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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)
jhucrowd_downsample_256	b992d3acc7f1e1aa0ed6265c48e9f065fa1d3085	Thai Thien	2020-09-16 16:02:19
adamw1_bigtail13i_t7_jhu	81e1a8ff445b76f0a9c9c9882a11da65b8b13088	Thai Thien	2020-09-10 18:19:34
adamw1_CompactCNNV7_t1_jhu	ed5c3696081241e7ebd7370863ea08ec83467a0b	Thai Thien	2020-09-10 18:12:05
no force cache	33946d1f09a1e77c7758acbcd61e385a1800e854	Thai Thien	2020-09-10 17:47:06
skip train eval	186cb449855cb567fc61801bd3a1a4c0767a7d35	Thai Thien	2020-09-10 17:17:10
70 cuda mem but why ?	f63f482418b6c85d2a289da9e9e43ca2b50cdd5d	Thai Thien	2020-09-10 16:42:00
n_thread int	f6243317336e07b9b6177c89ec3072c7d18189c6	Thai Thien	2020-09-10 16:22:36
t3 fix num thread	8981cf96a953c643b3443f39ad73487d2e7e044d	Thai Thien	2020-09-10 16:20:28
jhu float32	c0b004d6a733f83e469bb3d48f7e20a9c7113957	Thai Thien	2020-09-10 16:14:09
fix float	8867f5d5e160c38a1a7d67b2fac728cf60e6649e	Thai Thien	2020-09-10 16:03:15
try catch empty file	e7d392cd6ec807f3ae242ef5090f5725ced9f897	Thai Thien	2020-09-10 15:40:14
case no label at all	51b423b2ee9271e3639a332ecab092944636d8f2	Thai Thien	2020-09-09 13:49:29
remove experiment	06478164cb8e00ed5512a0d4db0dbc6edc1b5ad1	Thai Thien	2020-09-08 15:10:33
val train test	5a43d57ba16667cb96f9514dcf4b81167ff8bd5a	Thai Thien	2020-09-08 15:01:51
parallel, try catch excemption	9ac6a36880a113c1820b1e6a702ed5c08ebcb03f	Thai Thien	2020-09-08 14:55:08
now show the number of point in log	54a1a16adf32c866ed65808d16da5f997d27b54f	Thai Thien	2020-09-07 17:19:18
gt to ground-truth	3996d1c19d68f3669b5f54e5b621635f0b87a9fc	Thai Thien	2020-09-07 17:06:15
change data path	5e6605625ba130310fbeaade259e4a5b98987dad	Thai Thien	2020-09-07 17:04:43
test train val	e817f1d6f4743286735811fff60f64080a8e76ed	Thai Thien	2020-09-07 17:02:57
add profiler	fac9aa609d6de1e8cd9cb8a8a97f00063aaae310	Thai Thien	2020-09-07 16:59:36

Commit b992d3acc7f1e1aa0ed6265c48e9f065fa1d3085 - jhucrowd_downsample_256
Author: Thai Thien
Author date (UTC): 2020-09-16 16:02
Committer name: Thai Thien
Committer date (UTC): 2020-09-16 16:02
Parent(s): 81e1a8ff445b76f0a9c9c9882a11da65b8b13088
Signing key:
Tree: 8dc103ccb234f46ae997d656cee03d5dfbe1c909

File	Lines added	Lines deleted
data_flow.py	49	0
train_script/learnstuff/l3/adamw1_ccnnv7_t2_jhu.sh	2	2

File data_flow.py changed (mode: 100644) (index 0418e46..3347278)
...	...	def load_data_jhucrowd_256(img_path, train=True, debug=False):
1058	1058	return img, target1	return img, target1
1059	1059
1060	1060
	1061		def load_data_jhucrowd_downsample_256(img_path, train=True, debug=False):
	1062		"""
	1063		for jhucrowd
	1064		crop fixed 256, allow batch in non-uniform dataset
	1065		:param img_path:
	1066		:param train:
	1067		:return:
	1068		"""
	1069		gt_path = img_path.replace('.jpg', '.h5').replace('images', 'ground-truth-h5')
	1070		img_origin = Image.open(img_path).convert('RGB')
	1071		# downsample by half
	1072		gt_file = h5py.File(gt_path, 'r')
	1073		target = np.asarray(gt_file['density']).astype('float32')
	1074		downsample_factor = 2
	1075		target_factor = 8 / downsample_factor
	1076		crop_sq_size = 256 * downsample_factor
	1077		if train:
	1078		crop_size = (crop_sq_size, crop_sq_size)
	1079		dx = int(random.random() * (img_origin.size[0] - crop_sq_size))
	1080		dy = int(random.random() * (img_origin.size[1] - crop_sq_size))
	1081		if img_origin.size[0] - crop_sq_size < 0 or img_origin.size[1] - crop_sq_size < 0: # we crop more than we can chew, so...
	1082		return None, None
	1083		img = img_origin.crop((dx, dy, crop_size[0] + dx, crop_size[1] + dy))
	1084		img2 = img.resize((int(img_origin.size[0] / 2), int(img_origin.size[1] / 2)), resample=Image.ANTIALIAS)
	1085		target = target[dy:crop_size[1] + dy, dx:crop_size[0] + dx]
	1086
	1087		if random.random() > 0.8:
	1088		target = np.fliplr(target)
	1089		img2 = img2.transpose(Image.FLIP_LEFT_RIGHT)
	1090
	1091		if not train:
	1092		# get correct people head count from head annotation
	1093		txt_path = img_path.replace('.jpg', '.txt').replace('images', 'ground-truth')
	1094		gt_count = count_gt_annotation_jhu(txt_path)
	1095		img_out = img_origin.resize((int(img_origin.size[0] / 2), int(img_origin.size[1] / 2)), resample=Image.ANTIALIAS)
	1096		if debug:
	1097		gt_file = h5py.File(gt_path, 'r')
	1098		target = np.asarray(gt_file['density'])
	1099		return img_origin, gt_count, target
	1100		return img_out, gt_count
	1101
	1102		target1 = cv2.resize(target, (int(target.shape[1] / target_factor), int(target.shape[0] / target_factor)),
	1103		interpolation=cv2.INTER_CUBIC) * target_factor * target_factor
	1104		# target1 = target1.unsqueeze(0) # make dim (batch size, channel size, x, y) to make model output
	1105		target1 = np.expand_dims(target1, axis=0) # make dim (batch size, channel size, x, y) to make model output
	1106		return img, target1
	1107
1061	1108	def data_augmentation(img, target):	def data_augmentation(img, target):
1062	1109	"""	"""
1063	1110	return 1 pair of img, target after apply augmentation	return 1 pair of img, target after apply augmentation

...	...	class ListDataset(Dataset):
1154	1201	self.load_data_fn = load_data_shanghaitech_180	self.load_data_fn = load_data_shanghaitech_180
1155	1202	elif dataset_name == "shanghaitech_256":	elif dataset_name == "shanghaitech_256":
1156	1203	self.load_data_fn = load_data_shanghaitech_256	self.load_data_fn = load_data_shanghaitech_256
	1204		elif dataset_name == "jhucrowd_downsample_256":
	1205		self.load_data_fn = load_data_jhucrowd_downsample_256
1157	1206	elif dataset_name == "shanghaitech_non_overlap":	elif dataset_name == "shanghaitech_non_overlap":
1158	1207	self.load_data_fn = load_data_shanghaitech_non_overlap	self.load_data_fn = load_data_shanghaitech_non_overlap
1159	1208	elif dataset_name == "shanghaitech_non_overlap_downsample":	elif dataset_name == "shanghaitech_non_overlap_downsample":

File train_script/learnstuff/l3/adamw1_ccnnv7_t2_jhu.sh copied from file train_script/learnstuff/l3/adamw1_ccnnv7_t1_jhu.sh (similarity 87%) (mode: 100644) (index 99fb23e..63fd722)
1		task="adamw1_CompactCNNV7_t1_jhu"
	1		task="adamw1_CompactCNNV7_t2_jhu"
2	2
3	3	CUDA_VISIBLE_DEVICES=4 OMP_NUM_THREADS=6 PYTHONWARNINGS="ignore" HTTPS_PROXY="http://10.60.28.99:86" nohup python experiment_main.py \	CUDA_VISIBLE_DEVICES=4 OMP_NUM_THREADS=6 PYTHONWARNINGS="ignore" HTTPS_PROXY="http://10.60.28.99:86" nohup python experiment_main.py \
4	4	--task_id $task \	--task_id $task \

...	...	CUDA_VISIBLE_DEVICES=4 OMP_NUM_THREADS=6 PYTHONWARNINGS="ignore" HTTPS_PROXY="ht
9	9	--decay 0.1 \	--decay 0.1 \
10	10	--loss_fn "MSEL1Mean" \	--loss_fn "MSEL1Mean" \
11	11	--batch_size 50 \	--batch_size 50 \
12		--datasetname jhucrowd_256 \
	12		--datasetname jhucrowd_downsample_256 \
13	13	--optim adamw \	--optim adamw \
14	14	--skip_train_eval \	--skip_train_eval \
15	15	--epochs 401 > logs/$task.log &	--epochs 401 > logs/$task.log &

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