wehub-resource-sync
240 lines
7.7 KiB
Python
240 lines
7.7 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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import unittest
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from test_trt_explicit_quantization_model import TestExplicitQuantizationModel
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import paddle
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class ResNet:
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def __init__(self, layers=50, prefix_name=''):
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self.layers = layers
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self.prefix_name = prefix_name
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def net(self, input, class_dim=1000, conv1_name='conv1', fc_name=None):
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layers = self.layers
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prefix_name = (
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self.prefix_name
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if self.prefix_name == ''
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else self.prefix_name + '_'
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)
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supported_layers = [34, 50, 101, 152]
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assert layers in supported_layers, (
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f"supported layers are {supported_layers} but input layer is {layers}"
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)
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if layers == 34 or layers == 50:
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depth = [3, 4, 6, 3]
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elif layers == 101:
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depth = [3, 4, 23, 3]
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elif layers == 152:
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depth = [3, 8, 36, 3]
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num_filters = [64, 128, 256, 512]
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conv = self.conv_bn_layer(
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input=input,
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num_filters=64,
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filter_size=7,
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stride=2,
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act='relu',
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name=prefix_name + conv1_name,
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)
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if layers >= 50:
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for block in range(len(depth)):
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for i in range(depth[block]):
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if layers in [101, 152] and block == 2:
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if i == 0:
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conv_name = "res" + str(block + 2) + "a"
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else:
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conv_name = "res" + str(block + 2) + "b" + str(i)
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else:
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conv_name = "res" + str(block + 2) + chr(97 + i)
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conv_name = prefix_name + conv_name
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conv = self.bottleneck_block(
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input=conv,
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num_filters=num_filters[block],
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stride=2 if i == 0 and block != 0 else 1,
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name=conv_name,
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)
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pool = paddle.nn.functional.adaptive_avg_pool2d(conv, 1)
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pool = paddle.reshape(pool, [-1, 2048])
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stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
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fc_name = fc_name if fc_name is None else prefix_name + fc_name
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out = paddle.static.nn.fc(
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pool,
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class_dim,
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activation='softmax',
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name=fc_name,
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weight_attr=paddle.ParamAttr(
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initializer=paddle.nn.initializer.Uniform(-stdv, stdv)
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),
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)
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else:
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for block in range(len(depth)):
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for i in range(depth[block]):
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conv_name = "res" + str(block + 2) + chr(97 + i)
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conv_name = prefix_name + conv_name
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conv = self.basic_block(
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input=conv,
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num_filters=num_filters[block],
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stride=2 if i == 0 and block != 0 else 1,
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is_first=block == i == 0,
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name=conv_name,
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)
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pool = paddle.nn.functional.adaptive_avg_pool2d(conv, 1)
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stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
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fc_name = fc_name if fc_name is None else prefix_name + fc_name
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out = paddle.static.nn.fc(
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pool,
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class_dim,
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name=fc_name,
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weight_attr=paddle.ParamAttr(
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initializer=paddle.nn.initializer.Uniform(-stdv, stdv)
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),
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)
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return out
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def conv_bn_layer(
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self,
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input,
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num_filters,
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filter_size,
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stride=1,
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groups=1,
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act=None,
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name=None,
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):
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conv = paddle.static.nn.conv2d(
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input=input,
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num_filters=num_filters,
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filter_size=filter_size,
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stride=stride,
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padding=(filter_size - 1) // 2,
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groups=groups,
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act=None,
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param_attr=paddle.ParamAttr(name=name + "_weights"),
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bias_attr=False,
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name=name + '.conv2d.output.1',
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)
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if self.prefix_name == '':
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if name == "conv1":
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bn_name = "bn_" + name
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else:
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bn_name = "bn" + name[3:]
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else:
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if name.split("_")[1] == "conv1":
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bn_name = name.split("_", 1)[0] + "_bn_" + name.split("_", 1)[1]
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else:
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bn_name = (
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name.split("_", 1)[0] + "_bn" + name.split("_", 1)[1][3:]
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)
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return paddle.static.nn.batch_norm(
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input=conv,
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act=act,
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name=bn_name + '.output.1',
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param_attr=paddle.ParamAttr(name=bn_name + '_scale'),
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bias_attr=paddle.ParamAttr(bn_name + '_offset'),
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moving_mean_name=bn_name + '_mean',
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moving_variance_name=bn_name + '_variance',
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)
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def shortcut(self, input, ch_out, stride, is_first, name):
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ch_in = input.shape[1]
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if ch_in != ch_out or stride != 1 or is_first:
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return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
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else:
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return input
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def bottleneck_block(self, input, num_filters, stride, name):
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conv0 = self.conv_bn_layer(
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input=input,
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num_filters=num_filters,
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filter_size=1,
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act='relu',
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name=name + "_branch2a",
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)
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conv1 = self.conv_bn_layer(
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input=conv0,
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num_filters=num_filters,
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filter_size=3,
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stride=stride,
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act='relu',
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name=name + "_branch2b",
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)
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conv2 = self.conv_bn_layer(
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input=conv1,
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num_filters=num_filters * 4,
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filter_size=1,
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act=None,
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name=name + "_branch2c",
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)
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short = self.shortcut(
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input,
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num_filters * 4,
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stride,
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is_first=False,
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name=name + "_branch1",
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)
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out = paddle.add(x=short, y=conv2, name=name + ".add.output.5")
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return paddle.nn.functional.relu(out)
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def basic_block(self, input, num_filters, stride, is_first, name):
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conv0 = self.conv_bn_layer(
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input=input,
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num_filters=num_filters,
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filter_size=3,
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act='relu',
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stride=stride,
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name=name + "_branch2a",
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)
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conv1 = self.conv_bn_layer(
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input=conv0,
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num_filters=num_filters,
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filter_size=3,
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act=None,
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name=name + "_branch2b",
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)
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short = self.shortcut(
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input, num_filters, stride, is_first, name=name + "_branch1"
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)
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out = paddle.add(x=short, y=conv1)
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return paddle.nn.functional.relu(out)
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@unittest.skipIf(
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paddle.inference.get_trt_compile_version() < (8, 5, 1),
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"Quantization axis is consistent with Paddle after TRT 8.5.2.",
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)
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class TestExplicitQuantizationResNet(
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TestExplicitQuantizationModel, unittest.TestCase
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):
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def build_model(self):
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model = ResNet(layers=50, prefix_name='')
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return model
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if __name__ == '__main__':
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unittest.main()
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