# --- load data
# data_raw = json.load( open(f"../data/stats_raw_full.json", "r") )
data_raw = json.load(open(f"../data/{args.dataset}.json", "r"))
data, meta, meta_full = datalib.load_data(config.DATA_FILE, config.META_FILE)
config.init_dataset(meta_full)
label_id = meta_full['label']
label_type = meta_full['description'][label_id]['type']
print("config =", config)
print(
f"Using dataset {meta_full['name']} with {meta_full['samples']} samples and {meta_full['classes']} classes."
)
data_trn, data_val, data_tst, shuffle_idx = datalib.split(
data, config.DATASEED)
net = Net(meta)
env = SeqEnv(data_tst, meta)
agent = Agent(env, net, meta)
net.load(config.MODEL_FILE)
from pprint import pprint
# net.eval()
with torch.no_grad():
step = 0
tot_cst = 0
all_samples = []
step_dict = []
if config.SEED:
np.random.seed(config.SEED)
random.seed(config.SEED)
torch.manual_seed(config.SEED)
torch.cuda.manual_seed(config.SEED)
# --- load data
data, meta, meta_full = datalib.load_data(config.DATA_FILE, config.META_FILE)
config.init_dataset(meta_full)
print("config =", config)
print(
f"Using dataset {meta_full['name']} with {meta_full['samples']} samples and {meta_full['classes']} classes."
)
data_trn, data_val, data_tst = datalib.split(data, config.DATASEED)
net = Net(meta).to(config.DEVICE)
env = Env(data_trn, meta)
agent = Agent(env, net, meta)
log_trn = Log(data_trn, net, meta)
log_val = Log(data_val, net, meta)
log_tst = Log(data_tst, net, meta)
print(net)
fps = utils.Fps()
fps.start()