add Intel GPU support

easyocr/detection.py

@@ -40,6 +40,7 @@ def test_net(canvas_size, mag_ratio, net, image, text_threshold, link_threshold,
          for n_img in img_resized_list]
     x = torch.from_numpy(np.array(x))
     x = x.to(device)
+    net = net.to(device)
 
     # forward pass
     with torch.no_grad():
@@ -74,8 +75,9 @@ def test_net(canvas_size, mag_ratio, net, image, text_threshold, link_threshold,
 def get_detector(trained_model, device='cpu', quantize=True, cudnn_benchmark=False):
     net = CRAFT()
 
-    if device == 'cpu':
+    if device == 'cpu'or device == 'xpu':
         net.load_state_dict(copyStateDict(torch.load(trained_model, map_location=device, weights_only=False)))
+        net.to(device)
         if quantize:
             try:
                 torch.quantization.quantize_dynamic(net, dtype=torch.qint8, inplace=True)

easyocr/easyocr.py

@@ -10,6 +10,12 @@
 import numpy as np
 import cv2
 import torch
+
+try:
+    import intel_extension_for_pytorch as ipex
+except Exception:
+    pass
+
 import os
 import sys
 from PIL import Image
@@ -74,10 +80,12 @@ def __init__(self, lang_list, gpu=True, model_storage_directory=None,
                 self.device = 'cuda'
             elif torch.backends.mps.is_available():
                 self.device = 'mps'
+            elif torch.xpu.is_available():
+                self.device = 'xpu'
             else:
                 self.device = 'cpu'
                 if verbose:
-                    LOGGER.warning('Neither CUDA nor MPS are available - defaulting to CPU. Note: This module is much faster with a GPU.')
+                    LOGGER.warning('Neither CUDA/XPU/MPS are available - defaulting to CPU. Note: This module is much faster with a GPU.')
         else:
             self.device = gpu
 

easyocr/recognition.py

@@ -165,13 +165,14 @@ def get_recognizer(recog_network, network_params, character,\
         model_pkg = importlib.import_module(recog_network)
     model = model_pkg.Model(num_class=num_class, **network_params)
 
-    if device == 'cpu':
+    if device == 'cpu'or device == 'xpu':
         state_dict = torch.load(model_path, map_location=device, weights_only=False)
         new_state_dict = OrderedDict()
         for key, value in state_dict.items():
             new_key = key[7:]
             new_state_dict[new_key] = value
         model.load_state_dict(new_state_dict)
+        model.to(device)
         if quantize:
             try:
                 torch.quantization.quantize_dynamic(model, dtype=torch.qint8, inplace=True)

requirements.txt

@@ -1,5 +1,4 @@
-torch
-torchvision>=0.5
+
 opencv-python-headless
 scipy
 numpy