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data_loader.py
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data_loader.py
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import torch
import numpy as np
from utils import model_dict
from torch.utils.data import Dataset
from transformers import AutoTokenizer
class TextDataset(Dataset):
def __init__(self, session_ids, dataset_df, opt):
self.session_ids = session_ids
data_df = dataset_df[dataset_df["session"].isin(session_ids)]
if opt.quartile != -1:
data_df = data_df[data_df["quant"]==opt.quartile]
if opt.by_speaker == "therapist":
data_df = data_df[data_df["speaker"]=="I"]
elif opt.by_speaker == "client":
data_df = data_df[data_df["speaker"]=="P"]
self.data_df = data_df
self.tokenizer = AutoTokenizer.from_pretrained(model_dict[opt.text_model])
self.input_ids, self.attn_mask, self.lengths, self.speakers, self.global_ids, self.labels = [], [], [], [], [], []
for sess_id in self.session_ids:
sess_df = self.data_df[self.data_df["session"]==sess_id]
sess_len = len(sess_df)
sess_label = sess_df.empathy_label.values[0]
_global_ids = sess_df["global_id"].tolist()
_speakers = sess_df["speaker"].tolist()
utts = sess_df["normed_text"].tolist()
encodings = self.tokenizer(utts, truncation=True, padding=True)
_input_ids = encodings["input_ids"]
_attn_mask = encodings["attention_mask"]
offsets = range(0, sess_len, opt.hop)
for offset in offsets:
start_idx = offset
end_idx = min(offset+opt.context_window, sess_len)
global_ids = _global_ids[start_idx]
input_ids_tensor = torch.LongTensor(_input_ids[start_idx:end_idx])
attn_mask_tensor = torch.LongTensor(_attn_mask[start_idx:end_idx])
speakers_tensor = torch.LongTensor([x == "P" for x in _speakers[start_idx:end_idx]])
self.input_ids.append(input_ids_tensor)
self.attn_mask.append(attn_mask_tensor)
self.speakers.append(speakers_tensor)
self.global_ids.append(global_ids)
self.labels.append(sess_label)
def __len__(self):
return len(self.labels)
def get_labels(self):
return self.labels
def __getitem__(self, idx):
return self.input_ids[idx], self.attn_mask[idx], self.labels[idx], self.speakers[idx], self.global_ids[idx]
def collate_fn(self, data):
input_ids, attn_mask, labels, speakers, global_ids = zip(*data)
batch_size = len(input_ids)
lengths_dim1 = [x.shape[0] for x in input_ids]
lengths_dim2 = [x.shape[1] for x in input_ids]
labels = torch.LongTensor(labels)
global_ids = list(global_ids)
input_ids_tensor = torch.zeros(batch_size, max(lengths_dim1), max(lengths_dim2)).long()
attn_mask_tensor = torch.zeros(batch_size, max(lengths_dim1), max(lengths_dim2)).long()
# initializing to 2 since the speaker codes are 0 and 1
speaker_tensor = torch.ones(batch_size, max(lengths_dim1)).long() * 2
for idx, (length1, length2) in enumerate(zip(lengths_dim1, lengths_dim2)):
input_ids_tensor[idx, :length1, :length2] = input_ids[idx]
attn_mask_tensor[idx, :length1, :length2] = attn_mask[idx]
speaker_tensor[idx, :length1] = speakers[idx]
return input_ids_tensor, attn_mask_tensor, lengths_dim1, labels, speaker_tensor, global_ids
class AudioDataset(Dataset):
def __init__(self, session_ids, dataset_df, opt):
self.data_root = opt.data_root
self.session_ids = session_ids
data_df = dataset_df[dataset_df["session"].isin(session_ids)]
column_path = f"{opt.audio_feat}_path"
if opt.quartile != -1:
data_df = data_df[data_df["quant"]==opt.quartile]
if opt.by_speaker == "therapist":
data_df = data_df[data_df["speaker"]=="I"]
elif opt.by_speaker == "client":
data_df = data_df[data_df["speaker"]=="P"]
self.data_df = data_df
self.input_paths, self.speakers, self.global_ids, self.ids, self.labels = [], [], [], [], []
for sess_id in self.session_ids:
sess_df = self.data_df[self.data_df["session"]==sess_id]
sess_len = len(sess_df)
sess_label = sess_df.empathy_label.values[0]
_global_ids = sess_df["global_id"].tolist()
_speakers = sess_df["speaker"].tolist()
utt_paths = sess_df[column_path].tolist()
offsets = range(0, sess_len, opt.hop)
for offset in offsets:
start_idx = offset
end_idx = min(offset+opt.context_window, sess_len)
paths = utt_paths[start_idx:end_idx]
speakers_tensor = torch.LongTensor([x == "P" for x in _speakers[start_idx:end_idx]])
global_ids = _global_ids[start_idx]
self.input_paths.append(paths)
self.speakers.append(speakers_tensor)
self.global_ids.append(global_ids)
self.labels.append(sess_label)
def __len__(self):
return len(self.labels)
def get_labels(self):
return self.labels
def __getitem__(self, idx):
return self.input_paths[idx], self.labels[idx], self.speakers[idx], self.global_ids[idx],
def collate_fn(self, data):
input_paths, labels, speakers, global_ids = zip(*data)
labels = torch.LongTensor(labels)
global_ids = list(global_ids)
seq_frames = []
for seq in input_paths:
utt_frames = []
for utt_file in seq:
feat = np.load(utt_file.replace("/data/perception-temp/ttran", self.data_root)) # [T, feat_dim]
mean_frames = np.mean(feat, 0)
max_frames = np.max(feat, 0)
mean_max = np.hstack([mean_frames, max_frames])
utt_frames.append(mean_max)
utt_frames = np.stack(utt_frames, axis=0)
utt_frames = torch.from_numpy(utt_frames)
seq_frames.append(utt_frames)
batch_size = len(data)
lengths_dim = [len(x) for x in input_paths]
feat_dim = utt_frames.shape[1]
feature_tensor = torch.zeros(batch_size, max(lengths_dim), feat_dim)
speaker_tensor = torch.ones(batch_size, max(lengths_dim)).long()*2 # initializing to 2 since the speaker codes are 0 and 1
for idx, (length, utt_frames) in enumerate(zip(lengths_dim, seq_frames)):
feature_tensor[idx, :length, :] = utt_frames
speaker_tensor[idx, :length] = speakers[idx]
return feature_tensor, lengths_dim, labels, speaker_tensor, global_ids
class MultimodalDataset(Dataset):
def __init__(self, session_ids, dataset_df, opt):
self.data_root = opt.data_root
self.session_ids = session_ids
data_df = dataset_df[dataset_df["session"].isin(session_ids)]
column_path = f"{opt.audio_feat}_path"
if opt.quartile != -1:
data_df = data_df[data_df["quant"]==opt.quartile]
if opt.by_speaker == "therapist":
data_df = data_df[data_df["speaker"]=="I"]
elif opt.by_speaker == "client":
data_df = data_df[data_df["speaker"]=="P"]
self.data_df = data_df
self.tokenizer = AutoTokenizer.from_pretrained(model_dict[opt.text_model])
self.input_ids, self.attn_mask, self.lengths, self.speakers, self.global_ids, self.labels, self.input_paths = [], [], [], [], [], [], []
for sess_id in self.session_ids:
sess_df = self.data_df[self.data_df["session"]==sess_id]
sess_len = len(sess_df)
sess_label = sess_df.empathy_label.values[0]
_global_ids = sess_df["global_id"].tolist()
_speakers = sess_df["speaker"].tolist()
utts = sess_df["normed_text"].tolist()
encodings = self.tokenizer(utts, truncation=True, padding=True)
_input_ids = encodings["input_ids"]
_attn_mask = encodings["attention_mask"]
utt_paths = sess_df[column_path].tolist()
offsets = range(0, sess_len, opt.hop)
for offset in offsets:
start_idx = offset
end_idx = min(offset+opt.context_window, sess_len)
global_ids = _global_ids[start_idx]
input_ids_tensor = torch.LongTensor(_input_ids[start_idx:end_idx])
attn_mask_tensor = torch.LongTensor(_attn_mask[start_idx:end_idx])
speakers_tensor = torch.LongTensor([x == "P" for x in _speakers[start_idx:end_idx]])
paths = utt_paths[start_idx:end_idx]
self.input_ids.append(input_ids_tensor)
self.attn_mask.append(attn_mask_tensor)
self.speakers.append(speakers_tensor)
self.global_ids.append(global_ids)
self.labels.append(sess_label)
self.input_paths.append(paths)
def __len__(self):
return len(self.labels)
def get_labels(self):
return self.labels
def __getitem__(self, idx):
return self.input_ids[idx], self.attn_mask[idx], self.labels[idx], self.speakers[idx], self.global_ids[idx], self.input_paths[idx]
def collate_fn(self, data):
input_ids, attn_mask, labels, speakers, global_ids, input_paths = zip(*data)
batch_size = len(input_ids)
lengths_dim1 = [x.shape[0] for x in input_ids]
lengths_dim2 = [x.shape[1] for x in input_ids]
labels = torch.LongTensor(labels)
global_ids = list(global_ids)
input_ids_tensor = torch.zeros(batch_size, max(lengths_dim1), max(lengths_dim2)).long()
attn_mask_tensor = torch.zeros(batch_size, max(lengths_dim1), max(lengths_dim2)).long()
speaker_tensor = torch.ones(batch_size, max(lengths_dim1)).long()*2 # initializing to 2 since the speaker codes are 0 and 1
for idx, (length1, length2) in enumerate(zip(lengths_dim1, lengths_dim2)):
input_ids_tensor[idx, :length1, :length2] = input_ids[idx]
attn_mask_tensor[idx, :length1, :length2] = attn_mask[idx]
speaker_tensor[idx, :length1] = speakers[idx]
seq_frames = []
seq_lengths = []
for seq in input_paths:
utt_frames = []
frame_lengths = []
for utt_file in seq:
feat = np.load(utt_file.replace("/data/perception-temp/ttran", self.data_root)) # [T, feat_dim]
frame_lengths.append(feat.shape[0])
mean_frames = np.mean(feat, 0)
max_frames = np.max(feat, 0)
mean_max = np.hstack([mean_frames, max_frames])
utt_frames.append(mean_max)
seq_lengths.append(frame_lengths)
utt_frames = np.stack(utt_frames, axis=0)
utt_frames = torch.from_numpy(utt_frames)
seq_frames.append(utt_frames)
feat_dim = utt_frames.size(1)
feature_tensor = torch.zeros(batch_size, max(lengths_dim1), feat_dim)
for idx, (length, utt_frames) in enumerate(zip(lengths_dim1, seq_frames)):
feature_tensor[idx, :length, :] = utt_frames
return input_ids_tensor, attn_mask_tensor, lengths_dim1, feature_tensor, labels, seq_lengths, speaker_tensor, global_ids