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data_utils.py

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+import pandas as pd
+from PIL import Image
+import json
+import random
+from qwen_vl_utils import process_vision_info
+
+prompt_template="What do you see?"
+expert_template = """{{flowrate:{FLOW_RATE_VALUE}}}"""
+answer_template="""The flowrate is {FLOW_RATE_VALUE}."""
+
+with open("/home/cm2161/rds/hpc-work/llama-manufacturing/pr-intern/src/data/general_statements.json", 'r') as file:
+    general_statements = json.load(file)["general_statements"]
+with open("/home/cm2161/rds/hpc-work/llama-manufacturing/pr-intern/src/data/qual_over_extrusion.json", 'r') as file:
+    qual_over_extrusion = json.load(file)["over_extrusion_statements"]
+with open("/home/cm2161/rds/hpc-work/llama-manufacturing/pr-intern/src/data/qual_under_extrusion.json", 'r') as file:
+    qual_under_extrusion = json.load(file)["under_extrusion_statements"]
+with open("/home/cm2161/rds/hpc-work/llama-manufacturing/pr-intern/src/data/qual_good_extrusion.json", 'r') as file:
+    qual_good_extrusion = json.load(file)["good_extrusion_statements"]
+with open("/home/cm2161/rds/hpc-work/llama-manufacturing/pr-intern/src/data/quant_templates.json", 'r') as file:
+    quant_templates = json.load(file)["flow_rate_statements"]
+
+## note: reinstall bitsandbytes if not working. enforced version 0.37.2.
+
+def synthesize_answer(sample, general=True, quant=True, qual=True):
+    final_statement = ""
+
+    # General statement
+    if general:
+        gs = random.choice(general_statements)["statement"]
+        final_statement += f"{gs} "
+
+    # Quantitative statement
+    if quant:
+        quant_statement = random.choice(quant_templates)["statement"]
+        quant_statement = quant_statement.format(flow_rate=sample["flow_rate"])
+        final_statement += f"{quant_statement} "
+
+    # Qualitative statement
+    if qual:
+        flow_rate = float(sample['flow_rate'])
+        if flow_rate < 90:
+            qual_statement = random.choice(qual_under_extrusion)["statement"]
+        elif flow_rate > 110:
+            qual_statement = random.choice(qual_over_extrusion)["statement"]
+        else:
+            qual_statement = random.choice(qual_good_extrusion)["statement"]
+        final_statement += f"{qual_statement}"
+
+    return final_statement.strip()
+
+
+def format_data(sample, image=True, fr= False, train=True):
+
+    formatted_data = {"messages": [{"role": "user","content": []}]}
+
+    if image:
+        formatted_data["messages"][0]["content"].append(
+                            {
+                                "type": "image","image": Image.open(sample["full_img_path"]),
+                            }
+                        )
+
+    if fr:
+        formatted_data["messages"][0]["content"].append(
+                {
+                    "type": "text", "text": expert_template.format(FLOW_RATE_VALUE=fr)+prompt_template
+                }
+            )
+    else:
+        formatted_data["messages"][0]["content"].append(
+            {
+                "type": "text", "text": prompt_template
+            }
+        )
+        
+    if train:
+        formatted_data["messages"].append(
+            {
+                "role": "assistant",
+                "content": synthesize_answer(sample=sample)
+            }
+        )
+
+    return formatted_data
+
+
+def collate_vqa(examples, processor, expert):
+
+    if expert:
+        _,_,expert_batch = expert.active_learning(examples)
+        templates = [format_data(example, fr=fr, image=True, train=True) for example,fr in zip(examples,expert_batch)]
+    else:
+        templates = [format_data(example, image=True, train=True) for example in examples]
+
+    image_inputs = [process_vision_info(template["messages"])[0] for template in templates]
+    texts = [processor.apply_chat_template(template["messages"], tokenize=False) for template in templates] # puts in template, and <image> token is isolated from img
+    
+    batch = processor(text=texts, images=image_inputs, return_tensors="pt", padding=True)
+    
+    labels = batch["input_ids"].clone()
+    labels[labels == processor.tokenizer.pad_token_id] = -100  #
+    image_tokens = [processor.tokenizer.convert_tokens_to_ids(processor.image_token)]
+    for image_token_id in image_tokens:
+        labels[labels == image_token_id] = -100
+
+    batch["labels"] = labels
+
+    return batch
+
+
+def val_collate_fn(examples, processor, expert=False):
+
+    if expert:
+        _, _, expert_batch = expert.validate_step(examples)
+        templates= [format_data(example, fr=fr, image=True, train=False) for example,fr in zip(examples, expert_batch)]
+    else:
+        templates= [format_data(example, image=True, train=False) for example in examples]
+
+    image_inputs = [process_vision_info(template["messages"])[0] for template in templates]
+    texts = [processor.apply_chat_template(template["messages"], tokenize=False) for template in templates] # puts in template, and <image> token is isolated from img
+    
+    batch = processor(text=texts, images=image_inputs, return_tensors="pt", padding=True)
+    
+    return batch
+
+
+def load_dataset(file_path, base_dir):
+    data = pd.read_csv(file_path)
+    data= data.drop(
+        columns=["nozzle_tip_x", "nozzle_tip_y"]
+    )
+    data["full_img_path"] = base_dir + data["img_path"].astype(str)
+    data= [row for _, row in data.iterrows()]
+    return data
+