def main():
PROJECT_PATH = "/rapids/notebooks/host/BM_GPU"
config_path = f"{PROJECT_PATH}/config_aicrowd.yaml"
num_videos_per_clusters = 9
video_duration = 200 # frames
Data = Dataset(PROJECT_PATH, 'task1_etho', config_path)
Data.load_data()
# configuration
INFO = Data.info
INFO_values = Data.info_values
with open(f"{PROJECT_PATH}/config_aicrowd.yaml") as f:
config = yaml.load(f, Loader=yaml.FullLoader)
# config = Data.config
skeleton = config['skeleton']
skeleton_color= config['skeleton_color']
skeleton_fill = config['skeleton_fill']
# features
#tot_bp = Data.data_obj['rotated_bodypoints']
tot_bp = Data.data_obj['scaled_bodypoints']
# embeddings
tot_embed = Data.data_obj['all_embeddings']
# cluster
tot_clusters = Data.data_obj['cluster']
num_clusters = int(np.max(tot_clusters))+1
# Determine Which frames to Abstract
video_cluster_idx = {}
for clust_i in range(num_clusters):
clust_idx = np.where(tot_clusters==clust_i)[0]
difference = np.diff(clust_idx)
# Find consecutive break
break_idx = np.where(difference != 1)[0]
mod_break_idx = np.insert(break_idx, 0, 0)
break_difference = np.diff(mod_break_idx)
# Find max consecutive
sorted_idx = np.argsort(break_difference)
top_idx = sorted_idx[-num_videos_per_clusters:]
video_idx = np.array([[ clust_idx[mod_break_idx[idx]+1], clust_idx[mod_break_idx[idx+1]+1]] for idx in top_idx])
video_cluster_idx[clust_i] = video_idx
global_start_frames = np.array([val['global_start_fr'] for val in INFO_values])
global_stop_frames = np.array([val['global_stop_fr'] for val in INFO_values])
global_directories = np.array([val['directory'] for val in INFO_values])
for clust_i in range(0, num_clusters):
# Create video
fig, ax = plt.subplots(3,3,figsize=(10,10))
fig.suptitle(f"Cluster {clust_i} Sample Videos")
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
# # animal video data
# video_i, file_start_fr = {}, {}
# for i, (start, stop) in enumerate(tqdm(video_cluster_idx[clust_i], desc="Collecting Videos")):
# file_bool = start > global_start_frames
# if any(file_bool):
# file_start_fr[i] = global_start_frames[file_bool][-1]
# file_path = global_directories[file_bool][-1]
# file_key = file_path.split("/")[-1]
# video_path = glob(f"{VIDEO_PATH}/{file_key}.avi")[0]
# video = skvideo.io.vread(video_path)
# video_start = start-file_start_fr[i]
# video_stop = video_start+video_duration
# if video_stop < len(video):
# video_i[i] = video[video_start:video_stop]
# else:
# video_i[i] = video[video_start:]
# else:
# return # don't create a video
# video format
FFMpegWriter = animation.writers['ffmpeg']
writer = FFMpegWriter(fps=25)
SAVE_PATH=f"videos/task1_etho/mutivideo_cluster{clust_i}.mp4"
with writer.saving(fig, SAVE_PATH, dpi=300):
for fr_i in tqdm(np.arange(0, video_duration), desc=f"Cluster {clust_i} Frame Loop"):
for i, (start, stop) in enumerate(video_cluster_idx[clust_i]):
fr = start+fr_i
# configure plot
ax[i//3,i%3].clear()
ax[i//3,i%3].set_axis_off()
ax[i//3,i%3].set(title=f"Cluster {int(tot_clusters[fr])}")
ax[i//3,i%3].set(xlim=(-3,3), ylim=(-3,3))
# ax[i//3,i%3].imshow(video_i[i][fr_i])
for skeleton_i, color_i in zip(skeleton, skeleton_color):
ax[i//3,i%3].plot(tot_bp[fr,skeleton_i,0], tot_bp[fr,skeleton_i,1], marker="o", markersize=2,
linewidth=2, alpha=0.6, c=color_i)
for fill_obj in skeleton_fill:
ax[i//3,i%3].add_patch(matplotlib.patches.Polygon(xy=tot_bp[fr,fill_obj['trapezoid'],0:2], fill=True,
alpha=0.7, color=fill_obj['fill']))
writer.grab_frame()
plt.close()
if __name__ == '__main__':
args = yaml.load(open('./configs/runner.yml', 'r'), Loader=yaml.FullLoader)
args = {**args, **yaml.load(open('./configs/data.yml', 'r'), Loader=yaml.FullLoader)}
seed = args["seed"]
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
train_file, test_file, embedding_source = args["train_file"], args["test_file"], args["embedding_source"]
dataset = Dataset(data_columns=args["data_info"], batch_size=args["batch_size"], seq_len=args["seq_len"])
dataset.load_data(train_file, test_file, embedding_source)
vocab_size = len(dataset.vocab)
embeddings = dataset.embeddings
model = instantiate_model(args["model"], vocab_size, embeddings)
# tqdm_range = trange(args["epochs"], desc="Epoch", unit="epoch")
print("=" * 54)
for epoch in range(args["epochs"]):
_, _ = model.run_epoch(dataset.train_batch_loader, dataset.valid_batch_loader)
print("-" * 54)
train_acc = model.evaluate(dataset.train_batch_loader)
val_acc = model.evaluate(dataset.valid_batch_loader)
print("Epoch %3d ended | train acc.: %3.2f | val acc.: %.2f" % ((epoch + 1), train_acc * 100, val_acc * 100))
# tqdm_range.set_postfix_str("Loss: %g" % loss)
def main():
config_name = sys.argv[1]
# load configuration and data
with open(config_name) as f:
config = yaml.load(f, Loader=yaml.FullLoader)
PROJECT_PATH = config['GPU_project_path']
config_path = f"{PROJECT_PATH}/{config_name}"
Data = Dataset(PROJECT_PATH, config_path)
Data.load_data()
# configuration
INFO = Data.info
INFO_values = Data.info_values
skeleton = config['skeleton']
skeleton_color = config['skeleton_color']
skeleton_fill = config['skeleton_fill']
num_videos_per_clusters = config['num_sample_videos']
video_duration = config['max_video_length']
video_type = config['video_type']
# bodypoints
if video_type == 0:
tot_bp = Data.data_obj['bodypoints']
elif video_type == 1:
tot_bp = Data.data_obj['rotated_bodypoints']
elif video_type == 2:
tot_bp = Data.data_obj['scaled_bodypoints']
# embeddings
tot_embed = Data.data_obj['all_embeddings']
# cluster
tot_clusters = Data.data_obj['cluster']
num_clusters = int(np.max(tot_clusters)) + 1
# Determine Which frames to Abstract
video_cluster_idx = {}
for clust_i in range(num_clusters):
sorted_list_idx = sorted((list(y) for (x, y) in itertools.groupby((
enumerate(tot_clusters)), operator.itemgetter(1)) if x == clust_i),
key=len,
reverse=True)
top_start_stop_idx = map(lambda x: [x[0][0], x[-1][0]],
sorted_list_idx[0:num_videos_per_clusters])
video_cluster_idx[clust_i] = np.array(list(top_start_stop_idx))
global_start_frames = np.array(
[val['global_start_fr'] for val in INFO_values])
global_stop_frames = np.array(
[val['global_stop_fr'] for val in INFO_values])
global_directories = np.array([val['directory'] for val in INFO_values])
for clust_i in range(0, num_clusters):
# Create video
fig, ax = plt.subplots(3, 3, figsize=(10, 10))
fig.suptitle(f"Cluster {clust_i} Sample Videos")
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
# animal video data
if video_type == 0:
video_i, file_start_fr = {}, {}
for i, (start, stop) in enumerate(
tqdm(video_cluster_idx[clust_i],
desc="Collecting Videos")):
file_bool = start > global_start_frames
if any(file_bool):
file_start_fr[i] = global_start_frames[file_bool][-1]
file_path = global_directories[file_bool][-1]
file_key = file_path.split("/")[-1]
video_path = glob(
f"{config['raw_video_path']}/{file_key}.avi")[0]
video = skvideo.io.vread(video_path)
video_start = start - file_start_fr[i]
video_stop = video_start + video_duration
if video_stop < len(video):
video_i[i] = video[video_start:video_stop]
else:
video_i[i] = video[video_start:]
else:
return # don't create a video
# video format
FFMpegWriter = animation.writers['ffmpeg']
writer = FFMpegWriter(fps=25)
SAVE_PATH = f"{config['save_video_path']}/mutivideo_cluster{clust_i}.mp4"
if not os.path.exists(f"{config['save_video_path']}"):
os.makedirs(f"{config['save_video_path']}")
with writer.saving(fig, SAVE_PATH, dpi=300):
for fr_i in tqdm(np.arange(0, video_duration),
desc=f"Cluster {clust_i} Frame Loop"):
for i, (start, stop) in enumerate(video_cluster_idx[clust_i]):
fr = start + fr_i
# stop animation if video ends
if (video_type == 0) & (fr_i >= len(video_i[i])):
continue
# configure plot
ax[i // 3, i % 3].clear()
ax[i // 3, i % 3].set_axis_off()
ax[i // 3,
i % 3].set(title=f"Cluster {int(tot_clusters[fr])}")
if (video_type == 1) | (video_type == 2):
ax[i // 3, i % 3].set(xlim=(-3, 3), ylim=(-3, 3))
if video_type == 0:
ax[i // 3, i % 3].imshow(video_i[i][fr_i])
for skeleton_i, color_i in zip(skeleton, skeleton_color):
ax[i // 3, i % 3].plot(tot_bp[fr, skeleton_i, 0],
tot_bp[fr, skeleton_i, 1],
marker="o",
markersize=2,
linewidth=2,
alpha=0.6,
c=color_i)
for fill_obj in skeleton_fill:
ax[i // 3, i % 3].add_patch(
matplotlib.patches.Polygon(
xy=tot_bp[fr, fill_obj['trapezoid'], 0:2],
fill=True,
alpha=0.7,
color=fill_obj['fill']))
writer.grab_frame()
plt.close()
def main():
# grab arguments
config_name = sys.argv[1]
with open(config_name) as f:
config = yaml.load(f, Loader=yaml.FullLoader)
config_path = f"{config['GPU_project_path']}/{config_name}"
PROJECT_PATH = config['GPU_project_path']
Data = Dataset(PROJECT_PATH, config_path)
Data.load_data()
# configuration
INFO = Data.info
INFO_items = list(INFO.items())
INFO_items.sort(key=lambda x: x[1]['order'])
config = Data.config
with open(
f"{PROJECT_PATH}/{config['result_path']}/angle_scale_model.pickle",
'rb') as file:
angle_scaler = pickle.load(file)
with open(
f"{PROJECT_PATH}/{config['result_path']}/limb_scale_model.pickle",
'rb') as file:
limb_scaler = pickle.load(file)
# features
tot_bp = Data.data_obj['rotated_bodypoints'] * config['postural_weight']
tot_angle = angle_scaler.transform(
Data.data_obj['angles']) * config['postural_weight']
tot_limb = limb_scaler.transform(
Data.data_obj['limbs']) * config['postural_weight']
tot_marker_pwr = None # Implement this later maybe
tot_angle_pwr = Data.data_obj['angle_power'] * config['kinematic_weight']
tot_limb_pwr = Data.data_obj['limb_power'] * config['kinematic_weight']
# take out bad frames
tot_good_fr, tot_bad_fr, tot_disregard_fr = locate_bad_fr(config, tot_bp)
tot_angle = tot_angle[tot_good_fr]
tot_limb = tot_limb[tot_good_fr]
tot_angle_pwr = tot_angle_pwr[tot_good_fr]
tot_limb_pwr = tot_limb_pwr[tot_good_fr]
print("******************")
nan_fr = np.where(np.isnan(tot_limb_pwr))[0]
print(len(np.unique(nan_fr)))
print("******************")
# Postural Embedding
if config['include_marker_postural']:
start_timer = time.time()
print(f"::: Marker Postural ::: START")
p_marker_embed(config, INFO_items, tot_bp)
print(
f"::: Marker Postural ::: Computation Time: {time.time()-start_timer}"
)
if config['include_angle_postural']:
start_timer = time.time()
print(f"::: Angle Postural ::: START")
p_angle_embed(config, INFO_items, tot_angle)
print(
f"::: Angle Postural ::: Computation Time: {time.time()-start_timer}"
)
if config['include_limb_postural']:
start_timer = time.time()
print(f"::: Limb Postural ::: START")
p_limb_embed(config, INFO_items, tot_limb)
print(
f"::: Limb Postural ::: Computation Time: {time.time()-start_timer}"
)
if config['include_all_postural']:
start_timer = time.time()
print(f"::: Angle & Limb Postural ::: START Timer")
p_angle_limb_embed(config, INFO_items, tot_angle, tot_limb)
print(
f"::: Angle & Limb Postural ::: Time Stamp: {time.time()-start_timer}"
)
# Kinematic Embedding
if config['include_marker_kinematic']:
start_timer = time.time()
print(f"::: Marker Kinematic ::: START")
k_marker_embed(config, INFO_items, tot_marker_pwr)
print(
f"::: Marker Kinematic ::: Computation Time: {time.time()-start_timer}"
)
if config['include_angle_kinematic']:
start_timer = time.time()
print(f"::: Angle Kinematic ::: START")
k_angle_embed(config, INFO_items, tot_angle_pwr)
print(
f"::: Angle Kinematic ::: Computation Time: {time.time()-start_timer}"
)
if config['include_limb_kinematic']:
start_timer = time.time()
print(f"::: Limb Kinematic ::: START")
k_limb_embed(config, INFO_items, tot_limb_pwr)
print(
f"::: Limb Kinematic ::: Computation Time: {time.time()-start_timer}"
)
if config['include_all_kinematic']:
start_timer = time.time()
print(f"::: Angle & Limb Kinematic ::: START Timer")
k_angle_limb_embed(config, INFO_items, tot_angle_pwr, tot_limb_pwr)
print(
f"::: Angle & Limb Kinematic ::: Time Stamp: {time.time()-start_timer}"
)
if config['include_all_features']:
start_timer = time.time()
print(f"::: Angle & Limb Kinematic & Postural ::: START Timer")
kp_angle_limb_embed(config, INFO_items, tot_angle, tot_limb,
tot_angle_pwr, tot_limb_pwr)
print(
f"::: Angle & Limb Kinematic & Postural ::: Time Stamp: {time.time()-start_timer}"
)
def main():
PROJECT_PATH = "/rapids/notebooks/host/BM_GPU"
config_path = f"{PROJECT_PATH}/config_aicrowd.yaml"
Data = Dataset(PROJECT_PATH, 'task1_etho', config_path)
Data.load_data()
# configuration
INFO = Data.info
INFO_items = list(INFO.items())
INFO_items.sort(key=lambda x: x[1]['order'])
config = Data.config
with open (f"{PROJECT_PATH}/{config['result_path']}/angle_scale_model.pickle", 'rb') as file:
angle_scaler = pickle.load(file)
with open (f"{PROJECT_PATH}/{config['result_path']}/limb_scale_model.pickle", 'rb') as file:
limb_scaler = pickle.load(file)
# features
tot_bp = Data.data_obj['rotated_bodypoints']*config['postural_weight']
tot_angle = angle_scaler.transform(Data.data_obj['angles'])*config['postural_weight']
tot_limb = limb_scaler.transform(Data.data_obj['limbs'])*config['postural_weight']
tot_marker_pwr = None # Implement this later maybe
tot_angle_pwr = Data.data_obj['angle_power']*config['kinematic_weight']
tot_limb_pwr = Data.data_obj['limb_power']*config['kinematic_weight']
# Postural Embedding
if config['include_marker_postural']:
start_timer = time.time()
print(f"::: Marker Postural ::: START")
p_marker_embed(config, INFO_items, tot_bp)
print(f"::: Marker Postural ::: Computation Time: {time.time()-start_timer}")
if config['include_angle_postural']:
start_timer = time.time()
print(f"::: Angle Postural ::: START")
p_angle_embed(config, INFO_items, tot_angle)
print(f"::: Angle Postural ::: Computation Time: {time.time()-start_timer}")
if config['include_limb_postural']:
start_timer = time.time()
print(f"::: Limb Postural ::: START")
p_limb_embed(config, INFO_items, tot_limb)
print(f"::: Limb Postural ::: Computation Time: {time.time()-start_timer}")
if config['include_all_postural']:
start_timer = time.time()
print(f"::: Angle & Limb Postural ::: START Timer")
p_angle_limb_embed(config, INFO_items, tot_angle, tot_limb)
print(f"::: Angle & Limb Postural ::: Time Stamp: {time.time()-start_timer}")
# Kinematic Embedding
if config['include_marker_kinematic']:
start_timer = time.time()
print(f"::: Marker Kinematic ::: START")
k_marker_embed(config, INFO_items, tot_marker_pwr)
print(f"::: Marker Kinematic ::: Computation Time: {time.time()-start_timer}")
if config['include_angle_kinematic']:
start_timer = time.time()
print(f"::: Angle Kinematic ::: START")
k_angle_embed(config, INFO_items, tot_angle_pwr)
print(f"::: Angle Kinematic ::: Computation Time: {time.time()-start_timer}")
if config['include_limb_kinematic']:
start_timer = time.time()
print(f"::: Limb Kinematic ::: START")
k_limb_embed(config, INFO_items, tot_limb_pwr)
print(f"::: Limb Kinematic ::: Computation Time: {time.time()-start_timer}")
if config['include_all_kinematic']:
start_timer = time.time()
print(f"::: Angle & Limb Kinematic ::: START Timer")
k_angle_limb_embed(config, INFO_items, tot_angle_pwr, tot_limb_pwr)
print(f"::: Angle & Limb Kinematic ::: Time Stamp: {time.time()-start_timer}")
if config['include_all_features']:
start_timer = time.time()
print(f"::: Angle & Limb Kinematic & Postural ::: START Timer")
kp_angle_limb_embed(config, INFO_items, tot_angle, tot_limb, tot_angle_pwr, tot_limb_pwr)
print(f"::: Angle & Limb Kinematic & Postural ::: Time Stamp: {time.time()-start_timer}")
