resnet.py

import numpy as np
import argparse
import ast
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
from paddle.fluid.dygraph.base import to_variable

from paddle.fluid import framework

import math
import sys
import time

class ConvBNLayer(fluid.dygraph.Layer):
    def __init__(self,
                 num_channels,
                 num_filters,
                 filter_size,
                 stride=1,
                 groups=1,
                 act=None,
                 name=None):
        super(ConvBNLayer, self).__init__()

        self._conv = Conv2D(
            num_channels=num_channels,
            num_filters=num_filters,
            filter_size=filter_size,
            stride=stride,
            padding=(filter_size - 1) // 2,
            groups=groups,
            act=None,
            param_attr=ParamAttr(name=name + "_weights"),
            bias_attr=False)
        if name == "conv1":
            bn_name = "bn_" + name
        else:
            bn_name = "bn" + name[3:]
        self._batch_norm = BatchNorm(num_filters, 
                                     act=act,
                                     param_attr=ParamAttr(name=bn_name + '_scale'),
                                     bias_attr=ParamAttr(bn_name + '_offset'),
                                     moving_mean_name=bn_name + '_mean',
                                     moving_variance_name=bn_name + '_variance')

    def forward(self, inputs):
        y = self._conv(inputs)
        y = self._batch_norm(y)
        return y


class BottleneckBlock(fluid.dygraph.Layer):
    def __init__(self,
                 num_channels,
                 num_filters,
                 stride,
                 shortcut=True,
                 name=None):
        super(BottleneckBlock, self).__init__()

        self.conv0 = ConvBNLayer(
            num_channels=num_channels,
            num_filters=num_filters,
            filter_size=1,
            act='relu',
            name=name+"_branch2a")
        self.conv1 = ConvBNLayer(
            num_channels=num_filters,
            num_filters=num_filters,
            filter_size=3,
            stride=stride,
            act='relu',
            name=name+"_branch2b")
        self.conv2 = ConvBNLayer(
            num_channels=num_filters,
            num_filters=num_filters * 4,
            filter_size=1,
            act=None,
            name=name+"_branch2c")

        if not shortcut:
            self.short = ConvBNLayer(
                num_channels=num_channels,
                num_filters=num_filters * 4,
                filter_size=1,
                stride=stride,
                name=name + "_branch1")

        self.shortcut = shortcut

        self._num_channels_out = num_filters * 4

    def forward(self, inputs):
        y = self.conv0(inputs)
        conv1 = self.conv1(y)
        conv2 = self.conv2(conv1)

        if self.shortcut:
            short = inputs
        else:
            short = self.short(inputs)

        y = fluid.layers.elementwise_add(x=short, y=conv2)

        layer_helper = LayerHelper(self.full_name(), act='relu')
        return layer_helper.append_activation(y)


class ResNet(fluid.dygraph.Layer):
    def __init__(self, layers=50, class_dim=1000):
        super(ResNet, self).__init__()

        self.layers = layers
        supported_layers = [50, 101, 152]
        assert layers in supported_layers, \
            "supported layers are {} but input layer is {}".format(supported_layers, layers)
        self.fm = None

        if layers == 50:
            depth = [3, 4, 6, 3]
        elif layers == 101:
            depth = [3, 4, 23, 3]
        elif layers == 152:
            depth = [3, 8, 36, 3]
        num_channels = [64, 256, 512, 1024]
        num_filters = [64, 128, 256, 512]

        self.conv = ConvBNLayer(
            num_channels=3,
            num_filters=64,
            filter_size=7,
            stride=2,
            act='relu',
            name="conv1")
        self.pool2d_max = Pool2D(
            pool_size=3,
            pool_stride=2,
            pool_padding=1,
            pool_type='max')

        self.bottleneck_block_list = []
        for block in range(len(depth)):
            shortcut = False
            for i in range(depth[block]):
                if layers in [101, 152] and block == 2:
                    if i == 0:
                        conv_name="res"+str(block+2)+"a"
                    else:
                        conv_name="res"+str(block+2)+"b"+str(i)
                else:
                    conv_name="res"+str(block+2)+chr(97+i)
                bottleneck_block = self.add_sublayer(
                    'bb_%d_%d' % (block, i),
                    BottleneckBlock(
                        num_channels=num_channels[block]
                        if i == 0 else num_filters[block] * 4,
                        num_filters=num_filters[block],
                        stride=2 if i == 0 and block != 0 else 1,
                        shortcut=shortcut,
                        name=conv_name))
                self.bottleneck_block_list.append(bottleneck_block)
                shortcut = True

        self.pool2d_avg = Pool2D(
            pool_size=7, pool_type='avg', global_pooling=True)

        self.pool2d_avg_output = num_filters[len(num_filters) - 1] * 4 * 1 * 1

        stdv = 1.0 / math.sqrt(2048 * 1.0)

        self.out = Linear(self.pool2d_avg_output,
                      class_dim,
                      param_attr=ParamAttr(
                          initializer=fluid.initializer.Uniform(-stdv, stdv), name="fc_0.w_0"),
                      bias_attr=ParamAttr(name="fc_0.b_0"))

    def forward(self, inputs):
        y = self.conv(inputs)
        y = self.pool2d_max(y)
        self.fm = y
        for bottleneck_block in self.bottleneck_block_list:
            y = bottleneck_block(y)
        y = self.pool2d_avg(y)
        y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_output])
        y = self.out(y)
        return y, self.fm


def ResNet50(**args):
    model = ResNet(layers=50, **args)
    return model


def ResNet101(**args):
    model = ResNet(layers=101, **args)
    return model


def ResNet152(**args):
    model = ResNet(layers=152, **args)
    return model


if __name__ == "__main__":
    import numpy as np
    place = fluid.CPUPlace()
    with fluid.dygraph.guard(place): 
        model = ResNet50()
        img = np.random.uniform(0, 255, [1, 3, 224, 224]).astype('float32')
        img = fluid.dygraph.to_variable(img)
        res = model(img)
        print(res.shape)