def heatmaps_to_landmarks(self, hms):
lms = np.zeros((len(hms), self.num_landmarks, 2), dtype=int)
if hms.shape[1] > 3:
# print(hms.max())
for i in range(len(hms)):
heatmaps = to_numpy(hms[i])
for l in range(len(heatmaps)):
hm = heatmaps[self.landmark_id_to_heatmap_id(l)]
lms[i, l, :] = np.unravel_index(np.argmax(hm, axis=None),
hm.shape)[::-1]
elif hms.shape[1] == 3:
hms = to_numpy(hms)
def get_score_plane(h, lm_id, cn):
v = nn.lmcolors[lm_id, cn]
hcn = h[cn]
hcn[hcn < v - 2] = 0
hcn[hcn > v + 5] = 0
return hcn
hms *= 255
for i in range(len(hms)):
hm = hms[i]
for l in landmarks.config.LANDMARKS:
lm_score_map = get_score_plane(hm, l, 0) * get_score_plane(
hm, l, 1) * get_score_plane(hm, l, 2)
lms[i, l, :] = np.unravel_index(
np.argmax(lm_score_map, axis=None),
lm_score_map.shape)[::-1]
lm_scale = lmcfg.HEATMAP_SIZE / self.input_size
return lms / lm_scale
def show_images_in_batch(images, labels):
from datasets.emotiw import EmotiW
disp_imgs = to_numpy(images)
if labels is not None:
labels = to_numpy(labels)
disp_imgs = add_frames_to_images(images, labels, label_colors=EmotiW.colors_rgb)
vis_square(disp_imgs, fx=0.4, fy=0.4)
def calc_landmark_recon_error(X, X_recon, lms, return_maps=False, reduction="mean"):
assert len(X.shape) == 4
assert reduction in ["mean", "none"]
X = to_numpy(X)
X_recon = to_numpy(X_recon)
mask = np.zeros((X.shape[0], X.shape[2], X.shape[3]), dtype=np.float32)
input_size = X.shape[-1]
radius = input_size * 0.05
for img_id in range(len(mask)):
for lm in lms[img_id]:
cv2.circle(
mask[img_id],
(int(lm[0]), int(lm[1])),
radius=int(radius),
color=1,
thickness=-1,
)
err_maps = np.abs(X - X_recon).mean(axis=1) * 255.0
masked_err_maps = err_maps * mask
debug = False
if debug:
fig, ax = plt.subplots(1, 3)
ax[0].imshow(
vis.to_disp_image(
(X * mask[:, np.newaxis, :, :].repeat(3, axis=1))[0], denorm=True
)
)
ax[1].imshow(
vis.to_disp_image(
(X_recon * mask[:, np.newaxis, :, :].repeat(3, axis=1))[0], denorm=True
)
)
ax[2].imshow(masked_err_maps[0])
plt.show()
if reduction == "mean":
err = masked_err_maps.sum() / (mask.sum() * 3)
else:
# err = masked_err_maps.mean(axis=2).mean(axis=1)
err = masked_err_maps.sum(axis=2).sum(axis=1) / (
mask.reshape(len(mask), -1).sum(axis=1) * 3
)
if return_maps:
return err, masked_err_maps
else:
return err
def res_vec(self, exclude_fid):
if cfg.WITH_PARALLEL_DISENTANGLEMENT:
try:
h = torch.cat([self.f_parallel[i] for i in range(len(self.f_parallel)) if i != exclude_fid], dim=1)
return to_numpy(h)
except:
return None
def add_error_to_images(images, errors, loc='bl', size=0.65, vmin=0., vmax=30.0, thickness=1, format_string='{:.1f}'):
new_images = _to_disp_images(images)
colors = color_map(to_numpy(errors), cmap=plt.cm.jet, vmin=vmin, vmax=vmax)
for disp, err, color in zip(new_images, errors, colors):
pos = get_pos_in_image(loc, size, disp.shape)
cv2.putText(disp, format_string.format(err), pos, cv2.FONT_HERSHEY_DUPLEX, size, color, thickness, cv2.LINE_AA)
return new_images
def detect_landmarks(self, X):
X_recon = self.forward(X)
X_lm_hm = self.LMH(self.P)
X_lm_hm = landmarks.lmutils.decode_heatmap_blob(X_lm_hm)
X_lm_hm = landmarks.lmutils.smooth_heatmaps(X_lm_hm)
lm_preds = to_numpy(self.heatmaps_to_landmarks(X_lm_hm))
return X_recon, lm_preds, X_lm_hm
def emotions_pred(self):
try:
if self.emotion_probs is not None:
return np.argmax(to_numpy(self.emotion_probs), axis=1)
except AttributeError:
pass
return None
def add_error_to_images(
images,
errors,
loc="bl",
size=0.65,
vmin=0.0,
vmax=30.0,
thickness=1,
format_string="{:.1f}",
colors=None,
):
new_images = to_disp_images(images)
if colors is None:
colors = color_map(to_numpy(errors),
cmap=plt.cm.jet,
vmin=vmin,
vmax=vmax)
if images[0].dtype == np.uint8:
colors *= 255
for disp, err, color in zip(new_images, errors, colors):
pos = get_pos_in_image(loc, size, disp.shape)
cv2.putText(
disp,
format_string.format(err),
pos,
cv2.FONT_HERSHEY_DUPLEX,
size,
color,
thickness,
cv2.LINE_AA,
)
return new_images
def visualize_random_faces(net, nimgs=10, wait=10, f=1.0):
z_random = torch.randn(nimgs, net.z_dim).cuda()
with torch.no_grad():
X_gen_vis = net.P(z_random)[:, :3]
X_lm_hm = net.LMH(net.P)
pred_heatmaps = to_single_channel_heatmap(to_numpy(X_lm_hm[:nimgs]))
pred_heatmaps = [
cv2.resize(im, None, fx=f, fy=f, interpolation=cv2.INTER_NEAREST)
for im in pred_heatmaps
]
disp_X_gen = to_numpy(ds_utils.denormalized(X_gen_vis).permute(0, 2, 3, 1))
disp_X_gen = (disp_X_gen * 255).astype(np.uint8)
# disp_X_gen = [vis.overlay_heatmap(disp_X_gen[i], pred_heatmaps[i]) for i in range(len(pred_heatmaps))]
grid_img = vis.make_grid(disp_X_gen, nCols=nimgs // 2)
cv2.imshow("random faces", cv2.cvtColor(grid_img, cv2.COLOR_RGB2BGR))
cv2.waitKey(wait)
def smooth_heatmaps(hms):
assert len(hms.shape) == 4
hms = to_numpy(hms)
for i in range(hms.shape[0]):
for l in range(hms.shape[1]):
hms[i, l] = cv2.blur(hms[i, l], (9, 9), borderType=cv2.BORDER_CONSTANT)
# hms[i,l] = cv2.GaussianBlur(hms[i,l], (9,9), sigmaX=9, borderType=cv2.BORDER_CONSTANT)
return hms
def decode_heatmap_blob(hms):
assert len(hms.shape) == 4
if hms.shape[1] == lmcfg.NUM_LANDMARK_HEATMAPS: # no decoding necessary
return hms
assert hms.shape[1] == len(hm_code_mat)
hms68 = np.zeros((hms.shape[0], 68, hms.shape[2], hms.shape[3]),
dtype=np.float32)
for img_idx in range(len(hms)):
hms68[img_idx] = decode_heatmaps(to_numpy(hms[img_idx]))[0]
return hms68
def add_confs(disp_X_recon, lmids, loc):
means = lm_confs[:, lmids].mean(axis=1)
colors = vis.color_map(to_numpy(1 - means),
cmap=plt.cm.jet,
vmin=0.0,
vmax=0.4)
return vis.add_error_to_images(disp_X_recon,
means,
loc=loc,
format_string='{:>4.2f}',
colors=colors)
def __forward_disentanglement_parallel(self, z, Y=None):
iter_stats = {}
with torch.no_grad():
self.f_parallel = self.E(z)
z_recon = self.G(*self.f_parallel)
ft_id = 3
try:
y = Y[ft_id]
except TypeError:
y = None
y_f = self.f_parallel[3]
try:
y_p = self.f_parallel[0]
def calc_err(outputs, target):
return np.abs(np.rad2deg(F.l1_loss(outputs, target, reduction='none').detach().cpu().numpy()))
iter_stats['err_pose'] = calc_err(y_p, Y[0])
except TypeError:
pass
clprobs = self.znet(y_f)
self.emotion_probs = clprobs
if y is not None:
emotion_labels = y[:, 0].long()
loss_cls = self.cross_entropy_loss(clprobs, emotion_labels)
acc_cls = calc_acc(clprobs, emotion_labels)
iter_stats['loss_cls'] = loss_cls.item()
iter_stats['acc_cls'] = acc_cls
iter_stats['emotion_probs'] = to_numpy(clprobs)
iter_stats['emotion_labels'] = to_numpy(emotion_labels)
f_parallel_recon = self.E(self.Q(self.P(z_recon)[:,:3]))
l1_err = torch.abs(torch.cat(f_parallel_recon, dim=1) - torch.cat(self.f_parallel, dim=1)).mean(dim=1)
iter_stats['l1_dis_cycle'] = to_numpy(l1_err)
return z_recon, iter_stats, None
def heatmaps_to_landmarks(self, hms):
lms = np.zeros((len(hms), lmcfg.NUM_LANDMARKS, 2), dtype=int)
if hms.shape[1] > 3:
# print(hms.max())
for i in range(len(hms)):
if hms.shape[1] not in [19, 68, 98]:
_, lm_coords = landmarks.lmutils.decode_heatmaps(
to_numpy(hms[i]))
lms[i] = lm_coords
else:
heatmaps = to_numpy(hms[i])
for l in range(len(heatmaps)):
hm = heatmaps[lmcfg.LANDMARK_ID_TO_HEATMAP_ID[l]]
# hm = cv2.blur(hm, (9,9))
# hm = cv2.medianBlur(hm, 9,9)
lms[i,
l, :] = np.unravel_index(np.argmax(hm, axis=None),
hm.shape)[::-1]
elif hms.shape[1] == 3:
hms = to_numpy(hms)
def get_score_plane(h, lm_id, cn):
v = utils.nn.lmcolors[lm_id, cn]
hcn = h[cn]
hcn[hcn < v - 2] = 0
hcn[hcn > v + 5] = 0
return hcn
hms *= 255
for i in range(len(hms)):
hm = hms[i]
for l in landmarks.config.LANDMARKS:
lm_score_map = get_score_plane(hm, l, 0) * get_score_plane(
hm, l, 1) * get_score_plane(hm, l, 2)
lms[i, l, :] = np.unravel_index(
np.argmax(lm_score_map, axis=None),
lm_score_map.shape)[::-1]
lm_scale = lmcfg.HEATMAP_SIZE / cfg.INPUT_SIZE
return lms / lm_scale
def draw_z(z_vecs):
fy = 1
width = 10
z_zoomed = []
for lvl, _ft in enumerate(to_numpy(z_vecs)):
# _ft = (_ft-_ft.min())/(_ft.max()-_ft.min())
vmin = 0 if lvl == 0 else -1
canvas = np.zeros((int(fy*len(_ft)), width, 3))
canvas[:int(fy*len(_ft)), :] = color_map(cv2.resize(_ft.reshape(-1,1), dsize=(width, int(fy*len(_ft))),
interpolation=cv2.INTER_NEAREST), vmin=-1.0, vmax=1.0)
z_zoomed.append(canvas)
return make_grid(z_zoomed, nCols=len(z_vecs), padsize=1, padval=0).transpose((1,0,2))
def loss_struct(X,
X_recon,
torch_ssim,
calc_error_maps=False,
reduction="mean"):
cs_error_maps = []
nimgs = len(X)
errs = torch.zeros(nimgs, requires_grad=True).cuda()
for i in range(nimgs):
errs[i] = 1.0 - torch_ssim(X[i].unsqueeze(0), X_recon[i].unsqueeze(0))
if calc_error_maps:
cs_error_maps.append(1.0 - to_numpy(torch_ssim.cs_map))
loss = __reduce(errs, reduction)
if calc_error_maps:
return loss, np.vstack(cs_error_maps)
else:
return loss, None
def visualize_images(X,
X_lm_hm,
landmarks=None,
show_recon=True,
show_landmarks=True,
show_heatmaps=False,
draw_wireframe=False,
smoothing_level=2,
heatmap_opacity=0.8,
f=1):
if show_recon:
disp_X = vis.to_disp_images(X, denorm=True)
else:
disp_X = vis.to_disp_images(torch.zeros_like(X), denorm=False)
heatmap_opacity = 1
if X_lm_hm is not None:
if smoothing_level > 0:
X_lm_hm = smooth_heatmaps(X_lm_hm)
if smoothing_level > 1:
X_lm_hm = smooth_heatmaps(X_lm_hm)
if show_heatmaps:
pred_heatmaps = to_single_channel_heatmap(to_numpy(X_lm_hm))
pred_heatmaps = [
cv2.resize(im, None, fx=f, fy=f, interpolation=cv2.INTER_CUBIC)
for im in pred_heatmaps
]
disp_X = [
vis.overlay_heatmap(disp_X[i], pred_heatmaps[i], heatmap_opacity)
for i in range(len(pred_heatmaps))
]
if show_landmarks and landmarks is not None:
pred_color = (0, 255, 255)
disp_X = vis.add_landmarks_to_images(disp_X,
landmarks,
color=pred_color,
draw_wireframe=draw_wireframe)
return disp_X
def heatmaps_to_landmarks(hms, target_size):
def landmark_id_to_heatmap_id(lm_id):
return {lm: i for i, lm in enumerate(range(num_landmarks))}[lm_id]
assert len(hms.shape) == 4
num_images = hms.shape[0]
num_landmarks = hms.shape[1]
heatmap_size = hms.shape[-1]
lms = np.zeros((num_images, num_landmarks, 2), dtype=int)
if hms.shape[1] > 3:
# print(hms.max())
for i in range(len(hms)):
heatmaps = to_numpy(hms[i])
for l in range(len(heatmaps)):
hm = heatmaps[landmark_id_to_heatmap_id(l)]
lms[i, l, :] = np.unravel_index(np.argmax(hm, axis=None), hm.shape)[
::-1
]
lm_scale = heatmap_size / target_size
return lms / lm_scale
def poses_pred(self):
try:
return to_numpy(self.f_parallel[0])
except:
return None
def f_vec(self, i):
if cfg.WITH_PARALLEL_DISENTANGLEMENT:
try:
return to_numpy(self.f_parallel[i])
except:
return None
def _run_batch(self, batch, eval=False, ds=None):
time_dataloading = time.time() - self.iter_starttime
time_proc_start = time.time()
iter_stats = {"time_dataloading": time_dataloading}
self.saae.zero_grad()
self.saae.eval()
input_images = (batch.target_images
if batch.target_images is not None else batch.images)
with torch.set_grad_enabled(self.args.train_encoder):
z_sample = self.saae.Q(input_images)
iter_stats.update({"z_recon_mean": z_sample.mean().item()})
#######################
# Reconstruction phase
#######################
with torch.set_grad_enabled(self.args.train_encoder and not eval):
X_recon = self.saae.P(z_sample)
# calculate reconstruction error for debugging and reporting
with torch.no_grad():
iter_stats["loss_recon"] = aae_training.loss_recon(
batch.images, X_recon)
#######################
# Landmark predictions
#######################
train_lmhead = not eval
lm_preds_max = None
with torch.set_grad_enabled(train_lmhead):
self.saae.LMH.train(train_lmhead)
X_lm_hm = self.saae.LMH(self.saae.P)
if batch.lm_heatmaps is not None:
loss_lms = F.mse_loss(batch.lm_heatmaps, X_lm_hm) * 100 * 3
iter_stats.update({"loss_lms": loss_lms.item()})
if eval or self._is_printout_iter(eval):
# expensive, so only calculate when every N iterations
# X_lm_hm = lmutils.decode_heatmap_blob(X_lm_hm)
X_lm_hm = lmutils.smooth_heatmaps(X_lm_hm)
lm_preds_max = self.saae.heatmaps_to_landmarks(X_lm_hm)
if eval or self._is_printout_iter(eval):
lm_gt = to_numpy(batch.landmarks)
nmes = lmutils.calc_landmark_nme(
lm_gt,
lm_preds_max,
ocular_norm=self.args.ocular_norm,
image_size=self.args.input_size,
)
# nccs = lmutils.calc_landmark_ncc(batch.images, X_recon, lm_gt)
iter_stats.update({"nmes": nmes})
if train_lmhead:
# if self.args.train_encoder:
# loss_lms = loss_lms * 80.0
loss_lms.backward()
self.optimizer_lm_head.step()
if self.args.train_encoder:
self.optimizer_E.step()
# self.optimizer_G.step()
# statistics
iter_stats.update({
"epoch": self.epoch,
"timestamp": time.time(),
"iter_time": time.time() - self.iter_starttime,
"time_processing": time.time() - time_proc_start,
"iter": self.iter_in_epoch,
"total_iter": self.total_iter,
"batch_size": len(batch),
})
self.iter_starttime = time.time()
self.epoch_stats.append(iter_stats)
batch_samples = {
"batch": batch,
"X_recon": X_recon,
"X_lm_hm": X_lm_hm,
"lm_preds_max": lm_preds_max,
"ds": ds,
}
# print stats every N mini-batches
if self._is_printout_iter(eval):
self._print_iter_stats(
self.epoch_stats[-self._print_interval(eval):])
out_dir = os.path.join(
cfg.REPORT_DIR,
"landmark_predictions",
self.session_name,
str(self.epoch + 1),
)
io.makedirs(out_dir)
lmvis.visualize_batch(
batch.images,
batch.landmarks,
X_recon,
X_lm_hm,
lm_preds_max,
self.all_landmarks,
lm_heatmaps=batch.lm_heatmaps,
target_images=batch.target_images,
ds=ds,
ocular_norm=self.args.ocular_norm,
clean=False,
overlay_heatmaps_input=False,
overlay_heatmaps_recon=False,
f=1.0,
wait=self.wait,
skeleton=self.skeleton,
)
return batch_samples
def z_vecs_pre(self):
return [to_numpy(self.z_pre)]
def __train_disenglement_parallel(self, z, Y=None, train=True):
iter_stats = {}
self.E.train(train)
self.G.train(train)
self.optimizer_E.zero_grad()
self.optimizer_G.zero_grad()
#
# Autoencoding phase
#
fts = self.E(z)
fp, fi, fs, fe = fts
z_recon = self.G(fp, fi, fs, fe)
loss_z_recon = F.l1_loss(z, z_recon) * cfg.W_Z_RECON
if not cfg.WITH_Z_RECON_LOSS:
loss_z_recon *= 0
#
# Info min/max phase
#
loss_I = loss_z_recon
loss_G = torch.zeros(1, requires_grad=True).cuda()
def calc_err(outputs, target):
return np.abs(np.rad2deg(F.l1_loss(outputs, target, reduction='none').detach().cpu().numpy().mean(axis=0)))
def cosine_loss(outputs, targets):
return (1 - F.cosine_similarity(outputs, targets, dim=1)).mean()
if Y[3] is not None and Y[3].sum() > 0: # Has expression -> AffectNet
available_factors = [3,3,3]
if cfg.WITH_POSE:
available_factors = [0] + available_factors
elif Y[2][1] is not None: # has vids -> VoxCeleb
available_factors = [2]
elif Y[1] is not None: # Has identities
available_factors = [1,1,1]
if cfg.WITH_POSE:
available_factors = [0] + available_factors
elif Y[0] is not None: # Any dataset with pose
available_factors = [0,1,3]
lvl = available_factors[self.iter % len(available_factors)]
name = self.factors[lvl]
try:
y = Y[lvl]
except TypeError:
y = None
# if y is not None and name != 'shape':
def calc_feature_loss(name, y_f, y, show_triplets=False, wnd_title=None):
if name == 'id' or name == 'shape' or name == 'expression':
display_images = None
if show_triplets:
display_images = self.images
loss_I_f, err_f = calc_triplet_loss(y_f, y, return_acc=True, images=display_images, feature_name=name,
wnd_title=wnd_title)
if name == 'expression':
loss_I_f *= 2.0
elif name == 'pose':
# loss_I_f, err_f = F.l1_loss(y_f, y), calc_err(y_f, y)
loss_I_f, err_f = F.mse_loss(y_f, y)*1, calc_err(y_f, y)
# loss_I_f, err_f = cosine_loss(y_f, y), calc_err(y_f, y)
else:
raise ValueError("Unknown feature name!")
return loss_I_f, err_f
if y is not None and cfg.WITH_FEATURE_LOSS:
show_triplets = (self.iter + 1) % self.print_interval == 0
y_f = fts[lvl]
loss_I_f, err_f = calc_feature_loss(name, y_f, y, show_triplets=show_triplets)
loss_I += cfg.W_FEAT * loss_I_f
iter_stats[name+'_loss_f'] = loss_I_f.item()
iter_stats[name+'_err_f'] = np.mean(err_f)
# train expression classifier
if name == 'expression':
self.znet.zero_grad()
emotion_labels = y[:,0].long()
clprobs = self.znet(y_f.detach()) # train only znet
# clprobs = self.znet(y_f) # train enoder and znet
# loss_cls = self.cross_entropy_loss(clprobs, emotion_labels)
loss_cls = self.weighted_CE_loss(clprobs, emotion_labels)
acc_cls = calc_acc(clprobs, emotion_labels)
if train:
loss_cls.backward(retain_graph=False)
self.optimizer_znet.step()
iter_stats['loss_cls'] = loss_cls.item()
iter_stats['acc_cls'] = acc_cls
iter_stats['expression_y_probs'] = to_numpy(clprobs)
iter_stats['expression_y'] = to_numpy(y)
# cycle loss
# other_levels = [0,1,2,3]
# other_levels.remove(lvl)
# shuffle_lvl = np.random.permutation(other_levels)[0]
shuffle_lvl = lvl
# print("shuffling level {}...".format(shuffle_lvl))
if cfg.WITH_DISENT_CYCLE_LOSS:
# z_random = torch.rand_like(z).cuda()
# fts_random = self.E(z_random)
# create modified feature vectors
fts[0] = fts[0].detach()
fts[1] = fts[1].detach()
fts[2] = fts[2].detach()
fts[3] = fts[3].detach()
fts_mod = fts.copy()
shuffled_ids = torch.randperm(len(fts[shuffle_lvl]))
y_mod = None
if y is not None:
if name == 'shape':
y_mod = [y[0][shuffled_ids], y[1][shuffled_ids]]
else:
y_mod = y[shuffled_ids]
fts_mod[shuffle_lvl] = fts[shuffle_lvl][shuffled_ids]
# predict full cycle
z_random_mod = self.G(*fts_mod)
X_random_mod = self.P(z_random_mod)[:,:3]
z_random_mod_recon = self.Q(X_random_mod)
fts2 = self.E(z_random_mod_recon)
# recon error in unmodified part
# h = torch.cat([fts_mod[i] for i in range(len(fts_mod)) if i != lvl], dim=1)
# h2 = torch.cat([fts2[i] for i in range(len(fts2)) if i != lvl], dim=1)
# l1_err_h = torch.abs(h - h2).mean(dim=1)
# l1_err_h = torch.abs(torch.cat(fts_mod, dim=1) - torch.cat(fts2, dim=1)).mean(dim=1)
# recon error in modified part
# l1_err_f = np.rad2deg(to_numpy(torch.abs(fts_mod[lvl] - fts2[lvl]).mean(dim=1)))
# recon error in entire vector
l1_err = torch.abs(torch.cat(fts_mod, dim=1)[:,3:] - torch.cat(fts2, dim=1)[:,3:]).mean(dim=1)
loss_dis_cycle = F.l1_loss(torch.cat(fts_mod, dim=1)[:,3:], torch.cat(fts2, dim=1)[:,3:]) * cfg.W_CYCLE
iter_stats['loss_dis_cycle'] = loss_dis_cycle.item()
loss_I += loss_dis_cycle
# cycle augmentation loss
if cfg.WITH_AUGMENTATION_LOSS and y_mod is not None:
y_f_2 = fts2[lvl]
loss_I_f_2, err_f_2 = calc_feature_loss(name, y_f_2, y_mod, show_triplets=show_triplets, wnd_title='aug')
loss_I += loss_I_f_2 * cfg.W_AUG
iter_stats[name+'_loss_f_2'] = loss_I_f_2.item()
iter_stats[name+'_err_f_2'] = np.mean(err_f_2)
#
# Adversarial loss of modified generations
#
GAN = False
if GAN and train:
eps = 0.00001
# #######################
# # GAN discriminator phase
# #######################
update_D = False
if update_D:
self.D.zero_grad()
err_real = self.D(self.images)
err_fake = self.D(X_random_mod.detach())
# err_fake = self.D(X_z_recon.detach())
loss_D = -torch.mean(torch.log(err_real + eps) + torch.log(1.0 - err_fake + eps)) * 0.1
loss_D.backward()
self.optimizer_D.step()
iter_stats.update({'loss_D': loss_D.item()})
#######################
# Generator loss
#######################
self.D.zero_grad()
err_fake = self.D(X_random_mod)
# err_fake = self.D(X_z_recon)
loss_G += -torch.mean(torch.log(err_fake + eps))
iter_stats.update({'loss_G': loss_G.item()})
# iter_stats.update({'err_real': err_real.mean().item(), 'err_fake': loss_G.mean().item()})
# debug visualization
show = True
if show:
if (self.iter+1) % self.print_interval in [0,1]:
if Y[3] is None:
emotion_gt = np.zeros(len(z), dtype=int)
emotion_gt_mod = np.zeros(len(z), dtype=int)
else:
emotion_gt = Y[3][:,0].long()
emotion_gt_mod = Y[3][shuffled_ids,0].long()
with torch.no_grad():
self.znet.eval()
self.G.eval()
emotion_preds = torch.max(self.znet(fe.detach()), 1)[1]
emotion_mod = torch.max(self.znet(fts_mod[3].detach()), 1)[1]
emotion_mod_pred = torch.max(self.znet(fts2[3].detach()), 1)[1]
X_recon = self.P(z)[:,:3]
X_z_recon = self.P(z_recon)[:,:3]
X_random_mod_recon = self.P(self.G(*fts2))[:,:3]
self.znet.train(train)
self.G.train(train)
X_recon_errs = 255.0 * torch.abs(self.images - X_recon).reshape(len(self.images), -1).mean(dim=1)
X_z_recon_errs = 255.0 * torch.abs(self.images - X_z_recon).reshape(len(self.images), -1).mean(dim=1)
nimgs = 8
disp_input = vis.add_pose_to_images(ds_utils.denormalized(self.images)[:nimgs], Y[0], color=(0, 0, 1.0))
if name == 'expression':
disp_input = vis.add_emotion_to_images(disp_input, to_numpy(emotion_gt))
elif name == 'id':
disp_input = vis.add_id_to_images(disp_input, to_numpy(Y[1]))
disp_recon = vis.add_pose_to_images(ds_utils.denormalized(X_recon)[:nimgs], fts[0])
disp_recon = vis.add_error_to_images(disp_recon, errors=X_recon_errs, format_string='{:.1f}')
disp_z_recon = vis.add_pose_to_images(ds_utils.denormalized(X_z_recon)[:nimgs], fts[0])
disp_z_recon = vis.add_emotion_to_images(disp_z_recon, to_numpy(emotion_preds),
gt_emotions=to_numpy(emotion_gt) if name=='expression' else None)
disp_z_recon = vis.add_error_to_images(disp_z_recon, errors=X_z_recon_errs, format_string='{:.1f}')
disp_input_shuffle = vis.add_pose_to_images(ds_utils.denormalized(self.images[shuffled_ids])[:nimgs], fts[0][shuffled_ids])
disp_input_shuffle = vis.add_emotion_to_images(disp_input_shuffle, to_numpy(emotion_gt_mod))
if name == 'id':
disp_input_shuffle = vis.add_id_to_images(disp_input_shuffle, to_numpy(Y[1][shuffled_ids]))
disp_recon_shuffle = vis.add_pose_to_images(ds_utils.denormalized(X_random_mod)[:nimgs], fts_mod[0], color=(0, 0, 1.0))
disp_recon_shuffle = vis.add_emotion_to_images(disp_recon_shuffle, to_numpy(emotion_mod))
disp_cycle = vis.add_pose_to_images(ds_utils.denormalized(X_random_mod_recon)[:nimgs], fts2[0])
disp_cycle = vis.add_emotion_to_images(disp_cycle, to_numpy(emotion_mod_pred))
disp_cycle = vis.add_error_to_images(disp_cycle, errors=l1_err, format_string='{:.3f}',
size=0.6, thickness=2, vmin=0, vmax=0.1)
rows = [
# original input images
vis.make_grid(disp_input, nCols=nimgs),
# reconstructions without disentanglement
vis.make_grid(disp_recon, nCols=nimgs),
# reconstructions with disentanglement
vis.make_grid(disp_z_recon, nCols=nimgs),
# source for feature transfer
vis.make_grid(disp_input_shuffle, nCols=nimgs),
# reconstructions with modified feature vector (direkt)
vis.make_grid(disp_recon_shuffle, nCols=nimgs),
# reconstructions with modified feature vector (1 iters)
vis.make_grid(disp_cycle, nCols=nimgs)
]
f = 1.0 / cfg.INPUT_SCALE_FACTOR
disp_img = vis.make_grid(rows, nCols=1, normalize=False, fx=f, fy=f)
wnd_title = name
if self.current_dataset is not None:
wnd_title += ' ' + self.current_dataset.__class__.__name__
cv2.imshow(wnd_title, cv2.cvtColor(disp_img, cv2.COLOR_RGB2BGR))
cv2.waitKey(10)
loss_I *= cfg.W_DISENT
iter_stats['loss_disent'] = loss_I.item()
if train:
loss_I.backward(retain_graph=True)
return z_recon, iter_stats, loss_G[0]
def visualize_batch(images,
landmarks,
X_recon,
X_lm_hm,
lm_preds_max,
lm_heatmaps=None,
images_mod=None,
lm_preds_cnn=None,
ds=None,
wait=0,
ssim_maps=None,
landmarks_to_draw=lmcfg.ALL_LANDMARKS,
ocular_norm='outer',
horizontal=False,
f=1.0,
overlay_heatmaps_input=False,
overlay_heatmaps_recon=False,
clean=False):
gt_color = (0, 255, 0)
pred_color = (0, 0, 255)
nimgs = min(10, len(images))
images = nn.atleast4d(images)[:nimgs]
nme_per_lm = None
if landmarks is None:
# print('num landmarks', lmcfg.NUM_LANDMARKS)
lm_gt = np.zeros((nimgs, lmcfg.NUM_LANDMARKS, 2))
else:
lm_gt = nn.atleast3d(to_numpy(landmarks))[:nimgs]
nme_per_lm = calc_landmark_nme(lm_gt,
lm_preds_max[:nimgs],
ocular_norm=ocular_norm)
lm_ssim_errs = 1 - calc_landmark_ssim_score(images, X_recon[:nimgs],
lm_gt)
lm_confs = None
# show landmark heatmaps
pred_heatmaps = None
if X_lm_hm is not None:
pred_heatmaps = to_single_channel_heatmap(to_numpy(X_lm_hm[:nimgs]))
pred_heatmaps = [
cv2.resize(im, None, fx=f, fy=f, interpolation=cv2.INTER_NEAREST)
for im in pred_heatmaps
]
gt_heatmaps = None
if lm_heatmaps is not None:
gt_heatmaps = to_single_channel_heatmap(
to_numpy(lm_heatmaps[:nimgs]))
gt_heatmaps = np.array([
cv2.resize(im,
None,
fx=f,
fy=f,
interpolation=cv2.INTER_NEAREST)
for im in gt_heatmaps
])
show_landmark_heatmaps(pred_heatmaps, gt_heatmaps, nimgs, f=1)
lm_confs = to_numpy(X_lm_hm).reshape(X_lm_hm.shape[0],
X_lm_hm.shape[1], -1).max(axis=2)
# resize images for display and scale landmarks accordingly
lm_preds_max = lm_preds_max[:nimgs] * f
if lm_preds_cnn is not None:
lm_preds_cnn = lm_preds_cnn[:nimgs] * f
lm_gt *= f
input_images = vis._to_disp_images(images[:nimgs], denorm=True)
if images_mod is not None:
disp_images = vis._to_disp_images(images_mod[:nimgs], denorm=True)
else:
disp_images = vis._to_disp_images(images[:nimgs], denorm=True)
disp_images = [
cv2.resize(im, None, fx=f, fy=f, interpolation=cv2.INTER_NEAREST)
for im in disp_images
]
recon_images = vis._to_disp_images(X_recon[:nimgs], denorm=True)
disp_X_recon = [
cv2.resize(im, None, fx=f, fy=f, interpolation=cv2.INTER_NEAREST)
for im in recon_images.copy()
]
# overlay landmarks on input images
if pred_heatmaps is not None and overlay_heatmaps_input:
disp_images = [
vis.overlay_heatmap(disp_images[i], pred_heatmaps[i])
for i in range(len(pred_heatmaps))
]
if pred_heatmaps is not None and overlay_heatmaps_recon:
disp_X_recon = [
vis.overlay_heatmap(disp_X_recon[i], pred_heatmaps[i])
for i in range(len(pred_heatmaps))
]
#
# Show input images
#
disp_images = vis.add_landmarks_to_images(disp_images,
lm_gt[:nimgs],
color=gt_color)
disp_images = vis.add_landmarks_to_images(disp_images,
lm_preds_max[:nimgs],
lm_errs=nme_per_lm,
color=pred_color,
draw_wireframe=False,
gt_landmarks=lm_gt,
draw_gt_offsets=True)
# disp_images = vis.add_landmarks_to_images(disp_images, lm_gt[:nimgs], color=(1,1,1), radius=1,
# draw_dots=True, draw_wireframe=True, landmarks_to_draw=landmarks_to_draw)
# disp_images = vis.add_landmarks_to_images(disp_images, lm_preds_max[:nimgs], lm_errs=nme_per_lm,
# color=(1.0, 0.0, 0.0),
# draw_dots=True, draw_wireframe=True, radius=1,
# gt_landmarks=lm_gt, draw_gt_offsets=False,
# landmarks_to_draw=landmarks_to_draw)
#
# Show reconstructions
#
X_recon_errs = 255.0 * torch.abs(images - X_recon[:nimgs]).reshape(
len(images), -1).mean(dim=1)
if not clean:
disp_X_recon = vis.add_error_to_images(disp_X_recon[:nimgs],
errors=X_recon_errs,
format_string='{:>4.1f}')
# modes of heatmaps
# disp_X_recon = [overlay_heatmap(disp_X_recon[i], pred_heatmaps[i]) for i in range(len(pred_heatmaps))]
if not clean:
lm_errs_max = calc_landmark_nme_per_img(
lm_gt,
lm_preds_max,
ocular_norm=ocular_norm,
landmarks_to_eval=lmcfg.LANDMARKS_NO_OUTLINE)
lm_errs_max_outline = calc_landmark_nme_per_img(
lm_gt,
lm_preds_max,
ocular_norm=ocular_norm,
landmarks_to_eval=lmcfg.LANDMARKS_ONLY_OUTLINE)
lm_errs_max_all = calc_landmark_nme_per_img(
lm_gt,
lm_preds_max,
ocular_norm=ocular_norm,
landmarks_to_eval=lmcfg.ALL_LANDMARKS)
disp_X_recon = vis.add_error_to_images(disp_X_recon,
lm_errs_max,
loc='br-2',
format_string='{:>5.2f}',
vmax=15)
disp_X_recon = vis.add_error_to_images(disp_X_recon,
lm_errs_max_outline,
loc='br-1',
format_string='{:>5.2f}',
vmax=15)
disp_X_recon = vis.add_error_to_images(disp_X_recon,
lm_errs_max_all,
loc='br',
format_string='{:>5.2f}',
vmax=15)
disp_X_recon = vis.add_landmarks_to_images(disp_X_recon,
lm_gt,
color=gt_color,
draw_wireframe=True)
# disp_X_recon = vis.add_landmarks_to_images(disp_X_recon, lm_preds_max[:nimgs],
# color=pred_color, draw_wireframe=False,
# lm_errs=nme_per_lm, lm_confs=lm_confs,
# lm_rec_errs=lm_ssim_errs, gt_landmarks=lm_gt,
# draw_gt_offsets=True, draw_dots=True)
disp_X_recon = vis.add_landmarks_to_images(disp_X_recon,
lm_preds_max[:nimgs],
color=pred_color,
draw_wireframe=True,
gt_landmarks=lm_gt,
draw_gt_offsets=True,
lm_errs=nme_per_lm,
draw_dots=True,
radius=2)
def add_confs(disp_X_recon, lmids, loc):
means = lm_confs[:, lmids].mean(axis=1)
colors = vis.color_map(to_numpy(1 - means),
cmap=plt.cm.jet,
vmin=0.0,
vmax=0.4)
return vis.add_error_to_images(disp_X_recon,
means,
loc=loc,
format_string='{:>4.2f}',
colors=colors)
# disp_X_recon = add_confs(disp_X_recon, lmcfg.LANDMARKS_NO_OUTLINE, 'bm-2')
# disp_X_recon = add_confs(disp_X_recon, lmcfg.LANDMARKS_ONLY_OUTLINE, 'bm-1')
# disp_X_recon = add_confs(disp_X_recon, lmcfg.ALL_LANDMARKS, 'bm')
# print ssim errors
ssim = np.zeros(nimgs)
for i in range(nimgs):
ssim[i] = compare_ssim(input_images[i],
recon_images[i],
data_range=1.0,
multichannel=True)
if not clean:
disp_X_recon = vis.add_error_to_images(disp_X_recon,
1 - ssim,
loc='bl-1',
format_string='{:>4.2f}',
vmax=0.8,
vmin=0.2)
# print ssim torch errors
if ssim_maps is not None and not clean:
disp_X_recon = vis.add_error_to_images(disp_X_recon,
ssim_maps.reshape(
len(ssim_maps),
-1).mean(axis=1),
loc='bl-2',
format_string='{:>4.2f}',
vmin=0.0,
vmax=0.4)
rows = [vis.make_grid(disp_images, nCols=nimgs, normalize=False)]
rows.append(vis.make_grid(disp_X_recon, nCols=nimgs))
if ssim_maps is not None:
disp_ssim_maps = to_numpy(
ds_utils.denormalized(ssim_maps)[:nimgs].transpose(0, 2, 3, 1))
for i in range(len(disp_ssim_maps)):
disp_ssim_maps[i] = vis.color_map(disp_ssim_maps[i].mean(axis=2),
vmin=0.0,
vmax=2.0)
grid_ssim_maps = vis.make_grid(disp_ssim_maps, nCols=nimgs, fx=f, fy=f)
cv2.imshow('ssim errors',
cv2.cvtColor(grid_ssim_maps, cv2.COLOR_RGB2BGR))
if horizontal:
assert (nimgs == 1)
disp_rows = vis.make_grid(rows, nCols=2)
else:
disp_rows = vis.make_grid(rows, nCols=1)
wnd_title = 'Predicted Landmarks '
if ds is not None:
wnd_title += ds.__class__.__name__
cv2.imshow(wnd_title, cv2.cvtColor(disp_rows, cv2.COLOR_RGB2BGR))
cv2.waitKey(wait)
def z_vecs(self):
return [to_numpy(self.z)]
def visualize_batch_CVPR(images,
landmarks,
X_recon,
X_lm_hm,
lm_preds,
lm_heatmaps=None,
ds=None,
wait=0,
horizontal=False,
f=1.0,
radius=2):
gt_color = (0, 255, 0)
pred_color = (0, 255, 255)
nimgs = min(10, len(images))
images = nn.atleast4d(images)[:nimgs]
if landmarks is None:
print('num landmarks', lmcfg.NUM_LANDMARKS)
lm_gt = np.zeros((nimgs, lmcfg.NUM_LANDMARKS, 2))
else:
lm_gt = nn.atleast3d(to_numpy(landmarks))[:nimgs]
# show landmark heatmaps
pred_heatmaps = None
if X_lm_hm is not None:
pred_heatmaps = to_single_channel_heatmap(to_numpy(X_lm_hm[:nimgs]))
pred_heatmaps = [
cv2.resize(im, None, fx=f, fy=f, interpolation=cv2.INTER_NEAREST)
for im in pred_heatmaps
]
gt_heatmaps = None
if lm_heatmaps is not None:
gt_heatmaps = to_single_channel_heatmap(
to_numpy(lm_heatmaps[:nimgs]))
gt_heatmaps = np.array([
cv2.resize(im,
None,
fx=f,
fy=f,
interpolation=cv2.INTER_NEAREST)
for im in gt_heatmaps
])
show_landmark_heatmaps(pred_heatmaps, gt_heatmaps, nimgs, f=1)
lm_confs = to_numpy(X_lm_hm).reshape(X_lm_hm.shape[0],
X_lm_hm.shape[1], -1).max(axis=2)
# resize images for display and scale landmarks accordingly
lm_preds = lm_preds[:nimgs] * f
lm_gt *= f
rows = []
disp_images = vis._to_disp_images(images[:nimgs], denorm=True)
disp_images = [
cv2.resize(im, None, fx=f, fy=f, interpolation=cv2.INTER_NEAREST)
for im in disp_images
]
rows.append(vis.make_grid(disp_images, nCols=nimgs, normalize=False))
recon_images = vis._to_disp_images(X_recon[:nimgs], denorm=True)
disp_X_recon = [
cv2.resize(im, None, fx=f, fy=f, interpolation=cv2.INTER_NEAREST)
for im in recon_images.copy()
]
rows.append(vis.make_grid(disp_X_recon, nCols=nimgs))
# recon_images = vis._to_disp_images(X_recon[:nimgs], denorm=True)
disp_X_recon_pred = [
cv2.resize(im, None, fx=f, fy=f, interpolation=cv2.INTER_NEAREST)
for im in recon_images.copy()
]
disp_X_recon_pred = vis.add_landmarks_to_images(disp_X_recon_pred,
lm_preds,
color=pred_color,
radius=radius)
rows.append(vis.make_grid(disp_X_recon_pred, nCols=nimgs))
disp_X_recon_gt = [
cv2.resize(im, None, fx=f, fy=f, interpolation=cv2.INTER_NEAREST)
for im in recon_images.copy()
]
disp_X_recon_gt = vis.add_landmarks_to_images(disp_X_recon_gt,
lm_gt,
color=gt_color,
radius=radius)
rows.append(vis.make_grid(disp_X_recon_gt, nCols=nimgs))
# overlay landmarks on images
disp_X_recon_hm = [
cv2.resize(im, None, fx=f, fy=f, interpolation=cv2.INTER_NEAREST)
for im in recon_images.copy()
]
disp_X_recon_hm = [
vis.overlay_heatmap(disp_X_recon_hm[i], pred_heatmaps[i])
for i in range(len(pred_heatmaps))
]
rows.append(vis.make_grid(disp_X_recon_hm, nCols=nimgs))
# input images with prediction (and ground truth)
disp_images_pred = vis._to_disp_images(images[:nimgs], denorm=True)
disp_images_pred = [
cv2.resize(im, None, fx=f, fy=f, interpolation=cv2.INTER_NEAREST)
for im in disp_images_pred
]
# disp_images_pred = vis.add_landmarks_to_images(disp_images_pred, lm_gt, color=gt_color, radius=radius)
disp_images_pred = vis.add_landmarks_to_images(disp_images_pred,
lm_preds,
color=pred_color,
radius=radius)
rows.append(vis.make_grid(disp_images_pred, nCols=nimgs))
if horizontal:
assert (nimgs == 1)
disp_rows = vis.make_grid(rows, nCols=2)
else:
disp_rows = vis.make_grid(rows, nCols=1)
wnd_title = 'recon errors '
if ds is not None:
wnd_title += ds.__class__.__name__
cv2.imshow(wnd_title, cv2.cvtColor(disp_rows, cv2.COLOR_RGB2BGR))
cv2.waitKey(wait)
def visualize_batch(self,
batch,
X_recon,
ssim_maps,
nimgs=8,
ds=None,
wait=0):
nimgs = min(nimgs, len(batch))
train_state_D = self.saae.D.training
train_state_Q = self.saae.Q.training
train_state_P = self.saae.P.training
self.saae.D.eval()
self.saae.Q.eval()
self.saae.P.eval()
loc_err_gan = "tr"
text_size_errors = 0.65
input_images = vis.reconstruct_images(batch.images[:nimgs])
show_filenames = batch.filenames[:nimgs]
target_images = (batch.target_images
if batch.target_images is not None else batch.images)
disp_images = vis.reconstruct_images(target_images[:nimgs])
# draw GAN score
if self.args.with_gan:
with torch.no_grad():
err_gan_inputs = self.saae.D(batch.images[:nimgs])
disp_images = vis.add_error_to_images(
disp_images,
errors=1 - err_gan_inputs,
loc=loc_err_gan,
format_string="{:>5.2f}",
vmax=1.0,
)
# disp_images = vis.add_landmarks_to_images(disp_images, batch.landmarks[:nimgs], color=(0,1,0), radius=1,
# draw_wireframe=False)
rows = [vis.make_grid(disp_images, nCols=nimgs, normalize=False)]
recon_images = vis.reconstruct_images(X_recon[:nimgs])
disp_X_recon = recon_images.copy()
print_stats = True
if print_stats:
# lm_ssim_errs = None
# if batch.landmarks is not None:
# lm_recon_errs = lmutils.calc_landmark_recon_error(batch.images[:nimgs], X_recon[:nimgs], batch.landmarks[:nimgs], reduction='none')
# disp_X_recon = vis.add_error_to_images(disp_X_recon, lm_recon_errs, size=text_size_errors, loc='bm',
# format_string='({:>3.1f})', vmin=0, vmax=10)
# lm_ssim_errs = lmutils.calc_landmark_ssim_error(batch.images[:nimgs], X_recon[:nimgs], batch.landmarks[:nimgs])
# disp_X_recon = vis.add_error_to_images(disp_X_recon, lm_ssim_errs.mean(axis=1), size=text_size_errors, loc='bm-1',
# format_string='({:>3.2f})', vmin=0.2, vmax=0.8)
X_recon_errs = 255.0 * torch.abs(batch.images - X_recon).reshape(
len(batch.images), -1).mean(dim=1)
# disp_X_recon = vis.add_landmarks_to_images(disp_X_recon, batch.landmarks[:nimgs], radius=1, color=None,
# lm_errs=lm_ssim_errs, draw_wireframe=False)
disp_X_recon = vis.add_error_to_images(
disp_X_recon[:nimgs],
errors=X_recon_errs,
size=text_size_errors,
format_string="{:>4.1f}",
)
if self.args.with_gan:
with torch.no_grad():
err_gan = self.saae.D(X_recon[:nimgs])
disp_X_recon = vis.add_error_to_images(
disp_X_recon,
errors=1 - err_gan,
loc=loc_err_gan,
format_string="{:>5.2f}",
vmax=1.0,
)
ssim = np.zeros(nimgs)
for i in range(nimgs):
data_range = 255.0 if input_images[0].dtype == np.uint8 else 1.0
ssim[i] = compare_ssim(
input_images[i],
recon_images[i],
data_range=data_range,
multichannel=True,
)
disp_X_recon = vis.add_error_to_images(
disp_X_recon,
1 - ssim,
loc="bl-1",
size=text_size_errors,
format_string="{:>4.2f}",
vmin=0.2,
vmax=0.8,
)
if ssim_maps is not None:
disp_X_recon = vis.add_error_to_images(
disp_X_recon,
ssim_maps.reshape(len(ssim_maps), -1).mean(axis=1),
size=text_size_errors,
loc="bl-2",
format_string="{:>4.2f}",
vmin=0.0,
vmax=0.4,
)
rows.append(vis.make_grid(disp_X_recon, nCols=nimgs))
if ssim_maps is not None:
disp_ssim_maps = to_numpy(
nn.denormalized(ssim_maps)[:nimgs].transpose(0, 2, 3, 1))
if disp_ssim_maps.shape[3] == 1:
disp_ssim_maps = disp_ssim_maps.repeat(3, axis=3)
for i in range(len(disp_ssim_maps)):
disp_ssim_maps[i] = vis.color_map(
disp_ssim_maps[i].mean(axis=2), vmin=0.0, vmax=2.0)
grid_ssim_maps = vis.make_grid(disp_ssim_maps, nCols=nimgs)
cv2.imwrite("ssim errors.jpg",
cv2.cvtColor(grid_ssim_maps, cv2.COLOR_RGB2BGR))
self.saae.D.train(train_state_D)
self.saae.Q.train(train_state_Q)
self.saae.P.train(train_state_P)
f = 1
disp_rows = vis.make_grid(rows, nCols=1, normalize=False, fx=f, fy=f)
wnd_title = "recon errors "
if ds is not None:
wnd_title += ds.__class__.__name__
cv2.imwrite(wnd_title + ".jpg",
cv2.cvtColor(disp_rows, cv2.COLOR_RGB2BGR))
cv2.waitKey(wait)
def get_landmark_confs(X_lm_hm):
return np.clip(
to_numpy(X_lm_hm).reshape(X_lm_hm.shape[0], X_lm_hm.shape[1], -1).max(axis=2),
a_min=0,
a_max=1,
)
def eval_affectnet(net,
n=2000,
feat_type=3,
eval_notf=True,
only_good_images=True,
show=False):
print("Evaluating AffectNet...")
batch_size = 20 if show else 100
dataset = affectnet.AffectNet(train=False,
max_samples=n,
deterministic=True,
use_cache=True)
dataloader = td.DataLoader(dataset,
batch_size=batch_size,
shuffle=False,
num_workers=6)
print(dataset)
labels = []
clprobs = []
for iter, data in enumerate(dataloader):
batch = nn.Batch(data)
with torch.no_grad():
X_recon = net(batch.images, Y=None)[:, :3]
if show:
nimgs = 25
f = 1.0
img = saae.draw_results(batch.images,
X_recon,
net.z_vecs(),
emotions=batch.emotions,
emotions_pred=net.emotions_pred(),
fx=f,
fy=f,
ncols=10)
cv2.imshow('reconst', cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
cv2.waitKey()
clprobs.append(nn.to_numpy(net.emotion_probs))
labels.append(nn.to_numpy(batch.emotions))
if (iter % 10) == 0:
print(iter)
clprobs = np.vstack(clprobs)
labels = np.concatenate(labels).astype(int)
accuracy, auc, auc_micro, conf_matrix = evaluate(clprobs, labels)
print('Accuracy F: %2.5f+-%2.5f' % (np.mean(accuracy), np.std(accuracy)))
log.info("\nAUCs: {} ({})".format(auc, np.mean(list(auc.values()))))
log.info("\nAUC micro: {} ".format(auc_micro))
print(conf_matrix)
vis.plot_confusion_matrix(conf_matrix,
classes=affectnet.AffectNet.classes,
normalize=True)
plt.show()
def reformat(lms):
lms = to_numpy(lms)
if len(lms.shape) == 2:
lms = lms.reshape((1, -1, 2))
return lms
