def visual_evaluation(model, quadratic, writer, epoch):
gen = dataloader.generate_hard_input(quadratic=quadratic)
y_hats = []
ys = []
for X, y in gen:
X = X.cuda() / 255.
y = y / 255.
y_hat = model(X).detach().cpu().squeeze(dim=0)
y_hats.append(y_hat)
ys.append(y)
y_hats = torch.stack(y_hats).clamp(0, 1)
ys = torch.stack(ys)
# create image grid
grid = utilities.create_grid(y_hats, ys, padding=20, nrow=4)
writer.add_image('hard_examples', img_tensor=grid, global_step=epoch)
weight_in_current_cluster)
if curr_cost < min_cost:
# Found better package to go to next
min_index = i
min_cost = curr_cost
if min_index != -1:
print("Appended ", min_index, " with cost ", min_cost)
cluster.append(min_index)
packages_visited[min_index] = 1
num_packages_visited += 1
curr_x = packages[min_index].x
curr_y = packages[min_index].y
weight_in_current_cluster += packages[min_index].weight
clusters.append(cluster)
return clusters
if __name__ == '__main__':
packages = []
obstacles = []
num_r = 0
packages, obstacles, num_r = utilities.parse_input('3a.in')
maze = utilities.create_grid(packages, obstacles)
clusters = part3(packages)
print(clusters)
sequence = convert_clusters_to_sequence(packages, clusters, maze)
print(sequence)
build_output(packages, sequence, maze)