def forward(self, x, inp_dim, num_classes, confidence):
x = x.data
global args
prediction = x.to(args.device)
prediction = predict_transform(prediction, inp_dim, self.anchors,
num_classes, confidence, args)
return prediction
def forward(self, x, inp_dim, num_classes, confidence):
x = x.data
global CUDA
prediction = x
prediction = predict_transform(prediction, inp_dim, self.anchors,
num_classes, confidence, CUDA)
return prediction
def forward(self, x):
modules = self.blocks[1:]
outputs = {}
write = 0
# Use the modules as to determine the module by index
# Module list contains the details of the module
for i, module in enumerate(modules):
module_type = (module["type"])
if module_type == "convolutional" or module_type == "upsample":
x = self.module_list[i](x)
elif module_type == "route":
layers = module["layers"]
# List of the route layers
layers = [int(a) for a in layers]
if (layers[0]) > 0:
layers[0] = layers[0] - i
if len(layers) == 1:
x = outputs[i + (layers[0])]
else:
if(layers[1]) > 0:
layers[1] = layers[1] - i
map1 = outputs[i + layers[0]]
map2 = outputs[i + layers[1]]
# Concat on the depth
x = torch.cat((map1, map2), 1)
elif module_type == "shortcut":
from_ = int(module["from"])
x = outputs[i-1] + outputs[i+from_]
elif module_type == 'yolo':
anchors = self.module_list[i][0].anchors
#Get the input dimensions
inp_dim = int (self.net_info["height"])
#Get the number of classes
num_classes = int (module["classes"])
#Transform
x = x.data
x = predict_transform(x, inp_dim, anchors, num_classes)
if not write: #if no collector has been intialised.
detections = x
write = 1
else:
detections = torch.cat((detections, x), 1)
outputs[i] = x
return detections
def forward(self, x, CUDA):
modules = self.blocks[1:]
# Cache route layer output
outputs = {}
write = 0
for i, (config, module) in enumerate(zip(modules, self.module_list)):
if config["type"] in ["convolutional", "upsample"]:
x = module(x)
elif config["type"] == "route":
layers = [int(layer) for layer in config["layers"]]
if (layers[0]) > 0:
layers[0] = layers[0] - i
if len(layers) == 1:
x = outputs[i + (layers[0])]
else:
if (layers[1]) > 0:
layers[1] = layers[1] - i
map1 = outputs[i + layers[0]]
map2 = outputs[i + layers[1]]
x = torch.cat((map1, map2), 1)
elif config["type"] == "shortcut":
from_ = int(config["from"])
x = outputs[i - 1] + outputs[i + from_]
elif config["type"] == 'yolo':
anchors = module[0].anchors
# Get the input dimensions
inp_dim = int(self.net_info["height"])
# Get the number of classes
num_classes = int(config["classes"])
# Transform
x = x.data
x = predict_transform(x, inp_dim, anchors, num_classes, CUDA)
if not write:
detections = x
write = 1
else:
detections = torch.cat((detections, x), 1)
outputs[i] = x
return detections
def forward(self, x):
modules = self.blocks[1:]
outputs = {}
detections = []
for i, m in enumerate(modules):
mtype = m['type']
# print(m)
# print(mtype)
# print(x.size())
if mtype == 'convolutional' or mtype == 'upsample':
x = self.module_list[i](x)
elif mtype == 'route':
layers = m['layers']
layers = [int(x) for x in layers]
if layers[0] > 0:
layers[0] -= i
if len(layers) == 1:
x = outputs[i + layers[0]]
else:
if layers[1] > 0:
layers[1] -= i
x = torch.cat(
(outputs[layers[0] + i], outputs[layers[1] + i]), 1)
elif mtype == 'shortcut':
_from = int(m['from'])
x = outputs[i - 1] + outputs[i + _from]
elif mtype == 'yolo':
anchors = self.module_list[i][0].anchors
inp_dim = int(self.net_info['height'])
num_classes = int(m['classes'])
detections += [
predict_transform(x, inp_dim, anchors, num_classes)
]
outputs[i] = x
res = torch.cat(detections, dim=1)
# res = detections[0]
# for dets in detections[1:]:
# res = torch.cat([res, dets], dim=1)
return res
def forward(self, x, CUDA=False):
modules = self.blocks[
1:] # first element of blocks is a net block, which isn't part of the forward pass
outputs = {} # cache the outputs for the route layer
write = 0 # means the collector hasn't been initialized.
for i, module in enumerate(modules):
module_type = (module['type'])
if module_type == 'convolutional' or module_type == 'upsample':
x = self.module_list[i](x)
elif module_type == 'route':
layers = module['layers']
layers = [int(a) for a in layers]
if (layers[0]) > 0:
layers[0] = layers[0] - i
if len(layers) == 1:
x = outputs[i + (layers[0])]
else:
if (layers[1]) > 0:
layers[1] = layers[1] - i
map1 = outputs[i + layers[0]]
map2 = outputs[i + layers[1]]
x = torch.cat((map1, map2), 1)
elif module_type == 'shortcut':
from_ = int(module['from'])
x = outputs[i - 1] + outputs[i + from_]
elif module_type == 'yolo':
anchors = self.module_list[i][0].anchors
# Get the input dimensions
inp_dim = int(self.net_info['height'])
# Get number of classes
num_classes = int(module['classes'])
# Transform
x = x.data
x = predict_transform(x, inp_dim, anchors, num_classes, CUDA)
if not write:
detections = x
write = 1
else:
detections = torch.cat((detections, x), 1)
outputs[i] = x
return detections
def forward(self, x, CUDA=False):
modules = self.blocks[1:] # first element of blocks is a net block, which isn't part of the forward pass
outputs = {} # cache the outputs for the route layer
write = 0 # means the collector hasn't been initialized.
for i, module in enumerate(modules):
module_type = (module['type'])
if module_type == 'convolutional' or module_type == 'upsample':
x = self.module_list[i](x)
elif module_type == 'route':
layers = module['layers']
layers = [int(a) for a in layers]
if (layers[0]) > 0:
layers[0] = layers[0] - i
if len(layers) == 1:
x = outputs[i + (layers[0])]
else:
if (layers[1]) > 0:
layers[1] = layers[1] - i
map1 = outputs[i + layers[0]]
map2 = outputs[i + layers[1]]
x = torch.cat((map1, map2), 1)
elif module_type == 'shortcut':
from_ = int(module['from'])
x = outputs[i - 1] + outputs[i + from_]
elif module_type == 'yolo':
anchors = self.module_list[i][0].anchors
# Get the input dimensions
inp_dim = int(self.net_info['height'])
# Get number of classes
num_classes = int(module['classes'])
# Transform
x = x.data
x = predict_transform(x, inp_dim, anchors, num_classes, CUDA)
if not write:
detections = x
write = 1
else:
detections = torch.cat((detections, x), 1)
outputs[i] = x
return detections
def forward(self, x, CUDA):
detections = []
modules = self.blocks[
1:] #迭代 self.block[1:] 而不是 self.blocks，因为 self.blocks 的第一个元素是一个 net 块，它不属于前向传播。
outputs = {} #We cache the outputs for the route layer
write = 0
for i in range(len(modules)):
module_type = (modules[i]["type"])
if module_type == "convolutional" or module_type == "upsample" or module_type == "maxpool":
x = self.module_list[i](x)
outputs[i] = x
elif module_type == "route":
layers = modules[i]["layers"]
layers = [int(a) for a in layers]
if (layers[0]) > 0:
layers[0] = layers[0] - i
if len(layers) == 1:
x = outputs[i + (layers[0])]
else:
if (layers[1]) > 0:
layers[1] = layers[1] - i
map1 = outputs[i + layers[0]]
map2 = outputs[i + layers[1]]
x = torch.cat((map1, map2), 1)
outputs[i] = x
elif module_type == "shortcut":
from_ = int(modules[i]["from"])
x = outputs[i - 1] + outputs[i + from_]
outputs[i] = x
elif module_type == 'yolo':
anchors = self.module_list[i][0].anchors
#Get the input dimensions
inp_dim = int(self.net_info["height"])
#Get the number of classes
num_classes = int(modules[i]["classes"])
#Output the result
x = x.data ## 这里得到的是预测的yolo层feature map
x = predict_transform(x, inp_dim, anchors, num_classes, CUDA)
if type(x) == int:
continue
if not write:
detections = x
write = 1
else:
detections = torch.cat((detections, x), 1)
outputs[i] = outputs[i - 1]
try:
return detections
except:
return 0
def forward(self, x, CUDA):
modules = self.blocks[1:] # 而且层的顺序与cfg中定义的相同
outputs = {} # 存储route layer的输出 k: 层的索引, v: 层的输出特征
write = 0 # 用来标志是不是第一个尺度的特征，如果不是直接concate输出结果即可
for i, module in enumerate(modules):
module_type = module['type']
# 卷积层或者上采样层，直接forward即可
if module_type in ['convolutional', 'upsample']:
x = self.module_list[i](x)
# route层, concate操作
elif module_type == 'route':
layers = module['layers']
layers = [int(a) for a in layers]
if layers[0] > 0:
layers[0] = layers[0] - i
if len(layers) == 1:
x = outputs[i + layers[0]]
else:
if layers[1] > 0:
layers[1] = layers[1] - i
map1 = outputs[i + layers[0]]
map2 = outputs[i + layers[1]]
x = torch.cat((map1, map2), dim=1) # 在通道维度上concate
elif module_type == 'shortcut':
from_ = int(module['from'])
x = outputs[i - 1] + outputs[i + from_]
# 在该尺度输出
elif module_type == 'yolo':
anchors = self.module_list[i][0].anchors
inp_dim = int(self.net_info['height'])
num_classes = int(module["classes"])
# 开始转化
x = x.data
x = predict_transform(x, inp_dim, anchors, num_classes, CUDA)
if not write:
detections = x
write = 1
else:
detections = torch.cat((detections, x), dim=1)
# 把推理后的x赋值到outputs
outputs[i] = x
return detections
def forward(self, x, CUDA=True):
detections = []
modules = self.blocks[1:] # 除了net块之外的所有
outputs = {} # cache output for route layer
write = False # 拼接检测层结果
for i in range(len(modules)):
module_type = modules[i]["type"] #
if module_type == "convolutional" or module_type == "upsample" or module_type == "maxpool":
x = self.module_list[i](x)
outputs[i] = x #
elif module_type == "route":
layers = modules[i]["layers"]
layers = [int(a) for a in layers]
if (layers[0]) > 0:
layers[0] = layers[0] - i
if len(layers) == 1:
x = outputs[i + (layers[0])]
else:
if (layers[1]) > 0:
layers[1] = layers[1] - i
map1 = outputs[i + layers[0]]
map2 = outputs[i + layers[1]]
x = torch.cat((map1, map2), 1)
outputs[i] = x
elif module_type == "shortcut":
from_ = int(modules[i]["from"])
x = outputs[i - 1] + outputs[i + from_] # 求和运算
outputs[i] = x
elif module_type == 'yolo':
anchors = self.module_list[i][0].anchors
inp_dim = int(self.net_info["height"]) # 得到输入维度
num_classes = int(modules[i]["classes"]) # 得到类别数
x = x.data
x = predict_transform(x, inp_dim, anchors, num_classes,
CUDA) # 输出结果
if type(x) == int:
continue
if not write:
# 将在3个不同level的feature map上检测结果存储在 detections 里
detections = x
write = True
else:
detections = torch.cat((detections, x), 1)
outputs[i] = outputs[i - 1]
try:
return detections # 网络forward 执行完毕
except:
return 0
def forward(self, x, CUDA):
detections = []
modules = self.blocks[1:]
outputs = {}
write = 0
for i in range(len(modules)):
module_type = modules[i]["type"]
if (module_type == "convolutional" or module_type == "upsample"
or module_type == "maxpool"):
x = self.module_list[i](x)
outputs[i] = x
elif module_type == "route":
layers = modules[i]["layers"]
layers = [int(a) for a in layers]
if (layers[0]) > 0:
layers[0] = layers[0] - i
if len(layers) == 1:
x = outputs[i + (layers[0])]
else:
if (layers[1]) > 0:
layers[1] = layers[1] - i
map1 = outputs[i + layers[0]]
map2 = outputs[i + layers[1]]
x = torch.cat((map1, map2), 1)
outputs[i] = x
elif module_type == "shortcut":
from_ = int(modules[i]["from"])
x = outputs[i - 1] + outputs[i + from_]
outputs[i] = x
elif module_type == "yolo":
anchors = self.module_list[i][0].anchors
inp_dim = int(self.net_info["height"])
num_classes = int(modules[i]["classes"])
x = x.data
x = predict_transform(x, inp_dim, anchors, num_classes, CUDA)
if type(x) == int:
continue
if not write:
detections = x
write = 1
else:
detections = torch.cat((detections, x), 1)
outputs[i] = outputs[i - 1]
try:
return detections
except:
return 0
def forward(self,x,CUDA):
modules = self.blocks[1:]
outputs = {}
write = 0
for i,module in enumerate(modules):
module_type = (module["type"])
if module_type == "convolutional" or module_type == "upsample":
x = self.module_list[i](x) # forward
elif module_type == "route":
layers = module["layers"]
layers = [int(a) for a in layers]
if layers[0] > 0:
layers[0] = layers[0] - i
if len(layers) == 1:
x = outputs[i + layers[0]]
else:
if layers[1] > 0:
layers[1] = layers[1] - i
map1 = outputs[i + layers[0]]
map2 = outputs[i + layers[1]]
x = torch.cat((map1,map2),1)
elif module_type == "shortcut":
form_ = int(module["from"])
x = outputs[i-1] + outputs[i + form_] # 求和
elif module_type == "yolo":
anchors = self.module_list[i][0].anchors
inp_dim = int(self.net_info["height"])
num_classes = int(module["classes"])
x = x.data # 得到yolo层的feature map
x = predict_transform(x,inp_dim,anchors,num_classes,CUDA)
if not write:
detections = x
write = 1
else:
detections = torch.cat((detections,x),1)
outputs[i] = x
return detections
def forward(self, x, CUDA):
modules = self.blocks[1:]
outputs = {}
write = 0
for i, module in enumerate(modules):
if module['type'] == 'convolutional' or module[
'type'] == 'unsample':
x = self.module_lists[i](x)
elif module['type'] == 'route':
layers = module['layers']
layers = [int(x.strip()) for x in layers]
if layers[0] > 0:
layers[0] = layers[0] - i
if len(layers) == 1:
x = outputs[i + layers[0]]
else:
if layers[1] > 1:
layers[1] = layers[1] - i
map1 = outputs[i + layers[0]]
map2 = outputs[i + layers[1]]
x = torch.cat((map1, map2), 1)
elif module['type'] == 'shortcut':
from_ = module['from']
x = outputs[i - 1] + outputs[from_]
elif module['type'] == 'yolo':
x = x.data
anchors = self.module_lists[i][0].anchors
inp_size = int(self.netinfo['height'])
class_num = int(module['classes'])
x = predict_transform(x, inp_size, anchors, class_num, CUDA)
if not write:
detections = x
write = 1
else:
detections = torch.cat((detections, x), 1)
outputs[i] = x
return detections
def forward(self, x, CUDA):
modules = self.blocks[1:]
outputs = {}
write = 0
for i, module in enumerate(modules):
module_type = module['type']
if module_type in ['convolutional', 'upsample']:
x = self.module_list[i](x)
elif module_type == 'route':
layers = module['layers'].split(',')
layers = [int(a) for a in layers]
if len(layers) == 1:
x = outputs[i + layers[0]]
else:
map1 = outputs[i + layers[0]]
map2 = outputs[layers[1]]
# TODO: why 1
x = torch.cat((map1, map2), 1)
elif module_type == 'shortcut':
from_ = int(module['from'])
x = outputs[i - 1] + outputs[i + from_]
elif module_type == 'yolo':
anchors = self.module_list[i][0].anchors
inp_dim = int(self.net_info['height'])
num_classes = int(module['classes'])
x = x.data
x = predict_transform(x, inp_dim, anchors, num_classes, False)
if not write:
detections = x
write = 1
else:
detections = torch.cat((detections, x), 1)
outputs[i] = x
return detections
def forward(self, x, CUDA):
modules = self.blocks[1:]
outputs = {}
write = 0
for i, module in enumerate(modules):
module_type = (module["type"])
if module_type == "convolutional" or module_type == "upsample":
x = self.module_list[i](x)
elif module_type == "route":
layers = module["layers"]
layers = [int(a) for a in layers]
if(layers[0]) > 0:
layers[0] = layers[0] - i
if len(layers) == 1:
x = outputs[i + (layers[0])]
else:
if(layers[1]) > 0:
layers[1] = layers[1] - i
map1 = outputs[i + layers[0]]
map2 = outputs[i + layers[1]]
x = torch.cat((map1, map2), 1)
elif module_type == "shortcut":
from_ = int(module["from"])
x = outputs[i - 1] + outputs[i + from_]
elif module_type == "yolo":
anchors = self.module_list[i][0].anchors
# Get the input dimensions
inp_dim = int(self.net_info["height"])
# Get the number of classes
num_classes = int(module["classes"])
# Transform
x = x.data
x = util.predict_transform(x, inp_dim, anchors, num_classes, False)
if not write: # If no collector has bean initialized
detections = x
write = 1
else:
detections = torch.cat((detections, x), 1)
outputs[i] = x
return detections
def forward(self, input, CUDA):
block_without_net = self.block_list[1:]
outputs = {}
write = 0
detections = 0
for i, module in enumerate(block_without_net):
# for every node in block_list without net, total 106
# module: ['type':'yolo',]
module_type = module['type']
if module_type == 'convolutional' or module_type == 'upsample':
output = self.layer_list[i](input)
elif module_type == 'route':
#TODO: not the same
layers = module['layers']
layers = layers.split(',')
if len(layers) == 1:
output = outputs[i + int(layers[0].strip())]
else:
start = int(layers[0].strip())
end = int(layers[1].strip())
map1 = outputs[i + start]
map2 = outputs[end]
output = torch.cat((map1, map2), 1)
elif module_type == 'shortcut':
from_ = int(module['from'])
output = outputs[i-1] + outputs[i+from_]
elif module_type == 'yolo':
#TODO: self.layer_list[i][0]
anchors = block_without_net[i]['anchors']
inp_dim = int(self.net_info['height'])
num_classes = int(module['classes'])
input = input.data
output = predict_transform(input, inp_dim, anchors, num_classes, CUDA)
if not write:
detections = output
write = 1
else:
detections = torch.cat((detections. output), 1)
outputs[i] = output
return detections
def forward(self, x, cuda=True):
modules = self.blocks[1:]
outputs = {}
write = False
for idx, module in enumerate(modules):
module_type = module['type']
if module_type in ('convolutional', 'upsample'):
x = self.module_list[idx](x)
elif module_type == 'route':
if module['end'] == 0:
x = outputs[module['start']]
else:
x = torch.cat(
(outputs[module['start']], outputs[module['end']]), 1)
elif module_type == 'shortcut':
from_ = int(module['from'])
x = outputs[idx - 1] + outputs[idx + from_]
elif module_type == 'yolo':
anchors = self.module_list[idx][0].anchors
in_dim = int(self.net_info['height'])
num_classes = int(module['classes'])
# Transform
x = predict_transform(x.data, in_dim, anchors, num_classes,
cuda)
if not write:
detections = x
write = True
else:
detections = torch.cat((detections, x), 1)
outputs[idx] = x
return detections
def forward(self, x, CUDA):
detections = []
modules = self.blocks[1:]
outputs = {} #We cache the outputs for the route layer
write = 0
for i in range(len(modules)):
module_type = (modules[i]["type"])
if module_type == "convolutional" or module_type == "upsample" or module_type == "maxpool":
if i == 0:
print("Input to YOLO ", i)
print(x.permute(0, 2, 3, 1).size())
print(x.permute(0, 2, 3, 1))
# if i in [81, 93, 105]:
# print("Input to YOLO ", i)
# print(x.permute(0, 2, 3, 1).size())
# print(x.permute(0, 2, 3, 1))
# print("Weights to layer ", i)
# print(self.module_list[i][0].weight.permute(2,3,1,0).size())
# print(self.module_list[i][0].weight.permute(2,3,1,0))
x = self.module_list[i](x)
if i == 0:
print("Output of YOLO ", i)
print(x.permute(0, 2, 3, 1).size())
print(x.permute(0, 2, 3, 1))
outputs[i] = x
elif module_type == "route":
layers = modules[i]["layers"]
layers = [int(a) for a in layers]
if (layers[0]) > 0:
layers[0] = layers[0] - i
if len(layers) == 1:
x = outputs[i + (layers[0])]
else:
if (layers[1]) > 0:
layers[1] = layers[1] - i
map1 = outputs[i + layers[0]]
map2 = outputs[i + layers[1]]
x = torch.cat((map1, map2), 1)
outputs[i] = x
elif module_type == "shortcut":
from_ = int(modules[i]["from"])
x = outputs[i - 1] + outputs[i + from_]
outputs[i] = x
elif module_type == 'yolo':
anchors = self.module_list[i][0].anchors
#Get the input dimensions
inp_dim = int(self.net_info["height"])
#Get the number of classes
num_classes = int(modules[i]["classes"])
#Output the result
x = x.data
# print("Yolo ", i, " has input ")
# print(x.permute(0,2,3,1).size())
# print(x.permute(0,2,3,1))
x = predict_transform(x, inp_dim, anchors, num_classes, CUDA)
if type(x) == int:
continue
if not write:
detections = x
write = 1
else:
detections = torch.cat((detections, x), 1)
outputs[i] = outputs[i - 1]
try:
return detections
except:
return 0
def forward(self, x, CUDA):
detections = []
modules = self.blocks[
1:] # 除了net块之外的所有，forward这里用的是blocks列表中的各个block块字典
outputs = {} #We cache the outputs for the route layer
write = 0 #write表示我们是否遇到第一个检测。write=0，则收集器尚未初始化，write=1，则收集器已经初始化，我们只需要将检测图与收集器级联起来即可。
for i in range(len(modules)):
module_type = (modules[i]["type"])
if module_type == "convolutional" or module_type == "upsample" or module_type == "maxpool":
x = self.module_list[i](x)
outputs[i] = x
elif module_type == "route":
layers = modules[i]["layers"]
layers = [int(a) for a in layers]
if (layers[0]) > 0:
layers[0] = layers[0] - i
# 如果只有一层时。从前面的if (layers[0]) > 0:语句中可知，如果layer[0]>0，则输出的就是当前layer[0]这一层的特征,如果layer[0]<0，输出就是从route层(第i层)向后退layer[0]层那一层得到的特征
if len(layers) == 1:
x = outputs[i + (layers[0])]
# 第二个元素同理
else:
if (layers[1]) > 0:
layers[1] = layers[1] - i
map1 = outputs[i + layers[0]]
map2 = outputs[i + layers[1]]
x = torch.cat((map1, map2),
1) #第二个参数设为 1,这是因为我们希望将特征图沿anchor数量的维度级联起来。
outputs[i] = x
elif module_type == "shortcut":
from_ = int(modules[i]["from"])
x = outputs[i -
1] + outputs[i +
from_] # 求和运算，它只是将前一层的特征图添加到后面的层上而已
outputs[i] = x
elif module_type == 'yolo':
anchors = self.module_list[i][0].anchors
#Get the input dimensions#从net_info(实际就是blocks[0]，即[net])中get the input dimensions
inp_dim = int(self.net_info["height"])
#Get the number of classes
num_classes = int(modules[i]["classes"])
#Output the result
x = x.data # 这里得到的是预测的yolo层feature map
# 在util.py中的predict_transform()函数利用x(是传入yolo层的feature map)，得到每个格子所对应的anchor最终得到的目标
# 坐标与宽高，以及出现目标的得分与每种类别的得分。经过predict_transform变换后的x的维度是(batch_size, grid_size*grid_size*num_anchors, 5+类别数量)
x = predict_transform(x, inp_dim, anchors, num_classes, CUDA)
if type(x) == int:
continue
if not write: #if no collector has been intialised. 因为一个空的tensor无法与一个有数据的tensor进行concatenate操作，
detections = x #所以detections的初始化在有预测值出来时才进行，
write = 1 #用write = 1标记，当后面的分数出来后，直接concatenate操作即可。
else:
'''
变换后x的维度是(batch_size, grid_size * grid_size * num_anchors, 5 + 类别数量)，这里是在维度1上进行concatenate，即按照
anchor数量的维度进行连接，对应教程part3中的Bounding
Box
attributes图的行进行连接。yolov3中有3个yolo层，所以
对于每个yolo层的输出先用predict_transform()
变成每行为一个anchor对应的预测值的形式(不看batch_size这个维度，x剩下的
维度可以看成一个二维tensor)，这样3个yolo层的预测值按照每个方框对应的行的维度进行连接。得到了这张图处所有anchor的预测值，后面的NMS等操作可以一次完成
'''
detections = torch.cat((detections, x), 1)
outputs[i] = outputs[i - 1]
try:
return detections
except:
return 0
def forward(self, x, CUDA):
'''
输入参数: x：输入样本数据
CUDA：是否使用CUDA的标志位，为True则使用，False则不使用
输出参数：detection：Tensor类型，网络的输出，大小为[batch_size, all_anchors, (5+classes_num)]
'''
detections = []
modules = self.blocks[1:]
outputs = {
} # 由于route和shortcut层需要前面的层的输出图，因此将每个层的输出特征图缓存在字典outputs中。键是层的索引，值是特征图
write = 0 # 标志位，标志是否是第一次检测
for i in range(len(modules)):
module_type = (modules[i]["type"])
if module_type == "convolutional" or module_type == "upsample" or module_type == "maxpool":
x = self.module_list[i](x)
outputs[i] = x
elif module_type == "route":
layers = modules[i]["layers"]
layers = [int(a) for a in layers]
# 将参数大于零和小于零的情况都转换为从当前层往前数的层数
if (layers[0]) > 0:
layers[0] = layers[0] - i
# layers只有一个参数时直接输出对应层的特征图即可
if len(layers) == 1:
x = outputs[i + (layers[0])]
# 两个参数时需要按照深度对两层的特征图进行拼接
else:
if (layers[1]) > 0:
layers[1] = layers[1] - i
map1 = outputs[i + layers[0]]
map2 = outputs[i + layers[1]]
# 进行拼接
x = torch.cat((map1, map2), 1)
outputs[i] = x
elif module_type == "shortcut":
from_ = int(modules[i]["from"])
# 将前一层和from_指定的层的特征图进行相加
x = outputs[i - 1] + outputs[i + from_]
outputs[i] = x
elif module_type == 'yolo':
anchors = self.module_list[i][0].anchors
#Get the input dimensions
inp_dim = int(self.net_info["height"])
#Get the number of classes
num_classes = int(modules[i]["classes"])
#Output the result
x = x.data
x = predict_transform(x, inp_dim, anchors, num_classes, CUDA)
if type(x) == int:
continue
# 注意：无法将一个张量连接至一个空的张量，所以要分情况讨论
# 如果是第一次检测，则直接将网络预测输出赋值给detections
if not write:
detections = x
write = 1
# 如果不是第一检测，则执行连接操作
else:
detections = torch.cat((detections, x), 1)
outputs[i] = outputs[i - 1]
try:
return detections
except:
return 0
def forward(self, x, CUDA=False, Train=False):
detections = []
modules = self.blocks[1:]
outputs = {} # We cache the outputs for the route layer
write = 0
for i in range(len(modules)):
module_type = (modules[i]["type"])
if module_type == "convolutional" or module_type == "upsample" or module_type == "maxpool":
x = self.module_list[i](x)
outputs[i] = x
elif module_type == "route":
layers = modules[i]["layers"]
layers = [int(a) for a in layers]
if (layers[0]) > 0:
layers[0] = layers[0] - i
if len(layers) == 1:
x = outputs[i + (layers[0])]
else:
if (layers[1]) > 0:
layers[1] = layers[1] - i
map1 = outputs[i + layers[0]]
map2 = outputs[i + layers[1]]
x = torch.cat((map1, map2), 1)
outputs[i] = x
elif module_type == "shortcut":
from_ = int(modules[i]["from"])
x = outputs[i - 1] + outputs[i + from_]
outputs[i] = x
elif module_type == 'yolo':
anchors = self.module_list[i][0].anchors
# Get the input dimensions
inp_dim = int(self.net_info["height"])
# Get the number of classes
num_classes = int(modules[i]["classes"])
# Output the result
if Train:
x, box_b, pred = predict_transform(x, inp_dim, anchors,
num_classes, CUDA,
Train)
else:
x = predict_transform(x, inp_dim, anchors, num_classes,
CUDA, Train)
if type(x) == int:
continue
if not write:
detections = x
write = 1
if Train:
bbox = box_b
y_pred = pred
else:
detections = torch.cat((detections, x), 1)
if Train:
bbox = torch.cat((bbox, box_b), 1)
y_pred = torch.cat((y_pred, pred), 1)
outputs[i] = outputs[i - 1]
# self.anchors = torch.cat(self.anchors)
if Train:
return detections, bbox, self.input_size, self.anchors, y_pred
else:
return detections
def forward(self, x, CUDA = True):
detections = []
modules = self.blocks[1:]
outputs = {} #We cache the outputs for the route layer
write = 0
for i in range(len(modules)):
module_type = (modules[i]["type"])
print('index -> ', i)
print('type -> ', module_type)
if module_type == "convolutional" or module_type == "upsample" or module_type == "maxpool":
x = self.module_list[i](x)
outputs[i] = x
elif module_type == "route":
layers = modules[i]["layers"]
layers = [int(a) for a in layers]
if (layers[0]) > 0:
layers[0] = layers[0] - i
if len(layers) == 1:
x = outputs[i + (layers[0])]
else:
if (layers[1]) > 0:
layers[1] = layers[1] - i
map1 = outputs[i + layers[0]]
map2 = outputs[i + layers[1]]
x = torch.cat((map1, map2), 1)
outputs[i] = x
elif module_type == "shortcut":
from_ = int(modules[i]["from"])
x = outputs[i-1] + outputs[i+from_]
outputs[i] = x
elif module_type == 'yolo':
anchors = self.module_list[i][0].anchors
#Get the input dimensions
inp_dim = int (self.net_info["height"])
#Get the number of classes
num_classes = int (modules[i]["classes"])
#Output the result
x = x.data
x = predict_transform(x, inp_dim, anchors, num_classes, CUDA)
print('yolo shape: ', x.shape)
print('yolo : ', x)
if type(x) == int:
continue
if not write:
detections = x
write = 1
else:
detections = torch.cat((detections, x), 1)
outputs[i] = outputs[i-1]
print('outputs[81]: ', outputs[81].shape)
print('outputs[82]: ', outputs[82].shape)
try:
return detections
except:
return 0
def forward(self, x, targets=None, CUDA=False):
detections = []
modules = self.blocks[1:]
outputs = {} #We cache the outputs for the route layer
loss = 0
write = 0
device = "cpu"
if CUDA:
self.mse_loss = self.mse_loss.cuda()
self.bce_loss = self.bce_loss.cuda()
self.ce_loss = self.ce_loss.cuda()
device = "cuda"
for i in range(len(modules)):
module_type = (modules[i]["type"])
if module_type == "convolutional" or module_type == "upsample" or module_type == "maxpool":
x = self.module_list[i].to(device)(x)
outputs[i] = x
elif module_type == "route":
layers = modules[i]["layers"]
layers = [int(a) for a in layers]
if (layers[0]) > 0:
layers[0] = layers[0] - i
if len(layers) == 1:
x = outputs[i + (layers[0])]
else:
if (layers[1]) > 0:
layers[1] = layers[1] - i
map1 = outputs[i + layers[0]]
map2 = outputs[i + layers[1]]
x = torch.cat((map1, map2), 1)
outputs[i] = x
elif module_type == "shortcut":
from_ = int(modules[i]["from"])
x = outputs[i - 1] + outputs[i + from_]
outputs[i] = x
elif module_type == 'yolo':
anchors = self.module_list[i][0].anchors
#Get the input dimensions
inp_dim = int(self.net_info["height"])
batch_size = x.size(0)
#Get the number of classes
num_classes = int(modules[i]["classes"])
bbox_attrs = 5 + num_classes
num_anchors = len(anchors)
#Output the result
if targets is None:
x = x.data
x = predict_transform(x, inp_dim, anchors, num_classes,
CUDA)
if type(x) == int:
continue
if not write:
detections = x
write = 1
else:
detections = torch.cat((detections, x), 1)
else:
FloatTensor = torch.cuda.FloatTensor if x.is_cuda else torch.FloatTensor
LongTensor = torch.cuda.LongTensor if x.is_cuda else torch.LongTensor
ByteTensor = torch.cuda.ByteTensor if x.is_cuda else torch.ByteTensor
stride = inp_dim // x.size(2)
grid_size = inp_dim // stride
prediction = x.view(batch_size, num_anchors, bbox_attrs,
grid_size * grid_size)
prediction = prediction.transpose(2, 3).contiguous()
prediction = prediction.view(batch_size, num_anchors,
grid_size, grid_size,
bbox_attrs)
# Get outputs
x = torch.sigmoid(prediction[..., 0]) # Center x
y = torch.sigmoid(prediction[..., 1]) # Center y
w = prediction[..., 2] # Width
h = prediction[..., 3] # Height
pred_conf = torch.sigmoid(prediction[..., 4]) # Conf
pred_cls = torch.sigmoid(prediction[..., 5:]) # Cls pred.
grid_x = torch.arange(grid_size).repeat(grid_size, 1).view(
[1, 1, grid_size, grid_size]).type(FloatTensor)
grid_y = torch.arange(grid_size).repeat(
grid_size, 1).t().view([1, 1, grid_size,
grid_size]).type(FloatTensor)
scaled_anchors = FloatTensor([(a_w / stride, a_h / stride)
for a_w, a_h in anchors])
anchor_w = scaled_anchors[:, 0:1].view(
(1, num_anchors, 1, 1))
anchor_h = scaled_anchors[:, 1:2].view(
(1, num_anchors, 1, 1))
pred_boxes = FloatTensor(prediction[..., :4].shape)
pred_boxes[..., 0] = x.data + grid_x
pred_boxes[..., 1] = y.data + grid_y
pred_boxes[..., 2] = torch.exp(w.data) * anchor_w
pred_boxes[..., 3] = torch.exp(h.data) * anchor_h
pred_boxes = pred_boxes.to(device)
nGT, nCorrect, mask, conf_mask, tx, ty, tw, th, tconf, tcls = build_targets(
pred_boxes=pred_boxes.cpu().data,
pred_conf=pred_conf.cpu().data,
pred_cls=pred_cls.cpu().data,
target=targets,
anchors=scaled_anchors.cpu().data,
num_anchors=num_anchors,
num_classes=num_classes,
grid_size=grid_size,
ignore_thres=self.ignore_thres,
device=device)
# Handle masks
mask = Variable(mask.type(ByteTensor))
conf_mask = Variable(conf_mask.type(ByteTensor))
# Handle target variables
tx = Variable(tx.type(FloatTensor), requires_grad=False)
ty = Variable(ty.type(FloatTensor), requires_grad=False)
tw = Variable(tw.type(FloatTensor), requires_grad=False)
th = Variable(th.type(FloatTensor), requires_grad=False)
tconf = Variable(tconf.type(FloatTensor),
requires_grad=False)
tcls = Variable(tcls.type(LongTensor), requires_grad=False)
# Get conf mask where gt and where there is no gt
conf_mask_true = mask
conf_mask_false = conf_mask - mask
# Mask outputs to ignore non-existing objects
loss_x = self.mse_loss(x[mask], tx[mask])
loss_y = self.mse_loss(y[mask], ty[mask])
loss_w = self.mse_loss(w[mask], tw[mask])
loss_h = self.mse_loss(h[mask], th[mask])
loss_conf = self.bce_loss(
pred_conf[conf_mask_false],
tconf[conf_mask_false]) + self.bce_loss(
pred_conf[conf_mask_true], tconf[conf_mask_true])
loss_cls = (1 / batch_size) * self.ce_loss(
pred_cls[mask], torch.argmax(tcls[mask], 1))
return loss_x
loss += loss_x + loss_y + loss_w + loss_h + loss_conf + loss_cls
outputs[i] = outputs[i - 1]
if targets == None:
try:
return detections
except:
return 0
else:
return loss
def forward(self, x, CUDA):
detections = [] # return 하는 놈
modules = self.blocks[1:] # cfg 파일에서 가져온 놈
outputs = {} # We cache the outputs for the route layer
write = 0
for i in range(len(modules)):
module_type = (modules[i]["type"])
if module_type == "convolutional" or module_type == "upsample" or module_type == "maxpool":
x = self.module_list[i](x)
outputs[i] = x
elif module_type == "route":
layers = modules[i]["layers"]
layers = [int(a) for a in layers]
if (layers[0]) > 0:
layers[0] = layers[0] - i
if len(layers) == 1:
x = outputs[i + (layers[0])]
else:
if (layers[1]) > 0:
layers[1] = layers[1] - i
map1 = outputs[i + layers[0]]
map2 = outputs[i + layers[1]]
x = torch.cat((map1, map2), 1)
outputs[i] = x
elif module_type == "shortcut":
from_ = int(modules[i]["from"])
x = outputs[i - 1] + outputs[i + from_]
outputs[i] = x
elif module_type == 'yolo': #output을 내보내는 곳
anchors = self.module_list[i][0].anchors
#Get the input dimensions
inp_dim = int(self.net_info["height"])
#Get the number of classes
num_classes = int(modules[i]["classes"])
#print(i, " : ", np.shape(x))
#Output the result
x = x.data
x = predict_transform(x, inp_dim, anchors, num_classes, CUDA)
if type(x) == int:
continue
if not write:
detections = x
write = 1
else:
detections = torch.cat((detections, x), 1)
# x_tmp = x.permute(1, 2, 0)
#
# x_tmp = x_tmp.cpu()
# x_tmp = x_tmp.numpy()
#
# print(np.shape(x_tmp))
#
# if i == 82 :
# cv2.imshow("82", x_tmp)
# if i == 94 :
# cv2.imshow("94", x_tmp)
# if i == 106:
# cv2.imshow("106", x_tmp)
outputs[i] = outputs[i - 1]
# 각 레이어의 이미지를 보고 싶으면 이거 사용
# if i == 98 :
# x_tmp = x.squeeze()
# x_tmp = x_tmp.permute(1, 2, 0)
# x_tmp = x_tmp.cpu()
# x_tmp = x_tmp.detach().numpy()
#
# print("x_tmp shape :", np.shape(x_tmp))
# for i in range(20):#np.shape(x_tmp)[2]) :
#
# cv2.imshow(str(i), x_tmp[:,:,i])
#레이어의 shape를 보기 위해서 사용
#print("num : ", i, ", ", module_type, " : \t", np.shape(x))
try:
return detections
except:
return 0
def forward(self, x, device, flag_eval):
detections = []
modules = self.blocks[1:]
outputs = {} #We cache the outputs for the route layer
layer_index = 1
write = 0
for i in range(len(modules)):
module_type = (modules[i]["type"])
if module_type == "convolutional" or module_type == "upsample" or module_type == "maxpool":
x = self.module_list[i](x)
outputs[i] = x
# print(x.size(), layer_index, module_type)
layer_index += 1
elif module_type == "route":
layers = modules[i]["layers"]
layers = [int(a) for a in layers]
if (layers[0]) > 0:
layers[0] = layers[0] - i
if len(layers) == 1:
x = outputs[i + (layers[0])]
else:
if (layers[1]) > 0:
layers[1] = layers[1] - i
map1 = outputs[i + layers[0]]
map2 = outputs[i + layers[1]]
x = torch.cat((map1, map2), 1)
outputs[i] = x
# print(x.size(), layer_index, module_type)
layer_index += 1
elif module_type == "shortcut":
from_ = int(modules[i]["from"])
x = outputs[i - 1] + outputs[i + from_]
outputs[i] = x
# print(x.size(), layer_index, module_type)
if i == 61:
self.feature_map = x
layer_index += 1
elif module_type == 'yolo' and flag_eval:
anchors = self.module_list[i][0].anchors
#Get the input dimensions
inp_dim = int(self.net_info["height"])
#Get the number of classes
num_classes = int(modules[i]["classes"])
#Output the result
x = x.data
x = predict_transform(x, inp_dim, anchors, num_classes, device)
if type(x) == int:
continue
if not write:
detections = x
write = 1
else:
detections = torch.cat((detections, x), 1)
outputs[i] = outputs[i - 1]
if flag_eval:
try:
return detections
except:
return 0
else:
return outputs[61]
def forward(self, x, CUDA, vis=None, featureLayers=None, show=3, view='flat', conf=0.5):
detections = []
modules = self.blocks[1:]
outputs = {} #We cache the outputs for the route layer
write = 0
for i in range(len(modules)):
module_type = (modules[i]["type"])
if module_type == "convolutional" or module_type == "dilated_convolutional" or module_type == "upsample" or module_type == "maxpool" or module_type == "bilinear" or module_type == "avgpool":
x = self.module_list[i](x)
outputs[i] = x
#visualize feature map
if vis and module_type == "convolutional" and (featureLayers==None or len(featureLayers)==0 or featureLayers.split().count(str(i+1))>0):
self.visualizeFeatureMap(vis, i, x, show, view)
elif module_type == "softmax":
return F.softmax(x)
elif module_type == "route":
layers = modules[i]["layers"]
layers = [int(a) for a in layers]
if (layers[0]) > 0:
layers[0] = layers[0] - i
if len(layers) == 1:
x = outputs[i + (layers[0])]
else:
if (layers[1]) > 0:
layers[1] = layers[1] - i
map1 = outputs[i + layers[0]]
map2 = outputs[i + layers[1]]
x = torch.cat((map1, map2), 1)
outputs[i] = x
elif module_type == "shortcut":
from_ = int(modules[i]["from"])
x = outputs[i-1] + outputs[i+from_]
outputs[i] = x
elif module_type == 'yolo':
anchors = self.module_list[i][0].anchors
#Get the input dimensions
inp_dim = int (self.net_info["height"])
#Get the number of classes
num_classes = int (modules[i]["classes"])
grid = x.size(2)
#Output the result
x = x.data
x = predict_transform(x, inp_dim, anchors, num_classes, CUDA)
if type(x) == int:
continue
#visualize feature map
if vis and (featureLayers==None or len(featureLayers)==0 or featureLayers.split().count(str(i+1))>0):
#X = x.view(1, len(anchors)*(5+num_classes), grid, grid)
X = x
self.visualizeFeatureMap(vis, i, X, show, view, conf)
if not write:
detections = x
write = 1
else:
detections = torch.cat((detections, x), 1)
outputs[i] = outputs[i-1]
self.result = outputs[i]#for debug net structure
try:
return detections
except:
return 0
def create_yolo_model(model_list: list, input_shape=(416, 416, 3)):
"""
Create YOLOv3 model with list of layers which based on cfg_file.
:param input_shape: shape of input image.
:param model_list: A list of layers which needed.
:return: A functional Keras model
"""
# Define the input as a tensor with shape input_shape
X = Input(input_shape)
# Catch the output of each layer used for route layer and shortcut layer.
layer_output_list = []
write = False # flag of getting the first output layer
# Traverse the model_list
for layer_index, layer in enumerate(model_list):
# In model_list, both convNet layer and upsample layer are functional,
# to let layer work, just need a input.
# But route and shortcut layer, they need the output from other layers,
# so, when create model_list, this two layers set as dict to hold info,
# when building the model, get info from dict of layer, and get output
# from layer_output_list to implement route or shortcut layer.
layer_type = type(layer) # get layer type
if layer_type != dict:
# This layer is convolution or upsampling layer.
X = layer(X) # just get layer a input and get the output
else:
# This layer is route or shortcut or yolo layer.
if layer["layerType"] == "route": # This layer is route layer.
route_start = layer[
"start"] # get start layer index. Negative value
map_start = layer_output_list[
layer_index +
route_start] # get output tensor from start layer.
try:
# try to get end layer index. If exist, get the index and output tensor of that layer.
route_end = layer["end"] #Negative value
map_end = layer_output_list[layer_index + route_end]
# out put of route layer is concatenate two layers' output tensor at channel axis.
X = tf.concat(
[map_start, map_end], axis=-1
) # as tensor is 'channel last', concat at last axis
except Exception:
# If there is only start layer, the output tensor is start layer's output.
X = map_start
elif layer[
"layerType"] == "shortcut": # This layer is shortcut layer.
shortcut_from = layer["from"] # get add layer's index
# the sum of from_layer and previous layer as shortcut layer's output.
X = layer_output_list[layer_index -
1] + layer_output_list[layer_index +
shortcut_from]
elif layer["layerType"] == "yolo": # yolo layer
anchors: list = layer["anchors"] # get anchors' size
inp_dim = net_info["height"] # Get input image dimension
num_classes = layer["classes"] # Get the number of classes
# Transfer convNet output format to readable format
X = predict_transform(X,
inp_dim=inp_dim,
anchors=anchors,
num_classes=num_classes)
# check write flag which marks the model get first detection tensor or not.
if not write:
# if model hasn't get first detection, create detection tensor and
# raise the write flag.
detections = X
write = True
else:
# if model has got detection, concatenate detection tensor and
# new X tensor at axis=1
detections = tf.concat([detections, X], axis=1)
else:
# error
continue
layer_output_list.append(X) # append output in list
def forward(self, x, CUDA):
if CUDA:
x=x.cuda()
modules = self.blocks[1:]
outputs = {} #We cache the outputs for the route layer
detections=[]
detections_prev=[]
for i, module in enumerate(modules):
module_type = (module["type"])
if module_type == "convolutional" or module_type == "upsample":
x = self.module_list[i](x)
elif module_type == "route":
layers = module["layers"]
layers = [int(a) for a in layers]
if (layers[0]) > 0:
layers[0] = layers[0] - i
if len(layers) == 1:
x = outputs[i + (layers[0])]
else:
if (layers[1]) > 0:
layers[1] = layers[1] - i
map1 = outputs[i + layers[0]]
map2 = outputs[i + layers[1]]
x = torch.cat((map1, map2), 1)
elif module_type == "shortcut":
from_ = int(module["from"])
x = outputs[i-1] + outputs[i+from_]
elif module_type == 'yolo':
anchors = self.module_list[i][0].anchors
#Get the input dimensions
inp_dim = int (self.net_info["height"])
#Get the number of classes
num_classes = int (module["classes"])
#Transform
detections_prev.append(x)
x = x.data
self.module_list[i][0].stride = inp_dim // x.size(2)
self.module_list[i][0].grid_size = inp_dim // self.module_list[i][0].stride
x = util.predict_transform(x, inp_dim, anchors, num_classes, CUDA)
detections.append(x)
'''
if not write: #if no collector has been intialised.
detections = x
write = 1
else:
detections = torch.stack((detections, x), 0)
'''
outputs[i] = x
return detections_prev,detections
def forward(self, x, CUDA):
"""
| x | torch.Tensor | size=(batch,channel,height,width)
| CUDA | bool |
"""
detections = []
modules = self.blocks[1:]
outputs = {} #We cache the outputs for the route layer
write = 0
for i in range(len(modules)):
module_type = (modules[i]["type"])
if module_type == "convolutional" or module_type == "upsample" or module_type == "maxpool":
x = self.module_list[i](x)
outputs[i] = x
elif module_type == "route":
layers = modules[i]["layers"]
layers = [int(a) for a in layers]
if (layers[0]) > 0:
layers[0] = layers[0] - i
if len(layers) == 1:
x = outputs[i + (layers[0])]
else:
if (layers[1]) > 0:
layers[1] = layers[1] - i
map1 = outputs[i + layers[0]]
map2 = outputs[i + layers[1]]
x = torch.cat((map1, map2), 1)
outputs[i] = x
elif module_type == "shortcut":
from_ = int(modules[i]["from"])
x = outputs[i - 1] + outputs[i + from_]
outputs[i] = x
elif module_type == 'yolo':
# anchors
# yolo_82 | [(116,90), (156,198), (373,326)]
# yolo_94 | [( 30,61), ( 62, 45), ( 59,119)]
# yolo_106 | [( 10,13), ( 16, 30), ( 33, 23)]
anchors = self.module_list[i][0].anchors
#Get the input dimensions
inp_dim = int(self.net_info["height"])
#Get the number of classes
# classes=80 (MS COCO)
num_classes = int(modules[i]["classes"])
#Output the result
x = x.data
x = predict_transform(prediction=x,
inp_dim=inp_dim,
anchors=anchors,
num_classes=num_classes,
CUDA=CUDA)
if type(x) == int:
continue
if not write:
detections = x
write = 1 # 一度書き込んだかどうかのフラグ
else:
# concatenate along channel axis
detections = torch.cat((detections, x), 1)
outputs[i] = outputs[i - 1]
try:
return detections
except:
return 0
def forward(self, x):
x = x.data
prediction = x
prediction = predict_transform(prediction, self.inp_dim, self.anchors,
self.num_classes)
return prediction
def forward(self, x, CUDA):
"""
两个作用：1.计算输出 2.为了处理更方便，对输出的检测特征图进行变换
:param x:输入
:param CUDA:是否使用GPU加速训练，如果为True,就是用GPU
:return:
"""
detections = [] # 最后的预测结果列表
modules = self.blocks[1:] # 第一个层是net,和计算无关
# route和shortcut层需要前面的层的输出图，因此我们将每个层的输出特征图缓存在字典outputs中
outputs = {} # 键是层的索引，值是特征图
write = 0
for i in range(len(modules)):
module_type = (modules[i]["type"])
if module_type == "convolutional" or module_type == "upsample" or \
module_type == "maxpool":
x = self.module_list[i](x)
outputs[i] = x
elif module_type == "route":
layers = modules[i]["layers"]
layers = [int(a) for a in layers]
if layers[0] > 0:
layers[0] = layers[0] - i
if len(layers) == 1:
x = outputs[i + (layers[0])]
else:
if (layers[1]) > 0:
layers[1] = layers[1] - i
map1 = outputs[i + layers[0]]
map2 = outputs[i + layers[1]]
x = torch.cat((map1, map2), 1)
elif module_type == "shortcut":
from_ = int(modules[i]["from"])
x = outputs[i - 1] + outputs[i + from_]
outputs[i] = x
elif module_type == 'yolo':
anchors = self.module_list[i][0].anchors
# 获取输入维度信息
inp_dim = int(self.net_info["height"])
# 获取类别的数量
num_classes = int(modules[i]["classes"])
# 输出结果
x = x.data
x = predict_transform(x, inp_dim, anchors, num_classes, CUDA)
if type(x) == int:
continue
if not write:
detections = x
write = 1
else:
detections = torch.cat((detections, x), 1)
outputs[i] = outputs[i - 1]
try:
return detections
except:
return 0