import pathlib import random import numpy as np import omegaconf import pydiffvg import torch import torch.nn as nn from PIL import Image from pytorch_svgrender.diffvg_warp import DiffVGState from pytorch_svgrender.libs.modules.edge_map.DoG import XDoG from pytorch_svgrender.painter.clipasso import modified_clip as clip from pytorch_svgrender.painter.clipasso.grad_cam import gradCAM from torchvision import transforms class Painter(DiffVGState): def __init__( self, method_cfg: omegaconf.DictConfig, diffvg_cfg: omegaconf.DictConfig, num_strokes: int = 4, canvas_size: int = 224, device=None, target_im=None, mask=None ): super(Painter, self).__init__(device, print_timing=diffvg_cfg.print_timing, canvas_width=canvas_size, canvas_height=canvas_size) self.args = method_cfg self.num_paths = num_strokes self.num_segments = method_cfg.num_segments self.width = method_cfg.width self.control_points_per_seg = method_cfg.control_points_per_seg self.num_control_points = torch.zeros(self.num_segments, dtype=torch.int32) + (self.control_points_per_seg - 2) self.opacity_optim = method_cfg.force_sparse self.num_stages = method_cfg.num_stages self.noise_thresh = method_cfg.noise_thresh self.softmax_temp = method_cfg.softmax_temp self.add_random_noise = "noise" in method_cfg.augemntations self.optimize_points = method_cfg.optimize_points self.optimize_points_global = method_cfg.optimize_points self.points_init = [] # for mlp training self.color_vars_threshold = method_cfg.color_vars_threshold self.path_svg = method_cfg.path_svg self.strokes_per_stage = self.num_paths self.optimize_flag = [] # attention related for strokes initialisation self.attention_init = method_cfg.attention_init self.saliency_model = method_cfg.saliency_model self.xdog_intersec = method_cfg.xdog_intersec self.mask_object_attention = method_cfg.mask_object_attention self.text_target = method_cfg.text_target # for clip gradients self.saliency_clip_model = method_cfg.saliency_clip_model self.image2clip_input = self.clip_preprocess(target_im) self.mask = mask self.attention_map = self.set_attention_map() if self.attention_init else None self.thresh = self.set_attention_threshold_map() if self.attention_init else None self.strokes_counter = 0 # counts the number of calls to "get_path" self.epoch = 0 self.final_epoch = method_cfg.num_iter - 1 if "for" in method_cfg.loss_mask: # default for the mask is to mask out the background # if mask loss is for it means we want to maskout the foreground self.mask = 1 - mask self.mlp_train = method_cfg.mlp_train self.width_optim = method_cfg.width_optim self.width_optim_global = method_cfg.width_optim if self.width_optim: self.init_widths = torch.ones((self.num_paths)).to(device) * 1.5 self.mlp_width = WidthMLP(num_strokes=self.num_paths, num_cp=self.control_points_per_seg, width_optim=self.width_optim).to(device) self.mlp_width_weights_path = method_cfg.mlp_width_weights_path self.mlp_width_weight_init() self.gumbel_temp = method_cfg.gumbel_temp self.mlp = MLP(num_strokes=self.num_paths, num_cp=self.control_points_per_seg, width_optim=self.width_optim).to( device) if self.mlp_train else None self.mlp_points_weights_path = method_cfg.mlp_points_weights_path self.mlp_points_weight_init() self.out_of_canvas_mask = torch.ones((self.num_paths)).to(self.device) def turn_off_points_optim(self): self.optimize_points = False def switch_opt(self): self.width_optim = not self.width_optim self.optimize_points = not self.optimize_points def mlp_points_weight_init(self): if self.mlp_points_weights_path != "none": checkpoint = torch.load(self.mlp_points_weights_path) self.mlp.load_state_dict(checkpoint['model_state_dict']) print("mlp checkpoint loaded from ", self.mlp_points_weights_path) def mlp_width_weight_init(self): if self.mlp_width_weights_path == "none": self.mlp_width.apply(init_weights) else: checkpoint = torch.load(self.mlp_width_weights_path) self.mlp_width.load_state_dict(checkpoint['model_state_dict']) print("mlp checkpoint loaded from ", self.mlp_width_weights_path) def init_image(self, stage=0): if stage > 0: # Noting: if multi stages training than add new strokes on existing ones # don't optimize on previous strokes self.optimize_flag = [False for i in range(len(self.shapes))] for i in range(self.strokes_per_stage): stroke_color = torch.tensor([0.0, 0.0, 0.0, 1.0]) path = self.get_path() self.shapes.append(path) path_group = pydiffvg.ShapeGroup(shape_ids=torch.tensor([len(self.shapes) - 1]), fill_color=None, stroke_color=stroke_color) self.shape_groups.append(path_group) self.optimize_flag.append(True) else: num_paths_exists = 0 if self.path_svg is not None and pathlib.Path(self.path_svg).exists(): print(f"-> init svg from `{self.path_svg}` ...") self.canvas_width, self.canvas_height, self.shapes, self.shape_groups = self.load_svg(self.path_svg) # if you want to add more strokes to existing ones and optimize on all of them num_paths_exists = len(self.shapes) for path in self.shapes: self.points_init.append(path.points) for i in range(num_paths_exists, self.num_paths): stroke_color = torch.tensor([0.0, 0.0, 0.0, 1.0]) path = self.get_path() self.shapes.append(path) path_group = pydiffvg.ShapeGroup(shape_ids=torch.tensor([len(self.shapes) - 1]), fill_color=None, stroke_color=stroke_color) self.shape_groups.append(path_group) self.optimize_flag = [True for i in range(len(self.shapes))] def get_image(self, mode="train"): if self.mlp_train: img = self.mlp_pass(mode) else: img = self.render_warp(mode) opacity = img[:, :, 3:4] img = opacity * img[:, :, :3] + torch.ones(img.shape[0], img.shape[1], 3, device=self.device) * (1 - opacity) img = img[:, :, :3] # Convert img from HWC to NCHW img = img.unsqueeze(0) img = img.permute(0, 3, 1, 2).to(self.device) # NHWC -> NCHW return img def mlp_pass(self, mode, eps=1e-4): """ update self.shapes etc through mlp pass instead of directly (should be updated with the optimizer as well). """ if self.optimize_points_global: points_vars = self.points_init # reshape and normalise to [-1,1] range points_vars = torch.stack(points_vars).unsqueeze(0).to(self.device) points_vars = points_vars / self.canvas_width points_vars = 2 * points_vars - 1 if self.optimize_points: points = self.mlp(points_vars) else: with torch.no_grad(): points = self.mlp(points_vars) else: points = torch.stack(self.points_init).unsqueeze(0).to(self.device) if self.width_optim and mode != "init": # first iter use just the location mlp widths_ = self.mlp_width(self.init_widths).clamp(min=1e-8) mask_flipped = (1 - widths_).clamp(min=1e-8) v = torch.stack((torch.log(widths_), torch.log(mask_flipped)), dim=-1) hard_mask = torch.nn.functional.gumbel_softmax(v, self.gumbel_temp, False) self.stroke_probs = hard_mask[:, 0] * self.out_of_canvas_mask self.widths = self.stroke_probs * self.init_widths # normalize back to canvas size [0, 224] and reshape all_points = 0.5 * (points + 1.0) * self.canvas_width all_points = all_points + eps * torch.randn_like(all_points) all_points = all_points.reshape((-1, self.num_paths, self.control_points_per_seg, 2)) if self.width_optim_global and not self.width_optim: self.widths = self.widths.detach() # all_points = all_points.detach() # define new primitives to render shapes = [] shape_groups = [] for p in range(self.num_paths): width = torch.tensor(self.width) if self.width_optim_global and mode != "init": width = self.widths[p] path = pydiffvg.Path( num_control_points=self.num_control_points, points=all_points[:, p].reshape((-1, 2)), stroke_width=width, is_closed=False) if mode == "init": # do once at the begining, define a mask for strokes that are outside the canvas is_in_canvas_ = self.is_in_canvas(self.canvas_width, self.canvas_height, path) if not is_in_canvas_: self.out_of_canvas_mask[p] = 0 shapes.append(path) path_group = pydiffvg.ShapeGroup( shape_ids=torch.tensor([len(shapes) - 1]), fill_color=None, stroke_color=torch.tensor([0, 0, 0, 1])) shape_groups.append(path_group) _render = pydiffvg.RenderFunction.apply scene_method_cfg = pydiffvg.RenderFunction.serialize_scene( \ self.canvas_width, self.canvas_height, shapes, shape_groups) img = _render(self.canvas_width, # width self.canvas_height, # height 2, # num_samples_x 2, # num_samples_y 0, # seed None, *scene_method_cfg) self.shapes = shapes.copy() self.shape_groups = shape_groups.copy() return img def get_path(self): points = [] p0 = self.inds_normalised[self.strokes_counter] if self.attention_init else (random.random(), random.random()) points.append(p0) for j in range(self.num_segments): radius = 0.05 for k in range(self.control_points_per_seg - 1): p1 = (p0[0] + radius * (random.random() - 0.5), p0[1] + radius * (random.random() - 0.5)) points.append(p1) p0 = p1 points = torch.tensor(points).to(self.device) points[:, 0] *= self.canvas_width points[:, 1] *= self.canvas_height self.points_init.append(points) path = pydiffvg.Path(num_control_points=self.num_control_points, points=points, stroke_width=torch.tensor(self.width), is_closed=False) self.strokes_counter += 1 return path def render_warp(self, mode): if not self.mlp_train: if self.opacity_optim: for group in self.shape_groups: group.stroke_color.data[:3].clamp_(0., 0.) # to force black stroke group.stroke_color.data[-1].clamp_(0., 1.) # opacity # group.stroke_color.data[-1] = (group.stroke_color.data[-1] >= self.color_vars_threshold).float() # uncomment if you want to add random noise if self.add_random_noise: if random.random() > self.noise_thresh: eps = 0.01 * min(self.canvas_width, self.canvas_height) for path in self.shapes: path.points.data.add_(eps * torch.randn_like(path.points)) if self.width_optim and mode != "init": widths_ = self.mlp_width(self.init_widths).clamp(min=1e-8) mask_flipped = 1 - widths_ v = torch.stack((torch.log(widths_), torch.log(mask_flipped)), dim=-1) hard_mask = torch.nn.functional.gumbel_softmax(v, self.gumbel_temp, False) self.stroke_probs = hard_mask[:, 0] * self.out_of_canvas_mask self.widths = self.stroke_probs * self.init_widths if self.optimize_points: _render = pydiffvg.RenderFunction.apply scene_method_cfg = pydiffvg.RenderFunction.serialize_scene( \ self.canvas_width, self.canvas_height, self.shapes, self.shape_groups) img = _render(self.canvas_width, # width self.canvas_height, # height 2, # num_samples_x 2, # num_samples_y 0, # seed None, *scene_method_cfg) else: points = torch.stack(self.points_init).unsqueeze(0).to(self.device) shapes = [] shape_groups = [] for p in range(self.num_paths): width = torch.tensor(self.width) if self.width_optim: width = self.widths[p] path = pydiffvg.Path( num_control_points=self.num_control_points, points=points[:, p].reshape((-1, 2)), stroke_width=width, is_closed=False) shapes.append(path) path_group = pydiffvg.ShapeGroup( shape_ids=torch.tensor([len(shapes) - 1]), fill_color=None, stroke_color=torch.tensor([0, 0, 0, 1])) shape_groups.append(path_group) _render = pydiffvg.RenderFunction.apply scene_method_cfg = pydiffvg.RenderFunction.serialize_scene( \ self.canvas_width, self.canvas_height, shapes, shape_groups) img = _render(self.canvas_width, # width self.canvas_height, # height 2, # num_samples_x 2, # num_samples_y 0, # seed None, *scene_method_cfg) self.shapes = shapes.copy() self.shape_groups = shape_groups.copy() return img def parameters(self): if self.optimize_points: if self.mlp_train: self.points_vars = self.mlp.parameters() else: self.points_vars = [] # storkes' location optimization for i, path in enumerate(self.shapes): if self.optimize_flag[i]: path.points.requires_grad = True self.points_vars.append(path.points) self.optimize_flag[i] = False if self.width_optim: return self.points_vars, self.mlp_width.parameters() return self.points_vars def get_mlp(self): return self.mlp def get_width_mlp(self): if self.width_optim_global: return self.mlp_width else: return None def set_color_parameters(self): # for storkes' color optimization (opacity) self.color_vars = [] for i, group in enumerate(self.shape_groups): if self.optimize_flag[i]: group.stroke_color.requires_grad = True self.color_vars.append(group.stroke_color) return self.color_vars def get_color_parameters(self): return self.color_vars def get_widths(self): if self.width_optim_global: return self.stroke_probs return None def get_strokes_in_canvas_count(self): return self.out_of_canvas_mask.sum() def get_strokes_count(self): if self.width_optim_global: with torch.no_grad(): return torch.sum(self.stroke_probs) return self.num_paths def is_in_canvas(self, canvas_width, canvas_height, path): shapes, shape_groups = [], [] stroke_color = torch.tensor([0.0, 0.0, 0.0, 1.0]) shapes.append(path) path_group = pydiffvg.ShapeGroup(shape_ids=torch.tensor([len(shapes) - 1]), fill_color=None, stroke_color=stroke_color) shape_groups.append(path_group) _render = pydiffvg.RenderFunction.apply scene_method_cfg = pydiffvg.RenderFunction.serialize_scene( canvas_width, canvas_height, shapes, shape_groups) img = _render(canvas_width, # width canvas_height, # height 2, # num_samples_x 2, # num_samples_y 0, # seed None, *scene_method_cfg) img = img[:, :, 3:4] * img[:, :, :3] + \ torch.ones(img.shape[0], img.shape[1], 3, device=self.device) * (1 - img[:, :, 3:4]) img = img[:, :, :3].detach().cpu().numpy() return (1 - img).sum() def save_svg(self, output_dir, name): if not self.width_optim: pydiffvg.save_svg('{}/{}.svg'.format(output_dir, name), self.canvas_width, self.canvas_height, self.shapes, self.shape_groups) else: stroke_color = torch.tensor([0.0, 0.0, 0.0, 1.0]) new_shapes, new_shape_groups = [], [] for path in self.shapes: is_in_canvas_ = True w = path.stroke_width / 1.5 if w > 0.7 and is_in_canvas_: new_shapes.append(path) path_group = pydiffvg.ShapeGroup(shape_ids=torch.tensor([len(new_shapes) - 1]), fill_color=None, stroke_color=stroke_color) new_shape_groups.append(path_group) pydiffvg.save_svg('{}/{}.svg'.format(output_dir, name), self.canvas_width, self.canvas_height, new_shapes, new_shape_groups) def clip_preprocess(self, target_im): model, preprocess = clip.load(self.saliency_clip_model, device=self.device, jit=False) model.eval().to(self.device) data_transforms = transforms.Compose([ preprocess.transforms[-1], ]) return data_transforms(target_im).to(self.device) def dino_attn(self): patch_size = 8 # dino hyperparameter threshold = 0.6 # for dino model mean_imagenet = torch.Tensor([0.485, 0.456, 0.406])[None, :, None, None].to(self.device) std_imagenet = torch.Tensor([0.229, 0.224, 0.225])[None, :, None, None].to(self.device) totens = transforms.Compose([ transforms.Resize((self.canvas_height, self.canvas_width)), transforms.ToTensor() ]) dino_model = torch.hub.load('facebookresearch/dino:main', 'dino_vits8').eval().to(self.device) self.main_im = Image.open(self.target_path).convert("RGB") main_im_tensor = totens(self.main_im).to(self.device) img = (main_im_tensor.unsqueeze(0) - mean_imagenet) / std_imagenet w_featmap = img.shape[-2] // patch_size h_featmap = img.shape[-1] // patch_size with torch.no_grad(): attn = dino_model.get_last_selfattention(img).detach().cpu()[0] nh = attn.shape[0] attn = attn[:, 0, 1:].reshape(nh, -1) val, idx = torch.sort(attn) val /= torch.sum(val, dim=1, keepdim=True) cumval = torch.cumsum(val, dim=1) th_attn = cumval > (1 - threshold) idx2 = torch.method_cfgort(idx) for head in range(nh): th_attn[head] = th_attn[head][idx2[head]] th_attn = th_attn.reshape(nh, w_featmap, h_featmap).float() th_attn = nn.functional.interpolate(th_attn.unsqueeze(0), scale_factor=patch_size, mode="nearest")[0].cpu() attn = attn.reshape(nh, w_featmap, h_featmap).float() attn = nn.functional.interpolate(attn.unsqueeze(0), scale_factor=patch_size, mode="nearest")[0].cpu() return attn def clip_attn(self): model, preprocess = clip.load(self.saliency_clip_model, device=self.device, jit=False) model.eval().to(self.device) if "RN" in self.saliency_clip_model: text_input = clip.tokenize([self.text_target]).to(self.device) saliency_layer = "layer4" attn_map = gradCAM( model.visual, self.image2clip_input, model.encode_text(text_input).float(), getattr(model.visual, saliency_layer) ) attn_map = attn_map.squeeze().detach().cpu().numpy() attn_map = (attn_map - attn_map.min()) / (attn_map.max() - attn_map.min()) else: # ViT attn_map = interpret(self.image2clip_input, model, device=self.device) del model return attn_map def set_attention_map(self): assert self.saliency_model in ["dino", "clip"] if self.saliency_model == "dino": return self.dino_attn() elif self.saliency_model == "clip": return self.clip_attn() def softmax(self, x, tau=0.2): e_x = np.exp(x / tau) return e_x / e_x.sum() def set_inds_clip(self): attn_map = (self.attention_map - self.attention_map.min()) / ( self.attention_map.max() - self.attention_map.min()) if self.xdog_intersec: xdog = XDoG(k=10) im_xdog = xdog(self.image2clip_input[0].permute(1, 2, 0).cpu().numpy()) intersec_map = (1 - im_xdog) * attn_map attn_map = intersec_map if self.mask_object_attention: attn_map = attn_map * self.mask[0, 0].cpu().numpy() attn_map_soft = np.copy(attn_map) attn_map_soft[attn_map > 0] = self.softmax(attn_map[attn_map > 0], tau=self.softmax_temp) k = self.num_stages * self.num_paths self.inds = np.random.choice(range(attn_map.flatten().shape[0]), size=k, replace=False, p=attn_map_soft.flatten()) self.inds = np.array(np.unravel_index(self.inds, attn_map.shape)).T self.inds_normalised = np.zeros(self.inds.shape) self.inds_normalised[:, 0] = self.inds[:, 1] / self.canvas_width self.inds_normalised[:, 1] = self.inds[:, 0] / self.canvas_height self.inds_normalised = self.inds_normalised.tolist() return attn_map_soft def set_inds_dino(self): k = max(3, (self.num_stages * self.num_paths) // 6 + 1) # sample top 3 three points from each attention head num_heads = self.attention_map.shape[0] self.inds = np.zeros((k * num_heads, 2)) # "thresh" is used for visualisaiton purposes only thresh = torch.zeros(num_heads + 1, self.attention_map.shape[1], self.attention_map.shape[2]) softmax = nn.Softmax(dim=1) for i in range(num_heads): # replace "self.attention_map[i]" with "self.attention_map" to get the highest values among # all heads. topk, indices = np.unique(self.attention_map[i].numpy(), return_index=True) topk = topk[::-1][:k] cur_attn_map = self.attention_map[i].numpy() # prob function for uniform sampling prob = cur_attn_map.flatten() prob[prob > topk[-1]] = 1 prob[prob <= topk[-1]] = 0 prob = prob / prob.sum() thresh[i] = torch.Tensor(prob.reshape(cur_attn_map.shape)) # choose k pixels from each head inds = np.random.choice(range(cur_attn_map.flatten().shape[0]), size=k, replace=False, p=prob) inds = np.unravel_index(inds, cur_attn_map.shape) self.inds[i * k: i * k + k, 0] = inds[0] self.inds[i * k: i * k + k, 1] = inds[1] # for visualisaiton sum_attn = self.attention_map.sum(0).numpy() mask = np.zeros(sum_attn.shape) mask[thresh[:-1].sum(0) > 0] = 1 sum_attn = sum_attn * mask sum_attn = sum_attn / sum_attn.sum() thresh[-1] = torch.Tensor(sum_attn) # sample num_paths from the chosen pixels. prob_sum = sum_attn[self.inds[:, 0].astype(np.int), self.inds[:, 1].astype(np.int)] prob_sum = prob_sum / prob_sum.sum() new_inds = [] for i in range(self.num_stages): new_inds.extend(np.random.choice(range(self.inds.shape[0]), size=self.num_paths, replace=False, p=prob_sum)) self.inds = self.inds[new_inds] self.inds_normalised = np.zeros(self.inds.shape) self.inds_normalised[:, 0] = self.inds[:, 1] / self.canvas_width self.inds_normalised[:, 1] = self.inds[:, 0] / self.canvas_height self.inds_normalised = self.inds_normalised.tolist() return thresh def set_attention_threshold_map(self): assert self.saliency_model in ["dino", "clip"] if self.saliency_model == "dino": return self.set_inds_dino() elif self.saliency_model == "clip": return self.set_inds_clip() def get_attn(self): return self.attention_map def get_thresh(self): return self.thresh def get_inds(self): return self.inds def get_mask(self): return self.mask def set_random_noise(self, epoch): if epoch % self.args.save_step == 0: self.add_random_noise = False else: self.add_random_noise = "noise" in self.args.augemntations class PainterOptimizer: def __init__(self, args, renderer): self.renderer = renderer self.points_lr = args.lr self.color_lr = args.color_lr self.args = args self.optim_color = args.force_sparse self.width_optim = args.width_optim self.width_optim_global = args.width_optim self.width_lr = args.width_lr self.optimize_points = args.optimize_points self.optimize_points_global = args.optimize_points self.points_optim = None self.width_optimizer = None self.mlp_width_weights_path = args.mlp_width_weights_path self.mlp_points_weights_path = args.mlp_points_weights_path self.load_points_opt_weights = args.load_points_opt_weights # self.only_width = args.only_width def turn_off_points_optim(self): self.optimize_points = False def switch_opt(self): self.width_optim = not self.width_optim self.optimize_points = not self.optimize_points def init_optimizers(self): if self.width_optim: points_params, width_params = self.renderer.parameters() self.width_optimizer = torch.optim.Adam(width_params, lr=self.width_lr) if self.mlp_width_weights_path != "none": checkpoint = torch.load(self.mlp_width_weights_path) self.width_optimizer.load_state_dict(checkpoint['optimizer_state_dict']) print("optimizer checkpoint loaded from ", self.mlp_width_weights_path) else: points_params = self.renderer.parameters() if self.optimize_points: self.points_optim = torch.optim.Adam(points_params, lr=self.points_lr) if self.mlp_points_weights_path != "none" and self.load_points_opt_weights: checkpoint = torch.load(self.mlp_points_weights_path) self.points_optim.load_state_dict(checkpoint['optimizer_state_dict']) print("optimizer checkpoint loaded from ", self.mlp_points_weights_path) if self.optim_color: self.color_optim = torch.optim.Adam(self.renderer.set_color_parameters(), lr=self.color_lr) def zero_grad_(self): if self.optimize_points: self.points_optim.zero_grad() if self.width_optim: self.width_optimizer.zero_grad() if self.optim_color: self.color_optim.zero_grad() def step_(self): if self.optimize_points: self.points_optim.step() if self.width_optim: self.width_optimizer.step() if self.optim_color: self.color_optim.step() def get_lr(self, optim="points"): if optim == "points" and self.optimize_points_global: return self.points_optim.param_groups[0]['lr'] if optim == "width" and self.width_optim_global: return self.width_optimizer.param_groups[0]['lr'] else: return None def get_points_optim(self): return self.points_optim def get_width_optim(self): return self.width_optimizer class LinearDecayLR: def __init__(self, decay_every, decay_ratio): self.decay_every = decay_every self.decay_ratio = decay_ratio def __call__(self, n): decay_time = n // self.decay_every decay_step = n % self.decay_every lr_s = self.decay_ratio ** decay_time lr_e = self.decay_ratio ** (decay_time + 1) r = decay_step / self.decay_every lr = lr_s * (1 - r) + lr_e * r return lr def interpret(image, clip_model, device): # virtual forward to get attention map images = image.repeat(1, 1, 1, 1) _ = clip_model.encode_image(images) # ensure `attn_probs` in attention is not empty clip_model.zero_grad() image_attn_blocks = list(dict(clip_model.visual.transformer.resblocks.named_children()).values()) # create R to store attention map num_tokens = image_attn_blocks[0].attn_probs.shape[-1] R = torch.eye(num_tokens, num_tokens, dtype=image_attn_blocks[0].attn_probs.dtype).to(device) R = R.unsqueeze(0).expand(1, num_tokens, num_tokens) cams = [] for i, blk in enumerate(image_attn_blocks): # 12 attention blocks cam = blk.attn_probs.detach() # attn_probs shape: [12, 50, 50] # each patch is 7x7 so we have 49 pixels + 1 for positional encoding cam = cam.reshape(1, -1, cam.shape[-1], cam.shape[-1]) cam = cam.clamp(min=0) cam = cam.clamp(min=0).mean(dim=1) # mean of the 12 something cams.append(cam) R = R + torch.bmm(cam, R) cams_avg = torch.cat(cams) # [12, 50, 50] cams_avg = cams_avg[:, 0, 1:] # [12, 49] image_relevance = cams_avg.mean(dim=0).unsqueeze(0) # [1, 49] image_relevance = image_relevance.reshape(1, 1, 7, 7) # [1, 1, 7, 7] # interpolate: [1, 1, 7, 7] -> [1, 3, 224, 224] image_relevance = torch.nn.functional.interpolate(image_relevance, size=224, mode='bicubic') image_relevance = image_relevance.reshape(224, 224).data.cpu().numpy().astype(np.float32) # normalize the tensor to [0, 1] image_relevance = (image_relevance - image_relevance.min()) / (image_relevance.max() - image_relevance.min()) return image_relevance class MLP(nn.Module): def __init__(self, num_strokes, num_cp, width_optim=False): super().__init__() outdim = 1000 self.width_optim = width_optim self.layers_points = nn.Sequential( nn.Flatten(), nn.Linear(num_strokes * num_cp * 2, outdim), nn.SELU(inplace=True), nn.Linear(outdim, outdim), nn.SELU(inplace=True), nn.Linear(outdim, num_strokes * num_cp * 2), ) def forward(self, x, widths=None): '''Forward pass''' deltas = self.layers_points(x) # if self.width_optim: # return x.flatten() + 0.1 * deltas, self.layers_width(widths) return x.flatten() + 0.1 * deltas class WidthMLP(nn.Module): def __init__(self, num_strokes, num_cp, width_optim=False): super().__init__() outdim = 1000 self.width_optim = width_optim self.layers_width = nn.Sequential( nn.Linear(num_strokes, outdim), nn.SELU(inplace=True), nn.Linear(outdim, outdim), nn.SELU(inplace=True), nn.Linear(outdim, num_strokes), nn.Sigmoid() ) def forward(self, widths=None): '''Forward pass''' return self.layers_width(widths) def init_weights(m): if isinstance(m, nn.Linear): torch.nn.init.xavier_uniform(m.weight) m.bias.data.fill_(0.01)