Bienqq
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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-07-29 12:06:29 +02:00
parent 981abf679c
commit 4af819449c
1 changed files with 16 additions and 1 deletions
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17
models.py
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@ -1,6 +1,8 @@
from utils.parse_config import * from utils.parse_config import *
from utils.utils import * from utils.utils import *
import torch.nn.functional as F
ONNX_EXPORT = False ONNX_EXPORT = False
@ -49,11 +51,19 @@ def create_modules(module_defs):
layers = [int(x) for x in module_def['layers'].split(',')] layers = [int(x) for x in module_def['layers'].split(',')]
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])
modules.add_module('route_%d' % i, EmptyLayer()) modules.add_module('route_%d' % i, EmptyLayer())
# if module_defs[i+1]['type'] == 'reorg3d':
# upsample = nn.Upsample(scale_factor=1/float(module_defs[i+1]['stride']), mode='nearest')
# modules.add_module('reorg3d_%d' % i, upsample)
elif module_def['type'] == 'shortcut': elif module_def['type'] == 'shortcut':
filters = output_filters[int(module_def['from'])] filters = output_filters[int(module_def['from'])]
modules.add_module('shortcut_%d' % i, EmptyLayer()) modules.add_module('shortcut_%d' % i, EmptyLayer())
elif module_def['type'] == 'reorg3d':
# torch.Size([16, 128, 104, 104])
# torch.Size([16, 64, 208, 208]) <-- # stride 2 interpolate dimensions 2 and 3 to cat with prior layer
pass
elif module_def['type'] == 'yolo': elif module_def['type'] == 'yolo':
yolo_index += 1 yolo_index += 1
anchor_idxs = [int(x) for x in module_def['mask'].split(',')] anchor_idxs = [int(x) for x in module_def['mask'].split(',')]
@ -186,7 +196,12 @@ class Darknet(nn.Module):
if len(layer_i) == 1: if len(layer_i) == 1:
x = layer_outputs[layer_i[0]] x = layer_outputs[layer_i[0]]
else: else:
x = torch.cat([layer_outputs[i] for i in layer_i], 1) try:
x = torch.cat([layer_outputs[i] for i in layer_i], 1)
except: # apply stride 2 for darknet reorg layer
layer_outputs[layer_i[1]] = F.interpolate(layer_outputs[layer_i[1]], scale_factor=[0.5, 0.5])
x = torch.cat([layer_outputs[i] for i in layer_i], 1)
# print(''), [print(layer_outputs[i].shape) for i in layer_i], print(x.shape)
elif mtype == 'shortcut': elif mtype == 'shortcut':
layer_i = int(module_def['from']) layer_i = int(module_def['from'])
x = layer_outputs[-1] + layer_outputs[layer_i] x = layer_outputs[-1] + layer_outputs[layer_i]