fast-reid/fastreid/modeling/backbones/resnext.py

# encoding: utf-8
"""
@author:  xingyu liao
@contact: sherlockliao01@gmail.com
"""

# based on:
# https://github.com/XingangPan/IBN-Net/blob/master/models/imagenet/resnext_ibn_a.py

import logging
import math

import torch
import torch.nn as nn

from fastreid.layers import *
from fastreid.utils import comm
from fastreid.utils.checkpoint import get_missing_parameters_message, get_unexpected_parameters_message
from .build import BACKBONE_REGISTRY

logger = logging.getLogger(__name__)
model_urls = {
    'ibn_101x': 'https://github.com/XingangPan/IBN-Net/releases/download/v1.0/resnext101_ibn_a-6ace051d.pth',
}


class Bottleneck(nn.Module):
    """
    RexNeXt bottleneck type C
    """
    expansion = 4

    def __init__(self, inplanes, planes, bn_norm, num_splits, with_ibn, baseWidth, cardinality, stride=1,
                 downsample=None):
        """ Constructor
        Args:
            inplanes: input channel dimensionality
            planes: output channel dimensionality
            baseWidth: base width.
            cardinality: num of convolution groups.
            stride: conv stride. Replaces pooling layer.
        """
        super(Bottleneck, self).__init__()

        D = int(math.floor(planes * (baseWidth / 64)))
        C = cardinality
        self.conv1 = nn.Conv2d(inplanes, D * C, kernel_size=1, stride=1, padding=0, bias=False)
        if with_ibn:
            self.bn1 = IBN(D * C, bn_norm, num_splits)
        else:
            self.bn1 = get_norm(bn_norm, D * C, num_splits)
        self.conv2 = nn.Conv2d(D * C, D * C, kernel_size=3, stride=stride, padding=1, groups=C, bias=False)
        self.bn2 = get_norm(bn_norm, D * C, num_splits)
        self.conv3 = nn.Conv2d(D * C, planes * 4, kernel_size=1, stride=1, padding=0, bias=False)
        self.bn3 = get_norm(bn_norm, planes * 4, num_splits)
        self.relu = nn.ReLU(inplace=True)

        self.downsample = downsample

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class ResNeXt(nn.Module):
    """
    ResNext optimized for the ImageNet dataset, as specified in
    https://arxiv.org/pdf/1611.05431.pdf
    """

    def __init__(self, last_stride, bn_norm, num_splits, with_ibn, with_nl, block, layers, non_layers,
                 baseWidth=4, cardinality=32):
        """ Constructor
        Args:
            baseWidth: baseWidth for ResNeXt.
            cardinality: number of convolution groups.
            layers: config of layers, e.g., [3, 4, 6, 3]
        """
        super(ResNeXt, self).__init__()

        self.cardinality = cardinality
        self.baseWidth = baseWidth
        self.inplanes = 64
        self.output_size = 64

        self.conv1 = nn.Conv2d(3, 64, 7, 2, 3, bias=False)
        self.bn1 = get_norm(bn_norm, 64, num_splits)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0], 1, bn_norm, num_splits, with_ibn=with_ibn)
        self.layer2 = self._make_layer(block, 128, layers[1], 2, bn_norm, num_splits, with_ibn=with_ibn)
        self.layer3 = self._make_layer(block, 256, layers[2], 2, bn_norm, num_splits, with_ibn=with_ibn)
        self.layer4 = self._make_layer(block, 512, layers[3], last_stride, bn_norm, num_splits, with_ibn=with_ibn)

        self.random_init()

        # fmt: off
        if with_nl: self._build_nonlocal(layers, non_layers, bn_norm, num_splits)
        else:       self.NL_1_idx = self.NL_2_idx = self.NL_3_idx = self.NL_4_idx = []
        # fmt: on

    def _make_layer(self, block, planes, blocks, stride=1, bn_norm='BN', num_splits=1, with_ibn=False):
        """ Stack n bottleneck modules where n is inferred from the depth of the network.
        Args:
            block: block type used to construct ResNext
            planes: number of output channels (need to multiply by block.expansion)
            blocks: number of blocks to be built
            stride: factor to reduce the spatial dimensionality in the first bottleneck of the block.
        Returns: a Module consisting of n sequential bottlenecks.
        """
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                get_norm(bn_norm, planes * block.expansion, num_splits),
            )

        layers = []
        if planes == 512:
            with_ibn = False
        layers.append(block(self.inplanes, planes, bn_norm, num_splits, with_ibn,
                            self.baseWidth, self.cardinality, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(
                block(self.inplanes, planes, bn_norm, num_splits, with_ibn, self.baseWidth, self.cardinality, 1, None))

        return nn.Sequential(*layers)

    def _build_nonlocal(self, layers, non_layers, bn_norm, num_splits):
        self.NL_1 = nn.ModuleList(
            [Non_local(256, bn_norm, num_splits) for _ in range(non_layers[0])])
        self.NL_1_idx = sorted([layers[0] - (i + 1) for i in range(non_layers[0])])
        self.NL_2 = nn.ModuleList(
            [Non_local(512, bn_norm, num_splits) for _ in range(non_layers[1])])
        self.NL_2_idx = sorted([layers[1] - (i + 1) for i in range(non_layers[1])])
        self.NL_3 = nn.ModuleList(
            [Non_local(1024, bn_norm, num_splits) for _ in range(non_layers[2])])
        self.NL_3_idx = sorted([layers[2] - (i + 1) for i in range(non_layers[2])])
        self.NL_4 = nn.ModuleList(
            [Non_local(2048, bn_norm, num_splits) for _ in range(non_layers[3])])
        self.NL_4_idx = sorted([layers[3] - (i + 1) for i in range(non_layers[3])])

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool1(x)

        NL1_counter = 0
        if len(self.NL_1_idx) == 0:
            self.NL_1_idx = [-1]
        for i in range(len(self.layer1)):
            x = self.layer1[i](x)
            if i == self.NL_1_idx[NL1_counter]:
                _, C, H, W = x.shape
                x = self.NL_1[NL1_counter](x)
                NL1_counter += 1
        # Layer 2
        NL2_counter = 0
        if len(self.NL_2_idx) == 0:
            self.NL_2_idx = [-1]
        for i in range(len(self.layer2)):
            x = self.layer2[i](x)
            if i == self.NL_2_idx[NL2_counter]:
                _, C, H, W = x.shape
                x = self.NL_2[NL2_counter](x)
                NL2_counter += 1
        # Layer 3
        NL3_counter = 0
        if len(self.NL_3_idx) == 0:
            self.NL_3_idx = [-1]
        for i in range(len(self.layer3)):
            x = self.layer3[i](x)
            if i == self.NL_3_idx[NL3_counter]:
                _, C, H, W = x.shape
                x = self.NL_3[NL3_counter](x)
                NL3_counter += 1
        # Layer 4
        NL4_counter = 0
        if len(self.NL_4_idx) == 0:
            self.NL_4_idx = [-1]
        for i in range(len(self.layer4)):
            x = self.layer4[i](x)
            if i == self.NL_4_idx[NL4_counter]:
                _, C, H, W = x.shape
                x = self.NL_4[NL4_counter](x)
                NL4_counter += 1
        return x

    def random_init(self):
        self.conv1.weight.data.normal_(0, math.sqrt(2. / (7 * 7 * 64)))
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.InstanceNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()


def init_pretrained_weights(key):
    """Initializes model with pretrained weights.

    Layers that don't match with pretrained layers in name or size are kept unchanged.
    """
    import os
    import errno
    import gdown

    def _get_torch_home():
        ENV_TORCH_HOME = 'TORCH_HOME'
        ENV_XDG_CACHE_HOME = 'XDG_CACHE_HOME'
        DEFAULT_CACHE_DIR = '~/.cache'
        torch_home = os.path.expanduser(
            os.getenv(
                ENV_TORCH_HOME,
                os.path.join(
                    os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), 'torch'
                )
            )
        )
        return torch_home

    torch_home = _get_torch_home()
    model_dir = os.path.join(torch_home, 'checkpoints')
    try:
        os.makedirs(model_dir)
    except OSError as e:
        if e.errno == errno.EEXIST:
            # Directory already exists, ignore.
            pass
        else:
            # Unexpected OSError, re-raise.
            raise

    filename = model_urls[key].split('/')[-1]

    cached_file = os.path.join(model_dir, filename)

    if not os.path.exists(cached_file):
        if comm.is_main_process():
            gdown.download(model_urls[key], cached_file, quiet=False)

    comm.synchronize()

    logger.info(f"Loading pretrained model from {cached_file}")
    state_dict = torch.load(cached_file, map_location=torch.device('cpu'))

    return state_dict


@BACKBONE_REGISTRY.register()
def build_resnext_backbone(cfg):
    """
    Create a ResNeXt instance from config.
    Returns:
        ResNeXt: a :class:`ResNeXt` instance.
    """

    # fmt: off
    pretrain      = cfg.MODEL.BACKBONE.PRETRAIN
    pretrain_path = cfg.MODEL.BACKBONE.PRETRAIN_PATH
    last_stride   = cfg.MODEL.BACKBONE.LAST_STRIDE
    bn_norm       = cfg.MODEL.BACKBONE.NORM
    num_splits    = cfg.MODEL.BACKBONE.NORM_SPLIT
    with_ibn      = cfg.MODEL.BACKBONE.WITH_IBN
    with_nl       = cfg.MODEL.BACKBONE.WITH_NL
    depth         = cfg.MODEL.BACKBONE.DEPTH
    # fmt: on

    num_blocks_per_stage = {
        '50x': [3, 4, 6, 3],
        '101x': [3, 4, 23, 3],
        '152x': [3, 8, 36, 3], }[depth]
    nl_layers_per_stage = {
        '50x': [0, 2, 3, 0],
        '101x': [0, 2, 3, 0]}[depth]
    model = ResNeXt(last_stride, bn_norm, num_splits, with_ibn, with_nl, Bottleneck,
                    num_blocks_per_stage, nl_layers_per_stage)
    if pretrain:
        if pretrain_path:
            try:
                state_dict = torch.load(pretrain_path, map_location=torch.device('cpu'))['model']
                # Remove module.encoder in name
                new_state_dict = {}
                for k in state_dict:
                    new_k = '.'.join(k.split('.')[2:])
                    if new_k in model.state_dict() and (model.state_dict()[new_k].shape == state_dict[k].shape):
                        new_state_dict[new_k] = state_dict[k]
                state_dict = new_state_dict
                logger.info(f"Loading pretrained model from {pretrain_path}")
            except FileNotFoundError as e:
                logger.info(f'{pretrain_path} is not found! Please check this path.')
                raise e
            except KeyError as e:
                logger.info("State dict keys error! Please check the state dict.")
                raise e
        else:
            key = depth
            if with_ibn: key = 'ibn_' + key

            state_dict = init_pretrained_weights(key)

        incompatible = model.load_state_dict(state_dict, strict=False)
        if incompatible.missing_keys:
            logger.info(
                get_missing_parameters_message(incompatible.missing_keys)
            )
        if incompatible.unexpected_keys:
            logger.info(
                get_unexpected_parameters_message(incompatible.unexpected_keys)
            )

    return model