|
import numpy as np |
|
import torch |
|
import torch.nn as nn |
|
import gradio as gr |
|
from PIL import Image |
|
import torchvision.transforms as transforms |
|
|
|
norm_layer = nn.InstanceNorm2d |
|
|
|
class ResidualBlock(nn.Module): |
|
def __init__(self, in_features): |
|
super(ResidualBlock, self).__init__() |
|
|
|
conv_block = [ nn.ReflectionPad2d(1), |
|
nn.Conv2d(in_features, in_features, 3), |
|
norm_layer(in_features), |
|
nn.ReLU(inplace=True), |
|
nn.ReflectionPad2d(1), |
|
nn.Conv2d(in_features, in_features, 3), |
|
norm_layer(in_features) |
|
] |
|
|
|
self.conv_block = nn.Sequential(*conv_block) |
|
|
|
def forward(self, x): |
|
return x + self.conv_block(x) |
|
|
|
|
|
class Generator(nn.Module): |
|
def __init__(self, input_nc, output_nc, n_residual_blocks=9, sigmoid=True): |
|
super(Generator, self).__init__() |
|
|
|
|
|
model0 = [ nn.ReflectionPad2d(3), |
|
nn.Conv2d(input_nc, 64, 7), |
|
norm_layer(64), |
|
nn.ReLU(inplace=True) ] |
|
self.model0 = nn.Sequential(*model0) |
|
|
|
|
|
model1 = [] |
|
in_features = 64 |
|
out_features = in_features*2 |
|
for _ in range(2): |
|
model1 += [ nn.Conv2d(in_features, out_features, 3, stride=2, padding=1), |
|
norm_layer(out_features), |
|
nn.ReLU(inplace=True) ] |
|
in_features = out_features |
|
out_features = in_features*2 |
|
self.model1 = nn.Sequential(*model1) |
|
|
|
model2 = [] |
|
|
|
for _ in range(n_residual_blocks): |
|
model2 += [ResidualBlock(in_features)] |
|
self.model2 = nn.Sequential(*model2) |
|
|
|
|
|
model3 = [] |
|
out_features = in_features//2 |
|
for _ in range(2): |
|
model3 += [ nn.ConvTranspose2d(in_features, out_features, 3, stride=2, padding=1, output_padding=1), |
|
norm_layer(out_features), |
|
nn.ReLU(inplace=True) ] |
|
in_features = out_features |
|
out_features = in_features//2 |
|
self.model3 = nn.Sequential(*model3) |
|
|
|
|
|
model4 = [ nn.ReflectionPad2d(3), |
|
nn.Conv2d(64, output_nc, 7)] |
|
if sigmoid: |
|
model4 += [nn.Sigmoid()] |
|
|
|
self.model4 = nn.Sequential(*model4) |
|
|
|
def forward(self, x, cond=None): |
|
out = self.model0(x) |
|
out = self.model1(out) |
|
out = self.model2(out) |
|
out = self.model3(out) |
|
out = self.model4(out) |
|
|
|
return out |
|
|
|
model1 = Generator(3, 1, 3) |
|
model1.load_state_dict(torch.load('model.pth', map_location=torch.device('cpu'))) |
|
model1.eval() |
|
|
|
model2 = Generator(3, 1, 3) |
|
model2.load_state_dict(torch.load('model2.pth', map_location=torch.device('cpu'))) |
|
model2.eval() |
|
|
|
def predict(input_img, ver): |
|
input_img = Image.open(input_img) |
|
transform = transforms.Compose([transforms.Resize(256, Image.BICUBIC), transforms.ToTensor()]) |
|
input_img = transform(input_img) |
|
input_img = torch.unsqueeze(input_img, 0) |
|
|
|
drawing = 0 |
|
with torch.no_grad(): |
|
if ver == 'style 2': |
|
drawing = model2(input_img)[0].detach() |
|
else: |
|
drawing = model1(input_img)[0].detach() |
|
|
|
drawing = transforms.ToPILImage()(drawing) |
|
return drawing |
|
|
|
title="informative-drawings" |
|
description="""Gradio Demo for line drawing generation.<br> |
|
This Gradio Demo was build by <a href="https://huggingface.co/gstaff" target="_blank">Grant Stafford @gstaff</a>.""" |
|
|
|
|
|
examples=[['cat.png', 'style 1'], ['bridge.png', 'style 1'], ['lizard.png', 'style 2'],] |
|
|
|
|
|
iface = gr.Interface(predict, [gr.inputs.Image(type='filepath'), |
|
gr.inputs.Radio(['style 1','style 2'], type="value", default='style 1', label='version')], |
|
gr.outputs.Image(type="pil"), title=title,description=description,examples=examples, theme='gstaff/sketch') |
|
|
|
iface.launch() |
