以pytorch版CRNN为例,输出shape如下 {"Conv2d-1":{"input_shape":[1,1,32,128],"output_shape":[1,64,32,128],"trainable":true,"nb_params":576},"ReLU-2":{"input_shape":[1,64,32,128],"output_shape":[1,64,32,128],"nb_params":0},"MaxPool2d-3":{"input_shape":[1,64,32,...
summary_dict[i] = get_output_size(summary_dict[i],output[i])else:summary_dict['output_shape'] = list(output.size())return summary_dict def summary(input_size, model):def register_hook(module):def hook(module, input, output):class_name = str(module.__class__).split('.')[-1]....
inputs, labels = data optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() # 验证模型 with torch.no_grad(): # 计算准确率等指标 模型保存与加载(Model Saving and Loading) 在训练完成后,将模型保存到文件中以便后续使用。 import torch...
shape x_train, x_train.shape, y_train.min(), y_train.max() 5.2 定义模型 定义model的计算: 输入数据 * 权重 + 偏移量,初始化参数 import torch.nn.functional as F loss_func = F.cross_entropy def model(xb): return xb.mm(weights) + bias bs = 64 #batch = 64 xb = x_train[0:bs]...
print(modelviz) summary(modelviz, input_size=(8, 1, 8, 8), col_names=["kernel_size", "output_size", "num_params", "mult_adds"]) for p in modelviz.parameters(): if p.requires_grad: print(p.shape) 1. 2. 3. 4. 5.
(weight_file,1, n_classes, IN_IMAGE_H, IN_IMAGE_W)# 注释掉图片推理部分# session = onnxruntime.InferenceSession(onnx_path_demo)# session = onnx.load(onnx_path)# print("The model expects input shape: ", session.get_inputs()[0].shape)# image_src = cv2.imread(image_path)# ...
# 打印模型的参数defprint_model_parameters(model):forname,paraminmodel.named_parameters():ifparam.requires_grad:# 只打印需要梯度更新的参数print(f'Parameter:{name}| Shape:{param.shape}| Values:{param.data}')# 调用打印函数print_model_parameters(model) ...
model.eval()# Set model to evaluate mode,设置为测试模式 running_loss=0.0 running_corrects=0 # Iterate over data. forinputs, labelsindataloaders[phase]: inputs=inputs.to(device) labels=labels.to(device) # zero the parameter gradients ...
(): for inputs, labels in test_loader: inputs, labels = inputs.to(device), labels.to(device) outputs = model(inputs) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() accuracy = 100 * correct / total print(f"...
eval() # 进行转换 from x2paddle.convert import pytorch2paddle pytorch2paddle(torch_module, save_dir="pd_model_trace", jit_type="trace", input_examples=[torch.tensor(input_data)]) PyTorch-PaddlePaddle API映射表 API映射表梳理了PyTorch(v1.8.1)常用API与PaddlePaddle 2.0.0 API对应关系与差异...