res5 Taskonomy taskonomyencoder.py

taskonomyencoder.py

import math
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
import warnings

TASKONOMY_TASKS = "autoencoder class_1000 class_places curvature denoise edge2d edge3d inpainting_whole jigsaw keypoint2d keypoint3d reshade rgb2depth rgb2sfnorm rgb2mist room_layout segment25d segment2d segmentsemantic vanishing_point".split()
class TaskonomyEncoder(nn.Module):

    def __init__(self, normalize_outputs=True, eval_only=True):
        self.inplanes = 64
        super(TaskonomyEncoder, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=0)
        block = models.resnet.Bottleneck
        layers = [3,4,6,3]
        self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2])
        self.layer4 = self._make_layer(block, 512, layers[3])
        self.compress1 = nn.Conv2d(2048, 8, kernel_size=3, stride=1, padding=1, bias=False)
        # self.compress_bn = nn.BatchNorm2d(8)
        self.relu1 = nn.ReLU(inplace=True)
        self.groupnorm = nn.GroupNorm(8, 8, affine=False)
        self.normalize_outputs = normalize_outputs
        self.eval_only = eval_only
        if self.eval_only:
            self.eval()
        for p in self.parameters():
            p.requires_grad = False
    
    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        layers = []

        if self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                        kernel_size=1, stride=1, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )
        layers.append(block(self.inplanes, planes, downsample=downsample))

        self.inplanes = planes * block.expansion
        for i in range(1, blocks - 1):
            layers.append(block(self.inplanes, planes))

        downsample = None
        if stride != 1:
            downsample = nn.Sequential(
                nn.MaxPool2d( kernel_size=1, stride=stride ),
            )
        layers.append(block(self.inplanes, planes, stride, downsample))


        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = F.pad(x, (0,1,0,1), 'constant', 0)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.compress1(x)
        # x = self.compress_bn(x)
        x = self.relu1(x)
        if self.normalize_outputs:
            x = self.groupnorm(x)
        return x

    def train(self, val):
        if val and self.eval_only:
            warnings.warn("Ignoring 'train()' in TaskonomyEncoder since 'eval_only' was set during initialization.", RuntimeWarning)
        else:
            return super().train(val)