我们从Python开源项目中，提取了以下12个代码示例，用于说明如何使用data.v2()。
def __init__(self, phase, base, extras, head, num_classes): super(SSD, self).__init__() self.phase = phase self.num_classes = num_classes # TODO: implement __call__ in PriorBox self.priorbox = PriorBox(v2) self.priors = Variable(self.priorbox.forward(), volatile=True) self.size = 300 # SSD network self.vgg = nn.ModuleList(base) # Layer learns to scale the l2 normalized features from conv4_3 self.L2Norm = L2Norm(512, 20) self.extras = nn.ModuleList(extras) self.loc = nn.ModuleList(head[0]) self.conf = nn.ModuleList(head[1]) if phase == 'test': self.softmax = nn.Softmax() self.detect = Detect(num_classes, 0, 200, 0.01, 0.45)
def __init__(self, base, extras, head, num_classes): super(SSD, self).__init__() self.num_classes = num_classes # TODO: implement __call__ in PriorBox self.priorbox = PriorBox(v2) self.priors = Variable(self.priorbox.forward(), volatile=True) self.num_priors = self.priors.size(0) self.size = 300 # SSD network self.vgg = nn.ModuleList(base) # Layer learns to scale the l2 normalized features from conv4_3 self.L2Norm = L2Norm(512, 20) self.extras = nn.ModuleList(extras) self.loc = nn.ModuleList(head[0]) self.conf = nn.ModuleList(head[1]) self.softmax = nn.Softmax().cuda() # self.detect = Detect(num_classes, 0, 200, 0.001, 0.45)
def __init__(self, num_classes, overlap_thresh, prior_for_matching, bkg_label, neg_mining, neg_pos, neg_overlap, encode_target, use_gpu=True): super(MultiBoxLoss, self).__init__() self.use_gpu = use_gpu self.num_classes = num_classes self.threshold = overlap_thresh self.background_label = bkg_label self.encode_target = encode_target self.use_prior_for_matching = prior_for_matching self.do_neg_mining = neg_mining self.negpos_ratio = neg_pos self.neg_overlap = neg_overlap self.variance = cfg['variance']
def __init__(self, num_classes, bkg_label, top_k, conf_thresh, nms_thresh): self.num_classes = num_classes self.background_label = bkg_label self.top_k = top_k # Parameters used in nms. self.nms_thresh = nms_thresh if nms_thresh <= 0: raise ValueError('nms_threshold must be non negative.') self.conf_thresh = conf_thresh self.variance = cfg['variance'] self.output = torch.zeros(1, self.num_classes, self.top_k, 5)
def __init__(self,num_classes,overlap_thresh,prior_for_matching,bkg_label,neg_mining,neg_pos,neg_overlap,encode_target): super(MultiBoxLoss, self).__init__() self.num_classes = num_classes self.threshold = overlap_thresh self.background_label = bkg_label self.encode_target = encode_target self.use_prior_for_matching = prior_for_matching self.do_neg_mining = neg_mining self.negpos_ratio = neg_pos self.neg_overlap = neg_overlap self.variance = cfg['variance']
def __init__(self,num_classes,overlap_thresh,prior_for_matching,bkg_label,neg_mining,neg_pos,neg_overlap,encode_target,use_gpu=True): super(MultiBoxLoss, self).__init__() self.use_gpu = use_gpu self.num_classes = num_classes self.threshold = overlap_thresh self.background_label = bkg_label self.encode_target = encode_target self.use_prior_for_matching = prior_for_matching self.do_neg_mining = neg_mining self.negpos_ratio = neg_pos self.neg_overlap = neg_overlap self.variance = cfg['variance']
def __init__(self, num_classes, bkg_label, top_k, conf_thresh, nms_thresh): self.num_classes = num_classes self.background_label = bkg_label self.top_k = top_k # Parameters used in nms. self.nms_thresh = nms_thresh if nms_thresh <= 0: raise ValueError('nms_threshold must be non negative.') self.conf_thresh = conf_thresh self.variance = cfg['variance'] self.output = torch.zeros(1, self.num_classes+1, self.top_k, 5)
def forward(self): mean = [] # TODO merge these if self.version == 'v2': for k, f in enumerate(self.feature_maps): for i, j in product(range(f), repeat=2): f_k = self.image_size / self.steps[k] # unit center x,y cx = (j + 0.5) / f_k cy = (i + 0.5) / f_k # aspect_ratio: 1 # rel size: min_size s_k = self.min_sizes[k]/self.image_size mean += [cx, cy, s_k, s_k] # aspect_ratio: 1 # rel size: sqrt(s_k * s_(k+1)) s_k_prime = sqrt(s_k * (self.max_sizes[k]/self.image_size)) mean += [cx, cy, s_k_prime, s_k_prime] # rest of aspect ratios for ar in self.aspect_ratios[k]: mean += [cx, cy, s_k*sqrt(ar), s_k/sqrt(ar)] mean += [cx, cy, s_k/sqrt(ar), s_k*sqrt(ar)] else: # original version generation of prior (default) boxes for i, k in enumerate(self.feature_maps): step_x = step_y = self.image_size/k for h, w in product(range(k), repeat=2): c_x = ((w+0.5) * step_x) c_y = ((h+0.5) * step_y) c_w = c_h = self.min_sizes[i] / 2 s_k = self.image_size # 300 # aspect_ratio: 1, # size: min_size mean += [(c_x-c_w)/s_k, (c_y-c_h)/s_k, (c_x+c_w)/s_k, (c_y+c_h)/s_k] if self.max_sizes[i] > 0: # aspect_ratio: 1 # size: sqrt(min_size * max_size)/2 c_w = c_h = sqrt(self.min_sizes[i] * self.max_sizes[i])/2 mean += [(c_x-c_w)/s_k, (c_y-c_h)/s_k, (c_x+c_w)/s_k, (c_y+c_h)/s_k] # rest of prior boxes for ar in self.aspect_ratios[i]: if not (abs(ar-1) < 1e-6): c_w = self.min_sizes[i] * sqrt(ar)/2 c_h = self.min_sizes[i] / sqrt(ar)/2 mean += [(c_x-c_w)/s_k, (c_y-c_h)/s_k, (c_x+c_w)/s_k, (c_y+c_h)/s_k] # back to torch land output = torch.Tensor(mean).view(-1, 4) if self.clip: output.clamp_(max=1, min=0) return output
