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| import pathlib | |
| import random | |
| import omegaconf | |
| import pydiffvg | |
| import numpy as np | |
| import torch | |
| from torch.optim.lr_scheduler import LambdaLR | |
| from torchvision import transforms | |
| from pytorch_svgrender.diffvg_warp import DiffVGState | |
| from pytorch_svgrender.libs.modules.edge_map.DoG import XDoG | |
| from .grad_cam import gradCAM | |
| from . import modified_clip as clip | |
| 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.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.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 = 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 | |
| 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 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 | |
| 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 | |
| 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 set_point_parameters(self): | |
| self.point_vars = [] | |
| # storkes' location optimization | |
| for i, path in enumerate(self.shapes): | |
| if self.optimize_flag[i]: | |
| path.points.requires_grad = True | |
| self.point_vars.append(path.points) | |
| def get_point_parameters(self): | |
| return self.point_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) | |
| def get_color_parameters(self): | |
| return self.color_vars | |
| def save_svg(self, output_dir: str, name: str): | |
| pydiffvg.save_svg(f'{output_dir}/{name}.svg', | |
| self.canvas_width, self.canvas_height, self.shapes, self.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 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, self.device) | |
| del model | |
| return attn_map | |
| def set_attention_map(self): | |
| assert self.saliency_model in ["clip"] | |
| if 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 | |
| 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_attention_threshold_map(self): | |
| assert self.saliency_model in ["clip"] | |
| if 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 | |
| class PainterOptimizer: | |
| def __init__(self, renderer: Painter, num_iter: int, points_lr: float, force_sparse: bool, color_lr: float): | |
| self.renderer = renderer | |
| self.num_iter = num_iter | |
| self.points_lr = points_lr | |
| self.color_lr = color_lr | |
| self.optim_color = force_sparse | |
| self.points_optimizer, self.color_optimizer = None, None | |
| self.scheduler = None | |
| def init_optimizers(self): | |
| # optimizers | |
| self.renderer.set_point_parameters() | |
| self.points_optimizer = torch.optim.Adam(self.renderer.get_point_parameters(), lr=self.points_lr) | |
| if self.optim_color: | |
| self.renderer.set_color_parameters() | |
| self.color_optimizer = torch.optim.Adam(self.renderer.get_color_parameters(), lr=self.color_lr) | |
| # lr schedule | |
| lr_lambda_fn = LinearDecayLR(self.num_iter, 0.4) | |
| self.scheduler = LambdaLR(self.points_optimizer, lr_lambda=lr_lambda_fn, last_epoch=-1) | |
| def update_lr(self): | |
| self.scheduler.step() | |
| def zero_grad_(self): | |
| self.points_optimizer.zero_grad() | |
| if self.optim_color: | |
| self.color_optimizer.zero_grad() | |
| def step_(self): | |
| self.points_optimizer.step() | |
| if self.optim_color: | |
| self.color_optimizer.step() | |
| def get_lr(self): | |
| return self.points_optimizer.param_groups[0]['lr'] | |
| 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 | |