def load_12ECG_model(model_input):
"""Load Physionet2017 Model
model_input: This is an argument from running driver.py on command line. I think we just ignore it and hard-code
out model path.
"""
models_list = [
'ecgnet_0_fold_0.631593191670484', 'ecgnet_1_fold_0.6370736239012214',
'ecgnet_2_fold_0.6444454717434089', 'ecgnet_3_fold_0.6195938932528102',
'ecgnet_4_fold_0.6149398148500164', 'ecgnet_5_fold_0.6409127451470004'
]
os.makedirs(model_input + '/pretrained/', exist_ok=True)
# load the model
models = []
for i in models_list:
model_stack = Model(input_size=19000,
n_channels=15,
hparams=hparams,
gpu=[],
inference=True)
model_stack.model_load("./inference_models/" + i + ".pt")
model_stack.model_save(model_input + '/pretrained/' + i + ".pt")
models.append(model_stack)
return models
class Predict():
def __init__(self):
#load the model
self.model = Model()
self.model.model_load("./inference_models/ecgnet_0_fold_0.6078759902401878.pt")
#load preprocessng pipeline
#load thresholds
self.postptocessing = PostProcessing(0)
# threshold = 0
# for fold in range(6):
# threshold += float(open(f"threshold_{fold}.txt", "r").read())/6
#
# self.postptocessing.threshold = threshold
def predict(self,signal,meta):
############## Preprocessing ##############
#downsampling
X_resampled = np.zeros((signal.shape[0] // 2, 12))
for i in range(12):
X_resampled[:, i] = self.resampling.downsample(signal[:, 0], order=2)
#apply preprocessing
signal = self.apply_amplitude_scaling(X=X_resampled,y=meta)
# padding
sig_length = 19000
if X_resampled.shape[0] < sig_length:
padding = np.zeros((sig_length - X_resampled.shape[0], X_resampled.shape[1]))
X = np.concatenate([X_resampled, padding], axis=0)
if X_resampled.shape[0] > sig_length:
X_resampled = X_resampled[:sig_length, :]
############## Predictions ##############
predict = self.model.predict(X_resampled)
############## Postprocessing ##############
predict = self.postptocessing.run(predict)
predict = list(predict)
if predict[4] > 0 or predict[18] > 0:
predict[4] = 1
predict[18] = 1
if predict[23] > 0 or predict[12] > 0:
predict[23] = 1
predict[12] = 1
if predict[26] > 0 or predict[13] > 0:
predict[26] = 1
predict[13] = 1
return predict
@staticmethod
def apply_amplitude_scaling(X, y):
"""Get rpeaks for each channel and scale waveform amplitude by median rpeak amplitude of lead I."""
if y['rpeaks']:
for channel_rpeaks in y['rpeaks']:
if channel_rpeaks:
return X / np.median(X[y['rpeaks'][0], 0])
return (X - X.mean()) / X.std()
