def temp_visualize(eval_dataset, model, stats, opt):
# visualze model prediction batch by batch
model.eval()
data = np.load('./pics/pts1.npy').astype(np.float32)
data = data[:,2:]
# normalize the data
mean_vec = stats['mean_2d'][stats['dim_use_2d']]
std_vec = stats['std_2d'][stats['dim_use_2d']]
data = (data-mean_vec)/std_vec
data = torch.from_numpy(data.astype(np.float32))
data = data.cuda()
# forward pass to get prediction
prediction = model(data)
# un-normalize the data
skeleton_2d = data_utils.unNormalizeData(data.data.cpu().numpy(),
stats['mean_2d'], stats['std_2d'], stats['dim_ignore_2d'])
skeleton_3d_pred = data_utils.unNormalizeData(prediction.data.cpu().numpy(),
stats['mean_3d'], stats['std_3d'], stats['dim_ignore_3d'])
# visualizing
plt.figure()
ax = plt.subplot(1, 2, 1)
viz.show2Dpose(skeleton_2d[0], ax)
plt.gca().invert_yaxis()
ax = plt.subplot(1, 2, 2, projection='3d')
viz.show3Dpose(skeleton_3d_pred[0], ax, pred=True)
plt.show()
# rotate the axes and update
# for angle in range(0, 360, 5):
# for ax in axes:
# ax.view_init(30, angle)
# plt.draw()
# plt.pause(.001)
# input('Press enter to view next batch.')
return
def visualize(eval_dataset, model, stats, opt, save=False, save_dir=None):
# visualze model prediction batch by batch
batch_size = 9
# how many batches to save
if save:
num_batches = 10
current_batch = 1
model.eval()
eval_loader = torch.utils.data.DataLoader(eval_dataset, batch_size,
shuffle = True, num_workers = opt.num_threads)
for batch_idx, batch in enumerate(eval_loader):
if save and current_batch > num_batches:
break
data = batch[0]
target = batch[1]
if opt.cuda:
with torch.no_grad():
# move to GPU
data, target = data.cuda(), target.cuda()
# forward pass to get prediction
prediction = model(data)
# un-normalize the data
skeleton_2d = data_utils.unNormalizeData(data.data.cpu().numpy(),
stats['mean_2d'], stats['std_2d'], stats['dim_ignore_2d'])
skeleton_3d_gt = data_utils.unNormalizeData(target.data.cpu().numpy(),
stats['mean_3d'], stats['std_3d'], stats['dim_ignore_3d'])
skeleton_3d_pred = data_utils.unNormalizeData(prediction.data.cpu().numpy(),
stats['mean_3d'], stats['std_3d'], stats['dim_ignore_3d'])
# visualizing
if save:
plt.ioff()
f = plt.figure(figsize=(16, 8))
axes = []
for sample_idx in range(batch_size):
ax = plt.subplot(3, 9, 3*sample_idx + 1)
viz.show2Dpose(skeleton_2d[sample_idx], ax)
plt.gca().invert_yaxis()
ax = plt.subplot(3, 9, 3*sample_idx + 2, projection='3d')
viz.show3Dpose(skeleton_3d_gt[sample_idx], ax)
ax = plt.subplot(3, 9, 3*sample_idx + 3, projection='3d')
viz.show3Dpose(skeleton_3d_pred[sample_idx], ax, pred=True)
viz.show3Dpose(skeleton_3d_gt[sample_idx], ax, gt=True)
axes.append(ax)
adjust_figure(left = 0.05, right = 0.95, bottom = 0.05, top = 0.95,
wspace = 0.3, hspace = 0.3)
if not save:
plt.pause(0.5)
# rotate the axes and update
for angle in range(0, 360, 5):
for ax in axes:
ax.view_init(30, angle)
plt.draw()
plt.pause(.001)
input('Press enter to view next batch.')
else:
# save plot
f.savefig(save_dir +'/'+ str(current_batch) + '.png')
plt.close(f)
del axes
if save:
current_batch += 1
return
def visualize_cascade(eval_dataset, cascade, stats, opt, save=False, save_dir=None):
num_stages = len(cascade)
# visualze model prediction batch by batch
batch_size = 5
# how many batches to save
if save:
num_batches = 10
current_batch = 1
for stage_model in cascade:
stage_model.eval()
eval_loader = torch.utils.data.DataLoader(eval_dataset, batch_size,
shuffle = False,
num_workers = opt.num_threads)
for batch_idx, batch in enumerate(eval_loader):
if save and current_batch > num_batches:
break
data = batch[0]
## debug
# enc_in = np.array([[648., 266], [679, 311], [688, 320], [693, 161],
# [620, 244], [526, 156], [642, 160], [590, 310],
# [505, 350], [380, 375], [491, 285],
# [543, 190], [572, 119], [515, 417], [518, 514],
# [512, 638]],dtype=np.float32)
enc_in = data
enc_in = enc_in.reshape(1, 32)
# normalize
data_mean_2d = stats['mean_2d']
dim_to_use_2d = stats['dim_use_2d']
data_std_2d = stats['std_2d']
enc_in = (enc_in - data_mean_2d[dim_to_use_2d])/data_std_2d[dim_to_use_2d]
data = torch.from_numpy(enc_in.astype(np.float32))
## End experiment 2019/10/16
target = batch[1]
# store predictions for each stage
prediction_stages = []
if opt.cuda:
with torch.no_grad():
# move to GPU
data, target = data.cuda(), target.cuda()
# forward pass to get prediction for the first stage
prediction = cascade[0](data)
prediction_stages.append(prediction)
# prediction for later stages
for stage_idx in range(1, num_stages):
prediction = cascade[stage_idx](data)
prediction_stages.append(prediction_stages[stage_idx-1] + prediction)
# un-normalize the data
skeleton_2d = data_utils.unNormalizeData(data.data.cpu().numpy(),
stats['mean_2d'], stats['std_2d'], stats['dim_ignore_2d'])
skeleton_3d_gt = data_utils.unNormalizeData(target.data.cpu().numpy(),
stats['mean_3d'], stats['std_3d'], stats['dim_ignore_3d'])
for stage_idx in range(num_stages):
prediction_stages[stage_idx] = data_utils.unNormalizeData(prediction_stages[stage_idx].data.cpu().numpy(),
stats['mean_3d'], stats['std_3d'], stats['dim_ignore_3d'])
## save intermediate results
# import scipy.io as sio
# p3d = prediction_stages[0]
# sio.savemat('./teaser_pose3d.mat', {'pred_3d':p3d.reshape(32,3),
# 'pred_2d':np.array([[648., 266], [679, 311], [688, 320], [693, 161],
# [620, 244], [526, 156], [642, 160], [590, 310],
# [505, 350], [447, 348], [380, 375], [491, 285],
# [543, 190], [572, 119], [515, 417], [518, 514],
# [512, 638]])})
## End Experiment 2019/10/16
# visualizing
if save:
plt.ioff()
f = plt.figure(figsize=(16, 8))
axes = []
for sample_idx in range(batch_size):
for stage_idx in range(num_stages):
ax = plt.subplot(batch_size, num_stages+1, 1+(num_stages+1)*sample_idx)
viz.show2Dpose(skeleton_2d[sample_idx], ax)
plt.gca().invert_yaxis()
ax = plt.subplot(batch_size, num_stages+1,
2+stage_idx+(num_stages+1)*sample_idx, projection='3d')
viz.show3Dpose(prediction_stages[stage_idx][sample_idx], ax, pred=True)
viz.show3Dpose(skeleton_3d_gt[sample_idx], ax, gt=True)
axes.append(ax)
adjust_figure(left = 0.05, right = 0.95, bottom = 0.05, top = 0.95,
wspace = 0.3, hspace = 0.3)
if not save:
plt.pause(0.5)
# rotate the axes and update
# for angle in range(0, 360, 5):
# for ax in axes:
# ax.view_init(30, angle)
# plt.draw()
# plt.pause(.001)
input('Press enter to view next batch.')
else:
# save plot
f.savefig(save_dir +'/'+ str(current_batch) + '.png')
plt.close(f)
del axes
if save:
current_batch += 1
return