def __init__(self, image_dir, checkpoint_dir, checkpoint_iter, num_actions, num_gradients, state_type):
"""
:param image_dir: The test directory for images
:param checkpoint_dir: The checkpoint containing the best learnt model weights and biases
:param num_actions: Number of actions that the agent can take
:param num_gradients: Number of gradients to be used for each window
:param state_type: 'hog' for using windowed HOG gradient as state, 'image' for using raw images itself
"""
self.state_type = state_type
self.image_dir = image_dir
self.bins = np.array([x / float(NUM_BINS) for x in range(0, NUM_BINS, 1)])
self.sess = None
self.checkpoint_dir = checkpoint_dir
self.checkpoint_iter = checkpoint_iter
self.num_actions = num_actions
self.num_gradients = num_gradients
if self.state_type == 'hog':
self.input_channels = self.num_gradients
elif self.state_type == 'image':
self.input_channels = 1
else:
raise ValueError('State type not recognized, enter hog or image')
self.input_height = len(range(0, IMAGE_HEIGHT - SLIDING_STRIDE, SLIDING_STRIDE))
self.input_width = self.input_height
self.imagenet = None
# self.feature_dict = dict()
self.state_height = self.input_height
self.state_width = self.state_height
self.save_transform = True
self.im2f_loc = None
self.feature_size = None
Creator.__init__(self, self.input_channels, self.num_actions, self.input_height, self.input_width)
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