def test_mobilenet_can_use_testtime_dropout(self):
model = MobileNet(features=20, test_time_dropout=True)
model = model.eval()
inp = torch.randn(1, 3, 224, 224, device="cpu")
result1 = model(inp)
result2 = model(inp)
self.assertFalse(torch.all(result1.eq(result2)))
def update_train_features(torch_model, num_classes):
with torch.no_grad():
model = MobileNet(features=num_classes, pretrained=False)
model.load_state_dict(
torch.load(torch_model, map_location=torch.device('cpu')))
model = model.eval()
for i, train_img in enumerate(TrainImage.objects.all()):
if i % 10 == 0:
print(f'Updating image {i}')
image = Image.open(io.BytesIO(train_img.image))
data = preprocess(image)
img = data.repeat((3, 1, 1))
img = img.reshape((1, img.shape[0], img.shape[1], img.shape[2]))
features = model.features(img)
features = features.mean([2, 3])
byte_f = io.BytesIO()
torch.save(features, byte_f)
train_img.features = byte_f.getvalue()
train_img.save()