def test_evaluator(config):
transformer = volume_normalize('background')
db = layout_coco(config, 'val', transform=transformer)
output_dir = osp.join(db.cfg.model_dir, 'test_evaluator_coco')
maybe_create(output_dir)
ev = evaluator(db)
for i in range(0, len(db), 2):
# print('--------------------------------------')
entry_1 = db[i]
entry_2 = db[i+1]
scene_1 = db.scenedb[i]
scene_2 = db.scenedb[i+1]
name_1 = osp.splitext(osp.basename(entry_1['color_path']))[0]
name_2 = osp.splitext(osp.basename(entry_2['color_path']))[0]
graph_1 = scene_graph(db, scene_1, entry_1['out_inds'], True)
graph_2 = scene_graph(db, scene_2, entry_2['out_inds'], False)
color_1 = cv2.imread(entry_1['color_path'], cv2.IMREAD_COLOR)
color_2 = cv2.imread(entry_2['color_path'], cv2.IMREAD_COLOR)
color_1, _, _ = create_squared_image(color_1)
color_2, _, _ = create_squared_image(color_2)
color_1 = cv2.resize(color_1, (config.draw_size[0], config.draw_size[1]))
color_2 = cv2.resize(color_2, (config.draw_size[0], config.draw_size[1]))
color_1 = visualize_unigram(config, color_1, graph_1.unigrams, (225, 0, 0))
color_2 = visualize_unigram(config, color_2, graph_2.unigrams, (225, 0, 0))
color_1 = visualize_bigram(config, color_1, graph_1.bigrams, (0, 255, 255))
color_2 = visualize_bigram(config, color_2, graph_2.bigrams, (0, 255, 255))
scores = ev.evaluate_graph(graph_1, graph_2)
color_1 = visualize_unigram(config, color_1, ev.common_pred_unigrams, (0, 225, 0))
color_2 = visualize_unigram(config, color_2, ev.common_gt_unigrams, (0, 225, 0))
color_1 = visualize_bigram(config, color_1, ev.common_pred_bigrams, (255, 255, 0))
color_2 = visualize_bigram(config, color_2, ev.common_gt_bigrams, (255, 255, 0))
info = eval_info(config, scores[None, ...])
plt.switch_backend('agg')
fig = plt.figure(figsize=(16, 10))
title = entry_1['sentence'] + '\n' + entry_2['sentence'] + '\n'
title += 'unigram f3: %f, bigram f3: %f, bigram sim: %f\n'%(info.unigram_F3()[0], info.bigram_F3()[0], info.bigram_coord()[0])
title += 'scale: %f, ratio: %f, coord: %f \n'%(info.scale()[0], info.ratio()[0], info.unigram_coord()[0])
plt.suptitle(title)
plt.subplot(1, 2, 1); plt.imshow(color_1[:,:,::-1]); plt.axis('off')
plt.subplot(1, 2, 2); plt.imshow(color_2[:,:,::-1]); plt.axis('off')
out_path = osp.join(output_dir, name_1+'_'+name_2+'.png')
fig.savefig(out_path, bbox_inches='tight')
plt.close(fig)
if i > 40:
break
def test_simulator(config):
plt.switch_backend('agg')
output_dir = osp.join(config.model_dir, 'simulator')
maybe_create(output_dir)
transformer = volume_normalize('background')
db = layout_coco(config, 'val', transform=transformer)
loader = DataLoader(db,
batch_size=config.batch_size,
shuffle=False,
num_workers=config.num_workers)
env = simulator(db, config.batch_size)
env.reset()
for cnt, batched in enumerate(loader):
out_inds = batched['out_inds'].numpy()
gt_paths = batched['color_path']
img_inds = batched['image_idx']
sents = batched['sentence']
sequences = []
for i in range(out_inds.shape[1]):
frames = env.batch_render_to_pytorch(out_inds[:, i, :])
sequences.append(frames)
seqs1 = torch.stack(sequences, dim=1)
print('seqs1', seqs1.size())
seqs2 = env.batch_redraw(return_sequence=True)
seqs = seqs2
for i in range(len(seqs)):
imgs = seqs[i]
image_idx = img_inds[i]
name = '%03d_' % i + str(image_idx.item()).zfill(9)
out_path = osp.join(output_dir, name + '.png')
color = cv2.imread(gt_paths[i], cv2.IMREAD_COLOR)
color, _, _ = create_squared_image(color)
fig = plt.figure(figsize=(32, 16))
plt.suptitle(sents[i])
for j in range(len(imgs)):
plt.subplot(3, 5, j + 1)
plt.imshow(imgs[j])
plt.axis('off')
plt.subplot(3, 5, 15)
plt.imshow(color[:, :, ::-1])
plt.axis('off')
fig.savefig(out_path, bbox_inches='tight')
plt.close(fig)
break
def configure_output_dir(d=None):
"""
Set output directory to d, or to /tmp/somerandomnumber if d is None
"""
G.output_dir = d or "/tmp/experiments/%i"%int(time.time())
# assert not osp.exists(G.output_dir), "Log dir %s already exists! Delete it first or use a different dir"%G.output_dir
# os.makedirs(G.output_dir)
maybe_create(G.output_dir)
G.output_file = open(osp.join(G.output_dir, "log.txt"), 'w')
atexit.register(G.output_file.close)
print(colorize("Logging data to %s"%G.output_file.name, 'green', bold=True))
def test_topk(config):
import os.path as osp
from dataset import imdb
from utils import image_normalize, maybe_create
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
transformer = image_normalize('background')
db = imdb(config, split='test', transform=transformer)
net = DrawModel(db)
output_dir = osp.join(config.model_dir, 'test_topk')
maybe_create(output_dir)
pretrained_path = osp.join('data/caches/supervised_abstract.pkl')
assert osp.exists(pretrained_path)
if config.cuda:
states = torch.load(pretrained_path)
else:
states = torch.load(pretrained_path,
map_location=lambda storage, loc: storage)
net.load_state_dict(states)
plt.switch_backend('agg')
for i in range(len(db)):
entry = db[i]
scene = db.scenedb[i]
input_inds_np = entry['word_inds']
input_lens_np = entry['word_lens']
input_inds = torch.from_numpy(input_inds_np).long().unsqueeze(0)
input_lens = torch.from_numpy(input_lens_np).long().unsqueeze(0)
if config.cuda:
input_inds = input_inds.cuda()
input_lens = input_lens.cuda()
net.eval()
with torch.no_grad():
env = net.topk_inference(input_inds, input_lens, config.beam_size,
-1)
frames = env.batch_redraw(return_sequence=True)
gt_img = cv2.imread(entry['color_path'], cv2.IMREAD_COLOR)
for j in range(len(frames)):
fig = plt.figure(figsize=(40, 20))
title = entry['sentence']
# title = title + '\n reward: %f, scores: %f, %f, %f, %f, %f'%(rews[j], *(scores[j]))
plt.suptitle(title, fontsize=50)
imgs = frames[j]
for k in range(len(imgs)):
plt.subplot(3, 4, k + 1)
plt.imshow(imgs[k, :, :, ::-1])
plt.axis('off')
plt.subplot(3, 4, 12)
plt.imshow(gt_img[:, :, ::-1])
plt.axis('off')
output_path = osp.join(output_dir, '%03d_%03d.png' % (i, j))
fig.savefig(output_path, bbox_inches='tight')
plt.close(fig)
if i > 0:
break
def test_model(config):
transformer = image_normalize('background')
db = abstract_scene(config, 'val', transform=transformer)
net = DrawModel(db)
plt.switch_backend('agg')
output_dir = osp.join(config.model_dir, 'test_model')
maybe_create(output_dir)
pretrained_path = osp.join(
'../data/caches/abstract_ckpts/supervised_abstract_top1.pkl')
assert osp.exists(pretrained_path)
if config.cuda:
states = torch.load(pretrained_path)
else:
states = torch.load(pretrained_path,
map_location=lambda storage, loc: storage)
net.load_state_dict(states)
loader = DataLoader(db,
batch_size=config.batch_size,
shuffle=False,
num_workers=config.num_workers)
net.eval()
for cnt, batched in enumerate(loader):
word_inds = batched['word_inds'].long()
word_lens = batched['word_lens'].long()
bg_images = batched['background'].float()
fg_inds = batched['fg_inds'].long()
gt_inds = batched['out_inds'].long()
gt_msks = batched['out_msks'].float()
hmaps = batched['hmaps'].float()
fg_onehots = indices2onehots(fg_inds, config.output_cls_size)
fg_onehots = fg_onehots
inf_outs = net.teacher_forcing(word_inds, word_lens, bg_images,
fg_onehots, hmaps)
print('teacher forcing')
print('obj_logits ', inf_outs['obj_logits'].size())
print('coord_logits ', inf_outs['coord_logits'].size())
print('attri_logits ', inf_outs['attri_logits'].size())
if config.what_attn:
print('what_att_logits ', inf_outs['what_att_logits'].size())
if config.where_attn > 0:
print('where_att_logits ', inf_outs['where_att_logits'].size())
print('----------------------')
# inf_outs, env = net(word_inds, word_lens, -1, 0, 0, gt_inds)
inf_outs, env = net(word_inds, word_lens, 0, 1, 0, None)
print('scheduled sampling')
print('obj_logits ', inf_outs['obj_logits'].size())
print('coord_logits ', inf_outs['coord_logits'].size())
print('attri_logits ', inf_outs['attri_logits'].size())
if config.what_attn:
print('what_att_logits ', inf_outs['what_att_logits'].size())
if config.where_attn > 0:
print('where_att_logits ', inf_outs['where_att_logits'].size())
print('----------------------')
pred_out_inds, pred_out_msks = env.get_batch_inds_and_masks()
print('pred_out_inds', pred_out_inds[0, 1], pred_out_inds.shape)
print('gt_inds', gt_inds[0, 1], gt_inds.size())
print('pred_out_msks', pred_out_msks[0, 1], pred_out_msks.shape)
print('gt_msks', gt_msks[0, 1], gt_msks.size())
batch_frames = env.batch_redraw(True)
scene_inds = batched['scene_idx']
for i in range(len(scene_inds)):
sid = scene_inds[i]
entry = db[sid]
name = osp.splitext(osp.basename(entry['color_path']))[0]
imgs = batch_frames[i]
out_path = osp.join(output_dir, name + '.png')
fig = plt.figure(figsize=(16, 8))
plt.suptitle(entry['sentence'])
for j in range(len(imgs)):
plt.subplot(4, 3, j + 1)
plt.imshow(imgs[j, :, :, ::-1].astype(np.uint8))
plt.axis('off')
target = cv2.imread(entry['color_path'], cv2.IMREAD_COLOR)
plt.subplot(4, 3, 12)
plt.imshow(target[:, :, ::-1])
plt.axis('off')
fig.savefig(out_path, bbox_inches='tight')
plt.close(fig)
break
def test_simulator(config):
plt.switch_backend('agg')
output_dir = osp.join(config.model_dir, 'simulator')
maybe_create(output_dir)
transformer = image_normalize('background')
db = abstract_scene(config, 'val', transform=transformer)
loader = DataLoader(db,
batch_size=config.batch_size,
shuffle=False,
num_workers=config.num_workers)
env = simulator(db, config.batch_size)
env.reset()
for cnt, batched in enumerate(loader):
out_inds = batched['out_inds'].long().numpy()
gt_paths = batched['color_path']
img_inds = batched['image_idx']
sents = batched['sentence']
sequences = []
masks = []
for i in range(out_inds.shape[1]):
frames = env.batch_render_to_pytorch(out_inds[:, i])
frames = tensor_to_img(frames)
msks = env.batch_location_maps(out_inds[:, i, 3])
for j in range(len(frames)):
frame = frames[j]
msk = cv2.resize(msks[j], (frame.shape[0], frame.shape[1]),
cv2.INTER_NEAREST)
frames[j] = frame * (1.0 - msk[..., None])
sequences.append(frames)
seqs1 = np.stack(sequences, 1)
print('seqs1', seqs1.shape)
seqs2 = env.batch_redraw(return_sequence=True)
seqs = seqs2
for i in range(len(seqs)):
imgs = seqs[i]
image_idx = img_inds[i]
name = '%03d_' % i + str(image_idx).zfill(9)
out_path = osp.join(output_dir, name + '.png')
color = cv2.imread(gt_paths[i], cv2.IMREAD_COLOR)
# color, _, _ = create_squared_image(color)
fig = plt.figure(figsize=(32, 16))
plt.suptitle(sents[i])
for j in range(len(imgs)):
plt.subplot(3, 5, j + 1)
plt.imshow(imgs[j, :, :, ::-1])
plt.axis('off')
plt.subplot(3, 5, 15)
plt.imshow(color[:, :, ::-1])
plt.axis('off')
fig.savefig(out_path, bbox_inches='tight')
plt.close(fig)
break
