model = ContextEncoder() print(model) pretrained_dict = torch.load('E:/fin/models/gen.pth') print(pretrained_dict.keys())上述语句输出为:ContextEncoder( (encoder): Sequential( (0): Conv2d(4, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (1): LeakyReLU(negative_slope=0.2, inplace=True) (2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (4): LeakyReLU(negative_slope=0.2, inplace=True) (5): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (6): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (7): LeakyReLU(negative_slope=0.2, inplace=True) (8): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (9): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (10): LeakyReLU(negative_slope=0.2, inplace=True) (11): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (12): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (13): LeakyReLU(negative_slope=0.2, inplace=True) (14): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (15): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (16): LeakyReLU(negative_slope=0.2, inplace=True) (17): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (18): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (19): LeakyReLU(negative_slope=0.2, inplace=True) (20): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (21): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (22): LeakyReLU(negative_slope=0.2, inplace=True) (23): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (24): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (25): LeakyReLU(negative_slope=0.2, inplace=True) ) (decoder): Sequential( (0): ConvTranspose2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) (3): ConvTranspose2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ReLU(inplace=True) (6): ConvTranspose2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (7): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (8): ReLU(inplace=True) (9): ConvTranspose2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (10): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (11): ReLU(inplace=True) (12): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (13): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (14): ReLU(inplace=True) (15): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (16): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (17): ReLU(inplace=True) (18): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (19): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (20): ReLU(inplace=True) (21): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (22): Sigmoid() ) ) dict_keys(['iteration', 'generator'])
时间: 2023-06-16 16:03:48 浏览: 58
这段代码定义了一个名为ContextEncoder的模型,包含一个encoder和一个decoder。其中encoder是一个由多个卷积层和批归一化层组成的序列,用于将输入图像编码成一个特征向量。decoder则是由多个反卷积层和批归一化层组成的序列,用于将特征向量解码成一个输出图像。
通过torch.load()函数读取了预训练模型的参数,并将其保存在pretrained_dict中。可以看到pretrained_dict中包含两个键:'iteration'和'generator'。其中'iteration'键对应的值表示预训练模型的训练迭代次数,'generator'键对应的值则是一个包含了encoder和decoder参数的字典。
相关问题
model.load_state_dict(torch.load('./%s/net_params_%d.pkl' %
model.load_state_dict(torch.load('./%s/net_params_%d.pkl' % (filename, epoch)))
这行代码的作用是从指定路径加载模型的参数。其中,'./%s/net_params_%d.pkl'是一个格式化字符串,用于指定加载参数文件的路径和文件名。字符串中的%s和%d是占位符,分别对应于filename和epoch变量的值。
接下来,torch.load()函数用于加载保存的模型参数。参数文件的路径和文件名由格式化字符串的值确定。加载的参数会被存储在一个字典中。
最后,model.load_state_dict()函数将加载的参数字典加载到模型中。这样,模型的参数就被更新为之前保存的参数值。
pretrained_dict = torch.load(model_path)['state_dict']
这行代码是加载预训练模型的权重参数。`torch.load(model_path)`会加载模型参数文件,返回一个字典类型的对象,其中包含了模型的各个参数。`['state_dict']`是获取字典中的参数字典,因为模型参数保存在`state_dict`中。最后,将参数字典赋值给`pretrained_dict`变量。