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models.py
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import os
from collections import defaultdict,OrderedDict
import torch.nn as nn
from utils.parse_config import *
from utils.utils import *
from utils.syncbn import SyncBN
import time
import math
batch_norm=SyncBN #nn.BatchNorm2d
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()
yolo_layer_count = 0
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, batch_norm(filters))
if module_def['activation'] == 'leaky':
modules.add_module('leaky_%d' % i, nn.LeakyReLU(0.1))
elif module_def['type'] == 'maxpool':
kernel_size = int(module_def['size'])
stride = int(module_def['stride'])
if kernel_size == 2 and stride == 1:
modules.add_module('_debug_padding_%d' % i, nn.ZeroPad2d((0, 1, 0, 1)))
maxpool = nn.MaxPool2d(kernel_size=kernel_size, stride=stride, padding=int((kernel_size - 1) // 2))
modules.add_module('maxpool_%d' % i, maxpool)
elif module_def['type'] == 'upsample':
upsample = Upsample(scale_factor=int(module_def['stride']))
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[i + 1 if i > 0 else i] for 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]
nC = int(module_def['classes']) # number of classes
img_size = (int(hyperparams['width']),int(hyperparams['height']))
# Define detection layer
yolo_layer = YOLOLayer(anchors, nC, hyperparams['nID'], img_size, yolo_layer_count, cfg=hyperparams['cfg'])
modules.add_module('yolo_%d' % i, yolo_layer)
yolo_layer_count += 1
# 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__()
def forward(self, x):
return x
class Upsample(nn.Module):
# Custom Upsample layer (nn.Upsample gives deprecated warning message)
def __init__(self, scale_factor=1, mode='nearest'):
super(Upsample, self).__init__()
self.scale_factor = scale_factor
self.mode = mode
def forward(self, x):
return F.interpolate(x, scale_factor=self.scale_factor, mode=self.mode)
class YOLOLayer(nn.Module):
def __init__(self, anchors, nC, nID, img_size, yolo_layer, cfg):
super(YOLOLayer, self).__init__()
self.layer = yolo_layer
nA = len(anchors)
self.anchors = torch.FloatTensor(anchors)
self.nA = nA # number of anchors (3)
self.nC = nC # number of classes (80)
self.nID = nID # number of identities
self.img_size = 0
self.emb_dim = 512
self.SmoothL1Loss = nn.SmoothL1Loss()
self.SoftmaxLoss = nn.CrossEntropyLoss(ignore_index=-1)
self.CrossEntropyLoss = nn.CrossEntropyLoss()
self.IDLoss = nn.CrossEntropyLoss(ignore_index=-1)
self.s_c = nn.Parameter(-4.15*torch.ones(1)) # -4.15
self.s_r = nn.Parameter(-4.85*torch.ones(1)) # -4.85
self.s_id = nn.Parameter(-2.3*torch.ones(1)) # -2.3
self.emb_scale = math.sqrt(2) * math.log(self.nID-1)
def forward(self, p_cat, img_size, targets=None, classifier=None, test_emb=False):
p, p_emb = p_cat[:, :24, ...], p_cat[:, 24:, ...]
nB, nGh, nGw = p.shape[0], p.shape[-2], p.shape[-1]
if self.img_size != img_size:
create_grids(self, img_size, nGh, nGw)
if p.is_cuda:
self.grid_xy = self.grid_xy.cuda()
self.anchor_wh = self.anchor_wh.cuda()
p = p.view(nB, self.nA, self.nC + 5, nGh, nGw).permute(0, 1, 3, 4, 2).contiguous() # prediction
p_emb = p_emb.permute(0,2,3,1).contiguous()
p_box = p[..., :4]
p_conf = p[..., 4:6].permute(0, 4, 1, 2, 3) # Conf
# Training
if targets is not None:
if test_emb:
tconf, tbox, tids = build_targets_max(targets, self.anchor_vec.cuda(), self.nA, self.nC, nGh, nGw)
else:
tconf, tbox, tids = build_targets_thres(targets, self.anchor_vec.cuda(), self.nA, self.nC, nGh, nGw)
tconf, tbox, tids = tconf.cuda(), tbox.cuda(), tids.cuda()
mask = tconf > 0
# Compute losses
nT = sum([len(x) for x in targets]) # number of targets
nM = mask.sum().float() # number of anchors (assigned to targets)
nP = torch.ones_like(mask).sum().float()
if nM > 0:
lbox = self.SmoothL1Loss(p_box[mask], tbox[mask])
else:
FT = torch.cuda.FloatTensor if p_conf.is_cuda else torch.FloatTensor
lbox, lconf = FT([0]), FT([0])
lconf = self.SoftmaxLoss(p_conf, tconf)
lid = torch.Tensor(1).fill_(0).squeeze().cuda()
emb_mask,_ = mask.max(1)
# For convenience we use max(1) to decide the id, TODO: more reseanable strategy
tids,_ = tids.max(1)
tids = tids[emb_mask]
embedding = p_emb[emb_mask].contiguous()
embedding = self.emb_scale * F.normalize(embedding)
nI = emb_mask.sum().float()
if test_emb:
if np.prod(embedding.shape)==0 or np.prod(tids.shape) == 0:
return torch.zeros(0, self. emb_dim+1).cuda()
emb_and_gt = torch.cat([embedding, tids.float()], dim=1)
return emb_and_gt
if len(embedding) > 1:
logits = classifier(embedding).contiguous()
lid = self.IDLoss(logits, tids.squeeze())
# Sum loss components
loss = torch.exp(-self.s_r)*lbox + torch.exp(-self.s_c)*lconf + torch.exp(-self.s_id)*lid + \
(self.s_r + self.s_c + self.s_id)
loss *= 0.5
return loss, loss.item(), lbox.item(), lconf.item(), lid.item(), nT
else:
p_conf = torch.softmax(p_conf, dim=1)[:,1,...].unsqueeze(-1)
p_emb = p_emb.unsqueeze(1).repeat(1,self.nA,1,1,1).contiguous()
p_cls = torch.zeros(nB,self.nA,nGh,nGw,1).cuda() # Temp
p = torch.cat([p_box, p_conf, p_cls, p_emb], dim=-1)
p[..., :4] = decode_delta_map(p[..., :4], self.anchor_vec.to(p))
p[..., :4] *= self.stride
return p.view(nB, -1, p.shape[-1])
class Darknet(nn.Module):
"""YOLOv3 object detection model"""
def __init__(self, cfg_path, img_size=(1088, 608), nID=1591, test_emb=False):
super(Darknet, self).__init__()
self.module_defs = parse_model_cfg(cfg_path)
self.module_defs[0]['cfg'] = cfg_path
self.module_defs[0]['nID'] = nID
self.hyperparams, self.module_list = create_modules(self.module_defs)
self.img_size = img_size
self.loss_names = ['loss', 'box', 'conf', 'id', 'nT']
self.losses = OrderedDict()
for ln in self.loss_names:
self.losses[ln] = 0
self.emb_dim = 512
self.classifier = nn.Linear(self.emb_dim, nID)
self.test_emb=test_emb
def forward(self, x, targets=None, targets_len=None):
self.losses = OrderedDict()
for ln in self.loss_names:
self.losses[ln] = 0
is_training = (targets is not None) and (not self.test_emb)
#img_size = x.shape[-1]
layer_outputs = []
output = []
for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):
mtype = module_def['type']
if mtype in ['convolutional', 'upsample', 'maxpool']:
x = module(x)
elif mtype == 'route':
layer_i = [int(x) for x in module_def['layers'].split(',')]
if len(layer_i) == 1:
x = layer_outputs[layer_i[0]]
else:
x = torch.cat([layer_outputs[i] for i in layer_i], 1)
elif mtype == 'shortcut':
layer_i = int(module_def['from'])
x = layer_outputs[-1] + layer_outputs[layer_i]
elif mtype == 'yolo':
if is_training: # get loss
targets = [targets[i][:int(l)] for i,l in enumerate(targets_len)]
x, *losses = module[0](x, self.img_size, targets, self.classifier)
for name, loss in zip(self.loss_names, losses):
self.losses[name] += loss
elif self.test_emb:
targets = [targets[i][:int(l)] for i,l in enumerate(targets_len)]
x = module[0](x, self.img_size, targets, self.classifier, self.test_emb)
else: # get detections
x = module[0](x, self.img_size)
output.append(x)
layer_outputs.append(x)
if is_training:
self.losses['nT'] /= 3
output = [o.squeeze() for o in output]
return sum(output), torch.Tensor(list(self.losses.values())).cuda()
elif self.test_emb:
return torch.cat(output, 0)
return torch.cat(output, 1)
def create_grids(self, img_size, nGh, nGw):
self.stride = img_size[0]/nGw
assert self.stride == img_size[1] / nGh
# build xy offsets
grid_x = torch.arange(nGw).repeat((nGh, 1)).view((1, 1, nGh, nGw)).float()
grid_y = torch.arange(nGh).repeat((nGw, 1)).transpose(0,1).view((1, 1, nGh, nGw)).float()
#grid_y = grid_x.permute(0, 1, 3, 2)
self.grid_xy = torch.stack((grid_x, grid_y), 4)
# build wh gains
self.anchor_vec = self.anchors / self.stride
self.anchor_wh = self.anchor_vec.view(1, self.nA, 1, 1, 2)
def load_darknet_weights(self, weights, cutoff=-1):
# Parses and loads the weights stored in 'weights'
# cutoff: save layers between 0 and cutoff (if cutoff = -1 all are saved)
weights_file = weights.split(os.sep)[-1]
# Try to download weights if not available locally
if not os.path.isfile(weights):
try:
os.system('wget https://pjreddie.com/media/files/' + weights_file + ' -O ' + weights)
except IOError:
print(weights + ' not found')
# Establish cutoffs
if weights_file == 'darknet53.conv.74':
cutoff = 75
elif weights_file == 'yolov3-tiny.conv.15':
cutoff = 15
# Open the weights file
fp = open(weights, '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] # number of images seen during training
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
"""
@:param path - path of the new weights file
@:param cutoff - save layers between 0 and cutoff (cutoff = -1 -> all are saved)
"""
def save_weights(self, path, cutoff=-1):
fp = open(path, 'wb')
self.header_info[3] = self.seen # number of images seen during training
self.header_info.tofile(fp)
# Iterate through layers
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 batch norm, load bn first
if module_def['batch_normalize']:
bn_layer = module[1]
bn_layer.bias.data.cpu().numpy().tofile(fp)
bn_layer.weight.data.cpu().numpy().tofile(fp)
bn_layer.running_mean.data.cpu().numpy().tofile(fp)
bn_layer.running_var.data.cpu().numpy().tofile(fp)
# Load conv bias
else:
conv_layer.bias.data.cpu().numpy().tofile(fp)
# Load conv weights
conv_layer.weight.data.cpu().numpy().tofile(fp)
fp.close()