# Copyright (c) 2020 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 paddle import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr from paddle.fluid.layer_helper import LayerHelper from paddle.fluid.dygraph.nn import Conv2D, Pool2D, Linear from paddle.fluid.dygraph import SyncBatchNorm as BatchNorm __all__ = [ "HRNet_W18_Small_V1", "HRNet_W18_Small_V2", "HRNet_W18", "HRNet_W30", "HRNet_W32", "HRNet_W40", "HRNet_W44", "HRNet_W48", "HRNet_W60", "HRNet_W64", "SE_HRNet_W18_Small_V1", "SE_HRNet_W18_Small_V2", "SE_HRNet_W18", "SE_HRNet_W30", "SE_HRNet_W32", "SE_HRNet_W40", "SE_HRNet_W44", "SE_HRNet_W48", "SE_HRNet_W60", "SE_HRNet_W64" ] class HRNet(fluid.dygraph.Layer): def __init__(self, num_classes, stage1_num_modules=1, stage1_num_blocks=[4], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[4, 4], stage2_num_channels=[18, 36], stage3_num_modules=4, stage3_num_blocks=[4, 4, 4], stage3_num_channels=[18, 36, 72], stage4_num_modules=3, stage4_num_blocks=[4, 4, 4, 4], stage4_num_channels=[18, 36, 72, 144], has_se=False, ignore_index=255): super(HRNet, self).__init__() self.num_classes = num_classes self.stage1_num_modules = stage1_num_modules self.stage1_num_blocks = stage1_num_blocks self.stage1_num_channels = stage1_num_channels self.stage2_num_modules = stage2_num_modules self.stage2_num_blocks = stage2_num_blocks self.stage2_num_channels = stage2_num_channels self.stage3_num_modules = stage3_num_modules self.stage3_num_blocks = stage3_num_blocks self.stage3_num_channels = stage3_num_channels self.stage4_num_modules = stage4_num_modules self.stage4_num_blocks = stage4_num_blocks self.stage4_num_channels = stage4_num_channels self.has_se = has_se self.ignore_index = ignore_index self.EPS = 1e-5 self.conv_layer1_1 = ConvBNLayer( num_channels=3, num_filters=64, filter_size=3, stride=2, act='relu', name="layer1_1") self.conv_layer1_2 = ConvBNLayer( num_channels=64, num_filters=64, filter_size=3, stride=2, act='relu', name="layer1_2") self.la1 = Layer1( num_channels=64, num_blocks=self.stage1_num_blocks[0], num_filters=self.stage1_num_channels[0], has_se=has_se, name="layer2") self.tr1 = TransitionLayer( in_channels=[self.stage1_num_channels[0] * 4], out_channels=self.stage2_num_channels, name="tr1") self.st2 = Stage( num_channels=self.stage2_num_channels, num_modules=self.stage2_num_modules, num_blocks=self.stage2_num_blocks, num_filters=self.stage2_num_channels, has_se=self.has_se, name="st2") self.tr2 = TransitionLayer( in_channels=self.stage2_num_channels, out_channels=self.stage3_num_channels, name="tr2") self.st3 = Stage( num_channels=self.stage3_num_channels, num_modules=self.stage3_num_modules, num_blocks=self.stage3_num_blocks, num_filters=self.stage3_num_channels, name="st3") self.tr3 = TransitionLayer( in_channels=self.stage3_num_channels, out_channels=self.stage4_num_channels, name="tr3") self.st4 = Stage( num_channels=self.stage4_num_channels, num_modules=self.stage4_num_modules, num_blocks=self.stage4_num_blocks, num_filters=self.stage4_num_channels, name="st4") last_inp_channels = sum(self.stage4_num_channels) self.conv_last_2 = ConvBNLayer( num_channels=last_inp_channels, num_filters=last_inp_channels, filter_size=1, stride=1, name='conv-2') self.conv_last_1 = Conv2D( num_channels=last_inp_channels, num_filters=self.num_classes, filter_size=1, stride=1, padding=0, param_attr=ParamAttr(name='conv-1_weights')) def forward(self, x, label=None, mode='train'): input_shape = x.shape[2:] conv1 = self.conv_layer1_1(x) conv2 = self.conv_layer1_2(conv1) la1 = self.la1(conv2) tr1 = self.tr1([la1]) st2 = self.st2(tr1) tr2 = self.tr2(st2) st3 = self.st3(tr2) tr3 = self.tr3(st3) st4 = self.st4(tr3) x0_h, x0_w = st4[0].shape[2:] x1 = fluid.layers.resize_bilinear(st4[1], out_shape=(x0_h, x0_w)) x2 = fluid.layers.resize_bilinear(st4[2], out_shape=(x0_h, x0_w)) x3 = fluid.layers.resize_bilinear(st4[3], out_shape=(x0_h, x0_w)) x = fluid.layers.concat([st4[0], x1, x2, x3], axis=1) x = self.conv_last_2(x) logit = self.conv_last_1(x) logit = fluid.layers.resize_bilinear(logit, input_shape) if mode == 'train': if label is None: raise Exception('Label is need during training') return self._get_loss(logit, label) else: score_map = fluid.layers.softmax(logit, axis=1) score_map = fluid.layers.transpose(score_map, [0, 2, 3, 1]) pred = fluid.layers.argmax(score_map, axis=3) pred = fluid.layers.unsqueeze(pred, axes=[3]) return pred, score_map def _get_loss(self, logit, label): mask = label != self.ignore_index mask = fluid.layers.cast(mask, 'float32') loss, probs = fluid.layers.softmax_with_cross_entropy( logit, label, ignore_index=self.ignore_index, return_softmax=True, axis=1) loss = loss * mask avg_loss = fluid.layers.mean(loss) / ( fluid.layers.mean(mask) + self.EPS) label.stop_gradient = True mask.stop_gradient = True return avg_loss class ConvBNLayer(fluid.dygraph.Layer): def __init__(self, num_channels, num_filters, filter_size, stride=1, groups=1, act="relu", name=None): super(ConvBNLayer, self).__init__() self._conv = Conv2D( num_channels=num_channels, num_filters=num_filters, filter_size=filter_size, stride=stride, padding=(filter_size - 1) // 2, groups=groups, act=None, param_attr=ParamAttr(name=name + "_weights"), bias_attr=False) bn_name = name + '_bn' 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, input): y = self._conv(input) y = self._batch_norm(y) return y class Layer1(fluid.dygraph.Layer): def __init__(self, num_channels, num_filters, num_blocks, has_se=False, name=None): super(Layer1, self).__init__() self.bottleneck_block_list = [] for i in range(num_blocks): bottleneck_block = self.add_sublayer( "bb_{}_{}".format(name, i + 1), BottleneckBlock( num_channels=num_channels if i == 0 else num_filters * 4, num_filters=num_filters, has_se=has_se, stride=1, downsample=True if i == 0 else False, name=name + '_' + str(i + 1))) self.bottleneck_block_list.append(bottleneck_block) def forward(self, input): conv = input for block_func in self.bottleneck_block_list: conv = block_func(conv) return conv class TransitionLayer(fluid.dygraph.Layer): def __init__(self, in_channels, out_channels, name=None): super(TransitionLayer, self).__init__() num_in = len(in_channels) num_out = len(out_channels) self.conv_bn_func_list = [] for i in range(num_out): residual = None if i < num_in: if in_channels[i] != out_channels[i]: residual = self.add_sublayer( "transition_{}_layer_{}".format(name, i + 1), ConvBNLayer( num_channels=in_channels[i], num_filters=out_channels[i], filter_size=3, name=name + '_layer_' + str(i + 1))) else: residual = self.add_sublayer( "transition_{}_layer_{}".format(name, i + 1), ConvBNLayer( num_channels=in_channels[-1], num_filters=out_channels[i], filter_size=3, stride=2, name=name + '_layer_' + str(i + 1))) self.conv_bn_func_list.append(residual) def forward(self, input): outs = [] for idx, conv_bn_func in enumerate(self.conv_bn_func_list): if conv_bn_func is None: outs.append(input[idx]) else: if idx < len(input): outs.append(conv_bn_func(input[idx])) else: outs.append(conv_bn_func(input[-1])) return outs class Branches(fluid.dygraph.Layer): def __init__(self, num_blocks, in_channels, out_channels, has_se=False, name=None): super(Branches, self).__init__() self.basic_block_list = [] for i in range(len(out_channels)): self.basic_block_list.append([]) for j in range(num_blocks[i]): in_ch = in_channels[i] if j == 0 else out_channels[i] basic_block_func = self.add_sublayer( "bb_{}_branch_layer_{}_{}".format(name, i + 1, j + 1), BasicBlock( num_channels=in_ch, num_filters=out_channels[i], has_se=has_se, name=name + '_branch_layer_' + str(i + 1) + '_' + str(j + 1))) self.basic_block_list[i].append(basic_block_func) def forward(self, inputs): outs = [] for idx, input in enumerate(inputs): conv = input for basic_block_func in self.basic_block_list[idx]: conv = basic_block_func(conv) outs.append(conv) return outs class BottleneckBlock(fluid.dygraph.Layer): def __init__(self, num_channels, num_filters, has_se, stride=1, downsample=False, name=None): super(BottleneckBlock, self).__init__() self.has_se = has_se self.downsample = downsample self.conv1 = ConvBNLayer( num_channels=num_channels, num_filters=num_filters, filter_size=1, act="relu", name=name + "_conv1", ) self.conv2 = ConvBNLayer( num_channels=num_filters, num_filters=num_filters, filter_size=3, stride=stride, act="relu", name=name + "_conv2") self.conv3 = ConvBNLayer( num_channels=num_filters, num_filters=num_filters * 4, filter_size=1, act=None, name=name + "_conv3") if self.downsample: self.conv_down = ConvBNLayer( num_channels=num_channels, num_filters=num_filters * 4, filter_size=1, act=None, name=name + "_downsample") if self.has_se: self.se = SELayer( num_channels=num_filters * 4, num_filters=num_filters * 4, reduction_ratio=16, name=name + '_fc') def forward(self, input): residual = input conv1 = self.conv1(input) conv2 = self.conv2(conv1) conv3 = self.conv3(conv2) if self.downsample: residual = self.conv_down(input) if self.has_se: conv3 = self.se(conv3) y = fluid.layers.elementwise_add(x=conv3, y=residual, act="relu") return y class BasicBlock(fluid.dygraph.Layer): def __init__(self, num_channels, num_filters, stride=1, has_se=False, downsample=False, name=None): super(BasicBlock, self).__init__() self.has_se = has_se self.downsample = downsample self.conv1 = ConvBNLayer( num_channels=num_channels, num_filters=num_filters, filter_size=3, stride=stride, act="relu", name=name + "_conv1") self.conv2 = ConvBNLayer( num_channels=num_filters, num_filters=num_filters, filter_size=3, stride=1, act=None, name=name + "_conv2") if self.downsample: self.conv_down = ConvBNLayer( num_channels=num_channels, num_filters=num_filters * 4, filter_size=1, act="relu", name=name + "_downsample") if self.has_se: self.se = SELayer( num_channels=num_filters, num_filters=num_filters, reduction_ratio=16, name=name + '_fc') def forward(self, input): residual = input conv1 = self.conv1(input) conv2 = self.conv2(conv1) if self.downsample: residual = self.conv_down(input) if self.has_se: conv2 = self.se(conv2) y = fluid.layers.elementwise_add(x=conv2, y=residual, act="relu") return y class SELayer(fluid.dygraph.Layer): def __init__(self, num_channels, num_filters, reduction_ratio, name=None): super(SELayer, self).__init__() self.pool2d_gap = Pool2D(pool_type='avg', global_pooling=True) self._num_channels = num_channels med_ch = int(num_channels / reduction_ratio) stdv = 1.0 / math.sqrt(num_channels * 1.0) self.squeeze = Linear( num_channels, med_ch, act="relu", param_attr=ParamAttr( initializer=fluid.initializer.Uniform(-stdv, stdv), name=name + "_sqz_weights"), bias_attr=ParamAttr(name=name + '_sqz_offset')) stdv = 1.0 / math.sqrt(med_ch * 1.0) self.excitation = Linear( med_ch, num_filters, act="sigmoid", param_attr=ParamAttr( initializer=fluid.initializer.Uniform(-stdv, stdv), name=name + "_exc_weights"), bias_attr=ParamAttr(name=name + '_exc_offset')) def forward(self, input): pool = self.pool2d_gap(input) pool = fluid.layers.reshape(pool, shape=[-1, self._num_channels]) squeeze = self.squeeze(pool) excitation = self.excitation(squeeze) excitation = fluid.layers.reshape( excitation, shape=[-1, self._num_channels, 1, 1]) out = input * excitation return out class Stage(fluid.dygraph.Layer): def __init__(self, num_channels, num_modules, num_blocks, num_filters, has_se=False, multi_scale_output=True, name=None): super(Stage, self).__init__() self._num_modules = num_modules self.stage_func_list = [] for i in range(num_modules): if i == num_modules - 1 and not multi_scale_output: stage_func = self.add_sublayer( "stage_{}_{}".format(name, i + 1), HighResolutionModule( num_channels=num_channels, num_blocks=num_blocks, num_filters=num_filters, has_se=has_se, multi_scale_output=False, name=name + '_' + str(i + 1))) else: stage_func = self.add_sublayer( "stage_{}_{}".format(name, i + 1), HighResolutionModule( num_channels=num_channels, num_blocks=num_blocks, num_filters=num_filters, has_se=has_se, name=name + '_' + str(i + 1))) self.stage_func_list.append(stage_func) def forward(self, input): out = input for idx in range(self._num_modules): out = self.stage_func_list[idx](out) return out class HighResolutionModule(fluid.dygraph.Layer): def __init__(self, num_channels, num_blocks, num_filters, has_se=False, multi_scale_output=True, name=None): super(HighResolutionModule, self).__init__() self.branches_func = Branches( num_blocks=num_blocks, in_channels=num_channels, out_channels=num_filters, has_se=has_se, name=name) self.fuse_func = FuseLayers( in_channels=num_filters, out_channels=num_filters, multi_scale_output=multi_scale_output, name=name) def forward(self, input): out = self.branches_func(input) out = self.fuse_func(out) return out class FuseLayers(fluid.dygraph.Layer): def __init__(self, in_channels, out_channels, multi_scale_output=True, name=None): super(FuseLayers, self).__init__() self._actual_ch = len(in_channels) if multi_scale_output else 1 self._in_channels = in_channels self.residual_func_list = [] for i in range(self._actual_ch): for j in range(len(in_channels)): residual_func = None if j > i: residual_func = self.add_sublayer( "residual_{}_layer_{}_{}".format(name, i + 1, j + 1), ConvBNLayer( num_channels=in_channels[j], num_filters=out_channels[i], filter_size=1, stride=1, act=None, name=name + '_layer_' + str(i + 1) + '_' + str(j + 1))) self.residual_func_list.append(residual_func) elif j < i: pre_num_filters = in_channels[j] for k in range(i - j): if k == i - j - 1: residual_func = self.add_sublayer( "residual_{}_layer_{}_{}_{}".format( name, i + 1, j + 1, k + 1), ConvBNLayer( num_channels=pre_num_filters, num_filters=out_channels[i], filter_size=3, stride=2, act=None, name=name + '_layer_' + str(i + 1) + '_' + str(j + 1) + '_' + str(k + 1))) pre_num_filters = out_channels[i] else: residual_func = self.add_sublayer( "residual_{}_layer_{}_{}_{}".format( name, i + 1, j + 1, k + 1), ConvBNLayer( num_channels=pre_num_filters, num_filters=out_channels[j], filter_size=3, stride=2, act="relu", name=name + '_layer_' + str(i + 1) + '_' + str(j + 1) + '_' + str(k + 1))) pre_num_filters = out_channels[j] self.residual_func_list.append(residual_func) def forward(self, input): outs = [] residual_func_idx = 0 for i in range(self._actual_ch): residual = input[i] for j in range(len(self._in_channels)): if j > i: y = self.residual_func_list[residual_func_idx](input[j]) residual_func_idx += 1 y = fluid.layers.resize_nearest(input=y, scale=2**(j - i)) residual = fluid.layers.elementwise_add( x=residual, y=y, act=None) elif j < i: y = input[j] for k in range(i - j): y = self.residual_func_list[residual_func_idx](y) residual_func_idx += 1 residual = fluid.layers.elementwise_add( x=residual, y=y, act=None) layer_helper = LayerHelper(self.full_name(), act='relu') residual = layer_helper.append_activation(residual) outs.append(residual) return outs class LastClsOut(fluid.dygraph.Layer): def __init__(self, num_channel_list, has_se, num_filters_list=[32, 64, 128, 256], name=None): super(LastClsOut, self).__init__() self.func_list = [] for idx in range(len(num_channel_list)): func = self.add_sublayer( "conv_{}_conv_{}".format(name, idx + 1), BottleneckBlock( num_channels=num_channel_list[idx], num_filters=num_filters_list[idx], has_se=has_se, downsample=True, name=name + 'conv_' + str(idx + 1))) self.func_list.append(func) def forward(self, inputs): outs = [] for idx, input in enumerate(inputs): out = self.func_list[idx](input) outs.append(out) return outs def HRNet_W18_Small_V1(num_classes): model = HRNet( num_classes=num_classes, stage1_num_modules=1, stage1_num_blocks=[1], stage1_num_channels=[32], stage2_num_modules=1, stage2_num_blocks=[2, 2], stage2_num_channels=[16, 32], stage3_num_modules=1, stage3_num_blocks=[2, 2, 2], stage3_num_channels=[16, 32, 64], stage4_num_modules=1, stage4_num_blocks=[2, 2, 2, 2], stage4_num_channels=[16, 32, 64, 128]) return model def HRNet_W18_Small_V2(num_classes): model = HRNet( num_classes=num_classes, stage1_num_modules=1, stage1_num_blocks=[2], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[2, 2], stage2_num_channels=[18, 36], stage3_num_modules=1, stage3_num_blocks=[2, 2, 2], stage3_num_channels=[18, 36, 72], stage4_num_modules=1, stage4_num_blocks=[2, 2, 2, 2], stage4_num_channels=[18, 36, 72, 144]) return model def HRNet_W18(num_classes): model = HRNet( num_classes=num_classes, stage1_num_modules=1, stage1_num_blocks=[4], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[4, 4], stage2_num_channels=[18, 36], stage3_num_modules=4, stage3_num_blocks=[4, 4, 4], stage3_num_channels=[18, 36, 72], stage4_num_modules=3, stage4_num_blocks=[4, 4, 4, 4], stage4_num_channels=[18, 36, 72, 144]) return model def HRNet_W30(num_classes): model = HRNet( num_classes=num_classes, stage1_num_modules=1, stage1_num_blocks=[4], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[4, 4], stage2_num_channels=[30, 60], stage3_num_modules=4, stage3_num_blocks=[4, 4, 4], stage3_num_channels=[30, 60, 120], stage4_num_modules=3, stage4_num_blocks=[4, 4, 4, 4], stage4_num_channels=[30, 60, 120, 240]) return model def HRNet_W32(num_classes): model = HRNet( num_classes=num_classes, stage1_num_modules=1, stage1_num_blocks=[4], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[4, 4], stage2_num_channels=[32, 64], stage3_num_modules=4, stage3_num_blocks=[4, 4, 4], stage3_num_channels=[32, 64, 128], stage4_num_modules=3, stage4_num_blocks=[4, 4, 4, 4], stage4_num_channels=[32, 64, 128, 256]) return model def HRNet_W40(num_classes): model = HRNet( num_classes=num_classes, stage1_num_modules=1, stage1_num_blocks=[4], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[4, 4], stage2_num_channels=[40, 80], stage3_num_modules=4, stage3_num_blocks=[4, 4, 4], stage3_num_channels=[40, 80, 160], stage4_num_modules=3, stage4_num_blocks=[4, 4, 4, 4], stage4_num_channels=[40, 80, 160, 320]) return model def HRNet_W44(num_classes): model = HRNet( num_classes=num_classes, stage1_num_modules=1, stage1_num_blocks=[4], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[4, 4], stage2_num_channels=[44, 88], stage3_num_modules=4, stage3_num_blocks=[4, 4, 4], stage3_num_channels=[44, 88, 176], stage4_num_modules=3, stage4_num_blocks=[4, 4, 4, 4], stage4_num_channels=[44, 88, 176, 352]) return model def HRNet_W48(num_classes): model = HRNet( num_classes=num_classes, stage1_num_modules=1, stage1_num_blocks=[4], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[4, 4], stage2_num_channels=[48, 96], stage3_num_modules=4, stage3_num_blocks=[4, 4, 4], stage3_num_channels=[48, 96, 192], stage4_num_modules=3, stage4_num_blocks=[4, 4, 4, 4], stage4_num_channels=[48, 96, 192, 384]) return model def HRNet_W60(num_classes): model = HRNet( num_classes=num_classes, stage1_num_modules=1, stage1_num_blocks=[4], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[4, 4], stage2_num_channels=[60, 120], stage3_num_modules=4, stage3_num_blocks=[4, 4, 4], stage3_num_channels=[60, 120, 240], stage4_num_modules=3, stage4_num_blocks=[4, 4, 4, 4], stage4_num_channels=[60, 120, 240, 480]) return model def HRNet_W64(num_classes): model = HRNet( num_classes=num_classes, stage1_num_modules=1, stage1_num_blocks=[4], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[4, 4], stage2_num_channels=[64, 128], stage3_num_modules=4, stage3_num_blocks=[4, 4, 4], stage3_num_channels=[64, 128, 256], stage4_num_modules=3, stage4_num_blocks=[4, 4, 4, 4], stage4_num_channels=[64, 128, 256, 512]) return model def SE_HRNet_W18_Small_V1(num_classes): model = HRNet( num_classes=num_classes, stage1_num_modules=1, stage1_num_blocks=[1], stage1_num_channels=[32], stage2_num_modules=1, stage2_num_blocks=[2, 2], stage2_num_channels=[16, 32], stage3_num_modules=1, stage3_num_blocks=[2, 2, 2], stage3_num_channels=[16, 32, 64], stage4_num_modules=1, stage4_num_blocks=[2, 2, 2, 2], stage4_num_channels=[16, 32, 64, 128], has_se=True) return model def SE_HRNet_W18_Small_V2(num_classes): model = HRNet( num_classes=num_classes, stage1_num_modules=1, stage1_num_blocks=[2], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[2, 2], stage2_num_channels=[18, 36], stage3_num_modules=1, stage3_num_blocks=[2, 2, 2], stage3_num_channels=[18, 36, 72], stage4_num_modules=1, stage4_num_blocks=[2, 2, 2, 2], stage4_num_channels=[18, 36, 72, 144], has_se=True) return model def SE_HRNet_W18(num_classes): model = HRNet( num_classes=num_classes, stage1_num_modules=1, stage1_num_blocks=[4], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[4, 4], stage2_num_channels=[18, 36], stage3_num_modules=4, stage3_num_blocks=[4, 4, 4], stage3_num_channels=[18, 36, 72], stage4_num_modules=3, stage4_num_blocks=[4, 4, 4, 4], stage4_num_channels=[18, 36, 72, 144], has_se=True) return model def SE_HRNet_W30(num_classes): model = HRNet( num_classes=num_classes, stage1_num_modules=1, stage1_num_blocks=[4], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[4, 4], stage2_num_channels=[30, 60], stage3_num_modules=4, stage3_num_blocks=[4, 4, 4], stage3_num_channels=[30, 60, 120], stage4_num_modules=3, stage4_num_blocks=[4, 4, 4, 4], stage4_num_channels=[30, 60, 120, 240], has_se=True) return model def SE_HRNet_W32(num_classes): model = HRNet( num_classes=num_classes, stage1_num_modules=1, stage1_num_blocks=[4], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[4, 4], stage2_num_channels=[32, 64], stage3_num_modules=4, stage3_num_blocks=[4, 4, 4], stage3_num_channels=[32, 64, 128], stage4_num_modules=3, stage4_num_blocks=[4, 4, 4, 4], stage4_num_channels=[32, 64, 128, 256], has_se=True) return model def SE_HRNet_W40(num_classes): model = HRNet( num_classes=num_classes, stage1_num_modules=1, stage1_num_blocks=[4], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[4, 4], stage2_num_channels=[40, 80], stage3_num_modules=4, stage3_num_blocks=[4, 4, 4], stage3_num_channels=[40, 80, 160], stage4_num_modules=3, stage4_num_blocks=[4, 4, 4, 4], stage4_num_channels=[40, 80, 160, 320], has_se=True) return model def SE_HRNet_W44(num_classes): model = HRNet( num_classes=num_classes, stage1_num_modules=1, stage1_num_blocks=[4], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[4, 4], stage2_num_channels=[44, 88], stage3_num_modules=4, stage3_num_blocks=[4, 4, 4], stage3_num_channels=[44, 88, 176], stage4_num_modules=3, stage4_num_blocks=[4, 4, 4, 4], stage4_num_channels=[44, 88, 176, 352], has_se=True) return model def SE_HRNet_W48(num_classes): model = HRNet( num_classes=num_classes, stage1_num_modules=1, stage1_num_blocks=[4], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[4, 4], stage2_num_channels=[48, 96], stage3_num_modules=4, stage3_num_blocks=[4, 4, 4], stage3_num_channels=[48, 96, 192], stage4_num_modules=3, stage4_num_blocks=[4, 4, 4, 4], stage4_num_channels=[48, 96, 192, 384], has_se=True) return model def SE_HRNet_W60(num_classes): model = HRNet( num_classes=num_classes, stage1_num_modules=1, stage1_num_blocks=[4], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[4, 4], stage2_num_channels=[60, 120], stage3_num_modules=4, stage3_num_blocks=[4, 4, 4], stage3_num_channels=[60, 120, 240], stage4_num_modules=3, stage4_num_blocks=[4, 4, 4, 4], stage4_num_channels=[60, 120, 240, 480], has_se=True) return model def SE_HRNet_W64(num_classes): model = HRNet( num_classes=num_classes, stage1_num_modules=1, stage1_num_blocks=[4], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[4, 4], stage2_num_channels=[64, 128], stage3_num_modules=4, stage3_num_blocks=[4, 4, 4], stage3_num_channels=[64, 128, 256], stage4_num_modules=3, stage4_num_blocks=[4, 4, 4, 4], stage4_num_channels=[64, 128, 256, 512], has_se=True) return model