392 строки
15 KiB
Python
392 строки
15 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
import numpy as np
|
|
from region_loss import RegionLoss
|
|
from cfg import *
|
|
|
|
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 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
|
|
self.iter = 0
|
|
|
|
def forward(self, x):
|
|
ind = -2
|
|
self.loss = None
|
|
outputs = dict()
|
|
for block in self.blocks:
|
|
ind = ind + 1
|
|
#if ind > 0:
|
|
# return x
|
|
|
|
if block['type'] == 'net':
|
|
continue
|
|
elif block['type'] == 'convolutional' or block['type'] == 'maxpool' or block['type'] == 'reorg' or block['type'] == 'avgpool' or block['type'] == 'softmax' or block['type'] == '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'] == 'cost':
|
|
continue
|
|
else:
|
|
print('unknown type %s' % (block['type']))
|
|
return x
|
|
|
|
def print_network(self):
|
|
print_cfg(self.blocks)
|
|
|
|
def create_network(self, blocks):
|
|
models = nn.ModuleList()
|
|
|
|
prev_filters = 3
|
|
out_filters =[]
|
|
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, eps=1e-4))
|
|
#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)
|
|
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)
|
|
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_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)
|
|
models.append(model)
|
|
elif block['type'] == 'reorg':
|
|
stride = int(block['stride'])
|
|
prev_filters = stride * stride * prev_filters
|
|
out_filters.append(prev_filters)
|
|
models.append(Reorg(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]]
|
|
elif len(layers) == 2:
|
|
assert(layers[0] == ind - 1)
|
|
prev_filters = out_filters[layers[0]] + out_filters[layers[1]]
|
|
out_filters.append(prev_filters)
|
|
models.append(EmptyModule())
|
|
elif block['type'] == 'shortcut':
|
|
ind = len(models)
|
|
prev_filters = out_filters[ind-1]
|
|
out_filters.append(prev_filters)
|
|
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)
|
|
models.append(model)
|
|
elif block['type'] == 'region':
|
|
loss = RegionLoss()
|
|
anchors = block['anchors'].split(',')
|
|
if anchors == ['']:
|
|
loss.anchors = []
|
|
else:
|
|
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)
|
|
models.append(loss)
|
|
else:
|
|
print('unknown type %s' % (block['type']))
|
|
|
|
return models
|
|
|
|
def load_weights(self, weightfile):
|
|
fp = open(weightfile, 'rb')
|
|
header = np.fromfile(fp, count=4, 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'] == 'route':
|
|
pass
|
|
elif block['type'] == 'shortcut':
|
|
pass
|
|
elif block['type'] == 'region':
|
|
pass
|
|
elif block['type'] == 'avgpool':
|
|
pass
|
|
elif block['type'] == 'softmax':
|
|
pass
|
|
elif block['type'] == 'cost':
|
|
pass
|
|
else:
|
|
print('unknown type %s' % (block['type']))
|
|
|
|
def load_weights_until_last(self, weightfile):
|
|
fp = open(weightfile, 'rb')
|
|
header = np.fromfile(fp, count=4, 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
|
|
blocklen = len(self.blocks)
|
|
for i in range(blocklen-2):
|
|
block = self.blocks[i]
|
|
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'] == 'route':
|
|
pass
|
|
elif block['type'] == 'shortcut':
|
|
pass
|
|
elif block['type'] == 'region':
|
|
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'] == 'route':
|
|
pass
|
|
elif block['type'] == 'shortcut':
|
|
pass
|
|
elif block['type'] == 'region':
|
|
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()
|