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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)
some predict demo	7f2214ad2882874d5407a2d4d5bd115b7089a020	Thai Thien	2020-12-02 14:55:22
demo	3d5fa0d764843d4fd780e44547fd0b13a367c6bc	Thai Thien	2020-11-27 19:28:37
notable image	b0f2b0e9732c2f2031831eb80eeaf7e6dab1f5bd	Thai Thien	2020-11-22 07:11:16
notebook	5b8b840bc1fd0d806a13646477988534f4982fac	Thai Thien	2020-11-22 07:10:42
l2_adamw2_bigtail13i_t13_sha	7fd22510845dec808100058190a202b805bf6a02	Thai Thien	2020-09-23 14:17:00
l2_adamw2_bigtail13i_t12_sha	bf67476db55f55f348068bc7683f68834659cc96	Thai Thien	2020-09-23 14:14:35
l2_adamw2_bigtail13i_t11_sha	1365b305bf541a3d941d173b169fa14aa811beb1	Thai Thien	2020-09-22 19:31:17
l2_adamw2_bigtail13i_t10_sha	4f6f2b8d3ac4654cc2ed94ca6900643f3ca07be9	Thai Thien	2020-09-22 19:27:24
l2_adamw2_bigtail13i_t9_sha	576b424688dff640a0a7d448cbd615cfb4f33d54	Thai Thien	2020-09-22 19:21:17
t8	9e00ee3a50c93bbd726ebd8554a68f535d836691	Thai Thien	2020-09-22 19:11:37
split a	a7b2115bee8ab76497528b84aee09c79631575b1	Thai Thien	2020-09-22 17:48:37
load_data_shanghaitech_256_v2 and l2_adamw2_bigtail13i_t7_sha	07bca1a460c30ed7ec7269d72dad99eef6fc96b3	Thai Thien	2020-09-22 17:04:45
adamw1_ccnnv7_t7_jhu	c5f106103aa3696827627f5e815105d9432e2acb	Thai Thien	2020-09-16 18:21:22
adamw1_ccnnv7_t6_jhu.sh	5c8644da7445b3dd4f6d2611d564592e8e8ed45c	Thai Thien	2020-09-16 18:19:07
adamw1_CompactCNNV7_t6_jhu	1fa1d544a254cd042bcdd36e8de2984fe3d5244f	Thai Thien	2020-09-16 17:56:15
adamw1_CompactCNNV7_t5_jhu	40720162c9e9b731fb852ed7e3e191228e421ced	Thai Thien	2020-09-16 17:46:20
test_data cache	73633cbd6d70448268a3e1534440601ddcf75276	Thai Thien	2020-09-16 17:12:57
fix test data not exist	474f506204251e26825801499f6bc503f44f8410	Thai Thien	2020-09-16 16:41:00
fix test data not exist	6377e18d87e0fabbf9b4ed143e26ae5912c0b872	Thai Thien	2020-09-16 16:40:06
jhucrowd_downsample_512	30b8b9a63e93a5631f003858ef2ab63dc62c6563	Thai Thien	2020-09-16 16:17:02

Commit 7f2214ad2882874d5407a2d4d5bd115b7089a020 - some predict demo
Author: Thai Thien
Author date (UTC): 2020-12-02 14:55
Committer name: Thai Thien
Committer date (UTC): 2020-12-02 14:55
Parent(s): 3d5fa0d764843d4fd780e44547fd0b13a367c6bc
Signing key:
Tree: 5b33bdbdba44ee8f50c73caa8012c07236eb8ac6

File	Lines added	Lines deleted
data_flow.py	3	1
demo_app/predict_only.py	30	3
models/dccnn.py	68	0
predict/.gitkeep	1	0
visualize_util.py	23	0

File data_flow.py changed (mode: 100644) (index 029f79b..812e169)
...	...	def get_dataloader(train_list, val_list, test_list, dataset_name="shanghaitech",
1456	1456	def simple_predict_data_load_fn(img_path):	def simple_predict_data_load_fn(img_path):
1457	1457	img_name = img_path.split("/")[-1]	img_name = img_path.split("/")[-1]
1458	1458	# when debug, give information on p_count and img_name	# when debug, give information on p_count and img_name
	1459		img_id = img_name.split(".")[0].split("_")[-1]
1459	1460	debug_info = {"img_path":img_path,	debug_info = {"img_path":img_path,
1460		"name": img_name}
	1461		"name": img_name,
	1462		"id": img_id}
1461	1463	img_origin = Image.open(img_path).convert('RGB')	img_origin = Image.open(img_path).convert('RGB')
1462	1464	return img_origin, debug_info	return img_origin, debug_info
1463	1465

File demo_app/predict_only.py changed (mode: 100644) (index 6d18a33..d677298)
1	1	import os	import os
	2		import torch
2	3	from data_flow import get_predict_dataloader	from data_flow import get_predict_dataloader
	4		from models.dccnn import DCCNN
	5		from visualize_util import save_density_map_normalize, save_density_map
	6
3	7	if __name__ == "__main__":	if __name__ == "__main__":
4	8	"""	"""
5	9	predict all in folder	predict all in folder
6	10	output into another folder	output into another folder
7	11	output density map and count in csv	output density map and count in csv
8	12	"""	"""
9		INPUT_FOLDER = "/data/ShanghaiTech/part_B/test_data/images/"
	13		NAME="bao2"
	14		# INPUT_FOLDER = "/data/ShanghaiTech/part_B/test_data/images/"
	15		INPUT_FOLDER = "/home/tt/Downloads/bao2"
10	16	OUTPUT_FOLDER = "/data/apps/tmp"	OUTPUT_FOLDER = "/data/apps/tmp"
	17		MODEL = "/home/tt/project/C-3-folder/trained_model/adamw1_bigtail13i_t1_shb_checkpoint_valid_mae=-7.574910521507263.pth"
11	18	input_list = [os.path.join(INPUT_FOLDER, dir) for dir in os.listdir(INPUT_FOLDER)]	input_list = [os.path.join(INPUT_FOLDER, dir) for dir in os.listdir(INPUT_FOLDER)]
12	19	loader = get_predict_dataloader(input_list)	loader = get_predict_dataloader(input_list)
	20		loaded_file = torch.load(MODEL)
	21		model = DCCNN()
	22		model.load_state_dict(loaded_file['model'])
	23		model.eval()
	24		os.mkdir(os.path.join(OUTPUT_FOLDER, NAME))
	25		log_file = open(os.path.join(OUTPUT_FOLDER, NAME, NAME +".log"), 'w')
	26		limit_count = 100
	27		count = 0
13	28	for img, info in loader:	for img, info in loader:
14		print(img.shape)
15		print(info)
	29		if count > limit_count:
	30		break
	31		predict_name = "PRED_" + info["name"][0]
	32
	33		predict_path = os.path.join(OUTPUT_FOLDER, NAME, predict_name)
	34		pred = model(img)
	35		pred = pred.detach().numpy()[0][0]
	36		pred_count = pred.sum()
	37		log_line = info["name"][0] + "," + str(pred_count.item()) +"\n"
	38		log_file.write(log_line)
	39		save_density_map(pred, predict_path)
	40		print("save to ", predict_path)
	41		count += 1
	42		log_file.close()

File models/dccnn.py added (mode: 100644) (index 0000000..87b5e97)
	1	import torch.nn as nn
	2	import torch
	3	import collections
	4	import torch.nn.functional as F
	5
	6	class DCCNN(nn.Module):
	7	"""
	8	A REAL-TIME DEEP NETWORK FOR CROWD COUNTING
	9	https://arxiv.org/pdf/2002.06515.pdf
	10	"""
	11	def __init__(self, load_weights=False):
	12	super(DCCNN, self).__init__()
	13	self.model_note = "BigTail12i, batchnorm default setting, add bn red, green, blue, i mean discard inplace"
	14	self.red_cnn = nn.Conv2d(3, 10, 9, padding=4)
	15	self.green_cnn = nn.Conv2d(3, 14, 7, padding=3)
	16	self.blue_cnn = nn.Conv2d(3, 16, 5, padding=2)
	17
	18	self.c0 = nn.Conv2d(40, 40, 3, padding=2, dilation=2)
	19
	20	self.max_pooling = nn.MaxPool2d(kernel_size=2, stride=2)
	21	self.avg_pooling = nn.AvgPool2d(kernel_size=2, stride=2)
	22
	23	self.c1 = nn.Conv2d(40, 60, 3, padding=2, dilation=2)
	24	self.c2 = nn.Conv2d(60, 40, 3, padding=2, dilation=2)
	25	self.c3 = nn.Conv2d(40, 20, 3, padding=2, dilation=2)
	26	self.c4 = nn.Conv2d(20, 10, 3, padding=2, dilation=2)
	27	self.output = nn.Conv2d(10, 1, 1)
	28
	29	self.bn_red = nn.BatchNorm2d(10)
	30	self.bn_green = nn.BatchNorm2d(14)
	31	self.bn_blue = nn.BatchNorm2d(16)
	32
	33	self.bn00 = nn.BatchNorm2d(40)
	34	self.bn0 = nn.BatchNorm2d(40)
	35	self.bn1 = nn.BatchNorm2d(60)
	36	self.bn2 = nn.BatchNorm2d(40)
	37	self.bn3 = nn.BatchNorm2d(20)
	38	self.bn4 = nn.BatchNorm2d(10)
	39
	40	def forward(self,x):
	41	x_red = F.relu(self.red_cnn(x))
	42	x_red = self.bn_red(x_red)
	43	x_green = F.relu(self.green_cnn(x))
	44	x_green = self.bn_green(x_green)
	45	x_blue = F.relu(self.blue_cnn(x))
	46	x_blue = self.bn_blue(x_blue)
	47
	48	x = torch.cat((x_red, x_green, x_blue), 1)
	49	x = self.bn00(x)
	50	x = self.max_pooling(x)
	51
	52	x = F.relu(self.c0(x))
	53	x = self.bn0(x)
	54	x = F.relu(self.c1(x))
	55	x = self.bn1(x)
	56	x = self.avg_pooling(x)
	57
	58	x = F.relu(self.c2(x))
	59	x = self.bn2(x)
	60
	61	x = F.relu(self.c3(x))
	62	x = self.bn3(x)
	63	x = self.avg_pooling(x)
	64
	65	x = F.relu(self.c4(x))
	66	x = self.bn4(x)
	67	x = self.output(x)
	68	return x

File predict/.gitkeep added (mode: 100644) (index 0000000..02448eb)
	1		keep the folder

File visualize_util.py changed (mode: 100644) (index 1b0f845..449bb48)
...	...	def save_density_map(density_map, name):
17	17	plt.savefig(name, dpi=600, bbox_inches='tight', pad_inches=0)	plt.savefig(name, dpi=600, bbox_inches='tight', pad_inches=0)
18	18	plt.close()	plt.close()
19	19
	20
	21		def save_density_map_normalize(density_map, name):
	22		den = density_map / density_map.max(density_map + 1e-20)
	23		plt.figure(dpi=600)
	24		plt.axis('off')
	25		plt.margins(0, 0)
	26		plt.imshow(den, cmap=CM.jet)
	27		plt.savefig(name, dpi=600, bbox_inches='tight', pad_inches=0)
	28		plt.close()
	29
	30
	31
20	32	def save_density_map_with_colorrange(density_map, name, vmin, vmax):	def save_density_map_with_colorrange(density_map, name, vmin, vmax):
21	33	plt.figure(dpi=600)	plt.figure(dpi=600)
22	34	plt.axis('off')	plt.axis('off')

...	...	def save_density_map_with_colorrange(density_map, name, vmin, vmax):
26	38	plt.savefig(name, dpi=600, bbox_inches='tight', pad_inches=0)	plt.savefig(name, dpi=600, bbox_inches='tight', pad_inches=0)
27	39	plt.close()	plt.close()
28	40
	41		def save_density_map_with_colorrange_max(density_map, name, vmin, vmax):
	42		den = density_map/np.max(density_map+1e-20)
	43		plt.figure(dpi=600)
	44		plt.axis('off')
	45		plt.margins(0, 0)
	46		plt.imshow(den, cmap=CM.jet)
	47		plt.clim(vmin, vmax)
	48		plt.savefig(name, dpi=600, bbox_inches='tight', pad_inches=0)
	49		plt.close()
	50
	51
29	52	def save_img(imgnp, name):	def save_img(imgnp, name):
30	53	# plt.imshow(imgnp[0].permute(1, 2, 0).numpy())	# plt.imshow(imgnp[0].permute(1, 2, 0).numpy())
31	54	plt.imsave(name, imgnp[0].permute(1, 2, 0).numpy())	plt.imsave(name, imgnp[0].permute(1, 2, 0).numpy())

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