wehub-resource-sync
575 lines
19 KiB
Python
575 lines
19 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 unittest
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import numpy as np
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from op_test import get_device_place, is_custom_device
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from test_imperative_base import new_program_scope
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import paddle
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from paddle import base
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from paddle.base import core
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from paddle.base.layer_helper import LayerHelper
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from paddle.nn import BatchNorm
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batch_size = 8
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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": "piecewise_decay",
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"batch_size": batch_size,
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"epochs": [30, 60, 90],
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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": 0.1,
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"total_images": 6149,
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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 ls["name"] == "piecewise_decay":
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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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# TODO(Yancey1989): using lr decay if it is ready.
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# batch_size = ls["batch_size"]
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# step = int(total_images / batch_size + 1)
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# bd = [step * e for e in ls["epochs"]]
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# base_lr = params["lr"]
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# lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
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if base.in_dygraph_mode():
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optimizer = paddle.optimizer.SGD(
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learning_rate=0.01, parameters=parameter_list
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)
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else:
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optimizer = paddle.optimizer.SGD(learning_rate=0.01)
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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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bias_attr=None,
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)
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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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self._squeeze = paddle.nn.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.Constant(value=0.05)
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),
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)
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self.act_1 = paddle.nn.ReLU()
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self._excitation = paddle.nn.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.Constant(value=0.05)
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),
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)
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self.act_2 = paddle.nn.Softmax()
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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 = self.act_1(y)
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y = self._excitation(y)
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y = self.act_2(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, num_filters=num_filters, filter_size=1
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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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)
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self.conv2 = ConvBNLayer(
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num_channels=num_filters,
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num_filters=num_filters * 4,
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filter_size=1,
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act='relu',
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)
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self.scale = SqueezeExcitation(
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num_channels=num_filters * 4, 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 * 4,
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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 * 4
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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.add(x=short, y=scale)
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layer_helper = LayerHelper(self.full_name(), act='relu')
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y = layer_helper.append_activation(y)
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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=2,
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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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if layers == 152:
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num_channels = 128
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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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import math
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stdv = 1.0 / math.sqrt(2048 * 1.0)
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self.pool2d_avg_output = num_filters[-1] * 4 * 1 * 1
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self.out = paddle.nn.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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self.out_act = paddle.nn.Softmax()
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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(y)
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y = self.conv2(y)
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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 self.out_act(y)
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class TestImperativeResneXt(unittest.TestCase):
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def reader_decorator(self, reader):
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def _reader_simple():
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for item in reader():
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doc = np.array(item[0]).reshape(3, 224, 224)
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label = np.array(item[1]).astype('int64').reshape(1)
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yield doc, label
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return _reader_simple
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def test_se_resnext_float32(self):
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seed = 90
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batch_size = train_parameters["batch_size"]
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batch_num = 1
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epoch_num = 1
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def run_dygraph():
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paddle.seed(seed)
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paddle.framework.random._manual_program_seed(seed)
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se_resnext = SeResNeXt()
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optimizer = optimizer_setting(
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train_parameters, parameter_list=se_resnext.parameters()
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)
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np.random.seed(seed)
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batch_py_reader = base.io.PyReader(capacity=1)
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batch_py_reader.decorate_sample_list_generator(
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paddle.batch(
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self.reader_decorator(
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paddle.dataset.flowers.train(use_xmap=False)
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),
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batch_size=batch_size,
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drop_last=True,
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),
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places=base.CPUPlace(),
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)
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dy_param_init_value = {}
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for param in se_resnext.parameters():
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dy_param_init_value[param.name] = param.numpy()
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for epoch_id in range(epoch_num):
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for batch_id, data in enumerate(batch_py_reader()):
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if batch_id >= batch_num and batch_num != -1:
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break
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img = data[0]
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label = data[1]
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label.stop_gradient = True
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label.stop_gradient = True
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out = se_resnext(img)
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softmax_out = paddle.nn.functional.softmax(out)
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loss = paddle.nn.functional.cross_entropy(
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input=softmax_out,
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label=label,
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reduction='none',
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use_softmax=False,
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)
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avg_loss = paddle.mean(x=loss)
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dy_out = avg_loss.numpy()
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if batch_id == 0:
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for param in se_resnext.parameters():
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if param.name not in dy_param_init_value:
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dy_param_init_value[param.name] = param.numpy()
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avg_loss.backward()
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dy_grad_value = {}
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for param in se_resnext.parameters():
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if param.trainable:
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np_array = np.array(
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param._grad_ivar().value().get_tensor()
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)
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dy_grad_value[
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param.name + core.grad_var_suffix()
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] = np_array
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optimizer.minimize(avg_loss)
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se_resnext.clear_gradients()
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dy_param_value = {}
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for param in se_resnext.parameters():
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dy_param_value[param.name] = param.numpy()
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return (
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dy_out,
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dy_param_init_value,
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dy_param_value,
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dy_grad_value,
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)
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with base.dygraph.guard():
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(
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dy_out,
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dy_param_init_value,
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dy_param_value,
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dy_grad_value,
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) = run_dygraph()
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with base.dygraph.guard():
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(
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eager_out,
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eager_param_init_value,
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eager_param_value,
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eager_grad_value,
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) = run_dygraph()
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with new_program_scope():
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paddle.seed(seed)
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paddle.framework.random._manual_program_seed(seed)
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exe = base.Executor(
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base.CPUPlace()
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if not (core.is_compiled_with_cuda() or is_custom_device())
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else get_device_place()
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)
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se_resnext = SeResNeXt()
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optimizer = optimizer_setting(train_parameters)
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np.random.seed(seed)
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train_reader = paddle.batch(
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paddle.dataset.flowers.train(use_xmap=False),
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batch_size=batch_size,
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drop_last=True,
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)
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img = paddle.static.data(
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name='pixel', shape=[-1, 3, 224, 224], dtype='float32'
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)
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label = paddle.static.data(
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name='label', shape=[-1, 1], dtype='int64'
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)
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out = se_resnext(img)
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softmax_out = paddle.nn.functional.softmax(out)
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loss = paddle.nn.functional.cross_entropy(
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input=softmax_out,
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label=label,
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reduction='none',
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use_softmax=False,
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)
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avg_loss = paddle.mean(x=loss)
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optimizer.minimize(avg_loss)
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# initialize params and fetch them
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static_param_init_value = {}
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static_param_name_list = []
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static_grad_name_list = []
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for param in se_resnext.parameters():
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static_param_name_list.append(param.name)
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for param in se_resnext.parameters():
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if param.trainable:
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static_grad_name_list.append(
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param.name + core.grad_var_suffix()
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)
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out = exe.run(
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base.default_startup_program(),
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fetch_list=static_param_name_list,
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)
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for i in range(len(static_param_name_list)):
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static_param_init_value[static_param_name_list[i]] = out[i]
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for epoch_id in range(epoch_num):
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for batch_id, data in enumerate(train_reader()):
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if batch_id >= batch_num and batch_num != -1:
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break
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static_x_data = np.array(
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[x[0].reshape(3, 224, 224) for x in data]
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).astype('float32')
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y_data = (
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np.array([x[1] for x in data])
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.astype('int64')
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.reshape([batch_size, 1])
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)
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fetch_list = [avg_loss.name]
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fetch_list.extend(static_param_name_list)
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fetch_list.extend(static_grad_name_list)
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out = exe.run(
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base.default_main_program(),
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feed={"pixel": static_x_data, "label": y_data},
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fetch_list=fetch_list,
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)
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static_param_value = {}
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static_grad_value = {}
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static_out = out[0]
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param_start_pos = 1
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grad_start_pos = (
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len(static_param_name_list) + param_start_pos
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)
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for i in range(
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param_start_pos,
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len(static_param_name_list) + param_start_pos,
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):
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static_param_value[
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static_param_name_list[i - param_start_pos]
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] = out[i]
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for i in range(
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grad_start_pos,
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len(static_grad_name_list) + grad_start_pos,
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):
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static_grad_value[
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static_grad_name_list[i - grad_start_pos]
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] = out[i]
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np.testing.assert_allclose(static_out, dy_out, rtol=1e-05)
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self.assertEqual(len(dy_param_init_value), len(static_param_init_value))
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for key, value in static_param_init_value.items():
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np.testing.assert_allclose(
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value, dy_param_init_value[key], rtol=1e-05
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)
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self.assertTrue(np.isfinite(value.all()))
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self.assertFalse(np.isnan(value.any()))
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self.assertEqual(len(dy_grad_value), len(static_grad_value))
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for key, value in static_grad_value.items():
|
|
np.testing.assert_allclose(value, dy_grad_value[key], rtol=1e-05)
|
|
self.assertTrue(np.isfinite(value.all()))
|
|
self.assertFalse(np.isnan(value.any()))
|
|
|
|
self.assertEqual(len(dy_param_value), len(static_param_value))
|
|
for key, value in static_param_value.items():
|
|
np.testing.assert_allclose(value, dy_param_value[key], rtol=1e-05)
|
|
self.assertTrue(np.isfinite(value.all()))
|
|
self.assertFalse(np.isnan(value.any()))
|
|
|
|
# check eager
|
|
np.testing.assert_allclose(static_out, eager_out, rtol=1e-05)
|
|
|
|
self.assertEqual(
|
|
len(eager_param_init_value), len(static_param_init_value)
|
|
)
|
|
|
|
for key, value in static_param_init_value.items():
|
|
np.testing.assert_allclose(
|
|
value, eager_param_init_value[key], rtol=1e-05
|
|
)
|
|
|
|
self.assertEqual(len(eager_grad_value), len(static_grad_value))
|
|
|
|
for key, value in static_grad_value.items():
|
|
np.testing.assert_allclose(value, eager_grad_value[key], rtol=1e-05)
|
|
|
|
self.assertEqual(len(eager_param_value), len(static_param_value))
|
|
for key, value in static_param_value.items():
|
|
np.testing.assert_allclose(
|
|
value, eager_param_value[key], rtol=1e-05
|
|
)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
paddle.enable_static()
|
|
unittest.main()
|