346 lines
15 KiB
Python
346 lines
15 KiB
Python
from __future__ import division
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.autograd import Variable
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import numpy as np
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from utils.parse_config import *
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from utils.utils import build_targets, to_cpu, non_max_suppression
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import matplotlib.pyplot as plt
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import matplotlib.patches as patches
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def create_modules(module_defs):
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"""
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Constructs module list of layer blocks from module configuration in module_defs
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"""
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hyperparams = module_defs.pop(0)
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output_filters = [int(hyperparams["channels"])]
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module_list = nn.ModuleList()
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for module_i, module_def in enumerate(module_defs):
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modules = nn.Sequential()
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# 3合1层
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if module_def["type"] == "convolutional":
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bn = int(module_def["batch_normalize"])
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filters = int(module_def["filters"])
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kernel_size = int(module_def["size"])
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pad = (kernel_size - 1) // 2
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modules.add_module(
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f"conv_{module_i}",
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nn.Conv2d(
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in_channels=output_filters[-1],
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out_channels=filters,
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kernel_size=kernel_size,
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stride=int(module_def["stride"]),
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padding=pad,
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bias=not bn,
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),
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)
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if bn:
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modules.add_module(f"batch_norm_{module_i}", nn.BatchNorm2d(filters, momentum=0.9, eps=1e-5))
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if module_def["activation"] == "leaky":
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modules.add_module(f"leaky_{module_i}", nn.LeakyReLU(0.1))
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elif module_def["type"] == "maxpool":
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kernel_size = int(module_def["size"])
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stride = int(module_def["stride"])
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if kernel_size == 2 and stride == 1:
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modules.add_module(f"_debug_padding_{module_i}", nn.ZeroPad2d((0, 1, 0, 1)))
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maxpool = nn.MaxPool2d(kernel_size=kernel_size, stride=stride, padding=int((kernel_size - 1) // 2))
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modules.add_module(f"maxpool_{module_i}", maxpool)
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elif module_def["type"] == "resize":
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resize = Resize(scale_factor=int(module_def["stride"]), mode="nearest")
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modules.add_module(f"resize_{module_i}", resize)
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elif module_def["type"] == "route": # 输入1：26*26*256 输入2：26*26*128 输出：26*26*（256+128）
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layers = [int(x) for x in module_def["layers"].split(",")]
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filters = sum([output_filters[1:][i] for i in layers])
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modules.add_module(f"route_{module_i}", EmptyLayer())
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elif module_def["type"] == "shortcut":
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filters = output_filters[1:][int(module_def["from"])]
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modules.add_module(f"shortcut_{module_i}", EmptyLayer())
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elif module_def["type"] == "yolo":
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anchor_idxs = [int(x) for x in module_def["mask"].split(",")]
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# Extract anchors
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anchors = [int(x) for x in module_def["anchors"].split(",")]
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anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)]
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anchors = [anchors[i] for i in anchor_idxs]
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num_classes = int(module_def["classes"])
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img_size = int(hyperparams["height"])
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# Define detection layer
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yolo_layer = YOLOLayer(anchors, num_classes, img_size)
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modules.add_module(f"yolo_{module_i}", yolo_layer)
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# Register module list and number of output filters
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module_list.append(modules)
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output_filters.append(filters)
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return hyperparams, module_list
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class Resize(nn.Module):
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""" nn.Upsample is deprecated """
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def __init__(self, scale_factor, mode="nearest"):
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super(Resize, self).__init__()
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self.scale_factor = scale_factor
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self.mode = mode
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def forward(self, x):
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x = F.interpolate(x, scale_factor=self.scale_factor, mode=self.mode)
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return x
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class EmptyLayer(nn.Module):
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"""Placeholder for 'route' and 'shortcut' layers"""
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def __init__(self):
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super(EmptyLayer, self).__init__()
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class YOLOLayer(nn.Module):
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"""Detection layer"""
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def __init__(self, anchors, num_classes, img_dim=416):
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super(YOLOLayer, self).__init__()
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self.anchors = anchors
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self.num_anchors = len(anchors)
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self.num_classes = num_classes
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self.ignore_thres = 0.5
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self.mse_loss = nn.MSELoss()
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self.bce_loss = nn.BCELoss()
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self.obj_scale = 1
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self.noobj_scale = 100
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self.metrics = {}
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self.img_dim = img_dim
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self.grid_size = 0 # grid size
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def compute_grid_offsets(self, grid_size, cuda=True):
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self.grid_size = grid_size
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g = self.grid_size
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FloatTensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
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self.stride = self.img_dim / self.grid_size
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# Calculate offsets for each grid
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self.grid_x = torch.arange(g).repeat(g, 1).view([1, 1, g, g]).type(FloatTensor)
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self.grid_y = torch.arange(g).repeat(g, 1).t().view([1, 1, g, g]).type(FloatTensor)
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self.scaled_anchors = FloatTensor([(a_w / self.stride, a_h / self.stride) for a_w, a_h in self.anchors])
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self.anchor_w = self.scaled_anchors[:, 0:1].view((1, self.num_anchors, 1, 1))
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self.anchor_h = self.scaled_anchors[:, 1:2].view((1, self.num_anchors, 1, 1))
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def forward(self, x, targets=None, img_dim=None):
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# Tensors for cuda support
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print (x.shape)
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FloatTensor = torch.cuda.FloatTensor if x.is_cuda else torch.FloatTensor
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LongTensor = torch.cuda.LongTensor if x.is_cuda else torch.LongTensor
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ByteTensor = torch.cuda.ByteTensor if x.is_cuda else torch.ByteTensor
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self.img_dim = img_dim
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num_samples = x.size(0)
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grid_size = x.size(2)
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prediction = (
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x.view(num_samples, self.num_anchors, self.num_classes + 5, grid_size, grid_size)
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.permute(0, 1, 3, 4, 2)
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.contiguous()
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)
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print (prediction.shape)
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# Get outputs
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x = torch.sigmoid(prediction[..., 0]) # Center x
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y = torch.sigmoid(prediction[..., 1]) # Center y
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w = prediction[..., 2] # Width
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h = prediction[..., 3] # Height
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pred_conf = torch.sigmoid(prediction[..., 4]) # Conf
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pred_cls = torch.sigmoid(prediction[..., 5:]) # Cls pred.
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# If grid size does not match current we compute new offsets
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if grid_size != self.grid_size:
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self.compute_grid_offsets(grid_size, cuda=x.is_cuda) #相对位置得到对应的绝对位置比如之前的位置是0.5,0.5变为 11.5，11.5这样的
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# Add offset and scale with anchors #特征图中的实际位置
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pred_boxes = FloatTensor(prediction[..., :4].shape)
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pred_boxes[..., 0] = x.data + self.grid_x
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pred_boxes[..., 1] = y.data + self.grid_y
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pred_boxes[..., 2] = torch.exp(w.data) * self.anchor_w
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pred_boxes[..., 3] = torch.exp(h.data) * self.anchor_h
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output = torch.cat(
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(
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pred_boxes.view(num_samples, -1, 4) * self.stride, #还原到原始图中
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pred_conf.view(num_samples, -1, 1),
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pred_cls.view(num_samples, -1, self.num_classes),
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),
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-1,
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)
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if targets is None:
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return output, 0
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else:
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iou_scores, class_mask, obj_mask, noobj_mask, tx, ty, tw, th, tcls, tconf = build_targets(
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pred_boxes=pred_boxes,
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pred_cls=pred_cls,
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target=targets,
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anchors=self.scaled_anchors,
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ignore_thres=self.ignore_thres,
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)
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# iou_scores：真实值与最匹配的anchor的IOU得分值 class_mask：分类正确的索引 obj_mask：目标框所在位置的最好anchor置为1 noobj_mask obj_mask那里置0，还有计算的iou大于阈值的也置0，其他都为1 tx, ty, tw, th, 对应的对于该大小的特征图的xywh目标值也就是我们需要拟合的值 tconf 目标置信度
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# Loss : Mask outputs to ignore non-existing objects (except with conf. loss)
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loss_x = self.mse_loss(x[obj_mask], tx[obj_mask]) # 只计算有目标的
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loss_y = self.mse_loss(y[obj_mask], ty[obj_mask])
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loss_w = self.mse_loss(w[obj_mask], tw[obj_mask])
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loss_h = self.mse_loss(h[obj_mask], th[obj_mask])
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loss_conf_obj = self.bce_loss(pred_conf[obj_mask], tconf[obj_mask])
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loss_conf_noobj = self.bce_loss(pred_conf[noobj_mask], tconf[noobj_mask])
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loss_conf = self.obj_scale * loss_conf_obj + self.noobj_scale * loss_conf_noobj #有物体越接近1越好 没物体的越接近0越好
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loss_cls = self.bce_loss(pred_cls[obj_mask], tcls[obj_mask]) #分类损失
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total_loss = loss_x + loss_y + loss_w + loss_h + loss_conf + loss_cls #总损失
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# Metrics
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cls_acc = 100 * class_mask[obj_mask].mean()
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conf_obj = pred_conf[obj_mask].mean()
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conf_noobj = pred_conf[noobj_mask].mean()
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conf50 = (pred_conf > 0.5).float()
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iou50 = (iou_scores > 0.5).float()
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iou75 = (iou_scores > 0.75).float()
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detected_mask = conf50 * class_mask * tconf
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precision = torch.sum(iou50 * detected_mask) / (conf50.sum() + 1e-16)
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recall50 = torch.sum(iou50 * detected_mask) / (obj_mask.sum() + 1e-16)
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recall75 = torch.sum(iou75 * detected_mask) / (obj_mask.sum() + 1e-16)
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self.metrics = {
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"loss": to_cpu(total_loss).item(),
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"x": to_cpu(loss_x).item(),
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"y": to_cpu(loss_y).item(),
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"w": to_cpu(loss_w).item(),
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"h": to_cpu(loss_h).item(),
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"conf": to_cpu(loss_conf).item(),
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"cls": to_cpu(loss_cls).item(),
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"cls_acc": to_cpu(cls_acc).item(),
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"recall50": to_cpu(recall50).item(),
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"recall75": to_cpu(recall75).item(),
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"precision": to_cpu(precision).item(),
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"conf_obj": to_cpu(conf_obj).item(),
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"conf_noobj": to_cpu(conf_noobj).item(),
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"grid_size": grid_size,
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}
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return output, total_loss
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class Darknet(nn.Module):
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"""YOLOv3 object detection model"""
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def __init__(self, config_path, img_size=416):
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super(Darknet, self).__init__()
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self.module_defs = parse_model_config(config_path)
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self.hyperparams, self.module_list = create_modules(self.module_defs)
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self.yolo_layers = [layer[0] for layer in self.module_list if hasattr(layer[0], "metrics")]
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self.img_size = img_size
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self.seen = 0
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self.header_info = np.array([0, 0, 0, self.seen, 0], dtype=np.int32)
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def forward(self, x, targets=None):
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img_dim = x.shape[2]
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loss = 0
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layer_outputs, yolo_outputs = [], []
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for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):
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if module_def["type"] in ["convolutional", "resize", "maxpool"]:
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x = module(x)
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elif module_def["type"] == "route":
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x = torch.cat([layer_outputs[int(layer_i)] for layer_i in module_def["layers"].split(",")], 1)
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elif module_def["type"] == "shortcut":
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layer_i = int(module_def["from"])
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x = layer_outputs[-1] + layer_outputs[layer_i]
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elif module_def["type"] == "yolo":
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x, layer_loss = module[0](x, targets, img_dim)
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loss += layer_loss
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yolo_outputs.append(x)
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layer_outputs.append(x)
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yolo_outputs = to_cpu(torch.cat(yolo_outputs, 1))
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return yolo_outputs if targets is None else (loss, yolo_outputs)
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def load_darknet_weights(self, weights_path):
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"""Parses and loads the weights stored in 'weights_path'"""
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# Open the weights file
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with open(weights_path, "rb") as f:
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header = np.fromfile(f, dtype=np.int32, count=5) # First five are header values
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self.header_info = header # Needed to write header when saving weights
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self.seen = header[3] # number of images seen during training
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weights = np.fromfile(f, dtype=np.float32) # The rest are weights
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# Establish cutoff for loading backbone weights
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cutoff = None
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if "darknet53.conv.74" in weights_path:
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cutoff = 75
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ptr = 0
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for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):
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if i == cutoff:
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break
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if module_def["type"] == "convolutional":
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conv_layer = module[0]
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if module_def["batch_normalize"]:
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# Load BN bias, weights, running mean and running variance
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bn_layer = module[1]
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num_b = bn_layer.bias.numel() # Number of biases
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# Bias
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bn_b = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(bn_layer.bias)
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bn_layer.bias.data.copy_(bn_b)
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ptr += num_b
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# Weight
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bn_w = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(bn_layer.weight)
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bn_layer.weight.data.copy_(bn_w)
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ptr += num_b
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# Running Mean
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bn_rm = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(bn_layer.running_mean)
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bn_layer.running_mean.data.copy_(bn_rm)
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ptr += num_b
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# Running Var
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bn_rv = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(bn_layer.running_var)
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bn_layer.running_var.data.copy_(bn_rv)
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ptr += num_b
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else:
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# Load conv. bias
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num_b = conv_layer.bias.numel()
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conv_b = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(conv_layer.bias)
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conv_layer.bias.data.copy_(conv_b)
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ptr += num_b
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# Load conv. weights
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num_w = conv_layer.weight.numel()
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conv_w = torch.from_numpy(weights[ptr : ptr + num_w]).view_as(conv_layer.weight)
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conv_layer.weight.data.copy_(conv_w)
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ptr += num_w
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def save_darknet_weights(self, path, cutoff=-1):
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"""
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@:param path - path of the new weights file
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@:param cutoff - save layers between 0 and cutoff (cutoff = -1 -> all are saved)
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"""
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fp = open(path, "wb")
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self.header_info[3] = self.seen
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self.header_info.tofile(fp)
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# Iterate through layers
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for i, (module_def, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
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if module_def["type"] == "convolutional":
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conv_layer = module[0]
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# If batch norm, load bn first
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if module_def["batch_normalize"]:
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bn_layer = module[1]
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bn_layer.bias.data.cpu().numpy().tofile(fp)
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bn_layer.weight.data.cpu().numpy().tofile(fp)
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bn_layer.running_mean.data.cpu().numpy().tofile(fp)
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bn_layer.running_var.data.cpu().numpy().tofile(fp)
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# Load conv bias
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else:
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conv_layer.bias.data.cpu().numpy().tofile(fp)
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# Load conv weights
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conv_layer.weight.data.cpu().numpy().tofile(fp)
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fp.close()
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