BATCH_SIZE = CORE * 6
NUM_EPOCH = 6
if __name__ == '__main__':
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
writer = SummaryWriter(filename_suffix="cnn_cough_%s" % EXPERIMENT_NAME)
run = 0
for test_id, train_id in get_runs():
segmented_label_train, label_val = get_labels_by_ids(
EXPERIMENT_NAME, train_id, test_id, run)
round_loss = []
black_box = ModelC().to(DEVICE).double()
loader = DataLoader(dataset=CoughDataSet(segmented_label_train),
batch_size=BATCH_SIZE,
shuffle=True,
num_workers=CORE,
pin_memory=True)
loss_function = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(black_box.parameters(),
lr=0.001,
momentum=0.9,
nesterov=True,
dampening=0)
black_box.train()
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
from sklearn.metrics import precision_recall_curve, roc_curve, roc_auc_score
from tqdm import tqdm
from component.cnn_model import ModelC
from component.decision_maker_w_onboarding import prepare_eval_data, evaluate_result
from component.configuration import DEVICE
EXPERIMENT_NAME = 'Dec25_onboarding_'
black_box = ModelC().to(DEVICE).double()
print(black_box)
#test_table = pd.read_csv('label/Dec25_small_nosliding__val_run0.csv', index_col=0)
test_table = pd.read_csv('label/Dec25_onboarding__val_run0.csv', index_col=0)
# print(test_table.head(10))
#test_table = test_table.sample(frac=0.5, random_state=SEED)
#print(test_table.describe())
#black_box.load_state_dict(torch.load('./model/cnn_final_Dec25_small_nosliding__run_0.pkl'))
black_box.load_state_dict(
torch.load('model/cnn_final_Dec25_onboarding__run_0.pkl'))
black_box.eval()
y_true = []
y_scores = []
y_audio = []
BATCH_SIZE = 20
INPUT_PIXEL_WIDTH = 8
SEED = 2333333
DEVICE = 'cuda:0'
#DEVICE = 'cpu'
#RUN = 'Dec19-balanced-30dB'
RUN = 'Dec19-30dB-ratio9'
MODEL = 49
DATA_PATH = '/home/jlchen/sandbox/feature/dnn_paper'
TEST_LABEL = './label/test_set.csv'
THRESHOLD = 1
black_box = ModelC().to(DEVICE).double()
print(black_box)
black_box.load_state_dict(torch.load('./model/cnn_attempt_%s_epoch_%d.pkl' % (RUN, MODEL)))
black_box.eval()
test_df = pd.read_csv('./label/segmented_val_set.csv')
test_positive = test_df[test_df['label'] >= 0.9]
test_negative = test_df[test_df['label'] < 0.9]
count_positive = len(test_positive)
count_negative = len(test_negative)
print(count_negative, count_positive)
positive_index = np.arange(0, count_positive, 1)
negative_index = np.arange(0, count_negative, 1)
from component.cnn_model import ModelC
black_box = ModelC().double()
def get_n_params(model):
pp=0
for p in list(model.parameters()):
nn=1
for s in list(p.size()):
nn = nn*s
pp += nn
return pp
print(get_n_params(black_box))