3 2 pnnx.Input input 0 1 input F.max_pool2d op_0 1 1 input out kernel_size=%kernel_size stride=%stride padding=%padding ceil_mode=%ceil_mode return_indices=False pnnx.Output output 1 0 out )PNNXIR"; } }; REGISTER_GLOBAL_PNNX_NCNN_GRAPH_REWRITER_PASS(F_max_pool2d_1,20) ...
3.1. 一维卷积层:torch.nn.Conv1d() 3.2. 二维卷积层:torch.nn.Conv2d() 3.3. 三维卷积层:torch.nn.Conv3d() 四、池化层 4.1. 1维最大池化:torch.nn.MaxPool1d() 4.2. 2维最大池化:torch.nn.MaxPool2d() 4.3. 3维最大池化:torch.nn.MaxPool3d() 五、非线性激活层 5.1. torch.nn.ReLU() 5....
(x): # E: Function is missing a type annotation [no-untyped-def] loss = torch.nn.functional.max_pool2d(x, kernel_size=3, stride=2, padding=1).sum() return torch.autograd.grad(loss, x) y = x.clone() result, = compute_grad(y) compile32, = torch.compile(compute_grad)(x) ...
x = torch.zeros(5, 3, dtype=torch.long) print(x) 1. 2. 3. #构造一个张量,直接使用数据: x = torch.tensor([5.5, 3]) print(x) 1. 2. 3. #创建一个 tensor 基于已经存在的 tensor。 x = x.new_ones(5, 3, dtype=torch.double) print(x) x = torch.randn_like(x, dtype=torch.f...
maxpool2(x) o3 = self.maxpool3(x) return torch.cat([x, o1, o2, o3], dim=1) class SPPF(nn.Module): def __init__(self): super().__init__() self.maxpool = nn.MaxPool2d(5, 1, padding=2) def forward(self, x): o1 = self.maxpool(x) o2 = self.maxpool(o1) o3 = ...
conv2d_relu1 = tf.nn.relu(h_conv1 + b_conv1) pooling1 = max_pool_2x2(conv2d_relu1) #第二层卷积,提取64种特征 W_conv2 = tf.Variable(tf.random_uniform([5, 5, 32, 64])) b_conv2 = tf.constant(0.1, shape=[64]) h_conv2 = conv2d(pooling1, W_conv2) ...
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1.1.0 1.1.1 >>> import mindspore.ops as ops >>> >>> avg_pool = ops.AvgPool(ksize=2, padding='same') >>> max_pool = ops.MaxPool(ksize=2, padding='same') >>> max_pool_with_argmax = ops.MaxPoolWithArgmax(ksize=2, padding='same') >>> import mindspore.ops as ops >>...
maxpool = nn.MaxPool2d(kernel_size=2, stride=2) #对输入张量进行池化操作 output_tensor = maxpool(input_tensor) ``` 在上面的代码中,首先定义了一个3通道、高32、宽32的输入张量`input_tensor`,然后使用`nn.MaxPool2d`函数定义了一个2x2的池化核和2x2的步长。最后,使用`maxpool`对`input_tensor`...
pooled_grads_value, conv_layer_output_value=iterate([x]) foriinrange(512): conv_layer_output_value[:, :, i]*=pooled_grads_value[i] heatmap=np.mean(conv_layer_output_value, axis=-1) heatmap=np.maximum(heatmap,0) heatmap/=np.max(heatm...