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

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List of commits:

Subject	Hash	Author	Date (UTC)
shuffle non-overlap, msel1 loss	8ef0edfc8b459326b4c7a7044bc6dc6d30fd7fd6	Thai Thien	2020-07-13 10:48:54
MSEL1Loss	e2f4dc7bb1bee096ae17101331e0f221d05ba1af	Thai Thien	2020-07-13 09:34:11
t10	2f443c077e186901cdbe2c9faf27d2f37905a775	Thai Thien	2020-07-12 18:57:39
t10	87d6dd6e7debf45e16953e39471c13071039fa59	Thai Thien	2020-07-12 18:57:17
fix	45617766d305df841b7b0af3c2f6204a2e6d4f46	Thai Thien	2020-07-12 18:45:01
ccnn_v7_t8_shb	9f921d9415b89d9c9d4f4e8ef9b65b32663e06ec	Thai Thien	2020-07-12 16:00:42
fix shanghaitech_non_overlap	9332377476d2d7524822ba98e2321c75be90f709	Thai Thien	2020-07-12 15:57:08
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

Commit 8ef0edfc8b459326b4c7a7044bc6dc6d30fd7fd6 - shuffle non-overlap, msel1 loss
Author: Thai Thien
Author date (UTC): 2020-07-13 10:48
Committer name: Thai Thien
Committer date (UTC): 2020-07-13 10:48
Parent(s): e2f4dc7bb1bee096ae17101331e0f221d05ba1af
Signing key:
Tree: 9ea8b0cdb567d5f251b37ccbbdb92b899aca02e6

File	Lines added	Lines deleted
data_flow.py	46	2
experiment_main.py	4	1
mse_l1_loss.py	0	0

File data_flow.py changed (mode: 100644) (index 4d6f927..2dffa53)
...	...	def load_data_shanghaitech_non_overlap(img_path, train=True):
464	464	if x==1:	if x==1:
465	465	target = np.fliplr(target)	target = np.fliplr(target)
466	466	img = img.transpose(Image.FLIP_LEFT_RIGHT)	img = img.transpose(Image.FLIP_LEFT_RIGHT)
467
468
469	467	target1 = cv2.resize(target, (int(target.shape[1] / target_factor), int(target.shape[0] / target_factor)),	target1 = cv2.resize(target, (int(target.shape[1] / target_factor), int(target.shape[0] / target_factor)),
470	468	interpolation=cv2.INTER_CUBIC) * target_factor * target_factor	interpolation=cv2.INTER_CUBIC) * target_factor * target_factor
471	469	# target1 = target1.unsqueeze(0) # make dim (batch size, channel size, x, y) to make model output	# target1 = target1.unsqueeze(0) # make dim (batch size, channel size, x, y) to make model output
472	470	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
473	471	crop_img.append(img)	crop_img.append(img)
474	472	crop_label.append(target1)	crop_label.append(target1)
	473		# shuffle in pair
	474		tmp_pair = list(zip(crop_img, crop_label))
	475		random.shuffle(tmp_pair)
	476		crop_img, crop_label = zip(*tmp_pair)
475	477	return crop_img, crop_label	return crop_img, crop_label
476	478
477	479	if not train:	if not train:

...	...	def load_data_shanghaitech_non_overlap(img_path, train=True):
481	483	return img_origin, gt_count	return img_origin, gt_count
482	484
483	485
	486		def load_data_shanghaitech_non_overlap_noflip(img_path, train=True):
	487		"""
	488		per sample, crop 4, non-overlap
	489		:param img_path:
	490		:param train:
	491		:return:
	492		"""
	493		gt_path = img_path.replace('.jpg', '.h5').replace('images', 'ground-truth-h5')
	494		img_origin = Image.open(img_path).convert('RGB')
	495		crop_size = (int(img_origin.size[0] / 2), int(img_origin.size[1] / 2))
	496		gt_file = h5py.File(gt_path, 'r')
	497		target_origin = np.asarray(gt_file['density'])
	498		target_factor = 8
	499
	500		if train:
	501		# for each image
	502		# create 8 patches, 4 non-overlap 4 corner
	503		# for each of 4 patch, create another 4 flip
	504		crop_img = []
	505		crop_label = []
	506		for i in range(2):
	507		for j in range(2):
	508		# crop non-overlap
	509		dx = int(i * img_origin.size[0] * 1. / 2)
	510		dy = int(j * img_origin.size[1] * 1. / 2)
	511		img = img_origin.crop((dx, dy, crop_size[0] + dx, crop_size[1] + dy))
	512		target = target_origin[dy:crop_size[1] + dy, dx:crop_size[0] + dx]
	513
	514
	515		target1 = cv2.resize(target, (int(target.shape[1] / target_factor), int(target.shape[0] / target_factor)),
	516		interpolation=cv2.INTER_CUBIC) * target_factor * target_factor
	517		target1 = np.expand_dims(target1, axis=0) # make dim (batch size, channel size, x, y) to make model output
	518		crop_img.append(img)
	519		crop_label.append(target1)
	520		return crop_img, crop_label
	521
	522		if not train:
	523		# get correct people head count from head annotation
	524		mat_path = img_path.replace('.jpg', '.mat').replace('images', 'ground-truth').replace('IMG', 'GT_IMG')
	525		gt_count = count_gt_annotation_sha(mat_path)
	526		return img_origin, gt_count
	527
484	528	def load_data_shanghaitech_crop_random(img_path, train=True):	def load_data_shanghaitech_crop_random(img_path, train=True):
485	529	"""	"""
486	530	40 percent crop	40 percent crop

File experiment_main.py changed (mode: 100644) (index b6b690d..1ea7cdf)
...	...	from ignite.metrics import Loss
7	7	from ignite.handlers import Checkpoint, DiskSaver, Timer	from ignite.handlers import Checkpoint, DiskSaver, Timer
8	8	from crowd_counting_error_metrics import CrowdCountingMeanAbsoluteError, CrowdCountingMeanSquaredError, CrowdCountingMeanAbsoluteErrorWithCount, CrowdCountingMeanSquaredErrorWithCount	from crowd_counting_error_metrics import CrowdCountingMeanAbsoluteError, CrowdCountingMeanSquaredError, CrowdCountingMeanAbsoluteErrorWithCount, CrowdCountingMeanSquaredErrorWithCount
9	9	from visualize_util import get_readable_time	from visualize_util import get_readable_time
10
	10		from mse_l1_loss import MSEL1Loss
11	11	import torch	import torch
12	12	from torch import nn	from torch import nn
13	13	from models.meow_experiment.kitten_meow_1 import M1, M2, M3, M4	from models.meow_experiment.kitten_meow_1 import M1, M2, M3, M4

...	...	if __name__ == "__main__":
130	130	elif args.loss_fn == "MSEMean":	elif args.loss_fn == "MSEMean":
131	131	loss_fn = nn.MSELoss(reduction='mean').to(device)	loss_fn = nn.MSELoss(reduction='mean').to(device)
132	132	print("use MSEMean")	print("use MSEMean")
	133		elif args.loss_fn == "MSEL1Mean":
	134		loss_fn = MSEL1Loss(reduction='mean').to(device)
	135		print("use MSEL1Mean")
133	136	elif args.loss_fn == "MSENone":	elif args.loss_fn == "MSENone":
134	137	"""	"""
135	138	Doesnt work	Doesnt work

File mse_l1_loss.py renamed from MSEL1Loss.py (similarity 100%)

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