removed files

App.py

@@ -1,86 +0,0 @@
-import streamlit as st
-import torch
-import DCGAN
-import SRGAN
-from Utils import color_histogram_mapping, denormalize_images
-import torch.nn as nn
-import random
-
-device = torch.device("cpu")
-
-if torch.cuda.is_available():
-    device = torch.device("cuda")
-
-latent_size = 100
-display_width = 450
-checkpoint_path = "Checkpoints/150epochs.chkpt"
-
-st.title("Generating Abstract Art")
-st.text("start generating (left side bar)")
-st.text("Made by Xingyu B.")
-
-st.sidebar.subheader("Configurations")
-seed = st.sidebar.slider('Seed', -10000, 10000, 0)
-
-num_images = st.sidebar.slider('Number of Images', 1, 10, 1)
-
-use_srgan = st.sidebar.selectbox(
-    'Apply image enhancement',
-    ('Yes', 'No')
-)
-
-generate = st.sidebar.button("Generate")
-
-
-# caching the expensive model loading 
-
-@st.cache(allow_output_mutation=True)
-def load_dcgan():
-    model = torch.jit.load('Checkpoints/dcgan.pt', map_location=device)
-    return model 
-
-@st.cache(allow_output_mutation=True)
-def load_esrgan():
-    model_state_dict = torch.load("Checkpoints/esrgan.pt", map_location=device)
-    return model_state_dict
-
-# if the user wants to generate something new 
-if generate:
-    torch.manual_seed(seed)
-    random.seed(seed)
-    
-    sampled_noise = torch.randn(num_images, latent_size, 1, 1, device=device)
-    generator = load_dcgan()
-    generator.eval()
-
-    with torch.no_grad():
-        fakes = generator(sampled_noise).detach()
-
-    # use srgan for super resolution
-    if use_srgan == "Yes":
-        # restore to the checkpoint
-        st.write("Using DCGAN then ESRGAN upscale...")
-        esrgan_generator = SRGAN.GeneratorRRDB(channels=3, filters=64, num_res_blocks=23).to(device)
-        esrgan_checkpoint = load_esrgan()
-        esrgan_generator.load_state_dict(esrgan_checkpoint)
-
-        esrgan_generator.eval()
-        with torch.no_grad():
-            enhanced_fakes = esrgan_generator(fakes).detach().cpu()
-        color_match = color_histogram_mapping(enhanced_fakes, fakes.cpu())
-
-        for i in range(len(color_match)):
-            # denormalize and permute to correct color channel
-            st.image(denormalize_images(color_match[i]).permute(1, 2, 0).numpy(), width=display_width)
-
-
-    # default setting -> vanilla dcgan generation
-    if use_srgan == "No":
-        fakes = fakes.cpu()
-        st.write("Using DCGAN Model...")
-        for i in range(len(fakes)):
-            st.image(denormalize_images(fakes[i]).permute(1, 2, 0).numpy(), width=display_width)
-
-
-
-

Utils.py

@@ -1,40 +0,0 @@
-import matplotlib.pyplot as plt
-import numpy as np
-import torchvision.utils as vutils
-import torchvision.transforms as transforms
-from skimage.exposure import match_histograms
-import torch
-
-# contains utility functions that we need in the main program
-
-# matches the color histogram of original and the super resolution output
-def color_histogram_mapping(images, references):
-    matched_list = []
-    for i in range(len(images)):
-        matched = match_histograms(images[i].permute(1, 2, 0).numpy(), references[i].permute(1, 2, 0).numpy(),
-                                   channel_axis=-1)
-        matched_list.append(matched)
-    return torch.tensor(np.array(matched_list)).permute(0, 3, 1, 2)
-
-
-def visualize_generations(seed, images):
-    plt.figure(figsize=(16, 16))
-    plt.title(f"Seed: {seed}")
-    plt.axis("off")
-    plt.imshow(np.transpose(vutils.make_grid(images, padding=2, nrow=5, normalize=True), (2, 1, 0)))
-    plt.show()
-
-
-# denormalize the images for proper display
-def denormalize_images(images):
-    mean= [0.5, 0.5, 0.5]
-    std= [0.5, 0.5, 0.5]
-    inv_normalize = transforms.Normalize(
-        mean=[-m / s for m, s in zip(mean, std)],
-        std=[1 / s for s in std]
-    )
-    return inv_normalize(images)
-
-
-
-