wehub-resource-sync
488 lines
15 KiB
Python
488 lines
15 KiB
Python
# Copyright (c) 2020 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 os
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import tempfile
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import time
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import unittest
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import numpy as np
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from dygraph_to_static_utils import (
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Dy2StTestBase,
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enable_to_static_guard,
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static_guard,
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test_default_mode_only,
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)
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from predictor_utils import PredictorTools
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import paddle
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from paddle.base import core
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SEED = 2025
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IMAGENET1000 = 1281167
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base_lr = 0.001
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momentum_rate = 0.9
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l2_decay = 1e-4
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# NOTE: Reduce batch_size from 8 to 2 to avoid unittest timeout.
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batch_size = 2
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epoch_num = 1
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place = (
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paddle.CUDAPlace(0) if paddle.is_compiled_with_cuda() else paddle.CPUPlace()
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)
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if paddle.is_compiled_with_cuda():
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paddle.base.set_flags({'FLAGS_cudnn_deterministic': True})
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def optimizer_setting(parameter_list=None):
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optimizer = paddle.optimizer.Momentum(
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learning_rate=base_lr,
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momentum=momentum_rate,
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weight_decay=paddle.regularizer.L2Decay(l2_decay),
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parameters=parameter_list,
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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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bias_attr=False,
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)
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self._batch_norm = paddle.nn.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__(self, num_channels, num_filters, stride, shortcut=True):
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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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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 * 4,
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filter_size=1,
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act=None,
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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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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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# TODO: uncomment this lines to reproduce the oneDNN segment fault error.
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# layer_helper = paddle.base.layer_helper.LayerHelper(
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# self.full_name(), act='relu'
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# )
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# return layer_helper.append_activation(y)
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return paddle.nn.functional.relu(y)
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class ResNet(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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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, num_filters=64, filter_size=7, stride=2, act='relu'
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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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),
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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[len(num_filters) - 1] * 4 * 1 * 1
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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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in_features=self.pool2d_avg_output,
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out_features=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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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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pred = self.out(y)
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pred = paddle.nn.functional.softmax(pred)
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return pred
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def reader_decorator(reader):
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def __reader__():
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for item in reader():
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img = np.array(item[0]).astype('float32').reshape(3, 224, 224)
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label = np.array(item[1]).astype('int64').reshape(1)
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yield img, label
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return __reader__
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class TransedFlowerDataSet(paddle.io.Dataset):
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def __init__(self, length):
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self.img = []
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self.label = []
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self._generate(length)
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def _generate(self, length):
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for i, data in enumerate(range(1000)):
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image = paddle.randn((3, 224, 224)).astype("float32").cpu()
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label = np.array(
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[paddle.randint(0, 100, (1,)).astype("int64").item()]
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)
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if i >= length:
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break
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self.img.append(image)
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self.label.append(label)
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def __getitem__(self, idx):
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return self.img[idx], self.label[idx]
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def __len__(self):
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return len(self.img)
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class ResNetHelper:
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def __init__(self):
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self.temp_dir = tempfile.TemporaryDirectory()
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self.model_save_dir = os.path.join(self.temp_dir.name, "./inference")
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self.model_save_prefix = os.path.join(
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self.temp_dir.name, "./inference/resnet"
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)
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self.model_filename = (
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"resnet" + paddle.jit.translated_layer.INFER_MODEL_SUFFIX
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)
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self.pir_model_filename = (
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"resnet" + paddle.jit.pir_translated_layer.PIR_INFER_MODEL_SUFFIX
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)
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self.params_filename = (
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"resnet" + paddle.jit.translated_layer.INFER_PARAMS_SUFFIX
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)
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self.dy_state_dict_save_path = os.path.join(
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self.temp_dir.name, "./resnet.dygraph"
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)
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def __del__(self):
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self.temp_dir.cleanup()
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def train(self, to_static, build_strategy=None):
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"""
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Tests model decorated by `dygraph_to_static_output` in static graph mode. For users, the model is defined in dygraph mode and trained in static graph mode.
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"""
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np.random.seed(SEED)
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paddle.seed(SEED)
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paddle.framework.random._manual_program_seed(SEED)
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dataset = TransedFlowerDataSet(
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batch_size * (10 + 1),
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)
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data_loader = paddle.io.DataLoader(
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dataset, batch_size=batch_size, drop_last=True
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)
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resnet = paddle.jit.to_static(ResNet(), build_strategy=build_strategy)
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optimizer = optimizer_setting(parameter_list=resnet.parameters())
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for epoch in range(epoch_num):
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total_loss = 0.0
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total_acc1 = 0.0
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total_acc5 = 0.0
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total_sample = 0
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for batch_id, data in enumerate(data_loader()):
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start_time = time.time()
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img_, label = data
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expected_place = paddle.framework._current_expected_place()
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if img_.stop_gradient and not img_.place._equals(
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expected_place
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):
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img = img_._copy_to(expected_place, False)
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img.stop_gradient = True
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else:
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img = img_
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pred = resnet(img)
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loss = paddle.nn.functional.cross_entropy(
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input=pred, label=label
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)
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avg_loss = paddle.mean(x=loss)
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acc_top1 = paddle.metric.accuracy(input=pred, label=label, k=1)
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acc_top5 = paddle.metric.accuracy(input=pred, label=label, k=5)
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avg_loss.backward()
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optimizer.minimize(avg_loss)
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resnet.clear_gradients()
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total_loss += avg_loss
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total_acc1 += acc_top1
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total_acc5 += acc_top5
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total_sample += 1
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end_time = time.time()
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if batch_id % 2 == 0:
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print(
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f"epoch {epoch} | batch step {batch_id}, "
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f"loss {total_loss.numpy() / total_sample:0.3f}, "
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f"acc1 {total_acc1.numpy() / total_sample:0.3f}, "
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f"acc5 {total_acc5.numpy() / total_sample:0.3f}, "
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f"time {end_time - start_time:f}"
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)
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if batch_id == 10:
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if to_static:
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paddle.jit.save(resnet, self.model_save_prefix)
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else:
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paddle.save(
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resnet.state_dict(),
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self.dy_state_dict_save_path + '.pdparams',
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)
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break
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return total_loss.numpy()
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def predict_dygraph(self, data):
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with enable_to_static_guard(False):
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resnet = paddle.jit.to_static(ResNet())
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model_dict = paddle.load(self.dy_state_dict_save_path + '.pdparams')
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resnet.set_dict(model_dict)
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resnet.eval()
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pred_res = resnet(
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paddle.to_tensor(
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data=data, dtype=None, place=None, stop_gradient=True
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)
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)
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ret = pred_res.numpy()
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return ret
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def predict_static(self, data):
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with static_guard():
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exe = paddle.static.Executor(place)
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model_filename = self.pir_model_filename
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[
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inference_program,
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feed_target_names,
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fetch_targets,
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] = paddle.static.load_inference_model(
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self.model_save_dir,
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executor=exe,
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model_filename=model_filename,
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params_filename=self.params_filename,
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)
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pred_res = exe.run(
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inference_program,
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feed={feed_target_names[0]: data},
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fetch_list=fetch_targets,
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)
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return pred_res[0]
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def predict_dygraph_jit(self, data):
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resnet = paddle.jit.load(self.model_save_prefix)
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resnet.eval()
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pred_res = resnet(data)
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ret = pred_res.numpy()
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return ret
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def predict_analysis_inference(self, data):
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model_filename = self.pir_model_filename
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output = PredictorTools(
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self.model_save_dir,
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model_filename,
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self.params_filename,
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[data],
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)
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(out,) = output()
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return out
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class TestResnet(Dy2StTestBase):
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def setUp(self):
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self.resnet_helper = ResNetHelper()
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def train(self, to_static):
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with enable_to_static_guard(to_static):
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return self.resnet_helper.train(to_static)
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def verify_predict(self):
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image = np.random.random([1, 3, 224, 224]).astype('float32')
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dy_pre = self.resnet_helper.predict_dygraph(image)
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st_pre = self.resnet_helper.predict_static(image)
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dy_jit_pre = self.resnet_helper.predict_dygraph_jit(image)
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predictor_pre = self.resnet_helper.predict_analysis_inference(image)
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np.testing.assert_allclose(
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dy_pre,
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st_pre,
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rtol=1e-05,
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err_msg=f'dy_pre:\n {dy_pre}\n, st_pre: \n{st_pre}.',
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)
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np.testing.assert_allclose(
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dy_jit_pre,
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st_pre,
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rtol=1e-05,
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err_msg=f'dy_jit_pre:\n {dy_jit_pre}\n, st_pre: \n{st_pre}.',
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)
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np.testing.assert_allclose(
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predictor_pre,
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st_pre,
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rtol=1e-05,
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atol=1e-7,
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err_msg=f'predictor_pre:\n {predictor_pre}\n, st_pre: \n{st_pre}.',
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)
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@test_default_mode_only
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def test_resnet(self):
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static_loss = self.train(to_static=True)
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dygraph_loss = self.train(to_static=False)
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np.testing.assert_allclose(
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static_loss,
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dygraph_loss,
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rtol=1e-05,
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err_msg=f'static_loss: {static_loss} \n dygraph_loss: {dygraph_loss}',
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)
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self.verify_predict()
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@test_default_mode_only
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def test_resnet_composite(self):
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core._set_prim_backward_enabled(True)
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core._add_skip_comp_ops("batch_norm")
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static_loss = self.train(to_static=True)
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core._set_prim_backward_enabled(False)
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dygraph_loss = self.train(to_static=False)
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np.testing.assert_allclose(
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static_loss,
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dygraph_loss,
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rtol=1e-05,
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err_msg=f'static_loss: {static_loss} \n dygraph_loss: {dygraph_loss}',
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)
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@test_default_mode_only
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def test_in_static_mode_onednn(self):
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paddle.set_flags({'FLAGS_use_onednn': True})
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try:
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if paddle.base.core.is_compiled_with_onednn():
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self.train(to_static=True)
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finally:
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paddle.set_flags({'FLAGS_use_onednn': False})
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if __name__ == '__main__':
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unittest.main()
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