From 6940221948682ed55044de317252235750132352 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Wed, 26 Dec 2018 12:32:34 +0100 Subject: [PATCH] ONNX compatibility updates --- models.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/models.py b/models.py index b2191d97..943d4d30 100755 --- a/models.py +++ b/models.py @@ -246,6 +246,7 @@ class YOLOLayer(nn.Module): p[..., 4] = torch.sigmoid(p[..., 4]) # p_conf 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) @@ -263,10 +264,10 @@ class Darknet(nn.Module): 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): - is_training = targets is not None - output = [] self.losses = defaultdict(float) + is_training = targets is not None layer_outputs = [] + output = [] for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)): if module_def['type'] in ['convolutional', 'upsample', 'maxpool']: @@ -314,6 +315,12 @@ class Darknet(nn.Module): self.losses['nT'] /= 3 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)