optimizer.step()if(iter+1) % 10 ==0:print('iter [{}/{}], Loss: {:.4f}'.format(iter+1, 300, loss.item()))#writer.add_graph(model, input_to_model=train_text,verbose=False)writer.add_scalar('loss',loss.item(),global_step=iter+1) writer.flush() writer.close() model_path= ...
model.load_stat_dict(torch.load(model_path, map_location=torch.device('cpu'))); mocel.eval() with torch.no_grad(): for batch in test_loader: test_images. test_gts = test_batch[0].cuda(), test_batch[1].cuda() test_preds = model(test_iamges) # 保存模型输出的图片 1. 2. 3. ...
def evaluate(): import util import random import matplotlib.pyplot as plt net.load_state_dict(torch.load(model_path)) index = random.randint(0, len(val_data)-1) val_image, val_label = val_data[index] out = net(val_image.unsqueeze(0).to(device)) pred = out.argmax(dim=1).squeeze...
path.isdir('./models'): os.mkdir('./models') # Create directory of saving models. n_epochs, best_loss, step, early_stop_count = config['n_epochs'], math.inf, 0, 0 for epoch in range(n_epochs): print(epoch) model.train() # Set your model to train mode. loss_record = [] ...
(decoder) autoencoder = Model(inputs=input_layer, outputs=decoder) autoencoder.compile(optimizer='adam', loss='mean_squared_error', metrics=['mae']) # 模型保存为SofaSofa_model.h5,并开始训练模型 checkpointer = ModelCheckpoint(filepath="SofaSofa_model.h5", verbose=0, save_best_only=True) ...
CUDA_VISIBLE_DEVICES=0 python -m vcoder_llava.serve.cli \ --model-path shi-labs/vcoder_ds_llava-v1.5-13b \ --image-file "vcoder_llava/serve/examples/suits.jpg" \ --seg-image-file "vcoder_llava/serve/examples/suits_pan.png" \ # optional [reqd with depth input] --depth-image...
usage: train_resnet.py [-h] [--model_url MODEL_URL] [--model_res MODEL_RES] [--data_dir DATA_DIR] [--model_path MODEL_PATH] [--model_depth MODEL_DEPTH] [--model_size MODEL_SIZE] [--activation ACTIVATION] [--use_fp16 USE_FP16] [--image_size IMAGE_SIZE] [--batch_size ...
model com.azure.resourcemanager.resources.fluentcore.arm.models com.azure.resourcemanager.resources.fluent.models com.azure.resourcemanager.resources.models com.azure.resourcemanager.resources.fluentcore.policy com.azure.resourcemanager.resources com.azure.resourcemanager.resources.fluentcore.rest com.azure....
model.h status.h tensor.h types.h neural_network_runtime_type.h neural_network_runtime.h native_avcodec_audiodecoder.h native_avcodec_audioencoder.h native_avcodec_base.h native_avcodec_videodecoder.h native_avcodec_videoencoder.h native_averrors.h native_avformat.h native_...
"""model=model.eval()print(model.config)# BertConfig {# "_name_or_path": "../dataset/bert-base-uncased",# "architectures": [# "BertForMaskedLM"# ],# "attention_probs_dropout_prob": 0.1,# "classifier_dropout": null,# "gradient_checkpointing": false,# "hidden_act": "gelu",# ...