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main.py
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main.py
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import argparse
from explain_model import ExplainModel, load_visualize_heatmap
import fashion_mnist_dataset
import dogs_cats_dataset
from helpers import fit, evaluate, load_model
from matplotlib import pyplot as plt
import numpy as np
import scipy.ndimage
from simple_model import SimpleModel
import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchsummary import summary
import torch.utils.data
from torchvision import datasets, transforms
def main():
parser = argparse.ArgumentParser()
parser.add_argument("-fit", nargs = '?', default = False)
parser.add_argument("-model", nargs = '?', default = "simple_model")
parser.add_argument("-visualize", nargs = '?', default = False)
parser.add_argument("-evaluate", nargs = '?')
parser.add_argument("-dataset")
parser.add_argument("-model_path", nargs = '?')
parser.add_argument("-visualize_heatmap", nargs = '?')
args = parser.parse_args()
if args.dataset:
if args.dataset == 'fashion_mnist':
train_loader, test_loader = fashion_mnist_dataset.get_data_loaders()
visualize = fashion_mnist_dataset.visualize_dataset
elif args.dataset == "dogs_cats":
train_loader, test_loader = dogs_cats_dataset.get_data_loaders()
visualize = dogs_cats_dataset.visualize_dataset
if args.model:
if args.model == 'simple_model':
model = SimpleModel()
if args.model == 'explain_model':
model = ExplainModel()
if args.fit:
fit_classifier(model, train_loader, test_loader, args.model)
elif args.visualize:
visualize(train_loader)
elif args.evaluate and args.model_path:
model = load_model(model_path)
evaluate(model, test_loader)
elif args.visualize_heatmap and args.model_path:
load_visualize_heatmap(args.model_path, test_loader)
main()