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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)
a	147a73727888e4bbcd3584fb32ae60a62b43b77a	Thai Thien	2020-12-06 15:41:18
reduction = sum	caaf7ea2f013097c2d0275a3c49bffb6ae7e4b69	Thai Thien	2020-12-06 15:40:13
cuda() and we fix	41f49e7aa28595cb6438519dddb5e17434a44d3e	Thai Thien	2020-12-06 15:34:53
minor fix 138	6178f63cb061ec7086a1748a8e9d4f4a03ea96e5	Thai Thien	2020-12-06 15:33:08
fix measure ssim psnr	88bb9b78e8ae45199074aa1076b15c73f13e6cb6	Thai Thien	2020-12-06 15:30:58
print y and y_pred shape	90ab90465dabc4bd1171f4500eb01c45cca97420	Thai Thien	2020-12-06 15:23:09
ccnn baseline	f02c3084f28f811879e36e9a309993d468535dc3	Thai Thien	2020-12-06 15:08:11
ccnn shb and sha	d1d4152ada2c5dfc18c65299f32cb12935b90fe4	Thai Thien	2020-12-06 14:46:36
sha	3196ece275027e4d032a0bddb84ce310c15f2380	Thai Thien	2020-12-06 13:46:28
abs clamp	524c6f7196908527367ddcbfc340107e4092cc6e	Thai Thien	2020-12-06 11:16:12
use abs instead of ssim	92226d63c9732e66bf50f0b78e61f915ad2bce77	Thai Thien	2020-12-06 10:56:56
claim both y and y_pred	d1f6764a691fc2f2f1adc63ae65d63a8347d8a45	Thai Thien	2020-12-06 10:52:14
clamp 0.0	5569f7dad3c9e792e65636bb29b66a84ff250282	Thai Thien	2020-12-06 10:46:57
dataloader target crop	e41c17147de7c05f213329009c7662fbb2f1dc91	Thai Thien	2020-12-06 10:43:12
clamp min = 0	5245898e5282aa4cdc4b6539e68cb36a1c2d0c1f	Thai Thien	2020-12-06 10:40:47
fix metric	9739b99b836657377eb9dce05fafeef4a90546e9	Thai Thien	2020-12-06 10:38:37
eval density	d02232a419cd22bc79e0325c1c6791a7e5fc15b0	Thai Thien	2020-12-06 10:37:22
.	926498e13908770b072aeca0ccecbf5d3a64808e	Thai Thien	2020-12-06 10:27:13
if self.dataset_name == "shanghaitech_non_overlap_test_with_densitygt" and not self.train:	ca2bf6f9372ccdd5b880f2dd7be2023840afdd1d	Thai Thien	2020-12-06 10:24:21
yeah forgot to change ds name	bfdf3cfe27be5f51de300e231b6b79f903a71624	Thai Thien	2020-12-06 10:17:37

Commit 147a73727888e4bbcd3584fb32ae60a62b43b77a - a
Author: Thai Thien
Author date (UTC): 2020-12-06 15:41
Committer name: Thai Thien
Committer date (UTC): 2020-12-06 15:41
Parent(s): caaf7ea2f013097c2d0275a3c49bffb6ae7e4b69
Signer:
Signing key:
Signing status: N
Tree: f0c7af89feb25a5d0b83791874ad19651d7f1f4d

File	Lines added	Lines deleted
crowd_counting_error_metrics.py	4	4

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File crowd_counting_error_metrics.py changed (mode: 100644) (index 91ba7e1..a6b8fb2)
...	...	class CrowdCountingMeanSSIMabs(Metric):
138	138	pad_density_map_tensor[:, 0, :y_pred.shape[2],:y_pred.shape[3]] = y_pred	pad_density_map_tensor[:, 0, :y_pred.shape[2],:y_pred.shape[3]] = y_pred
139	139	y_pred = pad_density_map_tensor	y_pred = pad_density_map_tensor
140	140
141		ssim_metric = piq.ssim(y, y_pred, reduction=sum())
	141		ssim_metric = piq.ssim(y, y_pred, reduction="sum")
142	142
143	143	self._sum += ssim_metric.item()	self._sum += ssim_metric.item()
144	144	# we multiply because ssim calculate mean of each image in batch	# we multiply because ssim calculate mean of each image in batch

...	...	class CrowdCountingMeanPSNRabs(Metric):
178	178	pad_density_map_tensor[:, 0, :y_pred.shape[2], :y_pred.shape[3]] = y_pred	pad_density_map_tensor[:, 0, :y_pred.shape[2], :y_pred.shape[3]] = y_pred
179	179	y_pred = pad_density_map_tensor	y_pred = pad_density_map_tensor
180	180
181		psnr_metric = piq.psnr(y, y_pred, reduction=sum())
	181		psnr_metric = piq.psnr(y, y_pred, reduction="sum")
182	182
183	183	self._sum += psnr_metric.item()	self._sum += psnr_metric.item()
184	184	# we multiply because ssim calculate mean of each image in batch	# we multiply because ssim calculate mean of each image in batch

...	...	class CrowdCountingMeanSSIMclamp(Metric):
218	218	pad_density_map_tensor[:, 0, :y_pred.shape[2], :y_pred.shape[3]] = y_pred	pad_density_map_tensor[:, 0, :y_pred.shape[2], :y_pred.shape[3]] = y_pred
219	219	y_pred = pad_density_map_tensor	y_pred = pad_density_map_tensor
220	220
221		ssim_metric = piq.ssim(y, y_pred, reduction=sum())
	221		ssim_metric = piq.ssim(y, y_pred, reduction="sum")
222	222
223	223	self._sum += ssim_metric.item()	self._sum += ssim_metric.item()
224	224	# we multiply because ssim calculate mean of each image in batch	# we multiply because ssim calculate mean of each image in batch

...	...	class CrowdCountingMeanPSNRclamp(Metric):
256	256	pad_density_map_tensor[:, 0, :y_pred.shape[2], :y_pred.shape[3]] = y_pred	pad_density_map_tensor[:, 0, :y_pred.shape[2], :y_pred.shape[3]] = y_pred
257	257	y_pred = pad_density_map_tensor	y_pred = pad_density_map_tensor
258	258
259		psnr_metric = piq.psnr(y, y_pred, reduction=sum())
	259		psnr_metric = piq.psnr(y, y_pred, reduction="sum")
260	260
261	261	self._sum += psnr_metric.item()	self._sum += psnr_metric.item()
262	262	# we multiply because ssim calculate mean of each image in batch	# we multiply because ssim calculate mean of each image in batch