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[~] Refactor

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This commit is contained in:
Siarhei Siniak 2021-07-20 08:29:34 +03:00
parent af61c39377
commit d10c5664f5
1 changed files with 27 additions and 4 deletions
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31
python/tasks/mlb_player.py
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@ -489,8 +489,8 @@ def kernel_7(
feed = Variable(torch.from_numpy(img_test_pad)).cuda() feed = Variable(torch.from_numpy(img_test_pad)).cuda()
output1, output2 = model(feed) output1, output2 = model(feed)
print(output1.size()) #print(output1.size())
print(output2.size()) #print(output2.size())
heatmap = nn.UpsamplingBilinear2d((img_raw.shape[0], img_raw.shape[1])).cuda()(output2) heatmap = nn.UpsamplingBilinear2d((img_raw.shape[0], img_raw.shape[1])).cuda()(output2)
@ -752,7 +752,14 @@ def kernel_7(
model_pose = torch.nn.DataParallel(model_pose, device_ids=range(torch.cuda.device_count())) model_pose = torch.nn.DataParallel(model_pose, device_ids=range(torch.cuda.device_count()))
cudnn.benchmark = True cudnn.benchmark = True
def estimate_pose(img_ori, name=None): def estimate_pose(
img_ori,
name=None,
scale_param=None,
):
if scale_param is None:
scale_param = [0.5, 1.0, 1.5, 2.0]
if name is None: if name is None:
name = tempfile.mktemp( name = tempfile.mktemp(
dir='/kaggle/working', dir='/kaggle/working',
@ -763,7 +770,6 @@ def kernel_7(
) )
# People might be at different scales in the image, perform inference at multiple scales to boost results # People might be at different scales in the image, perform inference at multiple scales to boost results
scale_param = [0.5, 1.0, 1.5, 2.0]
# Predict Heatmaps for approximate joint position # Predict Heatmaps for approximate joint position
# Use Part Affinity Fields (PAF's) as guidance to link joints to form skeleton # Use Part Affinity Fields (PAF's) as guidance to link joints to form skeleton
@ -811,3 +817,20 @@ def kernel_8(
arch_image = o arch_image = o
img_ori = o_7['cv2'].imread(arch_image) img_ori = o_7['cv2'].imread(arch_image)
o_7['estimate_pose'](img_ori) o_7['estimate_pose'](img_ori)
def kernel_9_benchmark(
o_7,
):
t1 = o_7['cv2'].imread('../input/indonesian-traditional-dance/tgagrakanyar/tga_0000.jpg'
t5 = 10
t2 = datetime.datetime.now()
for k in range(t5):
o_7['estimate_pose'](
img_ori=t1,
scale_param=[1.0],
)
t3 = datetime.datetime.now()
t4 = (t3 - t2).totalseconds() / t5
pprint.pprint(
['kernel_9_benchmark', dict(t4=t4, t5=t5)]
)