]
NPY_7CHANNEL_STD = [
0.05338322, 0.04247037, 0.03543733, 0.00608935, 0.00623353, 0.00691525,
0.01309933
]
dataset = ResnetTrainDataset(csv_path,
img_path,
dtype,
img_ext=img_ext,
channel_means=NPY_7CHANNEL_MEAN,
channel_stds=NPY_7CHANNEL_STD)
train_loader, val_loader = generate_train_val_dataloader(
dataset,
batch_size=batch_size,
num_workers=num_workers,
use_fraction_of_data=use_fraction_of_data,
)
## pretrained resnet
model = resnet18(pretrained=False)
## resize last fully connected layer to match our problem
model.fc = nn.Linear(model.fc.in_features, 17)
## resize the first conv layer to match our problem
model.conv1 = nn.Conv2d(7,
64,
kernel_size=7,
stride=2,
padding=3,
bias=False)
model.type(dtype)
from_pickle = int(sys.argv[1])
except IndexError:
from_pickle = 1
## cpu dtype
dtype = torch.cuda.FloatTensor
save_model_path = "conv_6-layer_state_dict.pkl"
save_mat_path = "conv_6-layer_loss_and_acc.mat"
csv_path = '../../data/train_v2.csv'
img_path = '../../data/train-jpg'
img_ext = '.jpg'
dataset = AmazonDataset(csv_path, img_path, img_ext, dtype)
train_loader, val_loader = generate_train_val_dataloader(
dataset,
batch_size=128,
num_workers=0,
use_fraction_of_data=0.2,
)
## 6 conv layers
model = nn.Sequential(
## 256x256
nn.Conv2d(4, 16, kernel_size=3, stride=1),
nn.ReLU(inplace=True),
nn.BatchNorm2d(16),
nn.Conv2d(16, 16, kernel_size=3, stride=1),
nn.ReLU(inplace=True),
nn.BatchNorm2d(16),
nn.AdaptiveMaxPool2d(128),
## 128x128
nn.Conv2d(16, 32, kernel_size=3, stride=1),