Fix bugs
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@ -20,7 +20,7 @@ class MetaModelV1(super_core.SuperModule):
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time_dim,
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meta_timestamps,
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dropout: float = 0.1,
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seq_length: int = 10,
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seq_length: int = None,
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interval: float = None,
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thresh: float = None,
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):
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@ -33,8 +33,7 @@ class MetaModelV1(super_core.SuperModule):
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self._raw_meta_timestamps = meta_timestamps
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assert interval is not None
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self._interval = interval
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self._seq_length = seq_length
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self._thresh = interval * 50 if thresh is None else thresh
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self._thresh = interval * seq_length if thresh is None else thresh
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self.register_parameter(
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"_super_layer_embed",
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@ -45,10 +44,6 @@ class MetaModelV1(super_core.SuperModule):
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torch.nn.Parameter(torch.Tensor(len(meta_timestamps), time_dim)),
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)
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self.register_buffer("_meta_timestamps", torch.Tensor(meta_timestamps))
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# register a time difference buffer
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# time_interval = [-i * self._interval for i in range(self._seq_length)]
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# time_interval.reverse()
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# self.register_buffer("_time_interval", torch.Tensor(time_interval))
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self._time_embed_dim = time_dim
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self._append_meta_embed = dict(fixed=None, learnt=None)
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self._append_meta_timestamps = dict(fixed=None, learnt=None)
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@ -186,7 +181,6 @@ class MetaModelV1(super_core.SuperModule):
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def forward_raw(self, timestamps, time_embeds, tembed_only=False):
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if time_embeds is None:
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# time_seq = timestamps.view(-1, 1) + self._time_interval.view(1, -1)
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[B] = timestamps.shape
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time_embeds = self._obtain_time_embed(timestamps)
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else: # use the hyper-net only
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@ -210,7 +204,7 @@ class MetaModelV1(super_core.SuperModule):
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batch_containers.append(
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self._shape_container.translate(torch.split(weights.squeeze(0), 1))
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)
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return time_seq, batch_containers, time_embeds
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return batch_containers, time_embeds
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def forward_candidate(self, input):
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raise NotImplementedError
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@ -239,10 +233,10 @@ class MetaModelV1(super_core.SuperModule):
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best_new_param = new_param.detach().clone()
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for iepoch in range(epochs):
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optimizer.zero_grad()
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_, [_], time_embed = self(timestamp.view(1, 1), None)
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_, time_embed = self(timestamp.view(1), None)
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match_loss = criterion(new_param, time_embed)
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_, [container], time_embed = self(None, new_param.view(1, -1))
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[container], time_embed = self(None, new_param.view(1, -1))
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y_hat = base_model.forward_with_container(x, container)
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meta_loss = criterion(y_hat, y)
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loss = meta_loss + match_loss
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@ -46,8 +46,8 @@ def online_evaluate(env, meta_model, base_model, criterion, args, logger, save=F
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with torch.no_grad():
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meta_model.eval()
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base_model.eval()
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_, [future_container], time_embeds = meta_model(
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future_time.to(args.device).view(1, 1), None, False
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[future_container], time_embeds = meta_model(
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future_time.to(args.device).view(-1), None, False
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)
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if save:
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w_containers[idx] = future_container.no_grad_clone()
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@ -117,10 +117,10 @@ def meta_train_procedure(base_model, meta_model, criterion, xenv, args, logger):
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)
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# future loss
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total_future_losses, total_present_losses = [], []
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_, future_containers, _ = meta_model(
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future_containers, _ = meta_model(
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None, generated_time_embeds[batch_indexes], False
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)
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_, present_containers, _ = meta_model(
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present_containers, _ = meta_model(
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None, meta_model.super_meta_embed[batch_indexes], False
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)
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for ibatch, time_step in enumerate(raw_time_steps.cpu().tolist()):
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@ -1,6 +1,3 @@
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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 math
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import abc
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import numpy as np
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@ -13,11 +10,11 @@ class UnifiedSplit:
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"""A class to unify the split strategy."""
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def __init__(self, total_num, mode):
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# Training Set 65%
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num_of_train = int(total_num * 0.65)
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# Training Set 75%
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num_of_train = int(total_num * 0.75)
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# Validation Set 05%
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num_of_valid = int(total_num * 0.05)
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# Test Set 30%
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# Test Set 20%
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num_of_set = total_num - num_of_train - num_of_valid
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all_indexes = list(range(total_num))
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if mode is None:
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@ -1,27 +1,32 @@
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.01 #
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#####################################################
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import torch
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import torch.nn as nn
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import numpy as np
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def count_parameters_in_MB(model):
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return count_parameters(model, "mb")
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return count_parameters(model, "mb", deprecated=True)
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def count_parameters(model_or_parameters, unit="mb"):
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def count_parameters(model_or_parameters, unit="mb", deprecated=False):
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if isinstance(model_or_parameters, nn.Module):
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counts = sum(np.prod(v.size()) for v in model_or_parameters.parameters())
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elif isinstance(model_or_parameters, nn.Parameter):
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counts = models_or_parameters.numel()
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elif isinstance(model_or_parameters, (list, tuple)):
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counts = sum(count_parameters(x, None) for x in models_or_parameters)
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counts = sum(
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count_parameters(x, None, deprecated) for x in models_or_parameters
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)
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else:
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counts = sum(np.prod(v.size()) for v in model_or_parameters)
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if unit.lower() == "kb" or unit.lower() == "k":
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counts /= 2 ** 10 # changed from 1e3 to 2^10
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counts /= 1e3 if deprecated else 2 ** 10 # changed from 1e3 to 2^10
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elif unit.lower() == "mb" or unit.lower() == "m":
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counts /= 2 ** 20 # changed from 1e6 to 2^20
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counts /= 1e6 if deprecated else 2 ** 20 # changed from 1e6 to 2^20
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elif unit.lower() == "gb" or unit.lower() == "g":
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counts /= 2 ** 30 # changed from 1e9 to 2^30
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counts /= 1e9 if deprecated else 2 ** 30 # changed from 1e9 to 2^30
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elif unit is not None:
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raise ValueError("Unknow unit: {:}".format(unit))
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return counts
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