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# -*- coding: utf-8 -*-
"""cyclegan_inference.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/12lelsBZXqNOe7xaXI724rEHAbppRt07y
"""
import gradio as gr
import torch
import torchvision
from torch import nn
from typing import List
def ifnone(a, b): # a fastai-specific (fastcore) function used below, redefined so it's independent
"`b` if `a` is None else `a`"
return b if a is None else a
class ConvBlock(torch.nn.Module):
def __init__(self,input_size,output_size,kernel_size=4,stride=2,padding=1,activation='relu',batch_norm=True):
super(ConvBlock,self).__init__()
self.conv = torch.nn.Conv2d(input_size,output_size,kernel_size,stride,padding)
self.batch_norm = batch_norm
self.bn = torch.nn.InstanceNorm2d(output_size)
self.activation = activation
self.relu = torch.nn.ReLU(True)
self.lrelu = torch.nn.LeakyReLU(0.2,True)
self.tanh = torch.nn.Tanh()
self.sigmoid = torch.nn.Sigmoid()
def forward(self,x):
if self.batch_norm:
out = self.bn(self.conv(x))
else:
out = self.conv(x)
if self.activation == 'relu':
return self.relu(out)
elif self.activation == 'lrelu':
return self.lrelu(out)
elif self.activation == 'tanh':
return self.tanh(out)
elif self.activation == 'no_act':
return out
elif self.activation =='sigmoid':
return self.sigmoid(out)
class ResnetBlock(torch.nn.Module):
def __init__(self,num_filter,kernel_size=3,stride=1,padding=0):
super(ResnetBlock,self).__init__()
conv1 = torch.nn.Conv2d(num_filter,num_filter,kernel_size,stride,padding)
conv2 = torch.nn.Conv2d(num_filter,num_filter,kernel_size,stride,padding)
bn = torch.nn.InstanceNorm2d(num_filter)
relu = torch.nn.ReLU(True)
pad = torch.nn.ReflectionPad2d(1)
self.resnet_block = torch.nn.Sequential(
pad,
conv1,
bn,
relu,
pad,
conv2,
bn
)
def forward(self,x):
out = self.resnet_block(x)
return out
def resnet_generator(ch_in:int, ch_out:int, n_ftrs:int=64, norm_layer:nn.Module=None,
dropout:float=0., n_blocks:int=9, pad_mode:str='reflection')->nn.Module:
norm_layer = ifnone(norm_layer, nn.InstanceNorm2d)
bias = (norm_layer == nn.InstanceNorm2d)
layers = pad_conv_norm_relu(ch_in, n_ftrs, 'reflection', norm_layer, pad=3, ks=7, bias=bias)
for i in range(2):
layers += pad_conv_norm_relu(n_ftrs, n_ftrs *2, 'zeros', norm_layer, stride=2, bias=bias)
n_ftrs *= 2
layers += [ResnetBlock(n_ftrs, pad_mode, norm_layer, dropout, bias) for _ in range(n_blocks)]
for i in range(2):
layers += convT_norm_relu(n_ftrs, n_ftrs//2, norm_layer, bias=bias)
n_ftrs //= 2
layers += [nn.ReflectionPad2d(3), nn.Conv2d(n_ftrs, ch_out, kernel_size=7, padding=0), nn.Tanh()]
return nn.Sequential(*layers)
class DeconvBlock(torch.nn.Module):
def __init__(self,input_size,output_size,kernel_size=4,stride=2,padding=1,activation='relu',batch_norm=True):
super(DeconvBlock,self).__init__()
self.deconv = torch.nn.ConvTranspose2d(input_size,output_size,kernel_size,stride,padding)
self.batch_norm = batch_norm
self.bn = torch.nn.InstanceNorm2d(output_size)
self.activation = activation
self.relu = torch.nn.ReLU(True)
self.tanh = torch.nn.Tanh()
def forward(self,x):
if self.batch_norm:
out = self.bn(self.deconv(x))
else:
out = self.deconv(x)
if self.activation == 'relu':
return self.relu(out)
elif self.activation == 'lrelu':
return self.lrelu(out)
elif self.activation == 'tanh':
return self.tanh(out)
elif self.activation == 'no_act':
return out
class Generator(torch.nn.Module):
def __init__(self,input_dim,num_filter,output_dim,num_resnet):
super(Generator,self).__init__()
#Reflection padding
#self.pad = torch.nn.ReflectionPad2d(3)
#Encoder
self.conv1 = ConvBlock(input_dim,num_filter,kernel_size=4,stride=2,padding=1)
self.conv2 = ConvBlock(num_filter,num_filter*2)
#self.conv3 = ConvBlock(num_filter*2,num_filter*4)
#Resnet blocks
self.resnet_blocks = []
for i in range(num_resnet):
self.resnet_blocks.append(ResnetBlock(num_filter*2))
self.resnet_blocks = torch.nn.Sequential(*self.resnet_blocks)
#Decoder
self.deconv1 = DeconvBlock(num_filter*2,num_filter)
self.deconv2 = DeconvBlock(num_filter,output_dim,activation='tanh')
#self.deconv3 = ConvBlock(num_filter,output_dim,kernel_size=7,stride=1,padding=0,activation='tanh',batch_norm=False)
def forward(self,x):
#Encoder
enc1 = self.conv1(x)
enc2 = self.conv2(enc1)
#enc3 = self.conv3(enc2)
#Resnet blocks
res = self.resnet_blocks(enc2)
#Decoder
dec1 = self.deconv1(res)
dec2 = self.deconv2(dec1)
#out = self.deconv3(self.pad(dec2))
return dec2
def normal_weight_init(self,mean=0.0,std=0.02):
for m in self.children():
if isinstance(m,ConvBlock):
torch.nn.init.normal_(m.conv.weight,mean,std)
if isinstance(m,DeconvBlock):
torch.nn.init.normal_(m.deconv.weight,mean,std)
if isinstance(m,ResnetBlock):
torch.nn.init.normal_(m.conv.weight,mean,std)
torch.nn.init.constant_(m.conv.bias,0)
model = G_A = Generator(3, 32, 3, 4).cuda() # input_dim, num_filter, output_dim, num_resnet
model.load_state_dict(torch.load('G_A_HW4_SAVE.pt',map_location=torch.device('cpu')))
model.eval()
totensor = torchvision.transforms.ToTensor()
normalize_fn = torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
topilimage = torchvision.transforms.ToPILImage()
def predict(input):
im = normalize_fn(totensor(input))
print(im.shape)
preds = model(im.unsqueeze(0))/2 + 0.5
print(preds.shape)
return topilimage(preds.squeeze(0).detach())
gr_interface = gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(256, 256)), outputs="image", title='Horse-to-Zebra CycleGAN')
gr_interface.launch(inline=False,share=False) |