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import os
os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
import madmom
import torch
import numpy as np
from tqdm import tqdm
from torch.utils.data import DataLoader
from DilatedTransformer import Demixed_DilatedTransformerModel
from spectrogram_dataset import audioDataset
import scipy
#import seaborn as sns
import matplotlib.pyplot as plt
from utils import AverageMeter
import warnings
warnings.filterwarnings('ignore')
#data
SAMPLE_SIZE = None
FPS = 44100/1024
NUM_FOLDS = 8
FOLD = 0
#model
DEVICE='cuda:0'
NORM_FIRST=True
ATTN_LEN=5
INSTR=5
NTOKEN=2
DMODEL=256
NHEAD=8
DHID=1024
NLAYER=9
#directories
DATASET_PATH = './data/demix_spectrogram_data.npz'
ANNOTATION_PATH = 'data/full_beat_annotation.npz'
MODEL_PATH = "./checkpoints/fold_6_trf_param.pt"
DATA_TO_LOAD = ['gtzan'] #'carnetic', 'harmonix' # ballroom, 'gtzan', 'hainsworth', 'smc'
TEST_ONLY = ['gtzan']
DEMO_SAVE_ROOT = './save/visualization'
if not os.path.exists(DEMO_SAVE_ROOT):
os.mkdir(DEMO_SAVE_ROOT)
model = Demixed_DilatedTransformerModel(attn_len=ATTN_LEN,
instr=INSTR,
ntoken=NTOKEN,
dmodel=DMODEL,
nhead=NHEAD,
d_hid=DHID,
nlayers=NLAYER,
norm_first=NORM_FIRST
)
model.load_state_dict(torch.load(MODEL_PATH, map_location=torch.device('cpu'))['state_dict'])
model.to(DEVICE)
model.eval()
dataset = audioDataset(data_to_load=DATA_TO_LOAD,
test_only_data = TEST_ONLY,
data_path = DATASET_PATH,
annotation_path = ANNOTATION_PATH,
fps = FPS,
sample_size = None,
num_folds = NUM_FOLDS)
train_set, val_set, test_set = dataset.get_fold(fold=0)
loader = DataLoader(test_set, batch_size=1, shuffle=False)
beat_tracker = madmom.features.beats.DBNBeatTrackingProcessor(min_bpm=55.0, max_bpm=215.0, fps=FPS, transition_lambda=10, threshold=0.05)
downbeat_tracker = madmom.features.downbeats.DBNDownBeatTrackingProcessor(beats_per_bar=[3, 4], min_bpm=55.0, max_bpm=215.0, fps=FPS, transition_lambda=10)
#thresh_beat_meter = AverageMeter()
#pick_beat_meter = AverageMeter()
beat_DBN_meter = AverageMeter()
#thresh_downbeat_meter = AverageMeter()
#pick_downbeat_meter = AverageMeter()
downbeat_DBN_meter = AverageMeter()
with torch.no_grad():
for idx, (dataset_key, data, beat, downbeat, tempo, root) in tqdm(enumerate(loader), total=len(loader)):
#if idx == 0:
# continue
data = data.float().to(DEVICE) #(1, 5, T', 128)
dataset = dataset_key[0]
print(root)
#inference
pred, pred_t, attn = model.inference(data)
beat_pred = torch.sigmoid(pred[0, :, 0]).detach().cpu().numpy()
#np.savetxt(os.path.join(save_dir, 'beat_activation.txt'), beat_pred[:, np.newaxis])
downbeat_pred = torch.sigmoid(pred[0, :, 1]).detach().cpu().numpy()
#np.savetxt(os.path.join(save_dir, 'downbeat_activation.txt'), downbeat_pred[:, np.newaxis])
#gt beat
beat_gt = torch.nonzero(beat[0]>.5)[:, 0].detach().numpy() / (FPS)
dnb_beat_pred = beat_tracker(beat_pred)
downbeat_gt = torch.nonzero(downbeat[0]>.5)[:, 0].detach().numpy() / (FPS)
combined_act = np.concatenate((np.maximum(beat_pred - downbeat_pred, np.zeros(beat_pred.shape))[:, np.newaxis], downbeat_pred[:, np.newaxis]), axis=-1) #(T, 2)
dbn_downbeat_pred = downbeat_tracker(combined_act)
dbn_downbeat_pred = dbn_downbeat_pred[dbn_downbeat_pred[:, 1]==1][:, 0]
beat_score_DBN = madmom.evaluation.beats.BeatEvaluation(dnb_beat_pred, beat_gt)
downbeat_score_DBN = madmom.evaluation.beats.BeatEvaluation(dbn_downbeat_pred, downbeat_gt)
fig = plt.figure(figsize=(20, 60))
for i in range(1, 10):
layer_attn = attn[i].transpose(-2, -1).squeeze(0).cpu().detach().numpy()
#layer_attn = np.mean(layer_attn, axis=0)
layer_attn = layer_attn[2]
#print(layer_attn.shape)
fig.add_subplot(9, 4, 4*i-3)
plt.imshow(layer_attn[0, :, :], cmap='viridis')
plt.vlines(torch.nonzero(beat[0, :]>.5)[:, 0].detach().numpy(), 0, layer_attn.shape[-1], label='Beats', color='r', linestyle=':', linewidth=.01)
plt.hlines(torch.nonzero(beat[0, :]>.5)[:, 0].detach().numpy(), 0, layer_attn.shape[-1], label='Beats', color='g', linestyle=':', linewidth=.01)
fig.add_subplot(9, 4, 4*i-2)
plt.imshow(layer_attn[1, :, :], cmap='viridis')
plt.vlines(torch.nonzero(beat[0, :]>.5)[:, 0].detach().numpy(), 0, layer_attn.shape[-1], label='Beats', color='r', linestyle=':', linewidth=.01)
plt.hlines(torch.nonzero(beat[0, :]>.5)[:, 0].detach().numpy(), 0, layer_attn.shape[-1], label='Beats', color='g', linestyle=':', linewidth=.01)
fig.add_subplot(9, 4, 4*i-1)
plt.imshow(layer_attn[2, :, :], cmap='viridis')
plt.vlines(torch.nonzero(beat[0, :]>.5)[:, 0].detach().numpy(), 0, layer_attn.shape[-1], label='Beats', color='r', linestyle=':', linewidth=.01)
plt.hlines(torch.nonzero(beat[0, :]>.5)[:, 0].detach().numpy(), 0, layer_attn.shape[-1], label='Beats', color='g', linestyle=':', linewidth=.01)
fig.add_subplot(9, 4, 4*i)
plt.imshow(layer_attn[3, :, :], cmap='viridis')
plt.vlines(torch.nonzero(beat[0, :]>.5)[:, 0].detach().numpy(), 0, layer_attn.shape[-1], label='Beats', color='r', linestyle=':', linewidth=.01)
plt.hlines(torch.nonzero(beat[0, :]>.5)[:, 0].detach().numpy(), 0, layer_attn.shape[-1], label='Beats', color='g', linestyle=':', linewidth=.01)
plt.show()
#fig = ax.get_figure()
print('saving...')
plt.savefig(f"{DEMO_SAVE_ROOT}/{root[0].split('/')[-1].replace('.wav', '')}_attention_paterns.pdf", format='pdf', dpi=1200)
#ax = sns.heatmap(attn[1])
#print(attn)
print('beat accuracy:', beat_score_DBN.fmeasure, beat_score_DBN.cmlt, beat_score_DBN.amlt)
print('dowbbeat accuracy:', downbeat_score_DBN.fmeasure, downbeat_score_DBN.cmlt, downbeat_score_DBN.amlt)
break
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