403 lines
17 KiB
Python
403 lines
17 KiB
Python
import os
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from collections import defaultdict,OrderedDict
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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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import time
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import math
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try:
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from utils.syncbn import SyncBN
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batch_norm=SyncBN #nn.BatchNorm2d
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except ImportError:
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batch_norm=nn.BatchNorm2d
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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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yolo_layer_count = 0
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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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after_bn = batch_norm(filters)
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modules.add_module('batch_norm_%d' % i, after_bn)
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# BN is uniformly initialized by default in pytorch 1.0.1.
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# In pytorch>1.2.0, BN weights are initialized with constant 1,
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# but we find with the uniform initialization the model converges faster.
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nn.init.uniform_(after_bn.weight)
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nn.init.zeros_(after_bn.bias)
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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'] == '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('_debug_padding_%d' % 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('maxpool_%d' % i, maxpool)
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elif module_def['type'] == 'upsample':
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upsample = Upsample(scale_factor=int(module_def['stride']))
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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[i + 1 if i > 0 else i] for 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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nC = int(module_def['classes']) # number of classes
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img_size = (int(hyperparams['width']),int(hyperparams['height']))
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# Define detection layer
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yolo_layer = YOLOLayer(anchors, nC, int(hyperparams['nID']),
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int(hyperparams['embedding_dim']), img_size, yolo_layer_count)
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modules.add_module('yolo_%d' % i, yolo_layer)
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yolo_layer_count += 1
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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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def forward(self, x):
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return x
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class Upsample(nn.Module):
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# Custom Upsample layer (nn.Upsample gives deprecated warning message)
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def __init__(self, scale_factor=1, mode='nearest'):
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super(Upsample, 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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return F.interpolate(x, scale_factor=self.scale_factor, mode=self.mode)
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class YOLOLayer(nn.Module):
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def __init__(self, anchors, nC, nID, nE, img_size, yolo_layer):
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super(YOLOLayer, self).__init__()
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self.layer = yolo_layer
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nA = len(anchors)
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self.anchors = torch.FloatTensor(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.nID = nID # number of identities
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self.img_size = 0
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self.emb_dim = nE
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self.shift = [1, 3, 5]
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self.SmoothL1Loss = nn.SmoothL1Loss()
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self.SoftmaxLoss = nn.CrossEntropyLoss(ignore_index=-1)
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self.CrossEntropyLoss = nn.CrossEntropyLoss()
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self.IDLoss = nn.CrossEntropyLoss(ignore_index=-1)
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self.s_c = nn.Parameter(-4.15*torch.ones(1)) # -4.15
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self.s_r = nn.Parameter(-4.85*torch.ones(1)) # -4.85
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self.s_id = nn.Parameter(-2.3*torch.ones(1)) # -2.3
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self.emb_scale = math.sqrt(2) * math.log(self.nID-1) if self.nID>1 else 1
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def forward(self, p_cat, img_size, targets=None, classifier=None, test_emb=False):
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p, p_emb = p_cat[:, :24, ...], p_cat[:, 24:, ...]
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nB, nGh, nGw = p.shape[0], p.shape[-2], p.shape[-1]
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if self.img_size != img_size:
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create_grids(self, img_size, nGh, nGw)
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if p.is_cuda:
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self.grid_xy = self.grid_xy.cuda()
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self.anchor_wh = self.anchor_wh.cuda()
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p = p.view(nB, self.nA, self.nC + 5, nGh, nGw).permute(0, 1, 3, 4, 2).contiguous() # prediction
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p_emb = p_emb.permute(0,2,3,1).contiguous()
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p_box = p[..., :4]
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p_conf = p[..., 4:6].permute(0, 4, 1, 2, 3) # Conf
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# Training
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if targets is not None:
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if test_emb:
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tconf, tbox, tids = build_targets_max(targets, self.anchor_vec.cuda(), self.nA, self.nC, nGh, nGw)
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else:
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tconf, tbox, tids = build_targets_thres(targets, self.anchor_vec.cuda(), self.nA, self.nC, nGh, nGw)
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tconf, tbox, tids = tconf.cuda(), tbox.cuda(), tids.cuda()
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mask = tconf > 0
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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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nP = torch.ones_like(mask).sum().float()
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if nM > 0:
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lbox = self.SmoothL1Loss(p_box[mask], tbox[mask])
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else:
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FT = torch.cuda.FloatTensor if p_conf.is_cuda else torch.FloatTensor
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lbox, lconf = FT([0]), FT([0])
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lconf = self.SoftmaxLoss(p_conf, tconf)
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lid = torch.Tensor(1).fill_(0).squeeze().cuda()
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emb_mask,_ = mask.max(1)
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# For convenience we use max(1) to decide the id, TODO: more reseanable strategy
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tids,_ = tids.max(1)
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tids = tids[emb_mask]
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embedding = p_emb[emb_mask].contiguous()
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embedding = self.emb_scale * F.normalize(embedding)
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nI = emb_mask.sum().float()
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if test_emb:
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if np.prod(embedding.shape)==0 or np.prod(tids.shape) == 0:
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return torch.zeros(0, self.emb_dim+1).cuda()
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emb_and_gt = torch.cat([embedding, tids.float()], dim=1)
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return emb_and_gt
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if len(embedding) > 1:
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logits = classifier(embedding).contiguous()
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lid = self.IDLoss(logits, tids.squeeze())
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# Sum loss components
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loss = torch.exp(-self.s_r)*lbox + torch.exp(-self.s_c)*lconf + torch.exp(-self.s_id)*lid + \
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(self.s_r + self.s_c + self.s_id)
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loss *= 0.5
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return loss, loss.item(), lbox.item(), lconf.item(), lid.item(), nT
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else:
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p_conf = torch.softmax(p_conf, dim=1)[:,1,...].unsqueeze(-1)
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p_emb = F.normalize(p_emb.unsqueeze(1).repeat(1,self.nA,1,1,1).contiguous(), dim=-1)
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#p_emb_up = F.normalize(shift_tensor_vertically(p_emb, -self.shift[self.layer]), dim=-1)
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#p_emb_down = F.normalize(shift_tensor_vertically(p_emb, self.shift[self.layer]), dim=-1)
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p_cls = torch.zeros(nB,self.nA,nGh,nGw,1).cuda() # Temp
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p = torch.cat([p_box, p_conf, p_cls, p_emb], dim=-1)
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#p = torch.cat([p_box, p_conf, p_cls, p_emb, p_emb_up, p_emb_down], dim=-1)
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p[..., :4] = decode_delta_map(p[..., :4], self.anchor_vec.to(p))
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p[..., :4] *= self.stride
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return p.view(nB, -1, p.shape[-1])
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class Darknet(nn.Module):
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"""YOLOv3 object detection model"""
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def __init__(self, cfg_dict, nID=0, test_emb=False):
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super(Darknet, self).__init__()
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if isinstance(cfg_dict, str):
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cfg_dict = parse_model_cfg(cfg_dict)
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self.module_defs = cfg_dict
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self.module_defs[0]['nID'] = nID
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self.img_size = [int(self.module_defs[0]['width']), int(self.module_defs[0]['height'])]
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self.emb_dim = int(self.module_defs[0]['embedding_dim'])
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self.hyperparams, self.module_list = create_modules(self.module_defs)
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self.loss_names = ['loss', 'box', 'conf', 'id', 'nT']
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self.losses = OrderedDict()
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for ln in self.loss_names:
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self.losses[ln] = 0
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self.test_emb = test_emb
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self.classifier = nn.Linear(self.emb_dim, nID) if nID>0 else None
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def forward(self, x, targets=None, targets_len=None):
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self.losses = OrderedDict()
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for ln in self.loss_names:
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self.losses[ln] = 0
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is_training = (targets is not None) and (not self.test_emb)
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#img_size = x.shape[-1]
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layer_outputs = []
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output = []
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for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):
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mtype = module_def['type']
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if mtype in ['convolutional', 'upsample', 'maxpool']:
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x = module(x)
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elif mtype == 'route':
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layer_i = [int(x) for x in module_def['layers'].split(',')]
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if len(layer_i) == 1:
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x = layer_outputs[layer_i[0]]
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else:
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x = torch.cat([layer_outputs[i] for i in layer_i], 1)
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elif mtype == '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 mtype == 'yolo':
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if is_training: # get loss
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targets = [targets[i][:int(l)] for i,l in enumerate(targets_len)]
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x, *losses = module[0](x, self.img_size, targets, self.classifier)
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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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elif self.test_emb:
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if targets is not None:
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targets = [targets[i][:int(l)] for i,l in enumerate(targets_len)]
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x = module[0](x, self.img_size, targets, self.classifier, self.test_emb)
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else: # get detections
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x = module[0](x, self.img_size)
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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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output = [o.squeeze() for o in output]
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return sum(output), torch.Tensor(list(self.losses.values())).cuda()
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elif self.test_emb:
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return torch.cat(output, 0)
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return torch.cat(output, 1)
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def shift_tensor_vertically(t, delta):
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# t should be a 5-D tensor (nB, nA, nH, nW, nC)
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res = torch.zeros_like(t)
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if delta >= 0:
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res[:,:, :-delta, :, :] = t[:,:, delta:, :, :]
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else:
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res[:,:, -delta:, :, :] = t[:,:, :delta, :, :]
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return res
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def create_grids(self, img_size, nGh, nGw):
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self.stride = img_size[0]/nGw
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assert self.stride == img_size[1] / nGh, \
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"{} v.s. {}/{}".format(self.stride, img_size[1], nGh)
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# build xy offsets
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grid_x = torch.arange(nGw).repeat((nGh, 1)).view((1, 1, nGh, nGw)).float()
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grid_y = torch.arange(nGh).repeat((nGw, 1)).transpose(0,1).view((1, 1, nGh, nGw)).float()
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#grid_y = grid_x.permute(0, 1, 3, 2)
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self.grid_xy = torch.stack((grid_x, grid_y), 4)
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# build wh gains
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self.anchor_vec = self.anchors / self.stride
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self.anchor_wh = self.anchor_vec.view(1, self.nA, 1, 1, 2)
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def load_darknet_weights(self, weights, cutoff=-1):
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# Parses and loads the weights stored in 'weights'
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# cutoff: save layers between 0 and cutoff (if cutoff = -1 all are saved)
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weights_file = weights.split(os.sep)[-1]
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# Try to download weights if not available locally
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if not os.path.isfile(weights):
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try:
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os.system('wget https://pjreddie.com/media/files/' + weights_file + ' -O ' + weights)
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except IOError:
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print(weights + ' not found')
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# Establish cutoffs
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if weights_file == 'darknet53.conv.74':
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cutoff = 75
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elif weights_file == 'yolov3-tiny.conv.15':
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cutoff = 15
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# Open the weights file
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fp = open(weights, '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] # number of images seen during training
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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 # number of images seen during training
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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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