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Running
on
Zero
File size: 8,086 Bytes
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import numpy as np
import random
import torch
from data.data_processing import transpose, tensor_to_ind_tensor
from data.data_processing_reverse import tuples_to_str
import sys
sys.path.append("..")
from utils import get_n_instruments
import os
"""
Main data loader
"""
class Loader:
def __init__(self, data_folder, data, input_len, conditioning, save_input_dir=None, pad=True,
use_start_token=True, use_end_token=False, max_transpose=3, n_try=5,
bar_start_prob=0.5, debug=False, overfit=False, regression=False,
max_samples=None, min_n_instruments=3, use_cls_token=True,
always_use_discrete_condition=False):
self.data_folder = data_folder
self.bar_start_prob = bar_start_prob
self.save_input_dir = save_input_dir
self.input_len = input_len
self.n_try = n_try # max number of trials to find suitable sample
self.min_n_instruments = min_n_instruments
self.overfit = overfit
self.one_sample = None
self.transpose_options = list(range(-max_transpose, max_transpose + 1))
self.conditioning = conditioning
self.regression = regression
self.use_cls_token = use_cls_token
self.pad = pad
self.always_use_discrete_condition = always_use_discrete_condition
self.pad_token = '<PAD>' if pad else None
self.start_token = '<START>' if use_start_token else None
self.end_token = '<END>' if use_end_token else None
self.cls_token = "<CLS>"
if debug or overfit:
data_folder = data_folder + "_debug"
self.data = data
data_files = os.listdir(self.data_folder)
self.data = [sample for sample in self.data if sample["file"] + '.pt' in data_files]
maps_file = os.path.join(os.path.abspath(data_folder + "/.."), "maps.pt")
self.maps = torch.load(maps_file)
extra_tokens = []
if self.conditioning == "continuous_token":
# two condition tokens will be concatenated later
self.input_len -= 2
elif self.conditioning == "discrete_token":
# add emotion tokens to mappings
for sample in self.data:
for label in ["valence", "arousal"]:
token = sample[label]
if token not in extra_tokens:
extra_tokens.append(token)
extra_tokens = sorted(extra_tokens)
if self.regression and self.use_cls_token:
extra_tokens.append(self.cls_token)
if extra_tokens != []:
# add to maps
maps_list = list(self.maps["idx2tuple"].values())
maps_list += extra_tokens
self.maps["idx2tuple"] = {i: val for i, val in enumerate(maps_list)}
self.maps["tuple2idx"] = {val: i for i, val in enumerate(maps_list)}
if max_samples is not None and not debug and not overfit:
self.data = self.data[:max_samples]
# roughly / 256, but *4 for flexibility. it is later cut anyway
self.n_bars = max(round(input_len / 256 * 4), 1)
def get_vocab_len(self):
return len(self.maps["tuple2idx"])
def get_maps(self):
return self.maps
def get_pad_idx(self):
return self.maps["tuple2idx"][self.pad_token]
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
if not self.overfit or self.one_sample is None:
data_path = os.path.join(self.data_folder, self.data[idx]["file"] + ".pt")
item = torch.load(data_path)
all_bars = item["bars"]
n_instruments = 0
j = 0
while j < self.n_try and n_instruments < self.min_n_instruments:
# make sure to have n many instruments
# choose random bar
max_bar_start_idx = max(0, len(all_bars) - self.n_bars - 1)
bar_start_idx = random.randint(0, max_bar_start_idx)
bar_end_idx = min(len(all_bars), bar_start_idx + self.n_bars)
bars = all_bars[bar_start_idx:bar_end_idx]
# flatten
if bars != []:
bars = torch.cat(bars, dim=0)
symbols = tuples_to_str(bars.cpu().numpy(), self.maps["idx2event"])
n_instruments = get_n_instruments(symbols)
else:
n_instruments = 0
j += 1
if n_instruments < self.min_n_instruments:
return None, None, None
# transpose
if self.transpose_options != []:
n_transpose = random.choice(self.transpose_options)
bars = transpose(bars, n_transpose,
self.maps["transposable_event_inds"])
# convert to indices (final input)
bars = tensor_to_ind_tensor(bars, self.maps["tuple2idx"])
# Decide taking the sample from the start of a bar or not
r = np.random.uniform()
start_at_beginning = not (r > self.bar_start_prob and bars.size(0) > self.input_len)
if start_at_beginning:
# starts exactly at bar location
if self.start_token is not None:
# add start token
start_idx = torch.ShortTensor(
[self.maps["tuple2idx"][self.start_token]])
bars = torch.cat((start_idx, bars), dim=0)
else:
# it doesn't have to start at bar location so shift arbitrarily
start = np.random.randint(0, bars.size(0)-self.input_len)
bars = bars[start:start+self.input_len+1]
if self.regression and self.use_cls_token:
# prepend <CLS> token
cls_idx = torch.ShortTensor(
[self.maps["tuple2idx"][self.cls_token]])
bars = torch.cat((cls_idx, bars), 0)
# for now, no auxiliary conditions
condition = torch.FloatTensor([np.nan, np.nan])
if self.conditioning == "discrete_token" and \
(start_at_beginning or self.always_use_discrete_condition):
# add emotion tokens
valence, arousal = self.data[idx]["valence"], self.data[idx]["arousal"]
valence = torch.ShortTensor([self.maps["tuple2idx"][valence]])
arousal = torch.ShortTensor([self.maps["tuple2idx"][arousal]])
bars = torch.cat((valence, arousal, bars), dim=0)
elif self.conditioning in ("continuous_token", "continuous_concat") or self.regression:
# continuous conditions
condition = torch.FloatTensor([self.data[idx]["valence"], self.data[idx]["arousal"]])
bars = bars[:self.input_len + 1] # trim to length, +1 to include target
if self.pad_token is not None:
n_pad = self.input_len + 1 - bars.shape[0]
if n_pad > 0:
# pad if necessary
bars = torch.nn.functional.pad(bars, (0, n_pad), value=self.get_pad_idx())
bars = bars.long() # to int32
input_ = bars[:-1]
if self.regression:
target = None # will use condition as target
else:
target = bars[1:]
if self.conditioning == "continuous_token":
# pad target from left, because input will get conditions concatenated
# their sizes should match
target = torch.nn.functional.pad(target, (condition.size(0), 0), value=self.get_pad_idx())
if self.overfit:
self.one_sample = [input_, condition, target]
else:
# sanity check, using one sample repeatedly
input_, condition, target = self.one_sample
return input_, condition, target
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