wehub-resource-sync
469 lines
15 KiB
Python
469 lines
15 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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from utils import DyGraphProgramDescTracerTestHelper
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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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# NOTE(zhiqiu): run with FLAGS_cudnn_deterministic=1
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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": 1281164,
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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 = 1281167
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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(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 = []
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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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# TODO(minqiyang): Add learning rate scheduler support to dygraph mode
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# optimizer = base.optimizer.Momentum(
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# learning_rate=params["lr"],
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# learning_rate=paddle.optimizer.lr.piecewise_decay(
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# boundaries=bd, values=lr),
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# momentum=0.9,
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# regularization=paddle.regularizer.L2Decay(1e-4))
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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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use_cudnn=False,
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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=False,
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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 BottleneckBlock(paddle.nn.Layer):
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def __init__(
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self, num_channels, num_filters, stride, shortcut=True, use_cudnn=False
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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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use_cudnn=use_cudnn,
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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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act='relu',
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use_cudnn=use_cudnn,
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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=None,
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use_cudnn=use_cudnn,
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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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use_cudnn=use_cudnn,
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)
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self.shortcut = shortcut
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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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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=conv2)
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layer_helper = LayerHelper(self.full_name(), act='relu')
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return layer_helper.append_activation(y)
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class ResNet(paddle.nn.Layer):
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def __init__(self, layers=50, class_dim=102, use_cudnn=True):
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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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depth = [3, 4, 6, 3]
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elif layers == 101:
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depth = [3, 4, 23, 3]
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elif layers == 152:
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depth = [3, 8, 36, 3]
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num_channels = [64, 256, 512, 1024]
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num_filters = [64, 128, 256, 512]
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self.conv = 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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use_cudnn=use_cudnn,
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)
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self.pool2d_max = paddle.nn.MaxPool2D(
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kernel_size=3, stride=2, padding=1
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)
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self.bottleneck_block_list = []
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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=(
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num_channels[block]
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if i == 0
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else num_filters[block] * 4
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),
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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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shortcut=shortcut,
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use_cudnn=use_cudnn,
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),
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)
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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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self.pool2d_avg_output = num_filters[-1] * 4 * 1 * 1
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import math
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stdv = 1.0 / math.sqrt(2048 * 1.0)
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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=base.param_attr.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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y = self.conv(inputs)
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y = self.pool2d_max(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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y = paddle.nn.functional.softmax(y)
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return y
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class TestDygraphResnet(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_resnet_float32(self):
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seed = 90
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batch_size = train_parameters["batch_size"]
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batch_num = 10
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traced_layer = None
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with base.dygraph.guard():
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paddle.seed(seed)
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paddle.framework.random._manual_program_seed(seed)
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resnet = ResNet()
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optimizer = optimizer_setting(
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train_parameters, parameter_list=resnet.parameters()
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)
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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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)
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dy_param_init_value = {}
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for param in resnet.parameters():
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dy_param_init_value[param.name] = param.numpy()
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helper = DyGraphProgramDescTracerTestHelper(self)
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program = None
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for batch_id, data in enumerate(train_reader()):
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if batch_id >= batch_num:
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break
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dy_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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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 = None
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out = resnet(img)
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if traced_layer is not None:
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resnet.eval()
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traced_layer._switch(is_test=True)
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out_dygraph = resnet(img)
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out_static = traced_layer([img])
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traced_layer._switch(is_test=False)
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helper.assertEachVar(out_dygraph, out_static)
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resnet.train()
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loss = paddle.nn.functional.cross_entropy(
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input=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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dy_out = avg_loss.numpy()
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if batch_id == 0:
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for param in resnet.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 resnet.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[param.name + core.grad_var_suffix()] = (
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np_array
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)
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optimizer.minimize(avg_loss)
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resnet.clear_gradients()
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dy_param_value = {}
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for param in resnet.parameters():
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dy_param_value[param.name] = param.numpy()
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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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resnet = ResNet()
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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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)
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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 = resnet(img)
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loss = paddle.nn.functional.cross_entropy(
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input=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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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 resnet.parameters():
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static_param_name_list.append(param.name)
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for param in resnet.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 batch_id, data in enumerate(train_reader()):
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if batch_id >= batch_num:
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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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if traced_layer is not None:
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traced_layer([static_x_data])
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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 = len(static_param_name_list) + param_start_pos
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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, 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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print("static", static_out)
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print("dygraph", dy_out)
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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():
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np.testing.assert_allclose(value, dy_grad_value[key], rtol=1e-05)
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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_param_value), len(static_param_value))
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for key, value in static_param_value.items():
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np.testing.assert_allclose(value, dy_param_value[key], rtol=1e-05)
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self.assertTrue(np.isfinite(value.all()))
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self.assertFalse(np.isnan(value.any()))
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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