363 lines
15 KiB
Python
Executable File
363 lines
15 KiB
Python
Executable File
from collections import defaultdict
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import torch.nn as nn
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from utils.parse_config import *
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from utils.utils import *
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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 i, module_def in enumerate(module_defs):
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modules = nn.Sequential()
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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 if int(module_def['pad']) else 0
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modules.add_module('conv_%d' % i, nn.Conv2d(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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if bn:
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modules.add_module('batch_norm_%d' % i, nn.BatchNorm2d(filters))
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if module_def['activation'] == 'leaky':
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modules.add_module('leaky_%d' % i, nn.LeakyReLU(0.1))
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elif module_def['type'] == 'upsample':
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upsample = nn.Upsample(scale_factor=int(module_def['stride']), mode='nearest')
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modules.add_module('upsample_%d' % i, upsample)
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elif module_def['type'] == 'route':
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layers = [int(x) for x in module_def['layers'].split(',')]
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filters = sum([output_filters[layer_i] for layer_i in layers])
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modules.add_module('route_%d' % i, EmptyLayer())
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elif module_def['type'] == 'shortcut':
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filters = output_filters[int(module_def['from'])]
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modules.add_module('shortcut_%d' % 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 = [float(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_height = int(hyperparams['height'])
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# Define detection layer
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yolo_layer = YOLOLayer(anchors, num_classes, img_height, anchor_idxs)
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modules.add_module('yolo_%d' % 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 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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def __init__(self, anchors, nC, img_dim, anchor_idxs):
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super(YOLOLayer, self).__init__()
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anchors = [(a_w, a_h) for a_w, a_h in anchors] # (pixels)
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nA = len(anchors)
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self.anchors = anchors
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self.nA = nA # number of anchors (3)
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self.nC = nC # number of classes (80)
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self.bbox_attrs = 5 + nC
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self.img_dim = img_dim # from hyperparams in cfg file, NOT from parser
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if anchor_idxs[0] == (nA * 2): # 6
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stride = 32
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elif anchor_idxs[0] == nA: # 3
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stride = 16
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else:
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stride = 8
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# Build anchor grids
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nG = int(self.img_dim / stride) # number grid points
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self.grid_x = torch.arange(nG).repeat(nG, 1).view([1, 1, nG, nG]).float()
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self.grid_y = torch.arange(nG).repeat(nG, 1).t().view([1, 1, nG, nG]).float()
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self.scaled_anchors = torch.FloatTensor([(a_w / stride, a_h / stride) for a_w, a_h in anchors])
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self.anchor_w = self.scaled_anchors[:, 0:1].view((1, nA, 1, 1))
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self.anchor_h = self.scaled_anchors[:, 1:2].view((1, nA, 1, 1))
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self.weights = class_weights()
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self.loss_means = torch.zeros(6)
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def forward(self, p, targets=None, requestPrecision=False):
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FT = torch.cuda.FloatTensor if p.is_cuda else torch.FloatTensor
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bs = p.shape[0] # batch size
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nG = p.shape[2] # number of grid points
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stride = self.img_dim / nG
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if p.is_cuda and not self.grid_x.is_cuda:
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self.grid_x, self.grid_y = self.grid_x.cuda(), self.grid_y.cuda()
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self.anchor_w, self.anchor_h = self.anchor_w.cuda(), self.anchor_h.cuda()
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self.weights = self.weights.cuda()
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# p.view(12, 255, 13, 13) -- > (12, 3, 13, 13, 80) # (bs, anchors, grid, grid, classes + xywh)
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p = p.view(bs, self.nA, self.bbox_attrs, nG, nG).permute(0, 1, 3, 4, 2).contiguous() # prediction
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# Get outputs
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x = torch.sigmoid(p[..., 0]) # Center x
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y = torch.sigmoid(p[..., 1]) # Center y
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# Width and height (yolo method)
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w = p[..., 2] # Width
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h = p[..., 3] # Height
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width = torch.exp(w.data) * self.anchor_w
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height = torch.exp(h.data) * self.anchor_h
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# Width and height (power method)
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# w = torch.sigmoid(p[..., 2]) # Width
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# h = torch.sigmoid(p[..., 3]) # Height
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# width = ((w.data * 2) ** 2) * self.anchor_w
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# height = ((h.data * 2) ** 2) * self.anchor_h
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# Add offset and scale with anchors (in grid space, i.e. 0-13)
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pred_boxes = FT(bs, self.nA, nG, nG, 4)
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pred_conf = p[..., 4] # Conf
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pred_cls = p[..., 5:] # Class
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# Training
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if targets is not None:
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MSELoss = nn.MSELoss()
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BCEWithLogitsLoss = nn.BCEWithLogitsLoss()
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CrossEntropyLoss = nn.CrossEntropyLoss()
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if requestPrecision:
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gx = self.grid_x[:, :, :nG, :nG]
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gy = self.grid_y[:, :, :nG, :nG]
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pred_boxes[..., 0] = x.data + gx - width / 2
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pred_boxes[..., 1] = y.data + gy - height / 2
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pred_boxes[..., 2] = x.data + gx + width / 2
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pred_boxes[..., 3] = y.data + gy + height / 2
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tx, ty, tw, th, mask, tcls, TP, FP, FN, TC = \
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build_targets(pred_boxes, pred_conf, pred_cls, targets, self.scaled_anchors, self.nA, self.nC, nG,
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requestPrecision)
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tcls = tcls[mask]
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if x.is_cuda:
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tx, ty, tw, th, mask, tcls = tx.cuda(), ty.cuda(), tw.cuda(), th.cuda(), mask.cuda(), tcls.cuda()
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# Compute losses
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nT = sum([len(x) for x in targets]) # number of targets
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nM = mask.sum().float() # number of anchors (assigned to targets)
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nB = len(targets) # batch size
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k = nM / nB
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if nM > 0:
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lx = k * MSELoss(x[mask], tx[mask])
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ly = k * MSELoss(y[mask], ty[mask])
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lw = k * MSELoss(w[mask], tw[mask])
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lh = k * MSELoss(h[mask], th[mask])
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# lconf = k * BCEWithLogitsLoss(pred_conf[mask], mask[mask].float())
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lconf = (k * 1) * BCEWithLogitsLoss(pred_conf, mask.float())
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lcls = (k / 10) * CrossEntropyLoss(pred_cls[mask], torch.argmax(tcls, 1))
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# lcls = (k * 10) * BCEWithLogitsLoss(pred_cls[mask], tcls.float())
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else:
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lx, ly, lw, lh, lcls, lconf = FT([0]), FT([0]), FT([0]), FT([0]), FT([0]), FT([0])
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# Add confidence loss for background anchors (noobj)
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# lconf += k * BCEWithLogitsLoss(pred_conf[~mask], mask[~mask].float())
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# Sum loss components
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balance_losses_flag = False
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if balance_losses_flag:
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loss_vec = torch.FloatTensor([lx.data, ly.data, lw.data, lh.data, lconf.data, lcls.data])
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self.loss_means = self.loss_means * 0.99 + loss_vec * 0.01
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k = 1 / self.loss_means.clone()
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k /= k.sum()
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loss = (lx * k[0] + ly * k[1] + lw * k[2] + lh * k[3] + lconf * k[4] + lcls * k[5]) * loss_vec.sum()
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else:
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loss = lx + ly + lw + lh + lconf + lcls
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# Sum False Positives from unassigned anchors
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i = torch.sigmoid(pred_conf[~mask]) > 0.5
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if i.sum() > 0:
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FP_classes = torch.argmax(pred_cls[~mask][i], 1)
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FPe = torch.bincount(FP_classes, minlength=self.nC).float().cpu() # extra FPs
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else:
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FPe = torch.zeros(self.nC)
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return loss, loss.item(), lx.item(), ly.item(), lw.item(), lh.item(), lconf.item(), lcls.item(), \
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nT, TP, FP, FPe, FN, TC
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else:
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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] = width
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pred_boxes[..., 3] = height
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# If not in training phase return predictions
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output = torch.cat((pred_boxes.view(bs, -1, 4) * stride,
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torch.sigmoid(pred_conf.view(bs, -1, 1)), pred_cls.view(bs, -1, self.nC)), -1)
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return output.data
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class Darknet(nn.Module):
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"""YOLOv3 object detection model"""
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def __init__(self, cfg_path, img_size=416):
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super(Darknet, self).__init__()
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self.module_defs = parse_model_config(cfg_path)
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self.module_defs[0]['height'] = img_size
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self.hyperparams, self.module_list = create_modules(self.module_defs)
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self.img_size = img_size
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self.loss_names = ['loss', 'x', 'y', 'w', 'h', 'conf', 'cls', 'nT', 'TP', 'FP', 'FPe', 'FN', 'TC']
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def forward(self, x, targets=None, requestPrecision=False):
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is_training = targets is not None
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output = []
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self.losses = defaultdict(float)
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layer_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', 'upsample']:
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x = module(x)
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elif module_def['type'] == 'route':
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layer_i = [int(x) for x in module_def['layers'].split(',')]
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x = torch.cat([layer_outputs[i] for i in layer_i], 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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# Train phase: get loss
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if is_training:
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x, *losses = module[0](x, targets, requestPrecision)
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for name, loss in zip(self.loss_names, losses):
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self.losses[name] += loss
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# Test phase: Get detections
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else:
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x = module(x)
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output.append(x)
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layer_outputs.append(x)
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if is_training:
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self.losses['nT'] /= 3
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self.losses['TC'] /= 3 # target category
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metrics = torch.zeros(3, len(self.losses['FPe'])) # TP, FP, FN
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ui = np.unique(self.losses['TC'])[1:]
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for i in ui:
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j = self.losses['TC'] == float(i)
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metrics[0, i] = (self.losses['TP'][j] > 0).sum().float() # TP
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metrics[1, i] = (self.losses['FP'][j] > 0).sum().float() # FP
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metrics[2, i] = (self.losses['FN'][j] == 3).sum().float() # FN
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metrics[1] += self.losses['FPe']
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self.losses['TP'] = metrics[0].sum()
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self.losses['FP'] = metrics[1].sum()
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self.losses['FN'] = metrics[2].sum()
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self.losses['TC'] = 0
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self.losses['metrics'] = metrics
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return sum(output) if is_training else torch.cat(output, 1)
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def load_weights(self, weights_path, cutoff=-1):
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# Parses and loads the weights stored in 'weights_path'
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# @:param cutoff - save layers between 0 and cutoff (cutoff = -1 -> all are saved)
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if weights_path.endswith('darknet53.conv.74'):
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cutoff = 75
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# Open the weights file
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fp = open(weights_path, 'rb')
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header = np.fromfile(fp, dtype=np.int32, count=5) # First five are header values
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# Needed to write header when saving weights
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self.header_info = header
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self.seen = header[3]
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weights = np.fromfile(fp, dtype=np.float32) # The rest are weights
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fp.close()
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ptr = 0
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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 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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"""
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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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def save_weights(self, path, cutoff=-1):
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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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