From ca8c65e3d9c788dae24cfb5599ff198214b222fd Mon Sep 17 00:00:00 2001
From: David Rotermund <54365609+davrot@users.noreply.github.com>
Date: Sat, 29 Jul 2023 16:02:02 +0200
Subject: [PATCH] Delete todo directory

---
 todo/optimal_stimulus.py | 158 ---------------------------------------
 1 file changed, 158 deletions(-)
 delete mode 100644 todo/optimal_stimulus.py

diff --git a/todo/optimal_stimulus.py b/todo/optimal_stimulus.py
deleted file mode 100644
index 9c1a482..0000000
--- a/todo/optimal_stimulus.py
+++ /dev/null
@@ -1,158 +0,0 @@
-import torch
-import matplotlib.pyplot as plt
-import matplotlib.patches as patch
-import matplotlib as mpl
-from functions.analyse_network import analyse_network
-
-mpl.rcParams["text.usetex"] = True
-mpl.rcParams["font.family"] = "serif"
-
-
-# define parameters
-num_iterations: int = 100000
-learning_rate: float = 0.1  # 0.0005  #
-apply_input_mask: bool = True
-mark_region_in_plot: bool = False
-sheduler_patience: int = 500
-sheduler_factor: float = 0.9
-sheduler_eps = 1e-08
-target_image_active: float = 1e4
-
-# path to NN
-nn = "network_0_seed0_Coignless_83Epoch_2807-1455"
-PATH = f"./trained_models/{nn}.pt"
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-
-# load and eval model
-model = torch.load(PATH).to(device)
-model.eval()
-print("Full network:")
-print(model)
-print("")
-
-# enter index to plot:
-idx = int(input("Please select layer: "))
-print(f"Selected layer {idx}:")
-assert idx < len(model)
-model = model[: idx + 1]
-
-# random input
-input_img = torch.randn(1, 200, 200).to(device)
-input_img = input_img.unsqueeze(0)
-input_img.requires_grad_(True)  # type: ignore
-
-input_shape = input_img.shape
-assert input_shape[-2] == input_shape[-1]
-coordinate_list, layer_type_list, pixel_used = analyse_network(
-    model=model, input_shape=int(input_shape[-1])
-)
-
-output_shape = model(input_img).shape
-target_image = torch.zeros(
-    (*output_shape,), dtype=input_img.dtype, device=input_img.device
-)
-
-input_parameter = torch.nn.Parameter(input_img)
-
-
-print(
-    (
-        f"Available max positions: f:{target_image.shape[1]} "
-        f"x:{target_image.shape[2]} y:{target_image.shape[3]}"
-    )
-)
-
-# select neuron and plot for all feature maps (?)
-neuron_f = int(input("Please select neuron_f: "))
-neuron_x = target_image.shape[2] // 2
-neuron_y = target_image.shape[3] // 2
-print(f"Selected neuron {neuron_f}, {neuron_x}, {neuron_y}")
-
-# Input mask ->
-active_input_x = coordinate_list[-1][:, neuron_x].clone()
-active_input_y = coordinate_list[-1][:, neuron_y].clone()
-
-input_mask: torch.Tensor = torch.zeros_like(input_img)
-
-input_mask[
-    :,
-    :,
-    active_input_x.type(torch.int64).unsqueeze(-1),
-    active_input_y.type(torch.int64).unsqueeze(0),
-] = 1
-
-rect_x = [int(active_input_x.min()), int(active_input_x.max())]
-rect_y = [int(active_input_y.min()), int(active_input_y.max())]
-# <- Input mask
-
-if apply_input_mask:
-    with torch.no_grad():
-        input_img *= input_mask
-
-
-optimizer = torch.optim.Adam([{"params": input_parameter}], lr=learning_rate)
-
-scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
-    optimizer,
-    patience=sheduler_patience,
-    factor=sheduler_factor,
-    eps=sheduler_eps * 0.1,
-)
-
-target_image[0, neuron_f, neuron_x, neuron_y] = target_image_active
-
-backup_image = input_parameter.data.detach().clone()
-
-counter: int = 0
-while (optimizer.param_groups[0]["lr"] > sheduler_eps) and (counter < num_iterations):
-    optimizer.zero_grad()
-
-    output = model(input_parameter)
-
-    loss = torch.nn.functional.mse_loss(output, target_image)
-    loss.backward()
-
-    if counter % 1000 == 0:
-        print(
-            f"{counter} : loss={float(loss):.3e} lr={optimizer.param_groups[0]['lr']:.3e}"
-        )
-
-    optimizer.step()
-
-    if apply_input_mask:
-        with torch.no_grad():
-            input_parameter.data[torch.where(input_mask == 0)] = 0.0
-
-    if (
-        torch.isfinite(input_parameter.data).sum().cpu()
-        != torch.tensor(input_parameter.data.size()).prod()
-    ):
-        print(f"Found NaN in step: {counter}, use a smaller initial lr")
-        input_parameter.data = backup_image.clone()
-        exit()
-    else:
-        backup_image = input_parameter.data.detach().clone()
-
-    scheduler.step(float(loss))
-    counter += 1
-
-# plot image:
-_, ax = plt.subplots()
-
-ax.imshow(input_img.squeeze().detach().cpu().numpy(), cmap="gray")
-
-if mark_region_in_plot:
-    edgecolor = "sienna"
-    kernel = patch.Rectangle(
-        (rect_y[0], rect_x[0]),
-        int(rect_y[1] - rect_y[0]),
-        int(rect_x[1] - rect_x[0]),
-        linewidth=1.2,
-        edgecolor=edgecolor,
-        facecolor="none",
-    )
-    ax.add_patch(kernel)
-
-
-plt.show(block=True)