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GCANet/GCANet_train/tf_visualizer.py

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+import numpy as np
+import os
+import ntpath
+import time
+import utils
+from scipy.misc import imresize
+from tensorboardX import SummaryWriter
+
+
+class TFVisualizer():
+    def __init__(self, opt):
+        self.tf_visualizer = SummaryWriter(os.path.join(opt.logDir, opt.name))
+        self.opt = opt
+        self.saved = False
+        self.ncols = 4
+
+        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)
+
+    def reset(self):
+        self.saved = False
+
+    # |visuals|: dictionary of images to display or save
+    def display_current_results(self, visuals, iter_mark, epoch, save_result):
+        for label, image in visuals.items():
+            img_gid = utils.tensor2imgrid(image)
+            self.tf_visualizer.add_image(label, img_gid, iter_mark)
+
+    # losses: dictionary of error labels and values
+    def plot_current_losses(self, iter_mark, losses):
+        # for label, loss in losses.items():
+        #     self.tf_visualizer.add_scalar(label, loss, iter_mark)
+        self.tf_visualizer.add_scalars('training loss', losses, iter_mark)
+
+    def print_logs(self, message):
+        print(message)
+        with open(self.log_name, "a") as log_file:
+            log_file.write('%s\n' % message)
+