yolov3-polygon
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20 hours ago •
ming71
ming71
去掉了计算TN部分 训练时只保留了loss等
import torch.nn as nn
from utils.parse_config import * from utils.utils import *
def create_modules(module_defs): """ Constructs module list of layer blocks from module configuration in module_defs """ hyperparams = module_defs.pop(0) output_filters = [int(hyperparams['channels'])] module_list = nn.ModuleList() for i, module_def in enumerate(module_defs): modules = nn.Sequential()
if module_def['type'] == 'convolutional':
bn = int(module_def['batch_normalize'])
filters = int(module_def['filters'])
kernel_size = int(module_def['size'])
pad = (kernel_size - 1) // 2 if int(module_def['pad']) else 0
modules.add_module('conv_%d' % i, nn.Conv2d(in_channels=output_filters[-1],
out_channels=filters,
kernel_size=kernel_size,
stride=int(module_def['stride']),
padding=pad,
bias=not bn))
if bn:
modules.add_module('batch_norm_%d' % i, nn.BatchNorm2d(filters))
if module_def['activation'] == 'leaky':
modules.add_module('leaky_%d' % i, nn.LeakyReLU(0.1))
elif module_def['type'] == 'upsample':
upsample = nn.Upsample(scale_factor=int(module_def['stride']), mode='nearest')
modules.add_module('upsample_%d' % i, upsample)
elif module_def['type'] == 'route':
layers = [int(x) for x in module_def['layers'].split(',')]
filters = sum([output_filters[layer_i] for layer_i in layers])
modules.add_module('route_%d' % i, EmptyLayer())
elif module_def['type'] == 'shortcut':
filters = output_filters[int(module_def['from'])]
modules.add_module('shortcut_%d' % i, EmptyLayer())
elif module_def['type'] == 'yolo':
anchor_idxs = [int(x) for x in module_def['mask'].split(',')]
# Extract anchors
anchors = [float(x) for x in module_def['anchors'].split(',')]
anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)]
anchors = [anchors[i] for i in anchor_idxs]
num_classes = int(module_def['classes'])
img_height = int(hyperparams['height'])
# Define detection layer
yolo_layer = YOLOLayer(anchors, num_classes, img_height, anchor_idxs)
modules.add_module('yolo_%d' % i, yolo_layer)
# Register module list and number of output filters
module_list.append(modules)
output_filters.append(filters)
return hyperparams, module_list
class EmptyLayer(nn.Module): """Placeholder for 'route' and 'shortcut' layers"""
def __init__(self):
super(EmptyLayer, self).__init__()
class YOLOLayer(nn.Module):
def __init__(self, anchors, nC, img_dim, anchor_idxs):
super(YOLOLayer, self).__init__()
anchors = [(a_w, a_h) for a_w, a_h in anchors] # (pixels)
nA = len(anchors)
self.anchors = anchors
self.nA = nA # number of anchors (3)
self.nC = nC # number of classes (1)
self.bbox_attrs = 9 + nC
self.img_dim = img_dim # from hyperparams in cfg file, NOT from parser
if anchor_idxs[0] == (nA * 2): # 6
stride = 32
elif anchor_idxs[0] == nA: # 3
stride = 16
else:
stride = 8
# Build anchor grids
nG = int(self.img_dim / stride) # number grid points
self.grid_x = torch.arange(nG).repeat(nG, 1).view([1, 1, nG, nG]).float()
self.grid_y = torch.arange(nG).repeat(nG, 1).t().view([1, 1, nG, nG]).float()
self.scaled_anchors = torch.FloatTensor([(a_w / stride, a_h / stride) for a_w, a_h in anchors])
self.anchor_w = self.scaled_anchors[:, 0:1].view((1, nA, 1, 1))
self.anchor_h = self.scaled_anchors[:, 1:2].view((1, nA, 1, 1))
# self.weights = class_weights()
def forward(self, p, targets=None, requestPrecision=False):
FT = torch.cuda.FloatTensor if p.is_cuda else torch.FloatTensor
bs = p.shape[0] # batch size
nG = p.shape[2] # number of grid points
stride = self.img_dim / nG
if p.is_cuda and not self.grid_x.is_cuda:
self.grid_x, self.grid_y = self.grid_x.cuda(), self.grid_y.cuda()
self.anchor_w, self.anchor_h = self.anchor_w.cuda(), self.anchor_h.cuda()
# self.weights = self.weights.cuda()
# p.view(1, 30, 13, 13) -- > (1, 3, 13, 13, 10) # (bs, anchors, grid, grid, classes + xywh)
p = p.view(bs, self.nA, self.bbox_attrs, nG, nG).permute(0, 1, 3, 4, 2).contiguous() # prediction
# Get outputs
P1_x = p[..., 0] # Point1 x
P1_y = p[..., 1] # Point1 y
P2_x = p[..., 2] # Point2 x
P2_y = p[..., 3] # Point2 y
P3_x = p[..., 4] # Point3 x
P3_y = p[..., 5] # Point3 y
P4_x = p[..., 6] # Point4 x
P4_y = p[..., 7] # Point4 y
pred_boxes = FT(bs, self.nA, nG, nG, 8)
pred_conf = p[..., 8] # Conf
pred_cls = p[..., 9:] # Class
# Training
if targets is not None:
BCEWithLogitsLoss = nn.BCEWithLogitsLoss()
CrossEntropyLoss = nn.CrossEntropyLoss()
SmoothL1Loss = nn.SmoothL1Loss()
t1_x, t1_y, t2_x, t2_y, t3_x, t3_y, t4_x, t4_y, mask, tcls = \
build_targets(targets, self.scaled_anchors, self.nA, self.nC, nG)
tcls = tcls[mask]
if P1_x.is_cuda:
t1_x, t1_y, t2_x, t2_y, t3_x, t3_y, t4_x, t4_y, mask, tcls = \
t1_x.cuda(), t1_y.cuda(), t2_x.cuda(), t2_y.cuda(), t3_x.cuda(), t3_y.cuda(), t4_x.cuda(), t4_y.cuda(), mask.cuda(), tcls.cuda()
# Compute losses
nM = mask.sum().float() # Number of anchors (assigned to targets)
nB = len(targets) # Batch size
k = nM / nB
if nM > 0:
lx1 = (k) * SmoothL1Loss(P1_x[mask], t1_x[mask]) / 8
ly1 = (k) * SmoothL1Loss(P1_y[mask], t1_y[mask]) / 8
lx2 = (k) * SmoothL1Loss(P2_x[mask], t2_x[mask]) / 8
ly2 = (k) * SmoothL1Loss(P2_y[mask], t2_y[mask]) / 8
lx3 = (k) * SmoothL1Loss(P3_x[mask], t3_x[mask]) / 8
ly3 = (k) * SmoothL1Loss(P3_y[mask], t3_y[mask]) / 8
lx4 = (k) * SmoothL1Loss(P4_x[mask], t4_x[mask]) / 8
ly4 = (k) * SmoothL1Loss(P4_y[mask], t4_y[mask]) / 8
lconf = (k * 10) * BCEWithLogitsLoss(pred_conf, mask.float())
lcls = (k / self.nC) * CrossEntropyLoss(pred_cls[mask], torch.argmax(tcls, 1))
else:
lx1, ly1, lx2, ly2, lx3, ly3, lx4, ly4, lcls, lconf = \
FT([0]).requires_grad_(True), FT([0]).requires_grad_(True), FT([0]).requires_grad_(True), FT(
[0]).requires_grad_(True), FT([0]).requires_grad_(True), \
FT([0]).requires_grad_(True), FT([0]).requires_grad_(True), FT([0]).requires_grad_(True), FT(
[0]).requires_grad_(True), FT([0]).requires_grad_(True),
# Sum loss components
loss = lx1 + ly1 + lx2 + ly2 + lx3 + ly3 + lx4 + ly4 + lconf + lcls
return loss, loss.item(), lconf.item(), lcls.item()
else:
pred_boxes[..., 0] = P1_x + self.grid_x
pred_boxes[..., 1] = P1_y + self.grid_y
pred_boxes[..., 2] = P2_x + self.grid_x
pred_boxes[..., 3] = P2_y + self.grid_y
pred_boxes[..., 4] = P3_x + self.grid_x
pred_boxes[..., 5] = P3_y + self.grid_y
pred_boxes[..., 6] = P4_x + self.grid_x
pred_boxes[..., 7] = P4_y + self.grid_y
output = torch.cat((pred_boxes.view(bs, -1, 8) * stride,
torch.sigmoid(pred_conf.view(bs, -1, 1)), pred_cls.view(bs, -1, self.nC)), -1)
return output
class Darknet(nn.Module): """YOLOv3 object detection model"""
def __init__(self, cfg_path, img_size=416):
super(Darknet, self).__init__()
self.module_defs = parse_model_config(cfg_path)
self.module_defs[0]['height'] = img_size
self.hyperparams, self.module_list = create_modules(self.module_defs)
self.img_size = img_size
self.loss_names = ['loss', 'conf', 'cls']
def forward(self, x, targets=None, requestPrecision=False):
is_training = targets is not None
output = []
self.losses = defaultdict(float)
layer_outputs = []
for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):
if module_def['type'] in ['convolutional', 'upsample']:
x = module(x)
elif module_def['type'] == 'route':
layer_i = [int(x) for x in module_def['layers'].split(',')]
x = torch.cat([layer_outputs[i] for i in layer_i], 1)
elif module_def['type'] == 'shortcut':
layer_i = int(module_def['from'])
x = layer_outputs[-1] + layer_outputs[layer_i]
elif module_def['type'] == 'yolo':
# Train phase: get loss
if is_training:
# if module[0](x, targets, requestPrecision)[0].item()==0:
x, *losses = module[0](x, targets, requestPrecision)
for name, loss in zip(self.loss_names, losses):
self.losses[name] += loss
# Test phase: Get detections
else:
x = module(x)
output.append(x)
layer_outputs.append(x)
return sum(output) if is_training else torch.cat(output, 1)
def load_weights(self, weights_path, cutoff=-1):
# Parses and loads the weights stored in 'weights_path'
# @:param cutoff - save layers between 0 and cutoff (cutoff = -1 -> all are saved)
if weights_path.endswith('darknet53.conv.74'):
cutoff = 75
# Open the weights file
fp = open(weights_path, 'rb')
header = np.fromfile(fp, dtype=np.int32, count=5) # First five are header values
# Needed to write header when saving weights
self.header_info = header
self.seen = header[3]
weights = np.fromfile(fp, dtype=np.float32) # The rest are weights
fp.close()
ptr = 0
for i, (module_def, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
if module_def['type'] == 'convolutional':
conv_layer = module[0]
if module_def['batch_normalize']:
# Load BN bias, weights, running mean and running variance
bn_layer = module[1]
num_b = bn_layer.bias.numel() # Number of biases
# Bias
bn_b = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.bias)
bn_layer.bias.data.copy_(bn_b)
ptr += num_b
# Weight
bn_w = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.weight)
bn_layer.weight.data.copy_(bn_w)
ptr += num_b
# Running Mean
bn_rm = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.running_mean)
bn_layer.running_mean.data.copy_(bn_rm)
ptr += num_b
# Running Var
bn_rv = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.running_var)
bn_layer.running_var.data.copy_(bn_rv)
ptr += num_b
else:
# Load conv. bias
num_b = conv_layer.bias.numel()
conv_b = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(conv_layer.bias)
conv_layer.bias.data.copy_(conv_b)
ptr += num_b
# Load conv. weights
num_w = conv_layer.weight.numel()
conv_w = torch.from_numpy(weights[ptr:ptr + num_w]).view_as(conv_layer.weight)
conv_layer.weight.data.copy_(conv_w)
ptr += num_w
from collections import defaultdict
import torch.nn as nn
from utils.parse_config import * from utils.utils import *
def create_modules(module_defs): """ Constructs module list of layer blocks from module configuration in module_defs """ hyperparams = module_defs.pop(0) output_filters = [int(hyperparams['channels'])] module_list = nn.ModuleList() for i, module_def in enumerate(module_defs): modules = nn.Sequential()
if module_def['type'] == 'convolutional':
bn = int(module_def['batch_normalize'])
filters = int(module_def['filters'])
kernel_size = int(module_def['size'])
pad = (kernel_size - 1) // 2 if int(module_def['pad']) else 0
modules.add_module('conv_%d' % i, nn.Conv2d(in_channels=output_filters[-1],
out_channels=filters,
kernel_size=kernel_size,
stride=int(module_def['stride']),
padding=pad,
bias=not bn))
if bn:
modules.add_module('batch_norm_%d' % i, nn.BatchNorm2d(filters))
if module_def['activation'] == 'leaky':
modules.add_module('leaky_%d' % i, nn.LeakyReLU(0.1))
elif module_def['type'] == 'upsample':
upsample = nn.Upsample(scale_factor=int(module_def['stride']), mode='nearest')
modules.add_module('upsample_%d' % i, upsample)
elif module_def['type'] == 'route':
layers = [int(x) for x in module_def['layers'].split(',')]
filters = sum([output_filters[layer_i] for layer_i in layers])
modules.add_module('route_%d' % i, EmptyLayer())
elif module_def['type'] == 'shortcut':
filters = output_filters[int(module_def['from'])]
modules.add_module('shortcut_%d' % i, EmptyLayer())
elif module_def['type'] == 'yolo':
anchor_idxs = [int(x) for x in module_def['mask'].split(',')]
# Extract anchors
anchors = [float(x) for x in module_def['anchors'].split(',')]
anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)]
anchors = [anchors[i] for i in anchor_idxs]
num_classes = int(module_def['classes'])
img_height = int(hyperparams['height'])
# Define detection layer
yolo_layer = YOLOLayer(anchors, num_classes, img_height, anchor_idxs)
modules.add_module('yolo_%d' % i, yolo_layer)
# Register module list and number of output filters
module_list.append(modules)
output_filters.append(filters)
return hyperparams, module_list
class EmptyLayer(nn.Module): """Placeholder for 'route' and 'shortcut' layers"""
def __init__(self):
super(EmptyLayer, self).__init__()
class YOLOLayer(nn.Module):
def __init__(self, anchors, nC, img_dim, anchor_idxs):
super(YOLOLayer, self).__init__()
anchors = [(a_w, a_h) for a_w, a_h in anchors] # (pixels)
nA = len(anchors)
self.anchors = anchors
self.nA = nA # number of anchors (3)
self.nC = nC # number of classes (1)
self.bbox_attrs = 9 + nC
self.img_dim = img_dim # from hyperparams in cfg file, NOT from parser
if anchor_idxs[0] == (nA * 2): # 6
stride = 32
elif anchor_idxs[0] == nA: # 3
stride = 16
else:
stride = 8
# Build anchor grids
nG = int(self.img_dim / stride) # number grid points
self.grid_x = torch.arange(nG).repeat(nG, 1).view([1, 1, nG, nG]).float()
self.grid_y = torch.arange(nG).repeat(nG, 1).t().view([1, 1, nG, nG]).float()
self.scaled_anchors = torch.FloatTensor([(a_w / stride, a_h / stride) for a_w, a_h in anchors])
self.anchor_w = self.scaled_anchors[:, 0:1].view((1, nA, 1, 1))
self.anchor_h = self.scaled_anchors[:, 1:2].view((1, nA, 1, 1))
# self.weights = class_weights()
def forward(self, p, targets=None, requestPrecision=False):
FT = torch.cuda.FloatTensor if p.is_cuda else torch.FloatTensor
bs = p.shape[0] # batch size
nG = p.shape[2] # number of grid points
stride = self.img_dim / nG
if p.is_cuda and not self.grid_x.is_cuda:
self.grid_x, self.grid_y = self.grid_x.cuda(), self.grid_y.cuda()
self.anchor_w, self.anchor_h = self.anchor_w.cuda(), self.anchor_h.cuda()
# self.weights = self.weights.cuda()
# p.view(1, 30, 13, 13) -- > (1, 3, 13, 13, 10) # (bs, anchors, grid, grid, classes + xywh)
p = p.view(bs, self.nA, self.bbox_attrs, nG, nG).permute(0, 1, 3, 4, 2).contiguous() # prediction
# Get outputs
P1_x = p[..., 0] # Point1 x
P1_y = p[..., 1] # Point1 y
P2_x = p[..., 2] # Point2 x
P2_y = p[..., 3] # Point2 y
P3_x = p[..., 4] # Point3 x
P3_y = p[..., 5] # Point3 y
P4_x = p[..., 6] # Point4 x
P4_y = p[..., 7] # Point4 y
pred_boxes = FT(bs, self.nA, nG, nG, 8)
pred_conf = p[..., 8] # Conf
pred_cls = p[..., 9:] # Class
# Training
if targets is not None:
BCEWithLogitsLoss = nn.BCEWithLogitsLoss()
CrossEntropyLoss = nn.CrossEntropyLoss()
SmoothL1Loss = nn.SmoothL1Loss()
t1_x, t1_y, t2_x, t2_y, t3_x, t3_y, t4_x, t4_y, mask, tcls = \
build_targets(targets, self.scaled_anchors, self.nA, self.nC, nG)
tcls = tcls[mask]
if P1_x.is_cuda:
t1_x, t1_y, t2_x, t2_y, t3_x, t3_y, t4_x, t4_y, mask, tcls = \
t1_x.cuda(), t1_y.cuda(), t2_x.cuda(), t2_y.cuda(), t3_x.cuda(), t3_y.cuda(), t4_x.cuda(), t4_y.cuda(), mask.cuda(), tcls.cuda()
# Compute losses
nM = mask.sum().float() # Number of anchors (assigned to targets)
nB = len(targets) # Batch size
k = nM / nB
if nM > 0:
lx1 = (k) * SmoothL1Loss(P1_x[mask], t1_x[mask]) / 8
ly1 = (k) * SmoothL1Loss(P1_y[mask], t1_y[mask]) / 8
lx2 = (k) * SmoothL1Loss(P2_x[mask], t2_x[mask]) / 8
ly2 = (k) * SmoothL1Loss(P2_y[mask], t2_y[mask]) / 8
lx3 = (k) * SmoothL1Loss(P3_x[mask], t3_x[mask]) / 8
ly3 = (k) * SmoothL1Loss(P3_y[mask], t3_y[mask]) / 8
lx4 = (k) * SmoothL1Loss(P4_x[mask], t4_x[mask]) / 8
ly4 = (k) * SmoothL1Loss(P4_y[mask], t4_y[mask]) / 8
lconf = (k * 10) * BCEWithLogitsLoss(pred_conf, mask.float())
lcls = (k / self.nC) * CrossEntropyLoss(pred_cls[mask], torch.argmax(tcls, 1))
else:
lx1, ly1, lx2, ly2, lx3, ly3, lx4, ly4, lcls, lconf = \
FT([0]).requires_grad_(True), FT([0]).requires_grad_(True), FT([0]).requires_grad_(True), FT(
[0]).requires_grad_(True), FT([0]).requires_grad_(True), \
FT([0]).requires_grad_(True), FT([0]).requires_grad_(True), FT([0]).requires_grad_(True), FT(
[0]).requires_grad_(True), FT([0]).requires_grad_(True),
# Sum loss components
loss = lx1 + ly1 + lx2 + ly2 + lx3 + ly3 + lx4 + ly4 + lconf + lcls
return loss, loss.item(), lconf.item(), lcls.item()
else:
pred_boxes[..., 0] = P1_x + self.grid_x
pred_boxes[..., 1] = P1_y + self.grid_y
pred_boxes[..., 2] = P2_x + self.grid_x
pred_boxes[..., 3] = P2_y + self.grid_y
pred_boxes[..., 4] = P3_x + self.grid_x
pred_boxes[..., 5] = P3_y + self.grid_y
pred_boxes[..., 6] = P4_x + self.grid_x
pred_boxes[..., 7] = P4_y + self.grid_y
output = torch.cat((pred_boxes.view(bs, -1, 8) * stride,
torch.sigmoid(pred_conf.view(bs, -1, 1)), pred_cls.view(bs, -1, self.nC)), -1)
return output
class Darknet(nn.Module): """YOLOv3 object detection model"""
def __init__(self, cfg_path, img_size=416):
super(Darknet, self).__init__()
self.module_defs = parse_model_config(cfg_path)
self.module_defs[0]['height'] = img_size
self.hyperparams, self.module_list = create_modules(self.module_defs)
self.img_size = img_size
self.loss_names = ['loss', 'conf', 'cls']
def forward(self, x, targets=None, requestPrecision=False):
is_training = targets is not None
output = []
self.losses = defaultdict(float)
layer_outputs = []
for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):
if module_def['type'] in ['convolutional', 'upsample']:
x = module(x)
elif module_def['type'] == 'route':
layer_i = [int(x) for x in module_def['layers'].split(',')]
x = torch.cat([layer_outputs[i] for i in layer_i], 1)
elif module_def['type'] == 'shortcut':
layer_i = int(module_def['from'])
x = layer_outputs[-1] + layer_outputs[layer_i]
elif module_def['type'] == 'yolo':
# Train phase: get loss
if is_training:
# if module[0](x, targets, requestPrecision)[0].item()==0:
x, *losses = module[0](x, targets, requestPrecision)
for name, loss in zip(self.loss_names, losses):
self.losses[name] += loss
# Test phase: Get detections
else:
x = module(x)
output.append(x)
layer_outputs.append(x)
return sum(output) if is_training else torch.cat(output, 1)
def load_weights(self, weights_path, cutoff=-1):
# Parses and loads the weights stored in 'weights_path'
# @:param cutoff - save layers between 0 and cutoff (cutoff = -1 -> all are saved)
if weights_path.endswith('darknet53.conv.74'):
cutoff = 75
# Open the weights file
fp = open(weights_path, 'rb')
header = np.fromfile(fp, dtype=np.int32, count=5) # First five are header values
# Needed to write header when saving weights
self.header_info = header
self.seen = header[3]
weights = np.fromfile(fp, dtype=np.float32) # The rest are weights
fp.close()
ptr = 0
for i, (module_def, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
if module_def['type'] == 'convolutional':
conv_layer = module[0]
if module_def['batch_normalize']:
# Load BN bias, weights, running mean and running variance
bn_layer = module[1]
num_b = bn_layer.bias.numel() # Number of biases
# Bias
bn_b = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.bias)
bn_layer.bias.data.copy_(bn_b)
ptr += num_b
# Weight
bn_w = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.weight)
bn_layer.weight.data.copy_(bn_w)
ptr += num_b
# Running Mean
bn_rm = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.running_mean)
bn_layer.running_mean.data.copy_(bn_rm)
ptr += num_b
# Running Var
bn_rv = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.running_var)
bn_layer.running_var.data.copy_(bn_rv)
ptr += num_b
else:
# Load conv. bias
num_b = conv_layer.bias.numel()
conv_b = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(conv_layer.bias)
conv_layer.bias.data.copy_(conv_b)
ptr += num_b
# Load conv. weights
num_w = conv_layer.weight.numel()
conv_w = torch.from_numpy(weights[ptr:ptr + num_w]).view_as(conv_layer.weight)
conv_layer.weight.data.copy_(conv_w)
ptr += num_w
老哥能指一下删掉了哪些地方嘛，好多缩进改了不好diff找不到不同的地方😉
只改了yolo head的forward部分和train函数 把tp tn有关的都删了 git用的不是很熟😂 要不我晚上回去总结一下改了哪些地方再发给您 我现在在火车上
老哥能指一下删掉了哪些地方嘛，好多缩进改了不好diff找不到不同的地方wink
http://dayefuzi.cn/home_clouddisk 麻烦大佬去这个网址的共享网盘里下一下yolov3-polygon-master.tar.gz，在最下面，我只修改了yolo head的forward部分还有darknet，然后是train函数里把显示tp tn这些的去掉了。直接复制应该是没有问题的，不好意思我git用的不熟，因为最近要准备下年考研，不是太有时间看。
老哥能指一下删掉了哪些地方嘛，好多缩进改了不好diff找不到不同的地方wink
http://dayefuzi.cn/home_clouddisk 麻烦大佬去这个网址的共享网盘里下一下yolov3-polygon-master.tar.gz，在最下面，我只修改了yolo head的forward部分还有darknet，然后是train函数里把显示tp tn这些的去掉了。直接复制应该是没有问题的，不好意思我git用的不熟，因为最近要准备下年考研，不是太有时间看。
okk，祝考研上岸~~加油