API_URL = "https://api-inference.huggingface.co/models/CIS-5190-CIA/Ensamble"
from huggingface_hub import InferenceClient
client = InferenceClient(
"CIS-5190-CIA/Ensamble",
token="TOKEN HERE",
)
How to Run
In the notebook Run_ensamble.ipynb, replace the line:
dataset_test = load_dataset("gydou/released_img")
with the proper location of the testing dataset.
Training Dataset Statistics
lat_mean = 39.95173281562989
lat_std = 0.0006925131397316982
lon_mean = -75.19143805846498
lon_std = 0.0006552266653111098
Helper Functions to Predict from & Evaluate Ensamble
These functions will allow you to use the ensamble to predict and evaluate the model
They use the following paramaters:
- models: this is a dictionary of the models, in the format of:
models = { "RNNModel1": CNNModel1(num_outputs=2).to(device), "RNNModel2": CNNModel2(num_outputs=2).to(device), "RNNModel3": CNNModel3(num_outputs=2).to(device), } - dataloader: this is the data loader provided to us for the project
- lat_mean, lon_mean, lat_std, lon_std
def ensemble_predict(models, dataloader, lat_mean, lon_mean, lat_std, lon_std):
model_outputs = []
for model_name, model in models.items():
model.eval()
outputs = []
with torch.no_grad():
for images, _ in dataloader:
images = images.to(device)
outputs.append(model(images))
model_outputs.append(torch.cat(outputs, dim=0))
# average the predictions across all models
ensemble_output = torch.stack(model_outputs, dim=0).mean(dim=0)
# denormalize the ensemble predictions
ensemble_output_denorm = ensemble_output.cpu().numpy() * np.array([lat_std, lon_std]) + np.array([lat_mean, lon_mean])
return ensemble_output_denorm
# evaluate Ensemble with Geodesic Distance
def evaluate_ensemble(models, dataloader, lat_mean, lon_mean, lat_std, lon_std):
ensemble_outputs = ensemble_predict(models, dataloader, lat_mean, lon_mean, lat_std, lon_std)
all_targets = []
for _, targets in dataloader:
all_targets.append(targets)
all_targets = torch.cat(all_targets, dim=0).cpu().numpy()
all_targets_denorm = all_targets * np.array([lat_std, lon_std]) + np.array([lat_mean, lon_mean])
total_samples = all_targets_denorm.shape[0]
ensemble_loss = 0.0
# compute Geodesic Distance Metrics
for pred, actual in zip(ensemble_outputs, all_targets_denorm):
distance = geodesic((actual[0], actual[1]), (pred[0], pred[1])).meters
ensemble_loss += distance ** 2
ensemble_loss /= total_samples
ensemble_rmse = np.sqrt(ensemble_loss)
return ensemble_loss, ensemble_rmse
Our Custom Models for the ensamble
We used the following 3 model architectures and then created the ensamble to create an output
Model 1:
class CNNModel1(nn.Module):
def __init__(self, num_outputs=2):
super(CNNModel1, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.BatchNorm2d(64),
nn.Conv2d(64, 192, kernel_size=5, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.BatchNorm2d(192),
nn.Conv2d(192, 384, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(384, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2)
)
self.classifier = nn.Sequential(
nn.Dropout(),
nn.Linear(256 * 6 * 6, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(inplace=True),
nn.Linear(4096, num_outputs)
)
def forward(self, x):
x = self.features(x)
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
Model 2:
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride=1, downsample=None):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1)
self.bn1 = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm2d(out_channels)
self.downsample = downsample
def forward(self, x):
identity = x
if self.downsample:
identity = self.downsample(x)
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out += identity
out = self.relu(out)
return out
class CNNModel2(nn.Module):
def __init__(self, num_outputs=2):
super(CNNModel2, self).__init__()
self.in_channels = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(64, 2, stride=1)
self.layer2 = self._make_layer(128, 2, stride=2)
self.layer3 = self._make_layer(256, 2, stride=2)
self.layer4 = self._make_layer(512, 2, stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512, num_outputs)
def _make_layer(self, out_channels, blocks, stride):
downsample = None
if stride != 1 or self.in_channels != out_channels:
downsample = nn.Sequential(
nn.Conv2d(self.in_channels, out_channels, kernel_size=1, stride=stride),
nn.BatchNorm2d(out_channels)
)
layers = []
layers.append(ResidualBlock(self.in_channels, out_channels, stride, downsample))
self.in_channels = out_channels
for _ in range(1, blocks):
layers.append(ResidualBlock(out_channels, out_channels))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
Model 3:
class InceptionModule(nn.Module):
def __init__(self, in_channels, ch1x1, ch3x3_reduce, ch3x3, ch5x5_reduce, ch5x5, pool_proj):
super(InceptionModule, self).__init__()
self.branch1 = nn.Sequential(
nn.Conv2d(in_channels, ch1x1, kernel_size=1),
nn.ReLU(inplace=True)
)
self.branch2 = nn.Sequential(
nn.Conv2d(in_channels, ch3x3_reduce, kernel_size=1),
nn.ReLU(inplace=True),
nn.Conv2d(ch3x3_reduce, ch3x3, kernel_size=3, padding=1),
nn.ReLU(inplace=True)
)
self.branch3 = nn.Sequential(
nn.Conv2d(in_channels, ch5x5_reduce, kernel_size=1),
nn.ReLU(inplace=True),
nn.Conv2d(ch5x5_reduce, ch5x5, kernel_size=5, padding=2),
nn.ReLU(inplace=True)
)
self.branch4 = nn.Sequential(
nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
nn.Conv2d(in_channels, pool_proj, kernel_size=1),
nn.ReLU(inplace=True)
)
def forward(self, x):
branch1 = self.branch1(x)
branch2 = self.branch2(x)
branch3 = self.branch3(x)
branch4 = self.branch4(x)
outputs = torch.cat([branch1, branch2, branch3, branch4], 1)
return outputs
class CNNModel3(nn.Module):
def __init__(self, num_outputs=2):
super(CNNModel3, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
self.maxpool1 = nn.MaxPool2d(3, stride=2)
self.conv2 = nn.Conv2d(64, 192, kernel_size=3, padding=1)
self.maxpool2 = nn.MaxPool2d(3, stride=2)
self.inception3a = InceptionModule(192, 64, 96, 128, 16, 32, 32)
self.inception3b = InceptionModule(256, 128, 128, 192, 32, 96, 64)
self.maxpool3 = nn.MaxPool2d(3, stride=2)
self.inception4a = InceptionModule(480, 192, 96, 208, 16, 48, 64)
self.inception4b = InceptionModule(512, 160, 112, 224, 24, 64, 64)
self.maxpool4 = nn.MaxPool2d(3, stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.dropout = nn.Dropout(0.4)
self.fc = nn.Linear(512, num_outputs)
def forward(self, x):
x = self.conv1(x)
x = self.maxpool1(x)
x = self.conv2(x)
x = self.maxpool2(x)
x = self.inception3a(x)
x = self.inception3b(x)
x = self.maxpool3(x)
x = self.inception4a(x)
x = self.inception4b(x)
x = self.maxpool4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.dropout(x)
x = self.fc(x)
return x
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