tensorboard updates

train.py

@@ -228,9 +228,9 @@ def train(cfg,
             # Plot images with bounding boxes
             if epoch == 0 and i == 0:
                 fname = 'train_batch%g.jpg' % i
-                fig_data = plot_images(imgs=imgs, targets=targets, paths=paths, fname=fname)
+                plot_images(imgs=imgs, targets=targets, paths=paths, fname=fname)
                 if tb_writer:
-                    tb_writer.add_image(fname, fig_data, dataformats='HWC')
+                    tb_writer.add_image(fname, cv2.imread(fname)[:, :, ::-1], dataformats='HWC')
 
             # Hyperparameter burn-in
             # n_burn = nb - 1  # min(nb // 5 + 1, 1000)  # number of burn-in batches

utils/utils.py

@@ -697,9 +697,7 @@ def plot_images(imgs, targets, paths=None, fname='images.jpg'):
             plt.title(s[:min(len(s), 40)], fontdict={'size': 8})  # limit to 40 characters
     fig.tight_layout()
     fig.savefig(fname, dpi=200)
-    fig_image = fig_to_data(fig)
     plt.close()
-    return fig_image
 
 
 def plot_test_txt():  # from utils.utils import *; plot_test()
@@ -816,9 +814,3 @@ def plot_results_orig(start=0, stop=0):  # from utils.utils import *; plot_resul
 def version_to_tuple(version):
     # Used to compare versions of library
     return tuple(map(int, (version.split("."))))
-
-
-def fig_to_data(fig):
-    # Converts a matplotlib fig to 3D numpy array (fig is a matplotlib figure)
-    fig.canvas.draw()
-    return np.array(fig.canvas.renderer.buffer_rgba())[:, :, :3]  # RGB image