def init_saliency_map(args,
agent,
history,
first_frame=0,
prefix='QF_',
resolution=75,
save_dir='./movies/',
env_name='Breakout-v0'):
_, _, load_model, _ = build_network(agent.observation_shape,
agent.action_space_n)
#_, _,load_model_cam,_ = build_network(agent.observation_shape, agent.action_space_n)
#_, _, load_model_guided_backprop ,_= build_guided_model(agent.observation_shape, agent.action_space_n)
_, _, load_guided_model, _ = build_guided_model(agent.observation_shape,
agent.action_space_n)
load_model.load_weights(args.load_network_path)
load_guided_model.load_weights(args.load_network_path)
#load_model_guided_backprop.load_weights(args.load_network_path)
#load_model_grad_cam.load_weights(args.load_network_path)
frame_1 = np.zeros((84, 84))
total_frames = len(history['state'])
backprop_actor = init_guided_backprop(load_model, "dense_6")
backprop_critic = init_guided_backprop(load_model, "dense_5")
cam_actor = init_grad_cam(load_model, "convolution2d_4")
cam_critic = init_grad_cam(load_model, "convolution2d_4", False)
guidedBackprop_actor = init_guided_backprop(load_guided_model, "dense_9")
guidedBackprop_critic = init_guided_backprop(load_guided_model, "dense_8")
gradCAM_actor = init_grad_cam(load_guided_model, "convolution2d_7")
gradCAM_critic = init_grad_cam(load_guided_model, "convolution2d_7", False)
fig_array = np.zeros((4, 2, num_frames, 84, 84, 3))
for i in range(num_frames): #total_frames): #num_frames
ix = first_frame + i
if ix < total_frames: # prevent loop from trying to process a frame ix greater than rollout length
frame = history['state'][ix].copy()
action = history['action'][ix] #.copy()
frame = np.expand_dims(frame, axis=0)
if ix % 10 == 0:
print(ix)
actor_gbp_heatmap = guided_backprop(frame, backprop_actor)
actor_gbp_heatmap = np.asarray(actor_gbp_heatmap)
history['gradients_actor'].append(actor_gbp_heatmap)
actor_gbp_heatmap = guided_backprop(frame, backprop_critic)
actor_gbp_heatmap = np.asarray(actor_gbp_heatmap)
history['gradients_critic'].append(actor_gbp_heatmap)
Cam_heatmap = grad_cam(cam_actor, frame, action)
Cam_heatmap = np.asarray(Cam_heatmap)
history['gradCam_actor'].append(Cam_heatmap)
gradCam_heatmap = grad_cam(cam_critic, frame, action, False)
gradCam_heatmap = np.asarray(gradCam_heatmap)
history['gradCam_critic'].append(gradCam_heatmap)
actor_gbp_heatmap = guided_backprop(frame, guidedBackprop_actor)
actor_gbp_heatmap = np.asarray(actor_gbp_heatmap)
history['gdb_actor'].append(actor_gbp_heatmap)
critic_gbp_heatmap = guided_backprop(frame, guidedBackprop_critic)
critic_gbp_heatmap = np.asarray(critic_gbp_heatmap)
history['gdb_critic'].append(critic_gbp_heatmap)
gradCam_heatmap = grad_cam(gradCAM_actor, frame, action)
gradCam_heatmap = np.asarray(Cam_heatmap)
history['guidedGradCam_actor'].append(Cam_heatmap)
gradCam_heatmap = grad_cam(gradCAM_critic, frame, action, False)
gradCam_heatmap = np.asarray(gradCam_heatmap)
history['guidedGradCam_critic'].append(gradCam_heatmap)
history_gradients_actor = history['gradients_actor'].copy()
history_gradients_critic = history['gradients_critic'].copy()
history_gradCam_actor = history['gradCam_actor'].copy()
history_gradCam_critic = history['gradCam_critic'].copy()
history_gdb_actor = history['gdb_actor'].copy()
history_gdb_critic = history['gdb_critic'].copy()
history_guidedGradCam_actor = history['guidedGradCam_actor'].copy()
history_guidedGradCam_critic = history['guidedGradCam_critic'].copy()
fig_array[0, 0] = normalization(history_gradients_actor,
history,
"gdb",
GDB_actor=1)
fig_array[0, 1] = normalization(history_gradients_critic, history, 'gdb')
fig_array[1, 0] = normalization(
history_gradCam_actor,
history,
"cam",
)
fig_array[1, 1] = normalization(history_gradCam_critic, history, 'cam')
fig_array[2, 0] = normalization(history_gdb_actor,
history,
"gdb",
GDB_actor=1)
fig_array[2, 1] = normalization(history_gdb_critic, history, 'gdb')
fig_array[3, 0] = normalization(history_guidedGradCam_actor, history,
"cam")
fig_array[3, 1] = normalization(history_guidedGradCam_critic, history,
'cam')
make_movie(args, history, fig_array, first_frame, num_frames, resolution,
save_dir, prefix, env_name)
def init_saliency_map(args,
agent,
history,
first_frame=0,
num_frames=num_frames,
prefix='QF_',
resolution=450,
save_dir='./movies/',
env_name='Breakout-v0'):
#load_model = create_model(agent.input_shape, agent.num_actions, agent.net_mode, args, "QNet")
load_model = build_network(agent.input_shape, agent.num_actions)
load_guided_model = build_guided_model(agent.input_shape,
agent.num_actions)
load_model.load_weights(args.load_network_path)
load_guided_model.load_weights(args.load_network_path)
total_frames = len(history['state'])
backprop_actor = init_guided_backprop(load_model, "Attention A")
backprop_critic = init_guided_backprop(load_model, "Attention V")
cam_actor = init_grad_cam(load_model, "timedistributed_16")
cam_critic = init_grad_cam(load_model, "timedistributed_16", False)
guidedBackprop_actor = init_guided_backprop(load_guided_model,
"Attention A")
guidedBackprop_critic = init_guided_backprop(load_guided_model,
"Attention V")
gradCAM_actor = init_grad_cam(load_guided_model, "timedistributed_23")
gradCAM_critic = init_grad_cam(load_guided_model, "timedistributed_23",
False)
fig_array = np.zeros((6, 2, num_frames, 84, 84, 3))
for i in range(num_frames): #total_frames): #num_frames
ix = first_frame + i
if ix < total_frames: # prevent loop from trying to process a frame ix greater than rollout length
frame = history['state'][ix].copy()
action = history['action'][ix] #.copy()
frame = np.expand_dims(frame, axis=0)
if ix % 10 == 0:
print(ix)
actor_gbp_heatmap = guided_backprop(frame, backprop_actor)
actor_gbp_heatmap = np.asarray(actor_gbp_heatmap)
history['gradients_actor'].append(actor_gbp_heatmap)
actor_gbp_heatmap = guided_backprop(frame, backprop_critic)
actor_gbp_heatmap = np.asarray(actor_gbp_heatmap)
history['gradients_critic'].append(actor_gbp_heatmap)
Cam_heatmap = grad_cam(cam_actor, frame, action)
Cam_heatmap = np.asarray(Cam_heatmap)
history['gradCam_actor'].append(Cam_heatmap)
gradCam_heatmap = grad_cam(cam_critic, frame, action, False)
gradCam_heatmap = np.asarray(gradCam_heatmap)
history['gradCam_critic'].append(gradCam_heatmap)
actor_gbp_heatmap = guided_backprop(frame, guidedBackprop_actor)
actor_gbp_heatmap = np.asarray(actor_gbp_heatmap)
history['gdb_actor'].append(actor_gbp_heatmap)
critic_gbp_heatmap = guided_backprop(frame, guidedBackprop_critic)
critic_gbp_heatmap = np.asarray(critic_gbp_heatmap)
history['gdb_critic'].append(critic_gbp_heatmap)
gradCam_heatmap = grad_cam(gradCAM_actor, frame, action)
gradCam_heatmap = np.asarray(gradCam_heatmap)
history['guidedGradCam_actor'].append(gradCam_heatmap)
gradCam_heatmap = grad_cam(gradCAM_critic, frame, action, False)
gradCam_heatmap = np.asarray(gradCam_heatmap)
history['guidedGradCam_critic'].append(gradCam_heatmap)
history_gradients_actor = history['gradients_actor'].copy()
history_gradients_critic = history['gradients_critic'].copy()
history_gdb_actor = history['gdb_actor'].copy()
history_gdb_critic = history['gdb_critic'].copy()
history_gradCam_actor = history['gradCam_actor'].copy()
history_gradCam_critic = history['gradCam_critic'].copy()
history_gradCamGuided_actor = history['guidedGradCam_actor'].copy()
history_gradCamGuided_critic = history['guidedGradCam_critic'].copy()
fig_array[0, 0] = normalization(history_gradients_actor,
history,
"gdb",
GDB_actor=1)
fig_array[0, 1] = normalization(history_gradients_critic, history, 'gdb')
fig_array[1, 0] = normalization(history_gdb_actor,
history,
"gdb",
GDB_actor=1)
fig_array[1, 1] = normalization(history_gdb_critic, history, 'gdb')
fig_array[2, 0] = normalization(
history_gradCam_actor,
history,
"cam",
)
fig_array[2, 1] = normalization(history_gradCam_critic, history, "cam")
fig_array[3, 0] = normalization(history_gradCam_actor,
history,
"cam",
GDB_actor=1,
guided_model=history_gdb_actor)
fig_array[3, 1] = normalization(history_gradCam_critic,
history,
'cam',
guided_model=history_gdb_critic)
fig_array[4, 0] = normalization(history_gradCamGuided_actor, history,
"cam")
fig_array[4, 1] = normalization(history_gradCamGuided_critic, history,
"cam")
fig_array[5, 0] = normalization(history_gradCamGuided_actor,
history,
"cam",
GDB_actor=1,
guided_model=history_gdb_actor)
fig_array[5, 1] = normalization(history_gradCamGuided_critic,
history,
'cam',
guided_model=history_gdb_critic)
make_movie(args, history, fig_array, first_frame, num_frames, resolution,
save_dir, prefix, env_name)