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import numpy as np
import streamlit as st
import torch
import torch.nn as nn
from PIL import Image
from torchvision import transforms as tr
class ResidualBlock(nn.Module):
def __init__(self, channels):
super(ResidualBlock, self).__init__()
self.block = nn.Sequential(
nn.ReflectionPad2d(1),
nn.Conv2d(channels, channels, kernel_size=3, padding=0),
nn.InstanceNorm2d(channels),
nn.ReLU(inplace=True),
nn.ReflectionPad2d(1),
nn.Conv2d(channels, channels, kernel_size=3, padding=0),
nn.InstanceNorm2d(channels),
)
def forward(self, x):
return x + self.block(x)
class Generator(nn.Module):
def __init__(self, in_channels=3, out_channels=3, num_features=64, num_residual_blocks=9):
super(Generator, self).__init__()
model = [
nn.ReflectionPad2d(3),
nn.Conv2d(in_channels, num_features, kernel_size=7, padding=0),
nn.InstanceNorm2d(num_features),
nn.ReLU(inplace=True),
]
in_f = num_features
out_f = in_f * 2
for _ in range(2):
model += [
nn.Conv2d(in_f, out_f, kernel_size=3, stride=2, padding=1),
nn.InstanceNorm2d(out_f),
nn.ReLU(inplace=True),
]
in_f = out_f
out_f = in_f * 2
for _ in range(num_residual_blocks):
model += [ResidualBlock(in_f)]
out_f = in_f // 2
for _ in range(2):
model += [
nn.ConvTranspose2d(in_f, out_f, kernel_size=3, stride=2, padding=1, output_padding=1),
nn.InstanceNorm2d(out_f),
nn.ReLU(inplace=True),
]
in_f = out_f
out_f = in_f // 2
model += [
nn.ReflectionPad2d(3),
nn.Conv2d(num_features, out_channels, kernel_size=7, padding=0),
nn.Tanh(),
]
self.model = nn.Sequential(*model)
def forward(self, x):
return self.model(x)
class Discriminator(nn.Module):
def __init__(self, in_channels=3, num_features=64, num_layers=3):
super(Discriminator, self).__init__()
model = [
nn.Conv2d(in_channels, num_features, kernel_size=4, stride=2, padding=1),
nn.LeakyReLU(0.2, inplace=True),
]
in_f = num_features
out_f = in_f * 2
for _ in range(1, num_layers):
model += [
nn.Conv2d(in_f, out_f, kernel_size=4, stride=2, padding=1),
nn.InstanceNorm2d(out_f),
nn.LeakyReLU(0.2, inplace=True),
]
in_f = out_f
out_f = min(in_f * 2, 512)
model += [
nn.Conv2d(in_f, out_f, kernel_size=4, stride=1, padding=1),
nn.InstanceNorm2d(out_f),
nn.LeakyReLU(0.2, inplace=True),
]
model += [nn.Conv2d(out_f, 1, kernel_size=4, stride=1, padding=1)]
self.model = nn.Sequential(*model)
def forward(self, x):
return self.model(x)
class CycleGAN(nn.Module):
def __init__(self, in_channels=3, num_features_g=64, num_residual_blocks=9,
num_features_d=64, num_layers_d=3):
super(CycleGAN, self).__init__()
self.generators = nn.ModuleDict({
"a_to_b": Generator(in_channels, in_channels, num_features_g, num_residual_blocks),
"b_to_a": Generator(in_channels, in_channels, num_features_g, num_residual_blocks),
})
self.discriminators = nn.ModuleDict({
"a": Discriminator(in_channels, num_features_d, num_layers_d),
"b": Discriminator(in_channels, num_features_d, num_layers_d),
})
@st.cache_resource
def load_model():
model_dir = os.path.dirname(os.path.abspath(__file__))
model_path = os.path.join(model_dir, "cyclegan_model_v2.pt")
if not os.path.exists(model_path):
raise FileNotFoundError(f"Model file not found: {model_path}")
checkpoint = torch.load(model_path, map_location="cpu")
model = CycleGAN(**checkpoint["model_params"])
model.load_state_dict(checkpoint["model_state_dict"])
model.eval()
return model, checkpoint
def preprocess(image, mean, std, size=256):
transform = tr.Compose([
tr.Resize(size),
tr.CenterCrop(size),
tr.ToTensor(),
tr.Normalize(mean=mean, std=std),
])
return transform(image).unsqueeze(0)
def postprocess(tensor, mean, std):
img = tensor.squeeze(0).detach().cpu()
mean_t = torch.tensor(mean).view(3, 1, 1)
std_t = torch.tensor(std).view(3, 1, 1)
img = img * std_t + mean_t
img = img.permute(1, 2, 0).numpy()
img = np.clip(img * 255, 0, 255).astype(np.uint8)
return Image.fromarray(img)
st.set_page_config(page_title="CycleGAN: Summer <-> Winter", layout="wide")
st.title("CycleGAN: Summer ↔ Winter (Yosemite)")
st.markdown("Upload an image and transform it between summer and winter styles!")
if "result_image" not in st.session_state:
st.session_state.result_image = None
if "result_label" not in st.session_state:
st.session_state.result_label = None
direction = st.selectbox(
"Choose transformation direction:",
["Summer → Winter (A→B)", "Winter → Summer (B→A)"]
)
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
generate_clicked = st.button("Generate")
if uploaded_file is not None:
image = Image.open(uploaded_file).convert("RGB")
col1, col2 = st.columns(2)
with col1:
st.subheader("Original")
st.image(image, width=512)
if generate_clicked:
with st.spinner("Loading model..."):
model, checkpoint = load_model()
mean_a = checkpoint["channel_mean_a"]
std_a = checkpoint["channel_std_a"]
mean_b = checkpoint["channel_mean_b"]
std_b = checkpoint["channel_std_b"]
with torch.no_grad():
if "A→B" in direction:
input_tensor = preprocess(image, mean_a, std_a)
output_tensor = model.generators["a_to_b"](input_tensor)
result = postprocess(output_tensor, mean_b, std_b)
label = "Winter (Generated)"
else:
input_tensor = preprocess(image, mean_b, std_b)
output_tensor = model.generators["b_to_a"](input_tensor)
result = postprocess(output_tensor, mean_a, std_a)
label = "Summer (Generated)"
st.session_state.result_image = result
st.session_state.result_label = label
if st.session_state.result_image is not None:
with col2:
st.subheader(st.session_state.result_label)
st.image(st.session_state.result_image, width=512)
st.markdown("---")
st.markdown("Built with CycleGAN (Zhu et al., 2017). Dataset: summer2winter_yosemite.")
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