"""
# 16 44 117 119 143 151 206 242 267 290 308 354 380 410 421 456 517 573 598 622 638 663 676 688 715 725 749 752 820 851 866 922
# start at 16 since that's the start of a chord sequence (could choose any of the numbers above)
for offset in [16, 44, 308, 421, 517, 752, 866]:
print("sampling offset {}".format(offset))
x_rec_i = x_rec[offset:offset + num_each]
x_ts = piano_roll_imlike_to_image_array(x_rec_i, 0.25)
# cut off zero padding on the vertical axis
x_ts = x_ts[:, :35]
if not os.path.exists("samples"):
os.mkdir("samples")
save_image_array(
x_ts,
"samples/multichannel_pixel_cnn_gen_{}_seed_{}_temp_{}.png".format(
offset, args.seed, args.temp))
sample_flat_idx = flat_idx[-1000:]
p = sample_flat_idx[offset:offset + num_each]
note_to_norm_kv = d2["note_to_norm_kv"]
midi_to_norm_kv = d2["midi_to_norm_kv"]
# EEE more than 1 value maps to 0 in these kv lookups!
midi_to_norm_lu = {
int(k): int(v) + 1 if k != 0 else 0
for k, v in midi_to_norm_kv[p[0][0]]
}
norm_to_midi_lu = {v: k for k, v in midi_to_norm_lu.items()}
note_to_norm_lu = {
feed = {vs.images: x,
vs.bn_flag: 1.}
outs = [vs.z_e_x, vs.z_q_x, vs.z_i_x, vs.x_tilde]
r = sess.run(outs, feed_dict=feed)
x_rec = r[-1]
x_rec[x_rec > 0.5] = 1.
x_rec[x_rec <= 0.5] = 0.
x_rec = x_rec.sum(axis=-1)[..., None]
x = x.sum(axis=-1)[..., None]
x_rec[x_rec > 0.5] = 1.
x_rec[x_rec <= 0.5] = 0.
x[x > 0.5] = 1.
x[x <= 0.5] = 0.
x = x[:16]
x_rec = x_rec[:16]
diff = abs(x - x_rec)
diff[diff > 0] = 1.
# figure out piano roll / colored plot?
print("writing out multichannel_rec.png, multichannel_orig.png, multichannel_diff.png")
save_image_array(x_rec, "multichannel_rec.png")
save_image_array(x, "multichannel_orig.png")
save_image_array(diff, "multichannel_diff.png")
saver = tf.train.import_meta_graph(model_path + '.meta')
saver.restore(sess, model_path)
fields = ['images', 'bn_flag', 'z_e_x', 'z_q_x', 'z_i_x', 'x_tilde']
vs = namedtuple('Params',
fields)(*[tf.get_collection(name)[0] for name in fields])
x = image_data[:32]
feed = {vs.images: x, vs.bn_flag: 1.}
outs = [vs.z_e_x, vs.z_q_x, vs.z_i_x, vs.x_tilde]
r = sess.run(outs, feed_dict=feed)
x_rec = r[-1]
x_rec[x_rec > 0.5] = 1.
x_rec[x_rec <= 0.5] = 0.
#from IPython import embed; embed(); raise ValueError()
rr = quantized_imlike_to_image_array(image_data[:16], 0.25)
save_image_array(rr,
"orig_subroll_multichannel.png",
resize_multiplier=(4, 1),
gamma_multiplier=7,
flat_wide=True)
rr = quantized_imlike_to_image_array(x_rec[:16], 0.25)
save_image_array(rr,
"rec_subroll_multichannel.png",
resize_multiplier=(4, 1),
gamma_multiplier=7,
flat_wide=True)
print("wrote out 'orig_subroll_multichannel.png'")
print("wrote out 'rec_subroll_multichannel.png'")
if lcr_i[0] == 0:
print(n)
"""
# 16 44 117 119 143 151 206 242 267 290 308 354 380 410 421 456 517 573 598 622 638 663 676 688 715 725 749 752 820 851 866 922
# start at 16 since that's the start of a chord sequence (could choose any of the numbers above)
for offset in [16, 44, 308, 421, 517, 752, 866]:
print("sampling offset {}".format(offset))
x_rec_i = x_rec[offset:offset + num_each]
x_ts = piano_roll_imlike_to_image_array(x_rec_i, 0.25)
if not os.path.exists("samples"):
os.mkdir("samples")
save_image_array(
x_ts,
"samples/pianoroll_multichannel_pixel_cnn_markovm4_chords_gen_{}_seed_{}_temp_{}.png"
.format(offset, args.seed, args.temp))
sample_flat_idx = flat_idx[-1000:]
p = sample_flat_idx[offset:offset + num_each]
satb_midi = [[], [], [], []]
satb_notes = [[], [], [], []]
for n in range(len(x_rec_i)):
measure_len = x_rec_i[n].shape[1]
# 96 x 48 measure in
events = {}
for v in range(x_rec_i.shape[-1]):
all_up = zip(*np.where(x_rec_i[n][..., v]))
time_ordered = [
flat_scalenotes = [sn for sg in copy.deepcopy(d2["scalenotes"]) for sn in sg]
sample_scalenotes = flat_scalenotes[-1000:]
"""
# find some start points
for n in range(len(sample_labels)):
lcr_i = label_to_lcr[sample_labels[n, 0]]
if lcr_i[0] == 0:
print(n)
"""
# 16 44 117 119 143 151 206 242 267 290 308 354 380 410 421 456 517 573 598 622 638 663 676 688 715 725 749 752 820 851 866 922
# start at 16 since that's the start of a chord sequence (could choose any of the numbers above)
for offset in [16, 44, 308, 421, 517, 752, 866]:
print("sampling offset {}".format(offset))
x_rec_i = x_rec[offset:offset + num_each]
save_image_array(x_rec_i, "pixel_cnn_gen_{}.png".format(offset))
sample_flat_idx = flat_idx[-1000:]
p = sample_flat_idx[offset:offset + num_each]
note_to_norm_kv = d2["note_to_norm_kv"]
midi_to_norm_kv = d2["midi_to_norm_kv"]
# EEE more than 1 value maps to 0 in these kv lookups!
midi_to_norm_lu = {
int(k): int(v) + 1 if k != 0 else 0
for k, v in midi_to_norm_kv[p[0][0]]
}
norm_to_midi_lu = {v: k for k, v in midi_to_norm_lu.items()}
note_to_norm_lu = {
k: int(v) + 1 if k != "R" else 0
scalenotes = all_scalenotes[iii]
for mi in range(len(all_notes[iii])):
measure = all_notes[iii][mi]
midi_m = notes_to_midi(measure)
im = np.zeros((v_imsize, h_imsize, 4)).astype("uint8")
for v in range(len(midi_m[0])):
for t in range(len(midi_m)):
# skip rest items, we will infer them in decoding
if midi_m[t][v] != 0:
im[:, t, v] = oh_lu[midi_m[t][v]]
im = im.astype("float32")
measures_as_images.append(im)
# track in case something is skipped
all_measures_as_images.append(measures_as_images)
all_scalenotes_save.append([scalenotes] * len(measures_as_images))
all_chordnames_save.append(all_chordnames[iii])
"""
cc = np.concatenate([am[None] for am in all_measures_as_images[0][:16]], axis=0)
rr = piano_roll_imlike_to_image_array(cc, 0.25)
save_image_array(rr, "tmppr.png")
"""
final = {}
final["measures_as_images"] = all_measures_as_images
final["scalenotes"] = all_scalenotes_save
final["chordnames"] = all_chordnames_save
print("Dumping to music_data_pianoroll_multichannel.npz")
np.savez("music_data_pianoroll_multichannel.npz", **final)
import numpy as np
from tfbldr.datasets import piano_roll_imlike_to_image_array
from tfbldr.datasets import save_image_array
import sys
fname = sys.argv[1]
d = np.load(fname)
xr = d["pr"][8:16]
rr = piano_roll_imlike_to_image_array(xr, 0.25, background="white")
rr = rr[:, 40:80]
pngname = "samples/{}.png".format(fname.split("/")[-1].split(".")[0])
print("saving {}".format(pngname))
save_image_array(rr,
pngname,
resize_multiplier=(4, 1),
gamma_multiplier=7,
flat_wide=True)
print("image complete")
from IPython import embed
embed()
raise ValueError()
feed = {vs.images: x,
vs.bn_flag: 1.}
outs = [vs.z_e_x, vs.z_q_x, vs.z_i_x, vs.x_tilde]
r = sess.run(outs, feed_dict=feed)
x_tilde_lins = r[-1]
# gumbel sample to get mixture
idx = np.argmax(x_tilde_lins + -np.log(-np.log(random_state.uniform(1E-5, 1-1E-5, x_tilde_lins.shape))), axis=-1)
x_rec = idx
"""
# components, make it one hot
idx_oh = np.eye(x_tilde_lins.shape[-1])[idx.ravel()].reshape(idx.shape + (-1,))
# get rid of the useless channel
idx_oh = idx_oh[:, 0]
# transpose to h, w, c, time on horizontal
x_rec = idx_oh.transpose(0, 3, 1, 2)
"""
x = np.array(x).astype("int32")
x_rec = x_rec[:4]
x = x[:4]
x_img = [plot_piano_roll(x[i][0], 0.25) for i in range(len(x))]
x_rec_img = [plot_piano_roll(x_rec[i][0], 0.25) for i in range(len(x_rec))]
save_image_array(np.array(x_img)[0][None], "original_1d.png", rescale=False)
save_image_array(np.array(x_rec_img)[0][None], "reconstructed_1d.png", rescale=False)
# convert back into real pitches?
# jk its bad tho
image_data = image_data[-1000:]
#shuffle_random = np.random.RandomState(112)
#shuffle_random.shuffle(image_data)
# get images from the held out valid set
with tf.Session(config=config) as sess:
saver = tf.train.import_meta_graph(model_path + '.meta')
saver.restore(sess, model_path)
fields = ['images', 'bn_flag', 'z_e_x', 'z_q_x', 'z_i_x', 'x_tilde']
vs = namedtuple('Params',
fields)(*[tf.get_collection(name)[0] for name in fields])
x = image_data[:32]
feed = {vs.images: x, vs.bn_flag: 1.}
outs = [vs.z_e_x, vs.z_q_x, vs.z_i_x, vs.x_tilde]
r = sess.run(outs, feed_dict=feed)
x_rec = r[-1]
x_rec[x_rec > 0.5] = 1.
x_rec[x_rec <= 0.5] = 0.
x = x[:16]
x_rec = x_rec[:16]
diff = abs(x - x_rec)
diff[diff > 0] = 1.
print("writing out rec.png, orig.png, diff.png")
save_image_array(x_rec, "rec.png")
save_image_array(x, "orig.png")
save_image_array(diff, "diff.png")
np.sum(np.abs(np.diff(np.where(x_rec[i][:, :, 0])[0])))
for i in range(len(x_rec))
]
simul = [np.max(np.sum(x_rec[i][:, :, 0], axis=0)) for i in range(len(x_rec))]
non_boring_indices = [
i for i in range(len(x_rec)) if 0 < delta_counts[i] <= 12 and simul[i] <= 1
] # includes rest measures, rests arent boring
x_rec = x_rec[np.array(non_boring_indices)]
if len(x_rec) < (3 * num_to_plot):
raise ValueError("Removed too many boring ones, set num_to_plot lower")
x_rec = x_rec[:3 * num_to_plot]
x_rec = np.concatenate((x_rec[::3], x_rec[1::3], x_rec[2::3]), axis=-1)
save_image_array(x_rec, "samp_vq_pixel_cnn_music_bxe.png")
ce = copy.deepcopy(d1["centers"])
# only use bottom 3 voices
ce = [cei for cei in ce if len(cei) == 4]
# find all the ones with 0 or 1 rest
non_rest = [i for i in range(len(ce)) if sum(ce[i] == 0) == 0]
start_chunks = [
i for i in range(len(non_rest) - num_to_plot)
if np.max(np.diff(non_rest[i:i + num_to_plot])) == 1
]
random_state.shuffle(start_chunks)
random_state.shuffle(start_chunks)
ii = non_rest[start_chunks[0]]
skeleton = np.array([sk for sk in ce[ii:(ii + num_to_plot)]])
joined = np.zeros((len(x_rec), x_rec.shape[2], skeleton.shape[-1]))