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import pdb
from dataclasses import dataclass
from typing import Optional, List, Union
import pandas as pd
import torch
from videoalign.prompt_template import build_prompt
# from qwen_vl_utils import process_vision_info
from videoalign.vision_process import process_vision_info
from torch.utils.data import Dataset
import torchvision.transforms.functional as F
from videoalign.utils import save_video
@dataclass
class DataConfig:
meta_data: str = "/path/to/dataset/meta_data.csv"
data_dir: str = "/path/to/dataset"
meta_data_test: str = None
max_frame_pixels: int = 240 * 320
num_frames: float = None
fps: float = 2.0
p_shuffle_frames: float = 0.0
p_color_jitter: float = 0.0
eval_dim: Union[str, List[str]] = "VQ"
prompt_template_type: str = "none"
add_noise: bool = False
sample_type: str = "uniform"
use_tied_data: bool = True
def convert_GSB_csv_to_reward_data(example, data_dir, eval_dims=["VQ"], max_pixels=448 * 448, fps=2.0,
num_frames=None, prompt_template_type="none", sample_type="uniform"):
"""
Convert Good/Same/Bad csv data to reward data.
Args:
example (dict): A dataframe containing the GSB csv data.
data_dir (str): The directory path to the video files.
eval_dim (str): The dimension to evaluate ("VQ"/"MQ"/"TA").
max_pixels (int): The maximum number of pixels allowed for videos.
num_frames (float): Number of frames.
prompt_template_type (str): The type of prompt template to use ("none"/"simple"/"video_score").
Returns:
dict: A dictionary containing the reward data.
"""
A_data = [
{
"role": "user",
"content": [
{
"type": "video",
"video": f"file://{data_dir}/{example[f'path_A']}",
"max_pixels": max_pixels,
"fps": fps if num_frames is None else None,
"nframes": min(num_frames, example[f"num_frames_A"]) if num_frames is not None else None,
"sample_type": sample_type,
},
{"type": "text", "text": build_prompt(example["prompt"], eval_dims, prompt_template_type)},
],
}
]
B_data = [
{
"role": "user",
"content": [
{
"type": "video",
"video": f"file://{data_dir}/{example[f'path_B']}",
"max_pixels": max_pixels,
"fps": fps if num_frames is None else None,
"nframes": min(num_frames, example[f"num_frames_B"]) if num_frames is not None else None,
"sample_type": sample_type,
},
{"type": "text", "text": build_prompt(example["prompt"], eval_dims, prompt_template_type)},
],
}
]
chosen_labels = []
A_scores = []
B_scores = []
for eval_dim in eval_dims:
### chosen_label: 1 if A is chosen, -1 if B is chosen, 0 if tied.
### 22 if invalid. ooaaeeaa o.O
try:
if example[f"{eval_dim}"] is not None:
if example[f"{eval_dim}"] == "A":
chosen_label = 1
elif example[f"{eval_dim}"] == "B":
chosen_label = -1
elif example[f"{eval_dim}"] == "same":
chosen_label = 0
elif example[f"{eval_dim}"] == "invalid":
chosen_label = 22
else:
chosen_label = 22
else:
chosen_label = 22
except Exception as e:
chosen_label = 22
chosen_labels.append(chosen_label)
if f"MOS_A_{eval_dim}" in example and f"MOS_B_{eval_dim}" in example:
try:
A_score = example[f"MOS_A_{eval_dim}"] if example[f"MOS_A_{eval_dim}"] is not None else 0.0
B_score = example[f"MOS_B_{eval_dim}"] if example[f"MOS_B_{eval_dim}"] is not None else 0.0
except Exception as e:
A_score = 0.0
B_score = 0.0
A_scores.append(A_score)
B_scores.append(B_score)
else:
A_scores.append(0.0)
B_scores.append(0.0)
chosen_labels = torch.tensor(chosen_labels, dtype=torch.long)
A_scores = torch.tensor(A_scores, dtype=torch.float)
B_scores = torch.tensor(B_scores, dtype=torch.float)
metainfo_idx = None
if 'metainfo_idx' in example:
metainfo_idx = example['metainfo_idx']
return {"A_data": A_data, "B_data": B_data,
"A_scores": A_scores, "B_scores": B_scores,
"chosen_label": chosen_labels,
"metainfo_idx": metainfo_idx,}
class QWen2VLDataCollator():
def __init__(self, processor, add_noise=False, p_shuffle_frames=0.0, p_color_jitter=0.0):
self.processor = processor
self.add_noise = add_noise
self.set_noise_step = None
self.p_shuffle_frames = p_shuffle_frames
self.p_color_jitter = p_color_jitter
self.noise_adder = None
def _clean_message(self, message):
"""
remove unnecessary keys from message(very very necessary)
"""
out_message = [
{
"role": "user",
"content": [
{
"type": "video",
"video": message[0]["content"][0]["video"],
"max_pixels": message[0]["content"][0]["max_pixels"],
"fps": message[0]["content"][0]["fps"] if "fps" in message[0]["content"][0] else None,
"nframes": message[0]["content"][0]["nframes"] if "nframes" in message[0]["content"][0] else None,
"sample_type": message[0]["content"][0]["sample_type"] if "sample_type" in message[0]["content"][0] else "uniform",
},
{"type": "text", "text": message[0]["content"][1]["text"]},
],
}
]
if out_message[0]["content"][0]["fps"] is None:
out_message[0]["content"][0].pop("fps")
if out_message[0]["content"][0]["nframes"] is None:
out_message[0]["content"][0].pop("nframes")
return out_message
def _pad_sequence(self, sequences, attention_mask, max_len, padding_side='right'):
"""
Pad the sequences to the maximum length.
"""
assert padding_side in ['right', 'left']
if sequences.shape[1] >= max_len:
return sequences, attention_mask
pad_len = max_len - sequences.shape[1]
padding = (0, pad_len) if padding_side == 'right' else (pad_len, 0)
sequences_padded = torch.nn.functional.pad(sequences, padding, 'constant', self.processor.tokenizer.pad_token_id)
attention_mask_padded = torch.nn.functional.pad(attention_mask, padding, 'constant', 0)
return sequences_padded, attention_mask_padded
def __call__(self, features, enable_noise=True):
"""
Preprocess inputs to token sequences and return a batch
"""
# try:
features_A = []
features_B = []
# check if we have a margin. If we do, we need to batch it as well
# has_margin = "margin" in features[0]
has_idx = "metainfo_idx" in features[0] and features[0]["metainfo_idx"] is not None
for idx, feature in enumerate(features):
features_A.append(self._clean_message(feature["A_data"]))
features_B.append(self._clean_message(feature["B_data"]))
# import pdb; pdb.set_trace()
image_inputs_A, video_inputs_A = process_vision_info(features_A)
image_inputs_B, video_inputs_B = process_vision_info(features_B)
video_inputs_A = [video_inputs_A[i].float() / 255.0 for i in range(len(video_inputs_A))]
video_inputs_B = [video_inputs_B[i].float() / 255.0 for i in range(len(video_inputs_B))]
do_rescale = False
# print(f"{video_inputs_A[0].shape}, {video_inputs_B[0].shape}")
# if not enable_noise:
# print("Not training, no noise added.")
batch_A = self.processor(
text=self.processor.apply_chat_template(features_A, tokenize=False, add_generation_prompt=True),
images=image_inputs_A,
videos=video_inputs_A,
padding=True,
return_tensors="pt",
videos_kwargs={"do_rescale": do_rescale},
)
batch_B = self.processor(
text=self.processor.apply_chat_template(features_B, tokenize=False, add_generation_prompt=True),
images=image_inputs_B,
videos=video_inputs_B,
padding=True,
return_tensors="pt",
videos_kwargs={"do_rescale": do_rescale},
)
# pdb.set_trace()
max_len = max(batch_A["input_ids"].shape[1], batch_B["input_ids"].shape[1])
batch_A["input_ids"], batch_A["attention_mask"] = self._pad_sequence(batch_A["input_ids"], batch_A["attention_mask"], max_len, "right")
batch_B["input_ids"], batch_B["attention_mask"] = self._pad_sequence(batch_B["input_ids"], batch_B["attention_mask"], max_len, "right")
# print(f"Batch A: {batch_A['input_ids'].shape}, Batch B: {batch_B['input_ids'].shape}")
chosen_label = torch.stack([torch.tensor(feature["chosen_label"]) for feature in features])
A_scores = torch.stack([torch.tensor(feature["A_scores"]) for feature in features])
B_scores = torch.stack([torch.tensor(feature["B_scores"]) for feature in features])
batch = {
"A": batch_A,
"B": batch_B,
"return_loss": True,
"chosen_label": chosen_label,
"A_scores": A_scores,
"B_scores": B_scores,
}
if has_idx:
metainfo_idx = torch.stack([torch.tensor(feature["metainfo_idx"]) for feature in features])
batch["metainfo_idx"] = metainfo_idx
# pdb.set_trace()
return batch
# except Exception as e:
# print(f"Error processing batch: {e} in reading.")
# # get next batch
# return None
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