Upload import torch.py

import torch.py

@@ -0,0 +1,70 @@
+import torch
+from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
+from PIL import Image
+from torchvision import transforms
+
+model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+feature_extractor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+max_length = 16
+num_beams = 4
+gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
+
+def predict_step(image_paths):
+  images = []
+  for image_path in image_paths:
+    i_image = Image.open(image_path)
+    if i_image.mode != "RGB":
+      i_image = i_image.convert(mode="RGB")
+
+    images.append(i_image)
+
+  pixel_values = feature_extractor(images=images, return_tensors="pt").pixel_values
+  pixel_values = pixel_values.to(device)
+
+  output_ids = model.generate(pixel_values, **gen_kwargs)
+
+  preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+  preds = [pred.strip() for pred in preds]
+  return preds
+
+target_object = 'Desk'
+
+def predict_step(image_paths):
+    images = []
+    for image_path in image_paths:
+        i_image = Image.open(image_path)
+        if i_image.mode != "RGB":
+            i_image = i_image.convert(mode="RGB")
+
+        images.append(i_image)
+
+    pixel_values = feature_extractor(images=images, return_tensors="pt").pixel_values
+    pixel_values = pixel_values.to(device)
+
+    output_ids = model.generate(pixel_values, **gen_kwargs)
+
+    preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+    preds = [pred.strip() for pred in preds]
+
+    
+
+    # Check if the target object is mentioned in the captions
+    object_found = any(target_object.lower() in caption.lower() for caption in preds)
+
+
+    return object_found
+
+    # Check if the target object is present in the image
+
+
+result = predict_step(['D:\Sushant.jpg'])
+
+if result:
+    print(f"The object "+ target_object + " is present in the image.")
+else:
+    print(f"The object "+ target_object + " is not present in the image.")