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car-detection-bayes
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ONNX compatibility updates

This commit is contained in:
Glenn Jocher 2018-12-26 12:32:34 +01:00
parent 647e1c6f52
commit 6940221948
1 changed files with 9 additions and 2 deletions
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11
models.py
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@ -246,6 +246,7 @@ class YOLOLayer(nn.Module):
p[..., 4] = torch.sigmoid(p[..., 4]) # p_conf p[..., 4] = torch.sigmoid(p[..., 4]) # p_conf
p[..., :4] *= stride p[..., :4] *= stride
# reshape from [1, 3, 13, 13, 85] to [1, 507, 85]
return p.view(bs, self.nA * nG * nG, 5 + self.nC) return p.view(bs, self.nA * nG * nG, 5 + self.nC)
@ -263,10 +264,10 @@ class Darknet(nn.Module):
self.loss_names = ['loss', 'x', 'y', 'w', 'h', 'conf', 'cls', 'nT', 'TP', 'FP', 'FPe', 'FN', 'TC'] self.loss_names = ['loss', 'x', 'y', 'w', 'h', 'conf', 'cls', 'nT', 'TP', 'FP', 'FPe', 'FN', 'TC']
def forward(self, x, targets=None, batch_report=False, var=0): def forward(self, x, targets=None, batch_report=False, var=0):
is_training = targets is not None
output = []
self.losses = defaultdict(float) self.losses = defaultdict(float)
is_training = targets is not None
layer_outputs = [] layer_outputs = []
output = []
for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)): for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):
if module_def['type'] in ['convolutional', 'upsample', 'maxpool']: if module_def['type'] in ['convolutional', 'upsample', 'maxpool']:
@ -314,6 +315,12 @@ class Darknet(nn.Module):
self.losses['nT'] /= 3 self.losses['nT'] /= 3
self.losses['TC'] = 0 self.losses['TC'] = 0
ONNX_export = False
if ONNX_export:
output = output[0].squeeze().transpose(0, 1)
output[5:] = torch.nn.functional.softmax(torch.sigmoid(output[5:]) * output[4:5], dim=0) # SSD-like conf
return output[5:], output[:4] # ONNX scores, boxes
return sum(output) if is_training else torch.cat(output, 1) return sum(output) if is_training else torch.cat(output, 1)