isegm/model/modeling/hrnet_ocr.py

import os

import numpy as np
import torch
import torch._utils
import torch.nn as nn
import torch.nn.functional as F

from .ocr import SpatialGather_Module, SpatialOCR_Module
from .resnetv1b import BasicBlockV1b, BottleneckV1b

relu_inplace = True


class HighResolutionModule(nn.Module):
    def __init__(
        self,
        num_branches,
        blocks,
        num_blocks,
        num_inchannels,
        num_channels,
        fuse_method,
        multi_scale_output=True,
        norm_layer=nn.BatchNorm2d,
        align_corners=True,
    ):
        super(HighResolutionModule, self).__init__()
        self._check_branches(num_branches, num_blocks, num_inchannels, num_channels)

        self.num_inchannels = num_inchannels
        self.fuse_method = fuse_method
        self.num_branches = num_branches
        self.norm_layer = norm_layer
        self.align_corners = align_corners

        self.multi_scale_output = multi_scale_output

        self.branches = self._make_branches(
            num_branches, blocks, num_blocks, num_channels
        )
        self.fuse_layers = self._make_fuse_layers()
        self.relu = nn.ReLU(inplace=relu_inplace)

    def _check_branches(self, num_branches, num_blocks, num_inchannels, num_channels):
        if num_branches != len(num_blocks):
            error_msg = "NUM_BRANCHES({}) <> NUM_BLOCKS({})".format(
                num_branches, len(num_blocks)
            )
            raise ValueError(error_msg)

        if num_branches != len(num_channels):
            error_msg = "NUM_BRANCHES({}) <> NUM_CHANNELS({})".format(
                num_branches, len(num_channels)
            )
            raise ValueError(error_msg)

        if num_branches != len(num_inchannels):
            error_msg = "NUM_BRANCHES({}) <> NUM_INCHANNELS({})".format(
                num_branches, len(num_inchannels)
            )
            raise ValueError(error_msg)

    def _make_one_branch(self, branch_index, block, num_blocks, num_channels, stride=1):
        downsample = None
        if (
            stride != 1
            or self.num_inchannels[branch_index]
            != num_channels[branch_index] * block.expansion
        ):
            downsample = nn.Sequential(
                nn.Conv2d(
                    self.num_inchannels[branch_index],
                    num_channels[branch_index] * block.expansion,
                    kernel_size=1,
                    stride=stride,
                    bias=False,
                ),
                self.norm_layer(num_channels[branch_index] * block.expansion),
            )

        layers = []
        layers.append(
            block(
                self.num_inchannels[branch_index],
                num_channels[branch_index],
                stride,
                downsample=downsample,
                norm_layer=self.norm_layer,
            )
        )
        self.num_inchannels[branch_index] = num_channels[branch_index] * block.expansion
        for i in range(1, num_blocks[branch_index]):
            layers.append(
                block(
                    self.num_inchannels[branch_index],
                    num_channels[branch_index],
                    norm_layer=self.norm_layer,
                )
            )

        return nn.Sequential(*layers)

    def _make_branches(self, num_branches, block, num_blocks, num_channels):
        branches = []

        for i in range(num_branches):
            branches.append(self._make_one_branch(i, block, num_blocks, num_channels))

        return nn.ModuleList(branches)

    def _make_fuse_layers(self):
        if self.num_branches == 1:
            return None

        num_branches = self.num_branches
        num_inchannels = self.num_inchannels
        fuse_layers = []
        for i in range(num_branches if self.multi_scale_output else 1):
            fuse_layer = []
            for j in range(num_branches):
                if j > i:
                    fuse_layer.append(
                        nn.Sequential(
                            nn.Conv2d(
                                in_channels=num_inchannels[j],
                                out_channels=num_inchannels[i],
                                kernel_size=1,
                                bias=False,
                            ),
                            self.norm_layer(num_inchannels[i]),
                        )
                    )
                elif j == i:
                    fuse_layer.append(None)
                else:
                    conv3x3s = []
                    for k in range(i - j):
                        if k == i - j - 1:
                            num_outchannels_conv3x3 = num_inchannels[i]
                            conv3x3s.append(
                                nn.Sequential(
                                    nn.Conv2d(
                                        num_inchannels[j],
                                        num_outchannels_conv3x3,
                                        kernel_size=3,
                                        stride=2,
                                        padding=1,
                                        bias=False,
                                    ),
                                    self.norm_layer(num_outchannels_conv3x3),
                                )
                            )
                        else:
                            num_outchannels_conv3x3 = num_inchannels[j]
                            conv3x3s.append(
                                nn.Sequential(
                                    nn.Conv2d(
                                        num_inchannels[j],
                                        num_outchannels_conv3x3,
                                        kernel_size=3,
                                        stride=2,
                                        padding=1,
                                        bias=False,
                                    ),
                                    self.norm_layer(num_outchannels_conv3x3),
                                    nn.ReLU(inplace=relu_inplace),
                                )
                            )
                    fuse_layer.append(nn.Sequential(*conv3x3s))
            fuse_layers.append(nn.ModuleList(fuse_layer))

        return nn.ModuleList(fuse_layers)

    def get_num_inchannels(self):
        return self.num_inchannels

    def forward(self, x):
        if self.num_branches == 1:
            return [self.branches[0](x[0])]

        for i in range(self.num_branches):
            x[i] = self.branches[i](x[i])

        x_fuse = []
        for i in range(len(self.fuse_layers)):
            y = x[0] if i == 0 else self.fuse_layers[i][0](x[0])
            for j in range(1, self.num_branches):
                if i == j:
                    y = y + x[j]
                elif j > i:
                    width_output = x[i].shape[-1]
                    height_output = x[i].shape[-2]
                    y = y + F.interpolate(
                        self.fuse_layers[i][j](x[j]),
                        size=[height_output, width_output],
                        mode="bilinear",
                        align_corners=self.align_corners,
                    )
                else:
                    y = y + self.fuse_layers[i][j](x[j])
            x_fuse.append(self.relu(y))

        return x_fuse


class HighResolutionNet(nn.Module):
    def __init__(
        self,
        width,
        num_classes,
        ocr_width=256,
        small=False,
        norm_layer=nn.BatchNorm2d,
        align_corners=True,
    ):
        super(HighResolutionNet, self).__init__()
        self.norm_layer = norm_layer
        self.width = width
        self.ocr_width = ocr_width
        self.align_corners = align_corners

        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn1 = norm_layer(64)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn2 = norm_layer(64)
        self.relu = nn.ReLU(inplace=relu_inplace)

        num_blocks = 2 if small else 4

        stage1_num_channels = 64
        self.layer1 = self._make_layer(
            BottleneckV1b, 64, stage1_num_channels, blocks=num_blocks
        )
        stage1_out_channel = BottleneckV1b.expansion * stage1_num_channels

        self.stage2_num_branches = 2
        num_channels = [width, 2 * width]
        num_inchannels = [
            num_channels[i] * BasicBlockV1b.expansion for i in range(len(num_channels))
        ]
        self.transition1 = self._make_transition_layer(
            [stage1_out_channel], num_inchannels
        )
        self.stage2, pre_stage_channels = self._make_stage(
            BasicBlockV1b,
            num_inchannels=num_inchannels,
            num_modules=1,
            num_branches=self.stage2_num_branches,
            num_blocks=2 * [num_blocks],
            num_channels=num_channels,
        )

        self.stage3_num_branches = 3
        num_channels = [width, 2 * width, 4 * width]
        num_inchannels = [
            num_channels[i] * BasicBlockV1b.expansion for i in range(len(num_channels))
        ]
        self.transition2 = self._make_transition_layer(
            pre_stage_channels, num_inchannels
        )
        self.stage3, pre_stage_channels = self._make_stage(
            BasicBlockV1b,
            num_inchannels=num_inchannels,
            num_modules=3 if small else 4,
            num_branches=self.stage3_num_branches,
            num_blocks=3 * [num_blocks],
            num_channels=num_channels,
        )

        self.stage4_num_branches = 4
        num_channels = [width, 2 * width, 4 * width, 8 * width]
        num_inchannels = [
            num_channels[i] * BasicBlockV1b.expansion for i in range(len(num_channels))
        ]
        self.transition3 = self._make_transition_layer(
            pre_stage_channels, num_inchannels
        )
        self.stage4, pre_stage_channels = self._make_stage(
            BasicBlockV1b,
            num_inchannels=num_inchannels,
            num_modules=2 if small else 3,
            num_branches=self.stage4_num_branches,
            num_blocks=4 * [num_blocks],
            num_channels=num_channels,
        )

        last_inp_channels = np.int(np.sum(pre_stage_channels))
        if self.ocr_width > 0:
            ocr_mid_channels = 2 * self.ocr_width
            ocr_key_channels = self.ocr_width

            self.conv3x3_ocr = nn.Sequential(
                nn.Conv2d(
                    last_inp_channels,
                    ocr_mid_channels,
                    kernel_size=3,
                    stride=1,
                    padding=1,
                ),
                norm_layer(ocr_mid_channels),
                nn.ReLU(inplace=relu_inplace),
            )
            self.ocr_gather_head = SpatialGather_Module(num_classes)

            self.ocr_distri_head = SpatialOCR_Module(
                in_channels=ocr_mid_channels,
                key_channels=ocr_key_channels,
                out_channels=ocr_mid_channels,
                scale=1,
                dropout=0.05,
                norm_layer=norm_layer,
                align_corners=align_corners,
            )
            self.cls_head = nn.Conv2d(
                ocr_mid_channels,
                num_classes,
                kernel_size=1,
                stride=1,
                padding=0,
                bias=True,
            )

            self.aux_head = nn.Sequential(
                nn.Conv2d(
                    last_inp_channels,
                    last_inp_channels,
                    kernel_size=1,
                    stride=1,
                    padding=0,
                ),
                norm_layer(last_inp_channels),
                nn.ReLU(inplace=relu_inplace),
                nn.Conv2d(
                    last_inp_channels,
                    num_classes,
                    kernel_size=1,
                    stride=1,
                    padding=0,
                    bias=True,
                ),
            )
        else:
            self.cls_head = nn.Sequential(
                nn.Conv2d(
                    last_inp_channels,
                    last_inp_channels,
                    kernel_size=3,
                    stride=1,
                    padding=1,
                ),
                norm_layer(last_inp_channels),
                nn.ReLU(inplace=relu_inplace),
                nn.Conv2d(
                    last_inp_channels,
                    num_classes,
                    kernel_size=1,
                    stride=1,
                    padding=0,
                    bias=True,
                ),
            )

    def _make_transition_layer(self, num_channels_pre_layer, num_channels_cur_layer):
        num_branches_cur = len(num_channels_cur_layer)
        num_branches_pre = len(num_channels_pre_layer)

        transition_layers = []
        for i in range(num_branches_cur):
            if i < num_branches_pre:
                if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
                    transition_layers.append(
                        nn.Sequential(
                            nn.Conv2d(
                                num_channels_pre_layer[i],
                                num_channels_cur_layer[i],
                                kernel_size=3,
                                stride=1,
                                padding=1,
                                bias=False,
                            ),
                            self.norm_layer(num_channels_cur_layer[i]),
                            nn.ReLU(inplace=relu_inplace),
                        )
                    )
                else:
                    transition_layers.append(None)
            else:
                conv3x3s = []
                for j in range(i + 1 - num_branches_pre):
                    inchannels = num_channels_pre_layer[-1]
                    outchannels = (
                        num_channels_cur_layer[i]
                        if j == i - num_branches_pre
                        else inchannels
                    )
                    conv3x3s.append(
                        nn.Sequential(
                            nn.Conv2d(
                                inchannels,
                                outchannels,
                                kernel_size=3,
                                stride=2,
                                padding=1,
                                bias=False,
                            ),
                            self.norm_layer(outchannels),
                            nn.ReLU(inplace=relu_inplace),
                        )
                    )
                transition_layers.append(nn.Sequential(*conv3x3s))

        return nn.ModuleList(transition_layers)

    def _make_layer(self, block, inplanes, planes, blocks, stride=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,
                ),
                self.norm_layer(planes * block.expansion),
            )

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

        return nn.Sequential(*layers)

    def _make_stage(
        self,
        block,
        num_inchannels,
        num_modules,
        num_branches,
        num_blocks,
        num_channels,
        fuse_method="SUM",
        multi_scale_output=True,
    ):
        modules = []
        for i in range(num_modules):
            # multi_scale_output is only used last module
            if not multi_scale_output and i == num_modules - 1:
                reset_multi_scale_output = False
            else:
                reset_multi_scale_output = True
            modules.append(
                HighResolutionModule(
                    num_branches,
                    block,
                    num_blocks,
                    num_inchannels,
                    num_channels,
                    fuse_method,
                    reset_multi_scale_output,
                    norm_layer=self.norm_layer,
                    align_corners=self.align_corners,
                )
            )
            num_inchannels = modules[-1].get_num_inchannels()

        return nn.Sequential(*modules), num_inchannels

    def forward(self, x, additional_features=None):
        feats = self.compute_hrnet_feats(x, additional_features)
        if self.ocr_width > 0:
            out_aux = self.aux_head(feats)
            feats = self.conv3x3_ocr(feats)

            context = self.ocr_gather_head(feats, out_aux)
            feats = self.ocr_distri_head(feats, context)
            out = self.cls_head(feats)
            return [out, out_aux]
        else:
            return [self.cls_head(feats), None]

    def compute_hrnet_feats(self, x, additional_features):
        x = self.compute_pre_stage_features(x, additional_features)
        x = self.layer1(x)

        x_list = []
        for i in range(self.stage2_num_branches):
            if self.transition1[i] is not None:
                x_list.append(self.transition1[i](x))
            else:
                x_list.append(x)
        y_list = self.stage2(x_list)

        x_list = []
        for i in range(self.stage3_num_branches):
            if self.transition2[i] is not None:
                if i < self.stage2_num_branches:
                    x_list.append(self.transition2[i](y_list[i]))
                else:
                    x_list.append(self.transition2[i](y_list[-1]))
            else:
                x_list.append(y_list[i])
        y_list = self.stage3(x_list)

        x_list = []
        for i in range(self.stage4_num_branches):
            if self.transition3[i] is not None:
                if i < self.stage3_num_branches:
                    x_list.append(self.transition3[i](y_list[i]))
                else:
                    x_list.append(self.transition3[i](y_list[-1]))
            else:
                x_list.append(y_list[i])
        x = self.stage4(x_list)

        return self.aggregate_hrnet_features(x)

    def compute_pre_stage_features(self, x, additional_features):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        if additional_features is not None:
            x = x + additional_features
        x = self.conv2(x)
        x = self.bn2(x)
        return self.relu(x)

    def aggregate_hrnet_features(self, x):
        # Upsampling
        x0_h, x0_w = x[0].size(2), x[0].size(3)
        x1 = F.interpolate(
            x[1], size=(x0_h, x0_w), mode="bilinear", align_corners=self.align_corners
        )
        x2 = F.interpolate(
            x[2], size=(x0_h, x0_w), mode="bilinear", align_corners=self.align_corners
        )
        x3 = F.interpolate(
            x[3], size=(x0_h, x0_w), mode="bilinear", align_corners=self.align_corners
        )

        return torch.cat([x[0], x1, x2, x3], 1)

    def load_pretrained_weights(self, pretrained_path=""):
        model_dict = self.state_dict()

        if not os.path.exists(pretrained_path):
            print(f'\nFile "{pretrained_path}" does not exist.')
            print(
                "You need to specify the correct path to the pre-trained weights.\n"
                "You can download the weights for HRNet from the repository:\n"
                "https://github.com/HRNet/HRNet-Image-Classification"
            )
            exit(1)
        pretrained_dict = torch.load(pretrained_path, map_location={"cuda:0": "cpu"})
        pretrained_dict = {
            k.replace("last_layer", "aux_head").replace("model.", ""): v
            for k, v in pretrained_dict.items()
        }

        pretrained_dict = {
            k: v for k, v in pretrained_dict.items() if k in model_dict.keys()
        }

        model_dict.update(pretrained_dict)
        self.load_state_dict(model_dict)