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dataset.py
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dataset.py
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import os.path as osp
import pickle
import numpy as np
import torch
import torch.utils.data as data
from ipdb import set_trace
class TRNTHUMOSDataLayer(data.Dataset):
def __init__(self, args, phase="train"):
# args.data_root = '/home/aleflabo/ego_procedural/OadTR/data/assembly/old_split' # ! Loki path
# args.data_root = "/home/aleflabo/ego_procedural/OadTR/data/assembly/old_split_train+val/" # ! Loki path
# args.data_root = '/home/scofanol/data/EgoProcel/old_split' #!'/home/aleflabo/ego_procedural/OadTR/data/assembly/train' # DGX Path
args.data_root = "/home/aleflabo/ego_procedural/OadTR/data/assembly/OadTR_assembly/train+val_allMistakes_onlyThis/" # ! Loki path to allMistakes with only_this
self.pickle_root = args.data_root
self.data_root = args.data_root # data/THUMOS
self.pickle_root = args.data_root
self.data_root = args.data_root # data/THUMOS
self.sessions = getattr(args, phase + "_session_set") # video name
self.enc_steps = args.enc_layers
self.numclass = args.numclass
self.dec_steps = args.query_num
self.training = phase == "train"
if self.training:
self.sessions = self.sessions[:3]
if args.debug:
self.sessions = self.sessions[:3]
self.feature_pretrain = (
args.feature
) # 'Anet2016_feature' # IncepV3_feature Anet2016_feature
self.inputs = []
self.dataset = "assembly"
self.add_labels = args.add_labels
self.add_labels_mean = args.add_labels_mean
self.add_labels_seq = args.add_labels_seq
if self.dataset == "assembly":
self.subnet = "train" if self.training else "test"
else:
self.subnet = "val" if self.training else "test"
self.resize = args.resize_feature
if self.resize:
target_all = pickle.load(
open(
osp.join(
self.pickle_root,
self.dataset + "_" + self.subnet + "_anno_resize.pickle",
),
"rb",
)
)
else:
target_all = pickle.load(
open(
osp.join(
self.pickle_root,
self.dataset + "_" + self.subnet + "_anno.pickle",
),
"rb",
)
)
for session in self.sessions: # 改
# target = np.load(osp.join(self.data_root, 'target', session+'.npy')) # thumos_val_anno.pickle
target = target_all[session]["anno"]
seed = np.random.randint(self.enc_steps) if self.training else 0
# TODO: remove
seed = 9
for start, end in zip(
range(
seed, target.shape[0], 1
), # self.enc_steps #! Changed to 64 for test
range(seed + self.enc_steps, target.shape[0] - self.dec_steps, 1),
): #! Changed to 64 for test
enc_target = target[start:end]
# dec_target = self.get_dec_target(target[start:end + self.dec_steps])
dec_target = target[end : end + self.dec_steps]
distance_target, class_h_target = self.get_distance_target(
target[start:end]
)
if self.subnet == "train" and self.add_labels:
# !Take the additional class before the start of the sequence only if it is different, otherwise take the one before
if start == 0:
# all the classes are 0
additional_class = np.zeros(self.numclass)
else:
if self.add_labels_mean == True:
# ! Uncomment for mean
additional_class = [
target[start - 1 - i] for i in range(start, 0, -1)
]
additional_class = np.mean(additional_class, axis=0)
elif self.add_labels_seq == True:
window_size = 10
# if start - window_size < 0 then pad the additiona class until the start of the sequence
if start - window_size < 0:
additional_class = [
target[start - 1 - i] for i in range(start, 0, -1)
]
# pad the rest to cover the window size
additional_class = np.pad(
additional_class,
((0, window_size - start), (0, 0)),
"constant",
constant_values=0,
)
# revert the order so that the non zer elements are at the end
additional_class = additional_class[::-1]
else:
additional_class = [
target[start - 1 - i]
for i in range(start, start - window_size, -1)
]
# convert it to numpy array
additional_class = np.array(additional_class)
# ! Uncomment for last token only
# additional_class = target[start - 1]
self.inputs.append(
[
session,
start,
end,
enc_target,
distance_target,
class_h_target,
dec_target,
additional_class,
]
)
else:
self.inputs.append(
[
session,
start,
end,
enc_target,
distance_target,
class_h_target,
dec_target,
]
)
if "V3" in self.feature_pretrain:
if osp.exists(
osp.join(
self.pickle_root,
"thumos_all_feature_{}_V3.pickle".format(self.subnet),
)
):
self.feature_All = pickle.load(
open(
osp.join(
self.pickle_root,
"thumos_all_feature_{}_V3.pickle".format(self.subnet),
),
"rb",
)
)
print("load thumos_all_feature_{}_V3.pickle !".format(self.subnet))
else:
self.feature_All = {}
for session in self.sessions:
self.feature_All[session] = {}
self.feature_All[session]["rgb"] = np.load(
osp.join(
self.data_root, self.feature_pretrain, session + "_rgb.npy"
)
)
self.feature_All[session]["flow"] = np.load(
osp.join(
self.data_root, self.feature_pretrain, session + "_flow.npy"
)
)
with open(
osp.join(
self.pickle_root,
"thumos_all_feature_{}_V3.pickle".format(self.subnet),
),
"wb",
) as f:
pickle.dump(self.feature_All, f)
print("dump thumos_all_feature_{}_V3.pickle !".format(self.subnet))
elif "Anet2016_feature_v2" in self.feature_pretrain:
if self.resize:
if osp.exists(
osp.join(
self.pickle_root,
"{}_all_feature_{}_tsn_v2_resize.pickle".format(
self.dataset, self.subnet
),
)
):
self.feature_All = pickle.load(
open(
osp.join(
self.pickle_root,
"{}_all_feature_{}_tsn_v2_resize.pickle".format(
self.dataset, self.subnet
),
),
"rb",
)
)
print(
"load {}_all_feature_{}_tsn_v2_resize.pickle !".format(
self.dataset, self.subnet
)
)
else:
if osp.exists(
osp.join(
self.pickle_root,
"{}_all_feature_{}_tsn_v2.pickle".format(
self.dataset, self.subnet
),
)
):
self.feature_All = pickle.load(
open(
osp.join(
self.pickle_root,
"{}_all_feature_{}_tsn_v2.pickle".format(
self.dataset, self.subnet
),
),
"rb",
)
)
print(
"load {}_all_feature_{}_tsn_v2.pickle !".format(
self.dataset, self.subnet
)
)
else:
self.feature_All = {}
for session in self.sessions:
self.feature_All[session] = {}
self.feature_All[session]["rgb"] = np.load(
osp.join(
self.data_root,
self.feature_pretrain,
session + "_rgb.npy",
)
)
self.feature_All[session]["flow"] = np.load(
osp.join(
self.data_root,
self.feature_pretrain,
session + "_flow.npy",
)
)
with open(
osp.join(
self.pickle_root,
"{}_all_feature_{}_tsn_v2.pickle".format(
self.dataset, self.subnet
),
),
"wb",
) as f:
pickle.dump(self.feature_All, f)
print(
"dump {}_all_feature_{}_tsn_v2.pickle !".format(
self.dataset, self.subnet
)
)
else:
if osp.exists(
osp.join(
self.pickle_root, "thumos_all_feature_{}.pickle".format(self.subnet)
)
):
self.feature_All = pickle.load(
open(
osp.join(
self.pickle_root,
"thumos_all_feature_{}.pickle".format(self.subnet),
),
"rb",
)
)
print("load thumos_all_feature_{}.pickle !".format(self.subnet))
else:
self.feature_All = {}
for session in self.sessions:
self.feature_All[session] = {}
self.feature_All[session]["rgb"] = np.load(
osp.join(
self.data_root, self.feature_pretrain, session + "_rgb.npy"
)
)
self.feature_All[session]["flow"] = np.load(
osp.join(
self.data_root, self.feature_pretrain, session + "_flow.npy"
)
)
with open(
osp.join(
self.pickle_root,
"thumos_all_feature_{}.pickle".format(self.subnet),
),
"wb",
) as f:
pickle.dump(self.feature_All, f)
print("dump thumos_all_feature_{}.pickle !".format(self.subnet))
def get_dec_target(self, target_vector):
target_matrix = np.zeros(
(self.enc_steps, self.dec_steps, target_vector.shape[-1])
)
for i in range(self.enc_steps):
for j in range(self.dec_steps):
# 0 -> [1, 2, 3]
# target_matrix[i,j] = target_vector[i+j+1,:]
# 0 -> [0, 1, 2]
target_matrix[i, j] = target_vector[i + j, :]
return target_matrix
def get_distance_target(self, target_vector):
target_matrix = np.zeros(self.enc_steps - 1)
target_argmax = target_vector[self.enc_steps - 1].argmax()
for i in range(self.enc_steps - 1):
if target_vector[i].argmax() == target_argmax:
target_matrix[i] = 1.0
return target_matrix, target_vector[self.enc_steps - 1]
def __getitem__(self, index):
"""self.inputs.append([
session, start, end, enc_target, distance_target, class_h_target
])"""
if self.subnet == "train" and self.add_labels:
(
session,
start,
end,
enc_target,
distance_target,
class_h_target,
dec_target,
additional_class,
) = self.inputs[index]
try:
camera_inputs = self.feature_All[session]["rgb"][start:end]
except:
(
session,
start,
end,
enc_target,
distance_target,
class_h_target,
dec_target,
) = self.inputs[index - 1]
camera_inputs = self.feature_All[session]["rgb"][start:end]
camera_inputs = torch.tensor(camera_inputs)
motion_inputs = self.feature_All[session]["flow"][start:end]
motion_inputs = torch.tensor(motion_inputs)
enc_target = torch.tensor(enc_target)
distance_target = torch.tensor(distance_target)
class_h_target = torch.tensor(class_h_target)
dec_target = torch.tensor(dec_target)
additional_class = torch.tensor(additional_class)
# take the
if camera_inputs.shape[0] != 512:
camera_inputs = torch.cat(
[
camera_inputs,
torch.zeros(
512 - camera_inputs.shape[0], camera_inputs.shape[1]
),
],
axis=0,
)
motion_inputs = torch.cat(
[
motion_inputs,
torch.zeros(
512 - motion_inputs.shape[0], motion_inputs.shape[1]
),
],
axis=0,
)
return (
session,
start,
end,
camera_inputs,
motion_inputs,
enc_target,
distance_target,
class_h_target,
dec_target,
additional_class,
)
elif self.subnet == "test" or self.subnet == "train":
(
session,
start,
end,
enc_target,
distance_target,
class_h_target,
dec_target,
) = self.inputs[index]
try:
camera_inputs = self.feature_All[session]["rgb"][start:end]
except:
(
session,
start,
end,
enc_target,
distance_target,
class_h_target,
dec_target,
) = self.inputs[index - 1]
camera_inputs = self.feature_All[session]["rgb"][start:end]
camera_inputs = torch.tensor(camera_inputs)
motion_inputs = self.feature_All[session]["flow"][start:end]
motion_inputs = torch.tensor(motion_inputs)
enc_target = torch.tensor(enc_target)
distance_target = torch.tensor(distance_target)
class_h_target = torch.tensor(class_h_target)
dec_target = torch.tensor(dec_target)
# take the
if camera_inputs.shape[0] != 512:
camera_inputs = torch.cat(
[
camera_inputs,
torch.zeros(
512 - camera_inputs.shape[0], camera_inputs.shape[1]
),
],
axis=0,
)
motion_inputs = torch.cat(
[
motion_inputs,
torch.zeros(
512 - motion_inputs.shape[0], motion_inputs.shape[1]
),
],
axis=0,
)
return (
session,
start,
end,
camera_inputs,
motion_inputs,
enc_target,
distance_target,
class_h_target,
dec_target,
)
def __len__(self):
return len(self.inputs)