"""

Generate active learning samples, selecting the positive/negative pairs in which the model has the highest error

The saved information is:

- Current clusters. They are necessary to generate the same negative (as they select the mask)

- jpg negative images. Only needed to get the captions. Not needed for training (as they will be GAN-generated)

- Information to generate the negative images (masks and units, and the associated paths)

This information is saved in {args.active_learning_path}/{trainer.args.name_checkpoint}_{str(time.time())}/

When training with these images, it is recommended to start from the same checkpoint used for obtaining them.

After this, the next steps before running again the system with the new samples are:

- Collecting captions of the active learning samples (the negatives; the positives already have captions)

- Adding the new collected samples to the dataset. The can be added in a separate folder, and only modifying the

name_list_{}.txt files is enough. Note that the noise ID (and thus the name of the file) will already exist (for the

positive one), so save the new ones in an "active" subfolder.

"""

assert len(trainer.layer_list_all) == 1, 'Active learning is only implemented for a single layer ablations'

trainer.clusterer.save_results = True

clus, mean_clust, std_clust, _ = trainer.clusterer.create_clusters(iteration=0)

trainer.clusters = torch.FloatTensor(clus).cuda()

trainer.mean_clust = torch.FloatTensor(mean_clust)

trainer.std_clust = torch.FloatTensor(std_clust)

trainer.cluster_counts = 1 / trainer.clusters.max(1)[0]

trainer.clusters_unit = trainer.cluster_counts.view(trainer.clusters.size(0), 1).expand_as(trainer.clusters) * \

trainer.clusters

trainer.clusterer.name_with_images_clusters()

trainer.clusterer.name_clusters()

trainer.optimize_neurons()

if not trainer.args.use_cpu:

torch.cuda.synchronize()

data_time = utils.AverageMeter()

# Switch to train mode

trainer.model.eval()

active_learning_name = os.path.join(trainer.args.active_learning_path,

f'{trainer.args.name_checkpoint}_{str(time.time())}')

end = time.time()

all_loss = []

all_hmap = []

all_hmap_eval = []

all_units = []

all_paths = []

for batch_id, (image_input, audio_input, neg_images, nframes, path, image_raw) in \

enumerate(trainer.loaders['train']):

print(batch_id)

# Measure data loading time

data_time.update(time.time() - end)

if not trainer.args.use_cpu:

audio_input = audio_input.cuda(async=True)

if not trainer.args.loading_image:

if trainer.args.active_learning:

path_ints = [p.split('/')[-1] for p in path]

else:

path_ints = path

v_init = trainer.z[int(path_ints[0])]

z_img = torch.FloatTensor(image_input.size(0), v_init.shape[0])

for k in range(image_input.size(0)):

z_img[k, :] = trainer.z[int(path_ints[k])]

image_input = trainer.generator.generate_images(z_img, intervention=None)

image_input = utils.transform(image_input).detach()

else:

image_input = image_input.cuda()

neg_images = neg_images.cuda()

model_output = trainer.model(image_input, audio_input, [])

image_output = model_output[0]

audio_output = model_output[1]

neg_images = []

pooling_ratio = round(audio_input.size(3) / audio_output.size(3))

nframes.div_(pooling_ratio)

binary_mask_0 = None

if trainer.loss_type == 'negatives_edited' or trainer.loss_type == 'negatives_both':

limits = np.zeros((image_input.size(0), 2))

for i in range(image_input.size(0)):

pos_image = image_input[i, :, :, :]

nF = nframes[i]

matchmap = utils.compute_matchmap(image_output[i], audio_output[i][:, :, :nF])

positive_score = utils.matchmap_sim(matchmap).detach()

matchmap = matchmap.data.cpu().numpy().copy()

matchmap = matchmap.transpose(2, 0, 1) # l, h, w

matchmap = matchmap / (matchmap.max() + 1e-10)

matchmap_image = matchmap.max(axis=0)

threshold = 0.95

# ind_max = np.argmax(matchmap_image)

ind_max = np.argmax(matchmap)

ind_t = ind_max // (matchmap.shape[2] * matchmap.shape[1])

ind_h = (ind_max % (matchmap.shape[2] * matchmap.shape[1])) // matchmap.shape[1]

ind_w = (ind_max % (matchmap.shape[2] * matchmap.shape[1])) % matchmap.shape[1]

limits[i, 0] = ind_t

limits[i, 1] = ind_t + 1

v = (image_output[i][:, ind_h, ind_w] - trainer.mean_clust.cuda()) / (trainer.std_clust.cuda() + 1e-8)

normalized_clusters = np.matmul(trainer.clusters.cpu(), v.detach().cpu().numpy().transpose())

sorted_val = -np.sort(-normalized_clusters[:])

sorted_val = np.clip(sorted_val, 0, 4)

prob_samples = sorted_val / np.sum(sorted_val)

sorted_id = np.argsort(-normalized_clusters[:])

cluster_id = sorted_id[0]

norm = 0

threshold_random = 0.95

# The number of units to be ablated grows if we cannot generate a good (changed) negative

# The following numbers are the starting number of units to change

num_units_dict = {'layer2': 30, 'layer3': 30, 'layer4': 140, 'layer5': 30, 'layer6': 30}

thresold_heatmap = threshold

count = 0

binary_mask_eval = matchmap_image > (thresold_heatmap * matchmap_image.max())

binary_mask_eval = utils.geodesic_dilation(binary_mask_eval, (ind_h, ind_w))

binary_mask_eval = cv2.resize(binary_mask_eval, (128, 128))

all_hmap_eval.append(binary_mask_eval)

bmask = torch.Tensor(binary_mask_eval).cuda()

bmask = bmask.view(1, 128, 128).expand(3, 128, 128)

all_paths.append(path_ints[i])

while norm < threshold_random:

with torch.no_grad():

binary_mask = matchmap_image > (thresold_heatmap * matchmap_image.max())

binary_mask = utils.geodesic_dilation(binary_mask, (ind_h, ind_w))

if binary_mask_0 is None:

binary_mask_0 = cv2.resize(binary_mask, (224, 224))

z_img = trainer.z[int(path_ints[i])]

z_img = z_img[np.newaxis, :]

_ = trainer.generator.generate_images(z_img)

intervention = {}

for layer_n in trainer.layer_list_all: # This will only be one layer

units_ids = trainer.layers_units[layer_n][cluster_id][:num_units_dict[layer_n]]

layer_size = trainer.layers_dict[layer_n]['size']

layer_dim = trainer.layers_dict[layer_n]['depth']

ablation, replacement = trainer.get_ablation_replacement(params=[layer_dim, units_ids],

option='specific')

ablation_final = cv2.resize(binary_mask, (layer_size, layer_size))

ablation_final = np.tile(ablation_final, (layer_dim, 1, 1)).astype(np.float32)

ablation_final = torch.cuda.FloatTensor(ablation_final)

ablation_final = ablation.view(layer_dim, 1, 1).expand_as(ablation_final) * ablation_final

intervention[layer_n] = (ablation_final, replacement)

neg_img = trainer.generator.generate_images(z_img, intervention=intervention).detach()

neg_img_t = utils.transform(neg_img).detach()

binary_mask = cv2.resize(binary_mask, (128, 128))

norm = (neg_img_t[0, :, :, :] - pos_image.detach())

norm_im = torch.norm(norm, dim=0)

norm = norm * bmask

im_dif = norm

norm = torch.norm(torch.norm(torch.norm(norm, dim=2), dim=1), dim=0)

norm_normalized = norm / torch.norm(torch.norm(torch.norm(pos_image.detach() * bmask, dim=2),

dim=1), dim=0)

norm = norm_normalized.item()

for layer_n in trainer.layer_list_all:

num_units_dict[layer_n] = num_units_dict[layer_n] + 40 # increase units to change

thresold_heatmap = thresold_heatmap - 0.1

threshold_random = threshold_random - 0.05

cluster_id = np.random.choice(sorted_id, size=(1), p=prob_samples)[0]

count = count + 1

neg_images.append(neg_img.detach())

all_hmap.append(binary_mask)

all_units.append(units_ids)

neg_images = torch.cat(neg_images)

neg_images_t = utils.transform(neg_images)

image_output_neg, _, _ = trainer.model(neg_images_t, None, [])

loss_list = trainer.vectorized_negatives_loss(image_output, audio_output, image_output_neg, nframes)

loss_list = [x.detach() for x in loss_list]

all_loss.extend(loss_list)

all_loss = [x.view(1, -1) for x in all_loss]

all_loss = torch.cat(all_loss).view(-1, 1)

_, ind = all_loss.topk(3000, 0)

ind = [x.item() for x in ind]

a_units = [all_units[i] for i in ind]

a_paths = [all_paths[i] for i in ind]

a_hmaps = [all_hmap[i] for i in ind]

a_hmaps_eval = [all_hmap_eval[i] for i in ind]

torch.save(a_units, os.path.join(active_learning_name, 'units.pth'))

torch.save(a_paths, os.path.join(active_learning_name, 'a_paths.pth'))

torch.save(a_hmaps, os.path.join(active_learning_name, 'a_hmaps.pth'))

torch.save(a_hmaps_eval, os.path.join(active_learning_name, 'a_hmaps_eval.pth'))

os.makedirs(os.path.join(active_learning_name, 'images'), exist_ok=True)

os.makedirs(os.path.join(active_learning_name, 'hm'), exist_ok=True)

for j in range(len(a_units)):

path = a_paths[j]

units_ids = a_units[j]

binary_mask = a_hmaps[j]

layer_n = trainer.layer_list_all[0]

layer_size = trainer.layers_dict[layer_n]['size']

layer_dim = trainer.layers_dict[layer_n]['depth']

intervention = {}

ablation, replacement = trainer.get_ablation_replacement(params=[layer_dim, units_ids], option='specific')

ablation_final = cv2.resize(binary_mask, (layer_size, layer_size))

ablation_final = np.tile(ablation_final, (layer_dim, 1, 1)).astype(np.float32)

ablation_final = torch.cuda.FloatTensor(ablation_final)

ablation_final = ablation.view(layer_dim, 1, 1).expand_as(ablation_final) * ablation_final

intervention[layer_n] = (ablation_final, replacement)

z_img = trainer.z[int(path)]

z_img = z_img[np.newaxis, :]

neg_img = trainer.generator.generate_images(z_img, intervention=intervention).detach()

neg_im = neg_img[0, :, :, :].cpu().numpy().transpose(1, 2, 0)

neg_im = neg_im.astype(np.uint8)

neg_im = Image.fromarray(neg_im.astype('uint8'), 'RGB')

draw = ImageDraw.Draw(neg_im)

hm = a_hmaps_eval[j]

rows = np.any(hm, axis=1)

cols = np.any(hm, axis=0)

rmin, rmax = np.where(rows)[0][[0, -1]]

cmin, cmax = np.where(cols)[0][[0, -1]]

draw.rectangle(((cmin, rmin), (cmax, rmax)), outline='red')

neg_im.save(os.path.join(active_learning_name, 'images', f'{j}_hn.jpg'))

binary_mask_eval = cv2.resize(a_hmaps_eval[j], (128, 128))

mask_im = binary_mask_eval * 255

mask_im = mask_im.astype(np.uint8)

mask_im = mask_im.reshape((128, 128, 1))

mask_im = np.concatenate((mask_im, mask_im, mask_im), axis=2)

mask_im = Image.fromarray(mask_im.astype('uint8'), 'RGB')

assert len(trainer.layer_list_all) == 1, 'Active learning is only implemented for a single layer ablations'

assert args.clustering, 'Active learning samples are associated with a specific clustering. The clustering flag ' \

'is necessary'

active_paths = torch.load(os.path.join(args.active_learning_name, 'a_paths.pth'))

active_units = torch.load(os.path.join(args.active_learning_name, 'units.pth'))

active_binary_masks = torch.load(os.path.join(args.active_learning_name, 'a_hmaps.pth'))

trainer.active_dict = {}

for i, path in enumerate(active_paths):

trainer.active_dict[path] = {'mask': active_binary_masks[i], 'units': active_units[i], 'index': i}

cluster_path = os.path.join(args.active_learning_name, 'cluster')

trainer.clusterer = Clusterer(trainer.loaders['train'], model, path_store=cluster_path,

model_dim=args.embedding_dim, load_datapoints=True, load_histogram=True,

load_clustering=True, load_name_final=True, save_results=True,

output_size=output_size, args=args)

with torch.no_grad():

intervention = {}

data = trainer.active_dict[path_i]

binary_mask = data['mask']

units_ids = data['units']

layer_n = trainer.layer_list_all[0]

layer_size = trainer.layers_dict[layer_n]['size']

layer_dim = trainer.layers_dict[layer_n]['depth']

ablation, replacement = trainer.get_ablation_replacement(params=[layer_dim, units_ids], option='specific')

ablation_final = cv2.resize(binary_mask, (layer_size, layer_size))

ablation_final = np.tile(ablation_final, (layer_dim, 1, 1)).astype(np.float32)

ablation_final = torch.cuda.FloatTensor(ablation_final)

ablation_final = ablation.view(layer_dim, 1, 1).expand_as(ablation_final) * ablation_final

intervention[layer_n] = (ablation_final, replacement)

z_img = trainer.z[int(path_i)]

z_img = z_img[np.newaxis, :]

neg_img = trainer.generator.generate_images(z_img, intervention=intervention).detach()

if trainer.loss_type == 'negatives_edited' or trainer.loss_type == 'negatives_both':

# When we caption negative images in the active learning process in order to use them as positives, we

# still have to create them as negatives (with interventions), but then we move them to the "positive"

# tensor. And their negative is the image that originally was positive. So we basically swap these.

for i in range(image_input.size(0)):

if 'active' in path[i]: # we identify the active learning images with a 'active' in the path

aux = image_output[i]

image_output[i] = image_output_neg[i]

image_output_neg[i] = aux