class Logger(object): def __init__(self, log_dir): """Create a summary writer logging to log_dir.""" self.writer = SummaryWriter(log_dir=log_dir) def scalar_summary(self, tag, value, step): """Log a scalar variable.""" self.writer.add_summary(tag, value, step) def image_summary(self, tag, images, step): """Log a list of images.""" self.writer.add_image(tag, images, step) def graph_summary(self, model, dummy_input): self.writer.add_graph(model, (dummy_input, ))
class Logger(object): def __init__(self, log_dir, step=0): """Create a summary writer logging to log_dir.""" log_dir = os.path.join('log', log_dir) print('logging at: ', log_dir) self.writer = SummaryWriter(log_dir) self.step = step def incstep(self): self.step += 1 def scalar_summary(self, data): """Log a scalar variable.""" for tag, value in data.items(): self.writer.add_scalar(tag, value, self.step) def image_summary(self, tag, images, step): """Log a list of images.""" img_summaries = [] for i, img in enumerate(images): # Write the image to a string try: s = StringIO() except: s = BytesIO() scipy.misc.toimage(img).save(s, format="png") # Create an Image object img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(), height=img.shape[0], width=img.shape[1]) # Create a Summary value img_summaries.append( tf.Summary.Value(tag='%s/%d' % (tag, i), image=img_sum)) # Create and write Summary summary = tf.Summary(value=img_summaries) self.writer.add_summary(summary, step) def hist_summary(self, tag, values): """Log a histogram of the tensor of values.""" self.writer.add_histogram(tag, values, self.step, bins="auto")
class Logger(object): def __init__(self, log_dir): """Create a summary writer logging to log_dir.""" self.writer = SummaryWriter(log_dir=log_dir) def add_scalars(self, tag, value_dict, step): """Log a scalar variable.""" self.writer.add_scalars(tag, value_dict, step) def image_summary(self, tag, images, step): """Log a list of images.""" img_summaries = [] for i, img in enumerate(images): # Write the image to a string try: s = StringIO() except: s = BytesIO() scipy.misc.toimage(img).save(s, format="png") # Create an Image object img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(), height=img.shape[0], width=img.shape[1]) # Create a Summary value img_summaries.append( tf.Summary.Value(tag='%s/%d' % (tag, i), image=img_sum)) # Create and write Summary summary = tf.Summary(value=img_summaries) self.writer.add_summary(summary, step) def add_hist(self, tag, values, step, bins=1000): """Log a histogram of the tensor of values.""" # self.writer.add_histogram('hist', array, iteration) pass
class Logger(object): def __init__(self, log_dir): """Create a summary writer logging to log_dir.""" log_dir = os.path.join('log',log_dir) if not os.path.exists(log_dir): print('log dir', log_dir) #self.writer = tf.compat.v1.summary.FileWriter(log_dir) self.writer = SummaryWriter(log_dir) else: print('This training session name already exists. Please use a different name or delete it') self.step = 0 def incstep(self): self.step += 1 def scalar_summary(self, data): """Log a scalar variable.""" for tag, value in data.items(): self.writer.add_scalar(tag,value,self.step) #summary = tf.compat.v1.Summary(value=[tf.compat.v1.Summary.Value(tag=tag, simple_value=value)]) #self.writer.add_summary(summary, self.step) def image_summary(self, tag, images, step): """Log a list of images.""" img_summaries = [] for i, img in enumerate(images): # Write the image to a string try: s = StringIO() except: s = BytesIO() scipy.misc.toimage(img).save(s, format="png") # Create an Image object img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(), height=img.shape[0], width=img.shape[1]) # Create a Summary value img_summaries.append(tf.Summary.Value(tag='%s/%d' % (tag, i), image=img_sum)) # Create and write Summary summary = tf.Summary(value=img_summaries) self.writer.add_summary(summary, step) def hist_summary(self, tag, values, bins=5): """Log a histogram of the tensor of values.""" ''' # Create a histogram using numpy counts, bin_edges = np.histogram(values, bins=bins) # Fill the fields of the histogram proto hist = tf.compat.v1.HistogramProto() hist.min = float(np.min(values)) hist.max = float(np.max(values)) hist.num = int(np.prod(values.shape)) hist.sum = float(np.sum(values)) hist.sum_squares = float(np.sum(values**2)) # Drop the start of the first bin bin_edges = bin_edges[1:] # Add bin edges and counts for edge in bin_edges: hist.bucket_limit.append(edge) for c in counts: hist.bucket.append(c) ''' # Create and write Summary print('hello') self.writer.add_histogram(tag,values.clone().cpu().data.numpy(),self.step) print('hello') '''
class Visualizer: def __init__(self, opt): self.opt = opt self.tf_log = opt.isTrain and opt.tf_log self.tensorboard_log = opt.tensorboard_log self.win_size = opt.display_winsize self.name = opt.name if self.tensorboard_log: if self.opt.isTrain: self.log_dir = os.path.join(opt.checkpoints_dir, opt.name, "logs") if not os.path.exists(self.log_dir): os.makedirs(self.log_dir) self.writer = SummaryWriter(log_dir=self.log_dir) else: print("hi :)") self.log_dir = os.path.join(opt.checkpoints_dir, opt.name, opt.results_dir) if not os.path.exists(self.log_dir): os.makedirs(self.log_dir) if opt.isTrain: self.log_name = os.path.join(opt.checkpoints_dir, opt.name, "loss_log.txt") with open(self.log_name, "a") as log_file: now = time.strftime("%c") log_file.write("================ Training Loss (%s) ================\n" % now) # |visuals|: dictionary of images to display or save def display_current_results(self, visuals, epoch, step): all_tensor = [] if self.tensorboard_log: for key, tensor in visuals.items(): all_tensor.append((tensor.data.cpu() + 1) / 2) output = torch.cat(all_tensor, 0) img_grid = vutils.make_grid(output, nrow=self.opt.batchSize, padding=0, normalize=False) if self.opt.isTrain: self.writer.add_image("Face_SPADE/training_samples", img_grid, step) else: vutils.save_image( output, os.path.join(self.log_dir, str(step) + ".png"), nrow=self.opt.batchSize, padding=0, normalize=False, ) # errors: dictionary of error labels and values def plot_current_errors(self, errors, step): if self.tf_log: for tag, value in errors.items(): value = value.mean().float() summary = self.tf.Summary(value=[self.tf.Summary.Value(tag=tag, simple_value=value)]) self.writer.add_summary(summary, step) if self.tensorboard_log: self.writer.add_scalar("Loss/GAN_Feat", errors["GAN_Feat"].mean().float(), step) self.writer.add_scalar("Loss/VGG", errors["VGG"].mean().float(), step) self.writer.add_scalars( "Loss/GAN", { "G": errors["GAN"].mean().float(), "D": (errors["D_Fake"].mean().float() + errors["D_real"].mean().float()) / 2, }, step, ) # errors: same format as |errors| of plotCurrentErrors def print_current_errors(self, epoch, i, errors, t): message = "(epoch: %d, iters: %d, time: %.3f) " % (epoch, i, t) for k, v in errors.items(): v = v.mean().float() message += "%s: %.3f " % (k, v) print(message) with open(self.log_name, "a") as log_file: log_file.write("%s\n" % message) def convert_visuals_to_numpy(self, visuals): for key, t in visuals.items(): tile = self.opt.batchSize > 8 if "input_label" == key: t = util.tensor2label(t, self.opt.label_nc + 2, tile=tile) ## B*H*W*C 0-255 numpy else: t = util.tensor2im(t, tile=tile) visuals[key] = t return visuals # save image to the disk def save_images(self, webpage, visuals, image_path): visuals = self.convert_visuals_to_numpy(visuals) image_dir = webpage.get_image_dir() short_path = ntpath.basename(image_path[0]) name = os.path.splitext(short_path)[0] webpage.add_header(name) ims = [] txts = [] links = [] for label, image_numpy in visuals.items(): image_name = os.path.join(label, "%s.png" % (name)) save_path = os.path.join(image_dir, image_name) util.save_image(image_numpy, save_path, create_dir=True) ims.append(image_name) txts.append(label) links.append(image_name) webpage.add_images(ims, txts, links, width=self.win_size)
class Visualizer(): def __init__(self, opt): self.opt = opt self.tf_log = opt.isTrain and opt.tf_log self.tensorboard_log = opt.tensorboard_log self.use_html = opt.isTrain and not opt.no_html self.win_size = opt.display_winsize self.name = opt.name if self.tensorboard_log: if self.opt.isTrain: self.log_dir = os.path.join(opt.checkpoints_dir, opt.name, 'logs') if not os.path.exists(self.log_dir): os.makedirs(self.log_dir) self.writer = SummaryWriter(log_dir=self.log_dir) else: print("hi :)") self.log_dir = os.path.join(opt.checkpoints_dir, opt.name, opt.results_dir) if not os.path.exists(self.log_dir): os.makedirs(self.log_dir) # import tensorflow as tf # self.tf = tf # self.log_dir = os.path.join(opt.checkpoints_dir, opt.name, 'logs') # self.writer = tf.summary.FileWriter(self.log_dir) if opt.isTrain: self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt') with open(self.log_name, "a") as log_file: now = time.strftime("%c") log_file.write( '================ Training Loss (%s) ================\n' % now) # |visuals|: dictionary of images to display or save def display_current_results(self, visuals, epoch, step): all_tensor = [] if self.tensorboard_log: for key, tensor in visuals.items(): if key == 'input_label': tile = self.opt.batchSize > 1 t = util.tensor2label(tensor, self.opt.label_nc + 2, tile=tile) ## B*H*W*3 0-255 numpy t = np.transpose(t, (0, 3, 1, 2)) all_tensor.append(torch.tensor(t).float() / 255) else: all_tensor.append((tensor.data.cpu() + 1) / 2) output = torch.cat(all_tensor, 0) img_grid = vutils.make_grid(output, nrow=self.opt.batchSize, padding=0, normalize=False) if self.opt.isTrain: self.writer.add_image('Face_SPADE/training_samples', img_grid, step) else: # self.writer.add_image('Face_SPADE/test_samples',img_grid,step) vutils.save_image(output, os.path.join(self.log_dir, str(step) + '.png'), nrow=self.opt.batchSize, padding=0, normalize=False) ## convert tensors to numpy arrays # visuals = self.convert_visuals_to_numpy(visuals) # if self.tf_log: # show images in tensorboard output # img_summaries = [] # for label, image_numpy in visuals.items(): # # Write the image to a string # try: # s = StringIO() # except: # s = BytesIO() # if len(image_numpy.shape) >= 4: # image_numpy = image_numpy[0] # scipy.misc.toimage(image_numpy).save(s, format="jpeg") # # Create an Image object # img_sum = self.tf.Summary.Image(encoded_image_string=s.getvalue(), height=image_numpy.shape[0], width=image_numpy.shape[1]) # # Create a Summary value # img_summaries.append(self.tf.Summary.Value(tag=label, image=img_sum)) # # Create and write Summary # summary = self.tf.Summary(value=img_summaries) # self.writer.add_summary(summary, step) # errors: dictionary of error labels and values def plot_current_errors(self, errors, step): if self.tf_log: for tag, value in errors.items(): value = value.mean().float() summary = self.tf.Summary( value=[self.tf.Summary.Value(tag=tag, simple_value=value)]) self.writer.add_summary(summary, step) if self.tensorboard_log: # self.writer.add_scalar('G',errors['GAN'].item(),step) self.writer.add_scalar('Loss/GAN_Feat', errors['GAN_Feat'].mean().float(), step) self.writer.add_scalar('Loss/VGG', errors['VGG'].mean().float(), step) # self.writer.add_scalar('D',(errors['D_Fake'].item()+errors['D_real'].item())/2,step) self.writer.add_scalars( 'Loss/GAN', { 'G': errors['GAN'].mean().float(), 'D': (errors['D_Fake'].mean().float() + errors['D_real'].mean().float()) / 2 }, step) # errors: same format as |errors| of plotCurrentErrors def print_current_errors(self, epoch, i, errors, t): message = '(epoch: %d, iters: %d, time: %.3f) ' % (epoch, i, t) for k, v in errors.items(): #print(v) #if v != 0: v = v.mean().float() message += '%s: %.3f ' % (k, v) print(message) with open(self.log_name, "a") as log_file: log_file.write('%s\n' % message) def convert_visuals_to_numpy(self, visuals): for key, t in visuals.items(): tile = self.opt.batchSize > 8 if 'input_label' == key: t = util.tensor2label(t, self.opt.label_nc + 2, tile=tile) ## B*H*W*C 0-255 numpy else: t = util.tensor2im(t, tile=tile) visuals[key] = t return visuals # save image to the disk def save_images(self, webpage, visuals, image_path): visuals = self.convert_visuals_to_numpy(visuals) image_dir = webpage.get_image_dir() short_path = ntpath.basename(image_path[0]) name = os.path.splitext(short_path)[0] webpage.add_header(name) ims = [] txts = [] links = [] for label, image_numpy in visuals.items(): image_name = os.path.join(label, '%s.png' % (name)) save_path = os.path.join(image_dir, image_name) util.save_image(image_numpy, save_path, create_dir=True) ims.append(image_name) txts.append(label) links.append(image_name) webpage.add_images(ims, txts, links, width=self.win_size)