Update LFNA
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@ -1,7 +1,7 @@
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
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#####################################################
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# python exps/LFNA/lfna-tall-hpnet.py --env_version v1 --hidden_dim 16 --epochs 100000 --meta_batch 16
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# python exps/LFNA/lfna-tall-hpnet.py --env_version v1 --hidden_dim 16 --epochs 100000 --meta_batch 64
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#####################################################
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import sys, time, copy, torch, random, argparse
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from tqdm import tqdm
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@ -33,7 +33,7 @@ from lfna_models import HyperNet
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def main(args):
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logger, env_info, model_kwargs = lfna_setup(args)
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dynamic_env = env_info["dynamic_env"]
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model = get_model(dict(model_type="simple_mlp"), **model_kwargs)
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model = get_model(**model_kwargs)
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criterion = torch.nn.MSELoss()
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logger.log("There are {:} weights.".format(model.get_w_container().numel()))
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@ -72,7 +72,7 @@ def main(args):
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)
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limit_bar = float(iepoch + 1) / args.epochs * total_bar
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limit_bar = min(max(0, int(limit_bar)), total_bar)
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limit_bar = min(max(32, int(limit_bar)), total_bar)
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losses = []
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for ibatch in range(args.meta_batch):
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cur_time = random.randint(0, limit_bar)
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@ -1,7 +1,7 @@
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
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#####################################################
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# python exps/LFNA/lfna-test-hpnet.py --env_version v1 --hidden_dim 16
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# python exps/LFNA/lfna-test-hpnet.py --env_version v1 --hidden_dim 16 --layer_dim 32 --epochs 50000
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#####################################################
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import sys, time, copy, torch, random, argparse
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from tqdm import tqdm
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@ -33,17 +33,17 @@ from lfna_models import HyperNet
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def main(args):
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logger, env_info, model_kwargs = lfna_setup(args)
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dynamic_env = env_info["dynamic_env"]
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model = get_model(dict(model_type="simple_mlp"), **model_kwargs)
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model = get_model(**model_kwargs)
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model = model.to(args.device)
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criterion = torch.nn.MSELoss()
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logger.log("There are {:} weights.".format(model.get_w_container().numel()))
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shape_container = model.get_w_container().to_shape_container()
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hypernet = HyperNet(shape_container, args.hidden_dim, args.task_dim)
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hypernet = HyperNet(shape_container, args.layer_dim, args.task_dim)
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hypernet = hypernet.to(args.device)
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# task_embed = torch.nn.Parameter(torch.Tensor(env_info["total"], args.task_dim))
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total_bar = 10
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total_bar = 16
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task_embeds = []
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for i in range(total_bar):
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tensor = torch.Tensor(1, args.task_dim).to(args.device)
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@ -51,8 +51,12 @@ def main(args):
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for task_embed in task_embeds:
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trunc_normal_(task_embed, std=0.02)
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model.train()
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hypernet.train()
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parameters = list(hypernet.parameters()) + task_embeds
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optimizer = torch.optim.Adam(parameters, lr=args.init_lr, amsgrad=True)
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# optimizer = torch.optim.Adam(parameters, lr=args.init_lr, amsgrad=True)
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optimizer = torch.optim.Adam(parameters, lr=args.init_lr, weight_decay=1e-5)
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lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
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optimizer,
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milestones=[
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@ -98,7 +102,7 @@ def main(args):
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lr_scheduler.step()
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loss_meter.update(final_loss.item())
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if iepoch % 200 == 0:
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if iepoch % 100 == 0:
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logger.log(
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head_str
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+ " meta-loss: {:.4f} ({:.4f}) :: lr={:.5f}, batch={:}".format(
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@ -126,6 +130,26 @@ def main(args):
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print(model)
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print(hypernet)
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w_container_per_epoch = dict()
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for idx in range(0, total_bar):
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future_time = env_info["{:}-timestamp".format(idx)]
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future_x = env_info["{:}-x".format(idx)]
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future_y = env_info["{:}-y".format(idx)]
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future_container = hypernet(task_embeds[idx])
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w_container_per_epoch[idx] = future_container.no_grad_clone()
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with torch.no_grad():
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future_y_hat = model.forward_with_container(
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future_x, w_container_per_epoch[idx]
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)
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future_loss = criterion(future_y_hat, future_y)
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logger.log("meta-test: [{:03d}] -> loss={:.4f}".format(idx, future_loss.item()))
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save_checkpoint(
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{"w_container_per_epoch": w_container_per_epoch},
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logger.path(None) / "final-ckp.pth",
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logger,
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)
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logger.log("-" * 200 + "\n")
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logger.close()
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@ -150,6 +174,12 @@ if __name__ == "__main__":
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required=True,
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help="The hidden dimension.",
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)
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parser.add_argument(
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"--layer_dim",
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type=int,
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required=True,
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help="The hidden dimension.",
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)
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#####
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parser.add_argument(
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"--init_lr",
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@ -181,7 +211,7 @@ if __name__ == "__main__":
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if args.rand_seed is None or args.rand_seed < 0:
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args.rand_seed = random.randint(1, 100000)
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assert args.save_dir is not None, "The save dir argument can not be None"
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args.task_dim = args.hidden_dim
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args.task_dim = args.layer_dim
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args.save_dir = "{:}-{:}-d{:}".format(
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args.save_dir, args.env_version, args.hidden_dim
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)
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@ -31,7 +31,7 @@ from lfna_models import HyperNet_VX as HyperNet
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def main(args):
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logger, env_info, model_kwargs = lfna_setup(args)
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dynamic_env = env_info["dynamic_env"]
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model = get_model(dict(model_type="simple_mlp"), **model_kwargs)
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model = get_model(**model_kwargs)
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total_time = env_info["total"]
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for i in range(total_time):
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@ -4,6 +4,8 @@
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import copy
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import torch
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import torch.nn.functional as F
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from xlayers import super_core
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from xlayers import trunc_normal_
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from models.xcore import get_model
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@ -29,13 +31,15 @@ class HyperNet(super_core.SuperModule):
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trunc_normal_(self._super_layer_embed, std=0.02)
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model_kwargs = dict(
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config=dict(model_type="dual_norm_mlp"),
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input_dim=layer_embeding + task_embedding,
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output_dim=max(self._numel_per_layer),
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hidden_dims=[layer_embeding * 4] * 4,
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hidden_dims=[layer_embeding * 4] * 3,
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act_cls="gelu",
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norm_cls="layer_norm_1d",
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dropout=0.1,
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)
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self._generator = get_model(dict(model_type="norm_mlp"), **model_kwargs)
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self._generator = get_model(**model_kwargs)
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"""
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model_kwargs = dict(
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input_dim=layer_embeding + task_embedding,
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@ -50,8 +54,12 @@ class HyperNet(super_core.SuperModule):
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print("generator: {:}".format(self._generator))
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def forward_raw(self, task_embed):
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# task_embed = F.normalize(task_embed, dim=-1, p=2)
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# layer_embed = F.normalize(self._super_layer_embed, dim=-1, p=2)
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layer_embed = self._super_layer_embed
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task_embed = task_embed.view(1, -1).expand(self._num_layers, -1)
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joint_embed = torch.cat((task_embed, self._super_layer_embed), dim=-1)
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joint_embed = torch.cat((task_embed, layer_embed), dim=-1)
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weights = self._generator(joint_embed)
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if self._return_container:
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weights = torch.split(weights, 1)
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@ -11,6 +11,7 @@ __all__ = ["get_model"]
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from xlayers.super_core import SuperSequential
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from xlayers.super_core import SuperLinear
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from xlayers.super_core import SuperDropout
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from xlayers.super_core import super_name2norm
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from xlayers.super_core import super_name2activation
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@ -47,7 +48,20 @@ def get_model(config: Dict[Text, Any], **kwargs):
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last_dim = hidden_dim
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sub_layers.append(SuperLinear(last_dim, kwargs["output_dim"]))
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model = SuperSequential(*sub_layers)
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elif model_type == "dual_norm_mlp":
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act_cls = super_name2activation[kwargs["act_cls"]]
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norm_cls = super_name2norm[kwargs["norm_cls"]]
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sub_layers, last_dim = [], kwargs["input_dim"]
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for i, hidden_dim in enumerate(kwargs["hidden_dims"]):
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if i > 0:
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sub_layers.append(norm_cls(last_dim, elementwise_affine=False))
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sub_layers.append(SuperLinear(last_dim, hidden_dim))
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sub_layers.append(SuperDropout(kwargs["dropout"]))
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sub_layers.append(SuperLinear(hidden_dim, hidden_dim))
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sub_layers.append(act_cls())
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last_dim = hidden_dim
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sub_layers.append(SuperLinear(last_dim, kwargs["output_dim"]))
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model = SuperSequential(*sub_layers)
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else:
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raise TypeError("Unkonwn model type: {:}".format(model_type))
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return model
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@ -14,6 +14,7 @@ from .super_norm import SuperSimpleNorm
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from .super_norm import SuperLayerNorm1D
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from .super_norm import SuperSimpleLearnableNorm
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from .super_norm import SuperIdentity
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from .super_dropout import SuperDropout
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super_name2norm = {
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"simple_norm": SuperSimpleNorm,
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40
lib/xlayers/super_dropout.py
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40
lib/xlayers/super_dropout.py
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
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#####################################################
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import math
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from typing import Optional, Callable
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import spaces
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from .super_module import SuperModule
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from .super_module import IntSpaceType
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from .super_module import BoolSpaceType
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class SuperDropout(SuperModule):
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"""Applies a the dropout function element-wise."""
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def __init__(self, p: float = 0.5, inplace: bool = False) -> None:
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super(SuperDropout, self).__init__()
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self._p = p
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self._inplace = inplace
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@property
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def abstract_search_space(self):
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return spaces.VirtualNode(id(self))
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def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
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return self.forward_raw(input)
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def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
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return F.dropout(input, self._p, self.training, self._inplace)
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def forward_with_container(self, input, container, prefix=[]):
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return self.forward_raw(input)
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def extra_repr(self) -> str:
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xstr = "inplace=True" if self._inplace else ""
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return "p={:}".format(self._p) + ", " + xstr
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def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
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return F.layer_norm(input, (self.in_dim,), self.weight, self.bias, self.eps)
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def forward_with_container(self, input, container, prefix=[]):
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super_weight_name = ".".join(prefix + ["weight"])
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if container.has(super_weight_name):
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weight = container.query(super_weight_name)
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else:
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weight = None
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super_bias_name = ".".join(prefix + ["bias"])
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if container.has(super_bias_name):
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bias = container.query(super_bias_name)
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else:
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bias = None
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return F.layer_norm(input, (self.in_dim,), weight, bias, self.eps)
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def extra_repr(self) -> str:
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return (
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"shape={in_dim}, eps={eps}, elementwise_affine={elementwise_affine}".format(
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