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import os
import random
from typing import Dict, Optional, Sequence
from PIL import PngImagePlugin, Image, ImageFile
import torch
from torch.utils.data import Dataset
import torchvision.transforms as T
from torchvision.transforms.functional import InterpolationMode
from robohusky.train.tcsloader import TCSLoader
from robohusky.conversation import get_conv_template
IGNORE_INDEX = -100
Image.MAX_IMAGE_PIXELS = None
ImageFile.LOAD_TRUNCATED_IMAGES = True
MaximumDecompressedSize = 1024
MegaByte = 2 ** 20
PngImagePlugin.MAX_TEXT_CHUNK = MaximumDecompressedSize * MegaByte
DEFAULT_IMG_START_TOKEN = "<img>"
DEFAULT_IMG_END_TOKEN = "</img>"
DEFAULT_VIDEO_START_TOKEN = "<vid>"
DEFAULT_VIDEO_END_TOKEN = "</vid>"
def is_image(image_file):
if image_file.lower().endswith(('.bmp', '.dib', '.png', '.jpg', '.jpeg', '.pbm', '.pgm', '.ppm', '.tif', '.tiff')):
return True
else:
return False
def is_video(image_file):
if image_file.lower().endswith(('.mp4', '.mkv', '.avi', '.wmv', '.iso', ".webm")):
return True
else:
return False
def build_transform(input_size):
transform = T.Compose([
T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
T.ToTensor(),
T.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
])
return transform
def format_inputs(sources):
# Apply prompt templates
conv = get_conv_template("husky").copy()
roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
conversations = []
for i, source in enumerate(sources):
if roles[source[0]["from"]] != conv.roles[0]:
# Skip the first one if it is not from human
source = source[1:]
conv.messages = []
for j, sentence in enumerate(source):
role = roles[sentence["from"]]
assert role == conv.roles[j % 2], f"{i}"
# vision is only supported for the human input
if role == conv.roles[0]:
value = sentence["value"]
if "<image>" in value:
if value.endswith("\n<image>"):
value = "<image>\n" + value.replace("\n<image>", "")
image_query = DEFAULT_IMG_START_TOKEN + DEFAULT_IMG_END_TOKEN
sentence["value"] = value.replace("<image>", image_query)
elif "<video>" in value:
if value.endswith("\n<video>"):
value = "<video>\n" + value.replace("\n<video>", "")
video_query = DEFAULT_VIDEO_START_TOKEN + DEFAULT_VIDEO_END_TOKEN
sentence["value"] = value.replace("<video>", video_query)
conv.append_message(role, sentence["value"])
conversations.append(conv.get_prompt())
return conversations, conv
def process_func(examples, tokenizer, max_seq_length):
conversations, conv = format_inputs(examples['conversations'])
model_inputs = tokenizer(
conversations,
max_length=max_seq_length,
padding="max_length",
truncation=True,
return_tensors="pt",
)
model_inputs.pop("token_type_ids", None)
# If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
# padding in the loss.
targets = model_inputs["input_ids"].clone()
# Mask targets
sep = conv.sep + conv.roles[1] + ": "
for conversation, target in zip(conversations, targets):
total_len = int(target.ne(tokenizer.pad_token_id).sum())
turns = conversation.split(conv.sep2)
cur_len = 1
target[:cur_len] = IGNORE_INDEX
for i, turn in enumerate(turns):
if turn == "":
break
turn_len = len(tokenizer(turn).input_ids)
parts = turn.split(sep)
if len(parts) != 2:
break
parts[0] += sep
# "-2" is hardcoded for the Llama tokenizer to make the offset correct.
instruction_len = len(tokenizer(parts[0]).input_ids) - 2
if i != 0 and not tokenizer.legacy:
# The legacy and non-legacy modes handle special tokens differently
instruction_len -= 1
# Ignore the user instructions
target[cur_len: cur_len + instruction_len] = IGNORE_INDEX
cur_len += turn_len
if i != 0 and not tokenizer.legacy:
# The legacy and non-legacy modes handle special tokens differently
cur_len -= 1
target[cur_len:] = IGNORE_INDEX
if cur_len < tokenizer.model_max_length:
if cur_len != total_len:
target[:] = IGNORE_INDEX
model_inputs["labels"] = targets
return model_inputs
class BaseDataset(Dataset):
def __init__(self, dataset, processor, image_path="", input_size=224):
super(BaseDataset, self).__init__()
self.dataset = dataset
self.image_path = image_path
self.transform = build_transform(input_size)
self.husky_processor = processor
self.cached_data_dict = {}
def __len__(self):
return len(self.dataset)
def __getitem__(self, i) -> Dict[str, torch.Tensor]:
if i in self.cached_data_dict:
return self.cached_data_dict[i]
data = self.dataset[i]
image_file = data.pop("image", None)
if self.image_path != "":
image_file = os.path.join(self.image_path, image_file)
if not os.path.exists(image_file):
return self.__getitem__((i + 1) % len(self.dataset))
image = Image.open(image_file)
else:
image = Image.open(image_file)
for k, v in data.items():
data[k] = [v]
ret = self.husky_processor(data)
for k, v in ret.items():
ret[k] = v[0]
pixel_values = self.transform(image)
ret["pixel_values"] = pixel_values
self.cached_data_dict[i] = ret
return ret
class CephDataset(Dataset):
def __init__(self, dataset, processor, input_size=224):
super(CephDataset, self).__init__()
self.dataset = dataset
self.transform = build_transform(input_size)
self.husky_processor = processor
conf_path = "./petrelf.conf"
self.conf_path = os.path.abspath(conf_path)
self.initialized = False
self._init_memcached()
def _init_memcached(self):
if not self.initialized:
assert self.conf_path is not None
self.mt_loader = TCSLoader(self.conf_path)
self.initialized = True
def __len__(self):
return len(self.dataset)
def __getitem__(self, i) -> Dict[str, torch.Tensor]:
data = self.dataset[i]
image_file = data.pop("image", None)
try:
image = self.mt_loader(image_file).convert('RGB')
except (AttributeError, OSError):
with open("error.txt", 'a') as f:
f.write(image_file + '\n')
i = random.randint(0, len(self.dataset))
return self.__getitem__(i % len(self.dataset))
for k, v in data.items():
data[k] = [v]
ret = self.husky_processor(data)
for k, v in ret.items():
ret[k] = v[0]
pixel_values = self.transform(image)
ret["pixel_values"] = pixel_values
return ret
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