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import json
import math
import os
import random
from typing import Optional, Tuple, Union
import datasets
import torch
import transformers
from torch.nn import CrossEntropyLoss
from torch.utils.data import Subset
from torchvision.io import decode_image
from transformers import PreTrainedTokenizerFast, VisionEncoderDecoderModel
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_outputs import ModelOutput, Seq2SeqLMOutput
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import check_min_version, logging
from .configuration_cxrmate_ed import CXRMateEDConfig
from .dataset import PriorsDataset
from .prepare_dataset import prepare_dataset
from .utils import compute_time_delta
logger = logging.get_logger(__name__)
# Ordered by oblique, lateral, AP, and then PA views so that PA views are closest in position to the generated tokens (and oblique is furtherest).
VIEW_ORDER = [None, 'LPO', 'RAO', 'LAO', 'SWIMMERS', 'XTABLE LATERAL', 'LL', 'LATERAL', 'AP AXIAL', 'AP RLD', 'AP LLD', 'AP', 'PA RLD', 'PA LLD', 'PA']
def create_lookup_table(df, columns, start_idx):
df = df.groupby(columns).head(1)[columns].sort_values(by=columns)
indices = range(start_idx, start_idx + len(df))
df['index'] = indices
return df, indices[-1]
class FNNEncoder(torch.nn.Module):
def __init__(self, num_features, intermediate_size, decoder_hidden_size):
super().__init__()
self.up_proj = torch.nn.Linear(num_features, intermediate_size, bias=False)
self.down_proj = torch.nn.Linear(intermediate_size, decoder_hidden_size, bias=False)
self.act_fn = torch.nn.SiLU()
def forward(self, x):
return self.down_proj(self.act_fn(self.up_proj(x)))
class ProjectionHead(torch.nn.Module):
def __init__(self, input_size, hidden_size) -> None:
super().__init__()
# Layer normalisation before projection:
self.layer_norm = torch.nn.LayerNorm(input_size, eps=1e-6)
# No bias as following layer normalisation with bias:
self.projection = torch.nn.Linear(input_size, hidden_size, bias=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.layer_norm(x)
x = self.projection(x)
return x
class CXRStudyImagesEncoder(torch.nn.Module):
def __init__(self, encoder, decoder_config):
super().__init__()
self.encoder = encoder
self.config = encoder.config
self.adapter = ProjectionHead(self.config.embed_dim[-1], decoder_config.hidden_size)
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, ModelOutput]:
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# Flatten the batch and study_id dimensions:
assert len(pixel_values.shape) == 5, 'pixel_values must be B, S, C, H, W, where S is the max number of images for a study in the batch.'
last_hidden_state = self.encoder(pixel_values.view(-1, *pixel_values.shape[2:])).last_hidden_state
# Flatten h x w:
last_hidden_state = torch.flatten(last_hidden_state, 2) if last_hidden_state.dim() > 3 else last_hidden_state
# Project the features for each spatial position to the decoder's hidden size using the adapter network:
last_hidden_state = self.adapter(last_hidden_state)
# Concatenate the features for each chest X-ray:
last_hidden_state = last_hidden_state.view(pixel_values.shape[0], -1, last_hidden_state.shape[-1])
# Derive the attention mask from the pixel values:
mask = (pixel_values[:, :, 0, 0, 0] != 0.0)[:, :, None]
attention_mask = torch.ones(
[last_hidden_state.shape[0], pixel_values.shape[1], last_hidden_state.shape[1] // pixel_values.shape[1]],
dtype=torch.long,
device=mask.device,
)
attention_mask = attention_mask * mask
attention_mask = attention_mask.view(attention_mask.shape[0], -1)
if not return_dict:
return last_hidden_state
return ModelOutput(last_hidden_state=last_hidden_state, attention_mask=attention_mask)
class CXRMateEDModel(transformers.LlavaForConditionalGeneration):
config_class = CXRMateEDConfig
def __init__(self, config: CXRMateEDConfig):
check_min_version("4.46.0.dev0")
super(transformers.LlavaPreTrainedModel, self).__init__(config)
self.config = config
self.vocab_size = config.text_config.vocab_size
self.image_encoder = transformers.AutoModel.from_config(self.config.vision_config, trust_remote_code=True)
self.language_model = transformers.AutoModelForCausalLM.from_config(
config.text_config,
attn_implementation=config._attn_implementation,
)
self.image_encoder = CXRStudyImagesEncoder(self.image_encoder, config.text_config)
self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
# assert 'pad_token_id' in self.config.__dict__
# assert 'time_delta_monotonic_inversion' in self.config.__dict__
# assert 'add_time_deltas' in self.config.__dict__
# assert 'history' in self.config.__dict__
# assert 'tables_filter' in self.config.__dict__
# assert 'prompt_report_sections_filter' in self.config.__dict__
# assert isinstance(self.config.time_delta_monotonic_inversion, bool)
with open(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tables.json'), 'r') as f:
self.tables = json.load(f)
with open(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'lookup_tables.json'), 'r') as f:
self.luts = json.load(f)
with open(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'token_type_ids.json'), 'r') as f:
self.token_type_to_token_type_id = json.load(f)
self.tables = {k: self.tables[k] for k in self.config.tables_filter}
self.tables['mimic_cxr_sectioned']['text_columns'] = self.config.prompt_report_sections_filter
for k in self.tables.keys():
if self.luts[k]['total'] > 0:
setattr(
self,
f'{k}_index_value_encoder',
FNNEncoder(
num_features=self.luts[k]['total'],
intermediate_size=self.config.index_value_encoder_intermediate_size,
decoder_hidden_size=self.config.text_config.hidden_size,
),
)
if self.config.add_time_deltas:
self.time_delta_encoder = FNNEncoder(
num_features=1,
intermediate_size=self.config.index_value_encoder_intermediate_size,
decoder_hidden_size=self.config.text_config.hidden_size,
)
self.token_type_embeddings = torch.nn.Embedding(max(self.token_type_to_token_type_id.values()) + 1, self.config.text_config.hidden_size)
self.time_delta_map = lambda x: 1 / math.sqrt(x + 1)
self.zero_time_delta_value = self.time_delta_map(0)
self.inf_time_delta_value = self.time_delta_map(float('inf'))
self.post_init()
# @classmethod
# def from_encoder_decoder_pretrained(
# cls,
# encoder_pretrained_model_name_or_path: str = None,
# decoder_pretrained_model_name_or_path: str = None,
# *model_args,
# **kwargs,
# ) -> PreTrainedModel:
# r"""
# Instantiate an encoder and a decoder from one or two base classes of the library from pretrained model
# checkpoints.
# The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train
# the model, you need to first set it back in training mode with `model.train()`.
# Params:
# encoder_pretrained_model_name_or_path (`str`, *optional*):
# Information necessary to initiate the image encoder. Can be either:
# - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. An
# example is `google/vit-base-patch16-224-in21k`.
# - A path to a *directory* containing model weights saved using
# [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
# - A path or url to a *tensorflow index checkpoint file* (e.g, `./tf_model/model.ckpt.index`). In
# this case, `from_tf` should be set to `True` and a configuration object should be provided as
# `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a
# PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
# decoder_pretrained_model_name_or_path (`str`, *optional*, defaults to `None`):
# Information necessary to initiate the text decoder. Can be either:
# - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
# - A path to a *directory* containing model weights saved using
# [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
# - A path or url to a *tensorflow index checkpoint file* (e.g, `./tf_model/model.ckpt.index`). In
# this case, `from_tf` should be set to `True` and a configuration object should be provided as
# `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a
# PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
# model_args (remaining positional arguments, *optional*):
# All remaning positional arguments will be passed to the underlying model's `__init__` method.
# kwargs (remaining dictionary of keyword arguments, *optional*):
# Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
# `output_attentions=True`).
# - To update the encoder configuration, use the prefix *encoder_* for each configuration parameter.
# - To update the decoder configuration, use the prefix *decoder_* for each configuration parameter.
# - To update the parent model configuration, do not use a prefix for each configuration parameter.
# Behaves differently depending on whether a `config` is provided or automatically loaded.
# Example:
# ```python
# >>> from transformers import VisionEncoderDecoderModel
# >>> # initialize a vit-bert from a pretrained ViT and a pretrained BERT model. Note that the cross-attention layers will be randomly initialized
# >>> model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(
# ... "google/vit-base-patch16-224-in21k", "google-bert/bert-base-uncased"
# ... )
# >>> # saving model after fine-tuning
# >>> model.save_pretrained("./vit-bert")
# >>> # load fine-tuned model
# >>> model = VisionEncoderDecoderModel.from_pretrained("./vit-bert")
# ```"""
# kwargs_encoder = {
# argument[len("encoder_") :]: value for argument, value in kwargs.items() if argument.startswith("encoder_")
# }
# kwargs_decoder = {
# argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
# }
# # remove encoder, decoder kwargs from kwargs
# for key in kwargs_encoder.keys():
# del kwargs["encoder_" + key]
# for key in kwargs_decoder.keys():
# del kwargs["decoder_" + key]
# # Load and initialize the encoder and decoder
# # The distinction between encoder and decoder at the model level is made
# # by the value of the flag `is_decoder` that we need to set correctly.
# encoder = kwargs_encoder.pop("model", None)
# if encoder is None:
# if encoder_pretrained_model_name_or_path is None:
# raise ValueError(
# "If `encoder_model` is not defined as an argument, a `encoder_pretrained_model_name_or_path` has "
# "to be defined."
# )
# if "config" not in kwargs_encoder:
# encoder_config, kwargs_encoder = transformers.AutoConfig.from_pretrained(
# encoder_pretrained_model_name_or_path, **kwargs_encoder, return_unused_kwargs=True
# )
# if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True:
# logger.info(
# f"Initializing {encoder_pretrained_model_name_or_path} as a encoder model "
# "from a decoder model. Cross-attention and casual mask are disabled."
# )
# encoder_config.is_decoder = False
# encoder_config.add_cross_attention = False
# kwargs_encoder["config"] = encoder_config
# encoder = transformers.AutoModel.from_pretrained(encoder_pretrained_model_name_or_path, *model_args, **kwargs_encoder)
# decoder = kwargs_decoder.pop("model", None)
# if decoder is None:
# if decoder_pretrained_model_name_or_path is None:
# raise ValueError(
# "If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has "
# "to be defined."
# )
# if "config" not in kwargs_decoder:
# decoder_config, kwargs_decoder = transformers.AutoConfig.from_pretrained(
# decoder_pretrained_model_name_or_path, **kwargs_decoder, return_unused_kwargs=True
# )
# if decoder_config.is_decoder is False or decoder_config.add_cross_attention is False:
# logger.info(
# f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. Cross attention"
# f" layers are added to {decoder_pretrained_model_name_or_path} and randomly initialized if"
# f" {decoder_pretrained_model_name_or_path}'s architecture allows for cross attention layers."
# )
# decoder_config.is_decoder = True
# decoder_config.add_cross_attention = False
# kwargs_decoder["config"] = decoder_config
# if kwargs_decoder["config"].is_decoder is False or kwargs_decoder["config"].add_cross_attention is False:
# logger.warning(
# f"Decoder model {decoder_pretrained_model_name_or_path} is not initialized as a decoder. "
# f"In order to initialize {decoder_pretrained_model_name_or_path} as a decoder, "
# "make sure that the attributes `is_decoder` and `add_cross_attention` of `decoder_config` "
# "passed to `.from_encoder_decoder_pretrained(...)` are set to `True` or do not pass a "
# "`decoder_config` to `.from_encoder_decoder_pretrained(...)`"
# )
# decoder = transformers.AutoModelForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder)
# # instantiate config with corresponding kwargs
# config = CXRMateEDConfig.from_encoder_decoder_configs(encoder.config, decoder.config, **kwargs)
# # make sure input & output embeddings is not tied
# config.tie_word_embeddings = False
# config.is_encoder_decoder = False
# return cls(encoder=encoder, decoder=decoder, config=config)
def forward(
self,
decoder_position_ids: torch.LongTensor,
decoder_attention_mask: torch.FloatTensor,
decoder_token_type_ids: torch.LongTensor,
decoder_input_ids: Optional[torch.LongTensor] = None,
encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs,
) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
kwargs_decoder = {
argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
}
if decoder_inputs_embeds is None:
decoder_inputs_embeds = self.language_model.get_input_embeddings()(decoder_input_ids)
decoder_inputs_embeds += self.token_type_embeddings(decoder_token_type_ids)
if decoder_attention_mask.dim() == 4:
assert decoder_attention_mask.dtype == decoder_inputs_embeds.dtype, f'The dtype for {decoder_attention_mask} was {decoder_attention_mask.dtype}. It should be {decoder_inputs_embeds.dtype}'
else:
assert decoder_attention_mask.dtype == torch.long, f'The dtype for {decoder_attention_mask} was {decoder_attention_mask.dtype}. It should be torch.long'
# Generation:
decoder_outputs = self.language_model(
inputs_embeds=decoder_inputs_embeds,
attention_mask=decoder_attention_mask,
position_ids=decoder_position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
use_cache=use_cache,
past_key_values=past_key_values,
return_dict=return_dict,
**kwargs_decoder,
)
# Loss:
loss = None
if labels is not None:
logits = decoder_outputs.logits if return_dict else decoder_outputs[0]
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.reshape(-1, self.vocab_size), labels.reshape(-1))
if not return_dict:
if loss is not None:
return (loss,) + decoder_outputs + encoder_outputs
else:
return decoder_outputs + encoder_outputs
return Seq2SeqLMOutput(
loss=loss,
logits=decoder_outputs.logits,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
)
def prepare_inputs_for_generation(
self,
input_ids,
special_token_ids,
prompt_attention_mask,
prompt_position_ids,
past_key_values=None,
use_cache=None,
**kwargs,
):
"""
Modification of:
https://github.com/huggingface/transformers/blob/main/src/transformers/models/encoder_decoder/modeling_encoder_decoder.py#L660
"""
report_attention_mask = (input_ids != self.config.pad_token_id).long()
if len(past_key_values) == 0:
# 4D attention mask:
decoder_attention_mask = self.create_4d_attention_mask_mixed_causality(
prompt_attention_mask, report_attention_mask, dtype=kwargs['decoder_inputs_embeds'].dtype,
)
# Position identifiers accounting for padding:
report_position_ids = report_attention_mask.cumsum(-1) + prompt_position_ids.max(dim=1).values[:, None]
report_position_ids.masked_fill_(report_attention_mask == 0, 1)
decoder_position_ids = torch.cat([prompt_position_ids, report_position_ids], dim=1)
# `inputs_embeds` are only to be used in the 1st generation step:
inputs_embeds = torch.cat([kwargs['decoder_inputs_embeds'], self.language_model.get_input_embeddings()(input_ids)], dim=1)
decoder_token_type_ids = self.token_ids_to_token_type_ids(
input_ids, special_token_ids,
[self.token_type_to_token_type_id['findings'], self.token_type_to_token_type_id['impression']],
)
decoder_token_type_ids = torch.cat(
[
kwargs['decoder_token_type_ids'],
decoder_token_type_ids,
],
dim=1,
) # Add image token type identifiers.
input_dict = {
'decoder_input_ids': input_ids,
'decoder_inputs_embeds': inputs_embeds,
'decoder_token_type_ids': decoder_token_type_ids,
}
else:
# 4D attention mask:
decoder_attention_mask = self.create_4d_attention_mask_mixed_causality_past_key_values(
prompt_attention_mask, report_attention_mask, dtype=kwargs['decoder_inputs_embeds'].dtype,
)
# Position identifiers accounting for padding:
decoder_position_ids = report_attention_mask.cumsum(-1) + prompt_position_ids.max(dim=1).values[:, None]
decoder_position_ids.masked_fill_(report_attention_mask == 0, 1)
# Always place token_ids_to_token_type_ids_past_key_values before input_ids = input_ids[:, remove_prefix_length:]:
decoder_token_type_ids = self.token_ids_to_token_type_ids_past_key_values(
input_ids,
special_token_ids,
[self.token_type_to_token_type_id['findings'], self.token_type_to_token_type_id['impression']],
)
decoder_position_ids = decoder_position_ids[:, -1:]
past_length = past_key_values[0][0].shape[2]
# Some generation methods only pass the last input ID:
if input_ids.shape[1] > past_length:
remove_prefix_length = past_length
else:
# Keep only the final ID:
remove_prefix_length = input_ids.shape[1] - 1
input_ids = input_ids[:, remove_prefix_length:]
input_dict = {'decoder_input_ids': input_ids, 'decoder_token_type_ids': decoder_token_type_ids}
input_dict.update(
{
'decoder_attention_mask': decoder_attention_mask,
'decoder_position_ids': decoder_position_ids,
'past_key_values': past_key_values,
'use_cache': use_cache,
}
)
return input_dict
def token_ids_to_token_type_ids(self, token_ids, special_token_ids, token_type_id_sections):
"""
Extract token type identifiers from the token identifiers.
Argument/s:
token_ids - token identifiers.
special_token_ids - special token identifiers that indicate the separation between sections.
token_type_id_section - token type identifier for each section.
Returns:
token_type_ids - token type identifiers.
"""
token_type_id_sections = token_type_id_sections if token_type_id_sections is not None else list(range(len(special_token_ids) + 1))
mbatch_size, seq_len = token_ids.shape
token_type_ids = torch.full_like(token_ids, token_type_id_sections[0], dtype=torch.long, device=token_ids.device)
for i, j in enumerate(special_token_ids):
# Find first occurrence of special tokens that indicate the boundary between sections:
cols = (token_ids == j).int().argmax(dim=1)
rows = torch.arange(mbatch_size, device=token_ids.device)
# https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertTokenizer.create_token_type_ids_from_sequences.example
cols += 1
# Ensure that the column index is not out of bounds. If 0, then token_id not present.
# This is safe as index 0 is always a special token (now equal to 1 due to +1):
rows = rows[torch.logical_and(cols != 1, cols < seq_len)]
cols = cols[torch.logical_and(cols != 1, cols < seq_len)]
# Indices to that correspond to the second sequence:
if rows.nelement() != 0:
ids = torch.stack([
torch.stack([x, z]) for (x, y) in zip(rows, cols) for z in torch.arange(
y, seq_len, device=token_ids.device,
)
])
token_type_ids[ids[:, 0], ids[:, 1]] = token_type_id_sections[i + 1]
return token_type_ids
def token_ids_to_token_type_ids_past_key_values(self, token_ids, special_token_ids, token_type_id_sections):
"""
Extract token type identifiers from the token identifiers if past != None. Make sure to input all the
token_ids (e.g., do not input input_ids = input_ids[:, remove_prefix_length:] from prepare_inputs_for_generation).
Argument/s:
token_ids - token identifiers.
special_token_ids - special token identifiers that indicate the separation between sections.
Returns:
token_type_ids - token type identifiers.
"""
token_type_id_sections = token_type_id_sections if token_type_id_sections is not None else list(range(len(special_token_ids) + 1))
token_type_ids = torch.full([token_ids.shape[0], 1], token_type_id_sections[0], dtype=torch.long, device=token_ids.device)
# https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertTokenizer.create_token_type_ids_from_sequences.example
token_ids = token_ids[:, :-1]
for i, j in enumerate(special_token_ids):
# Find first occurrence of special token, which indicates the boundary between sections:
exists = torch.any(token_ids == j, dim=1, keepdim=True)
token_type_ids[exists] = token_type_id_sections[i + 1]
return token_type_ids
def tokenize_report_teacher_forcing(self, findings: str, impression: str, tokenizer: PreTrainedTokenizerFast, max_len: int):
"""
Tokenize the reports and creates the inputs and targets for teacher forcing.
Argument/s:
findings - findings sections.
impression - impression sections.
return_token_type_ids - return the token type identifiers.
tokenizer - Hugging Face tokenizer.
max_len - maximum number of tokens.
Returns:
decoder_input_ids - the token identifiers for the input of the decoder.
decoder_attention_mask - the attention mask for the decoder_input_ids.
label_ids - the label token identifiers for the decoder.
"""
# Prepare the sections for the tokenizer by placing special tokens between each section:
reports = [f'{tokenizer.bos_token}{i}{tokenizer.sep_token}{j}{tokenizer.eos_token}' for i, j in
zip(findings, impression)]
# Tokenize the report:
tokenized = tokenizer(
reports,
padding='longest',
truncation=True,
max_length=max_len + 1, # +1 to account for the bias between input and target.
return_tensors='pt',
return_token_type_ids=False,
add_special_tokens=False,
).to(self.device)
# Modify for language modelling:
batch_dict = {
# Labels for the decoder (shifted right by one for autoregression):
'label_ids': tokenized['input_ids'][:, 1:].detach().clone(),
# Remove last token identifier to match the sequence length of the labels:
'decoder_input_ids': tokenized['input_ids'][:, :-1],
# Attention mask for the decoder_input_ids (remove first token so that the eos_token_id is not considered):
'decoder_attention_mask': tokenized['attention_mask'][:, 1:],
}
return batch_dict
def tokenize_report_teacher_forcing_rev_a(self, tokenizer: PreTrainedTokenizerFast, max_len: int, findings: Optional[str] = None, impression: Optional[str] = None, reports: Optional[str] = None):
"""
Tokenize the reports and creates the inputs and targets for teacher forcing.
Argument/s:
tokenizer - Hugging Face tokenizer.
max_len - maximum number of tokens.
findings - findings sections.
impression - impression sections.
reports - prepared reports, with special tokens and report sections.
Returns:
decoder_input_ids - the token identifiers for the input of the decoder.
decoder_attention_mask - the attention mask for the decoder_input_ids.
label_ids - the label token identifiers for the decoder.
"""
# Prepare the sections for the tokenizer by placing special tokens between each section:
if reports is None:
assert findings and impression, "If 'reports' is not defined, 'findings' and 'impression' need to be defined."
reports = [f'{tokenizer.bos_token}{i}{tokenizer.sep_token}{j}{tokenizer.eos_token}' for i, j in
zip(findings, impression)]
# Tokenize the report:
tokenized = tokenizer(
reports,
padding='longest',
truncation=True,
max_length=max_len + 1, # +1 to account for the bias between input and target.
return_tensors='pt',
return_token_type_ids=False,
add_special_tokens=False,
).to(self.device)
# Modify for language modelling:
batch_dict = {
# Labels for the decoder (shifted right by one for autoregression):
'label_ids': tokenized['input_ids'][:, 1:].detach().clone(),
# Remove last token identifier to match the sequence length of the labels:
'decoder_input_ids': tokenized['input_ids'][:, :-1],
# Attention mask for the decoder_input_ids (remove first token so that the eos_token_id is not considered):
'decoder_attention_mask': tokenized['attention_mask'][:, 1:],
}
return batch_dict
def split_and_decode_sections(self, token_ids, special_token_ids, tokenizer: PreTrainedTokenizerFast):
"""
Split the token identifiers into sections, then convert the token identifiers into strings.
Argument/s:
token_ids - token identifiers.
special_token_ids - special token identifiers that indicate the end of each section.
tokenizer - Hugging Face tokenizer.
Returns:
token_type_ids - token type identifiers.
"""
_, seq_len = token_ids.shape
# The number of sections is the same as the number of special_token_ids:
num_sections = len(special_token_ids)
sections = {k: [] for k in range(num_sections)}
for i in token_ids:
prev_col = 0
for j, k in enumerate(special_token_ids):
# The maximum sequence length was exceeded, thus no more tokens:
if prev_col >= seq_len:
sections[j].append('')
continue
# Find first occurrence of special tokens that indicate the boundary between sections:
col = (i == k).int().argmax().item()
# If equal to 0, token was not found, set the column to the sequence length (as the decoder exceeded
# the maximum sequence length):
if col == 0:
col = seq_len
# Extract section token identifiers:
section_token_ids = i[prev_col:col]
prev_col = col
section_string = tokenizer.decode(section_token_ids, skip_special_tokens=True)
sections[j].append(section_string)
return tuple(sections.values())
def tokenize_text_prompt(self, tokenizer: PreTrainedTokenizerFast, **kwargs):
"""
Tokenize the text columns from MIMIC-IV ED and MIMIC-CXR (excluding the findings and impression sections).
Time deltas for the input_ids are also prepared here.
Argument/s:
tokenizer - Hugging Face tokenizer.
Returns:
ed - dictionary containing the input_ids, token_type_ids, attention_mask and time_deltas for the ED module columns.
cxr - dictionary containing the input_ids, token_type_ids, and attention_mask for MIMIC-CXR columns.
"""
batch_size = len(kwargs['study_id'])
tokenized = {
'input_ids': {i: [] for i in range(batch_size)},
'token_type_ids': {i: [] for i in range(batch_size)},
'time_delta': {i: [] for i in range(batch_size)},
'attention_mask': torch.empty(batch_size, 0, 1, device=self.device),
}
prompt_text_columns = [f'{k}_{j}' if k != 'mimic_cxr_sectioned' else j for k, v in self.tables.items() if 'text_columns' in v for j in (v['text_columns'] if isinstance(v['text_columns'], list) else [v['text_columns']])] + ['prior_findings', 'prior_impression']
for i in prompt_text_columns:
if i in kwargs:
if f'{i}_time_delta' not in kwargs:
kwargs[f'{i}_time_delta'] = [[self.zero_time_delta_value for _ in j] if j is not None else None for j in kwargs[i]]
for x, (y, z) in enumerate(zip(kwargs[i], kwargs[f'{i}_time_delta'])):
if y is not None:
assert isinstance(y, list)
assert isinstance(z, list)
for text, time_delta in zip(y, z):
if text is not None:
tokenized['input_ids'][x].append(
tokenizer(text, add_special_tokens=False, return_tensors='pt')['input_ids'].to(device=self.device)
)
tokenized['token_type_ids'][x].append(
torch.full(
(1, tokenized['input_ids'][x][-1].shape[-1]),
self.token_type_to_token_type_id[i],
dtype=torch.long,
device=self.device,
)
)
tokenized['time_delta'][x].append(
torch.full(
(1, tokenized['input_ids'][x][-1].shape[-1]),
time_delta,
dtype=torch.float32,
device=self.device,
)
)
tokenized['input_ids'] = [torch.cat(j, dim=1).T if j else torch.empty(0, 1, dtype=torch.long, device=self.device) for j in tokenized['input_ids'].values()]
tokenized['token_type_ids'] = [torch.cat(j, dim=1).T if j else torch.empty(0, 1, dtype=torch.long, device=self.device) for j in tokenized['token_type_ids'].values()]
tokenized['time_delta'] = [torch.cat(j, dim=1).T if j else torch.empty(0, 1, device=self.device) for j in tokenized['time_delta'].values()]
tokenized['input_ids'] = torch.nn.utils.rnn.pad_sequence(
tokenized['input_ids'], batch_first=True, padding_value=tokenizer.pad_token_id
)[:, :, 0]
tokenized['token_type_ids'] = torch.nn.utils.rnn.pad_sequence(
tokenized['token_type_ids'], batch_first=True, padding_value=0,
)[:, :, 0]
tokenized['attention_mask'] = (tokenized['input_ids'] != tokenizer.pad_token_id).int()
tokenized['time_delta'] = torch.nn.utils.rnn.pad_sequence(
tokenized['time_delta'], batch_first=True, padding_value=0,
)
return tokenized
def prepare_inputs(
self,
images,
tokenizer: PreTrainedTokenizerFast,
tokenized_report=None,
sep_token_id=None,
**batch,
):
"""
Tokenize the text columns from MIMIC-IV ED and MIMIC-CXR (excluding the findings and impression sections).
Argument/s:
images - images.
tokenizer - Hugging Face tokenizer.
tokenized_report - if training/teacher forcing, input the tokenized_report dict to include it in the prepared inputs.
separator_token_id - separator token identifier.
Returns:
inputs_embeds - input embeddings.
attention_mask - attention mask.
token_type_ids - token type identifiers.
position_ids - position identifiers.
bos_token_ids - bos_token_ids for generation.
"""
input_ids = []
inputs_embeds = []
token_type_ids = []
attention_mask = []
time_delta = []
position_ids = None
bos_token_ids = None
# Index and value columns:
batch_size = images.shape[0]
for k, v in self.tables.items():
if 'index_columns' in v or 'value_columns' in v:
if f'{k}_index_value_feats' not in batch:
batch[f'{k}_index_value_feats'] = torch.empty(batch_size, 0, self.luts[k]['total'], device=self.device)
inputs_embeds.append(
getattr(self, f'{k}_index_value_encoder')(batch[f'{k}_index_value_feats'])
)
token_type_ids.append(batch[f'{k}_index_value_token_type_ids'] if f'{k}_index_value_token_type_ids' in batch else torch.empty(batch_size, 0, dtype=torch.long, device=self.device))
attention_mask.append(batch[f'{k}_index_value_mask'] if f'{k}_index_value_mask' in batch else torch.empty(batch_size, 0, dtype=torch.long, device=self.device))
if f'{k}_index_value_time_delta' in batch:
time_delta.append(batch[f'{k}_index_value_time_delta'])
else:
time_delta_index_value = torch.zeros(*batch[f'{k}_index_value_mask'].shape, 1, device=self.device) if f'{k}_index_value_mask' in batch else torch.empty(batch_size, 0, 1, device=self.device)
time_delta.append(time_delta_index_value)
# Tokenize text columns for prompt:
tokenized = self.tokenize_text_prompt(tokenizer, **batch)
input_ids.append(tokenized['input_ids'])
token_type_ids.append(tokenized['token_type_ids'])
attention_mask.append(tokenized['attention_mask'])
time_delta.append(tokenized['time_delta'])
# Image encoder:
encoder_outputs = self.image_encoder(images)
inputs_embeds.append(encoder_outputs[0])
inputs_per_image = encoder_outputs[0].shape[-2] // images.shape[1]
time_delta_image_features = torch.tensor(batch['image_time_deltas'], device=self.device).repeat_interleave(inputs_per_image, dim=1)
token_type_ids.append(
torch.where(
torch.logical_or(
time_delta_image_features == self.zero_time_delta_value,
time_delta_image_features == self.inf_time_delta_value,
),
self.token_type_to_token_type_id['image'],
self.token_type_to_token_type_id['prior_image'],
),
)
attention_mask.append(encoder_outputs[1])
time_delta.append(time_delta_image_features[:, :, None])
# Compute embeddings from token identifiers:
input_ids = torch.cat(input_ids, dim=1)
inputs_embeds.append(self.language_model.get_input_embeddings()(input_ids))
# Concatentate time deltas and input embeddings before adding time delta embedding to prompt:
time_delta = torch.cat(time_delta, dim=1)
inputs_embeds = torch.cat(inputs_embeds, dim=1)
# Add time delta embeddings to prompt:
if time_delta.shape[1] > 0 and self.config.add_time_deltas:
time_delta = time_delta.to(dtype=inputs_embeds.dtype)
inputs_embeds += self.time_delta_encoder(time_delta)
# Concatentate the attention mask:
attention_mask = torch.cat(attention_mask, dim=1)
# Position identifiers:
position_ids = self.position_ids_from_time_deltas_and_attention_mask(time_delta, attention_mask)
# Tokenize report:
if tokenized_report is not None:
inputs_embeds = torch.cat([inputs_embeds, self.language_model.get_input_embeddings()(tokenized_report['decoder_input_ids'])], dim=1)
report_token_type_ids = self.token_ids_to_token_type_ids(
token_ids=tokenized_report['decoder_input_ids'],
special_token_ids=[sep_token_id],
token_type_id_sections=[self.token_type_to_token_type_id['findings'], self.token_type_to_token_type_id['impression']],
)
token_type_ids.append(report_token_type_ids)
# Position identifiers accounting for padding:
report_position_ids = tokenized_report['decoder_attention_mask'].cumsum(-1) + position_ids.max(dim=1).values[:, None]
report_position_ids.masked_fill_(tokenized_report['decoder_attention_mask'] == 0, 1)
position_ids = torch.cat([position_ids, report_position_ids], dim=1)
# 4D attention mask:
attention_mask = self.create_4d_attention_mask_mixed_causality(attention_mask, tokenized_report['decoder_attention_mask'], dtype=inputs_embeds.dtype)
# attention_mask = self.create_4d_attention_mask_mixed_causality(attention_mask, tokenized_report['decoder_attention_mask'])
# attention_mask_diagonal = torch.diagonal(attention_mask[:, 0], dim1=1, dim2=2)
else:
# BOS token identifiers for inference/generation:
bos_token_ids = torch.full((encoder_outputs[0].shape[0], 1), tokenizer.bos_token_id, dtype=torch.long, device=self.device)
# Concatentate the token type identifiers:
token_type_ids = torch.cat(token_type_ids, dim=1)
assert inputs_embeds.shape[1] == attention_mask.shape[-1]
assert inputs_embeds.shape[1] == token_type_ids.shape[1]
return inputs_embeds, attention_mask, token_type_ids, position_ids, bos_token_ids
@staticmethod
def create_4d_attention_mask_mixed_causality(non_causal_2d_attention_mask, causal_2d_attention_mask, dtype):
prompt_seq_len = non_causal_2d_attention_mask.shape[-1]
report_seq_len = causal_2d_attention_mask.shape[-1]
non_causal_2d_attention_mask = non_causal_2d_attention_mask[:, None, None, :]
causal_2d_attention_mask = causal_2d_attention_mask[:, None, None, :]
# Upper left of attention matrix:
upper_left = non_causal_2d_attention_mask.expand(-1, -1, prompt_seq_len, -1)
upper_left = upper_left * non_causal_2d_attention_mask
upper_left = upper_left * non_causal_2d_attention_mask.permute(0, 1, 3, 2)
causal_mask = torch.tril(
torch.ones(
(
report_seq_len,
report_seq_len,
),
dtype=torch.long,
device=causal_2d_attention_mask.device,
),
)
# Lower right of attention matrix:
lower_right = causal_2d_attention_mask.expand(-1, -1, report_seq_len, -1)
lower_right = lower_right * causal_2d_attention_mask.permute(0, 1, 3, 2)
lower_right = lower_right * causal_mask
# Upper right of attention matrix:
upper_right = torch.zeros(
causal_2d_attention_mask.shape[0],
1,
prompt_seq_len,
report_seq_len,
dtype=torch.long,
device=causal_2d_attention_mask.device,
)
# Lower left of attention matrix:
lower_left = non_causal_2d_attention_mask.expand(-1, -1, report_seq_len, -1)
lower_left = lower_left * causal_2d_attention_mask.permute(0, 1, 3, 2)
left = torch.cat((upper_left, lower_left), dim=2)
right = torch.cat((upper_right, lower_right), dim=2)
mixed_causality_4d_attention_mask = torch.cat((left, right), dim=-1)
mixed_causality_4d_attention_mask = mixed_causality_4d_attention_mask.to(dtype=dtype)
mixed_causality_4d_attention_mask[mixed_causality_4d_attention_mask == 0] = torch.finfo(mixed_causality_4d_attention_mask.dtype).min
mixed_causality_4d_attention_mask[mixed_causality_4d_attention_mask == 1] = 0.0
return mixed_causality_4d_attention_mask
@staticmethod
def create_4d_attention_mask_mixed_causality_past_key_values(non_causal_2d_attention_mask, causal_2d_attention_mask, dtype):
non_causal_2d_attention_mask = non_causal_2d_attention_mask[:, None, None, :]
causal_2d_attention_mask = causal_2d_attention_mask[:, None, None, :]
mixed_causality_4d_attention_mask = torch.cat((non_causal_2d_attention_mask, causal_2d_attention_mask), dim=-1)
mixed_causality_4d_attention_mask = mixed_causality_4d_attention_mask.to(dtype=dtype)
mixed_causality_4d_attention_mask[mixed_causality_4d_attention_mask == 0] = torch.finfo(mixed_causality_4d_attention_mask.dtype).min
mixed_causality_4d_attention_mask[mixed_causality_4d_attention_mask == 1] = 0.0
return mixed_causality_4d_attention_mask
# @staticmethod
# def create_4d_attention_mask_mixed_causality(non_causal_2d_attention_mask, causal_2d_attention_mask):
# prompt_seq_len = non_causal_2d_attention_mask.shape[-1]
# report_seq_len = causal_2d_attention_mask.shape[-1]
# non_causal_2d_attention_mask = non_causal_2d_attention_mask[:, None, None, :]
# causal_2d_attention_mask = causal_2d_attention_mask[:, None, None, :]
# # Upper left of attention matrix:
# upper_left = non_causal_2d_attention_mask.expand(-1, -1, prompt_seq_len, -1)
# upper_left = upper_left * non_causal_2d_attention_mask
# upper_left = upper_left * non_causal_2d_attention_mask.permute(0, 1, 3, 2)
# causal_mask = torch.tril(
# torch.ones(
# (
# report_seq_len,
# report_seq_len,
# ),
# dtype=torch.long,
# device=causal_2d_attention_mask.device,
# ),
# )
# # Lower right of attention matrix:
# lower_right = causal_2d_attention_mask.expand(-1, -1, report_seq_len, -1)
# lower_right = lower_right * causal_2d_attention_mask.permute(0, 1, 3, 2)
# lower_right = lower_right * causal_mask
# # Upper right of attention matrix:
# upper_right = torch.zeros(
# causal_2d_attention_mask.shape[0],
# 1,
# prompt_seq_len,
# report_seq_len,
# dtype=torch.long,
# device=causal_2d_attention_mask.device,
# )
# # Lower left of attention matrix:
# lower_left = non_causal_2d_attention_mask.expand(-1, -1, report_seq_len, -1)
# lower_left = lower_left * causal_2d_attention_mask.permute(0, 1, 3, 2)
# left = torch.cat((upper_left, lower_left), dim=2)
# right = torch.cat((upper_right, lower_right), dim=2)
# mixed_causality_4d_attention_mask = torch.cat((left, right), dim=-1)
# return mixed_causality_4d_attention_mask
# @staticmethod
# def create_4d_attention_mask_mixed_causality_past_key_values(non_causal_2d_attention_mask, causal_2d_attention_mask):
# non_causal_2d_attention_mask = non_causal_2d_attention_mask[:, None, None, :]
# causal_2d_attention_mask = causal_2d_attention_mask[:, None, None, :]
# mixed_causality_4d_attention_mask = torch.cat((non_causal_2d_attention_mask, causal_2d_attention_mask), dim=-1)
# return mixed_causality_4d_attention_mask
def position_ids_from_time_deltas_and_attention_mask(self, time_deltas, attention_mask):
mask_value = torch.finfo(time_deltas.dtype).max if self.config.time_delta_monotonic_inversion else torch.finfo(time_deltas.dtype).min
masked_time_deltas = torch.where(attention_mask == 1, time_deltas[:, :, 0], mask_value)
_, col_indices = torch.sort(masked_time_deltas, descending=not self.config.time_delta_monotonic_inversion)
num_rows, num_cols, _ = time_deltas.shape
row_indices = torch.arange(num_rows, device=time_deltas.device).view(-1, 1).repeat(1, num_cols).view(-1)
position_ids = torch.zeros_like(col_indices, device=time_deltas.device)
position_ids[row_indices, col_indices.flatten()] = torch.arange(num_cols, device=time_deltas.device)[None, :].expand(num_rows, -1).flatten()
position_ids.masked_fill_(attention_mask == 0, 1) # Following: https://github.com/huggingface/transformers/blob/c5f0288bc7d76f65996586f79f69fba8867a0e67/src/transformers/models/llama/modeling_llama.py#L1285
return position_ids
def get_dataset(self, dataset_path, train_transforms=None, test_transforms=None, max_train_images_per_study=None, study_id_split='mimic_iv_ed_mimic_cxr_jpg', test_set_only=False):
assert max_train_images_per_study is not None, 'max_train_images_per_study must be defined.'
assert test_transforms is not None, 'test_transforms must be defined.'
def train_set_transform(batch):
# Randomly select max_train_images_per_study if the number of images for a study exceeds max_train_images_per_study.
keys = ['images', 'dicom_id']
keys = keys + self.tables['mimic_cxr_2_0_0_metadata']['index_columns'] if 'mimic_cxr_2_0_0_metadata' in self.tables else keys
for i in range(len(batch['images'])):
if len(batch['images'][i]) > max_train_images_per_study:
paired = list(zip(*(batch[key][i] for key in keys)))
sampled_pairs = random.sample(paired, max_train_images_per_study)
unzipped_samples = zip(*sampled_pairs)
for key, values in zip(keys, unzipped_samples):
batch[key][i] = list(values)
batch['images'] = [[decode_image(torch.frombuffer(bytearray(j), dtype=torch.uint8)) for j in i] for i in batch['images']]
# Sort based on ViewPosition:
batch['images'] = [list(zip(*sorted(zip(i, v), key=lambda x: VIEW_ORDER.index(x[1]))))[0] for i, v in zip(batch['images'], batch['ViewPosition'])]
batch['images'] = [torch.stack([train_transforms(j) for j in i]) for i in batch['images']]
max_size = max(i.shape[0] for i in batch['images'])
batch['image_time_deltas'] = [[self.zero_time_delta_value if j < i.shape[0] else self.inf_time_delta_value for j in range(max_size)] for i in batch['images']]
batch['images'] = torch.nn.utils.rnn.pad_sequence(batch['images'], batch_first=True, padding_value=0.0)
for k, v in self.tables.items():
if 'index_columns' in v or 'value_columns' in v:
batch[f'{k}_index_value_feats'], batch[f'{k}_index_value_token_type_ids'], batch[f'{k}_index_value_time_delta'], batch[f'{k}_index_value_mask'] = self.prepare_index_value_feats(k, batch)
for k, v in self.tables.items():
if 'text_columns' in v:
for i in v['text_columns']:
key = f'{k}_{i}' if not k == 'mimic_cxr_sectioned' else i
batch[key], batch[f'{key}_time_delta'] = self.prepare_text_prompt(k, i, batch)
return batch
def test_set_transform(batch):
batch['images'] = [[decode_image(torch.frombuffer(bytearray(j), dtype=torch.uint8)) for j in i] for i in batch['images']]
# Sort based on ViewPosition:
batch['images'] = [list(zip(*sorted(zip(i, v), key=lambda x: VIEW_ORDER.index(x[1]))))[0] for i, v in zip(batch['images'], batch['ViewPosition'])]
batch['images'] = [torch.stack([test_transforms(j) for j in i]) for i in batch['images']]
max_size = max(i.shape[0] for i in batch['images'])
batch['image_time_deltas'] = [[self.zero_time_delta_value if j < i.shape[0] else self.inf_time_delta_value for j in range(max_size)] for i in batch['images']]
batch['images'] = torch.nn.utils.rnn.pad_sequence(batch['images'], batch_first=True, padding_value=0.0)
for k, v in self.tables.items():
if 'index_columns' in v or 'value_columns' in v:
batch[f'{k}_index_value_feats'], batch[f'{k}_index_value_token_type_ids'], batch[f'{k}_index_value_time_delta'], batch[f'{k}_index_value_mask'] = self.prepare_index_value_feats(k, batch)
for k, v in self.tables.items():
if 'text_columns' in v:
for i in v['text_columns']:
key = f'{k}_{i}' if not k == 'mimic_cxr_sectioned' else i
batch[key], batch[f'{key}_time_delta'] = self.prepare_text_prompt(k, i, batch)
return batch
dataset = datasets.load_from_disk(dataset_path)
# Train set:
if not test_set_only:
with open(os.path.join(os.path.dirname(os.path.abspath(__file__)), f'{study_id_split}_train_study_ids.json'), 'r') as f:
study_ids = json.load(f)
train_set = dataset['train']
train_set_study_ids = train_set['study_id']
index_map = {study_id: idx for idx, study_id in enumerate(train_set_study_ids)}
indices = [index_map[study_id] for study_id in study_ids if study_id in index_map]
indices.sort()
train_set = PriorsDataset(train_set, self.config.history, self.time_delta_map)
train_set.set_transform(train_set_transform)
train_set = Subset(train_set, indices)
else:
train_set = None
# Validation set:
if not test_set_only:
with open(os.path.join(os.path.dirname(os.path.abspath(__file__)), f'{study_id_split}_validate_study_ids.json'), 'r') as f:
study_ids = json.load(f)
val_set = dataset['validate']
val_set_study_ids = val_set['study_id']
index_map = {study_id: idx for idx, study_id in enumerate(val_set_study_ids)}
indices = [index_map[study_id] for study_id in study_ids if study_id in index_map]
indices.sort()
val_set = PriorsDataset(val_set, self.config.history, self.time_delta_map)
val_set.set_transform(test_set_transform)
val_set = Subset(val_set, indices)
else:
val_set = None
# Test set:
with open(os.path.join(os.path.dirname(os.path.abspath(__file__)), f'{study_id_split}_test_study_ids.json'), 'r') as f:
study_ids = json.load(f)
test_set = dataset['test']
test_set_study_ids = test_set['study_id']
index_map = {study_id: idx for idx, study_id in enumerate(test_set_study_ids)}
indices = [index_map[study_id] for study_id in study_ids if study_id in index_map]
indices.sort()
test_set = PriorsDataset(test_set, self.config.history, self.time_delta_map)
test_set.set_transform(test_set_transform)
test_set = Subset(test_set, indices)
if not test_set_only:
return train_set, val_set, test_set
else:
return test_set
def get_stage_1_dataset(self, dataset_path, train_transforms, test_transforms, max_train_images_per_study):
def train_set_transform(batch):
# Randomly select max_train_images_per_study if the number of images for a study exceeds max_train_images_per_study.
for i in range(len(batch['images'])):
if len(batch['images'][i]) > max_train_images_per_study:
paired = list(zip(batch['images'][i], batch['ViewPosition'][i]))
sampled_pairs = random.sample(paired, max_train_images_per_study)
batch['images'][i], batch['ViewPosition'][i] = zip(*sampled_pairs)
batch['images'] = [[decode_image(torch.frombuffer(bytearray(j), dtype=torch.uint8)) for j in i] for i in batch['images']]
# Sort based on ViewPosition:
batch['images'] = [list(zip(*sorted(zip(i, v), key=lambda x: VIEW_ORDER.index(x[1]))))[0] for i, v in zip(batch['images'], batch['ViewPosition'])]
batch['images'] = [torch.stack([train_transforms(j) for j in i]) for i in batch['images']]
max_size = max(i.shape[0] for i in batch['images'])
batch['image_time_deltas'] = [[self.zero_time_delta_value if j < i.shape[0] else self.inf_time_delta_value for j in range(max_size)] for i in batch['images']]
batch['images'] = torch.nn.utils.rnn.pad_sequence(batch['images'], batch_first=True, padding_value=0.0)
return batch
def test_set_transform(batch):
batch['images'] = [[decode_image(torch.frombuffer(bytearray(j), dtype=torch.uint8)) for j in i] for i in batch['images']]
# Sort based on ViewPosition:
batch['images'] = [list(zip(*sorted(zip(i, v), key=lambda x: VIEW_ORDER.index(x[1]))))[0] for i, v in zip(batch['images'], batch['ViewPosition'])]
batch['images'] = [torch.stack([test_transforms(j) for j in i]) for i in batch['images']]
max_size = max(i.shape[0] for i in batch['images'])
batch['image_time_deltas'] = [[self.zero_time_delta_value if j < i.shape[0] else self.inf_time_delta_value for j in range(max_size)] for i in batch['images']]
batch['images'] = torch.nn.utils.rnn.pad_sequence(batch['images'], batch_first=True, padding_value=0.0)
return batch
dataset = datasets.load_from_disk(dataset_path)
# Train set:
with open(os.path.join(os.path.dirname(os.path.abspath(__file__)), f'mimic_cxr_jpg_train_study_ids.json'), 'r') as f:
study_ids = json.load(f)
train_set = dataset['train']
train_set_study_ids = train_set['study_id']
index_map = {study_id: idx for idx, study_id in enumerate(train_set_study_ids)}
indices = [index_map[study_id] for study_id in study_ids if study_id in index_map]
indices.sort()
train_set = PriorsDataset(train_set, self.config.history, self.time_delta_map)
train_set.set_transform(train_set_transform)
train_set = Subset(train_set, indices)
# Validation set:
with open(os.path.join(os.path.dirname(os.path.abspath(__file__)), f'mimic_cxr_jpg_validate_study_ids.json'), 'r') as f:
study_ids = json.load(f)
val_set = dataset['validate']
val_set_study_ids = val_set['study_id']
index_map = {study_id: idx for idx, study_id in enumerate(val_set_study_ids)}
indices = [index_map[study_id] for study_id in study_ids if study_id in index_map]
indices.sort()
val_set = PriorsDataset(val_set, self.config.history, self.time_delta_map)
val_set.set_transform(test_set_transform)
val_set = Subset(val_set, indices)
# Test set:
with open(os.path.join(os.path.dirname(os.path.abspath(__file__)), f'mimic_cxr_jpg_test_study_ids.json'), 'r') as f:
study_ids = json.load(f)
test_set = dataset['test']
test_set_study_ids = test_set['study_id']
index_map = {study_id: idx for idx, study_id in enumerate(test_set_study_ids)}
indices = [index_map[study_id] for study_id in study_ids if study_id in index_map]
indices.sort()
test_set = PriorsDataset(test_set, self.config.history, self.time_delta_map)
test_set.set_transform(test_set_transform)
test_set = Subset(test_set, indices)
return train_set, val_set, test_set
def prepare_index_value_feats(self, table, batch):
index_value_columns = (self.tables[table].get('index_columns', []) + self.tables[table].get('value_columns', []))
index_value_columns = [f'{table}_{i}' for i in index_value_columns] if table != 'mimic_cxr_2_0_0_metadata' else index_value_columns
# Map to indices with lookup table:
if 'index_columns' in self.tables[table]:
for i in self.tables[table]['index_columns']:
k = f'{table}_{i}' if not table == 'mimic_cxr_2_0_0_metadata' else i
batch[k] = [
[self.luts[table][i][str(k)] if k is not None else None for k in j] if j is not None else None for j in batch[k]
]
batch_index_value_feats_list = []
batch_token_type_ids_list = []
batch_time_deltas_list = []
for batch_idx in range(len(batch['study_id'])):
if any([batch[k][batch_idx] for k in index_value_columns]):
num_rows = [len(batch[i][batch_idx]) for i in index_value_columns]
assert all(x == num_rows[0] for x in num_rows)
num_rows = num_rows[0]
# The y-index and the datetime for each group:
if isinstance(batch[self.tables[table]['groupby']][batch_idx], list):
y_indices = [d.setdefault(x, len(d)) for d in [{}] for x in batch[self.tables[table]['groupby']][batch_idx]]
datetime = [j for i, j in enumerate(batch[self.tables[table]['time_column']][batch_idx]) if j not in batch[self.tables[table]['time_column']][batch_idx][:i]]
assert len(set(y_indices)) == len(datetime)
else:
y_indices = [0] * num_rows
datetime = batch[self.tables[table]['time_column']][batch_idx] if 'time_column' in self.tables[table] else [batch['latest_study_datetime'][batch_idx]]
time_deltas = torch.tensor([compute_time_delta(i, batch['latest_study_datetime'][batch_idx], self.time_delta_map, to_tensor=False) for i in datetime])[:, None]
tensor = torch.zeros(max(y_indices) + 1, self.luts[table]['total'])
# Index columns to feats:
if 'index_columns' in self.tables[table]:
for i in self.tables[table]['index_columns']:
k = f'{table}_{i}' if not table == 'mimic_cxr_2_0_0_metadata' else i
y_indices_column = [y_idx for y_idx, x_idx in zip(y_indices, batch[k][batch_idx]) if x_idx is not None]
x_indices_column = [x_idx for x_idx in batch[k][batch_idx] if x_idx is not None]
tensor[y_indices_column, x_indices_column] = 1.0
if 'value_columns' in self.tables[table]:
for i in self.tables[table]['value_columns']:
k = f'{table}_{i}' if not table == 'mimic_cxr_2_0_0_metadata' else i
y_indices_column = [y_idx for y_idx, value in zip(y_indices, batch[k][batch_idx]) if value is not None]
x_indices_column = [self.luts[table][i] for value in batch[k][batch_idx] if value is not None]
values = [value for value in batch[k][batch_idx] if value is not None]
tensor[y_indices_column, x_indices_column] = torch.tensor(values, dtype=tensor.dtype)
assert not torch.isnan(tensor).any()
else:
tensor = torch.empty(0, self.luts[table]['total'])
time_deltas = torch.empty(0, 1)
batch_index_value_feats_list.append(tensor)
batch_token_type_ids_list.append(torch.full(
[tensor.shape[0]],
self.token_type_to_token_type_id[table],
dtype=torch.long,
)
)
batch_time_deltas_list.append(time_deltas)
assert tensor.shape[0] == batch_token_type_ids_list[-1].shape[0]
assert tensor.shape[0] == time_deltas.shape[0]
batch_index_value_feats = torch.nn.utils.rnn.pad_sequence(batch_index_value_feats_list, batch_first=True, padding_value=-1) # Pad value of -1 is not ideal. Need to use something else.
batch_token_type_ids = torch.nn.utils.rnn.pad_sequence(batch_token_type_ids_list, batch_first=True, padding_value=0)
batch_time_deltas = torch.nn.utils.rnn.pad_sequence(batch_time_deltas_list, batch_first=True, padding_value=0)
batch_mask = (batch_index_value_feats != -1).any(dim=-1).int()
return batch_index_value_feats, batch_token_type_ids, batch_time_deltas, batch_mask
def prepare_text_prompt(self, table, column, batch):
key = f'{table}_{column}' if not table == 'mimic_cxr_sectioned' else column
batch_text_list = []
batch_time_deltas_list = []
for batch_idx in range(len(batch['study_id'])):
if batch[key][batch_idx]:
num_rows = len(batch[key][batch_idx])
# The y-index and the datetime for each group:
if isinstance(batch[self.tables[table]['groupby']][batch_idx], list):
y_indices = [d.setdefault(x, len(d)) for d in [{}] for x in batch[self.tables[table]['groupby']][batch_idx]]
datetime = [j for i, j in enumerate(batch[self.tables[table]['time_column']][batch_idx]) if j not in batch[self.tables[table]['time_column']][batch_idx][:i]]
assert len(set(y_indices)) == len(datetime)
else:
y_indices = [0] * num_rows
datetime = batch[self.tables[table]['time_column']][batch_idx] if 'time_column' in self.tables[table] else [batch['latest_study_datetime'][batch_idx]]
# Remove None values:
text_rows = batch[key][batch_idx] if isinstance(batch[key][batch_idx], list) else [batch[key][batch_idx]]
y_indices = [i for i, j in zip(y_indices, text_rows) if j is not None]
text_rows = [i for i in text_rows if i is not None]
datetime = [datetime[i] for i in set(y_indices)]
if text_rows:
# Those in the same group (or those with the same y-index) get joined as the same string:
batch_text_list.append([', '.join([text_rows[j] for j in range(len(y_indices)) if y_indices[j] == k]) + '.' for k in set(y_indices)])
batch_time_deltas_list.append([compute_time_delta(i, batch['latest_study_datetime'][batch_idx], self.time_delta_map, to_tensor=False) for i in datetime])
assert len(batch_time_deltas_list[-1]) == len(batch_text_list[-1])
else:
batch_text_list.append([])
batch_time_deltas_list.append([])
else:
batch_text_list.append([])
batch_time_deltas_list.append([])
return batch_text_list, batch_time_deltas_list
@staticmethod
def collate_fn(batch):
keys = set().union(*(d.keys() for d in batch))
batch = {j: [i.setdefault(j, None) for i in batch] for j in keys}
batch = {k: torch.stack(v) if isinstance(v[0], torch.Tensor) else v for k, v in batch.items()}
return batch
@staticmethod
def prepare_dataset(physionet_dir: str, database_dir: str):
prepare_dataset(physionet_dir=physionet_dir, database_dir=database_dir)
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