wehub-resource-sync
381 lines
12 KiB
Python
381 lines
12 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import os
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import random
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import sys
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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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test_default_mode_only,
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)
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from tsm_config_utils import merge_configs, parse_config, print_configs
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import paddle
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from paddle.nn import BatchNorm, Linear
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random.seed(0)
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np.random.seed(0)
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def parse_args():
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parser = argparse.ArgumentParser("Paddle Video train script")
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parser.add_argument(
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'--config',
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type=str,
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default='tsm.yaml',
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help='path to config file of model',
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)
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parser.add_argument(
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'--use_gpu',
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type=bool,
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default=paddle.is_compiled_with_cuda(),
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help='default use gpu.',
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)
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args = parser.parse_args(
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['--config', __file__.rpartition('/')[0] + '/tsm.yaml']
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)
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return args
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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=1,
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weight_attr=paddle.ParamAttr(),
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bias_attr=False,
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)
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self._batch_norm = BatchNorm(
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num_filters,
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act=act,
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param_attr=paddle.ParamAttr(),
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bias_attr=paddle.ParamAttr(),
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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._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, seg_num=8
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):
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super().__init__()
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self.conv0 = ConvBNLayer(
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num_channels=num_channels,
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num_filters=num_filters,
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filter_size=1,
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act='relu',
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)
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self.conv1 = ConvBNLayer(
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num_channels=num_filters,
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num_filters=num_filters,
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filter_size=3,
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stride=stride,
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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.seg_num = seg_num
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self._num_channels_out = int(num_filters * 4)
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def forward(self, inputs):
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shifts = paddle.nn.functional.temporal_shift(
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inputs, self.seg_num, 1.0 / 8
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)
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y = self.conv0(shifts)
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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.nn.functional.relu(paddle.add(x=short, y=conv2))
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return y
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class TSM_ResNet(paddle.nn.Layer):
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def __init__(self, name_scope, config, mode):
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super().__init__(name_scope)
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self.layers = config.MODEL.num_layers
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self.seg_num = config.MODEL.seg_num
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self.class_dim = config.MODEL.num_classes
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self.reshape_list = [
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config.MODEL.seglen * 3,
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config[mode.upper()]['target_size'],
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config[mode.upper()]['target_size'],
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]
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if self.layers == 50:
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depth = [3, 4, 6, 3]
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else:
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raise NotImplementedError
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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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num_channels = 64
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for block in range(len(depth)):
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shortcut = False
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for i in range(depth[block]):
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bottleneck_block = self.add_sublayer(
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f'bb_{block}_{i}',
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BottleneckBlock(
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num_channels=num_channels,
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num_filters=num_filters[block],
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stride=2 if i == 0 and block != 0 else 1,
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shortcut=shortcut,
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seg_num=self.seg_num,
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),
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)
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num_channels = int(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.out = Linear(
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2048,
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self.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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bias_attr=paddle.ParamAttr(
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learning_rate=2.0, regularizer=paddle.regularizer.L1Decay()
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),
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)
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def forward(self, inputs):
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y = paddle.reshape(inputs, [-1, *self.reshape_list])
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y = self.conv(y)
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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.nn.functional.dropout(y, p=0.5)
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y = paddle.reshape(y, [-1, self.seg_num, y.shape[1]])
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y = paddle.mean(y, axis=1)
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y = paddle.reshape(y, shape=[-1, 2048])
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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 FakeDataReader:
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def __init__(self, mode, cfg):
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self.format = cfg.MODEL.format
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self.num_classes = cfg.MODEL.num_classes
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self.seg_num = cfg.MODEL.seg_num
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self.seglen = cfg.MODEL.seglen
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self.target_size = cfg[mode.upper()]['target_size']
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self.img_mean = (
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np.array(cfg.MODEL.image_mean).reshape([3, 1, 1]).astype(np.float32)
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)
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self.img_std = (
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np.array(cfg.MODEL.image_std).reshape([3, 1, 1]).astype(np.float32)
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)
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self.batch_size = (
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1
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if sys.platform == 'darwin' or os.name == 'nt'
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else cfg[mode.upper()]['batch_size']
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)
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self.generator_out = []
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self.total_iter = 3
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for i in range(self.total_iter):
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batch_out = []
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for j in range(self.batch_size):
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label = np.int64(random.randint(0, self.num_classes - 1))
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random_mean = self.img_mean[0][0][0]
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random_std = self.img_std[0][0][0]
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imgs = np.random.normal(
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random_mean,
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random_std,
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[
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self.seg_num,
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self.seglen * 3,
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self.target_size,
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self.target_size,
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],
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).astype(np.float32)
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batch_out.append((imgs, label))
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self.generator_out.append(batch_out)
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def create_reader(self):
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def batch_reader():
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for i in range(self.total_iter):
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yield self.generator_out[i]
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return batch_reader
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def create_optimizer(cfg, params):
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total_videos = cfg.total_videos
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batch_size = (
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1 if sys.platform == 'darwin' or os.name == 'nt' else cfg.batch_size
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)
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step = int(total_videos / batch_size + 1)
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bd = [e * step for e in cfg.decay_epochs]
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base_lr = cfg.learning_rate
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lr_decay = cfg.learning_rate_decay
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lr = [base_lr, base_lr * lr_decay, base_lr * lr_decay * lr_decay]
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l2_weight_decay = cfg.l2_weight_decay
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momentum = cfg.momentum
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optimizer = paddle.optimizer.Momentum(
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learning_rate=paddle.optimizer.lr.PiecewiseDecay(
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boundaries=bd, values=lr
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),
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momentum=momentum,
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weight_decay=paddle.regularizer.L2Decay(l2_weight_decay),
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parameters=params,
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)
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return optimizer
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def train(args, fake_data_reader):
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config = parse_config(args.config)
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train_config = merge_configs(config, 'train', vars(args))
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valid_config = merge_configs(config, 'valid', vars(args))
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print_configs(train_config, 'Train')
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random.seed(0)
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np.random.seed(0)
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paddle.seed(1000)
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paddle.framework.random._manual_program_seed(1000)
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video_model = paddle.jit.to_static(TSM_ResNet("TSM", train_config, 'Train'))
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optimizer = create_optimizer(train_config.TRAIN, video_model.parameters())
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train_reader = fake_data_reader.create_reader()
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ret = []
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for epoch in range(train_config.TRAIN.epoch):
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video_model.train()
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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(train_reader()):
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x_data = np.array([item[0] for item in data])
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y_data = np.array([item[1] for item in data]).reshape([-1, 1])
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imgs = paddle.to_tensor(x_data)
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labels = paddle.to_tensor(y_data)
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labels.stop_gradient = True
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outputs = video_model(imgs)
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loss = paddle.nn.functional.cross_entropy(
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input=outputs,
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label=labels,
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ignore_index=-1,
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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(loss)
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acc_top1 = paddle.static.accuracy(input=outputs, label=labels, k=1)
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acc_top5 = paddle.static.accuracy(input=outputs, label=labels, k=5)
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avg_loss.backward()
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optimizer.minimize(avg_loss)
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video_model.clear_gradients()
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total_loss += float(avg_loss)
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total_acc1 += float(acc_top1)
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total_acc5 += float(acc_top5)
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total_sample += 1
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print(
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f'TRAIN Epoch {epoch}, iter {batch_id}, loss = {float(avg_loss)}, acc1 {float(acc_top1)}, acc5 {float(acc_top5)}'
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)
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ret.extend(
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[
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float(avg_loss),
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float(acc_top1),
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float(acc_top5),
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]
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)
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print(
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f'TRAIN End, Epoch {epoch}, avg_loss= {total_loss / total_sample}, avg_acc1= {total_acc1 / total_sample}, avg_acc5= {total_acc5 / total_sample}'
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)
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return ret
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class TestTsm(Dy2StTestBase):
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@test_default_mode_only
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def test_dygraph_static_same_loss(self):
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if paddle.is_compiled_with_cuda():
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paddle.set_flags({"FLAGS_cudnn_deterministic": True})
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args = parse_args()
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fake_data_reader = FakeDataReader("train", parse_config(args.config))
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with enable_to_static_guard(False):
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dygraph_loss = train(args, fake_data_reader)
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static_loss = train(args, fake_data_reader)
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np.testing.assert_allclose(dygraph_loss, static_loss, rtol=1e-05)
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if __name__ == '__main__':
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unittest.main()
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