DL-Art-School/codes/models/archs/spinenet_arch.py

# Taken and modified from https://github.com/lucifer443/SpineNet-Pytorch/blob/master/mmdet/models/backbones/spinenet.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import kaiming_normal

from torchvision.models.resnet import BasicBlock, Bottleneck
from torch.nn.modules.batchnorm import _BatchNorm


''' Convenience class with Conv->BN->ReLU. Includes weight initialization and auto-padding for standard
    kernel sizes. '''
class ConvBnRelu(nn.Module):
    def __init__(self, filters_in, filters_out, kernel_size=3, stride=1, relu=True, bn=True, bias=True):
        super(ConvBnRelu, self).__init__()
        padding_map = {1: 0, 3: 1, 5: 2, 7: 3}
        assert kernel_size in padding_map.keys()
        self.conv = nn.Conv2d(filters_in, filters_out, kernel_size, stride, padding_map[kernel_size], bias=bias)
        if bn:
            self.bn = nn.BatchNorm2d(filters_out)
        else:
            self.bn = None
        if relu:
            self.relu = nn.ReLU()
        else:
            self.relu = None

        # Init params.
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu' if self.relu else 'linear')
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    def forward(self, x):
        x = self.conv(x)
        if self.bn:
            x = self.bn(x)
        if self.relu:
            return self.relu(x)
        else:
            return x


def constant_init(module, val, bias=0):
    if hasattr(module, 'weight') and module.weight is not None:
        nn.init.constant_(module.weight, val)
    if hasattr(module, 'bias') and module.bias is not None:
        nn.init.constant_(module.bias, bias)

def kaiming_init(module,
                 a=0,
                 mode='fan_out',
                 nonlinearity='relu',
                 bias=0,
                 distribution='normal'):
    assert distribution in ['uniform', 'normal']
    if distribution == 'uniform':
        nn.init.kaiming_uniform_(
            module.weight, a=a, mode=mode, nonlinearity=nonlinearity)
    else:
        nn.init.kaiming_normal_(
            module.weight, a=a, mode=mode, nonlinearity=nonlinearity)
    if hasattr(module, 'bias') and module.bias is not None:
        nn.init.constant_(module.bias, bias)

FILTER_SIZE_MAP = {
    1: 32,
    2: 64,
    3: 128,
    4: 256,
    5: 256,
    6: 256,
    7: 256,
}

def make_res_layer(block,
                   inplanes,
                   planes,
                   blocks,
                   stride=1,
                   dilation=1):
    downsample = None
    if stride != 1 or inplanes != planes * block.expansion:
        downsample = nn.Sequential(
            nn.Conv2d(
                inplanes,
                planes * block.expansion,
                kernel_size=1,
                stride=stride,
                bias=False),
            nn.BatchNorm2d(planes * block.expansion),
        )

    layers = []
    layers.append(
        block(
            inplanes=inplanes,
            planes=planes,
            stride=stride,
            dilation=dilation,
            downsample=downsample))
    inplanes = planes * block.expansion
    for i in range(1, blocks):
        layers.append(
            block(
                inplanes=inplanes,
                planes=planes,
                stride=1,
                dilation=dilation))

    return nn.Sequential(*layers)

# The fixed SpineNet architecture discovered by NAS.
# Each element represents a specification of a building block:
#   (block_level, block_fn, (input_offset0, input_offset1), is_output).
SPINENET_BLOCK_SPECS = [
    (2, Bottleneck, (None, None), False),  # init block
    (2, Bottleneck, (None, None), False),  # init block
    (2, Bottleneck, (0, 1), False),
    (4, BasicBlock, (0, 1), False),
    (3, Bottleneck, (2, 3), False),
    (4, Bottleneck, (2, 4), False),
    (6, BasicBlock, (3, 5), False),
    (4, Bottleneck, (3, 5), False),
    (5, BasicBlock, (6, 7), False),
    (7, BasicBlock, (6, 8), False),
    (5, Bottleneck, (8, 9), False),
    (5, Bottleneck, (8, 10), False),
    (4, Bottleneck, (5, 10), True),
    (3, Bottleneck, (4, 10), True),
    (5, Bottleneck, (7, 12), True),
    (7, Bottleneck, (5, 14), True),
    (6, Bottleneck, (12, 14), True),
]

SCALING_MAP = {
    '49S': {
        'endpoints_num_filters': 128,
        'filter_size_scale': 0.65,
        'resample_alpha': 0.5,
        'block_repeats': 1,
    },
    '49': {
        'endpoints_num_filters': 256,
        'filter_size_scale': 1.0,
        'resample_alpha': 0.5,
        'block_repeats': 1,
    },
    '96': {
        'endpoints_num_filters': 256,
        'filter_size_scale': 1.0,
        'resample_alpha': 0.5,
        'block_repeats': 2,
    },
    '143': {
        'endpoints_num_filters': 256,
        'filter_size_scale': 1.0,
        'resample_alpha': 1.0,
        'block_repeats': 3,
    },
    '190': {
        'endpoints_num_filters': 512,
        'filter_size_scale': 1.3,
        'resample_alpha': 1.0,
        'block_repeats': 4,
    },
}


class BlockSpec(object):
  """A container class that specifies the block configuration for SpineNet."""

  def __init__(self, level, block_fn, input_offsets, is_output):
    self.level = level
    self.block_fn = block_fn
    self.input_offsets = input_offsets
    self.is_output = is_output


def build_block_specs(block_specs=None):
  """Builds the list of BlockSpec objects for SpineNet."""
  if not block_specs:
    block_specs = SPINENET_BLOCK_SPECS
  return [BlockSpec(*b) for b in block_specs]


class Resample(nn.Module):
    def __init__(self, in_channels, out_channels, scale, block_type, alpha=1.0):
        super(Resample, self).__init__()
        self.scale = scale
        new_in_channels = int(in_channels * alpha)
        if block_type == Bottleneck:
            in_channels *= 4
        self.squeeze_conv = ConvBnRelu(in_channels, new_in_channels, kernel_size=1)
        if scale < 1:
            self.downsample_conv = ConvBnRelu(new_in_channels, new_in_channels, kernel_size=3, stride=2)
        self.expand_conv = ConvBnRelu(new_in_channels, out_channels, kernel_size=1, relu=False)

    def _resize(self, x):
        if self.scale == 1:
            return x
        elif self.scale > 1:
            return F.interpolate(x, scale_factor=self.scale, mode='nearest')
        else:
            x = self.downsample_conv(x)
            if self.scale < 0.5:
                new_kernel_size = 3 if self.scale >= 0.25 else 5
                x = F.max_pool2d(x, kernel_size=new_kernel_size, stride=int(0.5/self.scale), padding=new_kernel_size//2)
            return x

    def forward(self, inputs):
        feat = self.squeeze_conv(inputs)
        feat = self._resize(feat)
        feat = self.expand_conv(feat)
        return feat


class Merge(nn.Module):
    """Merge two input tensors"""
    def __init__(self, block_spec, alpha, filter_size_scale):
        super(Merge, self).__init__()
        out_channels = int(FILTER_SIZE_MAP[block_spec.level] * filter_size_scale)
        if block_spec.block_fn == Bottleneck:
            out_channels *= 4
        self.block = block_spec.block_fn
        self.resample_ops = nn.ModuleList()
        for spec_idx in block_spec.input_offsets:
            spec = BlockSpec(*SPINENET_BLOCK_SPECS[spec_idx])
            in_channels = int(FILTER_SIZE_MAP[spec.level] * filter_size_scale)
            scale = 2**(spec.level - block_spec.level)
            self.resample_ops.append(
                Resample(in_channels, out_channels, scale, spec.block_fn, alpha)
            )

    def forward(self, inputs):
        assert len(inputs) == len(self.resample_ops)
        parent0_feat = self.resample_ops[0](inputs[0])
        parent1_feat = self.resample_ops[1](inputs[1])
        target_feat = parent0_feat + parent1_feat
        return target_feat


class SpineNet(nn.Module):
    """Class to build SpineNet backbone"""
    def __init__(self,
                 arch,
                 in_channels=3,
                 output_level=[3, 4, 5, 6, 7],
                 conv_cfg=None,
                 norm_cfg=dict(type='BN', requires_grad=True),
                 zero_init_residual=True,
                 activation='relu',
                 use_input_norm=False,
                 double_reduce_early=True):
        super(SpineNet, self).__init__()
        self._block_specs = build_block_specs()[2:]
        self._endpoints_num_filters = SCALING_MAP[arch]['endpoints_num_filters']
        self._resample_alpha = SCALING_MAP[arch]['resample_alpha']
        self._block_repeats = SCALING_MAP[arch]['block_repeats']
        self._filter_size_scale = SCALING_MAP[arch]['filter_size_scale']
        self._init_block_fn = Bottleneck
        self._num_init_blocks = 2
        self._early_double_reduce = double_reduce_early
        self.zero_init_residual = zero_init_residual
        assert min(output_level) > 2 and max(output_level) < 8, "Output level out of range"
        self.output_level = output_level
        self.use_input_norm = use_input_norm

        self._make_stem_layer(in_channels)
        self._make_scale_permuted_network()
        self._make_endpoints()

    def _make_stem_layer(self, in_channels):
        """Build the stem network."""
        # Build the first conv and maxpooling layers.
        if self._early_double_reduce:
            self.conv1 = ConvBnRelu(
                in_channels,
                64,
                kernel_size=7,
                stride=2)
            self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        else:
            self.conv1 = ConvBnRelu(
                in_channels,
                64,
                kernel_size=7,
                stride=1)
            self.maxpool = None

        # Build the initial level 2 blocks.
        self.init_block1 = make_res_layer(
            self._init_block_fn,
            64,
            int(FILTER_SIZE_MAP[2] * self._filter_size_scale),
            self._block_repeats)
        self.init_block2 = make_res_layer(
            self._init_block_fn,
            int(FILTER_SIZE_MAP[2] * self._filter_size_scale) * 4,
            int(FILTER_SIZE_MAP[2] * self._filter_size_scale),
            self._block_repeats)

    def _make_endpoints(self):
        self.endpoint_convs = nn.ModuleDict()
        for block_spec in self._block_specs:
            if block_spec.is_output:
                in_channels = int(FILTER_SIZE_MAP[block_spec.level]*self._filter_size_scale) * 4
                self.endpoint_convs[str(block_spec.level)] = ConvBnRelu(in_channels,
                                                                   self._endpoints_num_filters,
                                                                   kernel_size=1,
                                                                   relu=False)

    def _make_scale_permuted_network(self):
        self.merge_ops = nn.ModuleList()
        self.scale_permuted_blocks = nn.ModuleList()
        for spec in self._block_specs:
            self.merge_ops.append(
                Merge(spec, self._resample_alpha, self._filter_size_scale)
            )
            channels = int(FILTER_SIZE_MAP[spec.level] * self._filter_size_scale)
            in_channels = channels * 4 if spec.block_fn == Bottleneck else channels
            self.scale_permuted_blocks.append(
                make_res_layer(spec.block_fn,
                               in_channels,
                               channels,
                               self._block_repeats)
            )

    def init_weights(self, pretrained=None):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                kaiming_init(m)
            elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
                constant_init(m, 1)
        if self.zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    constant_init(m.bn3, 0)
                elif isinstance(m, BasicBlock):
                    constant_init(m.bn2, 0)

    def forward(self, input):
        # Spinenet is pretrained on the standard pytorch input norm. The image will need to
        # be normalized before feeding it through.
        if self.use_input_norm:
            mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(input.device)
            std = torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(input.device)
            input = (input - mean) / std

        feat = self.conv1(input)
        if self.maxpool:
            feat = self.maxpool(feat)
        feat1 = self.init_block1(feat)
        feat2 = self.init_block2(feat1)
        block_feats = [feat1, feat2]
        output_feat = {}
        num_outgoing_connections = [0, 0]

        for i, spec in enumerate(self._block_specs):
            target_feat = self.merge_ops[i]([block_feats[feat_idx] for feat_idx in spec.input_offsets])
            # Connect intermediate blocks with outdegree 0 to the output block.
            if spec.is_output:
                for j, (j_feat, j_connections) in enumerate(
                        zip(block_feats, num_outgoing_connections)):
                    if j_connections == 0 and j_feat.shape == target_feat.shape:
                        target_feat += j_feat
                        num_outgoing_connections[j] += 1
            target_feat = F.relu(target_feat, inplace=True)
            target_feat = self.scale_permuted_blocks[i](target_feat)
            block_feats.append(target_feat)
            num_outgoing_connections.append(0)
            for feat_idx in spec.input_offsets:
                num_outgoing_connections[feat_idx] += 1
            if spec.is_output:
                output_feat[spec.level] = target_feat

        return tuple([self.endpoint_convs[str(level)](output_feat[level]) for level in self.output_level])