prigoyal

# Copyright 2024 MosaicML LLM Foundry authors
# SPDX-License-Identifier: Apache-2.0

"""MosaicML LLM Foundry package setup."""

import os
import re

import setuptools
from setuptools import setup

prigoyal

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):

prigoyal

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):

prigoyal

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):

prigoyal

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):

prigoyal

import torch.nn as nn
from torch.nn import Parameter, ModuleList
import torch.nn.functional as F
import torch
import multiprocessing
import numpy as np
import os
from gym import spaces
from torchvision.models import resnet18
from teas.models.taskonomyencoder import TaskonomyEncoder

prigoyal

import torch.nn as nn
from torch.nn import Parameter, ModuleList
import torch.nn.functional as F
import torch
import multiprocessing
import numpy as np
import os
from gym import spaces
from torchvision.models import resnet18
from teas.models.taskonomyencoder import TaskonomyEncoder

prigoyal

import math

from torch import nn
from torch.nn import init

# pre-trained SRResNet model
model_url = 'https://s3.amazonaws.com/pytorch/demos/srresnet-e10b2039.pth'

# model definition
def _initialize_orthogonal(conv):