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# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
from typing import List, Optional, Union, Dict
import numpy as np
import torch
from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import ImageInput, is_valid_image
from transformers.processing_utils import Unpack, _validate_images_text_input_order, ProcessorMixin
from transformers.tokenization_utils_base import AddedToken, PreTokenizedInput, TextInput
from transformers.utils import logging
from transformers.models.paligemma.processing_paligemma import (
make_batched_images,
build_string_from_input,
_is_str_or_image,
PaliGemmaProcessorKwargs,
IMAGE_TOKEN,
EXTRA_TOKENS
)
from .action_tokenizer import SpatialActionTokenizer
logger = logging.get_logger(__name__)
class SpatialVLAProcessor(ProcessorMixin):
attributes = ["image_processor", "tokenizer"]
valid_kwargs = ["chat_template"]
image_processor_class = "SiglipImageProcessor"
tokenizer_class = ("GemmaTokenizer", "GemmaTokenizerFast")
def __init__(
self,
image_processor=None,
tokenizer=None,
chat_template=None,
statistics: Optional[dict] = None,
bin_policy=None,
intrinsic_config=None,
action_config=None,
num_obs_steps=1,
obs_delta=1,
action_chunk_size=1,
min_sigma=0.0,
**kwargs,
):
if image_processor is None:
raise ValueError("You need to specify an `image_processor`.")
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`.")
if not hasattr(image_processor, "image_seq_length"):
raise ValueError("Image processor is missing an `image_seq_length` attribute.")
self.image_seq_length = image_processor.image_seq_length
if not hasattr(tokenizer, "image_token"):
image_token = AddedToken(IMAGE_TOKEN, normalized=False, special=True)
tokens_to_add = {"additional_special_tokens": [image_token]}
tokenizer.add_special_tokens(tokens_to_add)
self.image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
else:
self.image_token_id = tokenizer.image_token_id
tokenizer.add_tokens(EXTRA_TOKENS)
tokenizer.add_bos_token = False
tokenizer.add_eos_token = False
super().__init__(image_processor, tokenizer, chat_template=chat_template)
# action tokenizer
self.statistics = statistics if statistics else {}
self.bin_policy = bin_policy
self.min_sigma = min_sigma
self.intrinsic_config = intrinsic_config
self.action_config = action_config
self.num_obs_steps = num_obs_steps
self.obs_delta = obs_delta
self.action_chunk_size = action_chunk_size
self.dataset_intrinsics = {}
height, width = image_processor.size["height"], image_processor.size["width"]
# scale intrinsic matrix
for k, v in intrinsic_config.items():
K = torch.tensor(v["intrinsic"]).float()
K[:2] *= torch.tensor([width / v["width"], height / v["height"]])[:, None]
self.dataset_intrinsics[k] = K
self.action_tokenizer = SpatialActionTokenizer(
tokenizer=tokenizer, num_bins=action_config["num_bins"],
bin_policy=bin_policy, use_spherical=action_config["use_spherical"],
min_sigma=min_sigma,
)
def __call__(
self,
images: ImageInput = None,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
unnorm_key: Optional[str] = None,
suffix_actions: Optional[np.array] = None, # (t e)
**kwargs: Unpack[PaliGemmaProcessorKwargs],
) -> BatchFeature:
images, text = _validate_images_text_input_order(images, text)
output_kwargs = self._merge_kwargs(
PaliGemmaProcessorKwargs,
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
**kwargs,
)
if suffix_actions is not None:
action_tokens = self.action_tokenizer(suffix_actions) # (n,3)
suffix="".join(action_tokens.flatten())
else:
suffix = output_kwargs["text_kwargs"].pop("suffix", None)
return_token_type_ids = True if suffix is not None else False
if images is None:
raise ValueError("`images` are expected as arguments to a `PaliGemmaProcessor` instance.")
if text is None:
logger.warning_once( "You are using PaliGemma without a text prefix. It will perform as a picture-captioning model.")
text = ""
if _is_str_or_image(text):
text = [text]
elif isinstance(text, list) and _is_str_or_image(text[0]):
pass
if text is not None and images is not None:
if not any(IMAGE_TOKEN in sample for sample in text):
if isinstance(text, List) and isinstance(images, List):
if len(images) != len(text):
raise ValueError(
f"Received {len(images)} images for {len(text)} prompts. Each prompt should be associated with an image or list of images."
)
if is_valid_image(images):
images = [[images]]
elif isinstance(images, list) and is_valid_image(images[0]):
images = [[image] for image in images]
elif not (isinstance(images, list) and isinstance(images[0], list) and is_valid_image(images[0][0])):
raise ValueError("images must be an image, list of images or list of list of images")
if suffix is not None and _is_str_or_image(suffix): suffix = [suffix]
if suffix is not None: suffix = [sfx + self.tokenizer.eos_token for sfx in suffix]
input_strings = [
build_string_from_input(
prompt=prompt,
bos_token=self.tokenizer.bos_token,
image_seq_len=self.image_seq_length,
image_token=IMAGE_TOKEN,
num_images=len(image_list) if isinstance(image_list, list) else 1,
)
for prompt, image_list in zip(text, images)
]
images = make_batched_images(images)
else:
expanded_samples = []
for sample in text:
expanded_sample = sample.replace(IMAGE_TOKEN, IMAGE_TOKEN * self.image_seq_length)
bos_rfind_index = expanded_sample.rfind(IMAGE_TOKEN)
bos_index = bos_rfind_index + len(IMAGE_TOKEN) if bos_rfind_index != -1 else 0
expanded_sample = (
expanded_sample[:bos_index] + self.tokenizer.bos_token + expanded_sample[bos_index:]
)
expanded_samples.append(expanded_sample)
input_strings = [f"{sample}\n" for sample in expanded_samples]
pixel_values = self.image_processor(images, **output_kwargs["images_kwargs"])["pixel_values"]
if output_kwargs["text_kwargs"].get("max_length", None) is not None:
output_kwargs["text_kwargs"]["max_length"] += self.image_seq_length
inputs = self.tokenizer(
input_strings,
text_pair=suffix,
return_token_type_ids=return_token_type_ids,
**output_kwargs["text_kwargs"],
)
intrinsic = self.dataset_intrinsics[unnorm_key] if unnorm_key in self.dataset_intrinsics else self.dataset_intrinsics["default"]
return_data = {**inputs, "pixel_values": pixel_values, "intrinsic": intrinsic}
if return_token_type_ids:
labels = inputs["input_ids"].masked_fill(inputs["token_type_ids"] == 0, -100)
return_data.update({"labels": labels})
return BatchFeature(data=return_data)
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Gemma
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to GemmaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Gemma
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to GemmaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
@property
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
def decode_actions(
self,
generation_outputs: torch.Tensor,
unnorm_key: Optional[str] = None,
) -> Dict[str, torch.Tensor]:
action_token_num = 3 # translation + rotation + gripper
predicted_action_token_ids = generation_outputs[0, : action_token_num * self.action_chunk_size].detach().cpu().long().numpy()
assert self.tokenizer.eos_token != predicted_action_token_ids[-1], "[error] actions contain EOS token, please check you truncation settings!"
if predicted_action_token_ids.shape[0] < action_token_num * self.action_chunk_size: # pad with zeros
logger.warning(f"Padding zero action!")
predicted_action_token_ids = np.concatenate(
[
predicted_action_token_ids,
np.zeros(action_token_num * self.action_chunk_size - predicted_action_token_ids.shape[0], dtype=np.longlong),
]
)
predicted_action_token_ids = predicted_action_token_ids.reshape(-1, action_token_num)
normalized_action_chunks = self.action_tokenizer.decode_token_ids_to_actions(predicted_action_token_ids)
if unnorm_key is None:
logger.warning(f"unnorm_key {unnorm_key} is not in statistics, use next one")
unnorm_key = next(self.statistics.keys())
action_norm_stats = self.statistics[unnorm_key]["action"]
action_dim = len(action_norm_stats["q01"])
mask = np.array(action_norm_stats.get("mask", np.ones(action_dim)), dtype=bool)
action_high, action_low = np.array(action_norm_stats["q99"]), np.array(action_norm_stats["q01"])
actions = []
for normalized_actions in normalized_action_chunks:
action = np.where(
mask,
0.5 * (normalized_actions + 1) * (action_high - action_low) + action_low,
normalized_actions,
)
actions.append(action)
actions = np.stack(actions)
return {"actions": actions, "action_ids": predicted_action_token_ids} |