Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc. - pretrained-models.pytorch/examples/imagenet_eval.py at master · Cadene/pretrained-models.pytorch
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def test_single_stage_forward_cpu(cfg_file): model, train_cfg, test_cfg = _get_detector_cfg(cfg_file) model['pretrained'] = None from mmdet.models import build_detector detector = build_detector(model, train_cfg=train_cfg, test_cfg=test_cfg) input_shape = (1, 3, 300, 300) mm_in...
mx.rnn.inference,mx.lstm.inference,mx.gru.inferenceis used to create a custom RNN / LSTM / GRU inference model with parameterseq.len=1. We show the codes ofmx.lstm.inferenceand the other two are similar to it. The parameterarg.paramsstores the parameters of the trained unrolled rnn model...
parser = argparse.ArgumentParser(description="Simple example of a training script.") parser.add_argument( "--pretrained_model_name_or_path", type=str, default=None, required=True, help="Path to pretrained model or model identifier from huggingface.co/models.", ...
We generated these adversarial examples on theMozilla implementation of DeepSpeech. (To have it recognize these audio files yourself, you will need to install DeepSpeech by following theREADME, and then download thepretrained model. After extracting the tgz, the output_graph.pb file should have an...
model_1 = models.inception_v3(weights=models.Inception_V3_Weights.DEFAULT).to(device) # model_1 = models.googlenet(pretrained=True).to(device) print("True Image & Predicted Label") model_1.eval() correct = 0 total = 0 for images, labels in normal_loader: ...
这一小节的本质上就是我们常用的pretrained + fine-tuning的模式,最常见的就是直接在pretrianed的模型上直接fine-tuning参数,其他的还有可以在一个新的网络上使用pretrained的部分参数来初始化等等。 5.2Refine Meta-learnedθθ 该小节是基于meta learning的解决方法,利用元学习器学习一个好的初始化参数,之后在新的任...
model_id = model_id self.project_dir = project_dir self.create_model_dirs() resnet34 = models.resnet34() # load pretrained model: resnet34.load_state_dict(torch.load("/root/3DOD_thesis/pretrained_models/resnet/resnet34-333f7ec4.pth")) # remove fully connected layer: self.resnet34...
看了下问题,目前save_pretrained接口还不支持模型的attribute为非ErnieModel的网络。 所以ErnieCrfForTokenClassification不支持使用save_pretrained进行model_config.json的保存。 建议直接保存只需要保存模型参数,使用paddle.save(model.state_dict(), "/path/to/model") ...