initiaö commit

3 files changed, 78 insertions, 0 deletions

diff --git a/scripts/exec/eval.py b/scripts/exec/eval.py
new file mode 100644
index 0000000..96ed32d
--- /dev/null
+++ b/scripts/exec/eval.py
@@ -0,0 +1,28 @@
+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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diff --git a/scripts/exec/eval_savi.py b/scripts/exec/eval_savi.py
new file mode 100644
index 0000000..87d4e59
--- /dev/null
+++ b/scripts/exec/eval_savi.py
@@ -0,0 +1,17 @@
+import argparse
+import sys
+from data.datasets.ADEPT.dataset import AdeptDataset
+from scripts.evaluation_adept_savi import evaluate
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-load", default="", type=str, help='path to savi slots')
+    parser.add_argument("-n", default="", type=str, help='results name')
+
+    # Load configuration
+    args = parser.parse_args(sys.argv[1:])
+    print(f'Evaluating savi slots {args.load}')
+
+    # Load dataset
+    testset = AdeptDataset("./", 'adept', 'createdown',  (30 * 2**(2*2), 20 * 2**(2*2)))
+    evaluate(testset, args.load, args.n, plot_frequency= 1, plot_first_samples = 2)
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diff --git a/scripts/exec/train.py b/scripts/exec/train.py
new file mode 100644
index 0000000..f6e78fd
--- /dev/null
+++ b/scripts/exec/train.py
@@ -0,0 +1,33 @@
+import argparse
+import sys
+from scripts.utils.configuration import Configuration
+from scripts import training
+from data.datasets.CLEVRER.dataset import ClevrerDataset, ClevrerSample, RamImage
+from data.datasets.ADEPT.dataset import AdeptDataset
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-cfg", default="", help='path to the configuration file')
+    parser.add_argument("-n", default=-1, type=int, help='optional run number')
+    parser.add_argument("-load", default="", type=str, help='path to pretrained model or checkpoint')
+
+    # Load configuration
+    args = parser.parse_args(sys.argv[1:])
+    cfg = Configuration(args.cfg)
+    cfg.model_path = f"{cfg.model_path}"
+    if args.n >= 0:
+        cfg.model_path = f"{cfg.model_path}.run{args.n}"
+    print(f'Training model {cfg.model_path}')
+
+    # Load dataset
+    if cfg.datatype == "clevrer":
+        trainset = ClevrerDataset("./", cfg.dataset, "train", (cfg.model.latent_size[1] * 2**(cfg.model.level*2), cfg.model.latent_size[0] * 2**(cfg.model.level*2)), use_slotformer=False)
+        valset   = 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)
+    elif cfg.datatype == "adept":
+        trainset = AdeptDataset("./", cfg.dataset, "train", (cfg.model.latent_size[1] * 2**(cfg.model.level*2), cfg.model.latent_size[0] * 2**(cfg.model.level*2)))
+        valset   = AdeptDataset("./", cfg.dataset, "test",  (cfg.model.latent_size[1] * 2**(cfg.model.level*2), cfg.model.latent_size[0] * 2**(cfg.model.level*2)))
+    else:
+        raise Exception("Dataset not supported")
+    
+    # Final call
+    training.train_loci(cfg, trainset, valset, args.load)
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