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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2020-02-19 12:59:56 -08:00
parent ddd892dc20
commit f4a9e5cd58
1 changed files with 22 additions and 19 deletions
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models.py
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@ -70,7 +70,7 @@ def create_modules(module_defs, img_size, arc):
layers = [int(x) for x in mdef['from'].split(',')] layers = [int(x) for x in mdef['from'].split(',')]
filters = output_filters[layers[0]] filters = output_filters[layers[0]]
routs.extend([i + l if l < 0 else l for l in layers]) routs.extend([i + l if l < 0 else l for l in layers])
# modules = weightedFeatureFusion(layers=layers) modules = weightedFeatureFusion(layers=layers, weight='weights_type' in mdef)
elif mdef['type'] == 'reorg3d': # yolov3-spp-pan-scale elif mdef['type'] == 'reorg3d': # yolov3-spp-pan-scale
# torch.Size([16, 128, 104, 104]) # torch.Size([16, 128, 104, 104])
@ -119,20 +119,26 @@ def create_modules(module_defs, img_size, arc):
class weightedFeatureFusion(nn.Module): # weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070 class weightedFeatureFusion(nn.Module): # weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
def __init__(self, layers): def __init__(self, layers, weight=False):
super(weightedFeatureFusion, self).__init__() super(weightedFeatureFusion, self).__init__()
self.n = len(layers) + 1 # number of layers self.n = len(layers) + 1 # number of layers
self.layers = layers # layer indices self.layers = layers # layer indices
self.w = torch.nn.Parameter(torch.zeros(self.n)) # layer weights self.weight = weight # apply weights boolean
if weight:
self.w = torch.nn.Parameter(torch.zeros(self.n)) # layer weights
def forward(self, x, outputs): def forward(self, x, outputs):
w = torch.sigmoid(self.w) * (2 / self.n) # sigmoid weights (0-1) if self.weight:
if self.n == 2: w = torch.sigmoid(self.w) * (2 / self.n) # sigmoid weights (0-1)
return x * w[0] + outputs[self.layers[0]] * w[1] if self.n == 2:
elif self.n == 3: return x * w[0] + outputs[self.layers[0]] * w[1]
return x * w[0] + outputs[self.layers[0]] * w[1] + outputs[self.layers[1]] * w[2] elif self.n == 3:
return x * w[0] + outputs[self.layers[0]] * w[1] + outputs[self.layers[1]] * w[2]
else: else:
raise ValueError('weightedFeatureFusion() supports up to 3 layer inputs, %g attempted' % self.n) if self.n == 2:
return x + outputs[self.layers[0]]
elif self.n == 3:
return x + outputs[self.layers[0]] + outputs[self.layers[1]]
class SwishImplementation(torch.autograd.Function): class SwishImplementation(torch.autograd.Function):
@ -257,6 +263,10 @@ class Darknet(nn.Module):
mtype = mdef['type'] mtype = mdef['type']
if mtype in ['convolutional', 'upsample', 'maxpool']: if mtype in ['convolutional', 'upsample', 'maxpool']:
x = module(x) x = module(x)
elif mtype == 'shortcut': # sum
x = module(x, layer_outputs) # weightedFeatureFusion()
if verbose:
print('shortcut/add %s' % ([layer_outputs[i].shape for i in module.layers]))
elif mtype == 'route': # concat elif mtype == 'route': # concat
layers = [int(x) for x in mdef['layers'].split(',')] layers = [int(x) for x in mdef['layers'].split(',')]
if verbose: if verbose:
@ -270,25 +280,18 @@ class Darknet(nn.Module):
layer_outputs[layers[1]] = F.interpolate(layer_outputs[layers[1]], scale_factor=[0.5, 0.5]) layer_outputs[layers[1]] = F.interpolate(layer_outputs[layers[1]], scale_factor=[0.5, 0.5])
x = torch.cat([layer_outputs[i] for i in layers], 1) x = torch.cat([layer_outputs[i] for i in layers], 1)
# print(''), [print(layer_outputs[i].shape) for i in layers], print(x.shape) # print(''), [print(layer_outputs[i].shape) for i in layers], print(x.shape)
elif mtype == 'shortcut': # sum
# x = module(x, layer_outputs) # weightedFeatureFusion()
layers = [int(x) for x in mdef['from'].split(',')]
if verbose:
print('shortcut/add %s' % ([layer_outputs[i].shape for i in layers]))
for j in layers:
x = x + layer_outputs[j]
elif mtype == 'yolo': elif mtype == 'yolo':
output.append(module(x, img_size)) output.append(module(x, img_size))
layer_outputs.append(x if i in self.routs else []) layer_outputs.append(x if i in self.routs else [])
if verbose: if verbose:
print(i, x.shape) print(i, x.shape)
if self.training: if self.training: # train
return output return output
elif ONNX_EXPORT: elif ONNX_EXPORT: # export
x = [torch.cat(x, 0) for x in zip(*output)] x = [torch.cat(x, 0) for x in zip(*output)]
return x[0], torch.cat(x[1:3], 1) # scores, boxes: 3780x80, 3780x4 return x[0], torch.cat(x[1:3], 1) # scores, boxes: 3780x80, 3780x4
else: else: # test
io, p = zip(*output) # inference output, training output io, p = zip(*output) # inference output, training output
return torch.cat(io, 1), p return torch.cat(io, 1), p