from timeit import default_timer as timer NUM_EPOCHS = 18 for epoch in range(1, NUM_EPOCHS+1): start_time = timer() train_loss = train_epoch(transformer, optimizer) end_time = timer() val_loss = evaluate(transformer) print((f"Epoch: {epoch}, Train loss: {train_loss:.3f}, Val l...
model.to(DEVICE) model_input = model_input.to(DEVICE) print("slow path:") print("===") with torch.autograd.profiler.profile(use_cuda=True) as prof: for i in range(ITERATIONS): output = model(model_input) print(prof) model.eval() print("fast path:") print("===") with torch.au...
For the rest, strategy (a) is probably the better default, because of: Unless you want us to put upper bounds on all of our deps: this would prevent us from picking up prereleases, but it causes other problems down the line when the old upper bounds are overly aggressive. ...
All models have a common default configuration interface and API for accessing/changing the classifier - get_classifier and reset_classifier doing a forward pass on just the features - forward_features these makes it easy to write consistent network wrappers that work with any of the models All...
本教程适用于 PyTorch 版本 1.12 及更高版本。如果您使用的是早期版本,请将所有的 size_based_auto_wrap_policy 实例替换为 default_auto_wrap_policy。 # Based on: https://github.com/pytorch/examples/blob/master/mnist/main.pyimportosimportargparseimportfunctoolsimporttorchimporttorch.nnasnnimporttorch.nn...
train_iter = AG_NEWS(split="train")defyield_tokens(data_iter):for_, textindata_iter:yieldtokenizer(text) vocab = build_vocab_from_iterator(yield_tokens(train_iter), specials=["<unk>"]) vocab.set_default_index(vocab["<unk>"])
target_label = torch.cat([target_text_encoded,SEP_ID,decoder_padding]).to(device) # As we've added extra padding token with input encoding, during training, we don't want this token to be trained by model as there is nothing to learn in this token. # So, we'll use encoder mask ...
print("SEND_FILE_MAX_AGE_DEFAULT:", app.config['SEND_FILE_MAX_AGE_DEFAULT']) # 预先初始化模型 model_inited, opt = init_model() # 处理文件名的有效性 def allow_filename(filename): return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS ...
I've tried to keep the dependencies minimal, the setup is as per the PyTorch default install instructions for Conda: conda create -n torch-env conda activate torch-env conda install -c pytorch pytorch torchvision cudatoolkit=10 PyTorch Hub Models can be accessed via the PyTorch Hub API >>...
final - use either 'sigmoid' to squash to probs, or 'clamp' or pass in your own torch function. Should not use clamp with default logit loss opt - ?optimizer? should add an option for this device - torch device to construct the tensors ...