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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__()
# Initial convolution block
model0 = [ nn.ReflectionPad2d(3),
nn.Conv2d(input_nc, 64, 7),
norm_layer(64),
nn.ReLU(inplace=True) ]
self.model0 = nn.Sequential(*model0)
# Downsampling
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 = []
# Residual blocks
for _ in range(n_residual_blocks):
model2 += [ResidualBlock(in_features)]
self.model2 = nn.Sequential(*model2)
# More downsampling
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)
# Output layer
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()
model3 = Generator(3, 1, 3)
model3.load_state_dict(torch.load('model.pth', map_location=torch.device('cpu')))
model3.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 == 'Simple Lines':
drawing = model3(input_img)[0].detach()
else:
drawing = model1(input_img)[0].detach()
drawing = transforms.ToPILImage()(drawing)
return drawing
title="Image to Coloring Page Generator"
# examples=[
# ['01.jpeg', 'Complex Lines'],
#]
# iface = gr.Interface(predict,
# image,
# #gr.outputs.Image(type="pil"))
# image)
iface = gr.Interface(predict, [gr.inputs.Image(type='filepath'),
gr.inputs.Radio(['Complex Lines','Simple Lines'], type="value", default='Complex Lines', label='version')],
gr.outputs.Image(type="pil"))
iface.launch() |