import paddle from ppim import rednet_26 # Load the model with PPIM wheel package model, val_transforms = rednet_26(pretrained=True, return_transforms=True) # Load the model with paddle.hub API # paddlepaddle >= 2.1.0 ''' model, val_transforms = paddle.hub.load(  'AgentMaker/Paddle-Image-Models:dev',   'rednet_26',   source='github',   force_reload=False,   pretrained=True,   return_transforms=True ) ''' # Model summary  paddle.summary(model, input_size=(1, 3, 224, 224)) # Random a input x = paddle.randn(shape=(1, 3, 224, 224)) # Model forword out = model(x)

import paddle import paddle.nn as nn import paddle.vision.transforms as T from paddle.vision import Cifar100 from ppim import rexnet_1_0 # Load the model model, val_transforms = rexnet_1_0(pretrained=True, return_transforms=True, class_dim=100) # Use the PaddleHapi Model model = paddle.Model(model) # Set the optimizer opt = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters()) # Set the loss function loss = nn.CrossEntropyLoss() # Set the evaluate metric metric = paddle.metric.Accuracy(topk=(1, 5)) # Prepare the model  model.prepare(optimizer=opt, loss=loss, metrics=metric) # Set the data preprocess train_transforms = T.Compose([ T.Resize(256, interpolation='bicubic'), T.RandomCrop(224), T.ToTensor(), T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) # Load the Cifar100 dataset train_dataset = Cifar100(mode='train', transform=train_transforms, backend='pil') val_dataset = Cifar100(mode='test', transform=val_transforms, backend='pil') # Finetune the model  model.fit( train_data=train_dataset, eval_data=val_dataset, batch_size=256, epochs=2, eval_freq=1, log_freq=1, save_dir='save_models', save_freq=1, verbose=1, drop_last=False, shuffle=True, num_workers=0 )