replace idefics by a policy

operators/idefics2_op.py

@@ -1,61 +0,0 @@
-from dora import DoraStatus
-import pyarrow as pa
-
-
-import cv2
-
-from idefics2_utils import ask_vlm
-import pyttsx3
-
-
-CAMERA_WIDTH = 960
-CAMERA_HEIGHT = 540
-
-
-FONT = cv2.FONT_HERSHEY_SIMPLEX
-
-
-engine = pyttsx3.init("espeak")
-voices = engine.getProperty("voices")
-engine.setProperty("voice", voices[11].id)  # English
-
-
-def speak(text):
-    engine.say(text)
-    engine.runAndWait()
-
-
-class Operator:
-    def __init__(self):
-        self.instruction = "What is in the image?"
-        self.last_message = ""
-        self.image = None
-
-    def on_event(
-        self,
-        dora_event,
-        send_output,
-    ) -> DoraStatus:
-        if dora_event["type"] == "INPUT":
-            if dora_event["id"] == "image":
-                self.image = (
-                    dora_event["value"]
-                    .to_numpy()
-                    .reshape((CAMERA_HEIGHT, CAMERA_WIDTH, 3))
-                )
-            elif dora_event["id"] == "instruction":
-                self.instruction = dora_event["value"][0].as_py()
-                print("instructions: ", self.instruction, flush=True)
-
-                if self.image is not None:
-                    output = ask_vlm(self.image, self.instruction)
-                    speak(output)
-                    print("response: ", output, flush=True)
-                    send_output(
-                        "assistant_message",
-                        pa.array([output]),
-                        dora_event["metadata"],
-                    )
-
-                    self.last_message = output
-        return DoraStatus.CONTINUE

operators/idefics2_utils.py

@@ -1,69 +0,0 @@
-import requests
-import torch
-from PIL import Image
-from io import BytesIO
-
-from transformers import AutoProcessor, AutoModelForVision2Seq, AwqConfig
-
-
-MODE = "quantized"
-DEVICE = "cuda"
-PROCESSOR = AutoProcessor.from_pretrained("HuggingFaceM4/idefics2-tfrm-compatible")
-BAD_WORDS_IDS = PROCESSOR.tokenizer(
-    ["<image>", "<fake_token_around_image>"], add_special_tokens=False
-).input_ids
-EOS_WORDS_IDS = PROCESSOR.tokenizer(
-    "<end_of_utterance>", add_special_tokens=False
-).input_ids + [PROCESSOR.tokenizer.eos_token_id]
-
-# Load model
-if MODE == "regular":
-    model = AutoModelForVision2Seq.from_pretrained(
-        "HuggingFaceM4/idefics2-tfrm-compatible",
-        torch_dtype=torch.float16,
-        trust_remote_code=True,
-        _attn_implementation="flash_attention_2",
-        revision="3dc93be345d64fb6b1c550a233fe87ddb36f183d",
-    ).to(DEVICE)
-elif MODE == "quantized":
-    quant_path = "HuggingFaceM4/idefics2-tfrm-compatible-AWQ"
-    model = AutoModelForVision2Seq.from_pretrained(
-        quant_path, trust_remote_code=True
-    ).to(DEVICE)
-elif MODE == "fused_quantized":
-    quant_path = "HuggingFaceM4/idefics2-tfrm-compatible-AWQ"
-    quantization_config = AwqConfig(
-        bits=4,
-        fuse_max_seq_len=4096,
-        modules_to_fuse={
-            "attention": ["q_proj", "k_proj", "v_proj", "o_proj"],
-            "mlp": ["gate_proj", "up_proj", "down_proj"],
-            "layernorm": ["input_layernorm", "post_attention_layernorm", "norm"],
-            "use_alibi": False,
-            "num_attention_heads": 32,
-            "num_key_value_heads": 8,
-            "hidden_size": 4096,
-        },
-    )
-    model = AutoModelForVision2Seq.from_pretrained(
-        quant_path, quantization_config=quantization_config, trust_remote_code=True
-    ).to(DEVICE)
-else:
-    raise ValueError("Unknown mode")
-
-
-def ask_vlm(image, instruction):
-    prompts = [
-        "User:",
-        image,
-        f"{instruction}.<end_of_utterance>\n",
-        "Assistant:",
-    ]
-    inputs = PROCESSOR(prompts)
-    inputs = {k: torch.tensor(v).to(DEVICE) for k, v in inputs.items()}
-
-    generated_ids = model.generate(
-        **inputs, bad_words_ids=BAD_WORDS_IDS, max_new_tokens=10
-    )
-    generated_texts = PROCESSOR.batch_decode(generated_ids, skip_special_tokens=True)
-    return generated_texts[0].split("\nAssistant: ")[1]

operators/policy.py

@@ -10,7 +10,7 @@ HOME = np.array([[0.5, 0.0], [0.0, 0.0]]).ravel()
 
 ## Policy Operator
 class Operator:
-    def speak(text: str):
+    def speak(self, text: str):
         speak(text)
 
     def ask_model(self, image, text: str) -> bool:
@@ -21,9 +21,17 @@ class Operator:
         if dora_event["type"] == "INPUT":
             id = dora_event["id"]
             if id == "init":
-                send_output("go_to", pa.array([]))
+                send_output("go_to", pa.array(COUCH))
             elif id == "goal_reached":
+                print("goal reached", flush=True)
                 image = dora_event["value"].to_numpy().reshape((540, 960, 3))
-                pass
+                if self.ask_model(image, "Is there anyone with a bruise shirt?"):
+                    self.speak("I'm gonna go get coffee.")
+                    send_output("go_to", pa.array(KITCHEN))
+                    self.speak("I'm going to the kitchen.")
+                else:
+                    self.speak("There's no one with a bruise shirt.")
+                    send_output("go_to", pa.array(COUCH))
+                    self.speak("I'm going to the couch.")
 
         return DoraStatus.CONTINUE

operators/utils.py

@@ -22,6 +22,8 @@ def speak(text):
     engine.runAndWait()
 
 
+speak("hello")
+
 MODE = "quantized"
 DEVICE = "cuda"
 PROCESSOR = AutoProcessor.from_pretrained("HuggingFaceM4/idefics2-tfrm-compatible")
@@ -75,6 +77,7 @@ def ask_vlm(image, instruction):
         f"{instruction}.<end_of_utterance>\n",
         "Assistant:",
     ]
+    speak(instruction)
     inputs = PROCESSOR(prompts)
     inputs = {k: torch.tensor(v).to(DEVICE) for k, v in inputs.items()}
 
@@ -82,4 +85,39 @@ def ask_vlm(image, instruction):
         **inputs, bad_words_ids=BAD_WORDS_IDS, max_new_tokens=10
     )
     generated_texts = PROCESSOR.batch_decode(generated_ids, skip_special_tokens=True)
-    return generated_texts[0].split("\nAssistant: ")[1]
+
+    text = generated_texts[0].split("\nAssistant: ")[1]
+    speak(text)
+    return text
+
+
+# import requests
+# import torch
+# from PIL import Image
+# from io import BytesIO
+
+
+# def download_image(url):
+# try:
+# # Send a GET request to the URL to download the image
+# response = requests.get(url)
+# # Check if the request was successful (status code 200)
+# if response.status_code == 200:
+# # Open the image using PIL
+# image = Image.open(BytesIO(response.content))
+# # Return the PIL image object
+# return image
+# else:
+# print(f"Failed to download image. Status code: {response.status_code}")
+# return None
+# except Exception as e:
+# print(f"An error occurred: {e}")
+# return None
+
+
+# # Create inputs
+# image1 = download_image(
+# "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
+# )
+
+# print(ask_vlm(image1, "What is this?"))