diff --git a/ppcls/modeling/architectures/densenet.py b/ppcls/modeling/architectures/densenet.py index e8ba3818f8a09eff1659709395b77dfc0a553347..70fd058f024f0517560bb6ccb1a234a40da3d52c 100644 --- a/ppcls/modeling/architectures/densenet.py +++ b/ppcls/modeling/architectures/densenet.py @@ -1,42 +1,184 @@ -#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. - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import math - +import numpy as np 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, BatchNorm, Linear, Dropout + +import math __all__ = [ - "DenseNet", "DenseNet121", "DenseNet161", "DenseNet169", "DenseNet201", - "DenseNet264" + "DenseNet121", "DenseNet161", "DenseNet169", "DenseNet201", "DenseNet264" ] -class DenseNet(): - def __init__(self, layers=121): - self.layers = layers +class BNACConvLayer(fluid.dygraph.Layer): + def __init__(self, + num_channels, + num_filters, + filter_size, + stride=1, + pad=0, + groups=1, + act="relu", + name=None): + super(BNACConvLayer, self).__init__() + + self._batch_norm = BatchNorm( + num_channels, + act=act, + param_attr=ParamAttr(name=name + '_bn_scale'), + bias_attr=ParamAttr(name + '_bn_offset'), + moving_mean_name=name + '_bn_mean', + moving_variance_name=name + '_bn_variance') + + self._conv = Conv2D( + num_channels=num_channels, + num_filters=num_filters, + filter_size=filter_size, + stride=stride, + padding=pad, + groups=groups, + act=None, + param_attr=ParamAttr(name=name + "_weights"), + bias_attr=False) + + def forward(self, input): + y = self._batch_norm(input) + y = self._conv(y) + return y + + +class DenseLayer(fluid.dygraph.Layer): + def __init__(self, num_channels, growth_rate, bn_size, dropout, name=None): + super(DenseLayer, self).__init__() + self.dropout = dropout + + self.bn_ac_func1 = BNACConvLayer( + num_channels=num_channels, + num_filters=bn_size * growth_rate, + filter_size=1, + pad=0, + stride=1, + name=name + "_x1") + + self.bn_ac_func2 = BNACConvLayer( + num_channels=bn_size * growth_rate, + num_filters=growth_rate, + filter_size=3, + pad=1, + stride=1, + name=name + "_x2") + + if dropout: + self.dropout_func = Dropout(p=dropout) + + def forward(self, input): + conv = self.bn_ac_func1(input) + conv = self.bn_ac_func2(conv) + if self.dropout: + conv = self.dropout_func(conv) + conv = fluid.layers.concat([input, conv], axis=1) + return conv + + +class DenseBlock(fluid.dygraph.Layer): + def __init__(self, + num_channels, + num_layers, + bn_size, + growth_rate, + dropout, + name=None): + super(DenseBlock, self).__init__() + self.dropout = dropout + + self.dense_layer_func = [] + + pre_channel = num_channels + for layer in range(num_layers): + self.dense_layer_func.append( + self.add_sublayer( + "{}_{}".format(name, layer + 1), + DenseLayer( + num_channels=pre_channel, + growth_rate=growth_rate, + bn_size=bn_size, + dropout=dropout, + name=name + '_' + str(layer + 1)))) + pre_channel = pre_channel + growth_rate + + def forward(self, input): + conv = input + for func in self.dense_layer_func: + conv = func(conv) + return conv + + +class TransitionLayer(fluid.dygraph.Layer): + def __init__(self, num_channels, num_output_features, name=None): + super(TransitionLayer, self).__init__() + + self.conv_ac_func = BNACConvLayer( + num_channels=num_channels, + num_filters=num_output_features, + filter_size=1, + pad=0, + stride=1, + name=name) + + self.pool2d_avg = Pool2D(pool_size=2, pool_stride=2, pool_type='avg') + + def forward(self, input): + y = self.conv_ac_func(input) + y = self.pool2d_avg(y) + return y + + +class ConvBNLayer(fluid.dygraph.Layer): + def __init__(self, + num_channels, + num_filters, + filter_size, + stride=1, + pad=0, + 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=pad, + groups=groups, + act=None, + param_attr=ParamAttr(name=name + "_weights"), + bias_attr=False) + self._batch_norm = BatchNorm( + num_filters, + act=act, + param_attr=ParamAttr(name=name + '_bn_scale'), + bias_attr=ParamAttr(name + '_bn_offset'), + moving_mean_name=name + '_bn_mean', + moving_variance_name=name + '_bn_variance') + + def forward(self, input): + y = self._conv(input) + y = self._batch_norm(y) + return y + + +class DenseNet(fluid.dygraph.Layer): + def __init__(self, layers=60, bn_size=4, dropout=0, class_dim=1000): + super(DenseNet, self).__init__() - def net(self, input, bn_size=4, dropout=0, class_dim=1000): - layers = self.layers supported_layers = [121, 161, 169, 201, 264] assert layers in supported_layers, \ - "supported layers are {} but input layer is {}".format(supported_layers, layers) + "supported layers are {} but input layer is {}".format( + supported_layers, layers) densenet_spec = { 121: (64, 32, [6, 12, 24, 16]), 161: (96, 48, [6, 12, 36, 24]), @@ -44,139 +186,86 @@ class DenseNet(): 201: (64, 32, [6, 12, 48, 32]), 264: (64, 32, [6, 12, 64, 48]) } - num_init_features, growth_rate, block_config = densenet_spec[layers] - conv = fluid.layers.conv2d( - input=input, + + self.conv1_func = ConvBNLayer( + num_channels=3, num_filters=num_init_features, filter_size=7, stride=2, - padding=3, - act=None, - param_attr=ParamAttr(name="conv1_weights"), - bias_attr=False) - conv = fluid.layers.batch_norm( - input=conv, + pad=3, act='relu', - param_attr=ParamAttr(name='conv1_bn_scale'), - bias_attr=ParamAttr(name='conv1_bn_offset'), - moving_mean_name='conv1_bn_mean', - moving_variance_name='conv1_bn_variance') - conv = fluid.layers.pool2d( - input=conv, - pool_size=3, - pool_stride=2, - pool_padding=1, - pool_type='max') + name="conv1") + + self.pool2d_max = Pool2D( + pool_size=3, pool_stride=2, pool_padding=1, pool_type='max') + + self.block_config = block_config + + self.dense_block_func_list = [] + self.transition_func_list = [] + pre_num_channels = num_init_features num_features = num_init_features for i, num_layers in enumerate(block_config): - conv = self.make_dense_block( - conv, - num_layers, - bn_size, - growth_rate, - dropout, - name='conv' + str(i + 2)) + self.dense_block_func_list.append( + self.add_sublayer( + "db_conv_{}".format(i + 2), + DenseBlock( + num_channels=pre_num_channels, + num_layers=num_layers, + bn_size=bn_size, + growth_rate=growth_rate, + dropout=dropout, + name='conv' + str(i + 2)))) + num_features = num_features + num_layers * growth_rate + pre_num_channels = num_features + if i != len(block_config) - 1: - conv = self.make_transition( - conv, num_features // 2, name='conv' + str(i + 2) + '_blk') + self.transition_func_list.append( + self.add_sublayer( + "tr_conv{}_blk".format(i + 2), + TransitionLayer( + num_channels=pre_num_channels, + num_output_features=num_features // 2, + name='conv' + str(i + 2) + "_blk"))) + pre_num_channels = num_features // 2 num_features = num_features // 2 - conv = fluid.layers.batch_norm( - input=conv, - act='relu', + + self.batch_norm = BatchNorm( + num_features, + act="relu", param_attr=ParamAttr(name='conv5_blk_bn_scale'), bias_attr=ParamAttr(name='conv5_blk_bn_offset'), moving_mean_name='conv5_blk_bn_mean', moving_variance_name='conv5_blk_bn_variance') - conv = fluid.layers.pool2d( - input=conv, pool_type='avg', global_pooling=True) - stdv = 1.0 / math.sqrt(conv.shape[1] * 1.0) - out = fluid.layers.fc( - input=conv, - size=class_dim, - param_attr=fluid.param_attr.ParamAttr( + + self.pool2d_avg = Pool2D(pool_type='avg', global_pooling=True) + + stdv = 1.0 / math.sqrt(num_features * 1.0) + + self.out = Linear( + num_features, + class_dim, + param_attr=ParamAttr( initializer=fluid.initializer.Uniform(-stdv, stdv), name="fc_weights"), - bias_attr=ParamAttr(name='fc_offset')) - return out + bias_attr=ParamAttr(name="fc_offset")) - def make_transition(self, input, num_output_features, name=None): - bn_ac = fluid.layers.batch_norm( - input, - act='relu', - param_attr=ParamAttr(name=name + '_bn_scale'), - bias_attr=ParamAttr(name + '_bn_offset'), - moving_mean_name=name + '_bn_mean', - moving_variance_name=name + '_bn_variance') + def forward(self, input): + conv = self.conv1_func(input) + conv = self.pool2d_max(conv) - bn_ac_conv = fluid.layers.conv2d( - input=bn_ac, - num_filters=num_output_features, - filter_size=1, - stride=1, - act=None, - bias_attr=False, - param_attr=ParamAttr(name=name + "_weights")) - pool = fluid.layers.pool2d( - input=bn_ac_conv, pool_size=2, pool_stride=2, pool_type='avg') - return pool - - def make_dense_block(self, - input, - num_layers, - bn_size, - growth_rate, - dropout, - name=None): - conv = input - for layer in range(num_layers): - conv = self.make_dense_layer( - conv, - growth_rate, - bn_size, - dropout, - name=name + '_' + str(layer + 1)) - return conv + for i, num_layers in enumerate(self.block_config): + conv = self.dense_block_func_list[i](conv) + if i != len(self.block_config) - 1: + conv = self.transition_func_list[i](conv) - def make_dense_layer(self, input, growth_rate, bn_size, dropout, - name=None): - bn_ac = fluid.layers.batch_norm( - input, - act='relu', - param_attr=ParamAttr(name=name + '_x1_bn_scale'), - bias_attr=ParamAttr(name + '_x1_bn_offset'), - moving_mean_name=name + '_x1_bn_mean', - moving_variance_name=name + '_x1_bn_variance') - bn_ac_conv = fluid.layers.conv2d( - input=bn_ac, - num_filters=bn_size * growth_rate, - filter_size=1, - stride=1, - act=None, - bias_attr=False, - param_attr=ParamAttr(name=name + "_x1_weights")) - bn_ac = fluid.layers.batch_norm( - bn_ac_conv, - act='relu', - param_attr=ParamAttr(name=name + '_x2_bn_scale'), - bias_attr=ParamAttr(name + '_x2_bn_offset'), - moving_mean_name=name + '_x2_bn_mean', - moving_variance_name=name + '_x2_bn_variance') - bn_ac_conv = fluid.layers.conv2d( - input=bn_ac, - num_filters=growth_rate, - filter_size=3, - stride=1, - padding=1, - act=None, - bias_attr=False, - param_attr=ParamAttr(name=name + "_x2_weights")) - if dropout: - bn_ac_conv = fluid.layers.dropout( - x=bn_ac_conv, dropout_prob=dropout) - bn_ac_conv = fluid.layers.concat([input, bn_ac_conv], axis=1) - return bn_ac_conv + conv = self.batch_norm(conv) + y = self.pool2d_avg(conv) + y = fluid.layers.reshape(y, shape=[0, -1]) + y = self.out(y) + return y def DenseNet121(): diff --git a/ppcls/modeling/architectures/dpn.py b/ppcls/modeling/architectures/dpn.py index 61f8f596a4e878f0bd4ebb9124e1324f0577d10a..d4271061de32db04410cddac8e83b738cc8dbd10 100644 --- a/ppcls/modeling/architectures/dpn.py +++ b/ppcls/modeling/architectures/dpn.py @@ -1,40 +1,204 @@ -#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. - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os import numpy as np -import time import sys -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, BatchNorm, Linear + +import math + +__all__ = [ + "DPN", + "DPN68", + "DPN92", + "DPN98", + "DPN107", + "DPN131", +] + + +class ConvBNLayer(fluid.dygraph.Layer): + def __init__(self, + num_channels, + num_filters, + filter_size, + stride=1, + pad=0, + 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=pad, + groups=groups, + act=None, + param_attr=ParamAttr(name=name + "_weights"), + bias_attr=False) + self._batch_norm = BatchNorm( + num_filters, + act=act, + param_attr=ParamAttr(name=name + '_bn_scale'), + bias_attr=ParamAttr(name + '_bn_offset'), + moving_mean_name=name + '_bn_mean', + moving_variance_name=name + '_bn_variance') + + def forward(self, input): + y = self._conv(input) + y = self._batch_norm(y) + return y + + +class BNACConvLayer(fluid.dygraph.Layer): + def __init__(self, + num_channels, + num_filters, + filter_size, + stride=1, + pad=0, + groups=1, + act="relu", + name=None): + super(BNACConvLayer, self).__init__() + self.num_channels = num_channels + self.name = name + + self._batch_norm = BatchNorm( + num_channels, + act=act, + param_attr=ParamAttr(name=name + '_bn_scale'), + bias_attr=ParamAttr(name + '_bn_offset'), + moving_mean_name=name + '_bn_mean', + moving_variance_name=name + '_bn_variance') + + self._conv = Conv2D( + num_channels=num_channels, + num_filters=num_filters, + filter_size=filter_size, + stride=stride, + padding=pad, + groups=groups, + act=None, + param_attr=ParamAttr(name=name + "_weights"), + bias_attr=False) + + def forward(self, input): + y = self._batch_norm(input) + y = self._conv(y) + return y + + +class DualPathFactory(fluid.dygraph.Layer): + def __init__(self, + num_channels, + num_1x1_a, + num_3x3_b, + num_1x1_c, + inc, + G, + _type='normal', + name=None): + super(DualPathFactory, self).__init__() + + self.num_1x1_c = num_1x1_c + self.inc = inc + self.name = name + + kw = 3 + kh = 3 + pw = (kw - 1) // 2 + ph = (kh - 1) // 2 + + # type + if _type == 'proj': + key_stride = 1 + self.has_proj = True + elif _type == 'down': + key_stride = 2 + self.has_proj = True + elif _type == 'normal': + key_stride = 1 + self.has_proj = False + else: + print("not implemented now!!!") + sys.exit(1) + + data_in_ch = sum(num_channels) if isinstance(num_channels, + list) else num_channels + + if self.has_proj: + self.c1x1_w_func = BNACConvLayer( + num_channels=data_in_ch, + num_filters=num_1x1_c + 2 * inc, + filter_size=(1, 1), + pad=(0, 0), + stride=(key_stride, key_stride), + name=name + "_match") + + self.c1x1_a_func = BNACConvLayer( + num_channels=data_in_ch, + num_filters=num_1x1_a, + filter_size=(1, 1), + pad=(0, 0), + name=name + "_conv1") + + self.c3x3_b_func = BNACConvLayer( + num_channels=num_1x1_a, + num_filters=num_3x3_b, + filter_size=(kw, kh), + pad=(pw, ph), + stride=(key_stride, key_stride), + groups=G, + name=name + "_conv2") + + self.c1x1_c_func = BNACConvLayer( + num_channels=num_3x3_b, + num_filters=num_1x1_c + inc, + filter_size=(1, 1), + pad=(0, 0), + name=name + "_conv3") + + def forward(self, input): + # PROJ + if isinstance(input, list): + data_in = fluid.layers.concat([input[0], input[1]], axis=1) + else: + data_in = input + + if self.has_proj: + c1x1_w = self.c1x1_w_func(data_in) + data_o1, data_o2 = fluid.layers.split( + c1x1_w, num_or_sections=[self.num_1x1_c, 2 * self.inc], dim=1) + else: + data_o1 = input[0] + data_o2 = input[1] + + c1x1_a = self.c1x1_a_func(data_in) + c3x3_b = self.c3x3_b_func(c1x1_a) + c1x1_c = self.c1x1_c_func(c3x3_b) + + c1x1_c1, c1x1_c2 = fluid.layers.split( + c1x1_c, num_or_sections=[self.num_1x1_c, self.inc], dim=1) + + # OUTPUTS + summ = fluid.layers.elementwise_add(x=data_o1, y=c1x1_c1) + dense = fluid.layers.concat([data_o2, c1x1_c2], axis=1) + # tensor, channels + return [summ, dense] -__all__ = ["DPN", "DPN68", "DPN92", "DPN98", "DPN107", "DPN131"] +class DPN(fluid.dygraph.Layer): + def __init__(self, layers=60, class_dim=1000): + super(DPN, self).__init__() -class DPN(object): - def __init__(self, layers=68): - self.layers = layers + self._class_dim = class_dim - def net(self, input, class_dim=1000): - # get network args - args = self.get_net_args(self.layers) + args = self.get_net_args(layers) bws = args['bw'] inc_sec = args['inc_sec'] rs = args['r'] @@ -45,39 +209,23 @@ class DPN(object): init_filter_size = args['init_filter_size'] init_padding = args['init_padding'] - ## define Dual Path Network + self.k_sec = k_sec - # conv1 - conv1_x_1 = fluid.layers.conv2d( - input=input, + self.conv1_x_1_func = ConvBNLayer( + num_channels=3, num_filters=init_num_filter, - filter_size=init_filter_size, + filter_size=3, stride=2, - padding=init_padding, - groups=1, - act=None, - bias_attr=False, - name="conv1", - param_attr=ParamAttr(name="conv1_weights"), ) - - conv1_x_1 = fluid.layers.batch_norm( - input=conv1_x_1, + pad=1, act='relu', - is_test=False, - name="conv1_bn", - param_attr=ParamAttr(name='conv1_bn_scale'), - bias_attr=ParamAttr('conv1_bn_offset'), - moving_mean_name='conv1_bn_mean', - moving_variance_name='conv1_bn_variance', ) - - convX_x_x = fluid.layers.pool2d( - input=conv1_x_1, - pool_size=3, - pool_stride=2, - pool_padding=1, - pool_type='max', - name="pool1") + name="conv1") + + self.pool2d_max = Pool2D( + pool_size=3, pool_stride=2, pool_padding=1, pool_type='max') + num_channel_dpn = init_num_filter + + self.dpn_func_list = [] #conv2 - conv5 match_list, num = [], 0 for gc in range(4): @@ -93,43 +241,82 @@ class DPN(object): _type2 = 'normal' match = match + k_sec[gc - 1] match_list.append(match) + self.dpn_func_list.append( + self.add_sublayer( + "dpn{}".format(match), + DualPathFactory( + num_channels=num_channel_dpn, + num_1x1_a=R, + num_3x3_b=R, + num_1x1_c=bw, + inc=inc, + G=G, + _type=_type1, + name="dpn" + str(match)))) + num_channel_dpn = [bw, 3 * inc] - convX_x_x = self.dual_path_factory( - convX_x_x, R, R, bw, inc, G, _type1, name="dpn" + str(match)) for i_ly in range(2, k_sec[gc] + 1): num += 1 if num in match_list: num += 1 - convX_x_x = self.dual_path_factory( - convX_x_x, R, R, bw, inc, G, _type2, name="dpn" + str(num)) - - conv5_x_x = fluid.layers.concat(convX_x_x, axis=1) - conv5_x_x = fluid.layers.batch_norm( - input=conv5_x_x, - act='relu', - is_test=False, - name="final_concat_bn", + self.dpn_func_list.append( + self.add_sublayer( + "dpn{}".format(num), + DualPathFactory( + num_channels=num_channel_dpn, + num_1x1_a=R, + num_3x3_b=R, + num_1x1_c=bw, + inc=inc, + G=G, + _type=_type2, + name="dpn" + str(num)))) + + num_channel_dpn = [ + num_channel_dpn[0], num_channel_dpn[1] + inc + ] + + out_channel = sum(num_channel_dpn) + + self.conv5_x_x_bn = BatchNorm( + num_channels=sum(num_channel_dpn), + act="relu", param_attr=ParamAttr(name='final_concat_bn_scale'), bias_attr=ParamAttr('final_concat_bn_offset'), moving_mean_name='final_concat_bn_mean', - moving_variance_name='final_concat_bn_variance', ) - pool5 = fluid.layers.pool2d( - input=conv5_x_x, - pool_size=7, - pool_stride=1, - pool_padding=0, - pool_type='avg', ) + moving_variance_name='final_concat_bn_variance') + + self.pool2d_avg = Pool2D(pool_type='avg', global_pooling=True) stdv = 0.01 - fc6 = fluid.layers.fc( - input=pool5, - size=class_dim, + + self.out = Linear( + out_channel, + class_dim, param_attr=ParamAttr( initializer=fluid.initializer.Uniform(-stdv, stdv), - name='fc_weights'), - bias_attr=ParamAttr(name='fc_offset')) + name="fc_weights"), + bias_attr=ParamAttr(name="fc_offset")) + + def forward(self, input): + conv1_x_1 = self.conv1_x_1_func(input) + convX_x_x = self.pool2d_max(conv1_x_1) + + dpn_idx = 0 + for gc in range(4): + convX_x_x = self.dpn_func_list[dpn_idx](convX_x_x) + dpn_idx += 1 + for i_ly in range(2, self.k_sec[gc] + 1): + convX_x_x = self.dpn_func_list[dpn_idx](convX_x_x) + dpn_idx += 1 + + conv5_x_x = fluid.layers.concat(convX_x_x, axis=1) + conv5_x_x = self.conv5_x_x_bn(conv5_x_x) - return fc6 + y = self.pool2d_avg(conv5_x_x) + y = fluid.layers.reshape(y, shape=[0, -1]) + y = self.out(y) + return y def get_net_args(self, layers): if layers == 68: @@ -198,119 +385,6 @@ class DPN(object): return net_arg - def dual_path_factory(self, - data, - num_1x1_a, - num_3x3_b, - num_1x1_c, - inc, - G, - _type='normal', - name=None): - kw = 3 - kh = 3 - pw = (kw - 1) // 2 - ph = (kh - 1) // 2 - - # type - if _type is 'proj': - key_stride = 1 - has_proj = True - if _type is 'down': - key_stride = 2 - has_proj = True - if _type is 'normal': - key_stride = 1 - has_proj = False - - # PROJ - if type(data) is list: - data_in = fluid.layers.concat([data[0], data[1]], axis=1) - else: - data_in = data - - if has_proj: - c1x1_w = self.bn_ac_conv( - data=data_in, - num_filter=(num_1x1_c + 2 * inc), - kernel=(1, 1), - pad=(0, 0), - stride=(key_stride, key_stride), - name=name + "_match") - data_o1, data_o2 = fluid.layers.split( - c1x1_w, - num_or_sections=[num_1x1_c, 2 * inc], - dim=1, - name=name + "_match_conv_Slice") - else: - data_o1 = data[0] - data_o2 = data[1] - - # MAIN - c1x1_a = self.bn_ac_conv( - data=data_in, - num_filter=num_1x1_a, - kernel=(1, 1), - pad=(0, 0), - name=name + "_conv1") - c3x3_b = self.bn_ac_conv( - data=c1x1_a, - num_filter=num_3x3_b, - kernel=(kw, kh), - pad=(pw, ph), - stride=(key_stride, key_stride), - num_group=G, - name=name + "_conv2") - c1x1_c = self.bn_ac_conv( - data=c3x3_b, - num_filter=(num_1x1_c + inc), - kernel=(1, 1), - pad=(0, 0), - name=name + "_conv3") - - c1x1_c1, c1x1_c2 = fluid.layers.split( - c1x1_c, - num_or_sections=[num_1x1_c, inc], - dim=1, - name=name + "_conv3_Slice") - - # OUTPUTS - summ = fluid.layers.elementwise_add( - x=data_o1, y=c1x1_c1, name=name + "_elewise") - dense = fluid.layers.concat( - [data_o2, c1x1_c2], axis=1, name=name + "_concat") - - return [summ, dense] - - def bn_ac_conv(self, - data, - num_filter, - kernel, - pad, - stride=(1, 1), - num_group=1, - name=None): - bn_ac = fluid.layers.batch_norm( - input=data, - act='relu', - is_test=False, - name=name + '.output.1', - param_attr=ParamAttr(name=name + '_bn_scale'), - bias_attr=ParamAttr(name + '_bn_offset'), - moving_mean_name=name + '_bn_mean', - moving_variance_name=name + '_bn_variance', ) - bn_ac_conv = fluid.layers.conv2d( - input=bn_ac, - num_filters=num_filter, - filter_size=kernel, - stride=stride, - padding=pad, - groups=num_group, - act=None, - bias_attr=False, - param_attr=ParamAttr(name=name + "_weights")) - return bn_ac_conv - def DPN68(): model = DPN(layers=68) diff --git a/ppcls/modeling/architectures/hrnet.py b/ppcls/modeling/architectures/hrnet.py index 32f06df6a71e19e1b5dc3f3c50159d2bbafb23e9..467567e1926d448ae5f202935300ebb794ae60fb 100644 --- a/ppcls/modeling/architectures/hrnet.py +++ b/ppcls/modeling/architectures/hrnet.py @@ -1,382 +1,650 @@ -#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. - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import math - +import numpy as np import paddle import paddle.fluid as fluid -from paddle.fluid.initializer import MSRA from paddle.fluid.param_attr import ParamAttr +from paddle.fluid.layer_helper import LayerHelper +from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear + +import math __all__ = [ - "HRNet", "HRNet_W18_C", "HRNet_W30_C", "HRNet_W32_C", "HRNet_W40_C", - "HRNet_W44_C", "HRNet_W48_C", "HRNet_W60_C", "HRNet_W64_C", - "SE_HRNet_W18_C", "SE_HRNet_W30_C", "SE_HRNet_W32_C", "SE_HRNet_W40_C", - "SE_HRNet_W44_C", "SE_HRNet_W48_C", "SE_HRNet_W60_C", "SE_HRNet_W64_C" + "HRNet_W18_C", + "HRNet_W30_C", + "HRNet_W32_C", + "HRNet_W40_C", + "HRNet_W44_C", + "HRNet_W48_C", + "HRNet_W60_C", + "HRNet_W64_C", + "SE_HRNet_W18_C", + "SE_HRNet_W30_C", + "SE_HRNet_W32_C", + "SE_HRNet_W40_C", + "SE_HRNet_W44_C", + "SE_HRNet_W48_C", + "SE_HRNet_W60_C", + "SE_HRNet_W64_C", ] -class HRNet(): - def __init__(self, width=18, has_se=False): - self.width = width - self.has_se = has_se - self.channels = { - 18: [[18, 36], [18, 36, 72], [18, 36, 72, 144]], - 30: [[30, 60], [30, 60, 120], [30, 60, 120, 240]], - 32: [[32, 64], [32, 64, 128], [32, 64, 128, 256]], - 40: [[40, 80], [40, 80, 160], [40, 80, 160, 320]], - 44: [[44, 88], [44, 88, 176], [44, 88, 176, 352]], - 48: [[48, 96], [48, 96, 192], [48, 96, 192, 384]], - 60: [[60, 120], [60, 120, 240], [60, 120, 240, 480]], - 64: [[64, 128], [64, 128, 256], [64, 128, 256, 512]] - } +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__() - def net(self, input, class_dim=1000): - width = self.width - channels_2, channels_3, channels_4 = self.channels[width] - num_modules_2, num_modules_3, num_modules_4 = 1, 4, 3 + 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') - x = self.conv_bn_layer( - input=input, - filter_size=3, - num_filters=64, - stride=2, - if_act=True, - name='layer1_1') - x = self.conv_bn_layer( - input=x, - filter_size=3, - num_filters=64, - stride=2, - if_act=True, - name='layer1_2') - - la1 = self.layer1(x, name='layer2') - tr1 = self.transition_layer([la1], [256], channels_2, name='tr1') - st2 = self.stage(tr1, num_modules_2, channels_2, name='st2') - tr2 = self.transition_layer(st2, channels_2, channels_3, name='tr2') - st3 = self.stage(tr2, num_modules_3, channels_3, name='st3') - tr3 = self.transition_layer(st3, channels_3, channels_4, name='tr3') - st4 = self.stage(tr3, num_modules_4, channels_4, name='st4') - - #classification - last_cls = self.last_cls_out(x=st4, name='cls_head') - y = last_cls[0] - last_num_filters = [256, 512, 1024] - for i in range(3): - y = fluid.layers.elementwise_add( - last_cls[i + 1], - self.conv_bn_layer( - input=y, - filter_size=3, - num_filters=last_num_filters[i], - stride=2, - name='cls_head_add' + str(i + 1))) - - y = self.conv_bn_layer( - input=y, - filter_size=1, - num_filters=2048, - stride=1, - name='cls_head_last_conv') - pool = fluid.layers.pool2d( - input=y, pool_type='avg', global_pooling=True) - stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0) - out = fluid.layers.fc( - input=pool, - size=class_dim, - param_attr=ParamAttr( - name='fc_weights', - initializer=fluid.initializer.Uniform(-stdv, stdv)), - bias_attr=ParamAttr(name='fc_offset')) - return out + 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, has_se=False, name=None): + super(Layer1, self).__init__() + + self.bottleneck_block_list = [] - def layer1(self, input, name=None): - conv = input for i in range(4): - conv = self.bottleneck_block( - conv, - num_filters=64, - downsample=True if i == 0 else False, - name=name + '_' + str(i + 1)) + bottleneck_block = self.add_sublayer( + "bb_{}_{}".format(name, i + 1), + BottleneckBlock( + num_channels=num_channels if i == 0 else 256, + num_filters=64, + 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 - def transition_layer(self, x, in_channels, out_channels, name=None): + +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) out = [] + 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.conv_bn_layer( - x[i], - filter_size=3, + 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], - name=name + '_layer_' + str(i + 1)) - out.append(residual) - else: - out.append(x[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: - residual = self.conv_bn_layer( - x[-1], - filter_size=3, - num_filters=out_channels[i], - stride=2, - name=name + '_layer_' + str(i + 1)) - out.append(residual) - return out + if idx < len(input): + outs.append(conv_bn_func(input[idx])) + else: + outs.append(conv_bn_func(input[-1])) + return outs - def branches(self, x, block_num, channels, name=None): - out = [] - for i in range(len(channels)): - residual = x[i] - for j in range(block_num): - residual = self.basic_block( - residual, - channels[i], - name=name + '_branch_layer_' + str(i + 1) + '_' + - str(j + 1)) - out.append(residual) - return out - def fuse_layers(self, x, channels, multi_scale_output=True, name=None): - out = [] - for i in range(len(channels) if multi_scale_output else 1): - residual = x[i] - for j in range(len(channels)): - if j > i: - y = self.conv_bn_layer( - x[j], - filter_size=1, - num_filters=channels[i], - if_act=False, - name=name + '_layer_' + str(i + 1) + '_' + str(j + 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 = x[j] - for k in range(i - j): - if k == i - j - 1: - y = self.conv_bn_layer( - y, - filter_size=3, - num_filters=channels[i], - stride=2, - if_act=False, - name=name + '_layer_' + str(i + 1) + '_' + - str(j + 1) + '_' + str(k + 1)) - else: - y = self.conv_bn_layer( - y, - filter_size=3, - num_filters=channels[j], - stride=2, - name=name + '_layer_' + str(i + 1) + '_' + - str(j + 1) + '_' + str(k + 1)) - residual = fluid.layers.elementwise_add( - x=residual, y=y, act=None) +class Branches(fluid.dygraph.Layer): + def __init__(self, + block_num, + in_channels, + out_channels, + has_se=False, + name=None): + super(Branches, self).__init__() - residual = fluid.layers.relu(residual) - out.append(residual) - return out + self.basic_block_list = [] - def high_resolution_module(self, - x, - channels, - multi_scale_output=True, - name=None): - residual = self.branches(x, 4, channels, name=name) - out = self.fuse_layers( - residual, - channels, - multi_scale_output=multi_scale_output, - name=name) - return out + for i in range(len(out_channels)): + self.basic_block_list.append([]) + for j in range(block_num): + 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__() - def stage(self, - x, - num_modules, - channels, - multi_scale_output=True, - name=None): - out = x - for i in range(num_modules): - if i == num_modules - 1 and multi_scale_output == False: - out = self.high_resolution_module( - out, - channels, - multi_scale_output=False, - name=name + '_' + str(i + 1)) - else: - out = self.high_resolution_module( - out, channels, name=name + '_' + str(i + 1)) + self.has_se = has_se + self.downsample = downsample - return out + 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") - def last_cls_out(self, x, name=None): - out = [] - num_filters_list = [32, 64, 128, 256] - for i in range(len(x)): - out.append( - self.bottleneck_block( - input=x[i], - num_filters=num_filters_list[i], - name=name + 'conv_' + str(i + 1), - downsample=True)) + if self.downsample: + self.conv_down = ConvBNLayer( + num_channels=num_channels, + num_filters=num_filters * 4, + filter_size=1, + act=None, + name=name + "_downsample") - return out + if self.has_se: + self.se = SELayer( + num_channels=num_filters * 4, + num_filters=num_filters * 4, + reduction_ratio=16, + name='fc' + name) - def basic_block(self, - input, - num_filters, - stride=1, - downsample=False, - name=None): + def forward(self, input): residual = input - conv = self.conv_bn_layer( - input=input, - filter_size=3, + 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, - stride=stride, - name=name + '_conv1') - conv = self.conv_bn_layer( - input=conv, filter_size=3, + stride=stride, + act="relu", + name=name + "_conv1") + self.conv2 = ConvBNLayer( + num_channels=num_filters, num_filters=num_filters, - if_act=False, - name=name + '_conv2') - if downsample: - residual = self.conv_bn_layer( - input=input, + 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, - num_filters=num_filters, - if_act=False, - name=name + '_downsample') + act="relu", + name=name + "_downsample") + if self.has_se: - conv = self.squeeze_excitation( - input=conv, + self.se = SELayer( num_channels=num_filters, + num_filters=num_filters, reduction_ratio=16, - name=name + '_fc') - return fluid.layers.elementwise_add(x=residual, y=conv, act='relu') - - def bottleneck_block(self, - input, - num_filters, - stride=1, - downsample=False, - name=None): + name='fc' + name) + + def forward(self, input): residual = input - conv = self.conv_bn_layer( - input=input, - filter_size=1, - num_filters=num_filters, - name=name + '_conv1') - conv = self.conv_bn_layer( - input=conv, - filter_size=3, - num_filters=num_filters, - stride=stride, - name=name + '_conv2') - conv = self.conv_bn_layer( - input=conv, - filter_size=1, - num_filters=num_filters * 4, - if_act=False, - name=name + '_conv3') - if downsample: - residual = self.conv_bn_layer( - input=input, - filter_size=1, - num_filters=num_filters * 4, - if_act=False, - name=name + '_downsample') + conv1 = self.conv1(input) + conv2 = self.conv2(conv1) + + if self.downsample: + residual = self.conv_down(input) + if self.has_se: - conv = self.squeeze_excitation( - input=conv, - num_channels=num_filters * 4, - reduction_ratio=16, - name=name + '_fc') - return fluid.layers.elementwise_add(x=residual, y=conv, act='relu') - - def squeeze_excitation(self, - input, - num_channels, - reduction_ratio, - name=None): - pool = fluid.layers.pool2d( - input=input, pool_size=0, pool_type='avg', global_pooling=True) - stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0) - squeeze = fluid.layers.fc( - input=pool, - size=num_channels / reduction_ratio, - act='relu', - param_attr=fluid.param_attr.ParamAttr( + 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'), + name=name + "_sqz_weights"), bias_attr=ParamAttr(name=name + '_sqz_offset')) - stdv = 1.0 / math.sqrt(squeeze.shape[1] * 1.0) - excitation = fluid.layers.fc( - input=squeeze, - size=num_channels, - act='sigmoid', - param_attr=fluid.param_attr.ParamAttr( + + 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'), + name=name + "_exc_weights"), bias_attr=ParamAttr(name=name + '_exc_offset')) - scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0) - return scale - - def conv_bn_layer(self, - input, - filter_size, - num_filters, - stride=1, - padding=1, - num_groups=1, - if_act=True, - name=None): - conv = fluid.layers.conv2d( - input=input, - num_filters=num_filters, - filter_size=filter_size, - stride=stride, - padding=(filter_size - 1) // 2, - groups=num_groups, - act=None, - param_attr=ParamAttr( - initializer=MSRA(), name=name + '_weights'), - bias_attr=False) - bn_name = name + '_bn' - bn = fluid.layers.batch_norm( - input=conv, + + 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_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_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_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_filters, + has_se=False, + multi_scale_output=True, + name=None): + super(HighResolutionModule, self).__init__() + + self.branches_func = Branches( + block_num=4, + 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 + + +class HRNet(fluid.dygraph.Layer): + def __init__(self, width=18, has_se=False, class_dim=1000): + super(HRNet, self).__init__() + + self.width = width + self.has_se = has_se + self.channels = { + 18: [[18, 36], [18, 36, 72], [18, 36, 72, 144]], + 30: [[30, 60], [30, 60, 120], [30, 60, 120, 240]], + 32: [[32, 64], [32, 64, 128], [32, 64, 128, 256]], + 40: [[40, 80], [40, 80, 160], [40, 80, 160, 320]], + 44: [[44, 88], [44, 88, 176], [44, 88, 176, 352]], + 48: [[48, 96], [48, 96, 192], [48, 96, 192, 384]], + 60: [[60, 120], [60, 120, 240], [60, 120, 240, 480]], + 64: [[64, 128], [64, 128, 256], [64, 128, 256, 512]] + } + self._class_dim = class_dim + + channels_2, channels_3, channels_4 = self.channels[width] + num_modules_2, num_modules_3, num_modules_4 = 1, 4, 3 + + 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, has_se=has_se, name="layer2") + + self.tr1 = TransitionLayer( + in_channels=[256], out_channels=channels_2, name="tr1") + + self.st2 = Stage( + num_channels=channels_2, + num_modules=num_modules_2, + num_filters=channels_2, + has_se=self.has_se, + name="st2") + + self.tr2 = TransitionLayer( + in_channels=channels_2, out_channels=channels_3, name="tr2") + self.st3 = Stage( + num_channels=channels_3, + num_modules=num_modules_3, + num_filters=channels_3, + has_se=self.has_se, + name="st3") + + self.tr3 = TransitionLayer( + in_channels=channels_3, out_channels=channels_4, name="tr3") + self.st4 = Stage( + num_channels=channels_4, + num_modules=num_modules_4, + num_filters=channels_4, + has_se=self.has_se, + name="st4") + + # classification + num_filters_list = [32, 64, 128, 256] + self.last_cls = LastClsOut( + num_channel_list=channels_4, + has_se=self.has_se, + num_filters_list=num_filters_list, + name="cls_head", ) + + last_num_filters = [256, 512, 1024] + self.cls_head_conv_list = [] + for idx in range(3): + self.cls_head_conv_list.append( + self.add_sublayer( + "cls_head_add{}".format(idx + 1), + ConvBNLayer( + num_channels=num_filters_list[idx] * 4, + num_filters=last_num_filters[idx], + filter_size=3, + stride=2, + name="cls_head_add" + str(idx + 1)))) + + self.conv_last = ConvBNLayer( + num_channels=1024, + num_filters=2048, + filter_size=1, + stride=1, + name="cls_head_last_conv") + + self.pool2d_avg = Pool2D(pool_type='avg', global_pooling=True) + + stdv = 1.0 / math.sqrt(2048 * 1.0) + + self.out = Linear( + 2048, + class_dim, param_attr=ParamAttr( - name=bn_name + "_scale", - initializer=fluid.initializer.Constant(1.0)), - bias_attr=ParamAttr( - name=bn_name + "_offset", - initializer=fluid.initializer.Constant(0.0)), - moving_mean_name=bn_name + '_mean', - moving_variance_name=bn_name + '_variance') - if if_act: - bn = fluid.layers.relu(bn) - return bn + initializer=fluid.initializer.Uniform(-stdv, stdv), + name="fc_weights"), + bias_attr=ParamAttr(name="fc_offset")) + + def forward(self, input): + conv1 = self.conv_layer1_1(input) + 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) + + last_cls = self.last_cls(st4) + + y = last_cls[0] + for idx in range(3): + y = last_cls[idx + 1] + self.cls_head_conv_list[idx](y) + + y = self.conv_last(y) + y = self.pool2d_avg(y) + y = fluid.layers.reshape(y, shape=[0, -1]) + y = self.out(y) + return y def HRNet_W18_C():