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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)
shanghaitech same size density map	25fc2e9395dd67bb6cc273c7fe6cd64c3b154120	Thai Thien	2020-02-27 16:03:05
fix target1.unsqueeze(0)	bb5c46f121207fd9838ee085314df40513e2ed32	Thai Thien	2020-02-27 15:56:07
because GPU too small, we crop shanghaitech A	0f97517dd8de5d3d75cf49af9aa9d20e4595ea3e	Thai Thien	2020-02-27 15:52:21
meow	3b9576511e597b4894c7c95ca06dc5219ef9c524	tthien	2020-02-27 15:44:39
fix wrong py name in script, dataset use only 1 worker	3bd765c9aa79e47049a94547e57deb68c947001c	Thai Thien	2020-02-27 15:41:44
minor fix	5505b9a8b28df105e11a99d3462323ae9d507102	Thai Thien	2020-02-27 15:28:37
get ready for short training run with 30 epochs	66dda0858561897cd5f81e10077459adb39d86dd	Thai Thien	2020-02-27 15:22:01
implement attn_can_adcrowdnet	ffd38664a43d861c20cdc225746b9ce2a00260c7	Thai Thien	2020-02-27 15:10:27
WIP: add can-adcrowdnet	5620b83449b31d00a367c8de77e431e19a5ccfb3	Thai Thien	2020-02-25 11:31:38
add readable timestamp viz	ae1fdb49ddb9ea77659529dceb7fb87c2790c8dc	Thai Thien	2020-02-24 03:49:57
change save name prefix	c53a86f30fb8fd4e8f3a409eb67827d56a43ae5c	Thai Thien	2020-02-02 10:48:15
training flow that work	fb242273e8f696916f9d1ff4bb76b4e5869799ef	Thai Thien	2020-02-02 10:42:01
fix the dataloader for shanghaitech	5f2aee9f316e6555e6a70c6ad037a4e6b491867b	Thai Thien	2020-02-02 09:19:50
context aware visualize seem ok	1bdb6ffe77ca4e40ef8f299b2506df2266243db4	Thai Thien	2020-02-02 05:07:10
visualize eval context aware network seem ok	f3fe45c23dfeab3730624737efabb0b14d23c25b	Thai Thien	2020-02-02 04:50:34
visualize_shanghaitech_pacnn_with_perspective run without error	12366a2de2bd60ff4bd36e6132d44e37dedf7462	Thai Thien	2020-02-02 04:21:16
eval context aware network on ShanghaiTechB can run	e8c454d2b6d287c830c1286c9a37884b3cfc615f	Thai Thien	2020-02-02 04:09:14
import ShanghaiTechDataPath in data_util	e81eb56315d44375ff5c0e747d61456601492f8f	Thai Thien	2020-02-02 04:04:36
add model_context_aware_network.py	2a36025c001d85afc064c090f4d22987b328977b	Thai Thien	2020-02-02 03:46:38
PACNN (TODO: test this)	44d5ae7ec57c760fb4f105dd3e3492148a0cc075	Thai Thien	2020-02-02 03:40:26

Commit 25fc2e9395dd67bb6cc273c7fe6cd64c3b154120 - shanghaitech same size density map
Author: Thai Thien
Author date (UTC): 2020-02-27 16:03
Committer name: Thai Thien
Committer date (UTC): 2020-02-27 16:03
Parent(s): bb5c46f121207fd9838ee085314df40513e2ed32
Signer:
Signing key:
Signing status: N
Tree: 30ab9b91cdc305ecf8612cb31111158b761c9d9b

File	Lines added	Lines deleted
data_flow.py	30	0
train_attn_can_adcrowdnet.py	1	1

File data_flow.py changed (mode: 100644) (index 77d19e0..356acaf)
...	...	def load_data_shanghaitech(img_path, train=True):
94	94	return img, target1	return img, target1
95	95
96	96
	97		def load_data_shanghaitech_same_size_density_map(img_path, train=True):
	98		gt_path = img_path.replace('.jpg', '.h5').replace('images', 'ground-truth-h5')
	99		img = Image.open(img_path).convert('RGB')
	100		gt_file = h5py.File(gt_path, 'r')
	101		target = np.asarray(gt_file['density'])
	102
	103		if train:
	104		crop_size = (int(img.size[0] / 2), int(img.size[1] / 2))
	105		if random.randint(0, 9) <= -1:
	106
	107		dx = int(random.randint(0, 1) * img.size[0] * 1. / 2)
	108		dy = int(random.randint(0, 1) * img.size[1] * 1. / 2)
	109		else:
	110		dx = int(random.random() * img.size[0] * 1. / 2)
	111		dy = int(random.random() * img.size[1] * 1. / 2)
	112
	113		img = img.crop((dx, dy, crop_size[0] + dx, crop_size[1] + dy))
	114		target = target[dy:crop_size[1] + dy, dx:crop_size[0] + dx]
	115
	116		if random.random() > 0.8:
	117		target = np.fliplr(target)
	118		img = img.transpose(Image.FLIP_LEFT_RIGHT)
	119
	120		target1 = target
	121		# target1 = target1.unsqueeze(0) # make dim (batch size, channel size, x, y) to make model output
	122		target1 = np.expand_dims(target1, axis=0) # make dim (batch size, channel size, x, y) to make model output
	123		return img, target1
	124
97	125	def load_data_shanghaitech_keepfull(img_path, train=True):	def load_data_shanghaitech_keepfull(img_path, train=True):
98	126	gt_path = img_path.replace('.jpg', '.h5').replace('images', 'ground-truth-h5')	gt_path = img_path.replace('.jpg', '.h5').replace('images', 'ground-truth-h5')
99	127	img = Image.open(img_path).convert('RGB')	img = Image.open(img_path).convert('RGB')

...	...	class ListDataset(Dataset):
311	339	# load data fn	# load data fn
312	340	if dataset_name is "shanghaitech":	if dataset_name is "shanghaitech":
313	341	self.load_data_fn = load_data_shanghaitech	self.load_data_fn = load_data_shanghaitech
	342		if dataset_name is "shanghaitech_same_size_density_map":
	343		self.load_data_fn = load_data_shanghaitech_same_size_density_map
314	344	if dataset_name is "shanghaitech_keepfull":	if dataset_name is "shanghaitech_keepfull":
315	345	self.load_data_fn = load_data_shanghaitech_keepfull	self.load_data_fn = load_data_shanghaitech_keepfull
316	346	elif dataset_name is "ucf_cc_50":	elif dataset_name is "ucf_cc_50":

File train_attn_can_adcrowdnet.py changed (mode: 100644) (index db87802..6195c3d)
...	...	if __name__ == "__main__":
28	28	test_list = None	test_list = None
29	29
30	30	# create data loader	# create data loader
31		train_loader, val_loader, test_loader = get_dataloader(train_list, val_list, test_list, dataset_name="shanghaitech")
	31		train_loader, val_loader, test_loader = get_dataloader(train_list, val_list, test_list, dataset_name="shanghaitech_same_size_density_map")
32	32
33	33
34	34	# model	# model

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