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| import torch | |
| import torch.nn as nn | |
| from . import hybrid | |
| from . import vit | |
| from . import transformer | |
| class Model(nn.Module): | |
| def __init__(self, encoder, decoder, args): | |
| super().__init__() | |
| self.encoder = encoder | |
| self.decoder = decoder | |
| self.args = args | |
| def data_parallel(self, x: torch.Tensor, device_ids, output_device=None, **kwargs): | |
| if not device_ids or len(device_ids) == 1: | |
| return self(x, **kwargs) | |
| if output_device is None: | |
| output_device = device_ids[0] | |
| replicas = nn.parallel.replicate(self, device_ids) | |
| inputs = nn.parallel.scatter(x, device_ids) # Slices tensors into approximately equal chunks and distributes them across given GPUs. | |
| kwargs = nn.parallel.scatter(kwargs, device_ids) # Duplicates references to objects that are not tensors. | |
| replicas = replicas[:len(inputs)] | |
| kwargs = kwargs[:len(inputs)] | |
| outputs = nn.parallel.parallel_apply(replicas, inputs, kwargs) | |
| return nn.parallel.gather(outputs, output_device).mean() | |
| def forward(self, x: torch.Tensor, tgt_seq: torch.Tensor, **kwargs): | |
| encoded = self.encoder(x) | |
| out = self.decoder(tgt_seq, context=encoded, **kwargs) | |
| return out | |
| def generate(self, x: torch.Tensor, temperature: float = 0.25): | |
| return self.decoder.generate((torch.LongTensor([self.args.bos_token]*len(x))[:, None]).to(x.device), self.args.max_seq_len, | |
| eos_token=self.args.eos_token, context=self.encoder(x), temperature=temperature) | |
| def get_model(args): | |
| if args.encoder_structure.lower() == 'vit': | |
| encoder = vit.get_encoder(args) | |
| elif args.encoder_structure.lower() == 'hybrid': | |
| encoder = hybrid.get_encoder(args) | |
| else: | |
| raise NotImplementedError('Encoder structure "%s" not supported.' % args.encoder_structure) | |
| decoder = transformer.get_decoder(args) | |
| encoder.to(args.device) | |
| decoder.to(args.device) | |
| model = Model(encoder, decoder, args) | |
| if args.wandb: | |
| import wandb | |
| wandb.watch(model) | |
| return model | |