fix usage on README
#1
by harpreetsahota - opened
README.md
CHANGED
|
@@ -52,6 +52,27 @@ returns a list of (timestamps, object_id, pixel_x, pixel_y) output points.
|
|
| 52 |
|
| 53 |
### Video Pointing Example:
|
| 54 |
```python
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 55 |
video_path = "https://storage.googleapis.com/oe-training-public/demo_videos/many_penguins.mp4"
|
| 56 |
video_messages = [
|
| 57 |
{
|
|
@@ -73,12 +94,14 @@ inputs = processor.apply_chat_template(
|
|
| 73 |
return_pointing_metadata=True
|
| 74 |
)
|
| 75 |
|
|
|
|
|
|
|
| 76 |
inputs = {k: v.to("cuda") for k, v in inputs.items()}
|
| 77 |
|
| 78 |
with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
|
| 79 |
output = model.generate(
|
| 80 |
**inputs,
|
| 81 |
-
logits_processor=model.build_logit_processor_from_inputs(inputs)
|
| 82 |
max_new_tokens=200
|
| 83 |
)
|
| 84 |
|
|
|
|
| 52 |
|
| 53 |
### Video Pointing Example:
|
| 54 |
```python
|
| 55 |
+
|
| 56 |
+
from transformers import AutoProcessor, AutoModelForImageTextToText
|
| 57 |
+
import torch
|
| 58 |
+
import numpy as np
|
| 59 |
+
|
| 60 |
+
checkpoint_dir = "allenai/MolmoPoint-Vid-4B" # or path to a converted HF checkpoint
|
| 61 |
+
|
| 62 |
+
model = AutoModelForImageTextToText.from_pretrained(
|
| 63 |
+
checkpoint_dir,
|
| 64 |
+
trust_remote_code=True,
|
| 65 |
+
dtype="auto",
|
| 66 |
+
device_map="cuda",
|
| 67 |
+
)
|
| 68 |
+
|
| 69 |
+
processor = AutoProcessor.from_pretrained(
|
| 70 |
+
checkpoint_dir,
|
| 71 |
+
trust_remote_code=True,
|
| 72 |
+
padding_side="left",
|
| 73 |
+
)
|
| 74 |
+
|
| 75 |
+
|
| 76 |
video_path = "https://storage.googleapis.com/oe-training-public/demo_videos/many_penguins.mp4"
|
| 77 |
video_messages = [
|
| 78 |
{
|
|
|
|
| 94 |
return_pointing_metadata=True
|
| 95 |
)
|
| 96 |
|
| 97 |
+
metadata = inputs.pop("metadata")
|
| 98 |
+
|
| 99 |
inputs = {k: v.to("cuda") for k, v in inputs.items()}
|
| 100 |
|
| 101 |
with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
|
| 102 |
output = model.generate(
|
| 103 |
**inputs,
|
| 104 |
+
logits_processor=model.build_logit_processor_from_inputs(inputs),
|
| 105 |
max_new_tokens=200
|
| 106 |
)
|
| 107 |
|