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darknet.py
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darknet.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from region_loss import RegionLoss
from yolo_layer import YoloLayer
from cfg import *
#from layers.batchnorm.bn import BN2d
class MaxPoolStride1(nn.Module):
def __init__(self):
super(MaxPoolStride1, self).__init__()
def forward(self, x):
x = F.max_pool2d(F.pad(x, (0,1,0,1), mode='replicate'), 2, stride=1)
return x
class Upsample(nn.Module):
def __init__(self, stride=2):
super(Upsample, self).__init__()
self.stride = stride
def forward(self, x):
stride = self.stride
assert(x.data.dim() == 4)
B = x.data.size(0)
C = x.data.size(1)
H = x.data.size(2)
W = x.data.size(3)
ws = stride
hs = stride
x = x.view(B, C, H, 1, W, 1).expand(B, C, H, stride, W, stride).contiguous().view(B, C, H*stride, W*stride)
return x
class Reorg(nn.Module):
def __init__(self, stride=2):
super(Reorg, self).__init__()
self.stride = stride
def forward(self, x):
stride = self.stride
assert(x.data.dim() == 4)
B = x.data.size(0)
C = x.data.size(1)
H = x.data.size(2)
W = x.data.size(3)
assert(H % stride == 0)
assert(W % stride == 0)
ws = stride
hs = stride
x = x.view(B, C, H/hs, hs, W/ws, ws).transpose(3,4).contiguous()
x = x.view(B, C, H/hs*W/ws, hs*ws).transpose(2,3).contiguous()
x = x.view(B, C, hs*ws, H/hs, W/ws).transpose(1,2).contiguous()
x = x.view(B, hs*ws*C, H/hs, W/ws)
return x
class GlobalAvgPool2d(nn.Module):
def __init__(self):
super(GlobalAvgPool2d, self).__init__()
def forward(self, x):
N = x.data.size(0)
C = x.data.size(1)
H = x.data.size(2)
W = x.data.size(3)
x = F.avg_pool2d(x, (H, W))
x = x.view(N, C)
return x
# for route and shortcut
class EmptyModule(nn.Module):
def __init__(self):
super(EmptyModule, self).__init__()
def forward(self, x):
return x
# support route shortcut and reorg
class Darknet(nn.Module):
def __init__(self, cfgfile):
super(Darknet, self).__init__()
self.blocks = parse_cfg(cfgfile)
self.models = self.create_network(self.blocks) # merge conv, bn,leaky
self.loss = self.models[len(self.models)-1]
self.width = int(self.blocks[0]['width'])
self.height = int(self.blocks[0]['height'])
if self.blocks[(len(self.blocks)-1)]['type'] == 'region':
self.anchors = self.loss.anchors
self.num_anchors = self.loss.num_anchors
self.anchor_step = self.loss.anchor_step
self.num_classes = self.loss.num_classes
self.header = torch.IntTensor([0,0,0,0])
self.seen = 0
def forward(self, x):
ind = -2
self.loss = None
outputs = dict()
out_boxes = []
for block in self.blocks:
ind = ind + 1
#if ind > 0:
# return x
if block['type'] == 'net':
continue
elif block['type'] in ['convolutional', 'maxpool', 'reorg', 'upsample', 'avgpool', 'softmax', 'connected']:
x = self.models[ind](x)
outputs[ind] = x
elif block['type'] == 'route':
layers = block['layers'].split(',')
layers = [int(i) if int(i) > 0 else int(i)+ind for i in layers]
if len(layers) == 1:
x = outputs[layers[0]]
outputs[ind] = x
elif len(layers) == 2:
x1 = outputs[layers[0]]
x2 = outputs[layers[1]]
x = torch.cat((x1,x2),1)
outputs[ind] = x
elif block['type'] == 'shortcut':
from_layer = int(block['from'])
activation = block['activation']
from_layer = from_layer if from_layer > 0 else from_layer + ind
x1 = outputs[from_layer]
x2 = outputs[ind-1]
x = x1 + x2
if activation == 'leaky':
x = F.leaky_relu(x, 0.1, inplace=True)
elif activation == 'relu':
x = F.relu(x, inplace=True)
outputs[ind] = x
elif block['type'] == 'region':
continue
if self.loss:
self.loss = self.loss + self.models[ind](x)
else:
self.loss = self.models[ind](x)
outputs[ind] = None
elif block['type'] == 'yolo':
if self.training:
pass
else:
boxes = self.models[ind](x)
out_boxes.append(boxes)
elif block['type'] == 'cost':
continue
else:
print('unknown type %s' % (block['type']))
if self.training:
return loss
else:
return out_boxes
def print_network(self):
print_cfg(self.blocks)
def create_network(self, blocks):
models = nn.ModuleList()
prev_filters = 3
out_filters =[]
prev_stride = 1
out_strides = []
conv_id = 0
for block in blocks:
if block['type'] == 'net':
prev_filters = int(block['channels'])
continue
elif block['type'] == 'convolutional':
conv_id = conv_id + 1
batch_normalize = int(block['batch_normalize'])
filters = int(block['filters'])
kernel_size = int(block['size'])
stride = int(block['stride'])
is_pad = int(block['pad'])
pad = (kernel_size-1)/2 if is_pad else 0
activation = block['activation']
model = nn.Sequential()
if batch_normalize:
model.add_module('conv{0}'.format(conv_id), nn.Conv2d(prev_filters, filters, kernel_size, stride, pad, bias=False))
model.add_module('bn{0}'.format(conv_id), nn.BatchNorm2d(filters))
#model.add_module('bn{0}'.format(conv_id), BN2d(filters))
else:
model.add_module('conv{0}'.format(conv_id), nn.Conv2d(prev_filters, filters, kernel_size, stride, pad))
if activation == 'leaky':
model.add_module('leaky{0}'.format(conv_id), nn.LeakyReLU(0.1, inplace=True))
elif activation == 'relu':
model.add_module('relu{0}'.format(conv_id), nn.ReLU(inplace=True))
prev_filters = filters
out_filters.append(prev_filters)
prev_stride = stride * prev_stride
out_strides.append(prev_stride)
models.append(model)
elif block['type'] == 'maxpool':
pool_size = int(block['size'])
stride = int(block['stride'])
if stride > 1:
model = nn.MaxPool2d(pool_size, stride)
else:
model = MaxPoolStride1()
out_filters.append(prev_filters)
prev_stride = stride * prev_stride
out_strides.append(prev_stride)
models.append(model)
elif block['type'] == 'avgpool':
model = GlobalAvgPool2d()
out_filters.append(prev_filters)
models.append(model)
elif block['type'] == 'softmax':
model = nn.Softmax()
out_strides.append(prev_stride)
out_filters.append(prev_filters)
models.append(model)
elif block['type'] == 'cost':
if block['_type'] == 'sse':
model = nn.MSELoss(size_average=True)
elif block['_type'] == 'L1':
model = nn.L1Loss(size_average=True)
elif block['_type'] == 'smooth':
model = nn.SmoothL1Loss(size_average=True)
out_filters.append(1)
out_strides.append(prev_stride)
models.append(model)
elif block['type'] == 'reorg':
stride = int(block['stride'])
prev_filters = stride * stride * prev_filters
out_filters.append(prev_filters)
prev_stride = prev_stride * stride
out_strides.append(prev_stride)
models.append(Reorg(stride))
elif block['type'] == 'upsample':
stride = int(block['stride'])
out_filters.append(prev_filters)
prev_stride = prev_stride / stride
out_strides.append(prev_stride)
#models.append(nn.Upsample(scale_factor=stride, mode='nearest'))
models.append(Upsample(stride))
elif block['type'] == 'route':
layers = block['layers'].split(',')
ind = len(models)
layers = [int(i) if int(i) > 0 else int(i)+ind for i in layers]
if len(layers) == 1:
prev_filters = out_filters[layers[0]]
prev_stride = out_strides[layers[0]]
elif len(layers) == 2:
assert(layers[0] == ind - 1)
prev_filters = out_filters[layers[0]] + out_filters[layers[1]]
prev_stride = out_strides[layers[0]]
out_filters.append(prev_filters)
out_strides.append(prev_stride)
models.append(EmptyModule())
elif block['type'] == 'shortcut':
ind = len(models)
prev_filters = out_filters[ind-1]
out_filters.append(prev_filters)
prev_stride = out_strides[ind-1]
out_strides.append(prev_stride)
models.append(EmptyModule())
elif block['type'] == 'connected':
filters = int(block['output'])
if block['activation'] == 'linear':
model = nn.Linear(prev_filters, filters)
elif block['activation'] == 'leaky':
model = nn.Sequential(
nn.Linear(prev_filters, filters),
nn.LeakyReLU(0.1, inplace=True))
elif block['activation'] == 'relu':
model = nn.Sequential(
nn.Linear(prev_filters, filters),
nn.ReLU(inplace=True))
prev_filters = filters
out_filters.append(prev_filters)
out_strides.append(prev_stride)
models.append(model)
elif block['type'] == 'region':
loss = RegionLoss()
anchors = block['anchors'].split(',')
loss.anchors = [float(i) for i in anchors]
loss.num_classes = int(block['classes'])
loss.num_anchors = int(block['num'])
loss.anchor_step = len(loss.anchors)/loss.num_anchors
loss.object_scale = float(block['object_scale'])
loss.noobject_scale = float(block['noobject_scale'])
loss.class_scale = float(block['class_scale'])
loss.coord_scale = float(block['coord_scale'])
out_filters.append(prev_filters)
out_strides.append(prev_stride)
models.append(loss)
elif block['type'] == 'yolo':
yolo_layer = YoloLayer()
anchors = block['anchors'].split(',')
anchor_mask = block['mask'].split(',')
yolo_layer.anchor_mask = [int(i) for i in anchor_mask]
yolo_layer.anchors = [float(i) for i in anchors]
yolo_layer.num_classes = int(block['classes'])
yolo_layer.num_anchors = int(block['num'])
yolo_layer.anchor_step = len(yolo_layer.anchors)/yolo_layer.num_anchors
yolo_layer.stride = prev_stride
#yolo_layer.object_scale = float(block['object_scale'])
#yolo_layer.noobject_scale = float(block['noobject_scale'])
#yolo_layer.class_scale = float(block['class_scale'])
#yolo_layer.coord_scale = float(block['coord_scale'])
out_filters.append(prev_filters)
out_strides.append(prev_stride)
models.append(yolo_layer)
else:
print('unknown type %s' % (block['type']))
return models
def load_weights(self, weightfile):
fp = open(weightfile, 'rb')
header = np.fromfile(fp, count=5, dtype=np.int32)
self.header = torch.from_numpy(header)
self.seen = self.header[3]
buf = np.fromfile(fp, dtype = np.float32)
fp.close()
start = 0
ind = -2
for block in self.blocks:
if start >= buf.size:
break
ind = ind + 1
if block['type'] == 'net':
continue
elif block['type'] == 'convolutional':
model = self.models[ind]
batch_normalize = int(block['batch_normalize'])
if batch_normalize:
start = load_conv_bn(buf, start, model[0], model[1])
else:
start = load_conv(buf, start, model[0])
elif block['type'] == 'connected':
model = self.models[ind]
if block['activation'] != 'linear':
start = load_fc(buf, start, model[0])
else:
start = load_fc(buf, start, model)
elif block['type'] == 'maxpool':
pass
elif block['type'] == 'reorg':
pass
elif block['type'] == 'upsample':
pass
elif block['type'] == 'route':
pass
elif block['type'] == 'shortcut':
pass
elif block['type'] == 'region':
pass
elif block['type'] == 'yolo':
pass
elif block['type'] == 'avgpool':
pass
elif block['type'] == 'softmax':
pass
elif block['type'] == 'cost':
pass
else:
print('unknown type %s' % (block['type']))
def save_weights(self, outfile, cutoff=0):
if cutoff <= 0:
cutoff = len(self.blocks)-1
fp = open(outfile, 'wb')
self.header[3] = self.seen
header = self.header
header.numpy().tofile(fp)
ind = -1
for blockId in range(1, cutoff+1):
ind = ind + 1
block = self.blocks[blockId]
if block['type'] == 'convolutional':
model = self.models[ind]
batch_normalize = int(block['batch_normalize'])
if batch_normalize:
save_conv_bn(fp, model[0], model[1])
else:
save_conv(fp, model[0])
elif block['type'] == 'connected':
model = self.models[ind]
if block['activation'] != 'linear':
save_fc(fc, model)
else:
save_fc(fc, model[0])
elif block['type'] == 'maxpool':
pass
elif block['type'] == 'reorg':
pass
elif block['type'] == 'upsample':
pass
elif block['type'] == 'route':
pass
elif block['type'] == 'shortcut':
pass
elif block['type'] == 'region':
pass
elif block['type'] == 'yolo':
pass
elif block['type'] == 'avgpool':
pass
elif block['type'] == 'softmax':
pass
elif block['type'] == 'cost':
pass
else:
print('unknown type %s' % (block['type']))
fp.close()