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import torch
from torch import nn
from torchvision import transforms, models
class ActionClassifier(nn.Module):
def __init__(self, train_last_nlayer, hidden_size, dropout, ntargets):
super().__init__()
resnet = models.resnet50(weights=models.ResNet50_Weights.DEFAULT, progress=True)
modules = list(resnet.children())[:-1] # delete last layer
self.resnet = nn.Sequential(*modules)
for param in self.resnet[:-train_last_nlayer].parameters():
param.requires_grad = False
self.fc = nn.Sequential(
nn.Flatten(),
nn.BatchNorm1d(resnet.fc.in_features),
nn.Dropout(dropout),
nn.Linear(resnet.fc.in_features, hidden_size),
nn.ReLU(),
nn.BatchNorm1d(hidden_size),
nn.Dropout(dropout),
nn.Linear(hidden_size, ntargets),
nn.Sigmoid()
)
def forward(self, x):
x = self.resnet(x)
x = self.fc(x)
return x
def get_transform():
transform = transforms.Compose([
transforms.Resize([224, 244]),
models.ResNet50_Weights.DEFAULT.transforms()
])
return transform
# def get_transform():
# transform = transforms.Compose([
# transforms.Resize([224, 244]),
# transforms.ToTensor(),
# # std multiply by 255 to convert img of [0, 255]
# # to img of [0, 1]
# transforms.Normalize((0.485, 0.456, 0.406),
# (0.229*255, 0.224*255, 0.225*255))]
# )
# return transform
def get_model():
model = ActionClassifier(0, 512, 0.2, 15)
model.load_state_dict(torch.load('./model_weights.pth', map_location=torch.device('cpu')))
return model
def get_class(index):
ind2cat = [
'calling',
'clapping',
'cycling',
'dancing',
'drinking',
'eating',
'fighting',
'hugging',
'laughing',
'listening_to_music',
'running',
'sitting',
'sleeping',
'texting',
'using_laptop'
]
return ind2cat[index]
# img = Image.open('./inputs/Image_102.jpg').convert('RGB')
# #print(transform(img))
# img = transform(img)
# img = img.unsqueeze(dim=0)
# print(img.shape)
# model.eval()
# with torch.no_grad():
# out = model(img)
# out = nn.Softmax()(out).squeeze()
# print(out.shape)
# res = torch.argmax(out)
# print(ind2cat[res])
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