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hahattpro / crowd_counting_framework (public) (License: Unspecified) (since 2020-12-23) (hash sha1)

backupfrom https://github.com/ttpro1995/crowd_counting_framework

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List of commits:

Subject	Hash	Author	Date (UTC)
some more test for data loader	25173578cde7d4e9fe6c6140d1ee01caa4fcfc32	Thai Thien	2019-09-14 02:51:58
some visualize to debug data loader	e4f52007616acf307bddbde79c0fb4f8c649c785	Thai Thien	2019-09-13 17:35:45
wip	d7d44cad6774355bdfa45414258763f6c6a0c299	Thai Thien	2019-08-31 16:58:16
commit all	6dad7a58f7dbf9fc288ce9dd3e92be538851c2a7	Thai Thien	2019-08-29 19:10:44
input d1,d2,d3 match	fc2a809241f8b6356d964c63d40cbebd55ca5f6c	Thai Thien	2019-08-28 17:57:05
WIP	39eab26d061e61dfffbf164dbd5fd878299b7250	thient	2019-08-28 11:09:12
output of de is ok	dd770386674df3e0fbebafdfc48a9352bc28967d	thient	2019-08-28 10:54:09
code pacnn	c49537b5cc91e96e4e35c9338d2c95b9bb41c672	Thai Thien	2019-08-27 16:35:27
crowd counting stuff	da9f27a39cba9bdd021b6b5c562f5f7c2be50190	Thai Thien	2019-08-24 18:27:44
seem ok	53fa176c31669a0e89b04adf290cb398f0316c45	Thai Thien	2019-08-24 18:26:31
flow ok	ad849681000818dfbcd0c1715c2858aed7236041	Thai Thien	2019-08-24 17:00:02
wip	23c3ec48497782bbc91d829e1c8a682502360ab9	Thai Thien	2019-08-24 14:19:22
work in progress, try to use https://pytorch.org/ignite/quickstart.html	39c824fe8fc2501628ee42c236a844df45521007	Thai Thien	2019-08-24 07:41:46
Work in progress	984be31d85e5cbdb2af296ccdb128381fe9bf09e	Thai Thien	2019-08-24 05:30:51
README	045706df1fa3452d150a190675c60e80ebd18e08	Thai Thien	2019-08-24 04:11:49
init	18f7c296fb05c4340c1c20ca84c60fef4f93bb1f	Thai Thien	2019-08-24 04:08:16

Commit 25173578cde7d4e9fe6c6140d1ee01caa4fcfc32 - some more test for data loader
Author: Thai Thien
Author date (UTC): 2019-09-14 02:51
Committer name: Thai Thien
Committer date (UTC): 2019-09-14 02:51
Parent(s): e4f52007616acf307bddbde79c0fb4f8c649c785
Signing key:
Tree: 1298e0b3f1c95da07c0fc9566172b5954dc9d004

File	Lines added	Lines deleted
data_flow.py	5	0
visualize_data_loader.py	14	7

File data_flow.py changed (mode: 100644) (index baf3e25..906abd1)
...	...	def data_augmentation(img, target):
130	130
131	131	class ListDataset(Dataset):	class ListDataset(Dataset):
132	132	def __init__(self, root, shape=None, shuffle=True, transform=None, train=False, seen=0, batch_size=1,	def __init__(self, root, shape=None, shuffle=True, transform=None, train=False, seen=0, batch_size=1,
	133		debug=False,
133	134	num_workers=4, dataset_name="shanghaitech"):	num_workers=4, dataset_name="shanghaitech"):
134	135	"""	"""
135	136	if you have different image size, then batch_size must be 1	if you have different image size, then batch_size must be 1

...	...	class ListDataset(Dataset):
138	139	:param shuffle:	:param shuffle:
139	140	:param transform:	:param transform:
140	141	:param train:	:param train:
	142		:param debug: will print path of image
141	143	:param seen:	:param seen:
142	144	:param batch_size:	:param batch_size:
143	145	:param num_workers:	:param num_workers:

...	...	class ListDataset(Dataset):
151	153	self.lines = root	self.lines = root
152	154	self.transform = transform	self.transform = transform
153	155	self.train = train	self.train = train
	156		self.debug = debug
154	157	self.shape = shape	self.shape = shape
155	158	self.seen = seen	self.seen = seen
156	159	self.batch_size = batch_size	self.batch_size = batch_size

...	...	class ListDataset(Dataset):
170	173	def __getitem__(self, index):	def __getitem__(self, index):
171	174	assert index <= len(self), 'index range error'	assert index <= len(self), 'index range error'
172	175	img_path = self.lines[index]	img_path = self.lines[index]
	176		if self.debug:
	177		print(img_path)
173	178	img, target = self.load_data_fn(img_path, self.train)	img, target = self.load_data_fn(img_path, self.train)
174	179	if self.transform is not None:	if self.transform is not None:
175	180	img = self.transform(img)	img = self.transform(img)

File visualize_data_loader.py changed (mode: 100644) (index ad2acdc..6307e90)
...	...	import pytorch_ssim
16	16	from hard_code_variable import HardCodeVariable	from hard_code_variable import HardCodeVariable
17	17	from visualize_util import save_img, save_density_map	from visualize_util import save_img, save_density_map
18	18
19
20		if __name__ == "__main__":
	19		def visualize_ucf_cc_50_pacnn():
21	20	HARD_CODE = HardCodeVariable()	HARD_CODE = HardCodeVariable()
	21		saved_folder = "visualize/test_dataloader"
	22		os.makedirs(saved_folder, exist_ok=True)
22	23	DATA_PATH = HARD_CODE.UCF_CC_50_PATH	DATA_PATH = HARD_CODE.UCF_CC_50_PATH
23	24	train_list, val_list = get_train_val_list(DATA_PATH, test_size=0.2)	train_list, val_list = get_train_val_list(DATA_PATH, test_size=0.2)
24	25	test_list = None	test_list = None

...	...	if __name__ == "__main__":
33	34	]),	]),
34	35	train=True,	train=True,
35	36	batch_size=1,	batch_size=1,
36		num_workers=4, dataset_name="ucf_cc_50_pacnn"),
	37		num_workers=4, dataset_name="ucf_cc_50_pacnn", debug=True),
37	38	batch_size=1, num_workers=4)	batch_size=1, num_workers=4)
38	39
39	40	img, label = next(iter(train_loader_pacnn))	img, label = next(iter(train_loader_pacnn))
40	41
41	42	print(img.shape)	print(img.shape)
42		save_img(img, "pacnn_loader_img.png")
43		save_density_map(label[0].numpy()[0], "pacnn_loader_density1.png")
44		save_density_map(label[1].numpy()[0], "pacnn_loader_density2.png")
45		save_density_map(label[2].numpy()[0], "pacnn_loader_density3.png")
	43		save_img(img, os.path.join(saved_folder, "pacnn_loader_img.png"))
	44		save_density_map(label[0].numpy()[0], os.path.join(saved_folder,"pacnn_loader_density1.png"))
	45		save_density_map(label[1].numpy()[0], os.path.join(saved_folder,"pacnn_loader_density2.png"))
	46		save_density_map(label[2].numpy()[0], os.path.join(saved_folder,"pacnn_loader_density3.png"))
	47
	48
46	49
	50
	51
	52		if __name__ == "__main__":
	53		visualize_ucf_cc_50_pacnn()

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