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
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)
def __init__(self) -> None:
RefreshProgramLogs()
super().__init__()
self.video = cv2.VideoCapture(self.source)
self.model = Model(utilsdir=c.UTILSDIR,
modeldir=c.MODELDIR,
weights=c.WEIGHTS,
cfg=c.CFG,
labelsdir=c.LABELSDIR,
coco=c.COCONAMES,
cuda=False)
self.active_thread_count: int = None
self.p_time: float = 0
self.frame_counter: int = 0
def draw_feature(path='train_log/models/best-accuracy',
scale=1,
data_set='fashion_mnist'):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
checkpoint = torch.load(path, map_location=device)
data_loader_train, data_loader_test, data_train, data_test = load_dataset(
data_set=data_set)
net = Model(n_feature)
net.load_state_dict(checkpoint['model_state_dict'])
exact_list = ['feature']
feature_extractor = FeatureExtractor(net, exact_list)
feature_extractor.to(device)
# get weight
weight = checkpoint['model_state_dict']['pred.weight'].to('cpu').data
weight_norm = weight / torch.norm(weight, dim=1, keepdim=True)
print("weight_norm: ", torch.norm(weight, dim=1))
# get feature
features = []
labels = []
for data in data_loader_train:
X_train, y_train = data
X_train = Variable(X_train).to(device)
outputs = feature_extractor(X_train)['feature'].data
features.append(outputs)
labels.append(y_train)
features = torch.cat(features, dim=0).to('cpu').data
features_norm = features / torch.norm(features, dim=1, keepdim=True)
features = features.numpy()
features_norm = features_norm.numpy()
labels = torch.cat(labels, dim=0).to('cpu').data.numpy()
# draw features
label_list = get_label_list(data_set)
plt.figure(1, figsize=(20, 20))
plt.subplot(221)
for i in range(10):
plt.plot([0, scale * weight[i, 0]], [0, scale * weight[i, 1]],
color=color_list[i])
feature = features[labels == i]
plt.scatter(feature[:, 0],
feature[:, 1],
c=color_list[i],
marker='.',
label=label_list[i],
s=1)
plt.legend()
plt.subplot(223)
for i in range(10):
plt.plot([0, weight_norm[i, 0]], [0, weight_norm[i, 1]],
color=color_list[i])
feature = features_norm[labels == i]
plt.scatter(feature[:, 0],
feature[:, 1],
c=color_list[i],
marker='.',
label=label_list[i],
s=1)
plt.legend()
# get feature
features = []
labels = []
for data in data_loader_test:
X_test, y_test = data
X_test = Variable(X_test).to(device)
outputs = feature_extractor(X_test)['feature'].data
features.append(outputs)
labels.append(y_test)
features = torch.cat(features, dim=0).to('cpu').data
features_norm = features / torch.norm(features, dim=1, keepdim=True)
features = features.numpy()
features_norm = features_norm.numpy()
labels = torch.cat(labels, dim=0).to('cpu').data.numpy()
plt.subplot(222)
for i in range(10):
plt.plot([0, scale * weight[i, 0]], [0, scale * weight[i, 1]],
color=color_list[i])
feature = features[labels == i]
plt.scatter(feature[:, 0],
feature[:, 1],
c=color_list[i],
marker='.',
label=label_list[i],
s=1)
plt.legend()
plt.subplot(224)
for i in range(10):
plt.plot([0, weight_norm[i, 0]], [0, weight_norm[i, 1]],
color=color_list[i])
feature = features_norm[labels == i]
plt.scatter(feature[:, 0],
feature[:, 1],
c=color_list[i],
marker='.',
label=label_list[i],
s=1)
plt.legend()
title = os.path.basename(os.getcwd()) + '-' + os.path.basename(path)
plt.suptitle(title)
fname = 'train_log/feature-{}'.format(os.path.basename(path))
figname = 'train_log/{}.png'.format(fname)
os.remove(figname) if os.path.exists(figname) else None
plt.savefig(fname)
plt.close('all')
