updated app

app.py

@@ -80,6 +80,90 @@ elif modelName=="parseq":
     opt.scorer = "mean"
     opt.blackbg = True
 
+segmentation_fn = SegmentationAlgorithm('quickshift', kernel_size=4,
+                                        max_dist=200, ratio=0.2,
+                                        random_seed=random.randint(0, 1000))
+
+if modelName=="vitstr":
+    if opt.Transformer:
+        converter = TokenLabelConverter(opt)
+    elif 'CTC' in opt.Prediction:
+        converter = CTCLabelConverter(opt.character)
+    else:
+        converter = AttnLabelConverter(opt.character)
+    opt.num_class = len(converter.character)
+    if opt.rgb:
+        opt.input_channel = 3
+    model_obj = Model(opt)
+
+    model = torch.nn.DataParallel(model_obj).to(device)
+    modelCopy = copy.deepcopy(model)
+
+    """ evaluation """
+    scoring_singlechar = STRScore(opt=opt, converter=converter, device=device, enableSingleCharAttrAve=True)
+    super_pixel_model_singlechar = torch.nn.Sequential(
+        # super_pixler,
+        # numpy2torch_converter,
+        modelCopy,
+        scoring_singlechar
+    ).to(device)
+    modelCopy.eval()
+    scoring_singlechar.eval()
+    super_pixel_model_singlechar.eval()
+
+    # Single Char Attribution Averaging
+    # enableSingleCharAttrAve - set to True
+    scoring = STRScore(opt=opt, converter=converter, device=device)
+    super_pixel_model = torch.nn.Sequential(
+        # super_pixler,
+        # numpy2torch_converter,
+        model,
+        scoring
+    ).to(device)
+    model.eval()
+    scoring.eval()
+    super_pixel_model.eval()
+
+elif modelName=="parseq":
+    model = torch.hub.load('baudm/parseq', 'parseq', pretrained=True)
+    # checkpoint = torch.hub.load_state_dict_from_url('https://github.com/baudm/parseq/releases/download/v1.0.0/parseq-bb5792a6.pt', map_location="cpu")
+    # # state_dict = {key.replace("module.", ""): value for key, value in checkpoint["state_dict"].items()}
+    # model.load_state_dict(checkpoint)
+    model = model.to(device)
+    model_obj = model
+    converter = TokenLabelConverter(opt)
+    modelCopy = copy.deepcopy(model)
+
+    """ evaluation """
+    scoring_singlechar = STRScore(opt=opt, converter=converter, device=device, enableSingleCharAttrAve=True, model=modelCopy)
+    super_pixel_model_singlechar = torch.nn.Sequential(
+        # super_pixler,
+        # numpy2torch_converter,
+        modelCopy,
+        scoring_singlechar
+    ).to(device)
+    modelCopy.eval()
+    scoring_singlechar.eval()
+    super_pixel_model_singlechar.eval()
+
+    # Single Char Attribution Averaging
+    # enableSingleCharAttrAve - set to True
+    scoring = STRScore(opt=opt, converter=converter, device=device, model=model)
+    super_pixel_model = torch.nn.Sequential(
+        # super_pixler,
+        # numpy2torch_converter,
+        model,
+        scoring
+    ).to(device)
+    model.eval()
+    scoring.eval()
+    super_pixel_model.eval()
+
+if opt.blackbg:
+    shapImgLs = np.zeros(shape=(1, 1, 224, 224)).astype(np.float32)
+    trainList = np.array(shapImgLs)
+    background = torch.from_numpy(trainList).to(device)
+
 # x = st.slider('Select a value')
 # st.write(x, 'squared is', x * x)
 
@@ -99,6 +183,7 @@ if uploaded_file is not None:
     # To read file as bytes:
     bytes_data = uploaded_file.getvalue()
     pilImg = Image.open(uploaded_file)
+    pilImg = pilImg.resize((opt.imgW, opt.imgH))
 
     orig_img_tensors = transforms.ToTensor()(pilImg).unsqueeze(0)
     img1 = orig_img_tensors.to(device)