def __init__(self, n_timesteps, is_random_tr=True, is_random_te=False, is_shuffle_tr=True, is_shuffle_te=False, is_binarize_output=False):
"""
:param n_timesteps: How many timesteps per video.
:param is_random_tr: Sample random or uniform frames.
:param is_random_te: Sample random or uniform frames.
:param is_shuffle_tr: To shuffle data or not.
:param is_shuffle_te: To shuffle data or not.
"""
self.__is_random_tr = is_random_tr
self.__is_random_te = is_random_te
self.__is_shuffle_tr = is_shuffle_tr
self.__is_shuffle_te = is_shuffle_te
self.__n_timesteps = n_timesteps
self.__n_frames_per_segment = 8
self.__n_frames = self.__n_timesteps * self.__n_frames_per_segment
gt_activities_path = Pth('Breakfast/annotation/gt_activities.pkl')
frames_dict_path = Pth('Breakfast/annotation/frames_dict.pkl')
(self.__video_frames_dict_tr, self.__video_frames_dict_te) = utils.pkl_load(frames_dict_path)
(self.__video_ids_tr, self.__y_tr, self.__video_ids_te, self.__y_te) = utils.pkl_load(gt_activities_path)
if is_binarize_output:
classes = np.arange(0, 10)
self.__y_tr = utils.binarize_label(self.__y_tr, classes)
self.__y_te = utils.binarize_label(self.__y_te, classes)
def _05_visualize_attention_values():
# load data
n_timesteps = 64
n_centroids = 128
model_name = 'classifier_19.02.21-01:00:30'
features_path = Pth('Breakfast/features/features_i3d_mixed_5c_%d_frames.h5', (n_timesteps * 8,))
gt_activities_path = Pth('Breakfast/annotation/gt_activities.pkl')
frames_annot_path = Pth('Breakfast/annotation/annot_frames_i3d_%d.pkl', (512,))
attention_values_path = Pth('Breakfast/qualitative_results/node_attention_%s.pkl', (model_name,))
n_classes = ds_breakfast.N_CLASSES_ACTIVITIES
frames_annot = utils.pkl_load(frames_annot_path)
(video_ids_tr, y_tr), (video_ids_te, y_te) = utils.pkl_load(gt_activities_path)
y_tr = utils.debinarize_label(y_tr)
y_te = utils.debinarize_label(y_te)
(att_tr, att_te) = utils.pkl_load(attention_values_path) # (1357, 64, 128), (355, 64, 128)
attentions_tr = np.array([np.average(att_tr[np.where(y_tr == idx_class)[0]], axis=(0, 1)) for idx_class in range(n_classes)]) # (10, 128)
attentions_te = np.array([np.average(att_te[np.where(y_te == idx_class)[0]], axis=(0, 1)) for idx_class in range(n_classes)]) # (10, 128)
# remove least attended centroids
all_attn_vals = np.mean(attentions_tr, axis=1)
_ = 10
def __init__(self, n_timesteps, is_random_tr=True, is_random_te=False, is_shuffle_tr=True, is_shuffle_te=False):
"""
:param n_timesteps: How many timesteps per video.
:param is_random_tr: Sample random or uniform frames.
:param is_random_te: Sample random or uniform frames.
:param is_shuffle_tr: To shuffle data or not.
:param is_shuffle_te: To shuffle data or not.
"""
frames_dict_path = Pth('Charades/annotation/frames_dict_all_frames.pkl')
annotation_path = Pth('Charades/annotation/video_annotation.pkl')
self.__is_random_tr = is_random_tr
self.__is_random_te = is_random_te
self.__is_shuffle_tr = is_shuffle_tr
self.__is_shuffle_te = is_shuffle_te
self.__n_timesteps = n_timesteps
self.__n_frames_per_segment = 8
self.__n_frames = self.__n_timesteps * self.__n_frames_per_segment
(self.__video_frames_dict_tr, self.__video_frames_dict_te) = utils.pkl_load(frames_dict_path)
(self.__video_ids_tr, self.__y_tr, self.__video_ids_te, self.__y_te) = utils.pkl_load(annotation_path)
self.current_train = None
self.current_test = None
def _804_random_sample_frames_for_i3d_test_video_level_by_split(
split_type='train'):
assert split_type in ['train', 'test'
], 'Sorry, unknown split type: %s' % (split_type)
is_train = split_type == 'train'
file_name_suffix = 'tr' if is_train else 'te'
root_path_id = 0 if configs.is_local_machine() else 5
frames_root_path = Pth('EPIC-Kitchens/frames_rgb_resized/train',
root_type=c.ROOT_PATH_TYPES[root_path_id])
frame_relative_pathes_dict_path = Pth(
'EPIC-Kitchens/annotations/frame_relative_pathes_dict_%s.pkl',
(file_name_suffix, ))
video_names_splits_path = Pth(
'EPIC-Kitchens/annotations/video_names_splits.pkl')
(video_names_tr, video_names_te) = utils.pkl_load(video_names_splits_path)
video_names = video_names_tr if is_train else video_names_te
del video_names_tr
del video_names_te
frame_relative_pathes_dict = utils.pkl_load(
frame_relative_pathes_dict_path)
# loop on the videos and sample frames for i3d
n_timesteps = 64
n_frames_per_segment = 8
n_frames_per_video = n_timesteps * n_frames_per_segment
sampled_frames = __random_sample_frames_per_video_for_i3d(
video_names, frames_root_path, frame_relative_pathes_dict,
n_frames_per_segment, n_frames_per_video)
return sampled_frames
def _802_uniform_sample_frames_for_i3d_test_video_level():
video_names_splits_path = Pth(
'EPIC-Kitchens/annotations/video_names_splits.pkl')
frame_relative_pathes_dict_tr_path = Pth(
'EPIC-Kitchens/annotations/frame_relative_pathes_dict_tr.pkl')
frame_relative_pathes_dict_te_path = Pth(
'EPIC-Kitchens/annotations/frame_relative_pathes_dict_te.pkl')
sampled_frames_relative_pathes = Pth(
'EPIC-Kitchens/annotations/frame_relative_pathes_uniform_sample.pkl')
(video_names_tr, video_names_te) = utils.pkl_load(video_names_splits_path)
frame_relative_pathes_dict_tr = utils.pkl_load(
frame_relative_pathes_dict_tr_path)
frame_relative_pathes_dict_te = utils.pkl_load(
frame_relative_pathes_dict_te_path)
n_timesteps = 64
n_frames_per_segment = 8
n_frames_per_video = n_timesteps * n_frames_per_segment
sampled_frames_tr = __uniform_sample_frames_per_video_for_i3d(
video_names_tr, frame_relative_pathes_dict_tr, n_frames_per_segment,
n_frames_per_video)
sampled_frames_te = __uniform_sample_frames_per_video_for_i3d(
video_names_te, frame_relative_pathes_dict_te, n_frames_per_segment,
n_frames_per_video)
data = (sampled_frames_tr, sampled_frames_te)
utils.pkl_dump(data, sampled_frames_relative_pathes)
def _803_random_sample_frames_for_i3d_test_video_level():
video_names_splits_path = Pth(
'EPIC-Kitchens/annotations/video_names_splits.pkl')
frame_relative_pathes_dict_tr_path = Pth(
'EPIC-Kitchens/annotations/frame_relative_pathes_dict_tr.pkl')
frame_relative_pathes_dict_te_path = Pth(
'EPIC-Kitchens/annotations/frame_relative_pathes_dict_te.pkl')
root_path_id = 0 if configs.is_local_machine() else 5
frames_root_path = Pth('EPIC-Kitchens/frames_rgb_resized/train',
root_type=c.ROOT_PATH_TYPES[root_path_id])
(video_names_tr, video_names_te) = utils.pkl_load(video_names_splits_path)
frame_relative_pathes_dict_tr = utils.pkl_load(
frame_relative_pathes_dict_tr_path)
frame_relative_pathes_dict_te = utils.pkl_load(
frame_relative_pathes_dict_te_path)
# loop on the videos and sample frames for i3d
n_timesteps = 64
n_frames_per_segment = 8
n_frames_per_video = n_timesteps * n_frames_per_segment
sampled_frames_tr = __random_sample_frames_per_video_for_i3d(
video_names_tr, frames_root_path, frame_relative_pathes_dict_tr,
n_frames_per_segment, n_frames_per_video)
sampled_frames_te = __random_sample_frames_per_video_for_i3d(
video_names_te, frames_root_path, frame_relative_pathes_dict_te,
n_frames_per_segment, n_frames_per_video)
return (sampled_frames_tr, sampled_frames_te)
def __init__(self, n_timesteps, n_timesteps_total, featurenet_type, x_heavy_path, is_random_tr=True, is_random_te=False, is_shuffle_tr=True, is_shuffle_te=False):
"""
:param n_timesteps: How many timesteps per video.
:param is_random_tr: Sample random or uniform frames.
:param is_random_te: Sample random or uniform frames.
:param is_shuffle_tr: To shuffle data or not.
:param is_shuffle_te: To shuffle data or not.
"""
self.__is_random_tr = is_random_tr
self.__is_random_te = is_random_te
self.__is_shuffle_tr = is_shuffle_tr
self.__is_shuffle_te = is_shuffle_te
self.__n_timesteps = n_timesteps
self.__n_timesteps_total = n_timesteps_total
n_frames_per_segment = utils.get_model_n_frames_per_segment(featurenet_type)
n_frames = n_timesteps_total * n_frames_per_segment
gt_activities_path = Pth('Breakfast/annotation/gt_activities.pkl')
frames_annot_path = Pth('Breakfast/annotation/annot_frames_%s_%d.pkl', (featurenet_type, n_frames,))
(self.__video_ids_tr, self.__y_tr, self.__video_ids_te, self.__y_te) = utils.pkl_load(gt_activities_path)
(x_heavy_tr, x_heavy_te) = utils.h5_load_multi(x_heavy_path, ['x_tr', 'x_te']) # (B, C, T, H, W)
self.__x_heavy_tr = x_heavy_tr
self.__x_heavy_te = x_heavy_te
# select middle frame from each snippet
(frames_dict_tr, frames_dict_te) = utils.pkl_load(frames_annot_path)
frames_dict_tr = self.__select_middle_frame(frames_dict_tr, n_frames_per_segment)
frames_dict_te = self.__select_middle_frame(frames_dict_te, n_frames_per_segment)
self.__frames_dict_tr = frames_dict_tr
self.__frames_dict_te = frames_dict_te
def _703_prepare_data_splits():
"""
Sample fram pathes for the i3d model.
:return:
"""
annot_dict_path = Pth(
'EPIC-Kitchens/annotations/EPIC_train_action_labels_dict.pkl')
annot_idxes_many_shots_path = Pth(
'EPIC-Kitchens/annotations/annot_idxes_many_shots_noun_verb.pkl')
video_names_splits_path = Pth(
'EPIC-Kitchens/annotations/video_names_splits.pkl')
annot_idxes_many_shots = utils.pkl_load(annot_idxes_many_shots_path)
annot_dict = utils.pkl_load(annot_dict_path)
# split_ratio
split_ratio = 0.8
person_videos_dict = {}
# first loop to collect all unique video ids
for annot_id in annot_idxes_many_shots:
annot_line = annot_dict[annot_id]
person_id = annot_line[0]
video_id = annot_line[1]
if person_id not in person_videos_dict:
person_videos_dict[person_id] = []
person_videos_dict[person_id].append(video_id)
for person_id in person_videos_dict:
video_names = natsort.natsorted(
np.unique(person_videos_dict[person_id]))
person_videos_dict[person_id] = video_names
# now that we have collected the persons, and their videos, see how much videos if we split
video_names_tr = []
video_names_te = []
for person_id in person_videos_dict:
v_names = person_videos_dict[person_id]
idx = int(len(v_names) * split_ratio)
v_names_tr = v_names[:idx]
v_names_te = v_names[idx:]
video_names_tr += v_names_tr
video_names_te += v_names_te
video_names_tr = np.array(video_names_tr)
video_names_te = np.array(video_names_te)
print len(video_names_tr) + len(video_names_te)
print len(video_names_tr)
print len(video_names_te)
# save video names
utils.pkl_dump((video_names_tr, video_names_te), video_names_splits_path)
def __read_features(self, params):
features_path, idx_feature, idx_timestep = params
frame_feature = utils.pkl_load(features_path)
# add current feature to the list
self.__features[idx_feature, idx_timestep] = frame_feature
def __load_features(self, params):
idx_video = params[0]
feats_path = params[1]
try:
# load feature from file
feats = utils.pkl_load(feats_path)
n_feats = len(feats)
if self.__feat_map_side_dim == 1:
feats = np.expand_dims(feats, 1)
feats = np.expand_dims(feats, 1)
# some videos have frames less than required, then take all features
# and zero-pad (may be repeat the frames?) till the required is satisfied
if n_feats < self.__n_frames_per_video:
feats = self.__pad_video_feats(feats)
else:
# randomly sample only n frames
idx_feats = np.arange(n_feats)
np.random.shuffle(idx_feats)
idx_feats = idx_feats[:self.__n_frames_per_video]
feats = feats[idx_feats]
assert len(feats) == self.__n_frames_per_video
# as float
feats = feats.astype(np.float32)
self.__batch_features[idx_video] = feats
_ = 10
except Exception as exp:
print('\nSorry, error in loading feature %s' % (feats_path))
print(exp)
def _800_prepare_video_frames_path_dict():
frame_relative_pathes_dict_path = Pth(
'EPIC-Kitchens/annotations/frame_relative_pathes_dict.pkl')
video_names_splits_path = Pth(
'EPIC-Kitchens/annotations/video_names_splits.pkl')
imgs_root_path = Pth('EPIC-Kitchens/frames_rgb_resized/train')
(video_names_tr, video_names_te) = utils.pkl_load(video_names_splits_path)
video_names = np.hstack((video_names_tr, video_names_te))
frame_relative_pathes_dict = {}
n_videos = len(video_names)
for idx, video_id in enumerate(video_names):
utils.print_counter(idx, n_videos)
person_id = video_id.split('_')[0]
video_frames_root_path = '%s/%s/%s' % (imgs_root_path, person_id,
video_id)
video_frames_names = utils.file_names(video_frames_root_path,
is_nat_sort=True)
video_frames_names = np.array(video_frames_names)
video_frames_relative_pathes = np.array([
'%s/%s/%s' % (person_id, video_id, n) for n in video_frames_names
])
frame_relative_pathes_dict[video_id] = video_frames_relative_pathes
utils.pkl_dump(frame_relative_pathes_dict, frame_relative_pathes_dict_path)
def __count_how_many_videos_per_class():
root_path = c.data_root_path
annotation_path = '%s/Charades/annotation/video_annotation.pkl' % (
root_path)
(video_id_tr, y_tr, video_id_te, y_te) = utils.pkl_load(annotation_path)
n_classes = N_CLASSES
counts_tr = []
counts_te = []
for i in range(n_classes):
counts_tr.append(len(np.where(y_tr == i + 1)[0]))
counts_te.append(len(np.where(y_te == i + 1)[0]))
counts_tr = np.array(counts_tr)
counts_te = np.array(counts_te)
idx = np.argsort(counts_tr)[::-1]
counts_tr = counts_tr[idx]
counts_te = counts_te[idx]
counts = np.array([counts_tr, counts_te])
print(counts_tr)
print(counts_te)
utils.plot_multi(counts, title='Counts')
def _704_prepare_many_shots_noun_verb_action_ids():
"""
Prepeare two dicts of nouns and verbs to convert from id to many_shot id. All ids are zero-indexed.
71 noun classes
26 verb classes
xx actions
:return:
"""
annot_dict_path = Pth(
'EPIC-Kitchens/annotations/EPIC_train_action_labels_dict.pkl')
annot_idxes_many_shots_path = Pth(
'EPIC-Kitchens/annotations/annot_idxes_many_shots_noun_verb.pkl')
noun_ids_many_shots_dict_path = Pth(
'EPIC-Kitchens/annotations/noun_ids_many_shots_dict.pkl')
verb_ids_many_shots_dict_path = Pth(
'EPIC-Kitchens/annotations/verb_ids_many_shots_dict.pkl')
actn_ids_many_shots_dict_path = Pth(
'EPIC-Kitchens/annotations/actn_ids_many_shots_dict.pkl')
actn_ids_many_shots_list_path = Pth(
'EPIC-Kitchens/annotations//EPIC_many_shot_actions.csv')
annot_idxes_many_shots = utils.pkl_load(annot_idxes_many_shots_path)
annot_dict = utils.pkl_load(annot_dict_path)
# get all verb_ids, noun_ids
noun_ids = [
annot_dict[annot_id][10] for annot_id in annot_idxes_many_shots
]
verb_ids = [annot_dict[annot_id][8] for annot_id in annot_idxes_many_shots]
actn_ids = __get_action_ids_from_annotation(actn_ids_many_shots_list_path)
noun_ids = np.sort(np.unique(noun_ids))
verb_ids = np.sort(np.unique(verb_ids))
n_nouns = len(noun_ids)
n_verbs = len(verb_ids)
n_actns = len(actn_ids)
# these dictionaries get the id of many_shot (noun or verb) given the original (noun or verb)
many_shot_noun_ids_dict = dict(zip(noun_ids, np.arange(n_nouns)))
many_shot_verb_ids_dict = dict(zip(verb_ids, np.arange(n_verbs)))
many_shot_actn_ids_dict = dict(zip(actn_ids, np.arange(n_actns)))
utils.pkl_dump(many_shot_noun_ids_dict, noun_ids_many_shots_dict_path)
utils.pkl_dump(many_shot_verb_ids_dict, verb_ids_many_shots_dict_path)
utils.pkl_dump(many_shot_actn_ids_dict, actn_ids_many_shots_dict_path)
def __init__(self,
batch_size,
n_classes,
feature_dim,
feature_name,
is_training,
is_shuffle=True):
"""
Initialization
"""
self.batch_size = batch_size
self.is_training = is_training
self.n_classes = n_classes
self.feature_dim = feature_dim
self.feature_name = feature_name
# self.is_shuffle = is_shuffle
self.dataset_name = 'charades'
# load annotation
root_path = './data/charades'
annotation_path = '%s/annotation/video_annotation_py3.pkl' % (
root_path)
if self.is_training:
(video_names, y, _, _) = utils.pkl_load(annotation_path)
print(video_names)
else:
(_, _, video_names, y) = utils.pkl_load(annotation_path)
# in case of single label classification, debinarize the labels
if config.cfg.MODEL.CLASSIFICATION_TYPE == 'sl':
y = utils.debinarize_label(y)
# in any case, make sure target is float
y = y.astype(np.float32)
# convert relative to root pathes
feats_path = np.array([
'%s/%s/%s.pkl' % (root_path, feature_name, p) for p in video_names
])
n_samples = len(y)
self.n_samples = n_samples
self.n_batches = utils.calc_num_batches(n_samples, batch_size)
self.feats_path = feats_path
self.y = y
def _105_prepare_action_gt_timestamped():
"""
Get ground truth of unit-actions with their timestamps.
:return:
"""
root_path = c.DATA_ROOT_PATH
video_ids_path = Pth('Breakfast/annotation/video_ids_split.pkl')
unit_actions_path = Pth('Breakfast/annotation/unit_actions_list.pkl')
gt_actions_path = Pth(
'Breakfast/annotation/gt_unit_actions_timestamped.pkl')
(video_ids_tr, video_ids_te) = utils.pkl_load(video_ids_path)
unit_actions = utils.pkl_load(unit_actions_path)
video_pathes_tr = [
'%s/Breakfast/videos/%s' % (
root_path,
__video_video_id_to_video_relative_path(id, False),
) for id in video_ids_tr
]
video_pathes_te = [
'%s/Breakfast/videos/%s' % (
root_path,
__video_video_id_to_video_relative_path(id, False),
) for id in video_ids_te
]
gt_actions_te = __get_gt_actions_timestamped(video_pathes_te, unit_actions)
gt_actions_tr = __get_gt_actions_timestamped(video_pathes_tr, unit_actions)
gt_actions_tr = np.array(gt_actions_tr)
gt_actions_te = np.array(gt_actions_te)
l_tr = [len(i) for i in gt_actions_tr]
l_te = [len(i) for i in gt_actions_te]
print('mean, std, min, max for number of nodes in each video [tr/te]')
print np.mean(l_tr), np.std(l_tr), np.min(l_tr), np.max(l_tr)
print np.mean(l_te), np.std(l_te), np.min(l_te), np.max(l_te)
print gt_actions_tr.shape
print gt_actions_te.shape
utils.pkl_dump(
((video_ids_tr, gt_actions_tr), (video_ids_te, gt_actions_te)),
gt_actions_path)
def __init__(self, features_root_path, n_timesteps, n_timesteps_total, is_random_tr, is_random_te):
gt_activities_path = Pth('Breakfast/annotation/gt_activities.pkl')
(self.__video_ids_tr, self.__y_tr, self.__video_ids_te, self.__y_te) = utils.pkl_load(gt_activities_path)
self.__feature_root_path = features_root_path
self.__n_timesteps_total = n_timesteps_total
self.__n_timesteps = n_timesteps
self.__is_random_tr = is_random_tr
self.__is_random_te = is_random_te
def __init__(self, n_timesteps, is_random_tr=True, is_random_te=False, is_shuffle_tr=True, is_shuffle_te=False):
"""
:param n_timesteps: How many timesteps per video.
:param is_random_tr: Sample random or uniform frames.
:param is_random_te: Sample random or uniform frames.
:param is_shuffle_tr: To shuffle data or not.
:param is_shuffle_te: To shuffle data or not.
"""
self.__is_random_tr = is_random_tr
self.__is_random_te = is_random_te
self.__is_shuffle_tr = is_shuffle_tr
self.__is_shuffle_te = is_shuffle_te
self.__n_timesteps = n_timesteps
gt_activities_path = Pth('Breakfast/annotation/gt_activities.pkl')
frames_dict_path = Pth('Breakfast/annotation/frames_dict.pkl')
(self.__video_frames_dict_tr, self.__video_frames_dict_te) = utils.pkl_load(frames_dict_path)
(self.__video_ids_tr, self.__y_tr, self.__video_ids_te, self.__y_te) = utils.pkl_load(gt_activities_path)
def _202_spit_video_frames_relative_pathes():
video_names_splits_path = Pth('EPIC-Kitchens/annotation/video_names_splits.pkl')
frame_relative_pathes_dict_path = Pth('EPIC-Kitchens/annotation/frame_relative_pathes_dict.pkl')
frame_relative_pathes_dict_tr_path = Pth('EPIC-Kitchens/annotation/frame_relative_pathes_dict_tr.pkl')
frame_relative_pathes_dict_te_path = Pth('EPIC-Kitchens/annotation/frame_relative_pathes_dict_te.pkl')
(video_names_tr, video_names_te) = utils.pkl_load(video_names_splits_path)
frames_dict = utils.pkl_load(frame_relative_pathes_dict_path)
dict_tr = dict()
dict_te = dict()
for v_name in video_names_tr:
dict_tr[v_name] = frames_dict[v_name]
for v_name in video_names_te:
dict_te[v_name] = frames_dict[v_name]
utils.pkl_dump(dict_tr, frame_relative_pathes_dict_tr_path)
utils.pkl_dump(dict_te, frame_relative_pathes_dict_te_path)
def __read_features(self, params):
idx = params[0]
path = params[1]
# load feature
print(path)
feature = utils.pkl_load(path)
# add current feature to the list
self.__features[idx] = feature
def __init__(self, batch_size, n_classes, feature_dim, feature_name, is_training, is_shuffle=True):
"""
Initialization
"""
self.batch_size = batch_size #32
self.is_training = is_training #True
self.n_classes = n_classes #157
self.feature_dim = feature_dim #(1024, 32, 7, 7)
self.feature_name = feature_name #'features_i3d_pytorch_charades_rgb_mixed_5c_32f'
self.is_shuffle = is_shuffle
self.dataset_name = 'Charades'
# load annotation
root_path = './data/Charades'
annotation_path = '%s/annotation/video_annotation.pkl' % (root_path) #视频注释./data/Charades/annotation/video_annotation.pkl
if self.is_training:
(video_names, y, _, _) = utils.pkl_load(annotation_path) #video_names [b'001YG' b'004QE' b'00HFP' ... b'ZZDBH' b'ZZN85' b'ZZXQF'],y.shape=(7811, 157)
else:
(_, _, video_names, y) = utils.pkl_load(annotation_path) #y.shape = (1814,157)
# in case of single label classification, debinarize the labels,单标签
if config.cfg.MODEL.CLASSIFICATION_TYPE == 'sl':
y = utils.debinarize_label(y)
# in any case, make sure target is float
y = y.astype(np.float32)
# convert relative to root pathes,通过I3D进行特征提取的特征存放的路径
feats_path = np.array(['%s/%s/%s.pkl' % (root_path, feature_name, p.astype(str)) for p in video_names]) #原版的。#'./data/Charades/features_i3d_pytorch_charades_rgb_mixed_5c_32f/'
n_samples = len(y)
self.n_samples = n_samples
self.n_batches = utils.calc_num_batches(n_samples, batch_size) #计算batch的个数
self.feats_path = feats_path #特征存放的路径
self.y = y
# shuffle the data，打乱顺序
if self.is_shuffle:
self.__shuffle()
def _401_pickle_features_i3d_mixed_5c():
n_frames_per_video = 512
features_root_path = Pth('Breakfast/features_i3d_mixed_5c_%d_frames',
(n_frames_per_video, ))
features_path = Pth(
'Breakfast/features/features_i3d_mixed_5c_%d_frames.h5',
(n_frames_per_video, ))
video_ids_path = Pth('Breakfast/annotation/video_ids_split.pkl')
(video_ids_tr, video_ids_te) = utils.pkl_load(video_ids_path)
n_tr = len(video_ids_tr)
n_te = len(video_ids_te)
n_frames_per_segment = 8
n_segments = int(n_frames_per_video / n_frames_per_segment)
assert n_segments * n_frames_per_segment == n_frames_per_video
f_tr = np.zeros((n_tr, n_segments, 7, 7, 1024), dtype=np.float16)
f_te = np.zeros((n_te, n_segments, 7, 7, 1024), dtype=np.float16)
for i in range(n_tr):
utils.print_counter(i, n_tr, 100)
p = '%s/%s.pkl' % (features_root_path, video_ids_tr[i])
f = utils.pkl_load(p) # (T, 7, 7, 2048)
f_tr[i] = f
for i in range(n_te):
utils.print_counter(i, n_te, 100)
p = '%s/%s.pkl' % (features_root_path, video_ids_te[i])
f = utils.pkl_load(p) # (T, 7, 7, 2048)
f_te[i] = f
print f_tr.shape
print f_te.shape
print(utils.get_size_in_gb(utils.get_array_memory_size(f_tr)))
print(utils.get_size_in_gb(utils.get_array_memory_size(f_te)))
data_names = ['x_tr', 'x_te']
utils.h5_dump_multi((f_tr, f_te), data_names, features_path)
def __init__(self, features_path, n_timesteps, n_timesteps_total, is_random_tr=True, is_random_te=False, dataset_type=None):
if dataset_type == const.DATASET_TYPES.breakfast:
gt_activities_path = Pth('Breakfast/annotation/gt_activities.pkl')
(_, self.__y_tr, _, self.__y_te) = utils.pkl_load(gt_activities_path)
elif dataset_type == const.DATASET_TYPES.charades:
gt_activities_path = Pth('Charades/annotation/video_annotation.pkl')
(_, self.__y_tr, _, self.__y_te) = utils.pkl_load(gt_activities_path)
self.__y_tr = self.__y_tr.astype(np.float32)
self.__y_te = self.__y_te.astype(np.float32)
else:
raise Exception('Unknown Dataset Type: %s' % (dataset_type))
(self.__x_tr, self.__x_te) = utils.h5_load_multi(features_path, ['x_tr', 'x_te'])
self.__feature_root_path = features_path
self.__n_timesteps_total = n_timesteps_total
self.__n_timesteps = n_timesteps
self.__is_random_tr = is_random_tr
self.__is_random_te = is_random_te
def __init__(self, features_path, n_timesteps, n_timesteps_total, dataset_type=None):
if dataset_type == const.DATASET_TYPES.breakfast:
gt_activities_path = Pth('Breakfast/annotation/gt_activities.pkl')
(_, self.__y_tr, _, self.__y_te) = utils.pkl_load(gt_activities_path)
elif dataset_type == const.DATASET_TYPES.charades:
gt_activities_path = Pth('Charades/annotation/video_annotation.pkl')
(_, self.__y_tr, _, self.__y_te) = utils.pkl_load(gt_activities_path)
self.__y_tr = self.__y_tr.astype(np.float32)
self.__y_te = self.__y_te.astype(np.float32)
else:
raise Exception('Unknown Dataset Type: %s' % (dataset_type))
(x_tr, x_te) = utils.h5_load_multi(features_path, ['x_tr', 'x_te'])
step = n_timesteps_total / float(n_timesteps)
idxes = np.arange(0, n_timesteps_total, step, dtype=np.float32).astype(np.int32)
x_tr = x_tr[:, :, idxes]
x_te = x_te[:, :, idxes]
self.__x_tr = x_tr.astype(np.float32)
self.__x_te = x_te.astype(np.float32)
def _06_prepare_video_annotation_multi_label():
root_path = '.'
video_annotation_path = '%s/Charades/annotation/video_annotation.pkl' % (
root_path)
video_annotation_multi_label_path = '%s/Charades/annotation/video_annotation_multi_label.pkl' % (
root_path)
(video_id_tr, y_tr, video_id_te,
y_te) = utils.pkl_load(video_annotation_path)
video_ids_tr = np.unique(video_id_tr)
video_ids_te = np.unique(video_id_te)
n_tr = len(video_ids_tr)
n_te = len(video_ids_te)
n_classes = N_CLASSES
video_gt_dict_tr = dict()
video_gt_dict_te = dict()
for id in video_ids_tr:
video_gt_dict_tr[id] = []
for id in video_ids_te:
video_gt_dict_te[id] = []
"""
zip() 函数用于将可迭代的对象作为参数，将对象中对应的元素打包成一个个元组，然后返回由这些元组组成的列表。
如果各个迭代器的元素个数不一致，则返回列表长度与最短的对象相同，利用 * 号操作符，可以将元组解压为列表。
"""
for i, j in zip(video_id_tr, y_tr):
video_gt_dict_tr[i].append(j)
for i, j in zip(video_id_te, y_te):
video_gt_dict_te[i].append(j)
# binarize labels of videos
y_multi_label_tr = np.zeros((n_tr, n_classes), dtype=np.int)
y_multi_label_te = np.zeros((n_te, n_classes), dtype=np.int)
for idx_video, video_name in enumerate(video_ids_tr):
idx_class = np.add(video_gt_dict_tr[video_name], -1)
y_multi_label_tr[idx_video][idx_class] = 1
_ = 10
for idx_video, video_name in enumerate(video_ids_te):
idx_class = np.add(video_gt_dict_te[video_name], -1)
y_multi_label_te[idx_video][idx_class] = 1
_ = 10
data = (video_ids_tr, y_multi_label_tr, video_ids_te, y_multi_label_te)
utils.pkl_dump(data, video_annotation_multi_label_path)
def __getitem__(self, index):
"""
Generate one batch of data
"""
y = self.y[index]
p = self.feats_path[index]
x = utils.pkl_load(p) # (T, H, W, C)
# convert to channel last
x = np.transpose(x, (3, 0, 1, 2)) # (T, H, W, C)
return x, y
def __get_action_names_from_action_keys(action_keys):
root_path = c.DATA_ROOT_PATH
verb_dict_path = '%s/EPIC-Kitchens/annotation/EPIC_verb_classes_dict.pkl' % (root_path)
noun_dict_path = '%s/EPIC-Kitchens/annotation/EPIC_noun_classes_dict.pkl' % (root_path)
verb_dict = utils.pkl_load(verb_dict_path)
noun_dict = utils.pkl_load(noun_dict_path)
action_names = []
for key in action_keys:
verb_idx, noun_idx = key.split('_')
verb_idx = int(verb_idx)
noun_idx = int(noun_idx)
verb_name = verb_dict[verb_idx][0]
noun_name = noun_dict[noun_idx][0]
action_name = '%s_%s' % (verb_name, noun_name)
action_names.append(action_name)
action_names = np.array(action_names)
return action_names
# endregion
def __init__(self, img_root_path, is_shuffle_tr=True, is_shuffle_te=False):
annot_path = Pth('Hico/annotation/anno_hico.pkl')
(self.img_names_tr, self.y_tr, self.img_names_te, self.y_te) = utils.pkl_load(annot_path)
self.y_tr = self.y_tr.astype(np.float32)
self.y_te = self.y_te.astype(np.float32)
self.is_shuffle_tr = is_shuffle_tr
self.is_shuffle_te = is_shuffle_te
self.img_names_tr = np.array(['%s/%s' % (img_root_path, n) for n in self.img_names_tr])
self.img_names_te = np.array(['%s/%s' % (img_root_path, n) for n in self.img_names_te])
def _03_mean_std_of_nodes():
sns.set_style('whitegrid')
sns.set(style='darkgrid')
sns.set(style='darkgrid') # white, dark, whitegrid, darkgrid, ticks
n_epochs = 100
node_dim = 1024
n_centroids = 128
model_name = 'classifier_19.02.21-01:00:30'
nodes_root_path = Pth('Breakfast/qualitative_results/node_embedding_%s' % (model_name,))
# load nodes from files
nodes = []
nodes_file_pathes = utils.file_pathes(nodes_root_path, is_nat_sort=True)
for i in range(n_epochs):
n = utils.pkl_load(nodes_file_pathes[i])
nodes.append(n)
nodes = np.array(nodes) # (50, 128, 1024)
distances = []
for i in range(n_epochs):
n = nodes[i]
n = utils.normalize_l1(n)
d = distance.cdist(n, n, metric='euclidean')
d = np.mean(d)
distances.append(d)
distances = np.array(distances)
fig, ax = plt.subplots(nrows=1, ncols=1, num=1, figsize=(4, 2))
colors = plot_utils.tableau_category10()
ax.set_title('')
y = distances
x = np.arange(1, n_epochs + 1)
fit_fn = np.poly1d(np.polyfit(x, y, 4))
y_fit = fit_fn(x)
sigma = 0.005
plt.fill_between(x, y_fit + sigma, y_fit - sigma, facecolor=colors[0], alpha=0.25)
ax.plot(x, y, '.', c=colors[0], markersize=9, alpha=1.0)
ax.plot(x, y_fit, color='black', lw=1)
plt.tight_layout()
plt.subplots_adjust(left=0.2, right=0.9, top=0.9, bottom=0.3)
plt.grid('off')
plt.xlabel('Epoch Number')
plt.ylabel('Distance')
plt.show()
def _02_plot_nodes_over_epochs():
sns.set_style('whitegrid')
sns.set(style='darkgrid')
n_epochs = 50
node_dim = 1024
n_centroids = 128
# for plotting
is_async_tsne = True
window_size = 15
n_max_centroids = 40
model_name = 'classifier_19.02.21-01:00:30'
nodes_root_path = Pth('Breakfast/qualitative_results/node_embedding_%s' % (model_name,))
# load nodes from files
nodes = []
nodes_file_pathes = utils.file_pathes(nodes_root_path, is_nat_sort=True)
for i in range(n_epochs):
n = utils.pkl_load(nodes_file_pathes[i])
nodes.append(n)
nodes = np.array(nodes) # (50, 128, 1024)
nodes = nodes[:, 0:n_max_centroids] # (50, 100, 1024)
n_centroids = n_max_centroids
print nodes.shape
# embed the nodes
nodes_1 = nodes[:window_size]
nodes_2 = nodes[-window_size:]
print nodes_1.shape
print nodes_2.shape
nodes_1 = np.reshape(nodes_1, (-1, node_dim))
nodes_2 = np.reshape(nodes_2, (-1, node_dim))
print nodes_1.shape
print nodes_2.shape
nodes_1 = __async_tsne_embedding(nodes_1) if is_async_tsne else utils.learn_manifold(c.MANIFOLD_TYPES[0], nodes_1)
nodes_2 = __async_tsne_embedding(nodes_2) if is_async_tsne else utils.learn_manifold(c.MANIFOLD_TYPES[0], nodes_2)
nodes_1 = np.reshape(nodes_1, (window_size, n_centroids, 2)) # (50, 100, 1024)
nodes_2 = np.reshape(nodes_2, (window_size, n_centroids, 2)) # (50, 100, 1024)
print nodes_1.shape
print nodes_2.shape
# colors = plot_utils.tableau_category20()
colors = plot_utils.colors_256()
colors_1 = colors[:n_centroids]
colors_2 = colors[n_centroids + 1: n_centroids + n_centroids + 1]
__plot_centroids(nodes_1, window_size, n_centroids, colors_1, 1)
__plot_centroids(nodes_2, window_size, n_centroids, colors_2, 2)
def __init__(self, n_timesteps, is_random_tr=True, is_random_te=False, is_shuffle_tr=True, is_shuffle_te=False):
"""
:param n_timesteps: How many timesteps per video.
:param is_random_tr: Sample random or uniform frames.
:param is_random_te: Sample random or uniform frames.
:param is_shuffle_tr: To shuffle data or not.
:param is_shuffle_te: To shuffle data or not.
"""
self.__is_random_tr = is_random_tr
self.__is_random_te = is_random_te
self.__is_shuffle_tr = is_shuffle_tr
self.__is_shuffle_te = is_shuffle_te
self.__n_timesteps = n_timesteps
frames_dict_path = Pth('Charades/annotation/frames_dict_all_frames.pkl')
annotation_path = Pth('Charades/annotation/video_annotation.pkl')
(self.__video_frames_dict_tr, self.__video_frames_dict_te) = utils.pkl_load(frames_dict_path)
(self.__video_ids_tr, self.__y_tr, self.__video_ids_te, self.__y_te) = utils.pkl_load(annotation_path)
self.__y_tr = self.__y_tr.astype(np.float32)
self.__y_te = self.__y_te.astype(np.float32)