Pytorch Tensorboard SummaryWriter.add_video() Produces Bad Videos
I’m trying to create a video by generating a sequence of 500 matplotlib plots, converting each to a numpy array, stacking them and then passing them to a SummaryWriter()'s add_video(). When I do this, the colorbar is converted from colored to black &amp; white, and only a small number (~3-4) of the matplotlib plots are repeated. I confirmed that my numpy arrays are correct by using them to recreate a matplotlib figure.

My input tensor has shape (B,C,T,H,W), dtype np.uint8, and values between [0, 255].

Minimal working example below. To be clear, the code runs without any errors. My problem is that the resulting video is wrong.

import matplotlib.pyplot as plt
import numpy as np
import torch
from torch.utils.tensorboard import SummaryWriter


tensorboard_writer = SummaryWriter()
print(tensorboard_writer.get_logdir())


def fig2data(fig):

    # draw the renderer
    fig.canvas.draw()

    # Get the RGB buffer from the figure
    data = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
    return data


size = 500
x = np.random.uniform(0, 2., size=500)
y = np.random.uniform(0, 2., size=500)
trajectory_len = len(x)
trajectory_indices = np.arange(trajectory_len)
width, height = 3, 2

# tensorboard takes video of shape (B,C,T,H,W)
video_array = np.zeros(
    shape=(1, 3, trajectory_len, height*100, width*100),
    dtype=np.uint8)

for trajectory_idx in trajectory_indices:

    fig, axes = plt.subplots(
        1,
        2,
        figsize=(width, height),
        gridspec_kw={'width_ratios': [1, 0.05]})
    fig.suptitle('Example Trajectory')
    # plot the first trajectory
    sc = axes[0].scatter(
        x=[x[trajectory_idx]],
        y=[y[trajectory_idx]],
        c=[trajectory_indices[trajectory_idx]],
        s=4,
        vmin=0,
        vmax=trajectory_len,
        cmap=plt.cm.jet)

    axes[0].set_xlim(-0.25, 2.25)
    axes[0].set_ylim(-0.25, 2.25)

    colorbar = fig.colorbar(sc, cax=axes[1])
    colorbar.set_label('Trajectory Index Number')

    # extract numpy array of figure
    data = fig2data(fig)

    # UNCOMMENT IF YOU WANT TO VERIFY THAT THE NUMPY ARRAY WAS CORRECTLY EXTRACTED
    # plt.show()
    # fig2 = plt.figure()
    # ax2 = fig2.add_subplot(111, frameon=False)
    # ax2.imshow(data)
    # plt.show()

    # close figure to save memory
    plt.close(fig=fig)

    video_array[0, :, trajectory_idx, :, :] = np.transpose(data, (2, 0, 1))

# tensorboard takes video_array of shape (B,C,T,H,W)
tensorboard_writer.add_video(
    tag='sampled_trajectory',
    vid_tensor=torch.from_numpy(video_array),
    global_step=0,
    fps=4)

print('Added video')

tensorboard_writer.close()



According to pytorch docs, the video tensor should have shape (N,T,C,H,W), which I think it means: batch, time, channels, height and width. You said your tensor has shape (B,C,T,H,W). So it seems as your channels and time axis are swapped.
