import matplotlib.pyplot as plt
import torch
import argparse
import numpy as np
from mpl_toolkits.axes_grid1 import make_axes_locatable
from reversible import ReversibleModel
from audio_midi_dataset import get_dataset_individually, Spec2MidiDataset, SqueezingDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.sampler import SequentialSampler
from train_loop import normal_noise_like
import os
import re
import mpl_rc
import utils
import pretty_midi as pm
RCPARAMS = mpl_rc.default()
def collect_samples(device, model, loader, n_samples):
model.eval()
samples_x_true = []
samples_x_pred = []
for si in range(n_samples):
x_true = []
x_pred = []
for batch in loader:
x = batch['x']
y = batch['y']
y = y + normal_noise_like(y,
model.y_noise_scale) # tiny exaggeration
import matplotlib.pyplot as plt
import torch
import argparse
import numpy as np
from plot_input_output import plot_input_output
from reversible import ReversibleModel
from audio_midi_dataset import get_dataset_individually, Spec2MidiDataset, SqueezingDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.sampler import SequentialSampler
from train_loop import normal_noise_like
import os
import mpl_rc
import utils
rcParams = mpl_rc.default()
START = 100
END = 150
def collect_input_output(device, model, loader, n_samples):
model.eval()
samples_x_true = []
samples_x_invs = []
samples_x_edit = []
samples_x_zepa = []
samples_x_samp = []
samples_y_true = []
samples_y_pred = []
samples_y_edit = []