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import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.use('Agg')
import seaborn as sns
import numpy as np
sns.set_style('darkgrid')
import imageio
def make_rl_gif(action_logits, action_probs, actions, values, rewards, pre_activations, post_activations, inputs, filename):
n_steps = len(pre_activations)
pre_activations = pre_activations[:,0,:]
post_activations = post_activations[:,0,:]
if action_logits.shape[1] == 5:
class_labels = ['W', 'U', 'D', 'L', 'R']
elif action_logits.shape[1] == 2:
class_labels = ['L', 'R']
else:
class_labels = [str(i) for i in range(action_logits.shape[1])]
max_target = len(class_labels)
figscale = 0.28
frames = []
n_neurons_to_visualise = 15
# Create mosaic layout
mosaic = [['img_data', 'img_data', 'img_data', 'img_data', 'action_logits', 'action_logits', 'action_log_probs', 'action_log_probs'] for _ in range(2)] + \
[['img_data', 'img_data', 'img_data', 'img_data', 'action_logits', 'action_logits', 'action_log_probs', 'action_log_probs'] for _ in range(2)] + \
[['value', 'value', 'value', 'value', 'value', 'value', 'value', 'value']] + \
[['reward', 'reward', 'reward', 'reward', 'reward', 'reward', 'reward', 'reward']] + \
[[f'trace_{ti}', f'trace_{ti}', f'trace_{ti}', f'trace_{ti}', f'trace_{ti}', f'trace_{ti}', f'trace_{ti}', f'trace_{ti}'] for ti in range(n_neurons_to_visualise)]
# Main plotting loop
for stepi in range(n_steps):
fig_gif, axes_gif = plt.subplot_mosaic(mosaic=mosaic, figsize=(31*figscale*8/4, 76*figscale))
# Plot action logits
these_action_logits = np.array(action_logits)[:, :max_target]
colors = ['black' if i == actions[stepi] else ('b' if e >= 0 else 'r')
for i, e in enumerate(these_action_logits[stepi])]
sort_idxs = np.arange(len(these_action_logits[stepi]))
bars = axes_gif['action_logits'].bar(np.arange(len(these_action_logits[stepi][sort_idxs])), these_action_logits[stepi][sort_idxs], color=np.array(colors)[sort_idxs],width=0.9, alpha=0.5)
axes_gif['action_logits'].axis('off')
for bar, label in zip(bars, class_labels):
x = bar.get_x() + bar.get_width() / 2
axes_gif['action_logits'].annotate(label, xy=(x, 0), xytext=(1, 0),
textcoords="offset points",
ha='center', va='bottom', rotation=90)
axes_gif['action_logits'].set_ylim([np.min(these_action_logits), np.max(these_action_logits)])
# Plot action probs
these_action_log_probs = np.array(action_probs)[:, :max_target]
colors = ['black' if i == actions[stepi] else ('b' if e >= 0 else 'r')
for i, e in enumerate(these_action_log_probs[stepi])]
sort_idxs = np.arange(len(these_action_log_probs[stepi]))
bars = axes_gif['action_log_probs'].bar(np.arange(len(these_action_log_probs[stepi][sort_idxs])), these_action_log_probs[stepi][sort_idxs], color=np.array(colors)[sort_idxs],width=0.9, alpha=0.5)
axes_gif['action_log_probs'].axis('off')
for bar, label in zip(bars, class_labels):
x = bar.get_x() + bar.get_width() / 2
axes_gif['action_log_probs'].annotate(label, xy=(x, 0), xytext=(1, 0),
textcoords="offset points",
ha='center', va='bottom', rotation=90)
axes_gif['action_log_probs'].set_ylim([0,1])
# Plot value trace
ax_value = axes_gif['value']
ax_value.plot(np.arange(n_steps), values, 'b-', linewidth=2)
ax_value.axvline(x=stepi, color='k', linewidth=2, alpha=0.3)
ax_value.set_xticklabels([])
ax_value.set_yticklabels([])
ax_value.grid(False)
ax_value.set_xlim([0, n_steps-1])
# Plot reward trace
ax_reward = axes_gif['reward']
ax_reward.plot(np.arange(n_steps), rewards, 'g-', linewidth=2)
ax_reward.axvline(x=stepi, color='k', linewidth=2, alpha=0.3)
ax_reward.set_xticklabels([])
ax_reward.set_yticklabels([])
ax_reward.grid(False)
ax_reward.set_xlim([0, n_steps-1])
# Plot neuron traces
for neuroni in range(n_neurons_to_visualise):
ax = axes_gif[f'trace_{neuroni}']
pre_activation = pre_activations[:, neuroni]
post_activation = post_activations[:, neuroni]
ax_pre = ax.twinx()
pre_min, pre_max = np.min(pre_activation), np.max(pre_activation)
post_min, post_max = np.min(post_activation), np.max(post_activation)
ax_pre.plot(np.arange(n_steps), pre_activation,
color='grey',
linestyle='--',
linewidth=1,
alpha=0.4,
label='Pre-activation')
color = 'blue' if neuroni % 2 else 'red'
ax.plot(np.arange(n_steps), post_activation,
color=color,
linestyle='-',
linewidth=2,
alpha=1.0,
label='Post-activation')
ax.set_xlim([0, n_steps-1])
ax_pre.set_xlim([0, n_steps-1])
ax.set_ylim([post_min, post_max])
ax_pre.set_ylim([pre_min, pre_max])
ax.axvline(x=stepi, color='black', linewidth=1, alpha=0.5)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.grid(False)
ax_pre.set_xticklabels([])
ax_pre.set_yticklabels([])
ax_pre.grid(False)
ax.set_xlim([0, n_steps-1])
ax.set_xticklabels([])
ax.grid(False)
# Show input image
this_image = inputs[stepi]
axes_gif['img_data'].imshow(this_image, cmap='binary', vmin=0, vmax=1)
axes_gif['img_data'].grid(False)
axes_gif['img_data'].set_xticks([])
axes_gif['img_data'].set_yticks([])
# Save frames
fig_gif.tight_layout(pad=0.1)
if stepi == 0:
fig_gif.savefig(filename.split('.gif')[0]+'_frame0.png', dpi=100)
if stepi == 1:
fig_gif.savefig(filename.split('.gif')[0]+'_frame1.png', dpi=100)
if stepi == n_steps-1:
fig_gif.savefig(filename.split('.gif')[0]+'_frame-1.png', dpi=100)
# Convert to frame
canvas = fig_gif.canvas
canvas.draw()
image_numpy = np.frombuffer(canvas.buffer_rgba(), dtype='uint8')
image_numpy = image_numpy.reshape(*reversed(canvas.get_width_height()), 4)[:,:,:3]
frames.append(image_numpy)
plt.close(fig_gif)
imageio.mimsave(filename, frames, fps=15, loop=100)
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