Fixed autocorrect issues

app.py

@@ -1,5 +1,3 @@
-from data_loader_cache import normalize, im_reader, im_preprocess
-from models import *
 import cv2
 from skimage.restoration import denoise_nl_means, estimate_sigma
 import gradio as gr
@@ -19,48 +17,45 @@ os.system("git clone https://github.com/xuebinqin/DIS")
 os.system("mv DIS/IS-Net/* .")
 
 # project imports
+from data_loader_cache import normalize, im_reader, im_preprocess 
+from models import *
 
-# Helpers
+#Helpers
 device = 'cuda' if torch.cuda.is_available() else 'cpu'
 
 # Download official weights
 if not os.path.exists("saved_models"):
     os.mkdir("saved_models")
     os.system("mv isnet.pth saved_models/")
-
-
+    
 class GOSNormalize(object):
     '''
     Normalize the Image using torch.transforms
     '''
-
-    def __init__(self, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]):
+    def __init__(self, mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225]):
         self.mean = mean
         self.std = std
 
-    def __call__(self, image):
-        image = normalize(image, self.mean, self.std)
+    def __call__(self,image):
+        image = normalize(image,self.mean,self.std)
         return image
 
 
-transform = transforms.Compose(
-    [GOSNormalize([0.5, 0.5, 0.5], [1.0, 1.0, 1.0])])
-
+transform =  transforms.Compose([GOSNormalize([0.5,0.5,0.5],[1.0,1.0,1.0])])
 
 def load_image(im_path, hypar):
     im = im_reader(im_path)
     im, im_shp = im_preprocess(im, hypar["cache_size"])
-    im = torch.divide(im, 255.0)
+    im = torch.divide(im,255.0)
     shape = torch.from_numpy(np.array(im_shp))
-    # make a batch of image, shape
-    return transform(im).unsqueeze(0), shape.unsqueeze(0)
+    return transform(im).unsqueeze(0), shape.unsqueeze(0) # make a batch of image, shape
 
 
-def build_model(hypar, device):
-    net = hypar["model"]  # GOSNETINC(3,1)
+def build_model(hypar,device):
+    net = hypar["model"]#GOSNETINC(3,1)
 
     # convert to half precision
-    if (hypar["model_digit"] == "half"):
+    if(hypar["model_digit"]=="half"):
         net.half()
         for layer in net.modules():
             if isinstance(layer, nn.BatchNorm2d):
@@ -68,11 +63,10 @@ def build_model(hypar, device):
 
     net.to(device)
 
-    if (hypar["restore_model"] != ""):
-        net.load_state_dict(torch.load(
-            hypar["model_path"]+"/"+hypar["restore_model"], map_location=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()
+    net.eval()  
     return net
 
 
@@ -121,65 +115,56 @@ def smooth_and_denoise(mask):
     return denoised_mask
 
 
+    
 def predict(net,  inputs_val, shapes_val, hypar, device):
     '''
     Given an Image, predict the mask
     '''
     net.eval()
 
-    if (hypar["model_digit"] == "full"):
+    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
+  
+    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, :, :, :]
+    pred_val = ds_val[0][0,:,:,:] # B x 1 x H x W    # we want the first one which is the most accurate prediction
 
-    # 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'))
+    ## 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 == 'cuda':
-        torch.cuda.empty_cache()
-    # it is the mask we need
-    return (pred_val.detach().cpu().numpy()*255).astype(np.uint8)
-
+    pred_val = (pred_val-mi)/(ma-mi) # max = 1
 
+    if device == 'cuda': torch.cuda.empty_cache()
+    return (pred_val.detach().cpu().numpy()*255).astype(np.uint8) # it is the mask we need
+    
 # Set Parameters
-hypar = {}  # paramters for inferencing
+hypar = {} # paramters for inferencing
 
 
-hypar["model_path"] = "./saved_models"  # load trained weights from this path
-hypar["restore_model"] = "isnet.pth"  # name of the to-be-loaded weights
-# indicate if activate intermediate feature supervision
-hypar["interm_sup"] = False
+hypar["model_path"] ="./saved_models" ## load trained weights from this path
+hypar["restore_model"] = "isnet.pth" ## name of the to-be-loaded weights
+hypar["interm_sup"] = False ## indicate if activate intermediate feature supervision
 
-# choose floating point accuracy --
-# indicates "half" or "full" accuracy of float number
-hypar["model_digit"] = "full"
+##  choose floating point accuracy --
+hypar["model_digit"] = "full" ## indicates "half" or "full" accuracy of float number
 hypar["seed"] = 0
 
-# cached input spatial resolution, can be configured into different size
-hypar["cache_size"] = [1024, 1024]
+hypar["cache_size"] = [1024, 1024] ## cached input spatial resolution, can be configured into different size
 
-# 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]
+## data augmentation parameters ---
+hypar["input_size"] = [1024, 1024] ## mdoel input spatial size, usually use the same value hypar["cache_size"], which means we don't further resize the images
+hypar["crop_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["model"] = ISNetDIS()
 
-# Build Model
+ # Build Model
 net = build_model(hypar, device)
 
 
@@ -208,7 +193,6 @@ def inference(image):
 
     return [im_rgba, pil_mask, im_dark]
 
-
 title = "Mysign.id - Signature Background removal based on DIS"
 description = "ML Model based on ECCV2022/dis-background-removal specifically made for removing background from signatures."
 
@@ -221,4 +205,4 @@ interface = gr.Interface(
     description=description,
     allow_flagging='never',
     cache_examples=False,
-).queue(api_open=True).launch(show_api=True, show_error=True)
+    ).queue(api_open=True).launch(show_api=True, show_error=True)