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car-detection-bayes
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enable yolov3-tiny inference

This commit is contained in:
Glenn Jocher 2018-12-22 12:36:33 +01:00
parent 34fb1bb8a9
commit bd5b9693bf
1 changed files with 16 additions and 4 deletions
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20
models.py
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@ -32,13 +32,21 @@ def create_modules(module_defs):
if module_def['activation'] == 'leaky':
modules.add_module('leaky_%d' % i, nn.LeakyReLU(0.1))
elif module_def['type'] == 'maxpool':
kernel_size = int(module_def['size'])
stride = int(module_def['stride'])
if kernel_size == 2 and stride == 1:
modules.add_module('_debug_padding_%d' % i, nn.ZeroPad2d((0, 1, 0, 1)))
maxpool = nn.MaxPool2d(kernel_size=kernel_size, stride=stride, padding=int((kernel_size - 1) // 2))
modules.add_module('maxpool_%d' % i, maxpool)
elif module_def['type'] == 'upsample':
upsample = nn.Upsample(scale_factor=int(module_def['stride']), mode='nearest')
modules.add_module('upsample_%d' % i, upsample)
elif module_def['type'] == 'route':
layers = [int(x) for x in module_def['layers'].split(',')]
filters = sum([output_filters[layer_i] for layer_i in layers])
filters = sum([output_filters[i + 1 if i > 0 else i] for i in layers])
modules.add_module('route_%d' % i, EmptyLayer())
elif module_def['type'] == 'shortcut':
@ -54,7 +62,7 @@ def create_modules(module_defs):
num_classes = int(module_def['classes'])
img_height = int(hyperparams['height'])
# Define detection layer
yolo_layer = YOLOLayer(anchors, num_classes, img_height, anchor_idxs)
yolo_layer = YOLOLayer(anchors, num_classes, img_height, anchor_idxs, cfg=hyperparams['cfg'])
modules.add_module('yolo_%d' % i, yolo_layer)
# Register module list and number of output filters
@ -73,7 +81,7 @@ class EmptyLayer(nn.Module):
class YOLOLayer(nn.Module):
def __init__(self, anchors, nC, img_dim, anchor_idxs):
def __init__(self, anchors, nC, img_dim, anchor_idxs, cfg):
super(YOLOLayer, self).__init__()
anchors = [(a_w, a_h) for a_w, a_h in anchors] # (pixels)
@ -92,6 +100,9 @@ class YOLOLayer(nn.Module):
else:
stride = 8
if cfg.endswith('yolov3-tiny.cfg'):
stride *= 2
# Build anchor grids
nG = int(self.img_dim / stride) # number grid points
self.grid_x = torch.arange(nG).repeat(nG, 1).view([1, 1, nG, nG]).float()
@ -234,6 +245,7 @@ class Darknet(nn.Module):
super(Darknet, self).__init__()
self.module_defs = parse_model_config(cfg_path)
self.module_defs[0]['cfg'] = cfg_path
self.module_defs[0]['height'] = img_size
self.hyperparams, self.module_list = create_modules(self.module_defs)
self.img_size = img_size
@ -246,7 +258,7 @@ class Darknet(nn.Module):
layer_outputs = []
for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):
if module_def['type'] in ['convolutional', 'upsample']:
if module_def['type'] in ['convolutional', 'upsample', 'maxpool']:
x = module(x)
elif module_def['type'] == 'route':
layer_i = [int(x) for x in module_def['layers'].split(',')]