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- # copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
- #
- # 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.
- from __future__ import absolute_import, division, print_function
- import numpy as np
- import paddle
- from paddle import ParamAttr
- import paddle.nn as nn
- from paddle.nn import Conv2D, BatchNorm, Linear
- from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
- from paddle.nn.initializer import Uniform
- import math
- from paddlex.ppcls.arch.backbone.base.theseus_layer import TheseusLayer
- from paddlex.ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
- MODEL_URLS = {
- "ResNet18":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet18_pretrained.pdparams",
- "ResNet18_vd":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet18_vd_pretrained.pdparams",
- "ResNet34":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_pretrained.pdparams",
- "ResNet34_vd":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_pretrained.pdparams",
- "ResNet50":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_pretrained.pdparams",
- "ResNet50_vd":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_pretrained.pdparams",
- "ResNet101":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_pretrained.pdparams",
- "ResNet101_vd":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_pretrained.pdparams",
- "ResNet152":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet152_pretrained.pdparams",
- "ResNet152_vd":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet152_vd_pretrained.pdparams",
- "ResNet200_vd":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet200_vd_pretrained.pdparams",
- }
- __all__ = MODEL_URLS.keys()
- '''
- ResNet config: dict.
- key: depth of ResNet.
- values: config's dict of specific model.
- keys:
- block_type: Two different blocks in ResNet, BasicBlock and BottleneckBlock are optional.
- block_depth: The number of blocks in different stages in ResNet.
- num_channels: The number of channels to enter the next stage.
- '''
- NET_CONFIG = {
- "18": {
- "block_type": "BasicBlock",
- "block_depth": [2, 2, 2, 2],
- "num_channels": [64, 64, 128, 256]
- },
- "34": {
- "block_type": "BasicBlock",
- "block_depth": [3, 4, 6, 3],
- "num_channels": [64, 64, 128, 256]
- },
- "50": {
- "block_type": "BottleneckBlock",
- "block_depth": [3, 4, 6, 3],
- "num_channels": [64, 256, 512, 1024]
- },
- "101": {
- "block_type": "BottleneckBlock",
- "block_depth": [3, 4, 23, 3],
- "num_channels": [64, 256, 512, 1024]
- },
- "152": {
- "block_type": "BottleneckBlock",
- "block_depth": [3, 8, 36, 3],
- "num_channels": [64, 256, 512, 1024]
- },
- "200": {
- "block_type": "BottleneckBlock",
- "block_depth": [3, 12, 48, 3],
- "num_channels": [64, 256, 512, 1024]
- },
- }
- class ConvBNLayer(TheseusLayer):
- def __init__(self,
- num_channels,
- num_filters,
- filter_size,
- stride=1,
- groups=1,
- is_vd_mode=False,
- act=None,
- lr_mult=1.0,
- data_format="NCHW"):
- super().__init__()
- self.is_vd_mode = is_vd_mode
- self.act = act
- self.avg_pool = AvgPool2D(
- kernel_size=2, stride=2, padding=0, ceil_mode=True)
- self.conv = Conv2D(
- in_channels=num_channels,
- out_channels=num_filters,
- kernel_size=filter_size,
- stride=stride,
- padding=(filter_size - 1) // 2,
- groups=groups,
- weight_attr=ParamAttr(learning_rate=lr_mult),
- bias_attr=False,
- data_format=data_format)
- self.bn = BatchNorm(
- num_filters,
- param_attr=ParamAttr(learning_rate=lr_mult),
- bias_attr=ParamAttr(learning_rate=lr_mult),
- data_layout=data_format)
- self.relu = nn.ReLU()
- def forward(self, x):
- if self.is_vd_mode:
- x = self.avg_pool(x)
- x = self.conv(x)
- x = self.bn(x)
- if self.act:
- x = self.relu(x)
- return x
- class BottleneckBlock(TheseusLayer):
- def __init__(self,
- num_channels,
- num_filters,
- stride,
- shortcut=True,
- if_first=False,
- lr_mult=1.0,
- data_format="NCHW"):
- super().__init__()
- self.conv0 = ConvBNLayer(
- num_channels=num_channels,
- num_filters=num_filters,
- filter_size=1,
- act="relu",
- lr_mult=lr_mult,
- data_format=data_format)
- self.conv1 = ConvBNLayer(
- num_channels=num_filters,
- num_filters=num_filters,
- filter_size=3,
- stride=stride,
- act="relu",
- lr_mult=lr_mult,
- data_format=data_format)
- self.conv2 = ConvBNLayer(
- num_channels=num_filters,
- num_filters=num_filters * 4,
- filter_size=1,
- act=None,
- lr_mult=lr_mult,
- data_format=data_format)
- if not shortcut:
- self.short = ConvBNLayer(
- num_channels=num_channels,
- num_filters=num_filters * 4,
- filter_size=1,
- stride=stride if if_first else 1,
- is_vd_mode=False if if_first else True,
- lr_mult=lr_mult,
- data_format=data_format)
- self.relu = nn.ReLU()
- self.shortcut = shortcut
- def forward(self, x):
- identity = x
- x = self.conv0(x)
- x = self.conv1(x)
- x = self.conv2(x)
- if self.shortcut:
- short = identity
- else:
- short = self.short(identity)
- x = paddle.add(x=x, y=short)
- x = self.relu(x)
- return x
- class BasicBlock(TheseusLayer):
- def __init__(self,
- num_channels,
- num_filters,
- stride,
- shortcut=True,
- if_first=False,
- lr_mult=1.0,
- data_format="NCHW"):
- super().__init__()
- self.stride = stride
- self.conv0 = ConvBNLayer(
- num_channels=num_channels,
- num_filters=num_filters,
- filter_size=3,
- stride=stride,
- act="relu",
- lr_mult=lr_mult,
- data_format=data_format)
- self.conv1 = ConvBNLayer(
- num_channels=num_filters,
- num_filters=num_filters,
- filter_size=3,
- act=None,
- lr_mult=lr_mult,
- data_format=data_format)
- if not shortcut:
- self.short = ConvBNLayer(
- num_channels=num_channels,
- num_filters=num_filters,
- filter_size=1,
- stride=stride if if_first else 1,
- is_vd_mode=False if if_first else True,
- lr_mult=lr_mult,
- data_format=data_format)
- self.shortcut = shortcut
- self.relu = nn.ReLU()
- def forward(self, x):
- identity = x
- x = self.conv0(x)
- x = self.conv1(x)
- if self.shortcut:
- short = identity
- else:
- short = self.short(identity)
- x = paddle.add(x=x, y=short)
- x = self.relu(x)
- return x
- class ResNet(TheseusLayer):
- """
- ResNet
- Args:
- config: dict. config of ResNet.
- version: str="vb". Different version of ResNet, version vd can perform better.
- class_num: int=1000. The number of classes.
- lr_mult_list: list. Control the learning rate of different stages.
- Returns:
- model: nn.Layer. Specific ResNet model depends on args.
- """
- def __init__(self,
- config,
- version="vb",
- class_num=1000,
- lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0],
- data_format="NCHW",
- input_image_channel=3,
- return_patterns=None):
- super().__init__()
- self.cfg = config
- self.lr_mult_list = lr_mult_list
- self.is_vd_mode = version == "vd"
- self.class_num = class_num
- self.num_filters = [64, 128, 256, 512]
- self.block_depth = self.cfg["block_depth"]
- self.block_type = self.cfg["block_type"]
- self.num_channels = self.cfg["num_channels"]
- self.channels_mult = 1 if self.num_channels[-1] == 256 else 4
- assert isinstance(self.lr_mult_list, (
- list, tuple
- )), "lr_mult_list should be in (list, tuple) but got {}".format(
- type(self.lr_mult_list))
- assert len(self.lr_mult_list
- ) == 5, "lr_mult_list length should be 5 but got {}".format(
- len(self.lr_mult_list))
- self.stem_cfg = {
- #num_channels, num_filters, filter_size, stride
- "vb": [[input_image_channel, 64, 7, 2]],
- "vd":
- [[input_image_channel, 32, 3, 2], [32, 32, 3, 1], [32, 64, 3, 1]]
- }
- self.stem = nn.Sequential(*[
- ConvBNLayer(
- num_channels=in_c,
- num_filters=out_c,
- filter_size=k,
- stride=s,
- act="relu",
- lr_mult=self.lr_mult_list[0],
- data_format=data_format)
- for in_c, out_c, k, s in self.stem_cfg[version]
- ])
- self.max_pool = MaxPool2D(
- kernel_size=3, stride=2, padding=1, data_format=data_format)
- block_list = []
- for block_idx in range(len(self.block_depth)):
- shortcut = False
- for i in range(self.block_depth[block_idx]):
- block_list.append(globals()[self.block_type](
- num_channels=self.num_channels[block_idx] if i == 0 else
- self.num_filters[block_idx] * self.channels_mult,
- num_filters=self.num_filters[block_idx],
- stride=2 if i == 0 and block_idx != 0 else 1,
- shortcut=shortcut,
- if_first=block_idx == i == 0 if version == "vd" else True,
- lr_mult=self.lr_mult_list[block_idx + 1],
- data_format=data_format))
- shortcut = True
- self.blocks = nn.Sequential(*block_list)
- self.avg_pool = AdaptiveAvgPool2D(1, data_format=data_format)
- self.flatten = nn.Flatten()
- self.avg_pool_channels = self.num_channels[-1] * 2
- stdv = 1.0 / math.sqrt(self.avg_pool_channels * 1.0)
- self.fc = Linear(
- self.avg_pool_channels,
- self.class_num,
- weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)))
- self.data_format = data_format
- if return_patterns is not None:
- self.update_res(return_patterns)
- self.register_forward_post_hook(self._return_dict_hook)
- def forward(self, x):
- with paddle.static.amp.fp16_guard():
- if self.data_format == "NHWC":
- x = paddle.transpose(x, [0, 2, 3, 1])
- x.stop_gradient = True
- x = self.stem(x)
- fm = x
- x = self.max_pool(x)
- x = self.blocks(x)
- x = self.avg_pool(x)
- x = self.flatten(x)
- x = self.fc(x)
- return x, fm
- def _load_pretrained(pretrained, model, model_url, use_ssld):
- if pretrained is False:
- pass
- elif pretrained is True:
- load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
- elif isinstance(pretrained, str):
- load_dygraph_pretrain(model, pretrained)
- else:
- raise RuntimeError(
- "pretrained type is not available. Please use `string` or `boolean` type."
- )
- def ResNet18(pretrained=False, use_ssld=False, **kwargs):
- """
- ResNet18
- 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 `ResNet18` model depends on args.
- """
- model = ResNet(config=NET_CONFIG["18"], version="vb", **kwargs)
- _load_pretrained(pretrained, model, MODEL_URLS["ResNet18"], use_ssld)
- return model
- def ResNet18_vd(pretrained=False, use_ssld=False, **kwargs):
- """
- ResNet18_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 `ResNet18_vd` model depends on args.
- """
- model = ResNet(config=NET_CONFIG["18"], version="vd", **kwargs)
- _load_pretrained(pretrained, model, MODEL_URLS["ResNet18_vd"], use_ssld)
- return model
- def ResNet34(pretrained=False, use_ssld=False, **kwargs):
- """
- ResNet34
- 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 `ResNet34` model depends on args.
- """
- model = ResNet(config=NET_CONFIG["34"], version="vb", **kwargs)
- _load_pretrained(pretrained, model, MODEL_URLS["ResNet34"], use_ssld)
- return model
- def ResNet34_vd(pretrained=False, use_ssld=False, **kwargs):
- """
- ResNet34_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 `ResNet34_vd` model depends on args.
- """
- model = ResNet(config=NET_CONFIG["34"], version="vd", **kwargs)
- _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"], 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"], 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"], 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"], 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"], 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"], 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"], version="vd", **kwargs)
- _load_pretrained(pretrained, model, MODEL_URLS["ResNet200_vd"], use_ssld)
- return model
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