chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,239 @@
|
||||
# Copyright (c) 2023 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 math
|
||||
import unittest
|
||||
|
||||
from test_trt_explicit_quantization_model import TestExplicitQuantizationModel
|
||||
|
||||
import paddle
|
||||
|
||||
|
||||
class ResNet:
|
||||
def __init__(self, layers=50, prefix_name=''):
|
||||
self.layers = layers
|
||||
self.prefix_name = prefix_name
|
||||
|
||||
def net(self, input, class_dim=1000, conv1_name='conv1', fc_name=None):
|
||||
layers = self.layers
|
||||
prefix_name = (
|
||||
self.prefix_name
|
||||
if self.prefix_name == ''
|
||||
else self.prefix_name + '_'
|
||||
)
|
||||
supported_layers = [34, 50, 101, 152]
|
||||
assert layers in supported_layers, (
|
||||
f"supported layers are {supported_layers} but input layer is {layers}"
|
||||
)
|
||||
|
||||
if layers == 34 or layers == 50:
|
||||
depth = [3, 4, 6, 3]
|
||||
elif layers == 101:
|
||||
depth = [3, 4, 23, 3]
|
||||
elif layers == 152:
|
||||
depth = [3, 8, 36, 3]
|
||||
num_filters = [64, 128, 256, 512]
|
||||
|
||||
conv = self.conv_bn_layer(
|
||||
input=input,
|
||||
num_filters=64,
|
||||
filter_size=7,
|
||||
stride=2,
|
||||
act='relu',
|
||||
name=prefix_name + conv1_name,
|
||||
)
|
||||
|
||||
if layers >= 50:
|
||||
for block in range(len(depth)):
|
||||
for i in range(depth[block]):
|
||||
if layers in [101, 152] and block == 2:
|
||||
if i == 0:
|
||||
conv_name = "res" + str(block + 2) + "a"
|
||||
else:
|
||||
conv_name = "res" + str(block + 2) + "b" + str(i)
|
||||
else:
|
||||
conv_name = "res" + str(block + 2) + chr(97 + i)
|
||||
conv_name = prefix_name + conv_name
|
||||
conv = self.bottleneck_block(
|
||||
input=conv,
|
||||
num_filters=num_filters[block],
|
||||
stride=2 if i == 0 and block != 0 else 1,
|
||||
name=conv_name,
|
||||
)
|
||||
|
||||
pool = paddle.nn.functional.adaptive_avg_pool2d(conv, 1)
|
||||
pool = paddle.reshape(pool, [-1, 2048])
|
||||
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
|
||||
|
||||
fc_name = fc_name if fc_name is None else prefix_name + fc_name
|
||||
out = paddle.static.nn.fc(
|
||||
pool,
|
||||
class_dim,
|
||||
activation='softmax',
|
||||
name=fc_name,
|
||||
weight_attr=paddle.ParamAttr(
|
||||
initializer=paddle.nn.initializer.Uniform(-stdv, stdv)
|
||||
),
|
||||
)
|
||||
else:
|
||||
for block in range(len(depth)):
|
||||
for i in range(depth[block]):
|
||||
conv_name = "res" + str(block + 2) + chr(97 + i)
|
||||
conv_name = prefix_name + conv_name
|
||||
conv = self.basic_block(
|
||||
input=conv,
|
||||
num_filters=num_filters[block],
|
||||
stride=2 if i == 0 and block != 0 else 1,
|
||||
is_first=block == i == 0,
|
||||
name=conv_name,
|
||||
)
|
||||
|
||||
pool = paddle.nn.functional.adaptive_avg_pool2d(conv, 1)
|
||||
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
|
||||
fc_name = fc_name if fc_name is None else prefix_name + fc_name
|
||||
out = paddle.static.nn.fc(
|
||||
pool,
|
||||
class_dim,
|
||||
name=fc_name,
|
||||
weight_attr=paddle.ParamAttr(
|
||||
initializer=paddle.nn.initializer.Uniform(-stdv, stdv)
|
||||
),
|
||||
)
|
||||
|
||||
return out
|
||||
|
||||
def conv_bn_layer(
|
||||
self,
|
||||
input,
|
||||
num_filters,
|
||||
filter_size,
|
||||
stride=1,
|
||||
groups=1,
|
||||
act=None,
|
||||
name=None,
|
||||
):
|
||||
conv = paddle.static.nn.conv2d(
|
||||
input=input,
|
||||
num_filters=num_filters,
|
||||
filter_size=filter_size,
|
||||
stride=stride,
|
||||
padding=(filter_size - 1) // 2,
|
||||
groups=groups,
|
||||
act=None,
|
||||
param_attr=paddle.ParamAttr(name=name + "_weights"),
|
||||
bias_attr=False,
|
||||
name=name + '.conv2d.output.1',
|
||||
)
|
||||
if self.prefix_name == '':
|
||||
if name == "conv1":
|
||||
bn_name = "bn_" + name
|
||||
else:
|
||||
bn_name = "bn" + name[3:]
|
||||
else:
|
||||
if name.split("_")[1] == "conv1":
|
||||
bn_name = name.split("_", 1)[0] + "_bn_" + name.split("_", 1)[1]
|
||||
else:
|
||||
bn_name = (
|
||||
name.split("_", 1)[0] + "_bn" + name.split("_", 1)[1][3:]
|
||||
)
|
||||
return paddle.static.nn.batch_norm(
|
||||
input=conv,
|
||||
act=act,
|
||||
name=bn_name + '.output.1',
|
||||
param_attr=paddle.ParamAttr(name=bn_name + '_scale'),
|
||||
bias_attr=paddle.ParamAttr(bn_name + '_offset'),
|
||||
moving_mean_name=bn_name + '_mean',
|
||||
moving_variance_name=bn_name + '_variance',
|
||||
)
|
||||
|
||||
def shortcut(self, input, ch_out, stride, is_first, name):
|
||||
ch_in = input.shape[1]
|
||||
if ch_in != ch_out or stride != 1 or is_first:
|
||||
return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
|
||||
else:
|
||||
return input
|
||||
|
||||
def bottleneck_block(self, input, num_filters, stride, name):
|
||||
conv0 = self.conv_bn_layer(
|
||||
input=input,
|
||||
num_filters=num_filters,
|
||||
filter_size=1,
|
||||
act='relu',
|
||||
name=name + "_branch2a",
|
||||
)
|
||||
conv1 = self.conv_bn_layer(
|
||||
input=conv0,
|
||||
num_filters=num_filters,
|
||||
filter_size=3,
|
||||
stride=stride,
|
||||
act='relu',
|
||||
name=name + "_branch2b",
|
||||
)
|
||||
conv2 = self.conv_bn_layer(
|
||||
input=conv1,
|
||||
num_filters=num_filters * 4,
|
||||
filter_size=1,
|
||||
act=None,
|
||||
name=name + "_branch2c",
|
||||
)
|
||||
|
||||
short = self.shortcut(
|
||||
input,
|
||||
num_filters * 4,
|
||||
stride,
|
||||
is_first=False,
|
||||
name=name + "_branch1",
|
||||
)
|
||||
|
||||
out = paddle.add(x=short, y=conv2, name=name + ".add.output.5")
|
||||
return paddle.nn.functional.relu(out)
|
||||
|
||||
def basic_block(self, input, num_filters, stride, is_first, name):
|
||||
conv0 = self.conv_bn_layer(
|
||||
input=input,
|
||||
num_filters=num_filters,
|
||||
filter_size=3,
|
||||
act='relu',
|
||||
stride=stride,
|
||||
name=name + "_branch2a",
|
||||
)
|
||||
conv1 = self.conv_bn_layer(
|
||||
input=conv0,
|
||||
num_filters=num_filters,
|
||||
filter_size=3,
|
||||
act=None,
|
||||
name=name + "_branch2b",
|
||||
)
|
||||
short = self.shortcut(
|
||||
input, num_filters, stride, is_first, name=name + "_branch1"
|
||||
)
|
||||
|
||||
out = paddle.add(x=short, y=conv1)
|
||||
return paddle.nn.functional.relu(out)
|
||||
|
||||
|
||||
@unittest.skipIf(
|
||||
paddle.inference.get_trt_compile_version() < (8, 5, 1),
|
||||
"Quantization axis is consistent with Paddle after TRT 8.5.2.",
|
||||
)
|
||||
class TestExplicitQuantizationResNet(
|
||||
TestExplicitQuantizationModel, unittest.TestCase
|
||||
):
|
||||
def build_model(self):
|
||||
model = ResNet(layers=50, prefix_name='')
|
||||
return model
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
Reference in New Issue
Block a user