tdpeuter/2025ML-project-neural_compression
tdpeuter/2025ML-project-neural_compression
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This commit is contained in:
Tibo De Peuter 2025-12-09 15:10:12 +01:00
parent 8311eabd4d
commit 926cde17d3
Signed by: tdpeuter
GPG key ID: 38297DE43F75FFE2
3 changed files with 24 additions and 13 deletions
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6
main.py
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@ -29,7 +29,11 @@ def main():
) )
case 'compress': case 'compress':
compress(args.input_file) compress(device=device,
model_path=args.model_load_path,
input_file=args.input_file,
output_file=args.output_file
)
case _: case _:
raise NotImplementedError(f"Mode {args.mode} is not implemented yet") raise NotImplementedError(f"Mode {args.mode} is not implemented yet")

2
src/args.py
View file

@ -19,7 +19,7 @@ def parse_arguments():
help="Which model to use") help="Which model to use")
modelparser.add_argument("--model-load-path", type=str, required=False, modelparser.add_argument("--model-load-path", type=str, required=False,
help="Filepath to the model to load") help="Filepath to the model to load")
modelparser.add_argument("--model-save-path", type=str, required=True, modelparser.add_argument("--model-save-path", type=str, required=False,
help="Filepath to the model to save") help="Filepath to the model to save")
fileparser = ArgumentParser(add_help=False) fileparser = ArgumentParser(add_help=False)

11
src/utils/utils.py
View file

@ -1,3 +1,5 @@
from os import path
import torch import torch
from torch.utils.data import TensorDataset from torch.utils.data import TensorDataset
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
@ -14,7 +16,7 @@ def print_distribution(from_to: tuple[int, int], probabilities: list[float]):
plt.hist(range(from_to[0], from_to[1]), weights=probabilities) plt.hist(range(from_to[0], from_to[1]), weights=probabilities)
plt.show() plt.show()
def print_losses(train_losses: list[float], validation_losses: list[float], show=False): def print_losses(train_losses: list[float], validation_losses: list[float], filename: str | None = None, show=False):
plt.plot(train_losses, label="Training loss") plt.plot(train_losses, label="Training loss")
plt.plot(validation_losses, label="Validation loss") plt.plot(validation_losses, label="Validation loss")
plt.xlabel("Epoch") plt.xlabel("Epoch")
@ -23,7 +25,12 @@ def print_losses(train_losses: list[float], validation_losses: list[float], show
if show: if show:
plt.show() plt.show()
plt.savefig("losses.png")
if filename is None:
filename = path.join("results", "losses.png")
print(f"Saving losses to {filename}...")
plt.savefig(filename)
def load_data(path: str) -> bytes: def load_data(path: str) -> bytes: