def apply_themes(args, device, model):
img_path = os.path.join("photos", args.content_image)
content_image = Image.open(img_path).resize(eval_size)
masks = utils.get_masks(content_image, args.seg_threshold)
filter_ids = utils.select_filters(masks, content_image, total_filters)
content_transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Lambda(lambda x: x.mul(255))])
content_image = content_transform(content_image)
content_images = content_image.expand(len(filter_ids), -1, -1,
-1).to(device)
# one forward pass to render themes
with torch.no_grad():
if args.load_model is None or args.load_model == "None":
theme_model = model
else:
# our saved models were trained with gpu 3
#theme_model = torch.load(args.load_model, map_location={'cuda:3': 'cuda:0'})
theme_model = torch.load(args.load_model)
theme_model.eval()
theme_model.to(device)
output = theme_model(content_images, filter_ids).cpu()
output_list = []
for img in output:
img = img.clone().clamp(0, 255).numpy()
img = img.transpose(1, 2, 0).astype("uint8")
output_list.append(img)
rendered_themes = utils.render_themes(output_list, masks)
utils.draw(rendered_themes, args.content_image, args.output_image)
def walk_through_dataset(root_folder, depth, start_from=False, plot_evolution=False):
generator = utils.walk_level(root_folder, depth)
gens = [[r, f] for r, d, f in generator if len(r.split("/")) == len(root_folder.split("/")) + depth]
# This boolean controls whether the algorithm will be used on a specific image or not
if start_from:
go = False
else:
go = True
for root, files in gens:
images = utils.get_images(files, root)
masks = utils.get_masks(files, root)
cages = utils.get_cages(files, root)
if not images or not masks or not cages:
if not images:
print root, 'has no .png image'
if not masks:
print root, 'has no .png mask'
if not cages:
print root, 'has no .txt cages'
else:
# TODO: FIX TO ALLOW MORE IMAGES
for image in images:
if image.spec_name == start_from:
go = True
if not go:
continue
for mask in masks:
for cage in cages:
print '\nSegmenting', image.root
aux_cage = copy.deepcopy(cage)
resulting_cage = cac_segmenter.cac_segmenter(image, mask, aux_cage, None,
plot_evolution=plot_evolution)
evaluate_results(image, cage, mask, resulting_cage, files, root)
def inference_minor_util60(role_id, handcards, num, is_pair, dup_mask, main_cards_char):
for main_card in main_cards_char:
handcards.remove(main_card)
s = get_mask(handcards, action_space, None).astype(np.float32)
outputs = []
minor_type = 1 if is_pair else 0
for i in range(num):
input_single, input_pair, _, _ = get_masks(handcards, None)
_, _, _, _, _, _, minor_response_prob = func(
[np.array([role_id]), s.reshape(1, -1), np.zeros([1, 9085]), np.array([minor_type])]
)
# give minor cards
mask = None
if is_pair:
mask = np.concatenate([input_pair, [0, 0]]) * dup_mask
else:
mask = input_single * dup_mask
minor_response = take_action_from_prob(minor_response_prob, mask)
dup_mask[minor_response] = 0
# convert network output to char cards
handcards.remove(to_char(minor_response + 3))
if is_pair:
handcards.remove(to_char(minor_response + 3))
s = get_mask(handcards, action_space, None).astype(np.float32)
# save to output
outputs.append(to_char(minor_response + 3))
if is_pair:
outputs.append(to_char(minor_response + 3))
return outputs
def _load_all_masks(self, label_all, img_size):
print('preparing all masks data.....')
masks = []
for labels in label_all:
mask = get_masks(labels, img_size)
masks.append(mask)
return masks
def get_mask(self):
if self.act == ACT_TYPE.PASSIVE:
decision_mask, response_mask, bomb_mask, _ = get_mask_alter(
self.curr_handcards_char, to_char(self.last_cards_value),
self.category)
if self.mode == MODE.PASSIVE_DECISION:
return decision_mask
elif self.mode == MODE.PASSIVE_RESPONSE:
return response_mask
elif self.mode == MODE.PASSIVE_BOMB:
return bomb_mask
elif self.mode == MODE.MINOR_RESPONSE:
input_single, input_pair, _, _ = get_masks(
self.curr_handcards_char, None)
if self.minor_type == 1:
mask = np.append(input_pair, [0, 0])
else:
mask = input_single
for v in set(self.intention):
mask[v - 3] = 0
return mask
elif self.act == ACT_TYPE.ACTIVE:
decision_mask, response_mask, _, length_mask = get_mask_alter(
self.curr_handcards_char, [], self.category)
if self.mode == MODE.ACTIVE_DECISION:
return decision_mask
elif self.mode == MODE.ACTIVE_RESPONSE:
return response_mask[self.active_decision]
elif self.mode == MODE.ACTIVE_SEQ:
return length_mask[self.active_decision][self.active_response]
elif self.mode == MODE.MINOR_RESPONSE:
input_single, input_pair, _, _ = get_masks(
self.curr_handcards_char, None)
if self.minor_type == 1:
mask = np.append(input_pair, [0, 0])
else:
mask = input_single
for v in set(self.intention):
mask[v - 3] = 0
return mask
def __getitem__(self, index):
img = np.array(self.img_all[index], copy=True)
self.img_size = img.shape[0:2]
# seg_mask = np.array(self.segmask_all[index], copy=True)
labels = np.array(self.label_all[index], copy=True)
labels1 = np.array(self.label_all[index], copy=True)
masks = get_masks(labels1, self.img_size)
img_region = np.expand_dims(img, 0)
masks_region = masks
img_region = img_region / img_region.max()
return np.array(img_region), np.array(masks_region), np.array(labels)
def run(input_path):
print 'Read input'
imgs = utils.read_input(input_path)
print 'Calibrate camera'
camera_mat, dist_coeffs = utils.calibrate_camera()
print 'Undistort images'
imgs_undistorted = utils.undistort_imgs(imgs, camera_mat, dist_coeffs)
print 'Get masks'
masks = utils.get_masks(imgs_undistorted)
print 'Transform perspective'
masks_birdseye = utils.birdseye(masks)
print 'Find lines'
lines, history = utils.find_lines(masks_birdseye)
print 'Draw lanes'
imgs_superimposed = utils.draw_lane(imgs_undistorted, lines, history)
return imgs_superimposed
# Undistorted calibration image
calibration_img = utils.read_input('../data/camera_cal/calibration1.jpg')
camera_mat, dist_coeffs = utils.calibrate_camera()
cal_undistorted = utils.undistort_imgs(calibration_img, camera_mat,
dist_coeffs)
cal_save_path = join(c.SAVE_DIR, 'undistort_cal.jpg')
utils.save(cal_undistorted, cal_save_path)
# Undistorted road image
road_img = utils.read_input('../data/test_images/test3.jpg')
road_undistorted = utils.undistort_imgs(road_img, camera_mat, dist_coeffs)
road_save_path = join(c.SAVE_DIR, 'undistort_road.jpg')
utils.save(road_undistorted, road_save_path)
# Mask image
mask = utils.get_masks(road_undistorted)
mask_save_path = join(c.SAVE_DIR, 'mask.jpg')
utils.save(mask * 255, mask_save_path)
# Birdseye transform
birdseye = utils.birdseye(mask)
birdseye_save_path = join(c.SAVE_DIR, 'birdseye.jpg')
utils.save(birdseye * 255, birdseye_save_path)
# Fit lines
find_fit = utils.visualize_find_fit(birdseye[0])
find_fit_save_path = join(c.SAVE_DIR, 'find_fit.jpg')
utils.save(find_fit, find_fit_save_path)
# Output
lines, history = utils.find_lines(birdseye)
def evaluate(eval_loader, name='val'):
# Turn on evaluation mode which disables dropout.
model.eval()
start_time = time.time()
#NUM_Q_SAMPLES = opt.num_q_samples
NUM_Z_SAMPLES = opt.num_z_samples
NUM_ITERS = min(opt.num_iters, len(eval_loader))
MOD_STEP = opt.mod_step #5 #3
NUM_MODS = sorted(
list(
set([1] +
[n_mods for n_mods in range(2, num_modalities + 1, MOD_STEP)] +
[num_modalities])))
NUM_CONTEXTS = [0, 1, 2, 3, 4, 5]
NUM_TARGET = 5
MASK_STEP = opt.mask_step #10 #5
all_masks = []
for n_mods in NUM_MODS:
masks = get_masks(num_modalities, min_modes=n_mods, max_modes=n_mods)
masks = list(set(masks[::MASK_STEP] + [masks[-1]]))
all_masks += masks
m_indices = dict(
zip([get_str_from_mask(mask) for mask in all_masks],
[i for i in range(len(all_masks))]))
logging('num mods : {}'.format(NUM_MODS), path=opt.path)
logging('num ctxs : {}'.format(NUM_CONTEXTS), path=opt.path)
logging('num tgt : {}'.format(NUM_TARGET), path=opt.path)
logging('mask step: {}'.format(MASK_STEP), path=opt.path)
logging('masks : {}'.format(m_indices), path=opt.path)
total_avg_batch_sizes_per_nmod_nctx = [
[0 for i in range(len(NUM_CONTEXTS))] for j in range(num_modalities)
]
total_avg_acc1_per_nmod_nctx = [[0 for i in range(len(NUM_CONTEXTS))]
for j in range(num_modalities)]
total_avg_acc5_per_nmod_nctx = [[0 for i in range(len(NUM_CONTEXTS))]
for j in range(num_modalities)]
total_batch_sizes_per_nmod_nctx = [[0 for i in range(len(NUM_CONTEXTS))]
for j in range(len(all_masks))]
total_acc1_per_nmod_nctx = [[0 for i in range(len(NUM_CONTEXTS))]
for j in range(len(all_masks))]
total_acc5_per_nmod_nctx = [[0 for i in range(len(NUM_CONTEXTS))]
for j in range(len(all_masks))]
with torch.no_grad():
for batch_idx, (eval_info, eval_context,
eval_target) in enumerate(eval_loader):
# init batch
eval_context = batch_to_device(eval_context, device)
eval_target = batch_to_device(eval_target, device)
eval_all = merge_two_batch(eval_context, eval_target)
num_episodes = len(eval_context)
# select target
_new_eval_target = []
for target in eval_all:
_target = tuple([
target[i][-NUM_TARGET:] if target[i] is not None else None
for i in range(len(target))
])
_new_eval_target += [_target]
eval_target = _new_eval_target
# forward
for n_mods in NUM_MODS:
masks = get_masks(num_modalities,
min_modes=n_mods,
max_modes=n_mods)
masks = list(set(masks[::MASK_STEP] + [masks[-1]]))
for mask in masks:
n_mods = sum(mask)
avg_m_idx = n_mods - 1
m_idx = m_indices[get_str_from_mask(mask)]
for c_idx, num_context in enumerate(NUM_CONTEXTS):
# select context
_new_eval_context = []
for context in eval_all:
_context = tuple([
context[i][:num_context]
if context[i] is not None and num_context > 0
and mask[i // 2] else None
for i in range(len(context))
])
_new_eval_context += [_context]
eval_context = _new_eval_context
# get labels
eval_label = torch.Tensor(
[i for i in range(num_episodes)]).long().to(device)
# infer
logprobs_per_batch = []
for i_ep in range(num_episodes):
new_eval_target = [eval_target[i_ep]
] * num_episodes
# get dim size per episode
dim_per_eps = get_dim_size(
new_eval_target, is_grayscale=opt.grayscale)
# forward
logprobs = []
for j in range(NUM_Z_SAMPLES):
# forward
_, _, logprob, info = model.predict(
eval_context,
new_eval_target,
is_grayscale=opt.grayscale,
use_uint8=False)
# append to loss_logprobs
logprobs += [logprob.unsqueeze(1)]
# concat
logprobs = torch.cat(logprobs, dim=1)
# get logprob
logprobs = logprob_logsumexp(logprobs).detach()
# get logprob per dimension
for i in range(num_episodes):
logprobs[i:i + 1] /= float(dim_per_eps[i])
# append
logprobs_per_batch += [logprobs.unsqueeze(1)]
# concat
logprobs_per_batch = torch.cat(logprobs_per_batch,
dim=1)
# get acc
acc1, acc5 = accuracy(logprobs_per_batch,
eval_label,
topk=(1, 5))
cur_acc1 = acc1[0].item()
cur_acc5 = acc5[0].item()
total_avg_acc1_per_nmod_nctx[avg_m_idx][
c_idx] += cur_acc1 * num_episodes
total_avg_acc5_per_nmod_nctx[avg_m_idx][
c_idx] += cur_acc5 * num_episodes
total_avg_batch_sizes_per_nmod_nctx[avg_m_idx][
c_idx] += num_episodes
total_acc1_per_nmod_nctx[m_idx][
c_idx] += cur_acc1 * num_episodes
total_acc5_per_nmod_nctx[m_idx][
c_idx] += cur_acc5 * num_episodes
total_batch_sizes_per_nmod_nctx[m_idx][
c_idx] += num_episodes
# plot
if (batch_idx + 1) % opt.vis_interval == 0 or (
batch_idx + 1) == len(eval_loader):
elapsed = time.time() - start_time
start_time = time.time()
# print
logging('| {} '
'| {:5d}/{:5d} '
'| sec/step {:5.2f} '
'| acc (top1) {:.3f} '
'| acc (top5) {:.3f} '.format(
name,
batch_idx + 1,
len(eval_loader),
elapsed / opt.vis_interval,
cur_acc1,
cur_acc5,
),
path=opt.path)
if (batch_idx + 1) == NUM_ITERS:
break
# print
logging(''.join(
['masks V / # of context > '] +
[' {:4d}'.format(num_context) for num_context in NUM_CONTEXTS]),
path=opt.new_path)
logging('=' * 17 + ' acc1 ' + '=' * 17 + ' | ' + '=' * 17 + ' acc5 ' +
'=' * 17,
path=opt.new_path)
for mask in all_masks:
mask_str = get_str_from_mask(mask)
m_idx = m_indices[mask_str]
txt = ' {} |'.format(mask_str)
for c_idx, num_context in enumerate(NUM_CONTEXTS):
total_batch_size = total_batch_sizes_per_nmod_nctx[m_idx][c_idx]
total_acc1 = total_acc1_per_nmod_nctx[m_idx][
c_idx] / total_batch_size
writer.add_scalar('mask{}/{}/acc1'.format(mask_str, name),
total_acc1, num_context)
txt += ' {:3.1f}'.format(total_acc1)
txt += ' | '
for c_idx, num_context in enumerate(NUM_CONTEXTS):
total_batch_size = total_batch_sizes_per_nmod_nctx[m_idx][c_idx]
total_acc5 = total_acc5_per_nmod_nctx[m_idx][
c_idx] / total_batch_size
writer.add_scalar('mask{}/{}/acc5'.format(mask_str, name),
total_acc5, num_context)
txt += ' {:3.1f}'.format(total_acc5)
logging(txt, path=opt.new_path)
# print
logging('', path=opt.new_path)
logging('', path=opt.new_path)
logging(''.join(
['# of mods V / # of context > '] +
[' {:4d}'.format(num_context) for num_context in NUM_CONTEXTS]),
path=opt.new_path)
logging('=' * 17 + ' acc1 ' + '=' * 17 + ' | ' + '=' * 17 + ' acc5 ' +
'=' * 17,
path=opt.new_path)
for n_mods in NUM_MODS:
avg_m_idx = n_mods - 1
txt = ' {} |'.format(n_mods)
for c_idx, num_context in enumerate(NUM_CONTEXTS):
total_avg_batch_size = total_avg_batch_sizes_per_nmod_nctx[
avg_m_idx][c_idx]
total_avg_acc1 = total_avg_acc1_per_nmod_nctx[avg_m_idx][
c_idx] / total_avg_batch_size
writer.add_scalar('M{}/{}/acc1'.format(n_mods, name),
total_avg_acc1, num_context)
writer.add_scalar('C{}/{}/acc1'.format(num_context, name),
total_avg_acc1, n_mods)
txt += ' {:3.1f}'.format(total_avg_acc1)
txt += ' | '
for c_idx, num_context in enumerate(NUM_CONTEXTS):
total_avg_batch_size = total_avg_batch_sizes_per_nmod_nctx[
avg_m_idx][c_idx]
total_avg_acc5 = total_avg_acc5_per_nmod_nctx[avg_m_idx][
c_idx] / total_avg_batch_size
writer.add_scalar('M{}/{}/acc5'.format(n_mods, name),
total_avg_acc5, num_context)
writer.add_scalar('C{}/{}/acc5'.format(num_context, name),
total_avg_acc5, n_mods)
txt += ' {:3.1f}'.format(total_avg_acc5)
logging(txt, path=opt.new_path)
return total_acc1 / total_batch_size, total_acc5 / total_batch_size
def evaluate(eval_loader, test=False):
# Turn on evaluation mode which disables dropout.
name = 'test' if test else 'val'
model.eval()
transform = get_transform()
NUM_ITERS = min(opt.num_iters, len(eval_loader))
NUM_TEST = 5
NUM_CONTEXTS = sorted([nc for nc in opt.n_context])
if len(NUM_CONTEXTS) == 0:
NUM_CONTEXTS = [0, 1, 5, 10] #[0, 1, 5, 10, 15]
assert (NUM_TEST + NUM_CONTEXTS[-1]) <= dataset_info['nviews']
NUM_MODS = sorted([n_mods for n_mods in opt.n_mods])
if len(NUM_MODS) == 0:
NUM_MODS = [n_mod for n_mod in range(1, num_modalities + 1)]
assert NUM_MODS[-1] <= num_modalities
assert NUM_MODS[0] > 0
all_masks = []
for n_mods in NUM_MODS:
masks = get_masks(num_modalities, min_modes=n_mods, max_modes=n_mods)
all_masks += masks
logging('num mods : {}'.format(NUM_MODS), path=opt.path)
logging('num ctxs : {}'.format(NUM_CONTEXTS), path=opt.path)
logging('num tgt : {}'.format(NUM_TEST), path=opt.path)
logging('masks : {}'.format(all_masks), path=opt.path)
hpt_tgt_gen = {}
avg_diffs = {}
num_datas = {}
with torch.no_grad():
for i_sample in range(1, opt.num_samples + 1):
did_plot = [False] * num_classes
for batch_idx, (eval_info, eval_context,
eval_target) in enumerate(eval_loader):
# init batch
eval_context = batch_to_device(eval_context, device)
eval_target = batch_to_device(eval_target, device)
eval_all = merge_two_batch(eval_context, eval_target)
num_episodes = len(eval_context)
# get img_queries
img_queries = torch.from_numpy(
np.array(eval_info[0]['add_cameras'])).float()
# get true_images and hand_images
true_images = load_images(eval_info[0]['add_images'],
transform)
_true_images = get_grid_image(true_images,
16,
3,
64,
64,
nrow=4,
pad_value=0)
hand_images = load_images(eval_info[0]['hand_images'],
transform)
_hand_images = get_grid_image(hand_images,
15,
3,
64,
64,
nrow=15,
pad_value=0)
_data_images = []
for idx, (nchannels, nheight, nwidth, _,
mtype) in enumerate(dataset_info['dims']):
if mtype == 'image':
_data_images += [eval_all[0][idx * 2]]
_data_images = get_combined_visualization_image_data(
opt.dataset,
dataset_info['dims'],
_data_images,
dataset_info['nviews'],
min(4, num_episodes),
nrow=15,
pad_value=0)[0]
''' temporary '''
assert len(eval_context) == 1
assert len(eval_target) == 1
cls = eval_info[0]['class']
''' per class '''
# visualize per class
if not did_plot[cls]:
# change flag
did_plot[cls] = True
# draw true_images and hand_images
writer.add_image(
'{}/gt-img-cls{}-i{}'.format(name, cls, i_sample),
_true_images, 0)
writer.add_image(
'{}/hand-img-cls{}-i{}'.format(name, cls, i_sample),
_hand_images, 0)
writer.add_image(
'{}/data-img-cls{}-i{}'.format(name, cls, i_sample),
_data_images, 0)
for num_context in NUM_CONTEXTS:
_hand_images = get_grid_image(
hand_images[:num_context],
num_context,
3,
64,
64,
nrow=5,
pad_value=0)
writer.add_image(
'{}/ctx-hand-img-cls{}-i{}-nc{}'.format(
name, cls, i_sample, num_context),
_hand_images, 0)
def draw_img_gen(mask, num_context=0):
# get mask index
m_idx = sum(mask) - 1
# get context
new_eval_context, new_eval_target = trim_context_target(
eval_all,
num_context=num_context,
mask=mask,
num_modalities=num_modalities)
if sum([
int(new_eval_target[0][j * 2] is None)
for j in range(num_modalities)
]) == num_modalities:
return
# select test
_new_eval_target = []
for i in range(num_episodes):
_target = []
for idx, (nchannels, nheight, nwidth, _,
mtype) in enumerate(
dataset_info['dims']):
data, query = new_eval_target[i][
idx * 2], new_eval_target[i][idx * 2 + 1]
if mtype == 'haptic':
_target += [
data[-NUM_TEST:]
if data is not None else None
]
_target += [
query[-NUM_TEST:]
if data is not None else None
]
else:
_target += [data]
_target += [query]
_new_eval_target += [tuple(_target)]
new_eval_target = _new_eval_target
# get batch size
batch_size, mod_batch_sizes = get_batch_size(
new_eval_target)
# get queries
mod_queries, num_mod_queries = get_queries(
new_eval_target,
device,
num_hpt_queries=NUM_TEST,
img_queries=img_queries)
# forward
outputs, _, _, _ = model(new_eval_context,
new_eval_target,
is_grayscale=opt.grayscale)
# generate
gens, _ = model.generate(new_eval_context,
tuple(mod_queries),
is_grayscale=opt.grayscale)
# visualize
img_ctxs, img_tgts, img_outputs, img_gens = [], [], [], []
hpt_ctxs, hpt_tgts, hpt_outputs, hpt_gens = [], [], [], []
for idx, (nchannels, nheight, nwidth, _,
mtype) in enumerate(dataset_info['dims']):
# get output and gen
output = outputs[idx]
gen = gens[idx]
_num_mod_queries = num_mod_queries[idx]
# visualize
if mtype == 'image':
# grayscale
if opt.grayscale:
if output.size(0) > 0:
output = output.expand(
output.size(0), nchannels, nheight,
nwidth)
gen = gen.expand(gen.size(0), nchannels,
nheight, nwidth)
# get ctx, tgt
if num_context > 0 and mask[idx]:
sz = new_eval_context[0][idx *
2].size()[1:]
ctx = torch.cat([
new_eval_context[0][idx * 2],
gen.new_zeros(
dataset_info['nviews'] -
num_context, *sz)
],
dim=0)
num_target = new_eval_target[0][
idx * 2].size(0) if new_eval_target[0][
idx * 2] is not None else 0
assert num_target == output.size(0)
if num_target > 0:
tgt = torch.cat([
gen.new_zeros(
dataset_info['nviews'] -
num_target, *sz),
new_eval_target[0][idx * 2],
],
dim=0)
output = torch.cat([
gen.new_zeros(
dataset_info['nviews'] -
num_target, *sz),
output,
],
dim=0)
else:
tgt = gen.new_zeros(
dataset_info['nviews'] *
num_episodes, *sz)
output = gen.new_zeros(
dataset_info['nviews'] *
num_episodes, *sz)
else:
ctx = gen.new_zeros(
dataset_info['nviews'] * num_episodes,
nchannels, nheight, nwidth)
tgt = new_eval_target[0][idx * 2]
# append to list
img_gens += [gen]
img_outputs += [output]
img_ctxs += [ctx]
img_tgts += [tgt]
num_img_queries = _num_mod_queries
elif mtype == 'haptic':
ctx = new_eval_context[0][idx * 2]
tgt = new_eval_target[0][idx * 2]
# append to list
hpt_gens += [gen]
hpt_outputs += [output]
hpt_ctxs += [ctx]
hpt_tgts += [tgt]
num_hpt_queries = _num_mod_queries
else:
raise NotImplementedError
# combine haptic
if not get_str_from_mask(mask) in hpt_tgt_gen:
hpt_tgt_gen[get_str_from_mask(mask)] = {}
avg_diffs[get_str_from_mask(mask)] = np.zeros(
len(NUM_CONTEXTS))
num_datas[get_str_from_mask(mask)] = 0
hpt_tgts = torch.cat(hpt_tgts, dim=1)
hpt_gens = torch.cat(hpt_gens, dim=1)
hpt_tgt_gen[get_str_from_mask(mask)][num_context] = (
hpt_tgts, hpt_gens)
# visualize combined image
xgs = get_combined_visualization_image_data(
opt.dataset,
dataset_info['dims'],
img_gens,
num_img_queries,
min(4, num_episodes),
nrow=4,
pad_value=0)
xos = get_combined_visualization_image_data(
opt.dataset,
dataset_info['dims'],
img_outputs,
dataset_info['nviews'],
min(4, num_episodes),
nrow=4,
pad_value=0)
xcs = get_combined_visualization_image_data(
opt.dataset,
dataset_info['dims'],
img_ctxs,
dataset_info['nviews'],
min(4, num_episodes),
nrow=4,
pad_value=0)
_xcs = get_combined_visualization_image_data(
opt.dataset,
dataset_info['dims'],
img_ctxs,
num_context,
min(4, num_episodes),
nrow=5,
pad_value=0)
xts = get_combined_visualization_image_data(
opt.dataset,
dataset_info['dims'],
img_tgts,
dataset_info['nviews'],
min(4, num_episodes),
nrow=4,
pad_value=0)
for i, (xc, xt, xo,
xg) in enumerate(zip(xcs, xts, xos, xgs)):
writer.add_image(
'{}/ctx-cls{}-M{}-mask{}-i{}-nc{}/img'.format(
name, cls, m_idx + 1,
get_str_from_mask(mask), i, num_context),
_xcs[i], 0)
writer.add_image(
'{}/gen-cls{}-M{}-mask{}-i{}-nc{}/img'.format(
name, cls, m_idx + 1,
get_str_from_mask(mask), i, num_context),
xg, 0)
x = torch.cat([xc, xt, xo, xg], dim=2)
writer.add_image(
'{}/ctx-tgt-rec-gen-cls{}-M{}-mask{}-i{}-nc{}/img'
.format(name, cls, m_idx + 1,
get_str_from_mask(mask), i,
num_context), x, 0)
# run vis
for mask in all_masks:
for num_context in NUM_CONTEXTS:
draw_img_gen(mask=mask, num_context=num_context)
# visualize combined haptic
m_idx = sum(mask) - 1 # get mask index
xs, diffs = get_combined_visualization_haptic_data(
hpt_tgt_gen[get_str_from_mask(mask)],
title='mask: {}'.format(get_str_from_mask(mask)))
_diffs = [
np.mean([diffs[i][j] for i in range(len(diffs))])
for j in range(len(NUM_CONTEXTS))
]
num_datas[get_str_from_mask(mask)] += 1
for j, diff in enumerate(_diffs):
avg_diffs[get_str_from_mask(mask)][j:j + 1] += diff
num_context = NUM_CONTEXTS[j]
writer.add_scalar(
'{}/diff-cls{}-M{}-mask{}-all/hpt'.format(
name, cls, m_idx + 1,
get_str_from_mask(mask)), diff,
num_context)
for i, x in enumerate(xs):
writer.add_image(
'{}/tgt-gen-cls{}-M{}-mask{}-i{}/hpt'.format(
name, cls, m_idx + 1,
get_str_from_mask(mask), i),
convert_npimage_torchimage(x), 0)
for j, diff in enumerate(diffs[i]):
num_context = NUM_CONTEXTS[j]
writer.add_scalar(
'{}/diff-cls{}-M{}-mask{}-i{}/hpt'.format(
name, cls, m_idx + 1,
get_str_from_mask(mask), i), diff,
num_context)
if (batch_idx + 1) % 1 == 0:
print(batch_idx + 1, '/', NUM_ITERS, ' [',
len(eval_loader), ']')
if (batch_idx + 1) == NUM_ITERS:
break
return
args = parser.parse_args()
if args.dataset_folder:
X = pd.read_csv(args.dataset_folder + '/X.csv')
y = pd.read_csv(args.dataset_folder + '/y.csv')
networkx_graph = nx.read_graphml(args.dataset_folder + '/graph.graphml')
else:
X = pd.read_csv(args.X)
y = pd.read_csv(args.y)
networkx_graph = nx.read_graphml(args.graph)
networkx_graph = nx.relabel_nodes(networkx_graph, {str(i): i for i in range(len(networkx_graph))})
cat_features = args.cat_features
train_mask, val_mask, train_val_mask, test_mask = get_masks(X.shape[0],
train_size=args.train_size,
val_size=args.val_size,
random_seed=args.random_seed, )
os.makedirs('losses/', exist_ok=True)
if args.model.lower() == 'gbdt':
from models.GBDT import GBDT
model = GBDT(depth=args.depth)
model.fit(X, y, train_mask, val_mask, test_mask,
cat_features=cat_features, num_epochs=args.num_features, patience=args.patience,
learning_rate=args.learning_rate, plot=False, verbose=False,
loss_fn=args.loss_fn)
elif args.model.lower() == 'mlp':
from models.MLP import MLP
model = MLP(task=args.task)
def learn(self, batch_size=64, agent=0):
# DDPG implementation
if agent == 0:
experiences = self.memory_0.sample(batch_size)
if agent == 1:
experiences = self.memory_1.sample(batch_size)
states, actions, rewards, next_states, dones, batch_lengths = experiences
# Because different batch data is variable length, mask is used for cover the unused part.
masks = get_masks(batch_lengths)
# update critic
with torch.no_grad():
if agent == 0:
next_actions = torch.cat([
self.actor_target_0(next_states[:, :, :24]), actions[:, :,
2:]
],
dim=2)
if agent == 1:
next_actions = torch.cat([
actions[:, :, :2],
self.actor_target_1(next_states[:, :, 24:])
],
dim=2)
q_value_prime = self.critic_target(next_states, next_actions,
agent).squeeze(2) * masks
q_value = self.critic_local(states.requires_grad_(True), actions,
agent).squeeze(2) * masks
td_error = (rewards + self.gamma * q_value_prime *
(1 - dones) - q_value) * masks # one-step estimate
critic_loss = ((td_error**2).sum(1) / batch_lengths).mean()
self.critic_optimizer.zero_grad()
critic_loss.backward()
torch.nn.utils.clip_grad_norm_(self.critic_local.parameters(), 1)
self.critic_optimizer.step()
self.soft_update(self.critic_local, self.critic_target, tau=0.01)
# update actor
if agent == 0:
actions_pred = torch.cat(
[self.actor_local_0(states[:, :, :24]), actions[:, :, 2:]],
dim=2)
if agent == 1:
actions_pred = torch.cat(
[actions[:, :, :2],
self.actor_local_1(states[:, :, 24:])],
dim=2)
q_value = self.critic_local(states, actions_pred, agent)
actor_loss = -(
(q_value.squeeze() * masks).sum(1) / batch_lengths).mean()
if agent == 0:
self.actor_optimizer_0.zero_grad()
(actor_loss).backward()
torch.nn.utils.clip_grad_norm_(self.actor_local_0.parameters(), 1)
self.actor_optimizer_0.step()
self.soft_update(self.actor_local_0, self.actor_target_0, tau=0.01)
if agent == 1:
self.actor_optimizer_1.zero_grad()
(actor_loss).backward()
torch.nn.utils.clip_grad_norm_(self.actor_local_1.parameters(), 1)
self.actor_optimizer_1.step()
self.soft_update(self.actor_local_1, self.actor_target_1, tau=0.01)
self.noise.reset()
if self.t_step % 128 == 0:
print(f'q_value: {q_value.detach().mean().item():.5f}, '
f'critic_loss: {critic_loss.detach().mean().item():.5f}, '
f'actor_loss: {actor_loss.detach().mean().item():.5f} ')
