# copyright (c) 2020 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. import os import math import numpy as np import paddle from paddle import ParamAttr import paddle.nn as nn from paddle.nn import Conv2d, BatchNorm, Linear, Dropout from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d from paddle.nn.initializer import Uniform from paddlehub.module.module import moduleinfo from paddlehub.module.cv_module import ImageClassifierModule class ConvBNLayer(nn.Layer): """Basic conv bn layer.""" def __init__(self, num_channels: int, num_filters: int, filter_size: int, stride: int = 1, groups: int = 1, act: str = None, name: str = None): super(ConvBNLayer, self).__init__() 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(name=name + "_weights"), bias_attr=False) if name == "conv1": bn_name = "bn_" + name else: bn_name = "bn" + name[3:] self._batch_norm = BatchNorm(num_filters, act=act, param_attr=ParamAttr(name=bn_name + "_scale"), bias_attr=ParamAttr(bn_name + "_offset"), moving_mean_name=bn_name + "_mean", moving_variance_name=bn_name + "_variance") def forward(self, inputs: paddle.Tensor): y = self._conv(inputs) y = self._batch_norm(y) return y class BottleneckBlock(nn.Layer): """Bottleneck Block for ResNet18.""" def __init__(self, num_channels: int, num_filters: int, stride: int, shortcut: bool = True, name: str = None): super(BottleneckBlock, self).__init__() self.conv0 = ConvBNLayer(num_channels=num_channels, num_filters=num_filters, filter_size=1, act="relu", name=name + "_branch2a") self.conv1 = ConvBNLayer(num_channels=num_filters, num_filters=num_filters, filter_size=3, stride=stride, act="relu", name=name + "_branch2b") self.conv2 = ConvBNLayer(num_channels=num_filters, num_filters=num_filters * 4, filter_size=1, act=None, name=name + "_branch2c") if not shortcut: self.short = ConvBNLayer(num_channels=num_channels, num_filters=num_filters * 4, filter_size=1, stride=stride, name=name + "_branch1") self.shortcut = shortcut self._num_channels_out = num_filters * 4 def forward(self, inputs: paddle.Tensor): y = self.conv0(inputs) conv1 = self.conv1(y) conv2 = self.conv2(conv1) if self.shortcut: short = inputs else: short = self.short(inputs) y = paddle.elementwise_add(x=short, y=conv2, act="relu") return y class BasicBlock(nn.Layer): """Basic block for ResNet18.""" def __init__(self, num_channels: int, num_filters: int, stride: int, shortcut: bool = True, name: str = None): super(BasicBlock, self).__init__() self.stride = stride self.conv0 = ConvBNLayer(num_channels=num_channels, num_filters=num_filters, filter_size=3, stride=stride, act="relu", name=name + "_branch2a") self.conv1 = ConvBNLayer(num_channels=num_filters, num_filters=num_filters, filter_size=3, act=None, name=name + "_branch2b") if not shortcut: self.short = ConvBNLayer(num_channels=num_channels, num_filters=num_filters, filter_size=1, stride=stride, name=name + "_branch1") self.shortcut = shortcut def forward(self, inputs: paddle.Tensor): y = self.conv0(inputs) conv1 = self.conv1(y) if self.shortcut: short = inputs else: short = self.short(inputs) y = paddle.elementwise_add(x=short, y=conv1, act="relu") return y @moduleinfo(name="resnet18_imagenet", type="CV/classification", author="paddlepaddle", author_email="", summary="resnet18_imagenet is a classification model, " "this module is trained with Baidu open sourced dataset.", version="1.1.0", meta=ImageClassifierModule) class ResNet18(nn.Layer): """ResNet18 model.""" def __init__(self, class_dim: int = 1000, load_checkpoint: str = None): super(ResNet18, self).__init__() self.layers = 18 depth = [2, 2, 2, 2] num_channels = [64, 64, 128, 256] num_filters = [64, 128, 256, 512] self.conv = ConvBNLayer(num_channels=3, num_filters=64, filter_size=7, stride=2, act="relu", name="conv1") self.pool2d_max = MaxPool2d(kernel_size=3, stride=2, padding=1) self.block_list = [] for block in range(len(depth)): shortcut = False for i in range(depth[block]): conv_name = "res" + str(block + 2) + chr(97 + i) basic_block = self.add_sublayer( conv_name, BasicBlock(num_channels=num_channels[block] if i == 0 else num_filters[block], num_filters=num_filters[block], stride=2 if i == 0 and block != 0 else 1, shortcut=shortcut, name=conv_name)) self.block_list.append(basic_block) shortcut = True self.pool2d_avg = AdaptiveAvgPool2d(1) self.pool2d_avg_channels = num_channels[-1] * 2 stdv = 1.0 / math.sqrt(self.pool2d_avg_channels * 1.0) self.out = Linear(self.pool2d_avg_channels, class_dim, weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv), name="fc_0.w_0"), bias_attr=ParamAttr(name="fc_0.b_0")) if load_checkpoint is not None: model_dict = paddle.load(load_checkpoint)[0] self.set_dict(model_dict) print("load custom checkpoint success") else: checkpoint = os.path.join(self.directory, 'resnet18_imagenet.pdparams') if not os.path.exists(checkpoint): os.system( 'wget https://paddlehub.bj.bcebos.com/dygraph/image_classification/resnet18_imagenet.pdparams -O ' + checkpoint) model_dict = paddle.load(checkpoint)[0] self.set_dict(model_dict) print("load pretrained checkpoint success") def forward(self, inputs: paddle.Tensor): y = self.conv(inputs) y = self.pool2d_max(y) for block in self.block_list: y = block(y) y = self.pool2d_avg(y) y = paddle.reshape(y, shape=[-1, self.pool2d_avg_channels]) y = self.out(y) return y