File size: 4,123 Bytes
3743728 0f3cd84 5b29d56 764782e 3984452 41bee7b aef0baa 0f3cd84 aef0baa 379e978 bd4b429 3a27073 764782e aef0baa 3a27073 aef0baa 3a27073 3743728 764782e 03c67b1 aef0baa 764782e 3743728 aef0baa 3743728 0f3cd84 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 |
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)
# Upsampling
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()
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()])
transform = transforms.Compose([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. "
# article = "<p style='text-align: center'><a href='TODO' target='_blank'>Project Page</a> | <a href='codelink' target='_blank'>Github</a></p>"
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)
iface.launch() |