Delete main.py

main.py

@@ -1,162 +0,0 @@
-import gradio as gr
-import os
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from huggingface_hub import hf_hub_download
-from torch.autograd import Variable
-from PIL import Image
-
-
-def build_model(hypar, device):
-    net = hypar["model"]  # GOSNETINC(3,1)
-
-    # convert to half precision
-    if hypar["model_digit"] == "half":
-        net.half()
-        for layer in net.modules():
-            if isinstance(layer, nn.BatchNorm2d):
-                layer.float()
-
-    net.to(device)
-
-    if hypar["restore_model"] != "":
-        net.load_state_dict(
-            torch.load(
-                hypar["model_path"] + "/" + hypar["restore_model"],
-                map_location=device,
-            )
-        )
-        net.to(device)
-    net.eval()
-    return net
-
-
-if not os.path.exists("saved_models"):
-    os.mkdir("saved_models")
-    os.mkdir("git")
-    os.system("git clone https://github.com/xuebinqin/DIS git/xuebinqin/DIS")
-    hf_hub_download(
-        repo_id="NimaBoscarino/IS-Net_DIS-general-use",
-        filename="isnet-general-use.pth",
-        local_dir="saved_models",
-    )
-    os.system("rm -r git/xuebinqin/DIS/IS-Net/__pycache__")
-    os.system("mv git/xuebinqin/DIS/IS-Net/* .")
-
-import data_loader_cache
-import models
-
-device = "cpu"
-ISNetDIS = models.ISNetDIS
-normalize = data_loader_cache.normalize
-im_preprocess = data_loader_cache.im_preprocess
-
-# Set Parameters
-hypar = {}  # paramters for inferencing
-
-# load trained weights from this path
-hypar["model_path"] = "./saved_models"
-# name of the to-be-loaded weights
-hypar["restore_model"] = "isnet-general-use.pth"
-# indicate if activate intermediate feature supervision
-hypar["interm_sup"] = False
-
-# choose floating point accuracy --
-# indicates "half" or "full" accuracy of float number
-hypar["model_digit"] = "full"
-hypar["seed"] = 0
-
-# cached input spatial resolution, can be configured into different size
-hypar["cache_size"] = [1024, 1024]
-
-# data augmentation parameters ---
-# mdoel input spatial size, usually use the same value hypar["cache_size"], which means we don't further resize the images
-hypar["input_size"] = [1024, 1024]
-# random crop size from the input, it is usually set as smaller than hypar["cache_size"], e.g., [920,920] for data augmentation
-hypar["crop_size"] = [1024, 1024]
-
-hypar["model"] = ISNetDIS()
-
-# Build Model
-net = build_model(hypar, device)
-
-
-def predict(net, inputs_val, shapes_val, hypar, device):
-    """
-    Given an Image, predict the mask
-    """
-    net.eval()
-
-    if hypar["model_digit"] == "full":
-        inputs_val = inputs_val.type(torch.FloatTensor)
-    else:
-        inputs_val = inputs_val.type(torch.HalfTensor)
-
-    inputs_val_v = Variable(inputs_val, requires_grad=False).to(
-        device
-    )  # wrap inputs in Variable
-
-    ds_val = net(inputs_val_v)[0]  # list of 6 results
-
-    # B x 1 x H x W    # we want the first one which is the most accurate prediction
-    pred_val = ds_val[0][0, :, :, :]
-
-    # recover the prediction spatial size to the orignal image size
-    pred_val = torch.squeeze(
-        F.upsample(
-            torch.unsqueeze(pred_val, 0),
-            (shapes_val[0][0], shapes_val[0][1]),
-            mode="bilinear",
-        )
-    )
-
-    ma = torch.max(pred_val)
-    mi = torch.min(pred_val)
-    pred_val = (pred_val - mi) / (ma - mi)  # max = 1
-
-    if device == "cpu":
-        torch.cpu.empty_cache()
-    # it is the mask we need
-    return (pred_val.detach().cpu().numpy() * 255).astype(np.uint8)
-
-
-def load_image(im_pil, hypar):
-    im = np.array(im_pil)
-    im, im_shp = im_preprocess(im, hypar["cache_size"])
-    im = torch.divide(im, 255.0)
-    shape = torch.from_numpy(np.array(im_shp))
-    # make a batch of image, shape
-    aa = normalize(im, [0.5, 0.5, 0.5], [1.0, 1.0, 1.0])
-    return aa.unsqueeze(0), shape.unsqueeze(0)
-
-
-def remove_background(image):
-    image_tensor, orig_size = load_image(image, hypar)
-    mask = predict(net, image_tensor, orig_size, hypar, "cpu")
-
-    mask = Image.fromarray(mask).convert("L")
-    im_rgb = image.convert("RGB")
-
-    cropped = im_rgb.copy()
-    cropped.putalpha(mask)
-    return cropped
-
-
-inputs = gr.inputs.Image()
-outputs = gr.outputs.Image(type="pil")
-interface = gr.Interface(
-    fn=remove_background,
-    inputs=inputs,
-    outputs=outputs,
-    title="Remove Background",
-    description="This App removes the background from an image",
-    examples=[
-        "examples/input/1.jpeg",
-        "examples/input/2.jpeg",
-        "examples/input/3.jpeg",
-    ],
-    cache_examples=True,
-)
-interface.launch(enable_queue=True)