# Заменяем path на путь к понравившейся итерации модели
# Нам нужен файл *G_A.pth - генератор фото -> комикс
# output_path - имя файла для экспортируемой модели
# может быть любым с расширением *.jit, главное - не забыть, куда мы его сохранили

path= '/checkpoints/resnet9_nowd_nodo_128to400_c8/60_net_G_A.pth'
output_path ='/checkpoints/resnet9_nowd_nodo_128to400_c8/resnet9_nowd_nodo_128to400_c8_160-50-60_1.jit'

import torch
from torch import nn

class ResnetGenerator(nn.Module):
    """Resnet-based generator that consists of Resnet blocks between a few downsampling/upsampling operations.

    We adapt Torch code and idea from Justin Johnson's neural style transfer project(https://github.com/jcjohnson/fast-neural-style)
    """

    def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect'):
        """Construct a Resnet-based generator

        Parameters:
            input_nc (int)      -- the number of channels in input images
            output_nc (int)     -- the number of channels in output images
            ngf (int)           -- the number of filters in the last conv layer
            norm_layer          -- normalization layer
            use_dropout (bool)  -- if use dropout layers
            n_blocks (int)      -- the number of ResNet blocks
            padding_type (str)  -- the name of padding layer in conv layers: reflect | replicate | zero
        """
        assert(n_blocks >= 0)
        super(ResnetGenerator, self).__init__()
        if type(norm_layer) == functools.partial:
            use_bias = norm_layer.func == nn.InstanceNorm2d
        else:
            use_bias = norm_layer == nn.InstanceNorm2d

        model = [nn.ReflectionPad2d(3),
                 nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=use_bias),
                 norm_layer(ngf),
                 nn.ReLU(True)]

        n_downsampling = 2
        for i in range(n_downsampling):  # add downsampling layers
            mult = 2 ** i
            model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1, bias=use_bias),
                      norm_layer(ngf * mult * 2),
                      nn.ReLU(True)]

        mult = 2 ** n_downsampling
        for i in range(n_blocks):       # add ResNet blocks

            model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)]

        for i in range(n_downsampling):  # add upsampling layers
            mult = 2 ** (n_downsampling - i)
            model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2),
                                         kernel_size=3, stride=2,
                                         padding=1, output_padding=1,
                                         bias=use_bias),
                      norm_layer(int(ngf * mult / 2)),
                      nn.ReLU(True)]
        model += [nn.ReflectionPad2d(3)]
        model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
        model += [nn.Tanh()]

        self.model = nn.Sequential(*model)

    def forward(self, input):
        """Standard forward"""
        return self.model(input)

class ResnetBlock(nn.Module):
    """Define a Resnet block"""

    def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias):
        """Initialize the Resnet block

        A resnet block is a conv block with skip connections
        We construct a conv block with build_conv_block function,
        and implement skip connections in <forward> function.
        Original Resnet paper: https://arxiv.org/pdf/1512.03385.pdf
        """
        super(ResnetBlock, self).__init__()
        self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, use_dropout, use_bias)

    def build_conv_block(self, dim, padding_type, norm_layer, use_dropout, use_bias):
        """Construct a convolutional block.

        Parameters:
            dim (int)           -- the number of channels in the conv layer.
            padding_type (str)  -- the name of padding layer: reflect | replicate | zero
            norm_layer          -- normalization layer
            use_dropout (bool)  -- if use dropout layers.
            use_bias (bool)     -- if the conv layer uses bias or not

        Returns a conv block (with a conv layer, a normalization layer, and a non-linearity layer (ReLU))
        """
        conv_block = []
        p = 0
        if padding_type == 'reflect':
            conv_block += [nn.ReflectionPad2d(1)]
        elif padding_type == 'replicate':
            conv_block += [nn.ReplicationPad2d(1)]
        elif padding_type == 'zero':
            p = 1
        else:
            raise NotImplementedError('padding [%s] is not implemented' % padding_type)

        conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim), nn.ReLU(True)]
        if use_dropout:
            conv_block += [nn.Dropout(0.5)]

        p = 0
        if padding_type == 'reflect':
            conv_block += [nn.ReflectionPad2d(1)]
        elif padding_type == 'replicate':
            conv_block += [nn.ReplicationPad2d(1)]
        elif padding_type == 'zero':
            p = 1
        else:
            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
        conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim)]

        return nn.Sequential(*conv_block)

    def forward(self, x):
        """Forward function (with skip connections)"""
        out = x + self.conv_block(x)  # add skip connections
        return out

import functools

norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)

model = ResnetGenerator(3,3,64, norm_layer=norm_layer, use_dropout=False, n_blocks=9)

model.load_state_dict(torch.load(path))
model.eval()
model.cuda()
model.half()

trace_input = torch.ones(1,3,256,256).cuda()
model = model.eval()
jit_model = torch.jit.trace(model.float(), trace_input)

torch.jit.save(jit_model, output_path)

Документация: https://pytorch.org/docs/stable/jit.html
Если быть кратким, экспорт в jit позволяет сериализовать модель и не тащить за собой среду python, все зависимости и внешние модули, которые она могла использовать. Кроме torch, разумеется.
Хоть мы и будем хостить ее в python-среде, jit-модели можно использовать в самостоятельных приложениях.

import Algorithmia

import torch
import torchvision
import torchvision.transforms as transforms
import cv2
from torch import *
import uuid
import gc

import requests
import numpy as np

client = Algorithmia.client()

# Скачиваем модель по ссылке file_path, сохраняем на наш виртуальный сервер
# под именем model_file и загружаем ее.
def load_model():
    file_path = "{ ссылка на нашу загруженную модель }"
    model_file = client.file(file_path).getFile().name
    model = torch.jit.load(model_file).half().cuda()
    return model

model = load_model().eval()
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
torch.backends.cudnn.benchmark = True

# Скачиваем картинку по ссылке, уменьшаем до нужного размера, 
# преобразуем в тензор и нормализуем, 
# т.к. модель обучалась на нормализованных картинках

def preprocessing(image_path, max_size):

    response = requests.get(image_path)
    response = response.content
    nparr = np.frombuffer(response, np.uint8)
    img_res = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
    img_res = cv2.cvtColor(img_res, cv2.COLOR_BGR2RGB)

    x = img_res.shape[0]
    y = img_res.shape[1]

    #if image is bigger than the target max_size, downscale it
    if x>max_size and x<=y:
        y = y*(max_size/x)
        x = max_size

    if y>max_size and y<x:
        x = x*(max_size/y)
        y = max_size

    size = (int(y),int(x))
    img_res = cv2.resize(img_res,size)

    t = Tensor(img_res/255.)[None,:]
    t = t.permute(0,3,1,2).half().cuda()

    # standartize
    t = (t-0.5)/0.5

    return(t)

def predict(input):

    gc.collect()
    with torch.no_grad():
        res = model(input).detach()

    return res

# Денормализуем картинку обратно, сохраняем со случайным именем
# и загружаем в наше хранилище  
def save_file(res, file_uri):
    #de-standartize
    res = (res*0.5)+0.5
    tempfile = "/tmp/"+str(uuid.uuid4())+".jpg"
    torchvision.utils.save_image(res,tempfile)
    client.file(file_uri).putFile(tempfile)

# API calls will begin at the apply() method, with the request body passed as 'input'
# For more details, see algorithmia.com/developers/algorithm-development/languages
def apply(input):

    processed_data= preprocessing(input["in"], input["size"])
    res = predict(processed_data)
    save_file(res, input["out"])
    input = None
    res = None
    processed_data = None

    gc.collect()

    return "Success"