mirror of
https://github.com/iperov/DeepFaceLive
synced 2025-08-20 05:23:23 -07:00
code release
This commit is contained in:
iperov
parent
b941ba41a3
commit
a902f11f74
354 changed files with 826570 additions and 1 deletions
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modelhub/torch/CenterFace/CenterFace.pth
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modelhub/torch/CenterFace/CenterFace.pth
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modelhub/torch/CenterFace/CenterFace.py
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modelhub/torch/CenterFace/CenterFace.py
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from pathlib import Path
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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def CenterFace_to_onnx(onnx_filepath):
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"""Convert Pytorch CenterFace model to ONNX"""
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pth_file = Path(__file__).parent / 'CenterFace.pth'
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if not pth_file.exists():
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raise Exception(f'{pth_file} does not exist.')
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net = CenterFaceNet()
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net.load_state_dict( torch.load(pth_file) )
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torch.onnx.export(net,
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torch.from_numpy( np.zeros( (1,3,640,640), dtype=np.float32)),
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str(onnx_filepath),
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verbose=True,
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training=torch.onnx.TrainingMode.TRAINING,
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opset_version=12,
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do_constant_folding=False,
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input_names=['in'],
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output_names=['heatmap','scale','offset'],
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dynamic_axes={'in' : {0:'batch_size',2:'height',3:'width'},
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'heatmap' : {2:'height',3:'width'},
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'scale' : {2:'height',3:'width'},
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'offset' : {2:'height',3:'width'},
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},
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)
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# class BatchNorm2D(nn.Module):
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# def __init__(self, num_features, momentum=0.1, eps=1e-5):
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# super().__init__()
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# self.num_features = num_features
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# self.momentum = momentum
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# self.eps = 1e-5
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# self.weight = nn.Parameter(torch.Tensor(num_features))
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# self.bias = nn.Parameter(torch.Tensor(num_features))
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# self.register_buffer('running_mean', torch.zeros(num_features))
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# self.register_buffer('running_var', torch.ones(num_features))
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# self.register_buffer('num_batches_tracked', torch.tensor(0, dtype=torch.long))
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# def forward(self, input : torch.Tensor):
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# input_mean = input.mean([0,2,3], keepdim=True)
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# v = input-input_mean
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# var = (v*v).mean([0,2,3], keepdim=True)
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# return self.weight.view([1, self.num_features, 1, 1]) * v / (var + self.eps).sqrt() \
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# + self.bias.view([1, self.num_features, 1, 1])
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class CenterFaceNet(nn.Module):
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def __init__(self):
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super().__init__()
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self.conv_363 = nn.Conv2d(3, 32, 3, stride=2, padding=1, bias=False)
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self.bn_364 = nn.BatchNorm2d(32)
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self.dconv_366 = nn.Conv2d(32, 32, 3, padding=1, groups=32, bias=False)
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self.bn_367 = nn.BatchNorm2d(32)
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self.conv_369 = nn.Conv2d(32, 16, 1, padding=0, bias=False)
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self.bn_370 = nn.BatchNorm2d(16)
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self.conv_371 = nn.Conv2d(16, 96, 1, padding=0, bias=False)
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self.bn_372 = nn.BatchNorm2d(96)
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self.dconv_374 = nn.Conv2d(96, 96, 3, stride=2, padding=1, groups=96, bias=False)
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self.bn_375 = nn.BatchNorm2d(96)
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self.conv_377 = nn.Conv2d(96, 24, 1, padding=0, bias=False)
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self.bn_378 = nn.BatchNorm2d(24)
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self.conv_379 = nn.Conv2d(24, 144, 1, padding=0, bias=False)
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self.bn_380 = nn.BatchNorm2d(144)
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self.dconv_382 = nn.Conv2d(144, 144, 3, padding=1, groups=144, bias=False)
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self.bn_383 = nn.BatchNorm2d(144)
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self.conv_385 = nn.Conv2d(144, 24, 1, padding=0, bias=False)
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self.bn_386 = nn.BatchNorm2d(24)
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self.conv_388 = nn.Conv2d(24, 144, 1, padding=0, bias=False)
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self.bn_389 = nn.BatchNorm2d(144)
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self.dconv_391 = nn.Conv2d(144, 144, 3, stride=2, padding=1, groups=144, bias=False)
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self.bn_392 = nn.BatchNorm2d(144)
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self.conv_394 = nn.Conv2d(144, 32, 1, padding=0, bias=False)
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self.bn_395 = nn.BatchNorm2d(32)
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self.conv_396 = nn.Conv2d(32, 192, 1, padding=0, bias=False)
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self.bn_397 = nn.BatchNorm2d(192)
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self.dconv_399 = nn.Conv2d(192, 192, 3, padding=1, groups=192, bias=False)
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self.bn_400 = nn.BatchNorm2d(192)
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self.conv_402 = nn.Conv2d(192, 32, 1, padding=0, bias=False)
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self.bn_403 = nn.BatchNorm2d(32)
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self.conv_405 = nn.Conv2d(32, 192, 1, padding=0, bias=False)
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self.bn_406 = nn.BatchNorm2d(192)
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self.dconv_408 = nn.Conv2d(192, 192, 3, padding=1, groups=192, bias=False)
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self.bn_409 = nn.BatchNorm2d(192)
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self.conv_411 = nn.Conv2d(192, 32, 1, padding=0, bias=False)
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self.bn_412 = nn.BatchNorm2d(32)
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self.conv_414 = nn.Conv2d(32, 192, 1, padding=0, bias=False)
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self.bn_415 = nn.BatchNorm2d(192)
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self.dconv_417 = nn.Conv2d(192, 192, 3, stride=2, padding=1, groups=192, bias=False)
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self.bn_418 = nn.BatchNorm2d(192)
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self.conv_420 = nn.Conv2d(192, 64, 1, padding=0, bias=False)
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self.bn_421 = nn.BatchNorm2d(64)
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self.conv_422 = nn.Conv2d(64, 384, 1, padding=0, bias=False)
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self.bn_423 = nn.BatchNorm2d(384)
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self.dconv_425 = nn.Conv2d(384, 384, 3, padding=1, groups=384, bias=False)
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self.bn_426 = nn.BatchNorm2d(384)
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self.conv_428 = nn.Conv2d(384, 64, 1, padding=0, bias=False)
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self.bn_429 = nn.BatchNorm2d(64)
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self.conv_431 = nn.Conv2d(64, 384, 1, padding=0, bias=False)
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self.bn_432 = nn.BatchNorm2d(384)
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self.dconv_434 = nn.Conv2d(384, 384, 3, padding=1, groups=384, bias=False)
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self.bn_435 = nn.BatchNorm2d(384)
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self.conv_437 = nn.Conv2d(384, 64, 1, padding=0, bias=False)
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self.bn_438 = nn.BatchNorm2d(64)
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self.conv_440 = nn.Conv2d(64, 384, 1, padding=0, bias=False)
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self.bn_441 = nn.BatchNorm2d(384)
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self.dconv_443 = nn.Conv2d(384, 384, 3, padding=1, groups=384, bias=False)
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self.bn_444 = nn.BatchNorm2d(384)
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self.conv_446 = nn.Conv2d(384, 64, 1, padding=0, bias=False)
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self.bn_447 = nn.BatchNorm2d(64)
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self.conv_449 = nn.Conv2d(64, 384, 1, padding=0, bias=False)
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self.bn_450 = nn.BatchNorm2d(384)
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self.dconv_452 = nn.Conv2d(384, 384, 3, padding=1, groups=384, bias=False)
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self.bn_453 = nn.BatchNorm2d(384)
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self.conv_455 = nn.Conv2d(384, 96, 1, padding=0, bias=False)
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self.bn_456 = nn.BatchNorm2d(96)
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self.conv_457 = nn.Conv2d(96, 576, 1, padding=0, bias=False)
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self.bn_458 = nn.BatchNorm2d(576)
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self.dconv_460 = nn.Conv2d(576, 576, 3, padding=1, groups=576, bias=False)
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self.bn_461 = nn.BatchNorm2d(576)
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self.conv_463 = nn.Conv2d(576, 96, 1, padding=0, bias=False)
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self.bn_464 = nn.BatchNorm2d(96)
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self.conv_466 = nn.Conv2d(96, 576, 1, padding=0, bias=False)
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self.bn_467 = nn.BatchNorm2d(576)
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self.dconv_469 = nn.Conv2d(576, 576, 3, padding=1, groups=576, bias=False)
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self.bn_470 = nn.BatchNorm2d(576)
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self.conv_472 = nn.Conv2d(576, 96, 1, padding=0, bias=False)
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self.bn_473 = nn.BatchNorm2d(96)
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self.conv_475 = nn.Conv2d(96, 576, 1, padding=0, bias=False)
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self.bn_476 = nn.BatchNorm2d(576)
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self.dconv_478 = nn.Conv2d(576, 576, 3, stride=2, padding=1, groups=576, bias=False)
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self.bn_479 = nn.BatchNorm2d(576)
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self.conv_481 = nn.Conv2d(576, 160, 1, padding=0, bias=False)
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self.bn_482 = nn.BatchNorm2d(160)
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self.conv_483 = nn.Conv2d(160, 960, 1, padding=0, bias=False)
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self.bn_484 = nn.BatchNorm2d(960)
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self.dconv_486 = nn.Conv2d(960, 960, 3, padding=1, groups=960, bias=False)
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self.bn_487 = nn.BatchNorm2d(960)
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self.conv_489 = nn.Conv2d(960, 160, 1, padding=0, bias=False)
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self.bn_490 = nn.BatchNorm2d(160)
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self.conv_492 = nn.Conv2d(160, 960, 1, padding=0, bias=False)
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self.bn_493 = nn.BatchNorm2d(960)
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self.dconv_495 = nn.Conv2d(960, 960, 3, padding=1, groups=960, bias=False)
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self.bn_496 = nn.BatchNorm2d(960)
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self.conv_498 = nn.Conv2d(960, 160, 1, padding=0, bias=False)
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self.bn_499 = nn.BatchNorm2d(160)
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self.conv_501 = nn.Conv2d(160, 960, 1, padding=0, bias=False)
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self.bn_502 = nn.BatchNorm2d(960)
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self.dconv_504 = nn.Conv2d(960, 960, 3, padding=1, groups=960, bias=False)
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self.bn_505 = nn.BatchNorm2d(960)
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self.conv_507 = nn.Conv2d(960, 320, 1, padding=0, bias=False)
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self.bn_508 = nn.BatchNorm2d(320)
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self.conv_509 = nn.Conv2d(320, 24, 1, padding=0, bias=False)
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self.bn_510 = nn.BatchNorm2d(24)
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self.conv_512 = nn.ConvTranspose2d(24, 24, 2, stride=2, padding=0, bias=False)
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self.bn_513 = nn.BatchNorm2d(24)
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self.conv_515 = nn.Conv2d(96, 24, 1, padding=0, bias=False)
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self.bn_516 = nn.BatchNorm2d(24)
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self.conv_519 = nn.ConvTranspose2d(24,24, 2, stride=2, padding=0, bias=False)
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self.bn_520 = nn.BatchNorm2d(24)
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self.conv_522 = nn.Conv2d(32, 24, 1, padding=0, bias=False)
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self.bn_523 = nn.BatchNorm2d(24)
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self.conv_526 = nn.ConvTranspose2d(24,24, 2, stride=2, padding=0, bias=False)
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self.bn_527 = nn.BatchNorm2d(24)
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self.conv_529 = nn.Conv2d(24, 24, 1, padding=0, bias=False)
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self.bn_530 = nn.BatchNorm2d(24)
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self.conv_533 = nn.Conv2d(24, 24, 3, padding=1, bias=False)
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self.bn_534 = nn.BatchNorm2d(24)
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self.conv_536 = nn.Conv2d(24, 1, 1)
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self.conv_538 = nn.Conv2d(24, 2, 1)
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self.conv_539 = nn.Conv2d(24, 2, 1)
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self.conv_540 = nn.Conv2d(24, 10, 1)
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def forward(self, x):
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x = self.conv_363(x)
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x = self.bn_364(x)
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x = F.relu(x)
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x = self.dconv_366(x)
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x = self.bn_367(x)
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x = F.relu(x)
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x = self.conv_369(x)
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x = self.bn_370(x)
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x = self.conv_371(x)
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x = self.bn_372(x)
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x = F.relu(x)
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x = self.dconv_374(x)
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x = self.bn_375(x)
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x = F.relu(x)
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x = self.conv_377(x)
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x = x378 = self.bn_378(x)
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x = self.conv_379(x)
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x = self.bn_380(x)
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x = F.relu(x)
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x = self.dconv_382(x)
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x = self.bn_383(x)
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x = F.relu(x)
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x = self.conv_385(x)
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x = self.bn_386(x)
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x = x387 = x + x378
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x = self.conv_388(x)
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x = self.bn_389(x)
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x = F.relu(x)
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x = self.dconv_391(x)
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x = self.bn_392(x)
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x = F.relu(x)
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x = self.conv_394(x)
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x = x395 = self.bn_395(x)
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x = self.conv_396(x)
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x = self.bn_397(x)
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x = F.relu(x)
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x = self.dconv_399(x)
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x = self.bn_400(x)
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x = F.relu(x)
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x = self.conv_402(x)
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x = self.bn_403(x)
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x = x404 = x + x395
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x = self.conv_405(x)
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x = self.bn_406(x)
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x = F.relu(x)
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x = self.dconv_408(x)
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x = self.bn_409(x)
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x = F.relu(x)
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x = self.conv_411(x)
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x = self.bn_412(x)
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x = x413 = x + x404
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x = self.conv_414(x)
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x = self.bn_415(x)
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x = F.relu(x)
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x = self.dconv_417(x)
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x = self.bn_418(x)
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x = F.relu(x)
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x = self.conv_420(x)
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x = x421 = self.bn_421(x)
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x = self.conv_422(x)
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x = self.bn_423(x)
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x = F.relu(x)
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x = self.dconv_425(x)
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x = self.bn_426(x)
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x = F.relu(x)
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x = self.conv_428(x)
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x = self.bn_429(x)
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x = x430 = x + x421
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x = self.conv_431(x)
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x = self.bn_432(x)
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x = F.relu(x)
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x = self.dconv_434(x)
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x = self.bn_435(x)
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x = F.relu(x)
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x = self.conv_437(x)
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x = self.bn_438(x)
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x = x439 = x + x430
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x = self.conv_440(x)
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x = self.bn_441(x)
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x = F.relu(x)
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x = self.dconv_443(x)
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x = self.bn_444(x)
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x = F.relu(x)
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x = self.conv_446(x)
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x = self.bn_447(x)
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x = x + x439
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x = self.conv_449(x)
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x = self.bn_450(x)
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x = F.relu(x)
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x = self.dconv_452(x)
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x = self.bn_453(x)
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x = F.relu(x)
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x = self.conv_455(x)
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x = x456 = self.bn_456(x)
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x = self.conv_457(x)
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x = self.bn_458(x)
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x = F.relu(x)
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x = self.dconv_460(x)
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x = self.bn_461(x)
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x = F.relu(x)
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x = self.conv_463(x)
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x = self.bn_464(x)
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x = x465 = x + x456
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x = self.conv_466(x)
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x = self.bn_467(x)
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x = F.relu(x)
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x = self.dconv_469(x)
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x = self.bn_470(x)
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x = F.relu(x)
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x = self.conv_472(x)
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x = self.bn_473(x)
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x = x474 = x + x465
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x = self.conv_475(x)
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x = self.bn_476(x)
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x = F.relu(x)
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x = self.dconv_478(x)
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x = self.bn_479(x)
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x = F.relu(x)
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x = self.conv_481(x)
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x = x482 = self.bn_482(x)
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x = self.conv_483(x)
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x = self.bn_484(x)
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x = F.relu(x)
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x = self.dconv_486(x)
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x = self.bn_487(x)
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x = F.relu(x)
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x = self.conv_489(x)
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x = self.bn_490(x)
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x = x491 = x + x482
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x = self.conv_492(x)
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x = self.bn_493(x)
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x = F.relu(x)
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x = self.dconv_495(x)
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x = self.bn_496(x)
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x = F.relu(x)
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x = self.conv_498(x)
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x = self.bn_499(x)
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x = x + x491
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x = self.conv_501(x)
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x = self.bn_502(x)
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x = F.relu(x)
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x = self.dconv_504(x)
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x = self.bn_505(x)
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x = F.relu(x)
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x = self.conv_507(x)
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x = self.bn_508(x)
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x = self.conv_509(x)
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x = self.bn_510(x)
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x = F.relu(x)
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x = self.conv_512(x)
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x = self.bn_513(x)
|
||||
x = x514 = F.relu(x)
|
||||
|
||||
x = self.conv_515(x474)
|
||||
x = self.bn_516(x)
|
||||
x = F.relu(x)
|
||||
|
||||
x = x + x514
|
||||
|
||||
x = self.conv_519(x)
|
||||
x = self.bn_520(x)
|
||||
x = x521 = F.relu(x)
|
||||
|
||||
x = self.conv_522(x413)
|
||||
x = self.bn_523(x)
|
||||
x = F.relu(x)
|
||||
|
||||
x = x + x521
|
||||
|
||||
x = self.conv_526(x)
|
||||
x = self.bn_527(x)
|
||||
x = x528 = F.relu(x)
|
||||
|
||||
x = self.conv_529(x387)
|
||||
x = self.bn_530(x)
|
||||
x = F.relu(x)
|
||||
|
||||
x = x + x528
|
||||
|
||||
x = self.conv_533(x)
|
||||
x = self.bn_534(x)
|
||||
x = F.relu(x)
|
||||
|
||||
heatmap = torch.sigmoid( self.conv_536(x) )
|
||||
scale = self.conv_538(x)
|
||||
offset = self.conv_539(x)
|
||||
|
||||
return heatmap, scale, offset
|
||||
261
modelhub/torch/S3FD/S3FD.py
Normal file
261
modelhub/torch/S3FD/S3FD.py
Normal file
|
|
@ -0,0 +1,261 @@
|
|||
import operator
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from xlib import math as lib_math
|
||||
from xlib.image import ImageProcessor
|
||||
from xlib.torch import TorchDeviceInfo, get_cpu_device
|
||||
|
||||
|
||||
class S3FD:
|
||||
def __init__(self, device_info : TorchDeviceInfo = None ):
|
||||
if device_info is None:
|
||||
device_info = get_cpu_device()
|
||||
self.device_info = device_info
|
||||
|
||||
net = self.net = S3FDNet()
|
||||
net.load_state_dict( torch.load(str(Path(__file__).parent / 's3fd.pth')) )
|
||||
net.eval()
|
||||
|
||||
if not device_info.is_cpu():
|
||||
net.cuda(device_info.get_index())
|
||||
|
||||
def extract(self, img : np.ndarray, fixed_window, min_face_size=40):
|
||||
"""
|
||||
|
||||
"""
|
||||
ip = ImageProcessor(img)
|
||||
|
||||
if fixed_window != 0:
|
||||
fixed_window = max(64, max(1, fixed_window // 32) * 32 )
|
||||
img_scale = ip.fit_in(fixed_window, fixed_window, pad_to_target=True, allow_upscale=False)
|
||||
else:
|
||||
ip.pad_to_next_divisor(64, 64)
|
||||
img_scale = 1.0
|
||||
|
||||
img = ip.ch(3).as_float32().apply( lambda img: img - [104,117,123]).get_image('NCHW')
|
||||
|
||||
tensor = torch.from_numpy(img)
|
||||
if not self.device_info.is_cpu():
|
||||
tensor = tensor.cuda(self.device_info.get_index())
|
||||
|
||||
batches_bbox = [x.data.cpu().numpy() for x in self.net(tensor)]
|
||||
|
||||
faces_per_batch = []
|
||||
for batch in range(img.shape[0]):
|
||||
bbox = self.refine( [ x[batch] for x in batches_bbox ] )
|
||||
|
||||
faces = []
|
||||
for l,t,r,b,c in bbox:
|
||||
if img_scale != 1.0:
|
||||
l,t,r,b = l/img_scale, t/img_scale, r/img_scale, b/img_scale
|
||||
|
||||
bt = b-t
|
||||
if min(r-l,bt) < min_face_size:
|
||||
continue
|
||||
b += bt*0.1
|
||||
|
||||
faces.append ( (l,t,r,b) )
|
||||
|
||||
#sort by largest area first
|
||||
faces = [ [(l,t,r,b), (r-l)*(b-t) ] for (l,t,r,b) in faces ]
|
||||
faces = sorted(faces, key=operator.itemgetter(1), reverse=True )
|
||||
faces = [ x[0] for x in faces]
|
||||
faces_per_batch.append(faces)
|
||||
|
||||
return faces_per_batch
|
||||
|
||||
|
||||
def refine(self, olist):
|
||||
bboxlist = []
|
||||
variances = [0.1, 0.2]
|
||||
for i in range(len(olist) // 2):
|
||||
ocls, oreg = olist[i * 2], olist[i * 2 + 1]
|
||||
|
||||
stride = 2**(i + 2) # 4,8,16,32,64,128
|
||||
for hindex, windex in [*zip(*np.where(ocls[1, :, :] > 0.05))]:
|
||||
axc, ayc = stride / 2 + windex * stride, stride / 2 + hindex * stride
|
||||
score = ocls[1, hindex, windex]
|
||||
loc = np.ascontiguousarray(oreg[:, hindex, windex]).reshape((1, 4))
|
||||
priors = np.array([[axc, ayc, stride * 4, stride * 4]])
|
||||
bbox = np.concatenate((priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
|
||||
priors[:, 2:] * np.exp(loc[:, 2:] * variances[1])), 1)
|
||||
bbox[:, :2] -= bbox[:, 2:] / 2
|
||||
bbox[:, 2:] += bbox[:, :2]
|
||||
x1, y1, x2, y2 = bbox[0]
|
||||
bboxlist.append([x1, y1, x2, y2, score])
|
||||
|
||||
if len(bboxlist) != 0:
|
||||
bboxlist = np.array(bboxlist)
|
||||
bboxlist = bboxlist[ lib_math.nms(bboxlist[:,0], bboxlist[:,1], bboxlist[:,2], bboxlist[:,3], bboxlist[:,4], 0.3), : ]
|
||||
bboxlist = [x for x in bboxlist if x[-1] >= 0.5]
|
||||
|
||||
return bboxlist
|
||||
|
||||
|
||||
@staticmethod
|
||||
def save_as_onnx(onnx_filepath):
|
||||
s3fd = S3FD()
|
||||
|
||||
torch.onnx.export(s3fd.net,
|
||||
torch.from_numpy( np.zeros( (1,3,640,640), dtype=np.float32)),
|
||||
str(onnx_filepath),
|
||||
verbose=True,
|
||||
training=torch.onnx.TrainingMode.EVAL,
|
||||
opset_version=9,
|
||||
do_constant_folding=True,
|
||||
input_names=['in'],
|
||||
output_names=['cls1', 'reg1', 'cls2', 'reg2', 'cls3', 'reg3', 'cls4', 'reg4', 'cls5', 'reg5', 'cls6', 'reg6'],
|
||||
dynamic_axes={'in' : {0:'batch_size',2:'height',3:'width'},
|
||||
'cls1' : {2:'height',3:'width'},
|
||||
'reg1' : {2:'height',3:'width'},
|
||||
'cls2' : {2:'height',3:'width'},
|
||||
'reg2' : {2:'height',3:'width'},
|
||||
'cls3' : {2:'height',3:'width'},
|
||||
'reg3' : {2:'height',3:'width'},
|
||||
'cls4' : {2:'height',3:'width'},
|
||||
'reg4' : {2:'height',3:'width'},
|
||||
'cls5' : {2:'height',3:'width'},
|
||||
'reg5' : {2:'height',3:'width'},
|
||||
'cls6' : {2:'height',3:'width'},
|
||||
'reg6' : {2:'height',3:'width'},
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
|
||||
class L2Norm(nn.Module):
|
||||
def __init__(self, n_channels, scale=1.0):
|
||||
super().__init__()
|
||||
self.n_channels = n_channels
|
||||
self.scale = scale
|
||||
self.eps = 1e-10
|
||||
self.weight = nn.Parameter(torch.Tensor(self.n_channels))
|
||||
self.weight.data *= 0.0
|
||||
self.weight.data += self.scale
|
||||
|
||||
def forward(self, x):
|
||||
norm = x.pow(2).sum(dim=1, keepdim=True).sqrt() + self.eps
|
||||
x = x / norm * self.weight.view(1, -1, 1, 1)
|
||||
return x
|
||||
|
||||
|
||||
|
||||
class S3FDNet(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
|
||||
self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
|
||||
|
||||
self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
|
||||
self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
|
||||
|
||||
self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
|
||||
self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
|
||||
self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
|
||||
|
||||
self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
|
||||
self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
|
||||
self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
|
||||
|
||||
self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
|
||||
self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
|
||||
self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
|
||||
|
||||
self.fc6 = nn.Conv2d(512, 1024, kernel_size=3, stride=1, padding=3)
|
||||
self.fc7 = nn.Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0)
|
||||
|
||||
self.conv6_1 = nn.Conv2d(1024, 256, kernel_size=1, stride=1, padding=0)
|
||||
self.conv6_2 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1)
|
||||
|
||||
self.conv7_1 = nn.Conv2d(512, 128, kernel_size=1, stride=1, padding=0)
|
||||
self.conv7_2 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1)
|
||||
|
||||
self.conv3_3_norm = L2Norm(256, scale=10)
|
||||
self.conv4_3_norm = L2Norm(512, scale=8)
|
||||
self.conv5_3_norm = L2Norm(512, scale=5)
|
||||
|
||||
self.conv3_3_norm_mbox_conf = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1)
|
||||
self.conv3_3_norm_mbox_loc = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1)
|
||||
self.conv4_3_norm_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1)
|
||||
self.conv4_3_norm_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1)
|
||||
self.conv5_3_norm_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1)
|
||||
self.conv5_3_norm_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1)
|
||||
|
||||
self.fc7_mbox_conf = nn.Conv2d(1024, 2, kernel_size=3, stride=1, padding=1)
|
||||
self.fc7_mbox_loc = nn.Conv2d(1024, 4, kernel_size=3, stride=1, padding=1)
|
||||
self.conv6_2_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1)
|
||||
self.conv6_2_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1)
|
||||
self.conv7_2_mbox_conf = nn.Conv2d(256, 2, kernel_size=3, stride=1, padding=1)
|
||||
self.conv7_2_mbox_loc = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1)
|
||||
|
||||
def forward(self, x):
|
||||
h = F.relu(self.conv1_1(x))
|
||||
h = F.relu(self.conv1_2(h))
|
||||
h = F.max_pool2d(h, 2, 2)
|
||||
|
||||
h = F.relu(self.conv2_1(h))
|
||||
h = F.relu(self.conv2_2(h))
|
||||
h = F.max_pool2d(h, 2, 2)
|
||||
|
||||
h = F.relu(self.conv3_1(h))
|
||||
h = F.relu(self.conv3_2(h))
|
||||
h = F.relu(self.conv3_3(h))
|
||||
f3_3 = h
|
||||
h = F.max_pool2d(h, 2, 2)
|
||||
|
||||
h = F.relu(self.conv4_1(h))
|
||||
h = F.relu(self.conv4_2(h))
|
||||
h = F.relu(self.conv4_3(h))
|
||||
f4_3 = h
|
||||
h = F.max_pool2d(h, 2, 2)
|
||||
|
||||
h = F.relu(self.conv5_1(h))
|
||||
h = F.relu(self.conv5_2(h))
|
||||
h = F.relu(self.conv5_3(h))
|
||||
f5_3 = h
|
||||
h = F.max_pool2d(h, 2, 2)
|
||||
|
||||
h = F.relu(self.fc6(h))
|
||||
h = F.relu(self.fc7(h))
|
||||
ffc7 = h
|
||||
|
||||
h = F.relu(self.conv6_1(h))
|
||||
h = F.relu(self.conv6_2(h))
|
||||
|
||||
f6_2 = h
|
||||
|
||||
h = F.relu(self.conv7_1(h))
|
||||
h = F.relu(self.conv7_2(h))
|
||||
f7_2 = h
|
||||
|
||||
f3_3 = self.conv3_3_norm(f3_3)
|
||||
f4_3 = self.conv4_3_norm(f4_3)
|
||||
f5_3 = self.conv5_3_norm(f5_3)
|
||||
|
||||
cls1 = self.conv3_3_norm_mbox_conf(f3_3)
|
||||
reg1 = self.conv3_3_norm_mbox_loc(f3_3)
|
||||
cls2 = self.conv4_3_norm_mbox_conf(f4_3)
|
||||
reg2 = self.conv4_3_norm_mbox_loc(f4_3)
|
||||
cls3 = self.conv5_3_norm_mbox_conf(f5_3)
|
||||
reg3 = self.conv5_3_norm_mbox_loc(f5_3)
|
||||
|
||||
cls4 = self.fc7_mbox_conf(ffc7)
|
||||
reg4 = self.fc7_mbox_loc(ffc7)
|
||||
cls5 = self.conv6_2_mbox_conf(f6_2)
|
||||
reg5 = self.conv6_2_mbox_loc(f6_2)
|
||||
cls6 = self.conv7_2_mbox_conf(f7_2)
|
||||
reg6 = self.conv7_2_mbox_loc(f7_2)
|
||||
|
||||
# max-out background label
|
||||
chunk = torch.chunk(cls1, 4, 1)
|
||||
bmax = torch.max(torch.max(chunk[0], chunk[1]), chunk[2])
|
||||
cls1 = torch.cat ([bmax,chunk[3]], dim=1)
|
||||
cls1, cls2, cls3, cls4, cls5, cls6 = [ F.softmax(x, dim=1) for x in [cls1, cls2, cls3, cls4, cls5, cls6] ]
|
||||
|
||||
return [cls1, reg1, cls2, reg2, cls3, reg3, cls4, reg4, cls5, reg5, cls6, reg6]
|
||||
|
||||
|
||||
BIN
modelhub/torch/S3FD/s3fd.pth
Normal file
BIN
modelhub/torch/S3FD/s3fd.pth
Normal file
Binary file not shown.
2
modelhub/torch/__init__.py
Normal file
2
modelhub/torch/__init__.py
Normal file
|
|
@ -0,0 +1,2 @@
|
|||
from .CenterFace.CenterFace import CenterFace, CenterFace_to_onnx
|
||||
from .S3FD.S3FD import S3FD
|
||||
Loading…
Add table
Add a link
Reference in a new issue