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| import random | |
| import CLIP_.clip as clip | |
| import numpy as np | |
| import pydiffvg | |
| import sketch_utils as utils | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from PIL import Image | |
| from scipy.ndimage.filters import gaussian_filter | |
| from skimage.color import rgb2gray | |
| from skimage.filters import threshold_otsu | |
| from torchvision import transforms | |
| class Painter(torch.nn.Module): | |
| def __init__(self, args, | |
| num_strokes=4, | |
| num_segments=4, | |
| imsize=224, | |
| device=None, | |
| target_im=None, | |
| mask=None): | |
| super(Painter, self).__init__() | |
| self.args = args | |
| self.num_paths = num_strokes | |
| self.num_segments = num_segments | |
| self.width = args.width | |
| self.control_points_per_seg = args.control_points_per_seg | |
| self.opacity_optim = args.force_sparse | |
| self.num_stages = args.num_stages | |
| self.add_random_noise = "noise" in args.augemntations | |
| self.noise_thresh = args.noise_thresh | |
| self.softmax_temp = args.softmax_temp | |
| self.shapes = [] | |
| self.shape_groups = [] | |
| self.device = device | |
| self.canvas_width, self.canvas_height = imsize, imsize | |
| self.points_vars = [] | |
| self.color_vars = [] | |
| self.color_vars_threshold = args.color_vars_threshold | |
| self.path_svg = args.path_svg | |
| self.strokes_per_stage = self.num_paths | |
| self.optimize_flag = [] | |
| # attention related for strokes initialisation | |
| self.attention_init = args.attention_init | |
| self.target_path = args.target | |
| self.saliency_model = args.saliency_model | |
| self.xdog_intersec = args.xdog_intersec | |
| self.mask_object = args.mask_object_attention | |
| self.text_target = args.text_target # for clip gradients | |
| self.saliency_clip_model = args.saliency_clip_model | |
| self.define_attention_input(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 = args.num_iter - 1 | |
| def init_image(self, stage=0): | |
| if stage > 0: | |
| # 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 != "none": | |
| self.canvas_width, self.canvas_height, self.shapes, self.shape_groups = utils.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 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))] | |
| img = self.render_warp() | |
| 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] | |
| # Convert img from HWC to NCHW | |
| img = img.unsqueeze(0) | |
| img = img.permute(0, 3, 1, 2).to(self.device) # NHWC -> NCHW | |
| return img | |
| # utils.imwrite(img.cpu(), '{}/init.png'.format(args.output_dir), gamma=args.gamma, use_wandb=args.use_wandb, wandb_name="init") | |
| def get_image(self): | |
| img = self.render_warp() | |
| 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 get_path(self): | |
| points = [] | |
| self.num_control_points = torch.zeros(self.num_segments, dtype = torch.int32) + (self.control_points_per_seg - 2) | |
| 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 | |
| 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): | |
| 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() | |
| _render = pydiffvg.RenderFunction.apply | |
| # 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)) | |
| scene_args = 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_args) | |
| return img | |
| def parameters(self): | |
| 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) | |
| return self.points_vars | |
| def get_points_parans(self): | |
| return self.points_vars | |
| 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 save_svg(self, output_dir, name): | |
| pydiffvg.save_svg('{}/{}.svg'.format(output_dir, name), self.canvas_width, self.canvas_height, self.shapes, self.shape_groups) | |
| 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.argsort(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 define_attention_input(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], | |
| ]) | |
| self.image_input_attn_clip = data_transforms(target_im).to(self.device) | |
| def clip_attn(self): | |
| model, preprocess = clip.load(self.saliency_clip_model, device=self.device, jit=False) | |
| model.eval().to(self.device) | |
| text_input = clip.tokenize([self.text_target]).to(self.device) | |
| if "RN" in self.saliency_clip_model: | |
| saliency_layer = "layer4" | |
| attn_map = gradCAM( | |
| model.visual, | |
| self.image_input_attn_clip, | |
| 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: | |
| # attn_map = interpret(self.image_input_attn_clip, text_input, model, device=self.device, index=0).astype(np.float32) | |
| attn_map = interpret(self.image_input_attn_clip, text_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_() | |
| im_xdog = xdog(self.image_input_attn_clip[0].permute(1,2,0).cpu().numpy(), k=10) | |
| intersec_map = (1 - im_xdog) * attn_map | |
| attn_map = intersec_map | |
| 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] | |
| print("self.inds",self.inds.shape) | |
| 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_interval == 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 | |
| def init_optimizers(self): | |
| self.points_optim = torch.optim.Adam(self.renderer.parameters(), lr=self.points_lr) | |
| if self.optim_color: | |
| self.color_optim = torch.optim.Adam(self.renderer.set_color_parameters(), lr=self.color_lr) | |
| def update_lr(self, counter): | |
| new_lr = utils.get_epoch_lr(counter, self.args) | |
| for param_group in self.points_optim.param_groups: | |
| param_group["lr"] = new_lr | |
| def zero_grad_(self): | |
| self.points_optim.zero_grad() | |
| if self.optim_color: | |
| self.color_optim.zero_grad() | |
| def step_(self): | |
| self.points_optim.step() | |
| if self.optim_color: | |
| self.color_optim.step() | |
| def get_lr(self): | |
| return self.points_optim.param_groups[0]['lr'] | |
| class Hook: | |
| """Attaches to a module and records its activations and gradients.""" | |
| def __init__(self, module: nn.Module): | |
| self.data = None | |
| self.hook = module.register_forward_hook(self.save_grad) | |
| def save_grad(self, module, input, output): | |
| self.data = output | |
| output.requires_grad_(True) | |
| output.retain_grad() | |
| def __enter__(self): | |
| return self | |
| def __exit__(self, exc_type, exc_value, exc_traceback): | |
| self.hook.remove() | |
| def activation(self) -> torch.Tensor: | |
| return self.data | |
| def gradient(self) -> torch.Tensor: | |
| return self.data.grad | |
| def interpret(image, texts, model, device): | |
| images = image.repeat(1, 1, 1, 1) | |
| res = model.encode_image(images) | |
| model.zero_grad() | |
| image_attn_blocks = list(dict(model.visual.transformer.resblocks.named_children()).values()) | |
| 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 = [] # there are 12 attention blocks | |
| for i, blk in enumerate(image_attn_blocks): | |
| cam = blk.attn_probs.detach() #attn_probs shape is 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, 1, 49 | |
| image_relevance = cams_avg.mean(dim=0).unsqueeze(0) | |
| image_relevance = image_relevance.reshape(1, 1, 7, 7) | |
| 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) | |
| image_relevance = (image_relevance - image_relevance.min()) / (image_relevance.max() - image_relevance.min()) | |
| return image_relevance | |
| # Reference: https://arxiv.org/abs/1610.02391 | |
| def gradCAM( | |
| model: nn.Module, | |
| input: torch.Tensor, | |
| target: torch.Tensor, | |
| layer: nn.Module | |
| ) -> torch.Tensor: | |
| # Zero out any gradients at the input. | |
| if input.grad is not None: | |
| input.grad.data.zero_() | |
| # Disable gradient settings. | |
| requires_grad = {} | |
| for name, param in model.named_parameters(): | |
| requires_grad[name] = param.requires_grad | |
| param.requires_grad_(False) | |
| # Attach a hook to the model at the desired layer. | |
| assert isinstance(layer, nn.Module) | |
| with Hook(layer) as hook: | |
| # Do a forward and backward pass. | |
| output = model(input) | |
| output.backward(target) | |
| grad = hook.gradient.float() | |
| act = hook.activation.float() | |
| # Global average pool gradient across spatial dimension | |
| # to obtain importance weights. | |
| alpha = grad.mean(dim=(2, 3), keepdim=True) | |
| # Weighted combination of activation maps over channel | |
| # dimension. | |
| gradcam = torch.sum(act * alpha, dim=1, keepdim=True) | |
| # We only want neurons with positive influence so we | |
| # clamp any negative ones. | |
| gradcam = torch.clamp(gradcam, min=0) | |
| # Resize gradcam to input resolution. | |
| gradcam = F.interpolate( | |
| gradcam, | |
| input.shape[2:], | |
| mode='bicubic', | |
| align_corners=False) | |
| # Restore gradient settings. | |
| for name, param in model.named_parameters(): | |
| param.requires_grad_(requires_grad[name]) | |
| return gradcam | |
| class XDoG_(object): | |
| def __init__(self): | |
| super(XDoG_, self).__init__() | |
| self.gamma=0.98 | |
| self.phi=200 | |
| self.eps=-0.1 | |
| self.sigma=0.8 | |
| self.binarize=True | |
| def __call__(self, im, k=10): | |
| if im.shape[2] == 3: | |
| im = rgb2gray(im) | |
| imf1 = gaussian_filter(im, self.sigma) | |
| imf2 = gaussian_filter(im, self.sigma * k) | |
| imdiff = imf1 - self.gamma * imf2 | |
| imdiff = (imdiff < self.eps) * 1.0 + (imdiff >= self.eps) * (1.0 + np.tanh(self.phi * imdiff)) | |
| imdiff -= imdiff.min() | |
| imdiff /= imdiff.max() | |
| if self.binarize: | |
| th = threshold_otsu(imdiff) | |
| imdiff = imdiff >= th | |
| imdiff = imdiff.astype('float32') | |
| return imdiff | |