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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)
fix flatten collate	bb25738b0510915712a17daeb590b668b446b0be	Thai Thien	2020-07-12 15:17:14
ccnn_v7_t6_shb	c4f59ee01cbdab3f506302588b9667cd1c9f6411	Thai Thien	2020-07-12 14:19:03
do not *4 root if we have flatten augmentation list	c534aa36bf314ea32643e92231194bd020d7bf1f	Thai Thien	2020-07-12 14:19:00
train val split shb	61581543d16aaa2640bdee0b3573e41d1843770d	Thai Thien	2020-07-12 14:06:25
flatten collate	c04708ae0defc81dbf441395e1d27de6a1d598fc	Thai Thien	2020-07-12 13:54:01
travis remove nightly	d4b0c714823046bbafcd3c816d56f7079c76d126	Thai Thien	2020-07-12 13:27:55
travis	e6368ec3102e01f1bdc71a80a78f0db3617d7e08	Thai Thien	2020-07-12 12:33:21
flatten_collate	1e460396875c205c42de27449f56e73cd4ec10e0	Thai Thien	2020-07-12 12:23:40
train val split test ratio to 0.1	5091da3f0b45d875a38c2829e4fec5e61116e869	Thai Thien	2020-07-11 03:14:26
f	1defee5dc452c2da5fb540ff1050f6e01fe1878b	Thai Thien	2020-07-10 17:04:58
fix log	db4d655a313b8f3951baf225a4b197fce4bcdd4b	Thai Thien	2020-07-10 16:50:03
typo, trunc 3.0 not 4.0	ee29c49efd0a5087f11662997b8992d25671a33a	Thai Thien	2020-07-10 16:44:55
let try with trunc 3.0	f8eac179b1fa79d6451f19a9e6a35b82b94646a4	Thai Thien	2020-07-10 16:43:41
train script	7e6984a9eaa6609182ad6c786f6742dd29f1d017	Thai Thien	2020-06-14 10:23:05
fix H3	71d41ac82f273857f7643d8e63e502c701888a5b	Thai Thien	2020-06-14 10:10:39
typo	bc78059e62b17a5a617e3e0294f5c72dd4bd347e	Thai Thien	2020-06-14 10:06:35
H3_t1_sha H3_t2_sha H3_t2_shb_fixed ccnn_adam_t7_shb ccnn_adam_t7_sha	ca7e1ccab41314f8a30e48609421db3a2c41bbe0	Thai Thien	2020-06-14 09:59:17
train script	da9ad066d6d835f38eb9e9c2b771ff412124552a	Thai Thien	2020-06-13 18:06:22
train script ccnnv9	8ab76b70dad11b1458f8c1bfc2f1765c6ece984d	Thai Thien	2020-06-13 17:57:02
train script	7ab6cf827c811af9a0c9168de2fde4dbd0fb3e61	Thai Thien	2020-06-13 11:54:21

Commit bb25738b0510915712a17daeb590b668b446b0be - fix flatten collate
Author: Thai Thien
Author date (UTC): 2020-07-12 15:17
Committer name: Thai Thien
Committer date (UTC): 2020-07-12 15:17
Parent(s): c4f59ee01cbdab3f506302588b9667cd1c9f6411
Signing key:
Tree: bbd723e731f8f55310c18ea83d4bd542073b084e

File	Lines added	Lines deleted
data_flow.py	7	5
data_util/dataset_utils.py	23	2
data_util/test_dataset_utils.py	2	2
debug/explore_shb_fatten_list.py	3	3

File data_flow.py changed (mode: 100644) (index aaf7b31..806ba3c)
...	...	def load_data_shanghaitech_non_overlap(img_path, train=True):
473	473	target1 = np.expand_dims(target1, axis=0) # make dim (batch size, channel size, x, y) to make model output	target1 = np.expand_dims(target1, axis=0) # make dim (batch size, channel size, x, y) to make model output
474	474	crop_img.append(img)	crop_img.append(img)
475	475	crop_label.append(target1)	crop_label.append(target1)
476		return crop_img, crop_label
	476		return crop_img, crop_label
477	477
478	478	if not train:	if not train:
479	479	# get correct people head count from head annotation	# get correct people head count from head annotation

...	...	class ListDataset(Dataset):
937	937	if img is None or target is None:	if img is None or target is None:
938	938	return None	return None
939	939	if self.transform is not None:	if self.transform is not None:
940		img = self.transform(img)
	940		if isinstance(img, list):
	941		# for case of generate multiple augmentation per sample
	942		img_r = [self.transform(img_item) for img_item in img]
	943		img = img_r
	944		else:
	945		img = self.transform(img)
941	946	return img, target	return img, target
942	947
943
944
945
946	948	def get_dataloader(train_list, val_list, test_list, dataset_name="shanghaitech", visualize_mode=False, batch_size=1, train_loader_for_eval_check = False):	def get_dataloader(train_list, val_list, test_list, dataset_name="shanghaitech", visualize_mode=False, batch_size=1, train_loader_for_eval_check = False):
947	949	if visualize_mode:	if visualize_mode:
948	950	transformer = transforms.Compose([	transformer = transforms.Compose([

File data_util/dataset_utils.py changed (mode: 100644) (index 6e232b2..fdb1754)
...	...	def my_collate(batch): # batch size 4 [{tensor image, tensor label},{},{},{}] c
14	14	# so how to sample another dataset entry?	# so how to sample another dataset entry?
15	15	return torch.utils.data.dataloader.default_collate(batch)	return torch.utils.data.dataloader.default_collate(batch)
16	16
17		def flatten_collate(batch):
	17		def flatten_collate_broken(batch):
18	18	"""	"""
19	19
20		:param batch:
	20		:param batch: tuple of (data, label)
21	21	:return:	:return:
22	22	"""	"""
23	23	# remove null batch	# remove null batch

...	...	def flatten_collate(batch):
30	30	return out_batch	return out_batch
31	31
32	32
	33		def flatten_collate(batch):
	34		"""
	35
	36		:param batch: tuple of (data, label)
	37		:return:
	38		"""
	39		# remove null batch
	40		batch = list(filter(lambda x: x is not None, batch))
	41
	42		# flattening array
	43
	44		# more clarify version
	45		# out_batch = []
	46		# for data_pair in batch:
	47		# for img, label in zip(*data_pair):
	48		# out_batch.append((img, label))
	49
	50		# python List Comprehensions
	51		out_batch = [(img, label) for data_pair in batch for img, label in zip(*data_pair)]
	52
	53		return out_batch

File data_util/test_dataset_utils.py changed (mode: 100644) (index 3229ece..f62719e)
...	...	def test_flatten_collate_should_remove_null():
10	10
11	11
12	12	def test_flatten_list():	def test_flatten_list():
13		in_batch = [["s11", "s12", "s13"], ["s21", "s22", "s23"], ["s31", "s32", "s33"]]
14		out_batch = ["s11", "s12", "s13", "s21", "s22", "s23", "s31", "s32", "s33"]
	13		in_batch = [(["d11", "d12", "d13"],["l11", "l12", "l13"]),(["d21", "d22", "d23"],["l21", "l22", "l23"]), (["d31", "d32", "d33"],["l31", "l32", "l33"])]
	14		out_batch = [("d11", "l11"), ("d12", "l12"), ("d13", "l13"), ("d21", "l21"), ("d22", "l22"), ("d23", "l23"), ("d31", "l31"), ("d32", "l32"), ("d33", "l33")]
15	15	actual_output = flatten_collate(in_batch)	actual_output = flatten_collate(in_batch)
16	16	assert actual_output == out_batch	assert actual_output == out_batch

File debug/explore_shb_fatten_list.py copied from file debug/explore_shb.py (similarity 94%) (mode: 100644) (index d6814c7..c9fd3d5)
...	...	if __name__ == "__main__":
32	32	test_list = create_image_list(TEST_PATH)	test_list = create_image_list(TEST_PATH)
33	33
34	34	train_loader, train_loader_eval, val_loader, test_loader = get_dataloader(train_list, val_list, test_list,	train_loader, train_loader_eval, val_loader, test_loader = get_dataloader(train_list, val_list, test_list,
35		dataset_name="shanghaitech_more_random"
	35		dataset_name="shanghaitech_non_overlap"
36	36	, batch_size=20,	, batch_size=20,
37	37	train_loader_for_eval_check=True)	train_loader_for_eval_check=True)
38	38	print(len(train_loader))	print(len(train_loader))
39	39	print(len(val_loader))	print(len(val_loader))
40	40
41		for data, label in val_loader:
42		print(label)
	41		for obs in train_loader:
	42		print(len(obs))

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