TEST: debug gradio not rendering

app.py

@@ -16,36 +16,38 @@ STYLE = "shinkai_makoto"
 MODEL_PATH = "models"
 COLOUR_MODEL = "RGB"
 
-model = Transformer()
-model.load_state_dict(torch.load(os.path.join(MODEL_PATH, f"{STYLE}.pth")))
-model.eval()
+# model = Transformer()
+# model.load_state_dict(torch.load(os.path.join(MODEL_PATH, f"{STYLE}.pth")))
+# model.eval()
 
-disable_gpu = torch.cuda.is_available()
+# disable_gpu = torch.cuda.is_available()
 
 
 def inference(img):
-    # load image
-    input_image = img.convert(COLOUR_MODEL)
-    input_image = np.asarray(input_image)
-    # RGB -> BGR
-    input_image = input_image[:, :, [2, 1, 0]]
-    input_image = transforms.ToTensor()(input_image).unsqueeze(0)
-    # preprocess, (-1, 1)
-    input_image = -1 + 2 * input_image
-
-    if disable_gpu:
-        input_image = Variable(input_image).float()
-    else:
-        input_image = Variable(input_image).cuda()
-
-    # forward
-    output_image = model(input_image)
-    output_image = output_image[0]
-    # BGR -> RGB
-    output_image = output_image[[2, 1, 0], :, :]
-    output_image = output_image.data.cpu().float() * 0.5 + 0.5
-
-    return output_image
+    # # load image
+    # input_image = img.convert(COLOUR_MODEL)
+    # input_image = np.asarray(input_image)
+    # # RGB -> BGR
+    # input_image = input_image[:, :, [2, 1, 0]]
+    # input_image = transforms.ToTensor()(input_image).unsqueeze(0)
+    # # preprocess, (-1, 1)
+    # input_image = -1 + 2 * input_image
+
+    # if disable_gpu:
+    #     input_image = Variable(input_image).float()
+    # else:
+    #     input_image = Variable(input_image).cuda()
+
+    # # forward
+    # output_image = model(input_image)
+    # output_image = output_image[0]
+    # # BGR -> RGB
+    # output_image = output_image[[2, 1, 0], :, :]
+    # output_image = output_image.data.cpu().float() * 0.5 + 0.5
+
+    # return output_image
+
+    return ""
 
 
 title = "AnimeBackgroundGAN"

main.py

@@ -1,76 +0,0 @@
-import torch
-import os
-import numpy as np
-import torchvision.utils as vutils
-
-from PIL import Image
-import torchvision.transforms as transforms
-from torch.autograd import Variable
-
-from network.Transformer import Transformer
-import argparse
-
-parser = argparse.ArgumentParser()
-parser.add_argument("--input_dir", default="test_img")
-parser.add_argument("--load_size", default=1280)
-parser.add_argument("--model_path", default="./pretrained_model")
-parser.add_argument("--style", default="Shinkai")
-parser.add_argument("--output_dir", default="test_output")
-parser.add_argument("--gpu", type=int, default=0)
-
-opt = parser.parse_args()
-
-valid_ext = [".jpg", ".png"]
-
-# setup
-if not os.path.exists(opt.input_dir):
-    os.makedirs(opt.input_dir)
-if not os.path.exists(opt.output_dir):
-    os.makedirs(opt.output_dir)
-
-# load pretrained model
-model = Transformer()
-model.load_state_dict(
-    torch.load(os.path.join(opt.model_path, opt.style + "_net_G_float.pth"))
-)
-model.eval()
-
-disable_gpu = opt.gpu == -1 or not torch.cuda.is_available()
-
-if disable_gpu:
-    print("CPU mode")
-    model.float()
-else:
-    print("GPU mode")
-    model.cuda()
-
-for files in os.listdir(opt.input_dir):
-    ext = os.path.splitext(files)[1]
-    if ext not in valid_ext:
-        continue
-    # load image
-    input_image = Image.open(os.path.join(opt.input_dir, files)).convert("RGB")
-    input_image = np.asarray(input_image)
-    # RGB -> BGR
-    input_image = input_image[:, :, [2, 1, 0]]
-    input_image = transforms.ToTensor()(input_image).unsqueeze(0)
-    # preprocess, (-1, 1)
-    input_image = -1 + 2 * input_image
-    if disable_gpu:
-        input_image = Variable(input_image).float()
-    else:
-        input_image = Variable(input_image).cuda()
-
-    # forward
-    output_image = model(input_image)
-    output_image = output_image[0]
-    # BGR -> RGB
-    output_image = output_image[[2, 1, 0], :, :]
-    output_image = output_image.data.cpu().float() * 0.5 + 0.5
-    # save
-    vutils.save_image(
-        output_image,
-        os.path.join(opt.output_dir, files[:-4] + "_" + opt.style + ".jpg"),
-    )
-
-print("Done!")