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