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# ------------------------------------------------------------------------
# Copyright (c) 2023-present, BAAI. All Rights Reserved.
#
# 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.
# ------------------------------------------------------------------------
"""Image tokenizer."""
import numpy as np
import torch
from torch import nn
class ImageTokenizer(nn.Module):
"""Tokenize image regions with visual prompts."""
def __init__(
self,
image_encoder,
prompt_encoder,
image_decoder,
concept_projector=None,
text_tokenizer=None,
text_decoder=None,
pixel_mean=(103.53, 116.28, 123.675),
pixel_std=(57.375, 57.12, 58.395),
):
super(ImageTokenizer, self).__init__()
self.image_encoder = image_encoder
self.prompt_encoder = prompt_encoder
self.image_decoder = image_decoder
self.concept_projector = concept_projector
self.text_tokenizer = text_tokenizer
self.text_decoder = text_decoder
self.pixel_mean_value = pixel_mean # BGR order.
self.register_buffer("pixel_mean", torch.Tensor(pixel_mean))
self.register_buffer("pixel_rsig", torch.Tensor(pixel_std).reciprocal_())
def get_inputs(self, inputs, dtype=None):
"""Return the model inputs.
Parameters
----------
inputs : dict
The initial inputs.
dtype : torch.dtype, optional
The optional input dtype.
Returns
-------
dict
The model inputs.
"""
img_dtype, img_device = self.pixel_mean.dtype, self.pixel_mean.device
inputs["img"] = torch.as_tensor(inputs["img"], dtype=img_dtype, device=img_device)
inputs["img"] = inputs["img"].sub(self.pixel_mean).mul_(self.pixel_rsig).permute(0, 3, 1, 2)
inputs["img"] = inputs["img"].to(dtype=dtype) if dtype else inputs["img"]
return inputs
def get_features(self, inputs):
"""Return the image features.
Parameters
----------
inputs : dict
The inputs.
Returns
-------
dict
The image features.
"""
features = self.image_encoder(inputs["img"])
img_embeds = features[0].permute(0, 2, 3, 1).unsqueeze_(1)
return {"features": features, "img_embeds": img_embeds}
def get_outputs(self, inputs):
"""Return the model outputs.
Parameters
----------
inputs : dict
The model inputs.
Returns
-------
dict
The model outputs.
"""
inputs.update(self.prompt_encoder(inputs))
return self.image_decoder(inputs)
def forward(self, inputs):
"""Define the computation performed at every call.
Parameters
----------
inputs : dict
The initial inputs.
Returns
-------
dict
The model outputs.
"""
inputs = self.get_inputs(inputs)
inputs.update(self.get_features(inputs))
return self.get_outputs(inputs)
def upscale_masks(self, masks, size):
"""Upscale masks using bilinear interpolation.
Parameters
----------
masks : torch.Tensor
The input masks.
size : Union[int, Tuple[int]]
The output size.
Returns
-------
torch.Tensor
The output masks.
"""
return nn.functional.interpolate(masks, size, mode="bilinear", align_corners=False)
@torch.inference_mode()
def predict_concept(self, visual_embeds, k=1):
"""Predict top-k concepts based on visual embeddings.
Parameters
----------
visual_embeds: torch.Tensor
The embeddings to predict visual content.
k : int, optional, default=1
The k value.
Returns
-------
Tuple[numpy.ndarray, numpy.ndarray]
The concept scores and indices.
"""
return self.concept_projector.decode(visual_embeds, k)
@torch.inference_mode()
def generate_text(self, visual_tokens, max_gen_len=None, temperature=0):
"""Generate text sequences based on visual tokens.
Parameters
----------
visual_tokens: torch.Tensor
The tokens to prompt visual context.
max_gen_len : int, optional
The maximum length of the generated text sequences.
temperature : float, optional
The temperature for controlling randomness in sampling.
Returns
-------
np.ndarray
An array of generated texts.
"""
max_gen_len = max_gen_len or self.text_decoder.max_text_len
prompts = self.text_decoder.get_prompts(visual_tokens)
out_shape = (prompts.size(0), self.text_decoder.max_text_len)
tokens = np.full(out_shape, self.text_tokenizer.pad_id, "int64")
tokens[:, 0], prev_pos = self.text_tokenizer.bos_id, 0
eos_reached = np.array([False] * tokens.shape[0])
for cur_pos in range(1, max_gen_len):
decode_seq_len = cur_pos - prev_pos
x = torch.from_numpy(tokens[:, prev_pos:cur_pos]).to(device=prompts.device)
logits = self.text_decoder.transformer(prompts, x, prev_pos)
next_logits = logits[: x.size(0), decode_seq_len - 1]
if temperature > 0:
p = nn.functional.softmax(next_logits / temperature, dim=-1)
next_token = torch.multinomial(p, 1).cpu().numpy().flatten()
else:
next_token = next_logits.argmax(-1).cpu().numpy()
tokens[:, cur_pos] = next_token
eos_reached |= next_token == self.text_tokenizer.eos_id
prev_pos, logits, next_logits = cur_pos, None, None
if eos_reached.all():
break
return np.array(self.text_tokenizer.detokenize(tokens))
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