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import functools
import torch
import torch.nn as nn
from networks.base_model import BaseModel
import sys
from models import get_model
class Trainer(BaseModel):
def name(self):
return 'Trainer'
def __init__(self, opt):
super(Trainer, self).__init__(opt)
self.opt = opt
self.model = get_model("FeatureTransformer")
self.clip_model = get_model("CLIP:ViT-L/14")
# torch.nn.init.normal_(self.model.fc.weight.data, 0.0, opt.init_gain)
# if opt.fix_backbone:
params = []
for name, p in self.clip_model.named_parameters():
if name=="fc.weight" or name=="fc.bias":
params.append(p)
else:
p.requires_grad = False
del params
# else:
# print("Your backbone is not fixed. Are you sure you want to proceed? If this is a mistake, enable the --fix_backbone command during training and rerun")
# import time
# time.sleep(3)
# params = self.clip_model.parameters()
if opt.optim == 'adam':
self.optimizer = torch.optim.AdamW(self.model.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999), weight_decay=opt.weight_decay)
elif opt.optim == 'sgd':
self.optimizer = torch.optim.SGD(self.model.parameters(), lr=opt.lr, momentum=0.0, weight_decay=opt.weight_decay)
else:
raise ValueError("optim should be [adam, sgd]")
self.loss_fn = nn.BCEWithLogitsLoss()
self.model.to(self.device)
def adjust_learning_rate(self, min_lr=1e-6):
for param_group in self.optimizer.param_groups:
param_group['lr'] /= 10.
if param_group['lr'] < min_lr:
return False
return True
def set_input(self, input):
# self.input = torch.cat([self.clip_model.forward(x=video_frames, return_feature=True).unsqueeze(0) for video_frames in input[0]])
self.clip_model.to(self.device)
self.input = self.clip_model.forward(x=input[0].to(self.device).view(-1, 3, 224, 224), return_feature=True).view(-1, input[0].shape[1], 768)
self.clip_model.to('cpu')
self.input = self.input.to(self.device)
self.label = input[1].to(self.device).float()
def forward(self):
self.output = self.model(self.input)
self.output = self.output.view(-1).unsqueeze(1)
def get_loss(self):
return self.loss_fn(self.output.squeeze(1), self.label)
def optimize_parameters(self):
self.forward()
self.loss = self.loss_fn(self.output.squeeze(1), self.label)
self.optimizer.zero_grad()
self.loss.backward()
self.optimizer.step()
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