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"""
Code from:
https://github.com/DCASE-REPO/DESED_task
"""
from collections import OrderedDict
import numpy as np
import pandas as pd
from dcase_util.data import DecisionEncoder
class ManyHotEncoder:
""""
Adapted after DecisionEncoder.find_contiguous_regions method in
https://github.com/DCASE-REPO/dcase_util/blob/master/dcase_util/data/decisions.py
Encode labels into numpy arrays where 1 correspond to presence of the class and 0 absence.
Multiple 1 can appear on the same line, it is for multi label problem.
Args:
labels: list, the classes which will be encoded
n_frames: int, (Default value = None) only useful for strong labels. The number of frames of a segment.
Attributes:
labels: list, the classes which will be encoded
n_frames: int, only useful for strong labels. The number of frames of a segment.
"""
def __init__(
self, labels, audio_len=10, frame_hop=160, net_pooling=4, fs=16000
):
if type(labels) in [np.ndarray, np.array]:
labels = labels.tolist()
elif isinstance(labels, (dict, OrderedDict)):
labels = list(labels.keys())
self.labels = labels
self.audio_len = audio_len
self.frame_hop = frame_hop
self.fs = fs
self.net_pooling = net_pooling
n_frames = self.audio_len * self.fs
self.n_frames = int(int((n_frames / self.frame_hop)) / self.net_pooling)
def encode_weak(self, labels):
""" Encode a list of weak labels into a numpy array
Args:
labels: list, list of labels to encode (to a vector of 0 and 1)
Returns:
numpy.array
A vector containing 1 for each label, and 0 everywhere else
"""
# useful for tensor empty labels
if type(labels) is str:
if labels == "empty":
y = np.zeros(len(self.labels)) - 1
return y
else:
labels = labels.split(",")
if type(labels) is pd.DataFrame:
if labels.empty:
labels = []
elif "event_label" in labels.columns:
labels = labels["event_label"]
y = np.zeros(len(self.labels))
for label in labels:
if not pd.isna(label):
i = self.labels.index(label)
y[i] = 1
return y
def _time_to_frame(self, time):
samples = time * self.fs
frame = (samples) / self.frame_hop
return np.clip(frame / self.net_pooling, a_min=0, a_max=self.n_frames)
def _frame_to_time(self, frame):
frame = frame * self.net_pooling / (self.fs / self.frame_hop)
return np.clip(frame, a_min=0, a_max=self.audio_len)
def encode_strong_df(self, label_df):
"""Encode a list (or pandas Dataframe or Serie) of strong labels, they correspond to a given filename
Args:
label_df: pandas DataFrame or Series, contains filename, onset (in frames) and offset (in frames)
If only filename (no onset offset) is specified, it will return the event on all the frames
onset and offset should be in frames
Returns:
numpy.array
Encoded labels, 1 where the label is present, 0 otherwise
"""
assert any(
[x is not None for x in [self.audio_len, self.frame_hop]]
)
samples_len = self.n_frames
if type(label_df) is str:
if label_df == "empty":
y = np.zeros((samples_len, len(self.labels))) - 1
return y
y = np.zeros((samples_len, len(self.labels)))
if type(label_df) is pd.DataFrame:
if {"onset", "offset", "event_label"}.issubset(label_df.columns):
for _, row in label_df.iterrows():
if not pd.isna(row["event_label"]):
i = self.labels.index(row["event_label"])
onset = int(self._time_to_frame(row["onset"]))
offset = int(np.ceil(self._time_to_frame(row["offset"])))
if "confidence" in label_df.columns:
y[onset:offset, i] = row["confidence"] # support confidence
else:
y[
onset:offset, i
] = 1 # means offset not included (hypothesis of overlapping frames, so ok)
elif type(label_df) in [
pd.Series,
list,
np.ndarray,
]: # list of list or list of strings
if type(label_df) is pd.Series:
if {"onset", "offset", "event_label"}.issubset(
label_df.index
): # means only one value
if not pd.isna(label_df["event_label"]):
i = self.labels.index(label_df["event_label"])
onset = int(self._time_to_frame(label_df["onset"]))
offset = int(np.ceil(self._time_to_frame(label_df["offset"])))
if "confidence" in label_df.columns:
y[onset:offset, i] = label_df["confidence"]
else:
y[onset:offset, i] = 1
return y
for event_label in label_df:
# List of string, so weak labels to be encoded in strong
if type(event_label) is str:
if event_label != "":
i = self.labels.index(event_label)
y[:, i] = 1
# List of list, with [label, onset, offset]
elif len(event_label) == 3:
if event_label[0] != "":
i = self.labels.index(event_label[0])
onset = int(self._time_to_frame(event_label[1]))
offset = int(np.ceil(self._time_to_frame(event_label[2])))
y[onset:offset, i] = 1
# List of list, with [label, onset, offset, confidence]
elif len(event_label) == 4:
if event_label[0] != "":
i = self.labels.index(event_label[0])
onset = int(self._time_to_frame(event_label[1]))
offset = int(np.ceil(self._time_to_frame(event_label[2])))
y[onset:offset, i] = event_label[3]
else:
raise NotImplementedError(
"cannot encode strong, type mismatch: {}".format(
type(event_label)
)
)
else:
raise NotImplementedError(
"To encode_strong, type is pandas.Dataframe with onset, offset and event_label"
"columns, or it is a list or pandas Series of event labels, "
"type given: {}".format(type(label_df))
)
return y
def decode_weak(self, labels):
""" Decode the encoded weak labels
Args:
labels: numpy.array, the encoded labels to be decoded
Returns:
list
Decoded labels, list of string
"""
result_labels = []
for i, value in enumerate(labels):
if value == 1:
result_labels.append(self.labels[i])
return result_labels
def decode_strong(self, labels):
""" Decode the encoded strong labels
Args:
labels: numpy.array, the encoded labels to be decoded
Returns:
list
Decoded labels, list of list: [[label, onset offset], ...]
"""
result_labels = []
for i, label_column in enumerate(labels.T):
change_indices = DecisionEncoder().find_contiguous_regions(label_column)
# append [label, onset, offset] in the result list
for row in change_indices:
result_labels.append(
[
self.labels[i],
self._frame_to_time(row[0]),
self._frame_to_time(row[1]),
]
)
return result_labels
def state_dict(self):
return {
"labels": self.labels,
"audio_len": self.audio_len,
"frame_hop": self.frame_hop,
"net_pooling": self.net_pooling,
"fs": self.fs,
}
@classmethod
def load_state_dict(cls, state_dict):
labels = state_dict["labels"]
audio_len = state_dict["audio_len"]
frame_hop = state_dict["frame_hop"]
net_pooling = state_dict["net_pooling"]
fs = state_dict["fs"]
return cls(labels, audio_len, frame_hop, net_pooling, fs)
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