def test_folder_func(folder_name, FF_BATCH_SIZE):
STACKCOUNT = 15
TARGET_FPS = 20
take_last = lambda x: og_folder + "/" + x.split('/')[-1].split('.')[
0] + "_trained_on_" + folder_name.split("/")[-2]
# eval_files = [ "E:/Master/Converted Mocap/Eyes_Japan_Dataset/hamada/greeting-01-hello-hamada_poses.npz",
# "E:/Master/Converted Mocap/Eyes_Japan_Dataset/kudo/jump-12-boxer step-kudo_poses.npz",
# "E:/Master/Converted Mocap/KIT/576/MarcusS_AdrianM11_poses.npz",
# "E:/Master/Converted Mocap/KIT/513/balance_on_beam06_poses.npz",
# "E:/Master/Converted Mocap/Eyes_Japan_Dataset/hamada/gesture_etc-14-apologize-hamada_poses.npz",
# "E:/Master/Converted Mocap/Eyes_Japan_Dataset/kanno/walk-01-normal-kanno_poses.npz",
# "E:/Master/Converted Mocap/Eyes_Japan_Dataset/takiguchi/pose-10-recall blackmagic-takiguchi_poses.npz",
# "E:/Master/Converted Mocap/TotalCapture/s1/freestyle2_poses.npz",
# "E:/Master/Converted Mocap/Eyes_Japan_Dataset/hamada/accident-02-dodge fast-hamada_poses.npz",
# "E:/Master/Converted Mocap/BMLhandball/S07_Expert/Trial_upper_left_right_003_poses.npz"]
# eval_files_old = [ "E:/Master/Sorted Mocap/WALKING/WALKING_2265.npz",
# "E:/Master/Sorted Mocap/BASKETBALL/BASKETBALL_10.npz",
# "E:/Master/Sorted Mocap/BOXING/BOXING_64.npz",
# "E:/Master/Sorted Mocap/THROWING/THROWING_58.npz",
# "E:/Master/Sorted Mocap/INTERACTION/INTERACTION_1534.npz"
# ]
PATH_PREFIX = "E:/Master/Sorted Mocap/WALKING/WALKING_"
numbers = [
45,
720,
734, #bwd
338,
1148,
2112, #circle
650,
763,
2308, #diagonal
976,
1514,
2016, #fwd
12,
13,
772 #sideways
]
# numbers = []
eval_files = [PATH_PREFIX + str(elem) + ".npz" for elem in numbers]
# eval_files = [ "E:/Master/Sorted Mocap/WALKING/WALKING_42.npz",
# "E:/Master/Sorted Mocap/WALKING/WALKING_360.npz",
# "E:/Master/Sorted Mocap/WALKING/WALKING_420.npz",
# "E:/Master/Sorted Mocap/WALKING/WALKING_1337.npz",
# "E:/Master/Sorted Mocap/WALKING/WALKING_2265.npz",
# ]
training_prep = DataPreprocessor.ParalellMLPProcessor(
STACKCOUNT, 1.0 / TARGET_FPS, 5, use_weighted_sampling=True)
training_prep.append_folder(folder_name,
eval_files,
mirror=True,
reverse=True)
training_prep.finalize()
# training_prep.save(folder_name + "walking_augmented_5.npz")
# training_prep.load_np(folder_name + "walking_augmented_3.npz")
# from Helpers import StatsPrinter
# StatsPrinter.print_dirs(training_prep)
# training_prep.load_np(folder_name + "walking_augmented_2.npz")
# training_prep.load_np(folder_name + "walking_2.npz")
# training_prep.load_np(folder_name + "combined.npz")
# training_prep.append_subfolders("E:/Master/Converted Mocap/BMLhandball", eval_files)
# training_prep.append_subfolders("E:/Master/Converted Mocap/BMLmovi", eval_files)
# training_prep.append_subfolders("E:/Master/Converted Mocap/DFaust_67", eval_files)
# training_prep.append_subfolders("E:/Master/Converted Mocap/EKUT", eval_files)
# training_prep.append_subfolders("E:/Master/Converted Mocap/Eyes_Japan_Dataset", eval_files)
# training_prep.append_subfolders("E:/Master/Converted Mocap/HumanEva", eval_files)
# training_prep.append_subfolders("E:/Master/Converted Mocap/Kit", eval_files)
# training_prep.append_subfolders("E:/Master/Converted Mocap/MPI_HDM05", eval_files)
# training_prep.append_subfolders("E:/Master/Converted Moca/MPI_Limits", eval_files)
# training_prep.append_subfolders("E:/Master/Converted Mocap/MPI_mosh", eval_files)
# training_prep.append_subfolders("E:/Master/Converted Mocap/SFU", eval_files)
# training_prep.append_subfolders("E:/Master/Converted Mocap/TotalCapture", eval_files)
# training_prep.append_file("E:/Master/Sorted Mocap/WALKING/WALKING_2265.npz")
# training_prep.finalize()
# for idx in range(1,eval_files.__len__()):
# training_prep.append_file(eval_files[idx])
# training_prep.append_file(eval_files[1])
# loss_test_wrapper = ModelWrappers.ae_perceptual_loss(training_prep)
# loss_test_wrapper.train(400, 600, 0.01)
# gan_wrapper = None
timings_file = "timings.txt"
FileHelpers.clear_file(timings_file)
# gan_wrapper = ModelWrappers.gan_wrapper(training_prep)
# gan_wrapper.train(1, FF_BATCH_SIZE, 0.0001)
# GLOW
# glow_wrapper = ModelWrappers.glow_wrapper(training_prep)
#
# last_time = time.perf_counter()
#
# glow_wrapper.train(60, 180)
#
# comp_time = time.perf_counter()
# total_time = comp_time - last_time
# time_per_epoch = total_time / 60
# FileHelpers.append_line(timings_file, "GLOW Training, time per epoch:" + str(time_per_epoch) + "\t timing:" + str(total_time))
#
# for file in eval_files:
# eval_prep = DataPreprocessor.ParalellMLPProcessor(STACKCOUNT, 1.0 / TARGET_FPS, 5)
# eval_prep.append_file(file)
# eval_prep.finalize()
#
# glow_wrapper.predict(eval_prep)
#
# total_time = glow_wrapper.last_inference_time
# time_per_frame = total_time / glow_wrapper.final_outputs.shape[0]
# FileHelpers.append_line(timings_file, "GLOW, file: "+ str(file) + "\t time per frame: " + str(time_per_frame)+ "\t timing:" +str(total_time) + "\t length:" + str(glow_wrapper.final_outputs.shape[0]))
#
# glow_wrapper.save_prediction(take_last(file) + "_GLOW", gan_wrapper)
#
# torch.cuda.empty_cache()
# FF
# ff_wrapper = ModelWrappers.ff_wrapper(training_prep)
#
# last_time = time.perf_counter()
#
# ff_wrapper.train(120, FF_BATCH_SIZE, 0.0001)
#
# comp_time = time.perf_counter()
# total_time = comp_time - last_time
# time_per_epoch = total_time / 120
# FileHelpers.append_line(timings_file, "FF Training, time per epoch:" + str(time_per_epoch) + "\t timing:" + str(total_time))
#
# for file in eval_files:
# eval_prep = DataPreprocessor.ParalellMLPProcessor(STACKCOUNT, 1.0 / TARGET_FPS, 5)
# eval_prep.append_file(file)
# eval_prep.finalize()
#
# ff_wrapper.predict(eval_prep)
#
# total_time = ff_wrapper.last_inference_time
# time_per_frame = total_time / ff_wrapper.final_outputs.shape[0]
# FileHelpers.append_line(timings_file, "FF, file: "+ str(file) + "\t time per frame: " + str(time_per_frame)+ "\t timing:" +str(total_time) + "\t length:" + str(ff_wrapper.final_outputs.shape[0]))
#
# ff_wrapper.save_prediction(take_last(file) + "_FF", None)
#
# torch.cuda.empty_cache()
#
#rnn2
rnn_wrapper = ModelWrappers.rnn_wrapper_2(training_prep)
last_time = time.perf_counter()
# rnn_wrapper.train(80, FF_BATCH_SIZE, 0.0001)
rnn_wrapper.load_model('rnn_test_save')
comp_time = time.perf_counter()
total_time = comp_time - last_time
time_per_epoch = total_time / 120
FileHelpers.append_line(
timings_file, "RNN Training, time per epoch:" + str(time_per_epoch) +
"\t timing:" + str(total_time))
for file in eval_files:
eval_prep = DataPreprocessor.ParalellMLPProcessor(
STACKCOUNT, 1.0 / TARGET_FPS, 5)
eval_prep.append_file(file)
eval_prep.finalize()
rnn_wrapper.predict(eval_prep)
total_time = rnn_wrapper.last_inference_time
time_per_frame = total_time / rnn_wrapper.final_outputs.shape[0]
FileHelpers.append_line(
timings_file, "RNN, file: " + str(file) + "\t time per frame: " +
str(time_per_frame) + "\t timing:" + str(total_time) +
"\t length:" + str(rnn_wrapper.final_outputs.shape[0]))
rnn_wrapper.save_prediction(take_last(file) + "_RNN2", None)
ax.set_xticks(x)
ax.set_xticklabels(labels)
ax.legend()
def autolabel(rects):
"""Attach a text label above each bar in *rects*, displaying its height."""
for rect in rects:
height = rect.get_height()
ax.annotate(
'{}'.format(height),
xy=(rect.get_x(), height),
xytext=(0, 3), # 3 points vertical offset
textcoords="offset points",
ha='center',
va='bottom')
from Helpers import FileHelpers
FileHelpers.clear_file("file_count.txt")
values = ','.join([str(elem) for elem in values])
labels = ','.join([str(elem) for elem in labels])
FileHelpers.append_line("file_count.txt", values)
FileHelpers.append_line("file_count.txt", labels)
autolabel(rects1)
fig.tight_layout()
plt.show()