【情人节特辑】：虚拟女友教你如何正确“回答”

huashu_dict={'多喝热水':'a',            '你怎么又生气了':'b',            '你又怎么了':'c',            '你要这样想我也没办法':'d',            '随便你！你定吧':'e',            '哦':'f'}#请输入上面指定语句（粗糙版，请大家多多包涵）a = input('请输入直男语句:'+'\n')if a in huashu_dict:    print('已生成合适的话术'+'\n'+'请记住生成编号'+':'+huashu_dict.get(a))else:    print('不好意思，这句话我还没学会呢。')

import paddleimport paddle.nn as nnfrom paddle.io import Dataset, DataLoaderimport osimport cv2import numpy as npfrom tqdm import tqdmimport matplotlib.pyplot as pltimport PIL.Image as Image
%matplotlib inlineclass UnetGenerator(nn.Layer):
    def __init__(self, input_nc=3, output_nc=3, ngf=64):
        super(UnetGenerator, self).__init__()

        self.down1 = nn.Conv2D(input_nc, ngf, kernel_size=4, stride=2, padding=1)
        self.down2 = Downsample(ngf, ngf*2)
        self.down3 = Downsample(ngf*2, ngf*4)
        self.down4 = Downsample(ngf*4, ngf*8)
        self.down5 = Downsample(ngf*8, ngf*8)
        self.down6 = Downsample(ngf*8, ngf*8)
        self.down7 = Downsample(ngf*8, ngf*8)

        self.center = Downsample(ngf*8, ngf*8)

        self.up7 = Upsample(ngf*8, ngf*8, use_dropout=True)
        self.up6 = Upsample(ngf*8*2, ngf*8, use_dropout=True)
        self.up5 = Upsample(ngf*8*2, ngf*8, use_dropout=True)
        self.up4 = Upsample(ngf*8*2, ngf*8)
        self.up3 = Upsample(ngf*8*2, ngf*4)
        self.up2 = Upsample(ngf*4*2, ngf*2)
        self.up1 = Upsample(ngf*2*2, ngf)

        self.output_block = nn.Sequential(
            nn.ReLU(),
            nn.Conv2DTranspose(ngf*2, output_nc, kernel_size=4, stride=2, padding=1),
            nn.Tanh()
        )    def forward(self, x):
        d1 = self.down1(x)
        d2 = self.down2(d1)
        d3 = self.down3(d2)
        d4 = self.down4(d3)
        d5 = self.down5(d4)
        d6 = self.down6(d5)
        d7 = self.down7(d6)
        
        c = self.center(d7)
        
        x = self.up7(c, d7)
        x = self.up6(x, d6)
        x = self.up5(x, d5)
        x = self.up4(x, d4)
        x = self.up3(x, d3)
        x = self.up2(x, d2)
        x = self.up1(x, d1)

        x = self.output_block(x)        return xclass Downsample(nn.Layer):
    # LeakyReLU => conv => batch norm
    def __init__(self, in_dim, out_dim, kernel_size=4, stride=2, padding=1):
        super(Downsample, self).__init__()

        self.layers = nn.Sequential(
            nn.LeakyReLU(0.2),
            nn.Conv2D(in_dim, out_dim, kernel_size, stride, padding, bias_attr=False),
            nn.BatchNorm2D(out_dim)
        )    def forward(self, x):
        x = self.layers(x)        return xclass Upsample(nn.Layer):
    # ReLU => deconv => batch norm => dropout
    def __init__(self, in_dim, out_dim, kernel_size=4, stride=2, padding=1, use_dropout=False):
        super(Upsample, self).__init__()

        sequence = [
            nn.ReLU(),
            nn.Conv2DTranspose(in_dim, out_dim, kernel_size, stride, padding, bias_attr=False),
            nn.BatchNorm2D(out_dim)
        ]        if use_dropout:
            sequence.append(nn.Dropout(p=0.5))

        self.layers = nn.Sequential(*sequence)    def forward(self, x, skip):
        x = self.layers(x)
        x = paddle.concat([x, skip], axis=1)        return x#实例化生成器generator = UnetGenerator()#加载权重last_weights_path = 'data/data148534/epoch100.pdparams'print('加载权重:', last_weights_path)

model_state_dict = paddle.load(last_weights_path)
generator.load_dict(model_state_dict)
generator.eval()#读取数据image_name='01503.png'img_A2B = cv2.imread('work/'+image_name)
img_A = img_A2B[:, 256:]                                  # 卡通图（即输入）img_B = img_A2B[:, :256]                                  # 真人图（即预测结果）g_input = img_A.astype('float32') / 127.5 - 1             # 归一化g_input = g_input[np.newaxis, ...].transpose(0, 3, 1, 2)  # NHWC -> NCHWg_input = paddle.to_tensor(g_input)                       # numpy -> tensorg_output = generator(g_input)
g_output = g_output.detach().numpy()                      # tensor -> numpyg_output = g_output.transpose(0, 2, 3, 1)[0]              # NCHW -> NHWCg_output = g_output * 127.5 + 127.5                       # 反归一化g_output = g_output.astype(np.uint8)#只保存生成真人图像img = np.asarray(g_output).copy()
img = Image.fromarray(cv2.cvtColor(img,cv2.COLOR_BGR2RGB))# cv2 to Imageimg.save('work/'+'output_'+image_name)

img_show = np.hstack([img_A, g_output])[:,:,::-1]
plt.figure(figsize=(8, 8))
plt.imshow(img_show)
plt.show()

!tar xzvf bobo.tar.gz PaddleBoBo data nltk_data work

#这一步执行时间会比较久!pip install ppgan paddlespeech

%cd PaddleBoBo
!python create_virtual_human.py --config default.yaml

!python general_demo.py --human ./file/input/test.mp4 --output ../output.mp4 --text a