}'.format(fp32_model_path))
_logger.info('Dataset: {0}'.format(data_path))
......@@ -264,7 +264,7 @@ class QatInt8NLPComparisonTest(unittest.TestCase):
_logger.info('Accuracy drop threshold: {0}.'.format(acc_diff_threshold))
_logger.info('Quantized ops: {0}.'.format(self._quantized_ops))
_logger.info('--- QAT FP32 prediction start ---')
_logger.info('--- FP32 prediction start ---')
val_reader = paddle.batch(
self._reader_creator(data_path, labels_path), batch_size=batch_size)
fp32_acc, fp32_pps, fp32_lat = self._predict(
......
python/paddle/fluid/contrib/slim/tests/qat_int8_image_classification_comparison.py
浏览文件 @ c524b930
......@@ -25,7 +25,6 @@ import paddle
import paddle.fluid as fluid
from paddle.fluid.framework import IrGraph
from paddle.fluid.contrib.slim.quantization import QatInt8MkldnnPass
from paddle.fluid.contrib.slim.quantization import Qat2Int8MkldnnPass
from paddle.fluid import core
logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s')
......@@ -48,11 +47,6 @@ def parse_args():
help='If used, the graph of QAT model is drawn.')
parser.add_argument(
'--qat_model', type=str, default='', help='A path to a QAT model.')
parser.add_argument(
'--qat2',
action='store_true',
help='If used, the QAT model is treated as a second generation model for performance optimization.'
)
parser.add_argument('--infer_data', type=str, default='', help='Data file.')
parser.add_argument(
'--batch_num',
......@@ -65,14 +59,8 @@ def parse_args():
type=float,
default=0.01,
help='Accepted accuracy difference threshold.')
parser.add_argument(
'--quantized_ops',
type=str,
default='',
help='A comma separated list of quantized operators.')
test_args, args = parser.parse_known_args(namespace=unittest)
return test_args, sys.argv[:1] + args
......@@ -183,19 +171,9 @@ class QatInt8ImageClassificationComparisonTest(unittest.TestCase):
if (self._debug):
graph.draw('.', 'qat_orig', graph.all_op_nodes())
if (transform_to_int8):
if (test_case_args.qat2):
transform_to_mkldnn_int8_pass = Qat2Int8MkldnnPass(
self._quantized_ops,
_scope=inference_scope,
_place=place,
_core=core,
_debug=self._debug)
graph = transform_to_mkldnn_int8_pass.apply(graph)
else:
mkldnn_int8_pass = QatInt8MkldnnPass(
_scope=inference_scope, _place=place)
graph = mkldnn_int8_pass.apply(graph)
mkldnn_int8_pass = QatInt8MkldnnPass(
_scope=inference_scope, _place=place)
graph = mkldnn_int8_pass.apply(graph)
else:
graph = self._prepare_for_fp32_mkldnn(graph)
......@@ -208,8 +186,6 @@ class QatInt8ImageClassificationComparisonTest(unittest.TestCase):
fpses = []
batch_times = []
total_samples = 0
top1 = 0.0
top5 = 0.0
iters = 0
infer_start_time = time.time()
for data in test_reader():
......@@ -289,13 +265,14 @@ class QatInt8ImageClassificationComparisonTest(unittest.TestCase):
return
qat_model_path = test_case_args.qat_model
assert qat_model_path, 'The QAT model path cannot be empty. Please, use the --qat_model option.'
data_path = test_case_args.infer_data
assert data_path, 'The dataset path cannot be empty. Please, use the --infer_data option.'
batch_size = test_case_args.batch_size
batch_num = test_case_args.batch_num
skip_batch_num = test_case_args.skip_batch_num
acc_diff_threshold = test_case_args.acc_diff_threshold
self._debug = test_case_args.debug
self._quantized_ops = set(test_case_args.quantized_ops.split(','))
_logger.info('QAT FP32 & INT8 prediction run.')
_logger.info('QAT model: {0}'.format(qat_model_path))
......@@ -303,7 +280,6 @@ class QatInt8ImageClassificationComparisonTest(unittest.TestCase):
_logger.info('Batch size: {0}'.format(batch_size))
_logger.info('Batch number: {0}'.format(batch_num))
_logger.info('Accuracy drop threshold: {0}.'.format(acc_diff_threshold))
_logger.info('Quantized ops: {0}.'.format(self._quantized_ops))
_logger.info('--- QAT FP32 prediction start ---')
val_reader = paddle.batch(
......
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