def multimodel_timestep_hist(test_set, optimizer):
model_names = [
'quad-test_t1.345xsig_seed-11-test0f_vehicles-nobike_7-fold_G3-64_adam-lr0.00146-b10.9-b20.999_bs512_epochs600',
'cubic-test_t1.345xsig_seed-11-test0f_vehicles-nobike_7-fold_G3-64_adam-lr0.00146-b10.9-b20.999_bs512_epochs600',
'quartic-test_t1.345xsig_seed-11-test0f_vehicles-nobike_7-fold_G3-64_adam-lr0.00146-b10.9-b20.999_bs512_epochs600',
'quintic-test_t1.345xsig_seed-11-test0f_vehicles-nobike_7-fold_G3-64_adam-lr0.00146-b10.9-b20.999_bs512_epochs600',
'sextic-test_t1.345xsig_seed-11-test0f_vehicles-nobike_7-fold_G3-64_adam-lr0.00146-b10.9-b20.999_bs512_epochs600',
]
model_names = [
'd5_past2-test_t1.345xsig_seed-11-test0_vehicles-nobike_7-fold_G3-64_adam-lr0.00146-b10.9-b20.999_bs512_epochs600',
'd5_past3-test_t1.345xsig_seed-11-test0_vehicles-nobike_7-fold_G3-64_adam-lr0.00146-b10.9-b20.999_bs512_epochs600',
'd5_past5-test_t1.345xsig_seed-11-test0_vehicles-nobike_7-fold_G3-64_adam-lr0.00146-b10.9-b20.999_bs512_epochs600',
'd5_past7-test_t1.345xsig_seed-11-test0_vehicles-nobike_7-fold_G3-64_adam-lr0.00146-b10.9-b20.999_bs512_epochs600',
'quintic-test_t1.345xsig_seed-11-test0f_vehicles-nobike_7-fold_G3-64_adam-lr0.00146-b10.9-b20.999_bs512_epochs600'
]
epochs = [550,550,551,550,550]
past_frames = [2,3,5,7,10]
# x = x.reshape((-1, past_frames*4))
# y = y.reshape((-1, 4, 10, 1))
x, y, set_info = data_extract_1obj.get_kitti_raw_tracklets(np.linspace(0.1, 1.0, 10), sets=test_set, class_types=['Car', 'Van', 'Truck'], past_frames=10)
num_samples = y.shape[0]
all_ious = np.empty((len(model_names), num_samples, 10))
tau = 1.345
poly_order = 5
for i, model_name in enumerate(model_names):
#poly_order = 2 + i
x, y, set_info = data_extract_1obj.get_kitti_raw_tracklets(np.linspace(0.1, 1.0, 10), sets=test_set, class_types=['Car', 'Van', 'Truck'], past_frames=past_frames[i])
x = x.reshape((-1, past_frames[i]*4))
y = y.reshape((-1, 4, 10, 1))
weights_path = os.path.join('/playpen/mhudnell_cvpr_2019/mhudnell/maxgan/models', model_name, 'weights/m_weights_epoch-{}.h5'.format(epochs[i]))
M = poly_model.get_model_poly(None, poly_order, np.linspace(0.1, 1.0, 10), tau, past_frames[i], optimizer=optimizer, weights_path=weights_path)
out = M.predict(x)
gen_transforms = out[0]
print(weights_path)
print(gen_transforms.shape)
np.savez(
os.path.join(OUTPUT_DIR, "past_{}.npz".format(past_frames[i])),
target=y,
pred=gen_transforms)
ious = np.empty((num_samples, 10))
des = np.empty((num_samples, 10))
for j in range(num_samples):
ious[j], des[j] = calc_metrics_all(x[j][-4:], y[j], gen_transforms[j])
all_ious[i] = ious
print("ADE:", np.mean(des))
print("FDE:", np.mean(des[:, 9]))
print("FIOU:", np.mean(ious[:, 9]))
mult_iou_threshold(all_ious)
def run_tests(generator_model, discriminator_model, combined_model, model_name, seed=6, dataset='kitti_tracking'):
np.random.seed(seed)
if dataset == 'kitti_tracking':
all_sets = np.arange(21)
test_sets = np.random.choice(all_sets, 3, replace=False)
# train_sets = np.setdiff1d(all_sets, test_sets)
test_data, test_data_info = data_extract_1obj.get_kitti_data(test_sets)
elif dataset == 'kitti_raw_tracklets':
all_sets = np.arange(38)
test_sets = np.random.choice(all_sets, 8, replace=False)
test_data, test_data_info = data_extract_1obj.get_kitti_raw_tracklets(sets=test_sets)
# train_sets = np.setdiff1d(all_sets, test_sets)
else:
raise Exception("`dataset` parameter must be one of: ['kitti_tracking', 'kitti_raw_tracklets']")
gan_1obj.test_model_multiple(generator_model, discriminator_model, combined_model, model_name, test_data, test_data_info, dataset)
def get_baseline_data():
# get test sets
timepoints = np.linspace(0.1, 1.0, 10)
np.random.seed(11)
all_sets = np.arange(38)
test_sets = np.random.choice(all_sets, (3, 10), replace=False)
# get test data (ie X: the normalized bbs and Y: the offset transformations to the 10 future frames)
x_val, y_val, val_info = get_kitti_raw_tracklets(
timepoints,
sets=test_sets[0],
class_types=['Car', 'Van', 'Truck'],
past_frames=2,
offset_t=True)
y_val = np.reshape(y_val, (len(y_val), 4, 10, 1))
#print(np.shape(x_val))
#print(np.shape(y_val))
return x_val, y_val
ax2.set_title('+1.0s IoU distributions')
fig2.savefig(os.path.join(output_dir, 'd5-poly_past_threshold.png'))
for i in range(len(y)):
print(i+2, "AUC:", np.sum(y[i]) / 100)
if __name__ == '__main__':
optimizer = {
'name': 'adam',
'lr': .00146, # default: .001
'beta_1': .9, # default: .9
'beta_2': .999, # default: .999
'decay': 0 # default: 0
}
np.random.seed(DATA_SEED)
all_sets = np.arange(38)
test_sets = np.random.choice(all_sets, (3,10), replace=False)
#print(test_sets)
x_test, y_test, _ = data_extract_1obj.get_kitti_raw_tracklets(np.linspace(0.1, 1.0, 10), sets=test_sets[0], class_types=['Car', 'Van', 'Truck'], past_frames=PAST_FRAMES, offset_t=OFFSET_T)
weights_path = os.path.join(MODEL_DIR, MODEL_NAME, 'weights/m_weights_epoch-{}.h5'.format(EPOCH))
M = poly_model.get_model_poly(None, POLY_ORDER, np.linspace(0.1, 1.0, 10), TAU, PAST_FRAMES, optimizer=optimizer,
weights_path=weights_path)
get_metrics(M, x_test, y_test)
#multimodel_timestep_hist(test_sets[0], optimizer=optimizer)
def train_single(model_specs, train_sets, val_sets, dataset='kitti_tracking'):
"""Train a single model on the given data"""
[
model_name, starting_step, data_cols, label_cols, label_dim, optimizer,
w_adv, epochs, batch_size, k_d, k_g, show, output_dir
] = model_specs
timepoints = np.linspace(0.1, 1.0, 10)
# Get Data
if dataset == 'kitti_tracking':
...
# train_data, train_data_info = data_extract_1obj.get_kitti_data(train_sets)
# val_data, val_data_info = data_extract_1obj.get_kitti_data(val_sets)
elif dataset == 'kitti_raw_tracklets':
x_train, y_train, train_info = data_extract_1obj.get_kitti_raw_tracklets(
timepoints,
sets=train_sets,
class_types=['Car', 'Van', 'Truck'],
past_frames=PAST_FRAMES,
offset_t=OFFSET_T)
x_val, y_val, val_info = data_extract_1obj.get_kitti_raw_tracklets(
timepoints,
sets=val_sets,
class_types=['Car', 'Van', 'Truck'],
past_frames=PAST_FRAMES,
offset_t=OFFSET_T)
# print(train_data.shape, train_data.shape[0] / (train_data.shape[0] + val_data.shape[0]))
# print(val_data.shape, val_data.shape[0] / (train_data.shape[0] + val_data.shape[0]))
else:
raise Exception(
"`dataset` parameter must be one of: ['kitti_tracking', 'kitti_raw_tracklets']"
)
# Log hyperparameters
steps_per_epoch = len(x_train) // batch_size
nb_steps = steps_per_epoch * epochs
log_hyperparams(model_name=model_name,
output_dir=output_dir,
train_sets=train_sets,
val_sets=val_sets,
epochs=epochs,
batch_size=batch_size,
k_d=k_d,
k_g=k_g,
optimizer=optimizer,
show=show,
dataset=dataset)
# Get Model
M = poly_model.get_model_poly(output_dir,
POLY_ORDER,
timepoints,
TAU,
PAST_FRAMES,
optimizer=optimizer)
# Train Model
model_components = [
model_name,
starting_step,
data_cols,
label_cols,
label_dim, # optimizer,
M,
epochs,
batch_size,
k_d,
k_g,
show,
output_dir
]
# [G_losses, D_losses, val_losses, train_ious, val_ious, train_des, val_des, avg_gen_pred, avg_real_pred] = gan_1obj.training_steps_GAN(x_train, x_val, y_train, y_val, train_info, val_info, model_components)
[M_losses, val_losses, train_ious, val_ious, train_des,
val_des] = poly_model.train_poly(x_train, x_val, y_train, y_val,
train_info, val_info, model_components,
PAST_FRAMES)
# Save losses
save_losses(output_dir, val_losses, val_ious, val_des)
# Plot losses
plot_loss(model_name,
epochs,
nb_steps,
steps_per_epoch,
output_dir,
M_losses,
val_losses,
train_ious,
val_ious,
train_des,
val_des,
show_adv=(w_adv != 0.0))
min_smoothl1_idx = np.argmin(val_losses)
max_iou_idx = np.argmax(val_ious, axis=0)
min_de_idx = np.argmin(val_des, axis=0)
final_epoch = len(val_ious)
print("Training / Testing completed. Showing test scores:\n")
print("Best smoothL1 ({}): {}".format(min_smoothl1_idx + 1,
val_losses[min_smoothl1_idx]))
print("Best IoU ({}): {}".format(max_iou_idx + 1, val_ious[max_iou_idx]))
print("Best DE ({}): {}".format(min_de_idx + 1, val_des[min_de_idx]))
print("Final smoothL1 ({}): {}".format(final_epoch, val_losses[-1]))
print("Final IoU ({}): {}".format(final_epoch, val_ious[-1]))
print("Final DE ({}): {}".format(final_epoch, val_des[-1]))
resultsFile = open(os.path.join(model_specs[-1], 'results.txt'), 'w')
print("Training / Testing completed. Showing test scores:\n",
file=resultsFile)
print("Best smoothL1 ({}): {}".format(min_smoothl1_idx + 1,
val_losses[min_smoothl1_idx]),
file=resultsFile)
print("Best IoU ({}): {}".format(max_iou_idx + 1, val_ious[max_iou_idx]),
file=resultsFile)
print("Best DE ({}): {}".format(min_de_idx + 1, val_des[min_de_idx]),
file=resultsFile)
print("Final smoothL1 ({}): {}".format(final_epoch, val_losses[-1]),
file=resultsFile)
print("Final IoU ({}): {}".format(final_epoch, val_ious[-1]),
file=resultsFile)
print("Final DE ({}): {}".format(final_epoch, val_des[-1]),
file=resultsFile)
return [val_losses, val_ious, val_des]
print("ADE:", np.mean(des))
print("FDE:", np.mean(des[:, 9]))
print("FIOU:", np.mean(ious[:, 9]))
if __name__ == '__main__':
optimizer = {
'name': 'adam',
'lr': .00146, # default: .001
'beta_1': .9, # default: .9
'beta_2': .999, # default: .999
'decay': 0 # default: 0
}
np.random.seed(11)
all_sets = np.arange(38)
test_sets = np.random.choice(all_sets, (3, 10), replace=False)
timesteps = np.linspace(0.1, 1.0, 10)
x_test, y_test, _ = get_kitti_raw_tracklets(
timesteps, sets=test_sets[0], class_types=['Car', 'Van', 'Truck'], past_frames=PAST_FRAMES, offset_t=OFFSET_T)
weights_path = os.path.join(
MODEL_DIR, MODEL_NAME, 'weights', 'm_weights_epoch-{}.h5'.format(EPOCH)
)
M = get_model_poly(None, POLY_ORDER, timesteps,
TAU, PAST_FRAMES, optimizer=optimizer, weights_path=weights_path)
save_to_npz(M, x_test, y_test)
g_bb = data_extract_1obj.unnormalize_bb(g_bb, sample_set=sample_set)
target = Rect.make_cXcYWH(t_bb[0], t_bb[1], t_bb[2], t_bb[3])
generated = Rect.make_cXcYWH(g_bb[0], g_bb[1], g_bb[2], g_bb[3])
ious[i] = Rect.get_IoU(target, generated)
des[i] = Rect.get_DE(target, generated)
return ious, des
if __name__ == '__main__':
# samples, samples_info = data_extract_1obj.get_kitti_data(sets=None)
# samples, samples_info = data_extract_1obj.get_kitti_raw_data()
xs, ys, samples_info = data_extract_1obj.get_kitti_raw_tracklets(
np.linspace(0.1, 1.0, 10))
print(xs.shape)
# print(len(ys))
x = xs[0]
x_info = samples_info[0]
print(x)
print(x_info)
# drawFrameRects(x_info[0], x_info[1], x_info[2], x, False, None, dataset='kitti_raw', display=True)
# drawFrameRects(x_info[0], x_info[1], x_info[2], x, False, None, dataset='kitti_raw_tracklets', display=True)
# drawFrameRects(x_info[0], x_info[1], x_info[2], x, True, None, dataset='kitti_raw_tracklets', display=True)
# View multiple
# for i in range(5):