618 lines
21 KiB
Python
618 lines
21 KiB
Python
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
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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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# reference: https://arxiv.org/pdf/1512.03385
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from __future__ import absolute_import, division, print_function
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import numpy as np
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import paddle
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from paddle import ParamAttr
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import paddle.nn as nn
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from paddle.nn import Conv2D, BatchNorm, Linear, BatchNorm2D
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from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
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from paddle.nn.initializer import Uniform
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from paddle.regularizer import L2Decay
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import math
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from ....utils import logger
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from ..base.theseus_layer import TheseusLayer
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from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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MODEL_URLS = {
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"ResNet18":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet18_pretrained.pdparams",
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"ResNet18_vd":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet18_vd_pretrained.pdparams",
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"ResNet34":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_pretrained.pdparams",
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"ResNet34_vd":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_pretrained.pdparams",
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"ResNet50":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_pretrained.pdparams",
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"ResNet50_vd":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_pretrained.pdparams",
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"ResNet101":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_pretrained.pdparams",
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"ResNet101_vd":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_pretrained.pdparams",
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"ResNet152":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet152_pretrained.pdparams",
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"ResNet152_vd":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet152_vd_pretrained.pdparams",
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"ResNet200_vd":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet200_vd_pretrained.pdparams",
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}
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MODEL_STAGES_PATTERN = {
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"ResNet18": ["blocks[1]", "blocks[3]", "blocks[5]", "blocks[7]"],
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"ResNet34": ["blocks[2]", "blocks[6]", "blocks[12]", "blocks[15]"],
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"ResNet50": ["blocks[2]", "blocks[6]", "blocks[12]", "blocks[15]"],
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"ResNet101": ["blocks[2]", "blocks[6]", "blocks[29]", "blocks[32]"],
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"ResNet152": ["blocks[2]", "blocks[10]", "blocks[46]", "blocks[49]"],
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"ResNet200": ["blocks[2]", "blocks[14]", "blocks[62]", "blocks[65]"]
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}
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__all__ = MODEL_URLS.keys()
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'''
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ResNet config: dict.
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key: depth of ResNet.
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values: config's dict of specific model.
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keys:
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block_type: Two different blocks in ResNet, BasicBlock and BottleneckBlock are optional.
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block_depth: The number of blocks in different stages in ResNet.
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num_channels: The number of channels to enter the next stage.
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'''
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NET_CONFIG = {
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"18": {
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"block_type": "BasicBlock",
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"block_depth": [2, 2, 2, 2],
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"num_channels": [64, 64, 128, 256]
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},
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"34": {
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"block_type": "BasicBlock",
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"block_depth": [3, 4, 6, 3],
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"num_channels": [64, 64, 128, 256]
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},
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"50": {
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"block_type": "BottleneckBlock",
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"block_depth": [3, 4, 6, 3],
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"num_channels": [64, 256, 512, 1024]
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},
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"101": {
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"block_type": "BottleneckBlock",
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"block_depth": [3, 4, 23, 3],
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"num_channels": [64, 256, 512, 1024]
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},
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"152": {
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"block_type": "BottleneckBlock",
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"block_depth": [3, 8, 36, 3],
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"num_channels": [64, 256, 512, 1024]
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},
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"200": {
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"block_type": "BottleneckBlock",
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"block_depth": [3, 12, 48, 3],
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"num_channels": [64, 256, 512, 1024]
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},
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}
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class ConvBNLayer(TheseusLayer):
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def __init__(self,
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num_channels,
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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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is_vd_mode=False,
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act=None,
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lr_mult=1.0,
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data_format="NCHW"):
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super().__init__()
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self.is_vd_mode = is_vd_mode
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self.act = act
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self.avg_pool = AvgPool2D(
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kernel_size=2, stride=stride, padding="SAME", ceil_mode=True)
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self.conv = Conv2D(
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in_channels=num_channels,
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out_channels=num_filters,
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kernel_size=filter_size,
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stride=1 if is_vd_mode else stride,
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padding=(filter_size - 1) // 2,
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groups=groups,
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weight_attr=ParamAttr(learning_rate=lr_mult),
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bias_attr=False,
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data_format=data_format)
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self.bn = BatchNorm(
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num_filters,
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param_attr=ParamAttr(learning_rate=lr_mult),
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bias_attr=ParamAttr(learning_rate=lr_mult),
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data_layout=data_format)
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self.relu = nn.ReLU()
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def forward(self, x):
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if self.is_vd_mode:
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x = self.avg_pool(x)
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x = self.conv(x)
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x = self.bn(x)
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if self.act:
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x = self.relu(x)
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return x
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class BottleneckBlock(TheseusLayer):
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def __init__(self,
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num_channels,
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num_filters,
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stride,
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shortcut=True,
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if_first=False,
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lr_mult=1.0,
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data_format="NCHW"):
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super().__init__()
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self.conv0 = ConvBNLayer(
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num_channels=num_channels,
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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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lr_mult=lr_mult,
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data_format=data_format)
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self.conv1 = ConvBNLayer(
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num_channels=num_filters,
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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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lr_mult=lr_mult,
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data_format=data_format)
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self.conv2 = ConvBNLayer(
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num_channels=num_filters,
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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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lr_mult=lr_mult,
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data_format=data_format)
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if not shortcut:
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self.short = ConvBNLayer(
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num_channels=num_channels,
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num_filters=num_filters * 4,
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filter_size=1,
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stride=stride,
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is_vd_mode=False if if_first else True,
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lr_mult=lr_mult,
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data_format=data_format)
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self.relu = nn.ReLU()
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self.shortcut = shortcut
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def forward(self, x):
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identity = x
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x = self.conv0(x)
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x = self.conv1(x)
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x = self.conv2(x)
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if self.shortcut:
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short = identity
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else:
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short = self.short(identity)
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x = paddle.add(x=x, y=short)
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x = self.relu(x)
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return x
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class BasicBlock(TheseusLayer):
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def __init__(self,
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num_channels,
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num_filters,
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stride,
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shortcut=True,
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if_first=False,
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lr_mult=1.0,
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data_format="NCHW"):
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super().__init__()
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self.stride = stride
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self.conv0 = ConvBNLayer(
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num_channels=num_channels,
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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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lr_mult=lr_mult,
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data_format=data_format)
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self.conv1 = ConvBNLayer(
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num_channels=num_filters,
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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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lr_mult=lr_mult,
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data_format=data_format)
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if not shortcut:
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self.short = ConvBNLayer(
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num_channels=num_channels,
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num_filters=num_filters,
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filter_size=1,
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stride=stride,
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is_vd_mode=False if if_first else True,
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lr_mult=lr_mult,
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data_format=data_format)
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self.shortcut = shortcut
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self.relu = nn.ReLU()
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def forward(self, x):
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identity = x
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x = self.conv0(x)
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x = self.conv1(x)
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if self.shortcut:
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short = identity
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else:
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short = self.short(identity)
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x = paddle.add(x=x, y=short)
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x = self.relu(x)
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return x
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class ResNet(TheseusLayer):
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"""
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ResNet
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Args:
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config: dict. config of ResNet.
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version: str="vb". Different version of ResNet, version vd can perform better.
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class_num: int=1000. The number of classes.
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lr_mult_list: list. Control the learning rate of different stages.
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Returns:
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model: nn.Layer. Specific ResNet model depends on args.
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"""
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def __init__(self,
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config,
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stages_pattern,
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version="vb",
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stem_act="relu",
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class_num=1000,
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lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0],
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stride_list=[2, 2, 2, 2, 2],
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data_format="NCHW",
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input_image_channel=3,
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return_patterns=None,
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return_stages=None,
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**kargs):
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super().__init__()
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self.cfg = config
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self.lr_mult_list = lr_mult_list
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self.stride_list = stride_list
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self.is_vd_mode = version == "vd"
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self.class_num = class_num
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self.num_filters = [64, 128, 256, 512]
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self.block_depth = self.cfg["block_depth"]
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self.block_type = self.cfg["block_type"]
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self.num_channels = self.cfg["num_channels"]
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self.channels_mult = 1 if self.num_channels[-1] == 256 else 4
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assert isinstance(self.lr_mult_list, (
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list, tuple
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)), "lr_mult_list should be in (list, tuple) but got {}".format(
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type(self.lr_mult_list))
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if len(self.lr_mult_list) != 5:
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msg = "lr_mult_list length should be 5 but got {}, default lr_mult_list used".format(
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len(self.lr_mult_list))
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logger.warning(msg)
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self.lr_mult_list = [1.0, 1.0, 1.0, 1.0, 1.0]
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assert isinstance(self.stride_list, (
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list, tuple
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)), "stride_list should be in (list, tuple) but got {}".format(
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type(self.stride_list))
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assert len(self.stride_list
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) == 5, "stride_list length should be 5 but got {}".format(
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len(self.stride_list))
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self.stem_cfg = {
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#num_channels, num_filters, filter_size, stride
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"vb": [[input_image_channel, 64, 7, self.stride_list[0]]],
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"vd": [[input_image_channel, 32, 3, self.stride_list[0]],
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[32, 32, 3, 1], [32, 64, 3, 1]]
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}
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self.stem = nn.Sequential(* [
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ConvBNLayer(
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num_channels=in_c,
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num_filters=out_c,
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filter_size=k,
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stride=s,
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act=stem_act,
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lr_mult=self.lr_mult_list[0],
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data_format=data_format)
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for in_c, out_c, k, s in self.stem_cfg[version]
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])
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self.max_pool = MaxPool2D(
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kernel_size=3,
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stride=stride_list[1],
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padding=1,
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data_format=data_format)
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block_list = []
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for block_idx in range(len(self.block_depth)):
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shortcut = False
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for i in range(self.block_depth[block_idx]):
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block_list.append(globals()[self.block_type](
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num_channels=self.num_channels[block_idx] if i == 0 else
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self.num_filters[block_idx] * self.channels_mult,
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num_filters=self.num_filters[block_idx],
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stride=self.stride_list[block_idx + 1]
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if i == 0 and block_idx != 0 else 1,
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shortcut=shortcut,
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if_first=block_idx == i == 0 if version == "vd" else True,
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lr_mult=self.lr_mult_list[block_idx + 1],
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data_format=data_format))
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shortcut = True
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self.blocks = nn.Sequential(*block_list)
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self.avg_pool = AdaptiveAvgPool2D(1, data_format=data_format)
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self.flatten = nn.Flatten()
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self.avg_pool_channels = self.num_channels[-1] * 2
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stdv = 1.0 / math.sqrt(self.avg_pool_channels * 1.0)
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self.fc = Linear(
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self.avg_pool_channels,
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self.class_num,
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weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)))
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self.data_format = data_format
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super().init_res(
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stages_pattern,
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return_patterns=return_patterns,
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return_stages=return_stages)
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def forward(self, x):
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with paddle.static.amp.fp16_guard():
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if self.data_format == "NHWC":
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x = paddle.transpose(x, [0, 2, 3, 1])
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x.stop_gradient = True
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x = self.stem(x)
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x = self.max_pool(x)
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x = self.blocks(x)
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x = self.avg_pool(x)
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x = self.flatten(x)
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x = self.fc(x)
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return x
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def _load_pretrained(pretrained, model, model_url, use_ssld):
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if pretrained is False:
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pass
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elif pretrained is True:
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load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
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elif isinstance(pretrained, str):
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if 'http' in pretrained:
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load_dygraph_pretrain_from_url(model, pretrained, use_ssld=False)
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else:
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load_dygraph_pretrain(model, pretrained)
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else:
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raise RuntimeError(
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"pretrained type is not available. Please use `string` or `boolean` type."
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)
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def ResNet18(pretrained=False, use_ssld=False, **kwargs):
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"""
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ResNet18
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Args:
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pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
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If str, means the path of the pretrained model.
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use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
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Returns:
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model: nn.Layer. Specific `ResNet18` model depends on args.
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"""
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model = ResNet(
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config=NET_CONFIG["18"],
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stages_pattern=MODEL_STAGES_PATTERN["ResNet18"],
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version="vb",
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**kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["ResNet18"], use_ssld)
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return model
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def ResNet18_vd(pretrained=False, use_ssld=False, **kwargs):
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"""
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ResNet18_vd
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Args:
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pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
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If str, means the path of the pretrained model.
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use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
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Returns:
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model: nn.Layer. Specific `ResNet18_vd` model depends on args.
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"""
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model = ResNet(
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config=NET_CONFIG["18"],
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stages_pattern=MODEL_STAGES_PATTERN["ResNet18"],
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version="vd",
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**kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["ResNet18_vd"], use_ssld)
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return model
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def ResNet34(pretrained=False, use_ssld=False, **kwargs):
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"""
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ResNet34
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Args:
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pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
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If str, means the path of the pretrained model.
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use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
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Returns:
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model: nn.Layer. Specific `ResNet34` model depends on args.
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"""
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model = ResNet(
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config=NET_CONFIG["34"],
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stages_pattern=MODEL_STAGES_PATTERN["ResNet34"],
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version="vb",
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**kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["ResNet34"], use_ssld)
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return model
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def ResNet34_vd(pretrained=False, use_ssld=False, **kwargs):
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"""
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ResNet34_vd
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Args:
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pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
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If str, means the path of the pretrained model.
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use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
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Returns:
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model: nn.Layer. Specific `ResNet34_vd` model depends on args.
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"""
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model = ResNet(
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config=NET_CONFIG["34"],
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stages_pattern=MODEL_STAGES_PATTERN["ResNet34"],
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version="vd",
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**kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["ResNet34_vd"], use_ssld)
|
|
return model
|
|
|
|
|
|
def ResNet50(pretrained=False, use_ssld=False, **kwargs):
|
|
"""
|
|
ResNet50
|
|
Args:
|
|
pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
|
|
If str, means the path of the pretrained model.
|
|
use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
|
|
Returns:
|
|
model: nn.Layer. Specific `ResNet50` model depends on args.
|
|
"""
|
|
model = ResNet(
|
|
config=NET_CONFIG["50"],
|
|
stages_pattern=MODEL_STAGES_PATTERN["ResNet50"],
|
|
version="vb",
|
|
**kwargs)
|
|
_load_pretrained(pretrained, model, MODEL_URLS["ResNet50"], use_ssld)
|
|
return model
|
|
|
|
|
|
def ResNet50_vd(pretrained=False, use_ssld=False, **kwargs):
|
|
"""
|
|
ResNet50_vd
|
|
Args:
|
|
pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
|
|
If str, means the path of the pretrained model.
|
|
use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
|
|
Returns:
|
|
model: nn.Layer. Specific `ResNet50_vd` model depends on args.
|
|
"""
|
|
model = ResNet(
|
|
config=NET_CONFIG["50"],
|
|
stages_pattern=MODEL_STAGES_PATTERN["ResNet50"],
|
|
version="vd",
|
|
**kwargs)
|
|
_load_pretrained(pretrained, model, MODEL_URLS["ResNet50_vd"], use_ssld)
|
|
return model
|
|
|
|
|
|
def ResNet101(pretrained=False, use_ssld=False, **kwargs):
|
|
"""
|
|
ResNet101
|
|
Args:
|
|
pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
|
|
If str, means the path of the pretrained model.
|
|
use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
|
|
Returns:
|
|
model: nn.Layer. Specific `ResNet101` model depends on args.
|
|
"""
|
|
model = ResNet(
|
|
config=NET_CONFIG["101"],
|
|
stages_pattern=MODEL_STAGES_PATTERN["ResNet101"],
|
|
version="vb",
|
|
**kwargs)
|
|
_load_pretrained(pretrained, model, MODEL_URLS["ResNet101"], use_ssld)
|
|
return model
|
|
|
|
|
|
def ResNet101_vd(pretrained=False, use_ssld=False, **kwargs):
|
|
"""
|
|
ResNet101_vd
|
|
Args:
|
|
pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
|
|
If str, means the path of the pretrained model.
|
|
use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
|
|
Returns:
|
|
model: nn.Layer. Specific `ResNet101_vd` model depends on args.
|
|
"""
|
|
model = ResNet(
|
|
config=NET_CONFIG["101"],
|
|
stages_pattern=MODEL_STAGES_PATTERN["ResNet101"],
|
|
version="vd",
|
|
**kwargs)
|
|
_load_pretrained(pretrained, model, MODEL_URLS["ResNet101_vd"], use_ssld)
|
|
return model
|
|
|
|
|
|
def ResNet152(pretrained=False, use_ssld=False, **kwargs):
|
|
"""
|
|
ResNet152
|
|
Args:
|
|
pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
|
|
If str, means the path of the pretrained model.
|
|
use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
|
|
Returns:
|
|
model: nn.Layer. Specific `ResNet152` model depends on args.
|
|
"""
|
|
model = ResNet(
|
|
config=NET_CONFIG["152"],
|
|
stages_pattern=MODEL_STAGES_PATTERN["ResNet152"],
|
|
version="vb",
|
|
**kwargs)
|
|
_load_pretrained(pretrained, model, MODEL_URLS["ResNet152"], use_ssld)
|
|
return model
|
|
|
|
|
|
def ResNet152_vd(pretrained=False, use_ssld=False, **kwargs):
|
|
"""
|
|
ResNet152_vd
|
|
Args:
|
|
pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
|
|
If str, means the path of the pretrained model.
|
|
use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
|
|
Returns:
|
|
model: nn.Layer. Specific `ResNet152_vd` model depends on args.
|
|
"""
|
|
model = ResNet(
|
|
config=NET_CONFIG["152"],
|
|
stages_pattern=MODEL_STAGES_PATTERN["ResNet152"],
|
|
version="vd",
|
|
**kwargs)
|
|
_load_pretrained(pretrained, model, MODEL_URLS["ResNet152_vd"], use_ssld)
|
|
return model
|
|
|
|
|
|
def ResNet200_vd(pretrained=False, use_ssld=False, **kwargs):
|
|
"""
|
|
ResNet200_vd
|
|
Args:
|
|
pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
|
|
If str, means the path of the pretrained model.
|
|
use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
|
|
Returns:
|
|
model: nn.Layer. Specific `ResNet200_vd` model depends on args.
|
|
"""
|
|
model = ResNet(
|
|
config=NET_CONFIG["200"],
|
|
stages_pattern=MODEL_STAGES_PATTERN["ResNet200"],
|
|
version="vd",
|
|
**kwargs)
|
|
_load_pretrained(pretrained, model, MODEL_URLS["ResNet200_vd"], use_ssld)
|
|
return model
|