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import torch as th
from torch import nn
import numpy as np
import cv2
from einops import rearrange, repeat
import matplotlib.pyplot as plt
import PIL
from model.utils.nn_utils import Gaus2D, Vector2D
def preprocess(tensor, scale=1, normalize=False, mean_std_normalize=False):
if tensor is None:
return None
if normalize:
min_ = th.min(tensor)
max_ = th.max(tensor)
tensor = (tensor - min_) / (max_ - min_)
if mean_std_normalize:
mean = th.mean(tensor)
std = th.std(tensor)
tensor = th.clip((tensor - mean) / (2 * std), -1, 1) * 0.5 + 0.5
if scale > 1:
upsample = nn.Upsample(scale_factor=scale).to(tensor[0].device)
tensor = upsample(tensor)
return tensor
def preprocess_multi(*args, scale):
return [preprocess(a, scale) for a in args]
def color_mask(mask):
colors = th.tensor([
[ 255, 0, 0 ],
[ 0, 0, 255 ],
[ 255, 255, 0 ],
[ 255, 0, 255 ],
[ 0, 255, 255 ],
[ 0, 255, 0 ],
[ 255, 128, 0 ],
[ 128, 255, 0 ],
[ 128, 0, 255 ],
[ 255, 0, 128 ],
[ 0, 255, 128 ],
[ 0, 128, 255 ],
[ 255, 128, 128 ],
[ 128, 255, 128 ],
[ 128, 128, 255 ],
[ 255, 128, 128 ],
[ 128, 255, 128 ],
[ 128, 128, 255 ],
[ 255, 128, 255 ],
[ 128, 255, 255 ],
[ 128, 255, 255 ],
[ 255, 255, 128 ],
[ 255, 255, 128 ],
[ 255, 128, 255 ],
[ 128, 0, 0 ],
[ 0, 0, 128 ],
[ 128, 128, 0 ],
[ 128, 0, 128 ],
[ 0, 128, 128 ],
[ 0, 128, 0 ],
[ 128, 128, 0 ],
[ 128, 128, 0 ],
[ 128, 0, 128 ],
[ 128, 0, 128 ],
[ 0, 128, 128 ],
[ 0, 128, 128 ],
[ 128, 128, 128 ],
[ 128, 128, 128 ],
[ 128, 128, 128 ],
[ 128, 128, 128 ],
[ 128, 128, 128 ],
[ 128, 128, 128 ],
[ 128, 128, 128 ],
[ 128, 128, 128 ],
[ 128, 128, 128 ],
[ 128, 128, 128 ],
[ 128, 128, 128 ],
[ 128, 128, 128 ],
], device = mask.device) / 255.0
colors = colors.view(1, -1, 3, 1, 1)
mask = mask.unsqueeze(dim=2)
return th.sum(colors[:,:mask.shape[1]] * mask, dim=1)
def get_color(o):
colors = th.tensor([
[ 255, 0, 0 ],
[ 0, 0, 255 ],
[ 255, 255, 0 ],
[ 255, 0, 255 ],
[ 0, 255, 255 ],
[ 0, 255, 0 ],
[ 255, 128, 0 ],
[ 128, 255, 0 ],
[ 128, 0, 255 ],
[ 255, 0, 128 ],
[ 0, 255, 128 ],
[ 0, 128, 255 ],
[ 255, 128, 128 ],
[ 128, 255, 128 ],
[ 128, 128, 255 ],
[ 255, 128, 128 ],
[ 128, 255, 128 ],
[ 128, 128, 255 ],
[ 255, 128, 255 ],
[ 128, 255, 255 ],
[ 128, 255, 255 ],
[ 255, 255, 128 ],
[ 255, 255, 128 ],
[ 255, 128, 255 ],
[ 128, 0, 0 ],
[ 0, 0, 128 ],
[ 128, 128, 0 ],
[ 128, 0, 128 ],
[ 0, 128, 128 ],
[ 0, 128, 0 ],
[ 128, 128, 0 ],
[ 128, 128, 0 ],
[ 128, 0, 128 ],
[ 128, 0, 128 ],
[ 0, 128, 128 ],
[ 0, 128, 128 ],
[ 128, 128, 128 ],
[ 128, 128, 128 ],
[ 128, 128, 128 ],
[ 128, 128, 128 ],
[ 128, 128, 128 ],
[ 128, 128, 128 ],
[ 128, 128, 128 ],
[ 128, 128, 128 ],
[ 128, 128, 128 ],
[ 128, 128, 128 ],
[ 128, 128, 128 ],
[ 128, 128, 128 ],
]) / 255.0
colors = colors.view(48,3)
return colors[o]
def to_rgb(tensor: th.Tensor):
return th.cat((
tensor * 0.6 + 0.4,
tensor,
tensor
), dim=1)
def visualise_gate(gate, h, w):
bar = th.ones((1,h,w), device=gate.device) * 0.9
black = int(w*gate.item())
if black > 0:
bar[:,:, -black:] = 0
return bar
def get_highlighted_input(input, mask_cur):
# highlight error
highlighted_input = input
if mask_cur is not None:
grayscale = input[:,0:1] * 0.299 + input[:,1:2] * 0.587 + input[:,2:3] * 0.114
object_mask_cur = th.sum(mask_cur[:,:-1], dim=1).unsqueeze(dim=1)
highlighted_input = grayscale * (1 - object_mask_cur)
highlighted_input += grayscale * object_mask_cur * 0.3333333
cmask = color_mask(mask_cur[:,:-1])
highlighted_input = highlighted_input + cmask * 0.6666666
return highlighted_input
def color_slots(image, slots_bounded, slots_partially_occluded_cur, slots_occluded_cur):
image = (1-image) * slots_bounded + image * (1-slots_bounded)
image = th.clip(image - 0.3, 0,1) * slots_partially_occluded_cur + image * (1-slots_partially_occluded_cur)
image = th.clip(image - 0.3, 0,1) * slots_occluded_cur + image * (1-slots_occluded_cur)
return image
def compute_occlusion_mask(rawmask_cur, rawmask_next, mask_cur, mask_next, scale):
# compute occlusion mask
occluded_cur = th.clip(rawmask_cur - mask_cur, 0, 1)[:,:-1]
occluded_next = th.clip(rawmask_next - mask_next, 0, 1)[:,:-1]
# to rgb
rawmask_cur = repeat(rawmask_cur[:,:-1], 'b o h w -> b (o 3) h w')
rawmask_next = repeat(rawmask_next[:,:-1], 'b o h w -> b (o 3) h w')
# scale
occluded_next = preprocess(occluded_next, scale)
occluded_cur = preprocess(occluded_cur, scale)
rawmask_cur = preprocess(rawmask_cur, scale)
rawmask_next = preprocess(rawmask_next, scale)
# set occlusion to red
rawmask_cur = rearrange(rawmask_cur, 'b (o c) h w -> b o c h w', c = 3)
rawmask_cur[:,:,0] = rawmask_cur[:,:,0] * (1 - occluded_next)
rawmask_cur[:,:,1] = rawmask_cur[:,:,1] * (1 - occluded_next)
rawmask_next = rearrange(rawmask_next, 'b (o c) h w -> b o c h w', c = 3)
rawmask_next[:,:,0] = rawmask_next[:,:,0] * (1 - occluded_next)
rawmask_next[:,:,1] = rawmask_next[:,:,1] * (1 - occluded_next)
return rawmask_cur, rawmask_next
def plot_online_error_slots(errors, error_name, target, sequence_len, root_path, visibilty_memory, slots_bounded, ylim=0.3):
error_plots = []
if len(errors) > 0:
num_slots = int(th.sum(slots_bounded).item())
errors = rearrange(np.array(errors), '(l o) -> o l', o=len(slots_bounded))[:num_slots]
visibilty_memory = rearrange(np.array(visibilty_memory), '(l o) -> o l', o=len(slots_bounded))[:num_slots]
for error,visibility in zip(errors, visibilty_memory):
if len(error) < sequence_len:
fig, ax = plt.subplots(figsize=(round(target.shape[3]/100,2), round(target.shape[2]/100,2)))
plt.plot(error, label=error_name)
visibility = np.concatenate((visibility, np.ones(sequence_len-len(error))))
ax.fill_between(range(sequence_len), 0, 1, where=visibility==0, color='orange', alpha=0.3, transform=ax.get_xaxis_transform())
plt.xlim((0,sequence_len))
plt.ylim((0,ylim))
fig.tight_layout()
plt.savefig(f'{root_path}/tmp.jpg')
error_plot = PIL.Image.frombytes('RGB', fig.canvas.get_width_height(),fig.canvas.tostring_rgb())
error_plot = th.from_numpy(np.array(error_plot).transpose(2,0,1))
plt.close(fig)
error_plots.append(error_plot)
return error_plots
def plot_online_error(error, error_name, target, t, i, sequence_len, root_path, online_surprise = False):
fig = plt.figure(figsize=( round(target.shape[3]/50,2), round(target.shape[2]/50,2) ))
plt.plot(error, label=error_name)
if online_surprise:
# compute moving average of error
moving_average_length = 10
if t > moving_average_length:
moving_average_length += 1
average_error = np.mean(error[-moving_average_length:-1])
current_sd = np.std(error[-moving_average_length:-1])
current_error = error[-1]
if current_error > average_error + 2 * current_sd:
fig.set_facecolor('orange')
plt.xlim((0,sequence_len))
plt.legend()
# increase title size
plt.title(f'{error_name}', fontsize=20)
plt.xlabel('timestep')
plt.ylabel('error')
plt.savefig(f'{root_path}/tmp.jpg')
error_plot = PIL.Image.frombytes('RGB', fig.canvas.get_width_height(),fig.canvas.tostring_rgb())
error_plot = th.from_numpy(np.array(error_plot).transpose(2,0,1))
plt.close(fig)
return error_plot
def plot_object_view(error_plot, error_plot2, error_plot_slots, error_plot_slots2, highlighted_input, output_hidden, object_next, rawmask_next, velocity_next2d, target, slots_closed, gt_positions_target_next, association_table, size, num_objects, largest_object):
# add ground truth positions of objects to image
if gt_positions_target_next is not None:
for o in range(gt_positions_target_next.shape[1]):
position = gt_positions_target_next[0, o]
position = position/2 + 0.5
if position[2] > 0.0 and position[0] > 0.0 and position[0] < 1.0 and position[1] > 0.0 and position[1] < 1.0:
width = 5
w = np.clip(int(position[0]*target.shape[2]), width, target.shape[2]-width).item()
h = np.clip(int(position[1]*target.shape[3]), width, target.shape[3]-width).item()
col = get_color(o).view(3,1,1)
target[0,:,(w-width):(w+width), (h-width):(h+width)] = col
# add these positions to the associated slots velocity_next2d ilustration
slots = (association_table[0] == o).nonzero()
for s in slots.flatten():
velocity_next2d[s,:,(w-width):(w+width), (h-width):(h+width)] = col
if output_hidden is not None and s != largest_object:
output_hidden[0,:,(w-width):(w+width), (h-width):(h+width)] = col
gateheight = 60
ch = 40
gh = 40
gh_bar = gh-20
gh_margin = int((gh-gh_bar)/2)
margin = 20
slots_margin = 10
height = size[0] * 6 + 18*5
width = size[1] * 4 + 18*2 + size[1]*num_objects + 6*(num_objects+1) + slots_margin*(num_objects+1)
img = th.ones((3, height, width), device = object_next.device) * 0.4
row = (lambda row_index: [2*size[0]*row_index + (row_index+1)*margin, 2*size[0]*(row_index+1) + (row_index+1)*margin])
col1 = range(margin, margin + size[1]*2)
col2 = range(width-(margin+size[1]*2), width-margin)
img[:,row(0)[0]:row(0)[1], col1] = preprocess(highlighted_input.to(object_next.device), 2)[0]
img[:,row(1)[0]:row(1)[1], col1] = preprocess(output_hidden.to(object_next.device), 2)[0]
img[:,row(2)[0]:row(2)[1], col1] = preprocess(target.to(object_next.device), 2)[0]
if error_plot is not None:
img[:,row(0)[0]+gh+ch+2*margin-gh_margin:row(0)[1]+gh+ch+2*margin-gh_margin, col2] = preprocess(error_plot.to(object_next.device), normalize= True)
if error_plot2 is not None:
img[:,row(2)[0]:row(2)[1], col2] = preprocess(error_plot2.to(object_next.device), normalize= True)
for o in range(num_objects):
col = 18+size[1]*2+6+o*(6+size[1])+(o+1)*slots_margin
col = range(col, col + size[1])
# color bar for the gate
if (error_plot_slots2 is not None) and len(error_plot_slots2) > o:
img[:,margin:margin+ch, col] = get_color(o).view(3,1,1).to(object_next.device)
img[:,margin+ch+2*margin:2*margin+gh_bar+ch+margin, col] = visualise_gate(slots_closed[:,o, 0].to(object_next.device), h=gh_bar, w=len(col))
offset = gh+margin-gh_margin+ch+2*margin
row = (lambda row_index: [offset+(size[0]+6)*row_index, offset+size[0]*(row_index+1)+6*row_index])
img[:,row(0)[0]:row(0)[1], col] = preprocess(rawmask_next[0,o].to(object_next.device))
img[:,row(1)[0]:row(1)[1], col] = preprocess(object_next[:,o].to(object_next.device))
if (error_plot_slots2 is not None) and len(error_plot_slots2) > o:
img[:,row(2)[0]:row(2)[1], col] = preprocess(error_plot_slots2[o].to(object_next.device), normalize=True)
offset = margin*2-8
row = (lambda row_index: [offset+(size[0]+6)*row_index, offset+size[0]*(row_index+1)+6*row_index])
img[:,row(4)[0]-gh+gh_margin:row(4)[0]-gh_margin, col] = visualise_gate(slots_closed[:,o, 1].to(object_next.device), h=gh_bar, w=len(col))
img[:,row(4)[0]:row(4)[1], col] = preprocess(velocity_next2d[o].to(object_next.device), normalize=True)[0]
if (error_plot_slots is not None) and len(error_plot_slots) > o:
img[:,row(5)[0]:row(5)[1], col] = preprocess(error_plot_slots[o].to(object_next.device), normalize=True)
img = rearrange(img * 255, 'c h w -> h w c').cpu().numpy()
return img
def write_image(file, img):
img = rearrange(img * 255, 'c h w -> h w c').cpu().numpy()
cv2.imwrite(file, img)
pass
def plot_timestep(cfg, cfg_net, input, target, mask_cur, mask_next, output_next, position_encoder_cur, position_next, rawmask_hidden, rawmask_cur, rawmask_next, largest_object, object_cur, object_next, object_hidden, slots_bounded, slots_partially_occluded_cur, slots_occluded_cur, slots_partially_occluded_next, slots_occluded_next, slots_closed, gt_positions_target_next, association_table, error_next, output_hidden, object_view, individual_views, statistics_complete_slots, statistics_batch, sequence_len, root_path, plot_path, t_index, t, i):
# Create eposition helpers
size, gaus2d, vector2d, scale = get_position_helper(cfg_net, mask_cur.device)
# Compute plot content
highlighted_input = get_highlighted_input(input, mask_cur)
output = th.clip(output_next, 0, 1)
position_cur2d = gaus2d(rearrange(position_encoder_cur, 'b (o c) -> (b o) c', o=cfg_net.num_objects))
velocity_next2d = vector2d(rearrange(position_next, 'b (o c) -> (b o) c', o=cfg_net.num_objects))
# color slots
slots_bounded, slots_partially_occluded_cur, slots_occluded_cur, slots_partially_occluded_next, slots_occluded_next = reshape_slots(slots_bounded, slots_partially_occluded_cur, slots_occluded_cur, slots_partially_occluded_next, slots_occluded_next)
position_cur2d = color_slots(position_cur2d, slots_bounded, slots_partially_occluded_cur, slots_occluded_cur)
velocity_next2d = color_slots(velocity_next2d, slots_bounded, slots_partially_occluded_next, slots_occluded_next)
# compute occlusion
if (cfg.datatype == "adept"):
rawmask_cur_l, rawmask_next_l = compute_occlusion_mask(rawmask_cur, rawmask_next, mask_cur, mask_next, scale)
rawmask_cur_h, rawmask_next_h = compute_occlusion_mask(rawmask_cur, rawmask_hidden, mask_cur, mask_next, scale)
rawmask_cur_h[:,largest_object] = rawmask_cur_l[:,largest_object]
rawmask_next_h[:,largest_object] = rawmask_next_l[:,largest_object]
rawmask_cur = rawmask_cur_h
rawmask_next = rawmask_next_h
object_hidden[:, largest_object] = object_next[:, largest_object]
object_next = object_hidden
else:
rawmask_cur, rawmask_next = compute_occlusion_mask(rawmask_cur, rawmask_next, mask_cur, mask_next, scale)
# scale plot content
input, target, output, highlighted_input, object_next, object_cur, mask_next, error_next, output_hidden, output_next = preprocess_multi(input, target, output, highlighted_input, object_next, object_cur, mask_next, error_next, output_hidden, output_next, scale=scale)
# reshape
object_next = rearrange(object_next, 'b (o c) h w -> b o c h w', c = cfg_net.img_channels)
object_cur = rearrange(object_cur, 'b (o c) h w -> b o c h w', c = cfg_net.img_channels)
mask_next = rearrange(mask_next, 'b (o 1) h w -> b o 1 h w')
if object_view:
if (cfg.datatype == "adept"):
num_objects = 4
error_plot_slots = plot_online_error_slots(statistics_complete_slots['TE'][-cfg_net.num_objects*(t+1):], 'Tracking error', target, sequence_len, root_path, statistics_complete_slots['visible'][-cfg_net.num_objects*(t+1):], slots_bounded)
error_plot_slots2 = plot_online_error_slots(statistics_complete_slots['slot_error'][-cfg_net.num_objects*(t+1):], 'Image error', target, sequence_len, root_path, statistics_complete_slots['visible'][-cfg_net.num_objects*(t+1):], slots_bounded, ylim=0.0001)
error_plot = plot_online_error(statistics_batch['image_error'], 'Prediction error', target, t, i, sequence_len, root_path)
error_plot2 = plot_online_error(statistics_batch['TE'], 'Tracking error', target, t, i, sequence_len, root_path)
img = plot_object_view(error_plot, error_plot2, error_plot_slots, error_plot_slots2, highlighted_input, output_hidden, object_next, rawmask_next, velocity_next2d, target, slots_closed, gt_positions_target_next, association_table, size, num_objects, largest_object)
else:
num_objects = cfg_net.num_objects
error_plot_slots2 = plot_online_error_slots(statistics_complete_slots['slot_error'][-cfg_net.num_objects*(t+1):], 'Image error', target, sequence_len, root_path, statistics_complete_slots['slot_error'][-cfg_net.num_objects*(t+1):], slots_bounded, ylim=0.0001)
error_plot = plot_online_error(statistics_batch['image_error_mse'], 'Prediction error', target, t, i, sequence_len, root_path)
img = plot_object_view(error_plot, None, None, error_plot_slots2, highlighted_input, output_next, object_next, rawmask_next, velocity_next2d, target, slots_closed, None, None, size, num_objects, largest_object)
cv2.imwrite(f'{plot_path}object/gpnet-objects-{i:04d}-{t_index:03d}.jpg', img)
if individual_views:
# ['error', 'input', 'background', 'prediction', 'position', 'rawmask', 'mask', 'othermask']:
write_image(f'{plot_path}/individual/error/error-{i:04d}-{t_index:03d}.jpg', error_next[0])
write_image(f'{plot_path}/individual/input/input-{i:04d}-{t_index:03d}.jpg', input[0])
write_image(f'{plot_path}/individual/background/background-{i:04d}-{t_index:03d}.jpg', mask_next[0,-1])
write_image(f'{plot_path}/individual/imagination/imagination-{i:04d}-{t_index:03d}.jpg', output_hidden[0])
write_image(f'{plot_path}/individual/prediction/prediction-{i:04d}-{t_index:03d}.jpg', output_next[0])
pass
def get_position_helper(cfg_net, device):
size = cfg_net.input_size
gaus2d = Gaus2D(size).to(device)
vector2d = Vector2D(size).to(device)
scale = size[0] // (cfg_net.latent_size[0] * 2**(cfg_net.level*2))
return size,gaus2d,vector2d,scale
def reshape_slots(slots_bounded, slots_partially_occluded_cur, slots_occluded_cur, slots_partially_occluded_next, slots_occluded_next):
slots_bounded = th.squeeze(slots_bounded)[..., None,None,None]
slots_partially_occluded_cur = th.squeeze(slots_partially_occluded_cur)[..., None,None,None]
slots_occluded_cur = th.squeeze(slots_occluded_cur)[..., None,None,None]
slots_partially_occluded_next = th.squeeze(slots_partially_occluded_next)[..., None,None,None]
slots_occluded_next = th.squeeze(slots_occluded_next)[..., None,None,None]
return slots_bounded, slots_partially_occluded_cur, slots_occluded_cur, slots_partially_occluded_next, slots_occluded_next
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