def preprocess_observation(observation_set, device=None):
selected_frame_stacks = []
for observation in observation_set:
frames = np.reshape(observation[:-5],(1+HISTORY_QUEUE_LENGTH+breadcrumb_frames,SIDE_LENGTH*SIDE_LENGTH))
current_frame = np.reshape(frames[0],(SIDE_LENGTH,SIDE_LENGTH))
current_frame[current_frame == 3] = 0
current_frame[current_frame == 1] = 66
current_frame[current_frame == 2] = 100
current_frame[current_frame == 11] == 33
current_frame = current_frame/100
breadcrumbs = np.reshape(frames[-1],(SIDE_LENGTH,SIDE_LENGTH))/20
selected_frame_stacks.append(np.stack((current_frame,breadcrumbs),axis=-1))
input_array = np.array(selected_frame_stacks)
input_tensor = torch.tensor(input_array, device='cpu', dtype=torch.float)
return torch_ac.DictList({
"input": input_tensor,
})
def preprocess_obss(obss, device=None):
return torch_ac.DictList({
"image":
preprocess_images([obs["image"] for obs in obss],
device=device),
"text":
preprocess_texts([obs["mission"] for obs in obss],
vocab,
device=device)
})
def preprocess_obss(obss, device=None):
print("heare")
#
# print("----------------------")
# print(len(obss))
# print(type(obss[0]))
# print(obss[0])
# print(obss[0].keys())
return torch_ac.DictList({
"image": preprocess_images([obs["image"] for obs in obss], device=device),
"text": preprocess_texts([obs["mission"] for obs in obss], vocab, device=device)
})
def preprocess_obss(obss, device=None):
return torch_ac.DictList(
{"image": preprocess_images(obss, device=device)})
def preprocess_obss(obss, device=None):
return torch_ac.DictList({
"text": preprocess_texts([obs["text"] for obs in obss], vocab, vocab_space, gnn=gnn, device=device, ast=tree_builder)
})
def preprocess_obss(obss, device=None):
return torch_ac.DictList({
"progress_info": torch.stack([torch.tensor(obs["progress_info"], dtype=torch.float) for obs in obss], dim=0).to(device)
})
