99 lines
3.0 KiB
Python
99 lines
3.0 KiB
Python
import torch
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from torch.nn import functional as F
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from dassl.engine import TRAINER_REGISTRY, TrainerXU
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from dassl.modeling.ops import mixup
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from dassl.modeling.ops.utils import (
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sharpen_prob, create_onehot, linear_rampup, shuffle_index
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)
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@TRAINER_REGISTRY.register()
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class MixMatch(TrainerXU):
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"""MixMatch: A Holistic Approach to Semi-Supervised Learning.
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https://arxiv.org/abs/1905.02249.
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"""
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def __init__(self, cfg):
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super().__init__(cfg)
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self.weight_u = cfg.TRAINER.MIXMATCH.WEIGHT_U
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self.temp = cfg.TRAINER.MIXMATCH.TEMP
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self.beta = cfg.TRAINER.MIXMATCH.MIXUP_BETA
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self.rampup = cfg.TRAINER.MIXMATCH.RAMPUP
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def check_cfg(self, cfg):
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assert cfg.DATALOADER.K_TRANSFORMS > 1
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def forward_backward(self, batch_x, batch_u):
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input_x, label_x, input_u = self.parse_batch_train(batch_x, batch_u)
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num_x = input_x.shape[0]
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global_step = self.batch_idx + self.epoch * self.num_batches
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weight_u = self.weight_u * linear_rampup(global_step, self.rampup)
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# Generate pseudo-label for unlabeled data
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with torch.no_grad():
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output_u = 0
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for input_ui in input_u:
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output_ui = F.softmax(self.model(input_ui), 1)
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output_u += output_ui
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output_u /= len(input_u)
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label_u = sharpen_prob(output_u, self.temp)
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label_u = [label_u] * len(input_u)
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label_u = torch.cat(label_u, 0)
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input_u = torch.cat(input_u, 0)
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# Combine and shuffle labeled and unlabeled data
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input_xu = torch.cat([input_x, input_u], 0)
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label_xu = torch.cat([label_x, label_u], 0)
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input_xu, label_xu = shuffle_index(input_xu, label_xu)
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# Mixup
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input_x, label_x = mixup(
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input_x,
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input_xu[:num_x],
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label_x,
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label_xu[:num_x],
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self.beta,
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preserve_order=True,
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)
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input_u, label_u = mixup(
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input_u,
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input_xu[num_x:],
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label_u,
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label_xu[num_x:],
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self.beta,
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preserve_order=True,
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)
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# Compute losses
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output_x = F.softmax(self.model(input_x), 1)
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loss_x = (-label_x * torch.log(output_x + 1e-5)).sum(1).mean()
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output_u = F.softmax(self.model(input_u), 1)
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loss_u = ((label_u - output_u)**2).mean()
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loss = loss_x + loss_u*weight_u
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self.model_backward_and_update(loss)
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loss_summary = {"loss_x": loss_x.item(), "loss_u": loss_u.item()}
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if (self.batch_idx + 1) == self.num_batches:
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self.update_lr()
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return loss_summary
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def parse_batch_train(self, batch_x, batch_u):
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input_x = batch_x["img"][0]
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label_x = batch_x["label"]
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label_x = create_onehot(label_x, self.num_classes)
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input_u = batch_u["img"]
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input_x = input_x.to(self.device)
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label_x = label_x.to(self.device)
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input_u = [input_ui.to(self.device) for input_ui in input_u]
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return input_x, label_x, input_u
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