"""
"""
from copy import deepcopy
from functools import reduce
from typing import Any, List, Optional, Sequence, Union
import numpy as np
import pandas as pd
import torch
from torch import Tensor
try:
import torch_ecg # noqa: F401
except ModuleNotFoundError:
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).absolute().parents[2]))
from cfg import ModelCfg
from torch_ecg.cfg import CFG
from torch_ecg.components.outputs import MultiLableClassificationOutput, SequenceLabellingOutput
from torch_ecg.models import ECG_CRNN, ECG_SEQ_LAB_NET
from torch_ecg.utils.utils_data import mask_to_intervals
__all__ = [
"ECG_CRNN_CPSC2020",
"ECG_SEQ_LAB_NET_CPSC2020",
]
class ECG_CRNN_CPSC2020(ECG_CRNN):
""" """
__DEBUG__ = True
__name__ = "ECG_CRNN_CPSC2020"
def __init__(self, classes: Sequence[str], n_leads: int, config: Optional[CFG] = None, **kwargs: Any) -> None:
"""
Parameters
----------
classes: list,
list of the classes for classification
n_leads: int,
number of leads (number of input channels)
config: dict, optional,
other hyper-parameters, including kernel sizes, etc.
ref. the corresponding config file
"""
model_config = deepcopy(ModelCfg.crnn)
model_config.update(deepcopy(config) or {})
super().__init__(classes, n_leads, model_config, **kwargs)
@torch.no_grad()
def inference(
self,
input: Union[Sequence[float], np.ndarray, Tensor],
class_names: bool = False,
bin_pred_thr: float = 0.5,
) -> MultiLableClassificationOutput:
"""
auxiliary function to `forward`, for CPSC2020,
Parameters
----------
input: ndarray or Tensor,
input tensor, of shape (batch_size, channels, seq_len)
class_names: bool, default False,
if True, the returned scalar predictions will be a `DataFrame`,
with class names for each scalar prediction
bin_pred_thr: float, default 0.5,
the threshold for making binary predictions from scalar predictions
Returns
-------
MultiLabelClassificationOutput, with the following items:
classes: list,
list of the classes for classification
thr: float,
threshold for making binary predictions from scalar predictions
prob: ndarray or DataFrame,
scalar predictions, (and binary predictions if `class_names` is True)
prob: ndarray,
the array (with values 0, 1 for each class) of binary prediction
"""
self.eval()
_input = torch.as_tensor(input, dtype=self.dtype, device=self.device)
if _input.ndim == 2:
_input = _input.unsqueeze(0) # add a batch dimension
prob = self.sigmoid(self.forward(_input))
pred = (prob >= bin_pred_thr).int()
prob = prob.cpu().detach().numpy()
pred = pred.cpu().detach().numpy()
for row_idx, row in enumerate(pred):
row_max_prob = prob[row_idx, ...].max()
if row.sum() == 0:
pred[row_idx, ...] = (prob[row_idx, ...] == np.max(prob[row_idx, ...])).astype(int)
if class_names:
prob = pd.DataFrame(prob)
prob.columns = self.classes
prob["pred"] = ""
for row_idx in range(len(prob)):
prob.at[row_idx, "pred"] = np.array(self.classes)[np.where(pred == 1)[0]].tolist()
return MultiLableClassificationOutput(
classes=self.classes,
thr=bin_pred_thr,
prob=prob,
pred=pred,
)
@torch.no_grad()
def inference_CPSC2020(
self,
input: Union[np.ndarray, Tensor],
class_names: bool = False,
bin_pred_thr: float = 0.5,
) -> MultiLableClassificationOutput:
"""
alias for `self.inference`
"""
return self.inference(input, class_names, bin_pred_thr)
class ECG_SEQ_LAB_NET_CPSC2020(ECG_SEQ_LAB_NET):
""" """
__DEBUG__ = True
__name__ = "ECG_SEQ_LAB_NET_CPSC2020"
def __init__(self, classes: Sequence[str], n_leads: int, config: Optional[CFG] = None, **kwargs: Any) -> None:
"""
Parameters
----------
classes: list,
list of the classes for sequence labeling
n_leads: int,
number of leads (number of input channels)
config: dict, optional,
other hyper-parameters, including kernel sizes, etc.
ref. the corresponding config file
"""
model_config = deepcopy(ModelCfg.seq_lab)
model_config.update(deepcopy(config) or {})
super().__init__(classes, n_leads, model_config, **kwargs)
self.reduction = reduce(
lambda a, b: a * b,
self.config.cnn.multi_scopic.subsample_lengths,
1,
)
@torch.no_grad()
def inference(
self,
input: Union[Sequence[float], np.ndarray, Tensor],
bin_pred_thr: float = 0.5,
rpeak_inds: Optional[List[np.ndarray]] = None,
) -> SequenceLabellingOutput:
"""
auxiliary function to `forward`, for CPSC2020,
Parameters
----------
input: array_like,
input tensor, of shape (..., channels, seq_len)
bin_pred_thr: float, default 0.5,
the threshold for making binary predictions from scalar predictions
rpeak_inds: list of ndarray, optional,
indices of rpeaks for each batch data
Returns
-------
prob: ndarray or DataFrame,
scalar predictions, (and binary predictions if `class_names` is True)
SPB_indices: list,
list of predicted indices of SPB
PVC_indices: list,
list of predicted indices of PVC
"""
self.eval()
_input = torch.as_tensor(input, dtype=self.dtype, device=self.device)
if _input.ndim == 2:
_input = _input.unsqueeze(0) # add a batch dimension
batch_size, channels, seq_len = _input.shape
prob = self.forward(_input)
if self.n_classes == 2:
prob = self.sigmoid(prob) # (batch_size, seq_len, 2)
pred = (prob >= bin_pred_thr).int()
prob = prob.cpu().detach().numpy()
pred = pred.cpu().detach().numpy()
# aux used to filter out potential simultaneous predictions of SPB and PVC
aux = (prob == np.max(prob, axis=2, keepdims=True)).astype(int)
pred = aux * pred
elif self.n_classes == 3:
prob = self.softmax(prob) # (batch_size, seq_len, 3)
prob = prob.cpu().detach().numpy()
pred = np.argmax(prob, axis=2)
if rpeak_inds is not None:
assert len(rpeak_inds) == batch_size
rpeak_mask = np.zeros((batch_size, seq_len // self.reduction), dtype=int)
for i in range(batch_size):
batch_rpeak_inds = (rpeak_inds[i] / self.reduction).astype(int)
rpeak_mask[i, batch_rpeak_inds] = 1
else:
rpeak_mask = np.ones((batch_size, seq_len // self.reduction), dtype=int)
SPB_intervals = [
mask_to_intervals(seq * rpeak_mask[idx], 1) for idx, seq in enumerate(pred[..., self.classes.index("S")])
]
SPB_indices = [
[self.reduction * (itv[0] + itv[1]) // 2 for itv in l_itv] if len(l_itv) > 0 else [] for l_itv in SPB_intervals
]
PVC_intervals = [
mask_to_intervals(seq * rpeak_mask[idx], 1) for idx, seq in enumerate(pred[..., self.classes.index("V")])
]
PVC_indices = [
[self.reduction * (itv[0] + itv[1]) // 2 for itv in l_itv] if len(l_itv) > 0 else [] for l_itv in PVC_intervals
]
return SequenceLabellingOutput(
classes=self.classes,
prob=prob,
pred=pred,
SPB_indices=SPB_indices,
PVC_indices=PVC_indices,
)
@torch.no_grad()
def inference_CPSC2020(self, input: Union[np.ndarray, Tensor], bin_pred_thr: float = 0.5) -> SequenceLabellingOutput:
"""
alias for `self.inference`
"""
return self.inference(input, bin_pred_thr)
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