import numpy as np import torch import torch.nn.functional as F import imageio import os from skimage.draw import disk as circle #circle import matplotlib.pyplot as plt import collections class Logger: def __init__(self, log_dir, checkpoint_freq=100, visualizer_params=None, zfill_num=8, log_file_name='log.txt'): self.loss_list = [] self.cpk_dir = log_dir self.visualizations_dir = os.path.join(log_dir, 'train-vis') if not os.path.exists(self.visualizations_dir): os.makedirs(self.visualizations_dir) self.log_file = open(os.path.join(log_dir, log_file_name), 'a') self.zfill_num = zfill_num self.visualizer = Visualizer(**visualizer_params) self.checkpoint_freq = checkpoint_freq self.epoch = 0 self.best_loss = float('inf') self.names = None def log_scores(self, loss_names): loss_mean = np.array(self.loss_list).mean(axis=0) loss_string = "; ".join(["%s - %.5f" % (name, value) for name, value in zip(loss_names, loss_mean)]) loss_string = str(self.epoch).zfill(self.zfill_num) + ") " + loss_string print(loss_string, file=self.log_file) self.loss_list = [] self.log_file.flush() def visualize_rec(self, inp, out): image = self.visualizer.visualize(inp['driving'], inp['source'], out) imageio.imsave(os.path.join(self.visualizations_dir, "%s-rec.png" % str(self.epoch).zfill(self.zfill_num)), image) def save_cpk(self, emergent=False): cpk = {k: v.state_dict() for k, v in self.models.items()} cpk['epoch'] = self.epoch cpk_path = os.path.join(self.cpk_dir, '%s-checkpoint.pth.tar' % str(self.epoch).zfill(self.zfill_num)) if not (os.path.exists(cpk_path) and emergent): torch.save(cpk, cpk_path) @staticmethod def load_cpk(checkpoint_path, generator=None, discriminator=None, kp_detector=None, optimizer_generator=None, optimizer_discriminator=None, optimizer_kp_detector=None): checkpoint = torch.load(checkpoint_path) if generator is not None: generator.load_state_dict(checkpoint['generator']) if kp_detector is not None: kp_detector.load_state_dict(checkpoint['kp_detector']) if discriminator is not None: try: discriminator.load_state_dict(checkpoint['discriminator']) except: print ('No discriminator in the state-dict. Dicriminator will be randomly initialized') if optimizer_generator is not None: optimizer_generator.load_state_dict(checkpoint['optimizer_generator']) if optimizer_discriminator is not None: try: optimizer_discriminator.load_state_dict(checkpoint['optimizer_discriminator']) except RuntimeError as e: print ('No discriminator optimizer in the state-dict. Optimizer will be not initialized') if optimizer_kp_detector is not None: optimizer_kp_detector.load_state_dict(checkpoint['optimizer_kp_detector']) return checkpoint['epoch'] def __enter__(self): return self def __exit__(self, exc_type, exc_val, exc_tb): if 'models' in self.__dict__: self.save_cpk() self.log_file.close() def log_iter(self, losses): losses = collections.OrderedDict(losses.items()) if self.names is None: self.names = list(losses.keys()) self.loss_list.append(list(losses.values())) def log_epoch(self, epoch, models, inp, out): self.epoch = epoch self.models = models if (self.epoch + 1) % self.checkpoint_freq == 0: self.save_cpk() self.log_scores(self.names) self.visualize_rec(inp, out) class Visualizer: def __init__(self, kp_size=5, draw_border=False, colormap='gist_rainbow'): self.kp_size = kp_size self.draw_border = draw_border self.colormap = plt.get_cmap(colormap) def draw_image_with_kp(self, image, kp_array): image = np.copy(image) spatial_size = np.array(image.shape[:2][::-1])[np.newaxis] kp_array = spatial_size * (kp_array + 1) / 2 num_kp = kp_array.shape[0] for kp_ind, kp in enumerate(kp_array): rr, cc = circle(kp[1], kp[0], self.kp_size, shape=image.shape[:2]) image[rr, cc] = np.array(self.colormap(kp_ind / num_kp))[:3] return image def create_image_column_with_kp(self, images, kp): image_array = np.array([self.draw_image_with_kp(v, k) for v, k in zip(images, kp)]) return self.create_image_column(image_array) def create_image_column(self, images): if self.draw_border: images = np.copy(images) images[:, :, [0, -1]] = (1, 1, 1) images[:, :, [0, -1]] = (1, 1, 1) return np.concatenate(list(images), axis=0) def create_image_grid(self, *args): out = [] for arg in args: if type(arg) == tuple: out.append(self.create_image_column_with_kp(arg[0], arg[1])) else: out.append(self.create_image_column(arg)) return np.concatenate(out, axis=1) def visualize(self, driving, source, out): images = [] # Source image with keypoints source = source.data.cpu() kp_source = out['kp_source']['value'].data.cpu().numpy() source = np.transpose(source, [0, 2, 3, 1]) images.append((source, kp_source)) # Equivariance visualization if 'transformed_frame' in out: transformed = out['transformed_frame'].data.cpu().numpy() transformed = np.transpose(transformed, [0, 2, 3, 1]) transformed_kp = out['transformed_kp']['value'].data.cpu().numpy() images.append((transformed, transformed_kp)) # Driving image with keypoints kp_driving = out['kp_driving']['value'].data.cpu().numpy() driving = driving.data.cpu().numpy() driving = np.transpose(driving, [0, 2, 3, 1]) images.append((driving, kp_driving)) # Deformed image if 'deformed' in out: deformed = out['deformed'].data.cpu().numpy() deformed = np.transpose(deformed, [0, 2, 3, 1]) images.append(deformed) # Result with and without keypoints prediction = out['prediction'].data.cpu().numpy() prediction = np.transpose(prediction, [0, 2, 3, 1]) if 'kp_norm' in out: kp_norm = out['kp_norm']['value'].data.cpu().numpy() images.append((prediction, kp_norm)) images.append(prediction) ## Occlusion map if 'occlusion_map' in out: occlusion_map = out['occlusion_map'].data.cpu().repeat(1, 3, 1, 1) occlusion_map = F.interpolate(occlusion_map, size=source.shape[1:3]).numpy() occlusion_map = np.transpose(occlusion_map, [0, 2, 3, 1]) images.append(occlusion_map) # Deformed images according to each individual transform if 'sparse_deformed' in out: full_mask = [] for i in range(out['sparse_deformed'].shape[1]): image = out['sparse_deformed'][:, i].data.cpu() image = F.interpolate(image, size=source.shape[1:3]) mask = out['mask'][:, i:(i+1)].data.cpu().repeat(1, 3, 1, 1) mask = F.interpolate(mask, size=source.shape[1:3]) image = np.transpose(image.numpy(), (0, 2, 3, 1)) mask = np.transpose(mask.numpy(), (0, 2, 3, 1)) if i != 0: color = np.array(self.colormap((i - 1) / (out['sparse_deformed'].shape[1] - 1)))[:3] else: color = np.array((0, 0, 0)) color = color.reshape((1, 1, 1, 3)) images.append(image) if i != 0: images.append(mask * color) else: images.append(mask) full_mask.append(mask * color) images.append(sum(full_mask)) image = self.create_image_grid(*images) image = (255 * image).astype(np.uint8) return image