def __init__(self, m_feat, layer_ids, layer_wgts):
super().__init__()
self.m_feat = m_feat
self.loss_features = [self.m_feat[i] for i in layer_ids]
self.hooks = callbacks.hook_outputs(self.loss_features, detach=False)
self.wgts = layer_wgts
self.metric_names = [
'pixel',
] + [f'feat_{i}' for i in range(len(layer_ids))
] + [f'gram_{i}' for i in range(len(layer_ids))]
def __init__(self, m_feat, layer_ids, layer_wgts, base_loss=F.l1_loss):
super().__init__()
self.m_feat = m_feat
self.base_loss = base_loss
self.loss_features = [self.m_feat[i] for i in layer_ids]
self.hooks = hook_outputs(self.loss_features, detach=False)
self.wgts = layer_wgts
self.metric_names = [
'pixel',
]
def __init__(self, layer_wgts=[20,70,10]):
super().__init__()
self.m_feat = models.vgg16_bn(True).features.cuda().eval()
requires_grad(self.m_feat, False)
blocks = [i-1 for i,o in enumerate(children(self.m_feat)) if isinstance(o,nn.MaxPool2d)]
layer_ids = blocks[2:5]
self.loss_features = [self.m_feat[i] for i in layer_ids]
self.hooks = hook_outputs(self.loss_features, detach=False)
self.wgts = layer_wgts
self.metric_names = ['pixel',] + [f'feat_{i}' for i in range(len(layer_ids))]
self.base_loss = F.l1_loss
def __init__(self, model, layer_ids, layer_wgts, base_loss=F.l1_loss):
super().__init__()
self.model = nn.Sequential(*list(model.children())[:layer_ids[-1] + 1])
self.loss_features = [self.model[i] for i in layer_ids]
self.hooks = hook_outputs(self.loss_features, detach=False)
self.wgts = layer_wgts
self.metric_names = [
'pixel',
] + [f'feat_{i}' for i in range(len(layer_ids))
] + [f'gram_{i}'
for i in range(len(layer_ids))] + ['PSNR', 'SSIM', 'pEPs']
self.base_loss = base_loss
self.mse = nn.MSELoss()
self.ssim = SSIM(window_size=11)
from fastai.callbacks import hook_outputs from fastai.basic_data import DatasetType from fastai.torch_core import * # In[92]: [children(children(learner.model[1])[0])[-2]] # In[93]: # register a hook to grab features hook = hook_outputs([children(children(learner.model[1])[0])[-2]]) # In[94]: xb, yb = data.one_batch(ds_type=DatasetType.Valid) # In[95]: model = learner.model.eval() outb = model(xb.cuda())
def __init__(self, model, layer_ids, weights):
super().__init__()
self.model = model
self.important_layers = [self.model[i] for i in layer_ids]
self.hooks = hook_outputs(self.important_layers, detach=False)
self.weights = weights
