def prepare_image(self, image):
was_fixed_point = not image.is_floating_point()
image = torch.empty(image.shape, device='cpu', dtype=torch.float32).copy_(image)
if was_fixed_point:
image /= 255.0
if image.shape[-2:] != (256, 256):
image = resize(image, 256, 256)
image = color_normalize(image, Mpii.RGB_MEAN, Mpii.RGB_STDDEV)
return image
def prepare_image(self, image):
was_fixed_point = not image.is_floating_point()
image = torch.empty(image.shape, device='cpu',
dtype=torch.float32).copy_(image)
if was_fixed_point:
image /= 255.0
if image.shape[-2:] != (256, 256):
image = resize(image, 256, 256)
image = color_normalize(image, self.data_info.rgb_mean,
self.data_info.rgb_stddev)
return image
def example_input(man_running_image):
mean = torch.as_tensor([0.4404, 0.4440, 0.4327])
std = torch.as_tensor([0.2458, 0.2410, 0.2468])
image = resize(man_running_image, 256, 256)
image = color_normalize(image, mean, std)
return image.unsqueeze(0)