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import argparse
import sys
from data.datasets.ADEPT.dataset import AdeptDataset
from data.datasets.CLEVRER.dataset import ClevrerDataset, ClevrerSample, RamImage
from scripts.utils.configuration import Configuration
from scripts import evaluation_adept, evaluation_clevrer
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-cfg", default="", help='path to the configuration file')
parser.add_argument("-load", default="", type=str, help='path to model')
parser.add_argument("-n", default="", type=str, help='results name')
# Load configuration
args = parser.parse_args(sys.argv[1:])
cfg = Configuration(args.cfg)
print(f'Evaluating model {args.load}')
# Load dataset
if cfg.datatype == "clevrer":
testset = ClevrerDataset("./", cfg.dataset, 'val', (cfg.model.latent_size[1] * 2**(cfg.model.level*2), cfg.model.latent_size[0] * 2**(cfg.model.level*2)), use_slotformer=True, evaluation=True)
evaluation_clevrer.evaluate(cfg, testset, args.load, args.n, plot_frequency= 2, plot_first_samples = 3) # only plotting
evaluation_clevrer.evaluate(cfg, testset, args.load, args.n, plot_first_samples = 0) # evaluation
elif cfg.datatype == "adept":
testset = AdeptDataset("./", cfg.dataset, 'createdown', (cfg.model.latent_size[1] * 2**(cfg.model.level*2), cfg.model.latent_size[0] * 2**(cfg.model.level*2)))
evaluation_adept.evaluate(cfg, testset, args.load, args.n, plot_frequency= 1, plot_first_samples = 2)
else:
raise Exception("Dataset not supported")
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