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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2020-02-19 17:08:03 -08:00
parent f92ad043bd
commit 6fbab656c8
2 changed files with 23 additions and 18 deletions
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31
models.py
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@ -22,17 +22,16 @@ def create_modules(module_defs, img_size, arc):
# modules.add_module('BatchNorm2d_0', nn.BatchNorm2d(output_filters[-1], momentum=0.1)) # modules.add_module('BatchNorm2d_0', nn.BatchNorm2d(output_filters[-1], momentum=0.1))
if mdef['type'] == 'convolutional': if mdef['type'] == 'convolutional':
bn = int(mdef['batch_normalize']) bn = mdef['batch_normalize']
filters = int(mdef['filters']) filters = mdef['filters']
size = int(mdef['size']) size = mdef['size']
stride = int(mdef['stride']) if 'stride' in mdef else (int(mdef['stride_y']), int(mdef['stride_x'])) stride = mdef['stride'] if 'stride' in mdef else (mdef['stride_y'], mdef['stride_x'])
pad = (size - 1) // 2 if int(mdef['pad']) else 0
modules.add_module('Conv2d', nn.Conv2d(in_channels=output_filters[-1], modules.add_module('Conv2d', nn.Conv2d(in_channels=output_filters[-1],
out_channels=filters, out_channels=filters,
kernel_size=size, kernel_size=size,
stride=stride, stride=stride,
padding=pad, padding=(size - 1) // 2 if mdef['pad'] else 0,
groups=int(mdef['groups']) if 'groups' in mdef else 1, groups=mdef['groups'] if 'groups' in mdef else 1,
bias=not bn)) bias=not bn))
if bn: if bn:
modules.add_module('BatchNorm2d', nn.BatchNorm2d(filters, momentum=0.1)) modules.add_module('BatchNorm2d', nn.BatchNorm2d(filters, momentum=0.1))
@ -43,9 +42,9 @@ def create_modules(module_defs, img_size, arc):
modules.add_module('activation', Swish()) modules.add_module('activation', Swish())
elif mdef['type'] == 'maxpool': elif mdef['type'] == 'maxpool':
size = int(mdef['size']) size = mdef['size']
stride = int(mdef['stride']) stride = mdef['stride']
maxpool = nn.MaxPool2d(kernel_size=size, stride=stride, padding=int((size - 1) // 2)) maxpool = nn.MaxPool2d(kernel_size=size, stride=stride, padding=(size - 1) // 2)
if size == 2 and stride == 1: # yolov3-tiny if size == 2 and stride == 1: # yolov3-tiny
modules.add_module('ZeroPad2d', nn.ZeroPad2d((0, 1, 0, 1))) modules.add_module('ZeroPad2d', nn.ZeroPad2d((0, 1, 0, 1)))
modules.add_module('MaxPool2d', maxpool) modules.add_module('MaxPool2d', maxpool)
@ -57,17 +56,17 @@ def create_modules(module_defs, img_size, arc):
g = (yolo_index + 1) * 2 / 32 # gain g = (yolo_index + 1) * 2 / 32 # gain
modules = nn.Upsample(size=tuple(int(x * g) for x in img_size)) # img_size = (320, 192) modules = nn.Upsample(size=tuple(int(x * g) for x in img_size)) # img_size = (320, 192)
else: else:
modules = nn.Upsample(scale_factor=int(mdef['stride'])) modules = nn.Upsample(scale_factor=mdef['stride'])
elif mdef['type'] == 'route': # nn.Sequential() placeholder for 'route' layer elif mdef['type'] == 'route': # nn.Sequential() placeholder for 'route' layer
layers = [int(x) for x in mdef['layers'].split(',')] layers = mdef['layers']
filters = sum([output_filters[i + 1 if i > 0 else i] for i in layers]) filters = sum([output_filters[i + 1 if i > 0 else i] for i in layers])
routs.extend([l if l > 0 else l + i for l in layers]) routs.extend([l if l > 0 else l + i for l in layers])
# if mdef[i+1]['type'] == 'reorg3d': # if mdef[i+1]['type'] == 'reorg3d':
# modules = nn.Upsample(scale_factor=1/float(mdef[i+1]['stride']), mode='nearest') # reorg3d # modules = nn.Upsample(scale_factor=1/float(mdef[i+1]['stride']), mode='nearest') # reorg3d
elif mdef['type'] == 'shortcut': # nn.Sequential() placeholder for 'shortcut' layer elif mdef['type'] == 'shortcut': # nn.Sequential() placeholder for 'shortcut' layer
layers = [int(x) for x in mdef['from'].split(',')] layers = mdef['from']
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, weight='weights_type' in mdef) modules = weightedFeatureFusion(layers=layers, weight='weights_type' in mdef)
@ -79,9 +78,9 @@ def create_modules(module_defs, img_size, arc):
elif mdef['type'] == 'yolo': elif mdef['type'] == 'yolo':
yolo_index += 1 yolo_index += 1
mask = [int(x) for x in mdef['mask'].split(',')] # anchor mask mask = mdef['mask'] # anchor mask
modules = YOLOLayer(anchors=mdef['anchors'][mask], # anchor list modules = YOLOLayer(anchors=mdef['anchors'][mask], # anchor list
nc=int(mdef['classes']), # number of classes nc=mdef['classes'], # number of classes
img_size=img_size, # (416, 416) img_size=img_size, # (416, 416)
yolo_index=yolo_index, # 0, 1 or 2 yolo_index=yolo_index, # 0, 1 or 2
arc=arc) # yolo architecture arc=arc) # yolo architecture
@ -278,7 +277,7 @@ class Darknet(nn.Module):
print('shortcut/add %s + %s' % (list(x.shape), [list(out[i].shape) for i in module.layers])) print('shortcut/add %s + %s' % (list(x.shape), [list(out[i].shape) for i in module.layers]))
x = module(x, out) # weightedFeatureFusion() x = module(x, out) # weightedFeatureFusion()
elif mtype == 'route': # concat elif mtype == 'route': # concat
layers = [int(x) for x in mdef['layers'].split(',')] layers = mdef['layers']
if verbose: if verbose:
print('route/concatenate %s + %s' % (list(x.shape), [list(out[i].shape) for i in layers])) print('route/concatenate %s + %s' % (list(x.shape), [list(out[i].shape) for i in layers]))
if len(layers) == 1: if len(layers) == 1:

10
utils/parse_config.py
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@ -25,10 +25,16 @@ def parse_model_cfg(path):
key, val = line.split("=") key, val = line.split("=")
key = key.rstrip() key = key.rstrip()
if 'anchors' in key: if key == 'anchors': # return nparray
mdefs[-1][key] = np.array([float(x) for x in val.split(',')]).reshape((-1, 2)) # np anchors mdefs[-1][key] = np.array([float(x) for x in val.split(',')]).reshape((-1, 2)) # np anchors
elif key in ['from', 'layers', 'mask']: # return array
mdefs[-1][key] = [int(x) for x in val.split(',')]
else: else:
mdefs[-1][key] = val.strip() val = val.strip()
if val.isnumeric(): # return int or float
mdefs[-1][key] = int(val) if (int(val) - float(val)) == 0 else float(val)
else:
mdefs[-1][key] = val # return string
# Check all fields are supported # Check all fields are supported
supported = ['type', 'batch_normalize', 'filters', 'size', 'stride', 'pad', 'activation', 'layers', 'groups', supported = ['type', 'batch_normalize', 'filters', 'size', 'stride', 'pad', 'activation', 'layers', 'groups',