from einops import rearrange, reduce, repeat
import torch as th
from torch.utils.data import Dataset, DataLoader, Subset
import cv2
import numpy as np
import pandas as pd
import os
import motmetrics as mm
from scripts.evaluation_adept import calculate_tracking_error, get_evaluation_sets, update_mota_acc
from scripts.utils.eval_utils import boxes_to_centroids, masks_to_boxes, setup_result_folders, store_statistics
from scripts.utils.plot_utils import write_image
FG_THRE = 0.95
def evaluate(dataset: Dataset, file, n, model, plot_frequency= 1, plot_first_samples = 2):
assert model in ['savi', 'gswm']
# read pkl file
masks_complete = pd.read_pickle(file)
# plot config
color_list = [[255,0,0], [0,255,0], [0,0,255], [255,255,0], [0,255,255], [255,0,255], [255,255,255]]
dot_size = 2
if model == 'savi':
skip_frames = 2
offset = 15
elif model == 'gswm':
skip_frames = 2
offset = 0
# memory
statistics_complete_slots = {'set': [], 'evalmode': [], 'scene': [], 'frame': [], 'slot':[], 'TE': [], 'visible': [], 'bound': [], 'occluder': [], 'inimage': [], 'slot_error': [], 'mask_size': [], 'rawmask_size': [], 'rawmask_size_hidden': [], 'alpha_pos': [], 'alpha_ges': [], 'object_id': []}
acc_memory_eval = []
# load adept dataset
set_test_array, evaluation_modes = get_evaluation_sets(dataset)
control_samples = set_test_array[0]['samples'] # only consider control set
evalset = Subset(dataset, control_samples)
root_path, plot_path = setup_result_folders(file, n, set_test_array[0], evaluation_modes[0], True, False)
for i in range(len(evalset)):
print(f'Processing sample {i+1}/{len(evalset)}', flush=True)
input = evalset[i]
acc = mm.MOTAccumulator(auto_id=True)
# get input frame and target frame
tensor = th.tensor(input[0]).float().unsqueeze(0)
background_fix = th.tensor(input[1]).unsqueeze(0)
gt_object_positions = th.tensor(input[3]).unsqueeze(0)
gt_object_visibility = th.tensor(input[4]).unsqueeze(0)
gt_occluder_mask = th.tensor(input[5]).unsqueeze(0)
# apply skip frames
gt_object_positions = gt_object_positions[:,range(0, tensor.shape[1], skip_frames)]
gt_object_visibility = gt_object_visibility[:,range(0, tensor.shape[1], skip_frames)]
tensor = tensor[:,range(0, tensor.shape[1], skip_frames)]
sequence_len = tensor.shape[1]
if model == 'savi':
# load data
masks = th.tensor(masks_complete['test'][f'control_{i}.mp4']) # N, O, 1, H, W
masks_before_softmax = th.tensor(masks_complete['test_raw'][f'control_{i}.mp4'])
imgs_model = None
recons_model = None
# calculate rawmasks
bg_mask = masks_before_softmax.mean(dim=1)
masks_raw = compute_maskraw(masks_before_softmax, bg_mask, n_slots=7)
slots_bound = compute_slots_bound(masks_raw)
elif model == 'gswm':
# load data
masks = masks_complete[i]['visibility_mask'].squeeze(0)
masks_raw = masks_complete[i]['object_mask'].squeeze(0)
slots_bound = masks_complete[i]['z_pres'].squeeze(0)
slots_bound = (slots_bound > 0.9).float()
imgs_model = masks_complete[i]['imgs'].squeeze(0)
imgs_model[:] = imgs_model[:, [2,1,0]]
recons_model = masks_complete[i]['recon'].squeeze(0)
recons_model[:] = recons_model[:, [2,1,0]]
# consider only the first 7 slots
masks = masks[:,:7]
masks_raw = masks_raw[:,:7]
slots_bound = slots_bound[:,:7]
n_slots = masks.shape[1]
# threshold masks and calculate centroids
masks_binary = (masks_raw > FG_THRE).float()
masks2 = rearrange(masks_binary, 't o 1 h w -> (t o) h w')
boxes = masks_to_boxes(masks2.long())
boxes = boxes.reshape(1, masks.shape[0], n_slots, 4)
centroids = boxes_to_centroids(boxes)
# get rid of batch dimension
association_table = th.ones(n_slots) * -1
# iterate over frames
for t_index in range(offset,min(sequence_len,masks.shape[0])):
# move to next frame
input = tensor[:,t_index]
gt_positions_target = gt_object_positions[:,t_index]
gt_visibility_target = gt_object_visibility[:,t_index]
position_cur = centroids[t_index]
position_cur = rearrange(position_cur, 'o c -> 1 (o c)')
slots_bound_cur = slots_bound[t_index]
slots_bound_cur = rearrange(slots_bound_cur, 'o c -> 1 (o c)')
# calculate tracking error
tracking_error, tracking_error_perslot, association_table, slots_visible, slots_in_image, slots_occluder = calculate_tracking_error(gt_positions_target, gt_visibility_target, position_cur, n_slots, slots_bound_cur, None, association_table, gt_occluder_mask)
rawmask_size = reduce(masks_raw[t_index], 'o 1 h w-> 1 o', 'sum')
mask_size = reduce(masks[t_index], 'o 1 h w-> 1 o', 'sum')
statistics_complete_slots = store_statistics(statistics_complete_slots,
['control'] * n_slots,
['control'] * n_slots,
[control_samples[i]] * n_slots,
[t_index] * n_slots,
range(n_slots),
tracking_error_perslot.cpu().numpy().flatten(),
slots_visible.cpu().numpy().flatten().astype(int),
slots_bound_cur.cpu().numpy().flatten().astype(int),
slots_occluder.cpu().numpy().flatten().astype(int),
slots_in_image.cpu().numpy().flatten().astype(int),
[0] * n_slots,
mask_size.cpu().numpy().flatten(),
rawmask_size.cpu().numpy().flatten(),
[0] * n_slots,
[0] * n_slots,
[0] * n_slots,
association_table[0].cpu().numpy().flatten().astype(int),
extend = True)
acc = update_mota_acc(acc, gt_positions_target, position_cur, slots_bound_cur, n_slots, gt_occluder_mask, slots_occluder, None)
# plot_option
if (t_index % plot_frequency == 0) and (i < plot_first_samples) and (t_index >= 0):
masks_to_display = masks_binary.numpy() # masks_binary.numpy()
frame = tensor[0, t_index]
frame = frame.numpy().transpose(1,2,0)
frame = cv2.resize(frame, (64,64))
centroids_frame = centroids[t_index]
centroids_frame[:,0] = (centroids_frame[:,0] + 1) * 64 / 2
centroids_frame[:,1] = (centroids_frame[:,1] + 1) * 64 / 2
bound_frame = slots_bound[t_index]
for c_index,centroid_slot in enumerate(centroids_frame):
if bound_frame[c_index] == 1:
frame[int(centroid_slot[1]-dot_size):int(centroid_slot[1]+dot_size), int(centroid_slot[0]-dot_size):int(centroid_slot[0]+dot_size)] = color_list[c_index]
# slot images
slot_frame = masks_to_display[t_index].max(axis=0)
slot_frame = slot_frame.reshape((64,64,1)).repeat(3, axis=2)
if True:
for mask in masks_to_display[t_index]:
#slot_frame_single = mask.reshape((64,64,1)).repeat(3, axis=2)
slot_frame_single = mask.transpose((1,2,0)).repeat(3, axis=2)
slot_frame = np.concatenate((slot_frame, slot_frame_single), axis=1)
if imgs_model is not None:
frame_model = imgs_model[t_index].numpy().transpose(1,2,0)
recon_model = recons_model[t_index].numpy().transpose(1,2,0)
frame = np.concatenate((frame, frame_model, recon_model, slot_frame), axis=1)
else:
frame = np.concatenate((frame, slot_frame), axis=1)
cv2.imwrite(f'{plot_path}object/objects-{i:04d}-{t_index:03d}.jpg', frame*255)
acc_memory_eval.append(acc)
mh = mm.metrics.create()
summary = mh.compute_many(acc_memory_eval, metrics=mm.metrics.motchallenge_metrics, generate_overall=True)
summary['set'] = 'control'
summary['evalmode'] = 'control'
pd.DataFrame(summary).to_csv(os.path.join(root_path, 'statistics' , 'accframe.csv'))
pd.DataFrame(statistics_complete_slots).to_csv(os.path.join(root_path, 'statistics' , 'slotframe.csv'))
def compute_slots_bound(masks):
# take sum over axis 3,4 with th
masks_sum = masks.amax(dim=(3,4))
slots_bound = (masks_sum > FG_THRE).float()
return slots_bound
def compute_maskraw(mask, bg_mask, n_slots):
# d is a diagonal matrix which defines what to take the softmax over
d_mask = th.diag(th.ones(8))
d_mask[:,-1] = 1
d_mask[-1,-1] = 0
mask = mask.squeeze(2)
# take subset of maskraw with the diagonal matrix
maskraw = th.cat((mask, bg_mask), dim=1)
maskraw = repeat(maskraw, 'b o h w -> b r o h w', r = 8)
maskraw = maskraw[:,d_mask.bool()]
maskraw = rearrange(maskraw, 'b (o r) h w -> b o r h w', o = n_slots)
# take softmax between each object mask and the background mask
maskraw = th.squeeze(th.softmax(maskraw, dim=2)[:,:,0], dim=2)
maskraw = maskraw.unsqueeze(2)
return maskraw