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fix hrnet
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littletomatodonkey
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2bdf8a9b05
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03bbd585c2
@ -32,5 +32,6 @@ from .mobilenet_v2 import MobileNetV2_x0_25, MobileNetV2_x0_5, MobileNetV2_x0_75
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from .mobilenet_v3 import MobileNetV3_small_x0_35, MobileNetV3_small_x0_5, MobileNetV3_small_x0_75, MobileNetV3_small_x1_0, MobileNetV3_small_x1_25, MobileNetV3_large_x0_35, MobileNetV3_large_x0_5, MobileNetV3_large_x0_75, MobileNetV3_large_x1_0, MobileNetV3_large_x1_25
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from .shufflenet_v2 import ShuffleNetV2_x0_25, ShuffleNetV2_x0_33, ShuffleNetV2_x0_5, ShuffleNetV2, ShuffleNetV2_x1_5, ShuffleNetV2_x2_0, ShuffleNetV2_swish
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from .alexnet import AlexNet
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from .inception_v4 import InceptionV4
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from .distillation_models import ResNet50_vd_distill_MobileNetV3_large_x1_0
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@ -1,12 +1,29 @@
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# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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import paddle.fluid as fluid
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from paddle.fluid.param_attr import ParamAttr
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from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Dropout
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from paddle import ParamAttr
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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddle.nn import Conv2d, Pool2D, BatchNorm, Linear, Dropout
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from paddle.nn.initializer import Uniform
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import math
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__all__ = ["InceptionV4"]
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class ConvBNLayer(fluid.dygraph.Layer):
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class ConvBNLayer(nn.Layer):
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def __init__(self,
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num_channels,
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num_filters,
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@ -18,15 +35,14 @@ class ConvBNLayer(fluid.dygraph.Layer):
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name=None):
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super(ConvBNLayer, self).__init__()
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self._conv = Conv2D(
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num_channels=num_channels,
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num_filters=num_filters,
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filter_size=filter_size,
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self._conv = Conv2d(
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in_channels=num_channels,
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out_channels=num_filters,
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kernel_size=filter_size,
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stride=stride,
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padding=padding,
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groups=groups,
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act=None,
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param_attr=ParamAttr(name=name + "_weights"),
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weight_attr=ParamAttr(name=name + "_weights"),
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bias_attr=False)
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bn_name = name + "_bn"
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self._batch_norm = BatchNorm(
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@ -43,7 +59,7 @@ class ConvBNLayer(fluid.dygraph.Layer):
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return y
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class InceptionStem(fluid.dygraph.Layer):
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class InceptionStem(nn.Layer):
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def __init__(self):
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super(InceptionStem, self).__init__()
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self._conv_1 = ConvBNLayer(
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@ -84,7 +100,7 @@ class InceptionStem(fluid.dygraph.Layer):
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pool1 = self._pool(conv)
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conv2 = self._conv2(conv)
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concat = fluid.layers.concat([pool1, conv2], axis=1)
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concat = paddle.concat([pool1, conv2], axis=1)
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conv1 = self._conv1_1(concat)
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conv1 = self._conv1_2(conv1)
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@ -94,16 +110,16 @@ class InceptionStem(fluid.dygraph.Layer):
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conv2 = self._conv2_3(conv2)
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conv2 = self._conv2_4(conv2)
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concat = fluid.layers.concat([conv1, conv2], axis=1)
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concat = paddle.concat([conv1, conv2], axis=1)
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conv1 = self._conv3(concat)
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pool1 = self._pool(concat)
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concat = fluid.layers.concat([conv1, pool1], axis=1)
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concat = paddle.concat([conv1, pool1], axis=1)
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return concat
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class InceptionA(fluid.dygraph.Layer):
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class InceptionA(nn.Layer):
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def __init__(self, name):
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super(InceptionA, self).__init__()
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self._pool = Pool2D(pool_size=3, pool_type="avg", pool_padding=1)
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@ -154,11 +170,11 @@ class InceptionA(fluid.dygraph.Layer):
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conv4 = self._conv4_2(conv4)
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conv4 = self._conv4_3(conv4)
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concat = fluid.layers.concat([conv1, conv2, conv3, conv4], axis=1)
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concat = paddle.concat([conv1, conv2, conv3, conv4], axis=1)
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return concat
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class ReductionA(fluid.dygraph.Layer):
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class ReductionA(nn.Layer):
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def __init__(self):
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super(ReductionA, self).__init__()
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self._pool = Pool2D(pool_size=3, pool_type="max", pool_stride=2)
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@ -177,11 +193,11 @@ class ReductionA(fluid.dygraph.Layer):
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conv3 = self._conv3_1(inputs)
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conv3 = self._conv3_2(conv3)
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conv3 = self._conv3_3(conv3)
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concat = fluid.layers.concat([pool1, conv2, conv3], axis=1)
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concat = paddle.concat([pool1, conv2, conv3], axis=1)
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return concat
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class InceptionB(fluid.dygraph.Layer):
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class InceptionB(nn.Layer):
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def __init__(self, name=None):
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super(InceptionB, self).__init__()
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self._pool = Pool2D(pool_size=3, pool_type="avg", pool_padding=1)
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@ -254,11 +270,11 @@ class InceptionB(fluid.dygraph.Layer):
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conv4 = self._conv4_4(conv4)
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conv4 = self._conv4_5(conv4)
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concat = fluid.layers.concat([conv1, conv2, conv3, conv4], axis=1)
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concat = paddle.concat([conv1, conv2, conv3, conv4], axis=1)
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return concat
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class ReductionB(fluid.dygraph.Layer):
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class ReductionB(nn.Layer):
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def __init__(self):
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super(ReductionB, self).__init__()
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self._pool = Pool2D(pool_size=3, pool_type="max", pool_stride=2)
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@ -294,12 +310,12 @@ class ReductionB(fluid.dygraph.Layer):
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conv3 = self._conv3_3(conv3)
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conv3 = self._conv3_4(conv3)
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concat = fluid.layers.concat([pool1, conv2, conv3], axis=1)
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concat = paddle.concat([pool1, conv2, conv3], axis=1)
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return concat
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class InceptionC(fluid.dygraph.Layer):
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class InceptionC(nn.Layer):
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def __init__(self, name=None):
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super(InceptionC, self).__init__()
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self._pool = Pool2D(pool_size=3, pool_type="avg", pool_padding=1)
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@ -364,13 +380,13 @@ class InceptionC(fluid.dygraph.Layer):
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conv4_1 = self._conv4_1(conv4)
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conv4_2 = self._conv4_2(conv4)
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concat = fluid.layers.concat(
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concat = paddle.concat(
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[conv1, conv2, conv3_1, conv3_2, conv4_1, conv4_2], axis=1)
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return concat
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class InceptionV4DY(fluid.dygraph.Layer):
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class InceptionV4DY(nn.Layer):
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def __init__(self, class_dim=1000):
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super(InceptionV4DY, self).__init__()
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self._inception_stem = InceptionStem()
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@ -400,9 +416,8 @@ class InceptionV4DY(fluid.dygraph.Layer):
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self.out = Linear(
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1536,
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class_dim,
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param_attr=ParamAttr(
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initializer=fluid.initializer.Uniform(-stdv, stdv),
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name="final_fc_weights"),
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weight_attr=ParamAttr(
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initializer=Uniform(-stdv, stdv), name="final_fc_weights"),
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bias_attr=ParamAttr(name="final_fc_offset"))
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def forward(self, inputs):
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@ -428,7 +443,7 @@ class InceptionV4DY(fluid.dygraph.Layer):
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x = self._inceptionC_3(x)
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x = self.avg_pool(x)
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x = fluid.layers.squeeze(x, axes=[2, 3])
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x = paddle.squeeze(x, axis=[2, 3])
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x = self._drop(x)
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x = self.out(x)
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return x
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@ -436,4 +451,4 @@ class InceptionV4DY(fluid.dygraph.Layer):
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def InceptionV4(**args):
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model = InceptionV4DY(**args)
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return model
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return model
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