Python cfg.CUDA屬性代碼示例

本文整理匯總了Python中miscc.config.cfg.CUDA屬性的典型用法代碼示例。如果您正苦於以下問題：Python cfg.CUDA屬性的具體用法？Python cfg.CUDA怎麽用？Python cfg.CUDA使用的例子？那麽, 這裏精選的屬性代碼示例或許可以為您提供幫助。您也可以進一步了解該屬性所在類miscc.config.cfg的用法示例。

在下文中一共展示了cfg.CUDA屬性的15個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: load_network_stageI

# 需要導入模塊: from miscc.config import cfg [as 別名]
# 或者: from miscc.config.cfg import CUDA [as 別名]
def load_network_stageI(self):
        from model import STAGE1_G, STAGE1_D
        netG = STAGE1_G()
        netG.apply(weights_init)
        netD = STAGE1_D()
        netD.apply(weights_init)

        if cfg.NET_G != '':
            state_dict = \
                torch.load(cfg.NET_G, map_location=lambda storage, loc: storage)
            netG.load_state_dict(state_dict["netG"])
            print('Load from: ', cfg.NET_G)
        if cfg.NET_D != '':
            state_dict = \
                torch.load(cfg.NET_D,  map_location=lambda storage, loc: storage)
            netD.load_state_dict(state_dict)
            print('Load from: ', cfg.NET_D)
        if cfg.CUDA:
            netG.cuda()
            netD.cuda()
        return netG, netD

開發者ID:tohinz，項目名稱:multiple-objects-gan，代碼行數:23，代碼來源:trainer.py

示例2: load_network_stageI

# 需要導入模塊: from miscc.config import cfg [as 別名]
# 或者: from miscc.config.cfg import CUDA [as 別名]
def load_network_stageI(self):
        from model import STAGE1_G, STAGE1_D
        netG = STAGE1_G()
        netG.apply(weights_init)
        print(netG)
        netD = STAGE1_D()
        netD.apply(weights_init)
        print(netD)

        if cfg.NET_G != '':
            state_dict = \
                torch.load(cfg.NET_G, map_location=lambda storage, loc: storage)
            netG.load_state_dict(state_dict["netG"])
            print('Load from: ', cfg.NET_G)
        if cfg.NET_D != '':
            state_dict = \
                torch.load(cfg.NET_D,  map_location=lambda storage, loc: storage)
            netD.load_state_dict(state_dict)
            print('Load from: ', cfg.NET_D)
        if cfg.CUDA:
            netG.cuda()
            netD.cuda()
        return netG, netD

開發者ID:tohinz，項目名稱:multiple-objects-gan，代碼行數:25，代碼來源:trainer.py

示例3: load_network_stageI

# 需要導入模塊: from miscc.config import cfg [as 別名]
# 或者: from miscc.config.cfg import CUDA [as 別名]
def load_network_stageI(self):
        from model import STAGE1_G, STAGE1_D
        netG = STAGE1_G()
        netG.apply(weights_init)
        print(netG)
        netD = STAGE1_D()
        netD.apply(weights_init)
        print(netD)

        if cfg.NET_G != '':
            state_dict = \
                torch.load(cfg.NET_G, map_location=lambda storage, loc: storage)
            netG.load_state_dict(state_dict["netG"])
            print('Load from: ', cfg.NET_G)
        if cfg.NET_D != '':
            state_dict = \
                torch.load(cfg.NET_D,  map_location=lambda storage, loc: storage)
            netD.load_state_dict(state_dict)
            print('Load from: ', cfg.NET_D)
        if cfg.CUDA:
            netG.cuda()
            netD.cuda()
        return netG, netD

    # ############# For training stageII GAN  #############

開發者ID:tohinz，項目名稱:multiple-objects-gan，代碼行數:27，代碼來源:trainer.py

示例4: prepare_labels

# 需要導入模塊: from miscc.config import cfg [as 別名]
# 或者: from miscc.config.cfg import CUDA [as 別名]
def prepare_labels(self):
        if cfg.TRAIN.OPTIMIZE_DATA_LOADING:
            batch_sizes = self.batch_size
            real_labels, fake_labels, match_labels = [], [], []
            for batch_size in batch_sizes:
                real_labels.append(Variable(torch.FloatTensor(batch_size).fill_(1)))
                fake_labels.append(Variable(torch.FloatTensor(batch_size).fill_(0)))
                match_labels.append(Variable(torch.LongTensor(range(batch_size))))
            if cfg.CUDA:
                for idx in range(len(batch_sizes)):
                    real_labels[idx] = real_labels[idx].cuda().detach()
                    fake_labels[idx] = fake_labels[idx].cuda().detach()
                    match_labels[idx] = match_labels[idx].cuda().detach()
        else:
            batch_size = self.batch_size[0]
            real_labels = Variable(torch.FloatTensor(batch_size).fill_(1))
            fake_labels = Variable(torch.FloatTensor(batch_size).fill_(0))
            match_labels = Variable(torch.LongTensor(range(batch_size)))
            if cfg.CUDA:
                real_labels = real_labels.cuda().detach()
                fake_labels = fake_labels.cuda().detach()
                match_labels = match_labels.cuda().detach()

        return real_labels, fake_labels, match_labels

開發者ID:tohinz，項目名稱:semantic-object-accuracy-for-generative-text-to-image-synthesis，代碼行數:26，代碼來源:trainer.py

示例5: prepare_data

# 需要導入模塊: from miscc.config import cfg [as 別名]
# 或者: from miscc.config.cfg import CUDA [as 別名]
def prepare_data(self, data):
        fimgs, cimgs, c_code, _, warped_bbox = data

        real_vfimgs, real_vcimgs = [], []
        if cfg.CUDA:
            vc_code = Variable(c_code).cuda()
	    for i in range(len(warped_bbox)):
		warped_bbox[i] = Variable(warped_bbox[i]).float().cuda()

        else:
            vc_code = Variable(c_code)
	    for i in range(len(warped_bbox)):
		warped_bbox[i] = Variable(warped_bbox[i])

        if cfg.CUDA:
            real_vfimgs.append(Variable(fimgs[0]).cuda())
            real_vcimgs.append(Variable(cimgs[0]).cuda())
        else:
            real_vfimgs.append(Variable(fimgs[0]))
            real_vcimgs.append(Variable(cimgs[0]))

        return fimgs, real_vfimgs, real_vcimgs, vc_code, warped_bbox

開發者ID:kkanshul，項目名稱:finegan，代碼行數:24，代碼來源:trainer.py

示例6: prepare_data

# 需要導入模塊: from miscc.config import cfg [as 別名]
# 或者: from miscc.config.cfg import CUDA [as 別名]
def prepare_data(self, data):
        imgs, w_imgs, t_embedding, _ = data

        real_vimgs, wrong_vimgs = [], []
        if cfg.CUDA:
            vembedding = Variable(t_embedding).cuda()
        else:
            vembedding = Variable(t_embedding)
        for i in range(self.num_Ds):
            if cfg.CUDA:
                real_vimgs.append(Variable(imgs[i]).cuda())
                wrong_vimgs.append(Variable(w_imgs[i]).cuda())
            else:
                real_vimgs.append(Variable(imgs[i]))
                wrong_vimgs.append(Variable(w_imgs[i]))
        return imgs, real_vimgs, wrong_vimgs, vembedding

開發者ID:netanelyo，項目名稱:Recipe2ImageGAN，代碼行數:18，代碼來源:blah.py

示例7: prepare_data

# 需要導入模塊: from miscc.config import cfg [as 別名]
# 或者: from miscc.config.cfg import CUDA [as 別名]
def prepare_data(self, data):
        imgs, w_imgs, t_embedding, rec_id, im_id, _ = data

        real_vimgs, wrong_vimgs = [], []
        if cfg.CUDA:
            vembedding = Variable(t_embedding).cuda()
        else:
            vembedding = Variable(t_embedding)
        for i in range(self.num_Ds):
            if cfg.CUDA:
                real_vimgs.append(Variable(imgs[i]).cuda())
                wrong_vimgs.append(Variable(w_imgs[i]).cuda())
            else:
                real_vimgs.append(Variable(imgs[i]))
                wrong_vimgs.append(Variable(w_imgs[i]))
        return imgs, real_vimgs, wrong_vimgs, vembedding, rec_id, im_id

開發者ID:netanelyo，項目名稱:Recipe2ImageGAN，代碼行數:18，代碼來源:trainer.py

示例8: reparametrize

# 需要導入模塊: from miscc.config import cfg [as 別名]
# 或者: from miscc.config.cfg import CUDA [as 別名]
def reparametrize(self, mu, logvar):
        std = logvar.mul(0.5).exp_()
        
        # licht - printing many things
        
        #print "cfg.CUDA - ", cfg.CUDA
        
        if cfg.CUDA:
            eps = torch.cuda.FloatTensor(std.size()).normal_()
        else:
            eps = torch.FloatTensor(std.size()).normal_()
        
        # licht - moving the normal_() cmd to here    
        #eps.normal_()
        
        #print "eps type @Before@ Variable(eps)", type(eps)
        eps = Variable(eps)
        #print "eps type @After@ Variable(eps)", type(eps)
            
        # licht - printing type for mu and std
        #print "std type = ", std.data.type()
        #print "mu type = ", mu.data.type()
        return eps.mul(std).add_(mu)

開發者ID:netanelyo，項目名稱:Recipe2ImageGAN，代碼行數:25，代碼來源:model.py

示例9: define_module

# 需要導入模塊: from miscc.config import cfg [as 別名]
# 或者: from miscc.config.cfg import CUDA [as 別名]
def define_module(self):
        if cfg.GAN.B_CONDITION:
            #device = torch.device("cuda:0" if cfg.CUDA else "cpu")
            self.ca_net = CA_NET()#.to(device)

        if cfg.TREE.BRANCH_NUM > 0:
            self.h_net1 = INIT_STAGE_G(self.gf_dim * 16)
            self.img_net1 = GET_IMAGE_G(self.gf_dim)
        if cfg.TREE.BRANCH_NUM > 1:
            self.h_net2 = NEXT_STAGE_G(self.gf_dim)
            self.img_net2 = GET_IMAGE_G(self.gf_dim // 2)
        if cfg.TREE.BRANCH_NUM > 2:
            self.h_net3 = NEXT_STAGE_G(self.gf_dim // 2)
            self.img_net3 = GET_IMAGE_G(self.gf_dim // 4)
        if cfg.TREE.BRANCH_NUM > 3: # Recommended structure (mainly limited by GPU memory), and not test yet
            self.h_net4 = NEXT_STAGE_G(self.gf_dim // 4, num_residual=1)
            self.img_net4 = GET_IMAGE_G(self.gf_dim // 8)
        if cfg.TREE.BRANCH_NUM > 4:
            self.h_net4 = NEXT_STAGE_G(self.gf_dim // 8, num_residual=1)
            self.img_net4 = GET_IMAGE_G(self.gf_dim // 16)

開發者ID:netanelyo，項目名稱:Recipe2ImageGAN，代碼行數:22，代碼來源:model.py

示例10: models

# 需要導入模塊: from miscc.config import cfg [as 別名]
# 或者: from miscc.config.cfg import CUDA [as 別名]
def models(word_len):
    #print(word_len)
    text_encoder = cache.get('text_encoder')
    if text_encoder is None:
        #print("text_encoder not cached")
        text_encoder = RNN_ENCODER(word_len, nhidden=cfg.TEXT.EMBEDDING_DIM)
        state_dict = torch.load(cfg.TRAIN.NET_E, map_location=lambda storage, loc: storage)
        text_encoder.load_state_dict(state_dict)
        if cfg.CUDA:
            text_encoder.cuda()
        text_encoder.eval()
        cache.set('text_encoder', text_encoder, timeout=60 * 60 * 24)

    netG = cache.get('netG')
    if netG is None:
        #print("netG not cached")
        netG = G_NET()
        state_dict = torch.load(cfg.TRAIN.NET_G, map_location=lambda storage, loc: storage)
        netG.load_state_dict(state_dict)
        if cfg.CUDA:
            netG.cuda()
        netG.eval()
        cache.set('netG', netG, timeout=60 * 60 * 24)

    return text_encoder, netG

開發者ID:taoxugit，項目名稱:AttnGAN，代碼行數:27，代碼來源:eval.py

示例11: load_network_stageI

# 需要導入模塊: from miscc.config import cfg [as 別名]
# 或者: from miscc.config.cfg import CUDA [as 別名]
def load_network_stageI(self):
        from model import STAGE1_G, STAGE1_D
        netG = STAGE1_G()
        netG.apply(weights_init)
        print(netG)
        netD = STAGE1_D()
        netD.apply(weights_init)
        print(netD)

        if cfg.NET_G != '':
            state_dict = \
                torch.load(cfg.NET_G,
                           map_location=lambda storage, loc: storage)
            netG.load_state_dict(state_dict)
            print('Load from: ', cfg.NET_G)
        if cfg.NET_D != '':
            state_dict = \
                torch.load(cfg.NET_D,
                           map_location=lambda storage, loc: storage)
            netD.load_state_dict(state_dict)
            print('Load from: ', cfg.NET_D)
        if cfg.CUDA:
            netG.cuda()
            netD.cuda()
        return netG, netD

    # ############# For training stageII GAN  #############

開發者ID:hanzhanggit，項目名稱:StackGAN-Pytorch，代碼行數:29，代碼來源:trainer.py

示例12: load_network_stageII

# 需要導入模塊: from miscc.config import cfg [as 別名]
# 或者: from miscc.config.cfg import CUDA [as 別名]
def load_network_stageII(self):
        from model import STAGE1_G, STAGE2_G, STAGE2_D

        Stage1_G = STAGE1_G()
        netG = STAGE2_G(Stage1_G)
        netG.apply(weights_init)
        print(netG)
        if cfg.NET_G != '':
            state_dict = \
                torch.load(cfg.NET_G,
                           map_location=lambda storage, loc: storage)
            netG.load_state_dict(state_dict)
            print('Load from: ', cfg.NET_G)
        elif cfg.STAGE1_G != '':
            state_dict = \
                torch.load(cfg.STAGE1_G,
                           map_location=lambda storage, loc: storage)
            netG.STAGE1_G.load_state_dict(state_dict)
            print('Load from: ', cfg.STAGE1_G)
        else:
            print("Please give the Stage1_G path")
            return

        netD = STAGE2_D()
        netD.apply(weights_init)
        if cfg.NET_D != '':
            state_dict = \
                torch.load(cfg.NET_D,
                           map_location=lambda storage, loc: storage)
            netD.load_state_dict(state_dict)
            print('Load from: ', cfg.NET_D)
        print(netD)

        if cfg.CUDA:
            netG.cuda()
            netD.cuda()
        return netG, netD

開發者ID:hanzhanggit，項目名稱:StackGAN-Pytorch，代碼行數:39，代碼來源:trainer.py

示例13: reparametrize

# 需要導入模塊: from miscc.config import cfg [as 別名]
# 或者: from miscc.config.cfg import CUDA [as 別名]
def reparametrize(self, mu, logvar):
        std = logvar.mul(0.5).exp_()
        if cfg.CUDA:
            eps = torch.cuda.FloatTensor(std.size()).normal_()
        else:
            eps = torch.FloatTensor(std.size()).normal_()
        eps = Variable(eps)
        return eps.mul(std).add_(mu)

開發者ID:hanzhanggit，項目名稱:StackGAN-Pytorch，代碼行數:10，代碼來源:model.py

示例14: prepare_labels

# 需要導入模塊: from miscc.config import cfg [as 別名]
# 或者: from miscc.config.cfg import CUDA [as 別名]
def prepare_labels(self):
        batch_size = self.batch_size
        real_labels = Variable(torch.FloatTensor(batch_size).fill_(1))
        fake_labels = Variable(torch.FloatTensor(batch_size).fill_(0))
        match_labels = Variable(torch.LongTensor(range(batch_size)))
        if cfg.CUDA:
            real_labels = real_labels.cuda()
            fake_labels = fake_labels.cuda()
            match_labels = match_labels.cuda()

        return real_labels, fake_labels, match_labels

開發者ID:MinfengZhu，項目名稱:DM-GAN，代碼行數:13，代碼來源:trainer.py

示例15: sent_loss

# 需要導入模塊: from miscc.config import cfg [as 別名]
# 或者: from miscc.config.cfg import CUDA [as 別名]
def sent_loss(cnn_code, rnn_code, labels, class_ids,
              batch_size, eps=1e-8):
    # ### Mask mis-match samples  ###
    # that come from the same class as the real sample ###
    masks = []
    if class_ids is not None:
        for i in range(batch_size):
            mask = (class_ids == class_ids[i]).astype(np.uint8)
            mask[i] = 0
            masks.append(mask.reshape((1, -1)))
        masks = np.concatenate(masks, 0)
        # masks: batch_size x batch_size
        masks = torch.ByteTensor(masks)
        if cfg.CUDA:
            masks = masks.cuda()

    # --> seq_len x batch_size x nef
    if cnn_code.dim() == 2:
        cnn_code = cnn_code.unsqueeze(0)
        rnn_code = rnn_code.unsqueeze(0)

    # cnn_code_norm / rnn_code_norm: seq_len x batch_size x 1
    cnn_code_norm = torch.norm(cnn_code, 2, dim=2, keepdim=True)
    rnn_code_norm = torch.norm(rnn_code, 2, dim=2, keepdim=True)
    # scores* / norm*: seq_len x batch_size x batch_size
    scores0 = torch.bmm(cnn_code, rnn_code.transpose(1, 2))
    norm0 = torch.bmm(cnn_code_norm, rnn_code_norm.transpose(1, 2))
    scores0 = scores0 / norm0.clamp(min=eps) * cfg.TRAIN.SMOOTH.GAMMA3

    # --> batch_size x batch_size
    scores0 = scores0.squeeze()
    if class_ids is not None:
        scores0.data.masked_fill_(masks, -float('inf'))
    scores1 = scores0.transpose(0, 1)
    if labels is not None:
        loss0 = nn.CrossEntropyLoss()(scores0, labels)
        loss1 = nn.CrossEntropyLoss()(scores1, labels)
    else:
        loss0, loss1 = None, None
    return loss0, loss1

開發者ID:MinfengZhu，項目名稱:DM-GAN，代碼行數:42，代碼來源:losses.py

注：本文中的miscc.config.cfg.CUDA屬性示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。