-
Notifications
You must be signed in to change notification settings - Fork 9
/
test_gmm_v2.py
executable file
·208 lines (166 loc) · 7.09 KB
/
test_gmm_v2.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
'''
Controllability evaluation of Music FaderNets, GM-VAE version.
'''
import json
import torch
from gmm_model import *
import os
from sklearn.model_selection import train_test_split
from ptb_v2 import *
from torch.utils.data import Dataset, DataLoader
import numpy as np
import pretty_midi
from IPython.display import Audio
from tqdm import tqdm
from polyphonic_event_based_v2 import *
from collections import Counter
from torch.distributions import Normal
from sklearn.linear_model import LinearRegression
from scipy.stats import pearsonr
import matplotlib.pyplot as plt
import seaborn as sns
import random
from test_class import *
sns.set()
class GMMRhythmEvaluator(RhythmEvaluator):
def __init__(self, ds, epochs=10, num_of_samples=100):
super().__init__(ds, epochs=epochs, num_of_samples=num_of_samples)
def handle_z_output(self, res):
output, dis, z_out, logLogit_out, qy_x_out, y_out = res
return z_out
def handle_dis_output(self, res):
output, dis, z_out, logLogit_out, qy_x_out, y_out = res
return dis
class GMMNoteEvaluator(NoteEvaluator):
def __init__(self, ds, epochs=10, num_of_samples=100):
super().__init__(ds, epochs=epochs, num_of_samples=num_of_samples)
def handle_z_output(self, res):
output, dis, z_out, logLogit_out, qy_x_out, y_out = res
return z_out
def handle_dis_output(self, res):
output, dis, z_out, logLogit_out, qy_x_out, y_out = res
return dis
def run_through_gmm(dl):
r_mean, n_mean, t_mean, v_mean = [], [], [], []
r_lst, n_lst = [], []
a_lst = []
z_r_lst, z_n_lst = [], []
r_density_lst, n_density_lst = [], []
temp_count = 0
for j, x in tqdm(enumerate(dl), total=len(dl)):
d, r, n, c, r_density, n_density = x
d, r, n, c = d.cuda().long(), r.cuda().long(), \
n.cuda().long(), c.cuda().float()
r_lst.append(r)
n_lst.append(n)
r_density_lst.append(r_density.float())
n_density_lst.append(n_density.float())
d_oh = convert_to_one_hot(d, EVENT_DIMS)
r_oh = convert_to_one_hot(r, RHYTHM_DIMS)
n_oh = convert_to_one_hot(n, NOTE_DIMS)
res = model(d_oh, r_oh, n_oh, c)
# package output
output, dis, z_out, logLogit_out, qy_x_out, y_out = res
out, r_out, n_out, _, _ = output
z_r, z_n = z_out
dis_r, dis_n = dis
# hierachical part
z_r_lst.append(z_r.cpu().detach())
z_n_lst.append(z_n.cpu().detach())
r_mean.append(dis_r.mean.cpu().detach())
n_mean.append(dis_n.mean.cpu().detach())
r_mean = torch.cat(r_mean, dim=0).cpu().detach().numpy()
n_mean = torch.cat(n_mean, dim=0).cpu().detach().numpy()
r_density_lst = torch.cat(r_density_lst, dim=0).cpu().detach().numpy()
n_density_lst = torch.cat(n_density_lst, dim=0).cpu().detach().numpy()
r_lst = torch.cat(r_lst, dim=0).cpu().detach().numpy()
n_lst = torch.cat(n_lst, dim=0).cpu().detach().numpy()
z_r_lst = torch.cat(z_r_lst, dim=0).cpu().detach().numpy()
z_n_lst = torch.cat(z_n_lst, dim=0).cpu().detach().numpy()
# find value to set at z_r_0
z_r_0_lst = z_r_lst[:, 0]
z_r_rest_lst = z_r_lst[:, 1:]
z_n_0_lst = z_n_lst[:, 0]
z_n_rest_lst = z_n_lst[:, 1:]
r_min, r_max = np.amin(z_r_0_lst), np.amax(z_r_0_lst)
n_min, n_max = np.amin(z_n_0_lst), np.amax(z_n_0_lst)
return r_density_lst, n_density_lst, \
r_lst, n_lst, a_lst, \
r_mean, n_mean, \
z_r_0_lst, z_r_rest_lst, z_n_0_lst, z_n_rest_lst, \
r_min, r_max, n_min, n_max
def train_test_evaluation_gmm(dl):
r_density_lst, n_density_lst, \
r_lst, n_lst, a_lst, \
r_mean, n_mean, \
z_r_0_lst, z_r_rest_lst, z_n_0_lst, z_n_rest_lst, \
r_min, r_max, n_min, n_max = run_through_gmm(dl)
z_r_lst = np.concatenate([np.expand_dims(z_r_0_lst, axis=-1), z_r_rest_lst], axis=-1)
z_n_lst = np.concatenate([np.expand_dims(z_n_0_lst, axis=-1), z_n_rest_lst], axis=-1)
z_lst = np.concatenate([z_r_lst, z_n_lst], axis=-1)
# get r and n std
r_std = np.std(r_density_lst.squeeze())
n_std = np.std(n_density_lst.squeeze())
return r_min, r_max, n_min, n_max, r_std, n_std
if __name__ == "__main__":
# some initialization
with open('gmm_model_config.json') as f:
args = json.load(f)
if not os.path.isdir('log'):
os.mkdir('log')
if not os.path.isdir('params'):
os.mkdir('params')
from datetime import datetime
timestamp = str(datetime.now())
save_path_timing = 'params/{}.pt'.format(args['name'] + "_" + timestamp)
# model dimensions
EVENT_DIMS = 342
RHYTHM_DIMS = 3
NOTE_DIMS = 16
CHROMA_DIMS = 24
is_adversarial = False
save_path = "params/music_attr_vae_reg_gmm.pt"
model = MusicAttrRegGMVAE(roll_dims=EVENT_DIMS, rhythm_dims=RHYTHM_DIMS, note_dims=NOTE_DIMS,
chroma_dims=CHROMA_DIMS,
hidden_dims=args['hidden_dim'], z_dims=args['z_dim'],
n_step=args['time_step'],
n_component=2)
if os.path.exists(save_path):
print("Loading {}".format(save_path))
model.load_state_dict(torch.load(save_path))
else:
print("No save path!!")
if torch.cuda.is_available():
print('Using: ', torch.cuda.get_device_name(torch.cuda.current_device()))
model.cuda()
else:
print('CPU mode')
step, pre_epoch = 0, 0
batch_size = args["batch_size"]
# model.train()
# dataloaders
data_lst, rhythm_lst, note_density_lst, chroma_lst = get_classic_piano()
tlen, vlen = int(0.8 * len(data_lst)), int(0.9 * len(data_lst))
train_ds_dist = YamahaDataset(data_lst, rhythm_lst, note_density_lst,
chroma_lst, mode="train")
train_dl_dist = DataLoader(train_ds_dist, batch_size=batch_size, shuffle=False, num_workers=0)
val_ds_dist = YamahaDataset(data_lst, rhythm_lst, note_density_lst,
chroma_lst, mode="val")
val_dl_dist = DataLoader(val_ds_dist, batch_size=batch_size, shuffle=False, num_workers=0)
test_ds_dist = YamahaDataset(data_lst, rhythm_lst, note_density_lst,
chroma_lst, mode="test")
test_dl_dist = DataLoader(test_ds_dist, batch_size=batch_size, shuffle=False, num_workers=0)
dl = test_dl_dist
print(len(train_ds_dist), len(val_ds_dist), len(test_ds_dist))
# ================= Normal implementation =================== #
print("Train")
_, _, _, _, r_std, n_std = train_test_evaluation_gmm(train_dl_dist)
print("Test")
r_min, r_max, n_min, n_max, _, _ = train_test_evaluation_gmm(test_dl_dist)
print("STD: ", r_std, n_std)
rhythm_evaluator = GMMRhythmEvaluator(test_ds_dist, epochs=2, num_of_samples=20)
note_evaluator = GMMNoteEvaluator(test_ds_dist, epochs=2, num_of_samples=20)
print("Rhythm Generation")
rhythm_evaluator.evaluate(model, r_min, r_max, r_std, n_std)
print("Note Generation")
note_evaluator.evaluate(model, n_min, n_max, r_std, n_std)