wehub-resource-sync
359 lines
11 KiB
Python
359 lines
11 KiB
Python
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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 math
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import numpy as np
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from test_dist_base import TestParallelDyGraphRunnerBase, runtime_main
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import paddle
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from paddle import base
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from paddle.nn import Linear
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batch_size = 64
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momentum_rate = 0.9
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l2_decay = 1.2e-4
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train_parameters = {
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"input_size": [3, 224, 224],
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"input_mean": [0.485, 0.456, 0.406],
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"input_std": [0.229, 0.224, 0.225],
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"learning_strategy": {
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"name": "cosine_decay",
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"batch_size": batch_size,
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"epochs": [40, 80, 100],
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"steps": [0.1, 0.01, 0.001, 0.0001],
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},
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"batch_size": batch_size,
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"lr": 0.0125,
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"total_images": 6149,
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"num_epochs": 200,
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}
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def optimizer_setting(params, parameter_list=None):
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ls = params["learning_strategy"]
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if "total_images" not in params:
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total_images = 6149
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else:
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total_images = params["total_images"]
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batch_size = ls["batch_size"]
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step = int(math.ceil(float(total_images) / batch_size))
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bd = [step * e for e in ls["epochs"]]
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lr = params["lr"]
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num_epochs = params["num_epochs"]
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if base.in_dygraph_mode():
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optimizer = paddle.optimizer.Momentum(
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learning_rate=base.layers.cosine_decay(
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learning_rate=lr, step_each_epoch=step, epochs=num_epochs
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),
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momentum=momentum_rate,
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weight_decay=paddle.regularizer.L2Decay(l2_decay),
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parameter_list=parameter_list,
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)
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else:
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optimizer = paddle.optimizer.Momentum(
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learning_rate=paddle.optimizer.lr.cosine_decay(
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learning_rate=lr, step_each_epoch=step, epochs=num_epochs
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),
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momentum=momentum_rate,
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weight_decay=paddle.regularizer.L2Decay(l2_decay),
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)
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return optimizer
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class ConvBNLayer(paddle.nn.Layer):
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def __init__(
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self,
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num_channels,
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num_filters,
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filter_size,
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stride=1,
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groups=1,
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act=None,
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):
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super().__init__()
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self._conv = paddle.nn.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=(filter_size - 1) // 2,
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groups=groups,
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act=None,
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bias_attr=False,
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)
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# disable BatchNorm in multi-card. disable LayerNorm because of complex input_shape
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# self._batch_norm = BatchNorm(num_filters, act=act)
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def forward(self, inputs):
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y = self._conv(inputs)
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# y = self._batch_norm(y)
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return y
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class SqueezeExcitation(paddle.nn.Layer):
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def __init__(self, num_channels, reduction_ratio):
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super().__init__()
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self._num_channels = num_channels
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self._pool = paddle.nn.AdaptiveAvgPool2D(1)
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stdv = 1.0 / math.sqrt(num_channels * 1.0)
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self._squeeze = Linear(
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num_channels,
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num_channels // reduction_ratio,
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weight_attr=paddle.ParamAttr(
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initializer=paddle.nn.initializer.Uniform(-stdv, stdv)
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),
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)
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stdv = 1.0 / math.sqrt(num_channels / 16.0 * 1.0)
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self._excitation = Linear(
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num_channels // reduction_ratio,
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num_channels,
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weight_attr=paddle.ParamAttr(
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initializer=paddle.nn.initializer.Uniform(-stdv, stdv)
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),
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)
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def forward(self, input):
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y = self._pool(input)
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y = paddle.reshape(y, shape=[-1, self._num_channels])
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y = self._squeeze(y)
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y = paddle.nn.functional.relu(y)
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y = self._excitation(y)
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y = paddle.nn.functional.sigmoid(y)
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y = paddle.tensor.math._multiply_with_axis(x=input, y=y, axis=0)
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return y
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class BottleneckBlock(paddle.nn.Layer):
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def __init__(
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self,
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num_channels,
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num_filters,
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stride,
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cardinality,
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reduction_ratio,
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shortcut=True,
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):
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super().__init__()
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self.conv0 = ConvBNLayer(
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num_channels=num_channels,
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num_filters=num_filters,
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filter_size=1,
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act="relu",
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)
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self.conv1 = ConvBNLayer(
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num_channels=num_filters,
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num_filters=num_filters,
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filter_size=3,
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stride=stride,
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groups=cardinality,
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act="relu",
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)
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self.conv2 = ConvBNLayer(
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num_channels=num_filters,
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num_filters=num_filters * 2,
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filter_size=1,
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act=None,
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)
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self.scale = SqueezeExcitation(
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num_channels=num_filters * 2, reduction_ratio=reduction_ratio
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)
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if not shortcut:
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self.short = ConvBNLayer(
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num_channels=num_channels,
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num_filters=num_filters * 2,
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filter_size=1,
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stride=stride,
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)
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self.shortcut = shortcut
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self._num_channels_out = num_filters * 2
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def forward(self, inputs):
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y = self.conv0(inputs)
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conv1 = self.conv1(y)
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conv2 = self.conv2(conv1)
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scale = self.scale(conv2)
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if self.shortcut:
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short = inputs
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else:
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short = self.short(inputs)
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y = paddle.nn.functional.relu(paddle.add(x=short, y=scale))
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return y
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class SeResNeXt(paddle.nn.Layer):
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def __init__(self, layers=50, class_dim=102):
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super().__init__()
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self.layers = layers
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supported_layers = [50, 101, 152]
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assert layers in supported_layers, (
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f"supported layers are {supported_layers} but input layer is {layers}"
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)
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if layers == 50:
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cardinality = 32
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reduction_ratio = 16
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depth = [3, 4, 6, 3]
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num_filters = [128, 256, 512, 1024]
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self.conv0 = ConvBNLayer(
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num_channels=3,
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num_filters=64,
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filter_size=7,
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stride=2,
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act='relu',
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)
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self.pool = paddle.nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
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elif layers == 101:
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cardinality = 32
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reduction_ratio = 16
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depth = [3, 4, 23, 3]
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num_filters = [128, 256, 512, 1024]
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self.conv0 = ConvBNLayer(
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num_channels=3,
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num_filters=64,
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filter_size=7,
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stride=2,
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act='relu',
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)
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self.pool = paddle.nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
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elif layers == 152:
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cardinality = 64
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reduction_ratio = 16
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depth = [3, 8, 36, 3]
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num_filters = [128, 256, 512, 1024]
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self.conv0 = ConvBNLayer(
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num_channels=3,
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num_filters=64,
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filter_size=3,
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stride=2,
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act='relu',
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)
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self.conv1 = ConvBNLayer(
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num_channels=64,
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num_filters=64,
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filter_size=3,
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stride=1,
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act='relu',
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)
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self.conv2 = ConvBNLayer(
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num_channels=64,
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num_filters=128,
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filter_size=3,
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stride=1,
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act='relu',
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)
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self.pool = paddle.nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
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self.bottleneck_block_list = []
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num_channels = 64
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for block in range(len(depth)):
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shortcut = False
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for i in range(depth[block]):
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bottleneck_block = self.add_sublayer(
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f'bb_{block}_{i}',
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BottleneckBlock(
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num_channels=num_channels,
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num_filters=num_filters[block],
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stride=2 if i == 0 and block != 0 else 1,
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cardinality=cardinality,
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reduction_ratio=reduction_ratio,
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shortcut=shortcut,
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),
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)
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num_channels = bottleneck_block._num_channels_out
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self.bottleneck_block_list.append(bottleneck_block)
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shortcut = True
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self.pool2d_avg = paddle.nn.AdaptiveAvgPool2D(1)
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stdv = 1.0 / math.sqrt(2048 * 1.0)
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self.pool2d_avg_output = num_filters[len(num_filters) - 1] * 2 * 1 * 1
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self.out = Linear(
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self.pool2d_avg_output,
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class_dim,
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weight_attr=paddle.ParamAttr(
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initializer=paddle.nn.initializer.Uniform(-stdv, stdv)
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),
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)
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def forward(self, inputs):
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if self.layers == 50 or self.layers == 101:
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y = self.conv0(inputs)
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y = self.pool(y)
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elif self.layers == 152:
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y = self.conv0(inputs)
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y = self.conv1(inputs)
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y = self.conv2(inputs)
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y = self.pool(y)
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for bottleneck_block in self.bottleneck_block_list:
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y = bottleneck_block(y)
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y = self.pool2d_avg(y)
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y = paddle.reshape(y, shape=[-1, self.pool2d_avg_output])
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y = self.out(y)
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return y
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class TestSeResNeXt(TestParallelDyGraphRunnerBase):
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def get_model(self):
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model = SeResNeXt()
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train_reader = paddle.batch(
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paddle.dataset.flowers.test(use_xmap=False),
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batch_size=train_parameters["batch_size"],
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drop_last=True,
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)
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optimizer = optimizer_setting(
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train_parameters, parameter_list=model.parameters()
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)
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return model, train_reader, optimizer
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def run_one_loop(self, model, opt, data):
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bs = len(data)
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dy_x_data = np.array([x[0].reshape(3, 224, 224) for x in data]).astype(
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'float32'
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)
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dy_x_data = dy_x_data / 255.0
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y_data = np.array([x[1] for x in data]).astype('int64').reshape(bs, 1)
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img = paddle.to_tensor(dy_x_data)
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label = paddle.to_tensor(y_data)
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label.stop_gradient = True
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out = model(img)
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softmax_out = paddle.nn.functional.softmax(out, use_cudnn=False)
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loss = paddle.nn.functional.cross_entropy(
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input=softmax_out, label=label, reduction='none', use_softmax=False
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)
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avg_loss = paddle.mean(x=loss)
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return avg_loss
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if __name__ == "__main__":
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runtime_main(TestSeResNeXt)
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