remove .cuda() expressions

README.md

@@ -42,7 +42,7 @@ from transformers import AutoModel, AutoTokenizer
 
 tokenizer = AutoTokenizer.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True)
 model = AutoModel.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True, low_cpu_mem_usage=True, device_map='cuda', use_safetensors=True, pad_token_id=tokenizer.eos_token_id)
-model = model.eval().cuda()
+model = model.eval()
 
 
 # input your test image

modeling_GOT.py

@@ -558,7 +558,7 @@ class GOTQwenForCausalLM(Qwen2ForCausalLM):
 
         image_tensor_1 = image_processor_high(image)
 
-        input_ids = torch.as_tensor(inputs.input_ids).cuda()
+        input_ids = torch.as_tensor(inputs.input_ids)
 
         stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
         keywords = [stop_str]
@@ -569,7 +569,7 @@ class GOTQwenForCausalLM(Qwen2ForCausalLM):
             with torch.autocast("cuda", dtype=torch.bfloat16):
                 output_ids = self.generate(
                     input_ids,
-                    images=[image_tensor_1.unsqueeze(0).half().cuda()],
+                    images=[image_tensor_1.unsqueeze(0).half()],
                     do_sample=False,
                     num_beams = 1,
                     no_repeat_ngram_size = 20,
@@ -581,7 +581,7 @@ class GOTQwenForCausalLM(Qwen2ForCausalLM):
             with torch.autocast("cuda", dtype=torch.bfloat16):
                 output_ids = self.generate(
                     input_ids,
-                    images=[image_tensor_1.unsqueeze(0).half().cuda()],
+                    images=[image_tensor_1.unsqueeze(0).half()],
                     do_sample=False,
                     num_beams = 1,
                     no_repeat_ngram_size = 20,
@@ -812,7 +812,7 @@ class GOTQwenForCausalLM(Qwen2ForCausalLM):
 
         inputs = tokenizer([prompt])
 
-        input_ids = torch.as_tensor(inputs.input_ids).cuda()
+        input_ids = torch.as_tensor(inputs.input_ids)
 
         stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
         keywords = [stop_str]
@@ -823,7 +823,7 @@ class GOTQwenForCausalLM(Qwen2ForCausalLM):
             with torch.autocast("cuda", dtype=torch.bfloat16):
                 output_ids = self.generate(
                     input_ids,
-                    images=[image_list.half().cuda()],
+                    images=[image_list.half()],
                     do_sample=False,
                     num_beams = 1,
                     # no_repeat_ngram_size = 20,
@@ -835,7 +835,7 @@ class GOTQwenForCausalLM(Qwen2ForCausalLM):
             with torch.autocast("cuda", dtype=torch.bfloat16):
                 output_ids = self.generate(
                     input_ids,
-                    images=[image_list.half().cuda()],
+                    images=[image_list.half()],
                     do_sample=False,
                     num_beams = 1,
                     # no_repeat_ngram_size = 20,