Upload 2 files
Browse files- README.md +132 -0
- sampleRun_v4.ipynb +1580 -0
README.md
ADDED
|
@@ -0,0 +1,132 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# LAJ CNN Image-to-GPS Model Iteration 1
|
| 2 |
+
|
| 3 |
+
This project features a convolutional neural network (CNN) for predicting GPS coordinates (latitude and longitude) from image inputs. Below, you'll find details on loading the model, performing inference, and the architecture of the network.
|
| 4 |
+
|
| 5 |
+
---
|
| 6 |
+
|
| 7 |
+
## 1. Loading the Model
|
| 8 |
+
|
| 9 |
+
To load the model, look at the sampleRun_v4.ipynb and run the same commands.
|
| 10 |
+
|
| 11 |
+
## 2. Running the Model
|
| 12 |
+
|
| 13 |
+
To perform inference on our model, just normalize the latitudes and longitudes to our means and standard deviations below.
|
| 14 |
+
Then run code similar to the code provided to test code provided below:
|
| 15 |
+
|
| 16 |
+
```
|
| 17 |
+
# Evaluate on Test Set
|
| 18 |
+
model.eval()
|
| 19 |
+
all_preds, all_actuals = [], []
|
| 20 |
+
with torch.no_grad():
|
| 21 |
+
for images, gps_coords in val_loader:
|
| 22 |
+
images, gps_coords = images.to(device), gps_coords.to(device)
|
| 23 |
+
outputs = model(images)
|
| 24 |
+
all_preds.append(outputs.cpu())
|
| 25 |
+
all_actuals.append(gps_coords.cpu())
|
| 26 |
+
all_preds = torch.cat(all_preds).numpy()
|
| 27 |
+
all_actuals = torch.cat(all_actuals).numpy()
|
| 28 |
+
|
| 29 |
+
# Denormalize Predictions
|
| 30 |
+
all_preds_denorm = all_preds * np.array([lat_std, lon_std]) + np.array([lat_mean, lon_mean])
|
| 31 |
+
all_actuals_denorm = all_actuals * np.array([lat_std, lon_std]) + np.array([lat_mean, lon_mean])
|
| 32 |
+
|
| 33 |
+
# Compute Error Metrics
|
| 34 |
+
mae = mean_absolute_error(all_actuals_denorm, all_preds_denorm)
|
| 35 |
+
rmse = mean_squared_error(all_actuals_denorm, all_preds_denorm, squared=False)
|
| 36 |
+
print(f"Test Set Mean Absolute Error: {mae:}")
|
| 37 |
+
print(f"Test Set Root Mean Squared Error: {rmse:}")
|
| 38 |
+
```
|
| 39 |
+
|
| 40 |
+
## 3. Latitude and Longitude Means and Standard Deviations
|
| 41 |
+
|
| 42 |
+
The following values represent the **means** and **standard deviations** of the latitude and longitude used in this model:
|
| 43 |
+
|
| 44 |
+
- **Latitude Mean**: `39.95173729922173`
|
| 45 |
+
- **Latitude Standard Deviation**: `0.0006877829213952256`
|
| 46 |
+
- **Longitude Mean**: `-75.19138804851796`
|
| 47 |
+
- **Longitude Standard Deviation**: `0.0006182574854250925`
|
| 48 |
+
These values are used to normalize and denormalize the latitude and longitude predictions during inference.
|
| 49 |
+
|
| 50 |
+
## 4. CNN Architecture
|
| 51 |
+
|
| 52 |
+
Finally here is the architecture of the CNN we used:
|
| 53 |
+
|
| 54 |
+
```
|
| 55 |
+
|
| 56 |
+
# Model Definition
|
| 57 |
+
class CustomGPSModel(nn.Module):
|
| 58 |
+
def __init__(self):
|
| 59 |
+
super(CustomGPSModel, self).__init__()
|
| 60 |
+
|
| 61 |
+
# Load EfficientNetV2-S with pretrained weights
|
| 62 |
+
self.efficientnet = efficientnet_v2_s(pretrained=True)
|
| 63 |
+
|
| 64 |
+
# Modify the final layer for regression (predicting latitude and longitude)
|
| 65 |
+
num_features = self.efficientnet.classifier[1].in_features
|
| 66 |
+
self.efficientnet.classifier[1] = nn.Linear(num_features, 2) # Output layer has 2 outputs for latitude & longitude
|
| 67 |
+
|
| 68 |
+
# Don't freeze earlier layers
|
| 69 |
+
for param in self.efficientnet.features.parameters():
|
| 70 |
+
param.requires_grad = True
|
| 71 |
+
|
| 72 |
+
def forward(self, x):
|
| 73 |
+
return self.efficientnet(x) # Forward pass through EfficientNet
|
| 74 |
+
|
| 75 |
+
```
|
| 76 |
+
|
| 77 |
+
## 5. Sample Run Code (how to install and run everything)
|
| 78 |
+
|
| 79 |
+
```
|
| 80 |
+
!pip install datasets
|
| 81 |
+
!pip install huggingface_hub
|
| 82 |
+
!pip install requests
|
| 83 |
+
import torch
|
| 84 |
+
import torch.nn as nn
|
| 85 |
+
import torch.optim as optim
|
| 86 |
+
from torchvision.models import efficientnet_v2_s
|
| 87 |
+
from torch.optim.lr_scheduler import CosineAnnealingLR
|
| 88 |
+
from torchvision import transforms
|
| 89 |
+
from torch.utils.data import DataLoader, Dataset
|
| 90 |
+
from torchvision.transforms import functional as F
|
| 91 |
+
from PIL import Image
|
| 92 |
+
import numpy as np
|
| 93 |
+
from sklearn.metrics import mean_absolute_error, mean_squared_error
|
| 94 |
+
from huggingface_hub import PyTorchModelHubMixin
|
| 95 |
+
import os
|
| 96 |
+
|
| 97 |
+
|
| 98 |
+
# Model Definition
|
| 99 |
+
class CustomGPSModel(nn.Module):
|
| 100 |
+
def __init__(self):
|
| 101 |
+
super(CustomGPSModel, self).__init__()
|
| 102 |
+
|
| 103 |
+
# Load EfficientNetV2-S with pretrained weights
|
| 104 |
+
self.efficientnet = efficientnet_v2_s(pretrained=True)
|
| 105 |
+
|
| 106 |
+
# Modify the final layer for regression (predicting latitude and longitude)
|
| 107 |
+
num_features = self.efficientnet.classifier[1].in_features
|
| 108 |
+
self.efficientnet.classifier[1] = nn.Linear(num_features, 2) # Output layer has 2 outputs for latitude & longitude
|
| 109 |
+
|
| 110 |
+
# Don't freeze earlier layers
|
| 111 |
+
for param in self.efficientnet.features.parameters():
|
| 112 |
+
param.requires_grad = True
|
| 113 |
+
|
| 114 |
+
def forward(self, x):
|
| 115 |
+
return self.efficientnet(x) # Forward pass through EfficientNet
|
| 116 |
+
|
| 117 |
+
from huggingface_hub import hf_hub_download
|
| 118 |
+
import torch
|
| 119 |
+
path_name = "efficientnet_gps_regressor_complete_changed_betas_v2.pth"
|
| 120 |
+
repo_name = "CustomGPSModel_EfficientNetV2_Run3"
|
| 121 |
+
organization_name = "LAJ-519-Image-Project"
|
| 122 |
+
|
| 123 |
+
# Specify the repository and the filename of the model you want to load
|
| 124 |
+
repo_id = f"{organization_name}/{repo_name}"
|
| 125 |
+
filename = f"{path_name}"
|
| 126 |
+
|
| 127 |
+
model_path = hf_hub_download(repo_id=repo_id, filename=filename)
|
| 128 |
+
|
| 129 |
+
# Load the model using torch
|
| 130 |
+
model_test = torch.load(model_path)
|
| 131 |
+
model_test.eval()
|
| 132 |
+
```
|
sampleRun_v4.ipynb
ADDED
|
@@ -0,0 +1,1580 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"nbformat": 4,
|
| 3 |
+
"nbformat_minor": 0,
|
| 4 |
+
"metadata": {
|
| 5 |
+
"colab": {
|
| 6 |
+
"provenance": [],
|
| 7 |
+
"gpuType": "T4"
|
| 8 |
+
},
|
| 9 |
+
"kernelspec": {
|
| 10 |
+
"name": "python3",
|
| 11 |
+
"display_name": "Python 3"
|
| 12 |
+
},
|
| 13 |
+
"language_info": {
|
| 14 |
+
"name": "python"
|
| 15 |
+
},
|
| 16 |
+
"accelerator": "GPU",
|
| 17 |
+
"widgets": {
|
| 18 |
+
"application/vnd.jupyter.widget-state+json": {
|
| 19 |
+
"ea8bed4c2f59465c960a0f7959d11d5a": {
|
| 20 |
+
"model_module": "@jupyter-widgets/controls",
|
| 21 |
+
"model_name": "HBoxModel",
|
| 22 |
+
"model_module_version": "1.5.0",
|
| 23 |
+
"state": {
|
| 24 |
+
"_dom_classes": [],
|
| 25 |
+
"_model_module": "@jupyter-widgets/controls",
|
| 26 |
+
"_model_module_version": "1.5.0",
|
| 27 |
+
"_model_name": "HBoxModel",
|
| 28 |
+
"_view_count": null,
|
| 29 |
+
"_view_module": "@jupyter-widgets/controls",
|
| 30 |
+
"_view_module_version": "1.5.0",
|
| 31 |
+
"_view_name": "HBoxView",
|
| 32 |
+
"box_style": "",
|
| 33 |
+
"children": [
|
| 34 |
+
"IPY_MODEL_ba9f0a636b12483092c98a04425fabf3",
|
| 35 |
+
"IPY_MODEL_62270654be6d449e95ae164b45bedc7f",
|
| 36 |
+
"IPY_MODEL_b76d24c1fa2e4039a7ba04ca48c0a6fe"
|
| 37 |
+
],
|
| 38 |
+
"layout": "IPY_MODEL_97f44ea194ef4907848c95520687b5d5"
|
| 39 |
+
}
|
| 40 |
+
},
|
| 41 |
+
"ba9f0a636b12483092c98a04425fabf3": {
|
| 42 |
+
"model_module": "@jupyter-widgets/controls",
|
| 43 |
+
"model_name": "HTMLModel",
|
| 44 |
+
"model_module_version": "1.5.0",
|
| 45 |
+
"state": {
|
| 46 |
+
"_dom_classes": [],
|
| 47 |
+
"_model_module": "@jupyter-widgets/controls",
|
| 48 |
+
"_model_module_version": "1.5.0",
|
| 49 |
+
"_model_name": "HTMLModel",
|
| 50 |
+
"_view_count": null,
|
| 51 |
+
"_view_module": "@jupyter-widgets/controls",
|
| 52 |
+
"_view_module_version": "1.5.0",
|
| 53 |
+
"_view_name": "HTMLView",
|
| 54 |
+
"description": "",
|
| 55 |
+
"description_tooltip": null,
|
| 56 |
+
"layout": "IPY_MODEL_a1d0729c068349818e258c1adafa0444",
|
| 57 |
+
"placeholder": "",
|
| 58 |
+
"style": "IPY_MODEL_d5e5e8f006b24da282268d6169cb0d2d",
|
| 59 |
+
"value": "(…)gps_regressor_complete_changed_betas.pth: 100%"
|
| 60 |
+
}
|
| 61 |
+
},
|
| 62 |
+
"62270654be6d449e95ae164b45bedc7f": {
|
| 63 |
+
"model_module": "@jupyter-widgets/controls",
|
| 64 |
+
"model_name": "FloatProgressModel",
|
| 65 |
+
"model_module_version": "1.5.0",
|
| 66 |
+
"state": {
|
| 67 |
+
"_dom_classes": [],
|
| 68 |
+
"_model_module": "@jupyter-widgets/controls",
|
| 69 |
+
"_model_module_version": "1.5.0",
|
| 70 |
+
"_model_name": "FloatProgressModel",
|
| 71 |
+
"_view_count": null,
|
| 72 |
+
"_view_module": "@jupyter-widgets/controls",
|
| 73 |
+
"_view_module_version": "1.5.0",
|
| 74 |
+
"_view_name": "ProgressView",
|
| 75 |
+
"bar_style": "success",
|
| 76 |
+
"description": "",
|
| 77 |
+
"description_tooltip": null,
|
| 78 |
+
"layout": "IPY_MODEL_48807f50f9e94dacb2234a05e0d6b261",
|
| 79 |
+
"max": 81775818,
|
| 80 |
+
"min": 0,
|
| 81 |
+
"orientation": "horizontal",
|
| 82 |
+
"style": "IPY_MODEL_f6a23b7080bd4281ad660b06a2817e8b",
|
| 83 |
+
"value": 81775818
|
| 84 |
+
}
|
| 85 |
+
},
|
| 86 |
+
"b76d24c1fa2e4039a7ba04ca48c0a6fe": {
|
| 87 |
+
"model_module": "@jupyter-widgets/controls",
|
| 88 |
+
"model_name": "HTMLModel",
|
| 89 |
+
"model_module_version": "1.5.0",
|
| 90 |
+
"state": {
|
| 91 |
+
"_dom_classes": [],
|
| 92 |
+
"_model_module": "@jupyter-widgets/controls",
|
| 93 |
+
"_model_module_version": "1.5.0",
|
| 94 |
+
"_model_name": "HTMLModel",
|
| 95 |
+
"_view_count": null,
|
| 96 |
+
"_view_module": "@jupyter-widgets/controls",
|
| 97 |
+
"_view_module_version": "1.5.0",
|
| 98 |
+
"_view_name": "HTMLView",
|
| 99 |
+
"description": "",
|
| 100 |
+
"description_tooltip": null,
|
| 101 |
+
"layout": "IPY_MODEL_b8ddf21d3a104f35b6c978287e4bc550",
|
| 102 |
+
"placeholder": "",
|
| 103 |
+
"style": "IPY_MODEL_00f4efad12aa4342a2b312ddc0c8d91c",
|
| 104 |
+
"value": " 81.8M/81.8M [00:03<00:00, 26.2MB/s]"
|
| 105 |
+
}
|
| 106 |
+
},
|
| 107 |
+
"97f44ea194ef4907848c95520687b5d5": {
|
| 108 |
+
"model_module": "@jupyter-widgets/base",
|
| 109 |
+
"model_name": "LayoutModel",
|
| 110 |
+
"model_module_version": "1.2.0",
|
| 111 |
+
"state": {
|
| 112 |
+
"_model_module": "@jupyter-widgets/base",
|
| 113 |
+
"_model_module_version": "1.2.0",
|
| 114 |
+
"_model_name": "LayoutModel",
|
| 115 |
+
"_view_count": null,
|
| 116 |
+
"_view_module": "@jupyter-widgets/base",
|
| 117 |
+
"_view_module_version": "1.2.0",
|
| 118 |
+
"_view_name": "LayoutView",
|
| 119 |
+
"align_content": null,
|
| 120 |
+
"align_items": null,
|
| 121 |
+
"align_self": null,
|
| 122 |
+
"border": null,
|
| 123 |
+
"bottom": null,
|
| 124 |
+
"display": null,
|
| 125 |
+
"flex": null,
|
| 126 |
+
"flex_flow": null,
|
| 127 |
+
"grid_area": null,
|
| 128 |
+
"grid_auto_columns": null,
|
| 129 |
+
"grid_auto_flow": null,
|
| 130 |
+
"grid_auto_rows": null,
|
| 131 |
+
"grid_column": null,
|
| 132 |
+
"grid_gap": null,
|
| 133 |
+
"grid_row": null,
|
| 134 |
+
"grid_template_areas": null,
|
| 135 |
+
"grid_template_columns": null,
|
| 136 |
+
"grid_template_rows": null,
|
| 137 |
+
"height": null,
|
| 138 |
+
"justify_content": null,
|
| 139 |
+
"justify_items": null,
|
| 140 |
+
"left": null,
|
| 141 |
+
"margin": null,
|
| 142 |
+
"max_height": null,
|
| 143 |
+
"max_width": null,
|
| 144 |
+
"min_height": null,
|
| 145 |
+
"min_width": null,
|
| 146 |
+
"object_fit": null,
|
| 147 |
+
"object_position": null,
|
| 148 |
+
"order": null,
|
| 149 |
+
"overflow": null,
|
| 150 |
+
"overflow_x": null,
|
| 151 |
+
"overflow_y": null,
|
| 152 |
+
"padding": null,
|
| 153 |
+
"right": null,
|
| 154 |
+
"top": null,
|
| 155 |
+
"visibility": null,
|
| 156 |
+
"width": null
|
| 157 |
+
}
|
| 158 |
+
},
|
| 159 |
+
"a1d0729c068349818e258c1adafa0444": {
|
| 160 |
+
"model_module": "@jupyter-widgets/base",
|
| 161 |
+
"model_name": "LayoutModel",
|
| 162 |
+
"model_module_version": "1.2.0",
|
| 163 |
+
"state": {
|
| 164 |
+
"_model_module": "@jupyter-widgets/base",
|
| 165 |
+
"_model_module_version": "1.2.0",
|
| 166 |
+
"_model_name": "LayoutModel",
|
| 167 |
+
"_view_count": null,
|
| 168 |
+
"_view_module": "@jupyter-widgets/base",
|
| 169 |
+
"_view_module_version": "1.2.0",
|
| 170 |
+
"_view_name": "LayoutView",
|
| 171 |
+
"align_content": null,
|
| 172 |
+
"align_items": null,
|
| 173 |
+
"align_self": null,
|
| 174 |
+
"border": null,
|
| 175 |
+
"bottom": null,
|
| 176 |
+
"display": null,
|
| 177 |
+
"flex": null,
|
| 178 |
+
"flex_flow": null,
|
| 179 |
+
"grid_area": null,
|
| 180 |
+
"grid_auto_columns": null,
|
| 181 |
+
"grid_auto_flow": null,
|
| 182 |
+
"grid_auto_rows": null,
|
| 183 |
+
"grid_column": null,
|
| 184 |
+
"grid_gap": null,
|
| 185 |
+
"grid_row": null,
|
| 186 |
+
"grid_template_areas": null,
|
| 187 |
+
"grid_template_columns": null,
|
| 188 |
+
"grid_template_rows": null,
|
| 189 |
+
"height": null,
|
| 190 |
+
"justify_content": null,
|
| 191 |
+
"justify_items": null,
|
| 192 |
+
"left": null,
|
| 193 |
+
"margin": null,
|
| 194 |
+
"max_height": null,
|
| 195 |
+
"max_width": null,
|
| 196 |
+
"min_height": null,
|
| 197 |
+
"min_width": null,
|
| 198 |
+
"object_fit": null,
|
| 199 |
+
"object_position": null,
|
| 200 |
+
"order": null,
|
| 201 |
+
"overflow": null,
|
| 202 |
+
"overflow_x": null,
|
| 203 |
+
"overflow_y": null,
|
| 204 |
+
"padding": null,
|
| 205 |
+
"right": null,
|
| 206 |
+
"top": null,
|
| 207 |
+
"visibility": null,
|
| 208 |
+
"width": null
|
| 209 |
+
}
|
| 210 |
+
},
|
| 211 |
+
"d5e5e8f006b24da282268d6169cb0d2d": {
|
| 212 |
+
"model_module": "@jupyter-widgets/controls",
|
| 213 |
+
"model_name": "DescriptionStyleModel",
|
| 214 |
+
"model_module_version": "1.5.0",
|
| 215 |
+
"state": {
|
| 216 |
+
"_model_module": "@jupyter-widgets/controls",
|
| 217 |
+
"_model_module_version": "1.5.0",
|
| 218 |
+
"_model_name": "DescriptionStyleModel",
|
| 219 |
+
"_view_count": null,
|
| 220 |
+
"_view_module": "@jupyter-widgets/base",
|
| 221 |
+
"_view_module_version": "1.2.0",
|
| 222 |
+
"_view_name": "StyleView",
|
| 223 |
+
"description_width": ""
|
| 224 |
+
}
|
| 225 |
+
},
|
| 226 |
+
"48807f50f9e94dacb2234a05e0d6b261": {
|
| 227 |
+
"model_module": "@jupyter-widgets/base",
|
| 228 |
+
"model_name": "LayoutModel",
|
| 229 |
+
"model_module_version": "1.2.0",
|
| 230 |
+
"state": {
|
| 231 |
+
"_model_module": "@jupyter-widgets/base",
|
| 232 |
+
"_model_module_version": "1.2.0",
|
| 233 |
+
"_model_name": "LayoutModel",
|
| 234 |
+
"_view_count": null,
|
| 235 |
+
"_view_module": "@jupyter-widgets/base",
|
| 236 |
+
"_view_module_version": "1.2.0",
|
| 237 |
+
"_view_name": "LayoutView",
|
| 238 |
+
"align_content": null,
|
| 239 |
+
"align_items": null,
|
| 240 |
+
"align_self": null,
|
| 241 |
+
"border": null,
|
| 242 |
+
"bottom": null,
|
| 243 |
+
"display": null,
|
| 244 |
+
"flex": null,
|
| 245 |
+
"flex_flow": null,
|
| 246 |
+
"grid_area": null,
|
| 247 |
+
"grid_auto_columns": null,
|
| 248 |
+
"grid_auto_flow": null,
|
| 249 |
+
"grid_auto_rows": null,
|
| 250 |
+
"grid_column": null,
|
| 251 |
+
"grid_gap": null,
|
| 252 |
+
"grid_row": null,
|
| 253 |
+
"grid_template_areas": null,
|
| 254 |
+
"grid_template_columns": null,
|
| 255 |
+
"grid_template_rows": null,
|
| 256 |
+
"height": null,
|
| 257 |
+
"justify_content": null,
|
| 258 |
+
"justify_items": null,
|
| 259 |
+
"left": null,
|
| 260 |
+
"margin": null,
|
| 261 |
+
"max_height": null,
|
| 262 |
+
"max_width": null,
|
| 263 |
+
"min_height": null,
|
| 264 |
+
"min_width": null,
|
| 265 |
+
"object_fit": null,
|
| 266 |
+
"object_position": null,
|
| 267 |
+
"order": null,
|
| 268 |
+
"overflow": null,
|
| 269 |
+
"overflow_x": null,
|
| 270 |
+
"overflow_y": null,
|
| 271 |
+
"padding": null,
|
| 272 |
+
"right": null,
|
| 273 |
+
"top": null,
|
| 274 |
+
"visibility": null,
|
| 275 |
+
"width": null
|
| 276 |
+
}
|
| 277 |
+
},
|
| 278 |
+
"f6a23b7080bd4281ad660b06a2817e8b": {
|
| 279 |
+
"model_module": "@jupyter-widgets/controls",
|
| 280 |
+
"model_name": "ProgressStyleModel",
|
| 281 |
+
"model_module_version": "1.5.0",
|
| 282 |
+
"state": {
|
| 283 |
+
"_model_module": "@jupyter-widgets/controls",
|
| 284 |
+
"_model_module_version": "1.5.0",
|
| 285 |
+
"_model_name": "ProgressStyleModel",
|
| 286 |
+
"_view_count": null,
|
| 287 |
+
"_view_module": "@jupyter-widgets/base",
|
| 288 |
+
"_view_module_version": "1.2.0",
|
| 289 |
+
"_view_name": "StyleView",
|
| 290 |
+
"bar_color": null,
|
| 291 |
+
"description_width": ""
|
| 292 |
+
}
|
| 293 |
+
},
|
| 294 |
+
"b8ddf21d3a104f35b6c978287e4bc550": {
|
| 295 |
+
"model_module": "@jupyter-widgets/base",
|
| 296 |
+
"model_name": "LayoutModel",
|
| 297 |
+
"model_module_version": "1.2.0",
|
| 298 |
+
"state": {
|
| 299 |
+
"_model_module": "@jupyter-widgets/base",
|
| 300 |
+
"_model_module_version": "1.2.0",
|
| 301 |
+
"_model_name": "LayoutModel",
|
| 302 |
+
"_view_count": null,
|
| 303 |
+
"_view_module": "@jupyter-widgets/base",
|
| 304 |
+
"_view_module_version": "1.2.0",
|
| 305 |
+
"_view_name": "LayoutView",
|
| 306 |
+
"align_content": null,
|
| 307 |
+
"align_items": null,
|
| 308 |
+
"align_self": null,
|
| 309 |
+
"border": null,
|
| 310 |
+
"bottom": null,
|
| 311 |
+
"display": null,
|
| 312 |
+
"flex": null,
|
| 313 |
+
"flex_flow": null,
|
| 314 |
+
"grid_area": null,
|
| 315 |
+
"grid_auto_columns": null,
|
| 316 |
+
"grid_auto_flow": null,
|
| 317 |
+
"grid_auto_rows": null,
|
| 318 |
+
"grid_column": null,
|
| 319 |
+
"grid_gap": null,
|
| 320 |
+
"grid_row": null,
|
| 321 |
+
"grid_template_areas": null,
|
| 322 |
+
"grid_template_columns": null,
|
| 323 |
+
"grid_template_rows": null,
|
| 324 |
+
"height": null,
|
| 325 |
+
"justify_content": null,
|
| 326 |
+
"justify_items": null,
|
| 327 |
+
"left": null,
|
| 328 |
+
"margin": null,
|
| 329 |
+
"max_height": null,
|
| 330 |
+
"max_width": null,
|
| 331 |
+
"min_height": null,
|
| 332 |
+
"min_width": null,
|
| 333 |
+
"object_fit": null,
|
| 334 |
+
"object_position": null,
|
| 335 |
+
"order": null,
|
| 336 |
+
"overflow": null,
|
| 337 |
+
"overflow_x": null,
|
| 338 |
+
"overflow_y": null,
|
| 339 |
+
"padding": null,
|
| 340 |
+
"right": null,
|
| 341 |
+
"top": null,
|
| 342 |
+
"visibility": null,
|
| 343 |
+
"width": null
|
| 344 |
+
}
|
| 345 |
+
},
|
| 346 |
+
"00f4efad12aa4342a2b312ddc0c8d91c": {
|
| 347 |
+
"model_module": "@jupyter-widgets/controls",
|
| 348 |
+
"model_name": "DescriptionStyleModel",
|
| 349 |
+
"model_module_version": "1.5.0",
|
| 350 |
+
"state": {
|
| 351 |
+
"_model_module": "@jupyter-widgets/controls",
|
| 352 |
+
"_model_module_version": "1.5.0",
|
| 353 |
+
"_model_name": "DescriptionStyleModel",
|
| 354 |
+
"_view_count": null,
|
| 355 |
+
"_view_module": "@jupyter-widgets/base",
|
| 356 |
+
"_view_module_version": "1.2.0",
|
| 357 |
+
"_view_name": "StyleView",
|
| 358 |
+
"description_width": ""
|
| 359 |
+
}
|
| 360 |
+
}
|
| 361 |
+
}
|
| 362 |
+
}
|
| 363 |
+
},
|
| 364 |
+
"cells": [
|
| 365 |
+
{
|
| 366 |
+
"cell_type": "code",
|
| 367 |
+
"execution_count": null,
|
| 368 |
+
"metadata": {
|
| 369 |
+
"colab": {
|
| 370 |
+
"base_uri": "https://localhost:8080/"
|
| 371 |
+
},
|
| 372 |
+
"id": "uoEz8DyTYUts",
|
| 373 |
+
"outputId": "9fcffbe6-f9a0-4848-c83f-4463f73631c5"
|
| 374 |
+
},
|
| 375 |
+
"outputs": [
|
| 376 |
+
{
|
| 377 |
+
"output_type": "stream",
|
| 378 |
+
"name": "stdout",
|
| 379 |
+
"text": [
|
| 380 |
+
"Collecting datasets\n",
|
| 381 |
+
" Downloading datasets-3.2.0-py3-none-any.whl.metadata (20 kB)\n",
|
| 382 |
+
"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.16.1)\n",
|
| 383 |
+
"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.26.4)\n",
|
| 384 |
+
"Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (17.0.0)\n",
|
| 385 |
+
"Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
|
| 386 |
+
" Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n",
|
| 387 |
+
"Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.2.2)\n",
|
| 388 |
+
"Requirement already satisfied: requests>=2.32.2 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.32.3)\n",
|
| 389 |
+
"Requirement already satisfied: tqdm>=4.66.3 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.6)\n",
|
| 390 |
+
"Collecting xxhash (from datasets)\n",
|
| 391 |
+
" Downloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
|
| 392 |
+
"Collecting multiprocess<0.70.17 (from datasets)\n",
|
| 393 |
+
" Downloading multiprocess-0.70.16-py310-none-any.whl.metadata (7.2 kB)\n",
|
| 394 |
+
"Collecting fsspec<=2024.9.0,>=2023.1.0 (from fsspec[http]<=2024.9.0,>=2023.1.0->datasets)\n",
|
| 395 |
+
" Downloading fsspec-2024.9.0-py3-none-any.whl.metadata (11 kB)\n",
|
| 396 |
+
"Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.11.10)\n",
|
| 397 |
+
"Requirement already satisfied: huggingface-hub>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.26.5)\n",
|
| 398 |
+
"Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (24.2)\n",
|
| 399 |
+
"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0.2)\n",
|
| 400 |
+
"Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (2.4.4)\n",
|
| 401 |
+
"Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
|
| 402 |
+
"Requirement already satisfied: async-timeout<6.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
|
| 403 |
+
"Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (24.2.0)\n",
|
| 404 |
+
"Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.5.0)\n",
|
| 405 |
+
"Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.1.0)\n",
|
| 406 |
+
"Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (0.2.1)\n",
|
| 407 |
+
"Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.18.3)\n",
|
| 408 |
+
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.0->datasets) (4.12.2)\n",
|
| 409 |
+
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (3.4.0)\n",
|
| 410 |
+
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (3.10)\n",
|
| 411 |
+
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2.2.3)\n",
|
| 412 |
+
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2024.8.30)\n",
|
| 413 |
+
"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
|
| 414 |
+
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.2)\n",
|
| 415 |
+
"Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.2)\n",
|
| 416 |
+
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n",
|
| 417 |
+
"Downloading datasets-3.2.0-py3-none-any.whl (480 kB)\n",
|
| 418 |
+
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m480.6/480.6 kB\u001b[0m \u001b[31m15.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 419 |
+
"\u001b[?25hDownloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
|
| 420 |
+
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 421 |
+
"\u001b[?25hDownloading fsspec-2024.9.0-py3-none-any.whl (179 kB)\n",
|
| 422 |
+
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.3/179.3 kB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 423 |
+
"\u001b[?25hDownloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
|
| 424 |
+
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 425 |
+
"\u001b[?25hDownloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
|
| 426 |
+
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 427 |
+
"\u001b[?25hInstalling collected packages: xxhash, fsspec, dill, multiprocess, datasets\n",
|
| 428 |
+
" Attempting uninstall: fsspec\n",
|
| 429 |
+
" Found existing installation: fsspec 2024.10.0\n",
|
| 430 |
+
" Uninstalling fsspec-2024.10.0:\n",
|
| 431 |
+
" Successfully uninstalled fsspec-2024.10.0\n",
|
| 432 |
+
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
|
| 433 |
+
"gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
|
| 434 |
+
"\u001b[0mSuccessfully installed datasets-3.2.0 dill-0.3.8 fsspec-2024.9.0 multiprocess-0.70.16 xxhash-3.5.0\n"
|
| 435 |
+
]
|
| 436 |
+
}
|
| 437 |
+
],
|
| 438 |
+
"source": [
|
| 439 |
+
"!pip install datasets"
|
| 440 |
+
]
|
| 441 |
+
},
|
| 442 |
+
{
|
| 443 |
+
"cell_type": "code",
|
| 444 |
+
"source": [
|
| 445 |
+
"!pip install huggingface_hub"
|
| 446 |
+
],
|
| 447 |
+
"metadata": {
|
| 448 |
+
"colab": {
|
| 449 |
+
"base_uri": "https://localhost:8080/"
|
| 450 |
+
},
|
| 451 |
+
"id": "pnoqloVaYcZc",
|
| 452 |
+
"outputId": "f5d8c8c2-5c6d-4b95-b10a-1d2b95be1d1d"
|
| 453 |
+
},
|
| 454 |
+
"execution_count": null,
|
| 455 |
+
"outputs": [
|
| 456 |
+
{
|
| 457 |
+
"output_type": "stream",
|
| 458 |
+
"name": "stdout",
|
| 459 |
+
"text": [
|
| 460 |
+
"Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (0.26.5)\n",
|
| 461 |
+
"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (3.16.1)\n",
|
| 462 |
+
"Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2024.9.0)\n",
|
| 463 |
+
"Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (24.2)\n",
|
| 464 |
+
"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (6.0.2)\n",
|
| 465 |
+
"Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2.32.3)\n",
|
| 466 |
+
"Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.66.6)\n",
|
| 467 |
+
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.12.2)\n",
|
| 468 |
+
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.4.0)\n",
|
| 469 |
+
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.10)\n",
|
| 470 |
+
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2.2.3)\n",
|
| 471 |
+
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2024.8.30)\n"
|
| 472 |
+
]
|
| 473 |
+
}
|
| 474 |
+
]
|
| 475 |
+
},
|
| 476 |
+
{
|
| 477 |
+
"cell_type": "code",
|
| 478 |
+
"source": [
|
| 479 |
+
"!pip install requests"
|
| 480 |
+
],
|
| 481 |
+
"metadata": {
|
| 482 |
+
"colab": {
|
| 483 |
+
"base_uri": "https://localhost:8080/"
|
| 484 |
+
},
|
| 485 |
+
"id": "AV0maTmHY-ub",
|
| 486 |
+
"outputId": "e79de750-361d-4272-fcd7-fc83bde58d6d"
|
| 487 |
+
},
|
| 488 |
+
"execution_count": null,
|
| 489 |
+
"outputs": [
|
| 490 |
+
{
|
| 491 |
+
"output_type": "stream",
|
| 492 |
+
"name": "stdout",
|
| 493 |
+
"text": [
|
| 494 |
+
"Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (2.32.3)\n",
|
| 495 |
+
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests) (3.4.0)\n",
|
| 496 |
+
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests) (3.10)\n",
|
| 497 |
+
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests) (2.2.3)\n",
|
| 498 |
+
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests) (2024.8.30)\n"
|
| 499 |
+
]
|
| 500 |
+
}
|
| 501 |
+
]
|
| 502 |
+
},
|
| 503 |
+
{
|
| 504 |
+
"cell_type": "code",
|
| 505 |
+
"source": [
|
| 506 |
+
"import torch\n",
|
| 507 |
+
"import torch.nn as nn\n",
|
| 508 |
+
"import torch.optim as optim\n",
|
| 509 |
+
"from torchvision.models import efficientnet_v2_s\n",
|
| 510 |
+
"from torch.optim.lr_scheduler import CosineAnnealingLR\n",
|
| 511 |
+
"from torchvision import transforms\n",
|
| 512 |
+
"from torch.utils.data import DataLoader, Dataset\n",
|
| 513 |
+
"from torchvision.transforms import functional as F\n",
|
| 514 |
+
"from PIL import Image\n",
|
| 515 |
+
"import numpy as np\n",
|
| 516 |
+
"from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
|
| 517 |
+
"from huggingface_hub import PyTorchModelHubMixin\n",
|
| 518 |
+
"import os\n",
|
| 519 |
+
"\n",
|
| 520 |
+
"\n",
|
| 521 |
+
"# Model Definition\n",
|
| 522 |
+
"class CustomGPSModel(nn.Module):\n",
|
| 523 |
+
" def __init__(self):\n",
|
| 524 |
+
" super(CustomGPSModel, self).__init__()\n",
|
| 525 |
+
"\n",
|
| 526 |
+
" # Load EfficientNetV2-S with pretrained weights\n",
|
| 527 |
+
" self.efficientnet = efficientnet_v2_s(pretrained=True)\n",
|
| 528 |
+
"\n",
|
| 529 |
+
" # Modify the final layer for regression (predicting latitude and longitude)\n",
|
| 530 |
+
" num_features = self.efficientnet.classifier[1].in_features\n",
|
| 531 |
+
" self.efficientnet.classifier[1] = nn.Linear(num_features, 2) # Output layer has 2 outputs for latitude & longitude\n",
|
| 532 |
+
"\n",
|
| 533 |
+
" # Don't freeze earlier layers\n",
|
| 534 |
+
" for param in self.efficientnet.features.parameters():\n",
|
| 535 |
+
" param.requires_grad = True\n",
|
| 536 |
+
"\n",
|
| 537 |
+
" def forward(self, x):\n",
|
| 538 |
+
" return self.efficientnet(x) # Forward pass through EfficientNet"
|
| 539 |
+
],
|
| 540 |
+
"metadata": {
|
| 541 |
+
"id": "uBlw8T7r-EmY"
|
| 542 |
+
},
|
| 543 |
+
"execution_count": null,
|
| 544 |
+
"outputs": []
|
| 545 |
+
},
|
| 546 |
+
{
|
| 547 |
+
"cell_type": "code",
|
| 548 |
+
"source": [
|
| 549 |
+
"from huggingface_hub import hf_hub_download\n",
|
| 550 |
+
"import torch\n",
|
| 551 |
+
"\n",
|
| 552 |
+
"path_name = \"efficientnet_gps_regressor_complete_changed_betas_v2.pth\"\n",
|
| 553 |
+
"repo_name = \"CustomGPSModel_EfficientNetV2_Run3\"\n",
|
| 554 |
+
"organization_name = \"LAJ-519-Image-Project\"\n",
|
| 555 |
+
"\n",
|
| 556 |
+
"# Specify the repository and the filename of the model you want to load\n",
|
| 557 |
+
"repo_id = f\"{organization_name}/{repo_name}\"\n",
|
| 558 |
+
"filename = f\"{path_name}\"\n",
|
| 559 |
+
"\n",
|
| 560 |
+
"model_path = hf_hub_download(repo_id=repo_id, filename=filename)\n",
|
| 561 |
+
"\n",
|
| 562 |
+
"# Load the model using torch\n",
|
| 563 |
+
"model_test = torch.load(model_path)\n",
|
| 564 |
+
"model_test.eval()"
|
| 565 |
+
],
|
| 566 |
+
"metadata": {
|
| 567 |
+
"colab": {
|
| 568 |
+
"base_uri": "https://localhost:8080/",
|
| 569 |
+
"height": 1000,
|
| 570 |
+
"referenced_widgets": [
|
| 571 |
+
"ea8bed4c2f59465c960a0f7959d11d5a",
|
| 572 |
+
"ba9f0a636b12483092c98a04425fabf3",
|
| 573 |
+
"62270654be6d449e95ae164b45bedc7f",
|
| 574 |
+
"b76d24c1fa2e4039a7ba04ca48c0a6fe",
|
| 575 |
+
"97f44ea194ef4907848c95520687b5d5",
|
| 576 |
+
"a1d0729c068349818e258c1adafa0444",
|
| 577 |
+
"d5e5e8f006b24da282268d6169cb0d2d",
|
| 578 |
+
"48807f50f9e94dacb2234a05e0d6b261",
|
| 579 |
+
"f6a23b7080bd4281ad660b06a2817e8b",
|
| 580 |
+
"b8ddf21d3a104f35b6c978287e4bc550",
|
| 581 |
+
"00f4efad12aa4342a2b312ddc0c8d91c"
|
| 582 |
+
]
|
| 583 |
+
},
|
| 584 |
+
"id": "eVrRN-sKZQbq",
|
| 585 |
+
"outputId": "6ac1b12c-0128-4333-e513-d83cc24abf0f"
|
| 586 |
+
},
|
| 587 |
+
"execution_count": null,
|
| 588 |
+
"outputs": [
|
| 589 |
+
{
|
| 590 |
+
"output_type": "stream",
|
| 591 |
+
"name": "stderr",
|
| 592 |
+
"text": [
|
| 593 |
+
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
|
| 594 |
+
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
|
| 595 |
+
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
|
| 596 |
+
"You will be able to reuse this secret in all of your notebooks.\n",
|
| 597 |
+
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
|
| 598 |
+
" warnings.warn(\n"
|
| 599 |
+
]
|
| 600 |
+
},
|
| 601 |
+
{
|
| 602 |
+
"output_type": "display_data",
|
| 603 |
+
"data": {
|
| 604 |
+
"text/plain": [
|
| 605 |
+
"(…)gps_regressor_complete_changed_betas.pth: 0%| | 0.00/81.8M [00:00<?, ?B/s]"
|
| 606 |
+
],
|
| 607 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 608 |
+
"version_major": 2,
|
| 609 |
+
"version_minor": 0,
|
| 610 |
+
"model_id": "ea8bed4c2f59465c960a0f7959d11d5a"
|
| 611 |
+
}
|
| 612 |
+
},
|
| 613 |
+
"metadata": {}
|
| 614 |
+
},
|
| 615 |
+
{
|
| 616 |
+
"output_type": "stream",
|
| 617 |
+
"name": "stderr",
|
| 618 |
+
"text": [
|
| 619 |
+
"<ipython-input-5-e047d6f1184c>:15: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
|
| 620 |
+
" model_test = torch.load(model_path)\n"
|
| 621 |
+
]
|
| 622 |
+
},
|
| 623 |
+
{
|
| 624 |
+
"output_type": "execute_result",
|
| 625 |
+
"data": {
|
| 626 |
+
"text/plain": [
|
| 627 |
+
"CustomGPSModel(\n",
|
| 628 |
+
" (efficientnet): EfficientNet(\n",
|
| 629 |
+
" (features): Sequential(\n",
|
| 630 |
+
" (0): Conv2dNormActivation(\n",
|
| 631 |
+
" (0): Conv2d(3, 24, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
|
| 632 |
+
" (1): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 633 |
+
" (2): SiLU(inplace=True)\n",
|
| 634 |
+
" )\n",
|
| 635 |
+
" (1): Sequential(\n",
|
| 636 |
+
" (0): FusedMBConv(\n",
|
| 637 |
+
" (block): Sequential(\n",
|
| 638 |
+
" (0): Conv2dNormActivation(\n",
|
| 639 |
+
" (0): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
| 640 |
+
" (1): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 641 |
+
" (2): SiLU(inplace=True)\n",
|
| 642 |
+
" )\n",
|
| 643 |
+
" )\n",
|
| 644 |
+
" (stochastic_depth): StochasticDepth(p=0.0, mode=row)\n",
|
| 645 |
+
" )\n",
|
| 646 |
+
" (1): FusedMBConv(\n",
|
| 647 |
+
" (block): Sequential(\n",
|
| 648 |
+
" (0): Conv2dNormActivation(\n",
|
| 649 |
+
" (0): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
| 650 |
+
" (1): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 651 |
+
" (2): SiLU(inplace=True)\n",
|
| 652 |
+
" )\n",
|
| 653 |
+
" )\n",
|
| 654 |
+
" (stochastic_depth): StochasticDepth(p=0.005, mode=row)\n",
|
| 655 |
+
" )\n",
|
| 656 |
+
" )\n",
|
| 657 |
+
" (2): Sequential(\n",
|
| 658 |
+
" (0): FusedMBConv(\n",
|
| 659 |
+
" (block): Sequential(\n",
|
| 660 |
+
" (0): Conv2dNormActivation(\n",
|
| 661 |
+
" (0): Conv2d(24, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
|
| 662 |
+
" (1): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 663 |
+
" (2): SiLU(inplace=True)\n",
|
| 664 |
+
" )\n",
|
| 665 |
+
" (1): Conv2dNormActivation(\n",
|
| 666 |
+
" (0): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 667 |
+
" (1): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 668 |
+
" )\n",
|
| 669 |
+
" )\n",
|
| 670 |
+
" (stochastic_depth): StochasticDepth(p=0.01, mode=row)\n",
|
| 671 |
+
" )\n",
|
| 672 |
+
" (1): FusedMBConv(\n",
|
| 673 |
+
" (block): Sequential(\n",
|
| 674 |
+
" (0): Conv2dNormActivation(\n",
|
| 675 |
+
" (0): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
| 676 |
+
" (1): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 677 |
+
" (2): SiLU(inplace=True)\n",
|
| 678 |
+
" )\n",
|
| 679 |
+
" (1): Conv2dNormActivation(\n",
|
| 680 |
+
" (0): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 681 |
+
" (1): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 682 |
+
" )\n",
|
| 683 |
+
" )\n",
|
| 684 |
+
" (stochastic_depth): StochasticDepth(p=0.015000000000000003, mode=row)\n",
|
| 685 |
+
" )\n",
|
| 686 |
+
" (2): FusedMBConv(\n",
|
| 687 |
+
" (block): Sequential(\n",
|
| 688 |
+
" (0): Conv2dNormActivation(\n",
|
| 689 |
+
" (0): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
| 690 |
+
" (1): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 691 |
+
" (2): SiLU(inplace=True)\n",
|
| 692 |
+
" )\n",
|
| 693 |
+
" (1): Conv2dNormActivation(\n",
|
| 694 |
+
" (0): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 695 |
+
" (1): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 696 |
+
" )\n",
|
| 697 |
+
" )\n",
|
| 698 |
+
" (stochastic_depth): StochasticDepth(p=0.02, mode=row)\n",
|
| 699 |
+
" )\n",
|
| 700 |
+
" (3): FusedMBConv(\n",
|
| 701 |
+
" (block): Sequential(\n",
|
| 702 |
+
" (0): Conv2dNormActivation(\n",
|
| 703 |
+
" (0): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
| 704 |
+
" (1): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 705 |
+
" (2): SiLU(inplace=True)\n",
|
| 706 |
+
" )\n",
|
| 707 |
+
" (1): Conv2dNormActivation(\n",
|
| 708 |
+
" (0): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 709 |
+
" (1): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 710 |
+
" )\n",
|
| 711 |
+
" )\n",
|
| 712 |
+
" (stochastic_depth): StochasticDepth(p=0.025, mode=row)\n",
|
| 713 |
+
" )\n",
|
| 714 |
+
" )\n",
|
| 715 |
+
" (3): Sequential(\n",
|
| 716 |
+
" (0): FusedMBConv(\n",
|
| 717 |
+
" (block): Sequential(\n",
|
| 718 |
+
" (0): Conv2dNormActivation(\n",
|
| 719 |
+
" (0): Conv2d(48, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
|
| 720 |
+
" (1): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 721 |
+
" (2): SiLU(inplace=True)\n",
|
| 722 |
+
" )\n",
|
| 723 |
+
" (1): Conv2dNormActivation(\n",
|
| 724 |
+
" (0): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 725 |
+
" (1): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 726 |
+
" )\n",
|
| 727 |
+
" )\n",
|
| 728 |
+
" (stochastic_depth): StochasticDepth(p=0.030000000000000006, mode=row)\n",
|
| 729 |
+
" )\n",
|
| 730 |
+
" (1): FusedMBConv(\n",
|
| 731 |
+
" (block): Sequential(\n",
|
| 732 |
+
" (0): Conv2dNormActivation(\n",
|
| 733 |
+
" (0): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
| 734 |
+
" (1): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 735 |
+
" (2): SiLU(inplace=True)\n",
|
| 736 |
+
" )\n",
|
| 737 |
+
" (1): Conv2dNormActivation(\n",
|
| 738 |
+
" (0): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 739 |
+
" (1): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 740 |
+
" )\n",
|
| 741 |
+
" )\n",
|
| 742 |
+
" (stochastic_depth): StochasticDepth(p=0.035, mode=row)\n",
|
| 743 |
+
" )\n",
|
| 744 |
+
" (2): FusedMBConv(\n",
|
| 745 |
+
" (block): Sequential(\n",
|
| 746 |
+
" (0): Conv2dNormActivation(\n",
|
| 747 |
+
" (0): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
| 748 |
+
" (1): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 749 |
+
" (2): SiLU(inplace=True)\n",
|
| 750 |
+
" )\n",
|
| 751 |
+
" (1): Conv2dNormActivation(\n",
|
| 752 |
+
" (0): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 753 |
+
" (1): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 754 |
+
" )\n",
|
| 755 |
+
" )\n",
|
| 756 |
+
" (stochastic_depth): StochasticDepth(p=0.04, mode=row)\n",
|
| 757 |
+
" )\n",
|
| 758 |
+
" (3): FusedMBConv(\n",
|
| 759 |
+
" (block): Sequential(\n",
|
| 760 |
+
" (0): Conv2dNormActivation(\n",
|
| 761 |
+
" (0): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
| 762 |
+
" (1): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 763 |
+
" (2): SiLU(inplace=True)\n",
|
| 764 |
+
" )\n",
|
| 765 |
+
" (1): Conv2dNormActivation(\n",
|
| 766 |
+
" (0): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 767 |
+
" (1): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 768 |
+
" )\n",
|
| 769 |
+
" )\n",
|
| 770 |
+
" (stochastic_depth): StochasticDepth(p=0.045, mode=row)\n",
|
| 771 |
+
" )\n",
|
| 772 |
+
" )\n",
|
| 773 |
+
" (4): Sequential(\n",
|
| 774 |
+
" (0): MBConv(\n",
|
| 775 |
+
" (block): Sequential(\n",
|
| 776 |
+
" (0): Conv2dNormActivation(\n",
|
| 777 |
+
" (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 778 |
+
" (1): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 779 |
+
" (2): SiLU(inplace=True)\n",
|
| 780 |
+
" )\n",
|
| 781 |
+
" (1): Conv2dNormActivation(\n",
|
| 782 |
+
" (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=256, bias=False)\n",
|
| 783 |
+
" (1): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 784 |
+
" (2): SiLU(inplace=True)\n",
|
| 785 |
+
" )\n",
|
| 786 |
+
" (2): SqueezeExcitation(\n",
|
| 787 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 788 |
+
" (fc1): Conv2d(256, 16, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 789 |
+
" (fc2): Conv2d(16, 256, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 790 |
+
" (activation): SiLU(inplace=True)\n",
|
| 791 |
+
" (scale_activation): Sigmoid()\n",
|
| 792 |
+
" )\n",
|
| 793 |
+
" (3): Conv2dNormActivation(\n",
|
| 794 |
+
" (0): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 795 |
+
" (1): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 796 |
+
" )\n",
|
| 797 |
+
" )\n",
|
| 798 |
+
" (stochastic_depth): StochasticDepth(p=0.05, mode=row)\n",
|
| 799 |
+
" )\n",
|
| 800 |
+
" (1): MBConv(\n",
|
| 801 |
+
" (block): Sequential(\n",
|
| 802 |
+
" (0): Conv2dNormActivation(\n",
|
| 803 |
+
" (0): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 804 |
+
" (1): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 805 |
+
" (2): SiLU(inplace=True)\n",
|
| 806 |
+
" )\n",
|
| 807 |
+
" (1): Conv2dNormActivation(\n",
|
| 808 |
+
" (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512, bias=False)\n",
|
| 809 |
+
" (1): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 810 |
+
" (2): SiLU(inplace=True)\n",
|
| 811 |
+
" )\n",
|
| 812 |
+
" (2): SqueezeExcitation(\n",
|
| 813 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 814 |
+
" (fc1): Conv2d(512, 32, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 815 |
+
" (fc2): Conv2d(32, 512, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 816 |
+
" (activation): SiLU(inplace=True)\n",
|
| 817 |
+
" (scale_activation): Sigmoid()\n",
|
| 818 |
+
" )\n",
|
| 819 |
+
" (3): Conv2dNormActivation(\n",
|
| 820 |
+
" (0): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 821 |
+
" (1): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 822 |
+
" )\n",
|
| 823 |
+
" )\n",
|
| 824 |
+
" (stochastic_depth): StochasticDepth(p=0.05500000000000001, mode=row)\n",
|
| 825 |
+
" )\n",
|
| 826 |
+
" (2): MBConv(\n",
|
| 827 |
+
" (block): Sequential(\n",
|
| 828 |
+
" (0): Conv2dNormActivation(\n",
|
| 829 |
+
" (0): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 830 |
+
" (1): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 831 |
+
" (2): SiLU(inplace=True)\n",
|
| 832 |
+
" )\n",
|
| 833 |
+
" (1): Conv2dNormActivation(\n",
|
| 834 |
+
" (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512, bias=False)\n",
|
| 835 |
+
" (1): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 836 |
+
" (2): SiLU(inplace=True)\n",
|
| 837 |
+
" )\n",
|
| 838 |
+
" (2): SqueezeExcitation(\n",
|
| 839 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 840 |
+
" (fc1): Conv2d(512, 32, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 841 |
+
" (fc2): Conv2d(32, 512, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 842 |
+
" (activation): SiLU(inplace=True)\n",
|
| 843 |
+
" (scale_activation): Sigmoid()\n",
|
| 844 |
+
" )\n",
|
| 845 |
+
" (3): Conv2dNormActivation(\n",
|
| 846 |
+
" (0): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 847 |
+
" (1): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 848 |
+
" )\n",
|
| 849 |
+
" )\n",
|
| 850 |
+
" (stochastic_depth): StochasticDepth(p=0.06000000000000001, mode=row)\n",
|
| 851 |
+
" )\n",
|
| 852 |
+
" (3): MBConv(\n",
|
| 853 |
+
" (block): Sequential(\n",
|
| 854 |
+
" (0): Conv2dNormActivation(\n",
|
| 855 |
+
" (0): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 856 |
+
" (1): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 857 |
+
" (2): SiLU(inplace=True)\n",
|
| 858 |
+
" )\n",
|
| 859 |
+
" (1): Conv2dNormActivation(\n",
|
| 860 |
+
" (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512, bias=False)\n",
|
| 861 |
+
" (1): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 862 |
+
" (2): SiLU(inplace=True)\n",
|
| 863 |
+
" )\n",
|
| 864 |
+
" (2): SqueezeExcitation(\n",
|
| 865 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 866 |
+
" (fc1): Conv2d(512, 32, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 867 |
+
" (fc2): Conv2d(32, 512, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 868 |
+
" (activation): SiLU(inplace=True)\n",
|
| 869 |
+
" (scale_activation): Sigmoid()\n",
|
| 870 |
+
" )\n",
|
| 871 |
+
" (3): Conv2dNormActivation(\n",
|
| 872 |
+
" (0): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 873 |
+
" (1): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 874 |
+
" )\n",
|
| 875 |
+
" )\n",
|
| 876 |
+
" (stochastic_depth): StochasticDepth(p=0.065, mode=row)\n",
|
| 877 |
+
" )\n",
|
| 878 |
+
" (4): MBConv(\n",
|
| 879 |
+
" (block): Sequential(\n",
|
| 880 |
+
" (0): Conv2dNormActivation(\n",
|
| 881 |
+
" (0): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 882 |
+
" (1): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 883 |
+
" (2): SiLU(inplace=True)\n",
|
| 884 |
+
" )\n",
|
| 885 |
+
" (1): Conv2dNormActivation(\n",
|
| 886 |
+
" (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512, bias=False)\n",
|
| 887 |
+
" (1): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 888 |
+
" (2): SiLU(inplace=True)\n",
|
| 889 |
+
" )\n",
|
| 890 |
+
" (2): SqueezeExcitation(\n",
|
| 891 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 892 |
+
" (fc1): Conv2d(512, 32, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 893 |
+
" (fc2): Conv2d(32, 512, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 894 |
+
" (activation): SiLU(inplace=True)\n",
|
| 895 |
+
" (scale_activation): Sigmoid()\n",
|
| 896 |
+
" )\n",
|
| 897 |
+
" (3): Conv2dNormActivation(\n",
|
| 898 |
+
" (0): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 899 |
+
" (1): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 900 |
+
" )\n",
|
| 901 |
+
" )\n",
|
| 902 |
+
" (stochastic_depth): StochasticDepth(p=0.07, mode=row)\n",
|
| 903 |
+
" )\n",
|
| 904 |
+
" (5): MBConv(\n",
|
| 905 |
+
" (block): Sequential(\n",
|
| 906 |
+
" (0): Conv2dNormActivation(\n",
|
| 907 |
+
" (0): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 908 |
+
" (1): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 909 |
+
" (2): SiLU(inplace=True)\n",
|
| 910 |
+
" )\n",
|
| 911 |
+
" (1): Conv2dNormActivation(\n",
|
| 912 |
+
" (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512, bias=False)\n",
|
| 913 |
+
" (1): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 914 |
+
" (2): SiLU(inplace=True)\n",
|
| 915 |
+
" )\n",
|
| 916 |
+
" (2): SqueezeExcitation(\n",
|
| 917 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 918 |
+
" (fc1): Conv2d(512, 32, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 919 |
+
" (fc2): Conv2d(32, 512, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 920 |
+
" (activation): SiLU(inplace=True)\n",
|
| 921 |
+
" (scale_activation): Sigmoid()\n",
|
| 922 |
+
" )\n",
|
| 923 |
+
" (3): Conv2dNormActivation(\n",
|
| 924 |
+
" (0): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 925 |
+
" (1): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 926 |
+
" )\n",
|
| 927 |
+
" )\n",
|
| 928 |
+
" (stochastic_depth): StochasticDepth(p=0.075, mode=row)\n",
|
| 929 |
+
" )\n",
|
| 930 |
+
" )\n",
|
| 931 |
+
" (5): Sequential(\n",
|
| 932 |
+
" (0): MBConv(\n",
|
| 933 |
+
" (block): Sequential(\n",
|
| 934 |
+
" (0): Conv2dNormActivation(\n",
|
| 935 |
+
" (0): Conv2d(128, 768, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 936 |
+
" (1): BatchNorm2d(768, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 937 |
+
" (2): SiLU(inplace=True)\n",
|
| 938 |
+
" )\n",
|
| 939 |
+
" (1): Conv2dNormActivation(\n",
|
| 940 |
+
" (0): Conv2d(768, 768, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=768, bias=False)\n",
|
| 941 |
+
" (1): BatchNorm2d(768, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 942 |
+
" (2): SiLU(inplace=True)\n",
|
| 943 |
+
" )\n",
|
| 944 |
+
" (2): SqueezeExcitation(\n",
|
| 945 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 946 |
+
" (fc1): Conv2d(768, 32, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 947 |
+
" (fc2): Conv2d(32, 768, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 948 |
+
" (activation): SiLU(inplace=True)\n",
|
| 949 |
+
" (scale_activation): Sigmoid()\n",
|
| 950 |
+
" )\n",
|
| 951 |
+
" (3): Conv2dNormActivation(\n",
|
| 952 |
+
" (0): Conv2d(768, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 953 |
+
" (1): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 954 |
+
" )\n",
|
| 955 |
+
" )\n",
|
| 956 |
+
" (stochastic_depth): StochasticDepth(p=0.08, mode=row)\n",
|
| 957 |
+
" )\n",
|
| 958 |
+
" (1): MBConv(\n",
|
| 959 |
+
" (block): Sequential(\n",
|
| 960 |
+
" (0): Conv2dNormActivation(\n",
|
| 961 |
+
" (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 962 |
+
" (1): BatchNorm2d(960, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 963 |
+
" (2): SiLU(inplace=True)\n",
|
| 964 |
+
" )\n",
|
| 965 |
+
" (1): Conv2dNormActivation(\n",
|
| 966 |
+
" (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
|
| 967 |
+
" (1): BatchNorm2d(960, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 968 |
+
" (2): SiLU(inplace=True)\n",
|
| 969 |
+
" )\n",
|
| 970 |
+
" (2): SqueezeExcitation(\n",
|
| 971 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 972 |
+
" (fc1): Conv2d(960, 40, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 973 |
+
" (fc2): Conv2d(40, 960, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 974 |
+
" (activation): SiLU(inplace=True)\n",
|
| 975 |
+
" (scale_activation): Sigmoid()\n",
|
| 976 |
+
" )\n",
|
| 977 |
+
" (3): Conv2dNormActivation(\n",
|
| 978 |
+
" (0): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 979 |
+
" (1): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 980 |
+
" )\n",
|
| 981 |
+
" )\n",
|
| 982 |
+
" (stochastic_depth): StochasticDepth(p=0.085, mode=row)\n",
|
| 983 |
+
" )\n",
|
| 984 |
+
" (2): MBConv(\n",
|
| 985 |
+
" (block): Sequential(\n",
|
| 986 |
+
" (0): Conv2dNormActivation(\n",
|
| 987 |
+
" (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 988 |
+
" (1): BatchNorm2d(960, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 989 |
+
" (2): SiLU(inplace=True)\n",
|
| 990 |
+
" )\n",
|
| 991 |
+
" (1): Conv2dNormActivation(\n",
|
| 992 |
+
" (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
|
| 993 |
+
" (1): BatchNorm2d(960, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 994 |
+
" (2): SiLU(inplace=True)\n",
|
| 995 |
+
" )\n",
|
| 996 |
+
" (2): SqueezeExcitation(\n",
|
| 997 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 998 |
+
" (fc1): Conv2d(960, 40, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 999 |
+
" (fc2): Conv2d(40, 960, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1000 |
+
" (activation): SiLU(inplace=True)\n",
|
| 1001 |
+
" (scale_activation): Sigmoid()\n",
|
| 1002 |
+
" )\n",
|
| 1003 |
+
" (3): Conv2dNormActivation(\n",
|
| 1004 |
+
" (0): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1005 |
+
" (1): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1006 |
+
" )\n",
|
| 1007 |
+
" )\n",
|
| 1008 |
+
" (stochastic_depth): StochasticDepth(p=0.09, mode=row)\n",
|
| 1009 |
+
" )\n",
|
| 1010 |
+
" (3): MBConv(\n",
|
| 1011 |
+
" (block): Sequential(\n",
|
| 1012 |
+
" (0): Conv2dNormActivation(\n",
|
| 1013 |
+
" (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1014 |
+
" (1): BatchNorm2d(960, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1015 |
+
" (2): SiLU(inplace=True)\n",
|
| 1016 |
+
" )\n",
|
| 1017 |
+
" (1): Conv2dNormActivation(\n",
|
| 1018 |
+
" (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
|
| 1019 |
+
" (1): BatchNorm2d(960, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1020 |
+
" (2): SiLU(inplace=True)\n",
|
| 1021 |
+
" )\n",
|
| 1022 |
+
" (2): SqueezeExcitation(\n",
|
| 1023 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 1024 |
+
" (fc1): Conv2d(960, 40, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1025 |
+
" (fc2): Conv2d(40, 960, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1026 |
+
" (activation): SiLU(inplace=True)\n",
|
| 1027 |
+
" (scale_activation): Sigmoid()\n",
|
| 1028 |
+
" )\n",
|
| 1029 |
+
" (3): Conv2dNormActivation(\n",
|
| 1030 |
+
" (0): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1031 |
+
" (1): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1032 |
+
" )\n",
|
| 1033 |
+
" )\n",
|
| 1034 |
+
" (stochastic_depth): StochasticDepth(p=0.095, mode=row)\n",
|
| 1035 |
+
" )\n",
|
| 1036 |
+
" (4): MBConv(\n",
|
| 1037 |
+
" (block): Sequential(\n",
|
| 1038 |
+
" (0): Conv2dNormActivation(\n",
|
| 1039 |
+
" (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1040 |
+
" (1): BatchNorm2d(960, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1041 |
+
" (2): SiLU(inplace=True)\n",
|
| 1042 |
+
" )\n",
|
| 1043 |
+
" (1): Conv2dNormActivation(\n",
|
| 1044 |
+
" (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
|
| 1045 |
+
" (1): BatchNorm2d(960, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1046 |
+
" (2): SiLU(inplace=True)\n",
|
| 1047 |
+
" )\n",
|
| 1048 |
+
" (2): SqueezeExcitation(\n",
|
| 1049 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 1050 |
+
" (fc1): Conv2d(960, 40, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1051 |
+
" (fc2): Conv2d(40, 960, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1052 |
+
" (activation): SiLU(inplace=True)\n",
|
| 1053 |
+
" (scale_activation): Sigmoid()\n",
|
| 1054 |
+
" )\n",
|
| 1055 |
+
" (3): Conv2dNormActivation(\n",
|
| 1056 |
+
" (0): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1057 |
+
" (1): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1058 |
+
" )\n",
|
| 1059 |
+
" )\n",
|
| 1060 |
+
" (stochastic_depth): StochasticDepth(p=0.1, mode=row)\n",
|
| 1061 |
+
" )\n",
|
| 1062 |
+
" (5): MBConv(\n",
|
| 1063 |
+
" (block): Sequential(\n",
|
| 1064 |
+
" (0): Conv2dNormActivation(\n",
|
| 1065 |
+
" (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1066 |
+
" (1): BatchNorm2d(960, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1067 |
+
" (2): SiLU(inplace=True)\n",
|
| 1068 |
+
" )\n",
|
| 1069 |
+
" (1): Conv2dNormActivation(\n",
|
| 1070 |
+
" (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
|
| 1071 |
+
" (1): BatchNorm2d(960, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1072 |
+
" (2): SiLU(inplace=True)\n",
|
| 1073 |
+
" )\n",
|
| 1074 |
+
" (2): SqueezeExcitation(\n",
|
| 1075 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 1076 |
+
" (fc1): Conv2d(960, 40, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1077 |
+
" (fc2): Conv2d(40, 960, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1078 |
+
" (activation): SiLU(inplace=True)\n",
|
| 1079 |
+
" (scale_activation): Sigmoid()\n",
|
| 1080 |
+
" )\n",
|
| 1081 |
+
" (3): Conv2dNormActivation(\n",
|
| 1082 |
+
" (0): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1083 |
+
" (1): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1084 |
+
" )\n",
|
| 1085 |
+
" )\n",
|
| 1086 |
+
" (stochastic_depth): StochasticDepth(p=0.10500000000000001, mode=row)\n",
|
| 1087 |
+
" )\n",
|
| 1088 |
+
" (6): MBConv(\n",
|
| 1089 |
+
" (block): Sequential(\n",
|
| 1090 |
+
" (0): Conv2dNormActivation(\n",
|
| 1091 |
+
" (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1092 |
+
" (1): BatchNorm2d(960, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1093 |
+
" (2): SiLU(inplace=True)\n",
|
| 1094 |
+
" )\n",
|
| 1095 |
+
" (1): Conv2dNormActivation(\n",
|
| 1096 |
+
" (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
|
| 1097 |
+
" (1): BatchNorm2d(960, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1098 |
+
" (2): SiLU(inplace=True)\n",
|
| 1099 |
+
" )\n",
|
| 1100 |
+
" (2): SqueezeExcitation(\n",
|
| 1101 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 1102 |
+
" (fc1): Conv2d(960, 40, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1103 |
+
" (fc2): Conv2d(40, 960, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1104 |
+
" (activation): SiLU(inplace=True)\n",
|
| 1105 |
+
" (scale_activation): Sigmoid()\n",
|
| 1106 |
+
" )\n",
|
| 1107 |
+
" (3): Conv2dNormActivation(\n",
|
| 1108 |
+
" (0): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1109 |
+
" (1): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1110 |
+
" )\n",
|
| 1111 |
+
" )\n",
|
| 1112 |
+
" (stochastic_depth): StochasticDepth(p=0.11000000000000001, mode=row)\n",
|
| 1113 |
+
" )\n",
|
| 1114 |
+
" (7): MBConv(\n",
|
| 1115 |
+
" (block): Sequential(\n",
|
| 1116 |
+
" (0): Conv2dNormActivation(\n",
|
| 1117 |
+
" (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1118 |
+
" (1): BatchNorm2d(960, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1119 |
+
" (2): SiLU(inplace=True)\n",
|
| 1120 |
+
" )\n",
|
| 1121 |
+
" (1): Conv2dNormActivation(\n",
|
| 1122 |
+
" (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
|
| 1123 |
+
" (1): BatchNorm2d(960, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1124 |
+
" (2): SiLU(inplace=True)\n",
|
| 1125 |
+
" )\n",
|
| 1126 |
+
" (2): SqueezeExcitation(\n",
|
| 1127 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 1128 |
+
" (fc1): Conv2d(960, 40, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1129 |
+
" (fc2): Conv2d(40, 960, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1130 |
+
" (activation): SiLU(inplace=True)\n",
|
| 1131 |
+
" (scale_activation): Sigmoid()\n",
|
| 1132 |
+
" )\n",
|
| 1133 |
+
" (3): Conv2dNormActivation(\n",
|
| 1134 |
+
" (0): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1135 |
+
" (1): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1136 |
+
" )\n",
|
| 1137 |
+
" )\n",
|
| 1138 |
+
" (stochastic_depth): StochasticDepth(p=0.11500000000000002, mode=row)\n",
|
| 1139 |
+
" )\n",
|
| 1140 |
+
" (8): MBConv(\n",
|
| 1141 |
+
" (block): Sequential(\n",
|
| 1142 |
+
" (0): Conv2dNormActivation(\n",
|
| 1143 |
+
" (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1144 |
+
" (1): BatchNorm2d(960, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1145 |
+
" (2): SiLU(inplace=True)\n",
|
| 1146 |
+
" )\n",
|
| 1147 |
+
" (1): Conv2dNormActivation(\n",
|
| 1148 |
+
" (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
|
| 1149 |
+
" (1): BatchNorm2d(960, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1150 |
+
" (2): SiLU(inplace=True)\n",
|
| 1151 |
+
" )\n",
|
| 1152 |
+
" (2): SqueezeExcitation(\n",
|
| 1153 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 1154 |
+
" (fc1): Conv2d(960, 40, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1155 |
+
" (fc2): Conv2d(40, 960, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1156 |
+
" (activation): SiLU(inplace=True)\n",
|
| 1157 |
+
" (scale_activation): Sigmoid()\n",
|
| 1158 |
+
" )\n",
|
| 1159 |
+
" (3): Conv2dNormActivation(\n",
|
| 1160 |
+
" (0): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1161 |
+
" (1): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1162 |
+
" )\n",
|
| 1163 |
+
" )\n",
|
| 1164 |
+
" (stochastic_depth): StochasticDepth(p=0.12000000000000002, mode=row)\n",
|
| 1165 |
+
" )\n",
|
| 1166 |
+
" )\n",
|
| 1167 |
+
" (6): Sequential(\n",
|
| 1168 |
+
" (0): MBConv(\n",
|
| 1169 |
+
" (block): Sequential(\n",
|
| 1170 |
+
" (0): Conv2dNormActivation(\n",
|
| 1171 |
+
" (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1172 |
+
" (1): BatchNorm2d(960, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1173 |
+
" (2): SiLU(inplace=True)\n",
|
| 1174 |
+
" )\n",
|
| 1175 |
+
" (1): Conv2dNormActivation(\n",
|
| 1176 |
+
" (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=960, bias=False)\n",
|
| 1177 |
+
" (1): BatchNorm2d(960, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1178 |
+
" (2): SiLU(inplace=True)\n",
|
| 1179 |
+
" )\n",
|
| 1180 |
+
" (2): SqueezeExcitation(\n",
|
| 1181 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 1182 |
+
" (fc1): Conv2d(960, 40, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1183 |
+
" (fc2): Conv2d(40, 960, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1184 |
+
" (activation): SiLU(inplace=True)\n",
|
| 1185 |
+
" (scale_activation): Sigmoid()\n",
|
| 1186 |
+
" )\n",
|
| 1187 |
+
" (3): Conv2dNormActivation(\n",
|
| 1188 |
+
" (0): Conv2d(960, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1189 |
+
" (1): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1190 |
+
" )\n",
|
| 1191 |
+
" )\n",
|
| 1192 |
+
" (stochastic_depth): StochasticDepth(p=0.125, mode=row)\n",
|
| 1193 |
+
" )\n",
|
| 1194 |
+
" (1): MBConv(\n",
|
| 1195 |
+
" (block): Sequential(\n",
|
| 1196 |
+
" (0): Conv2dNormActivation(\n",
|
| 1197 |
+
" (0): Conv2d(256, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1198 |
+
" (1): BatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1199 |
+
" (2): SiLU(inplace=True)\n",
|
| 1200 |
+
" )\n",
|
| 1201 |
+
" (1): Conv2dNormActivation(\n",
|
| 1202 |
+
" (0): Conv2d(1536, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1536, bias=False)\n",
|
| 1203 |
+
" (1): BatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1204 |
+
" (2): SiLU(inplace=True)\n",
|
| 1205 |
+
" )\n",
|
| 1206 |
+
" (2): SqueezeExcitation(\n",
|
| 1207 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 1208 |
+
" (fc1): Conv2d(1536, 64, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1209 |
+
" (fc2): Conv2d(64, 1536, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1210 |
+
" (activation): SiLU(inplace=True)\n",
|
| 1211 |
+
" (scale_activation): Sigmoid()\n",
|
| 1212 |
+
" )\n",
|
| 1213 |
+
" (3): Conv2dNormActivation(\n",
|
| 1214 |
+
" (0): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1215 |
+
" (1): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1216 |
+
" )\n",
|
| 1217 |
+
" )\n",
|
| 1218 |
+
" (stochastic_depth): StochasticDepth(p=0.13, mode=row)\n",
|
| 1219 |
+
" )\n",
|
| 1220 |
+
" (2): MBConv(\n",
|
| 1221 |
+
" (block): Sequential(\n",
|
| 1222 |
+
" (0): Conv2dNormActivation(\n",
|
| 1223 |
+
" (0): Conv2d(256, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1224 |
+
" (1): BatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1225 |
+
" (2): SiLU(inplace=True)\n",
|
| 1226 |
+
" )\n",
|
| 1227 |
+
" (1): Conv2dNormActivation(\n",
|
| 1228 |
+
" (0): Conv2d(1536, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1536, bias=False)\n",
|
| 1229 |
+
" (1): BatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1230 |
+
" (2): SiLU(inplace=True)\n",
|
| 1231 |
+
" )\n",
|
| 1232 |
+
" (2): SqueezeExcitation(\n",
|
| 1233 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 1234 |
+
" (fc1): Conv2d(1536, 64, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1235 |
+
" (fc2): Conv2d(64, 1536, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1236 |
+
" (activation): SiLU(inplace=True)\n",
|
| 1237 |
+
" (scale_activation): Sigmoid()\n",
|
| 1238 |
+
" )\n",
|
| 1239 |
+
" (3): Conv2dNormActivation(\n",
|
| 1240 |
+
" (0): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1241 |
+
" (1): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1242 |
+
" )\n",
|
| 1243 |
+
" )\n",
|
| 1244 |
+
" (stochastic_depth): StochasticDepth(p=0.135, mode=row)\n",
|
| 1245 |
+
" )\n",
|
| 1246 |
+
" (3): MBConv(\n",
|
| 1247 |
+
" (block): Sequential(\n",
|
| 1248 |
+
" (0): Conv2dNormActivation(\n",
|
| 1249 |
+
" (0): Conv2d(256, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1250 |
+
" (1): BatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1251 |
+
" (2): SiLU(inplace=True)\n",
|
| 1252 |
+
" )\n",
|
| 1253 |
+
" (1): Conv2dNormActivation(\n",
|
| 1254 |
+
" (0): Conv2d(1536, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1536, bias=False)\n",
|
| 1255 |
+
" (1): BatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1256 |
+
" (2): SiLU(inplace=True)\n",
|
| 1257 |
+
" )\n",
|
| 1258 |
+
" (2): SqueezeExcitation(\n",
|
| 1259 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 1260 |
+
" (fc1): Conv2d(1536, 64, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1261 |
+
" (fc2): Conv2d(64, 1536, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1262 |
+
" (activation): SiLU(inplace=True)\n",
|
| 1263 |
+
" (scale_activation): Sigmoid()\n",
|
| 1264 |
+
" )\n",
|
| 1265 |
+
" (3): Conv2dNormActivation(\n",
|
| 1266 |
+
" (0): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1267 |
+
" (1): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1268 |
+
" )\n",
|
| 1269 |
+
" )\n",
|
| 1270 |
+
" (stochastic_depth): StochasticDepth(p=0.14, mode=row)\n",
|
| 1271 |
+
" )\n",
|
| 1272 |
+
" (4): MBConv(\n",
|
| 1273 |
+
" (block): Sequential(\n",
|
| 1274 |
+
" (0): Conv2dNormActivation(\n",
|
| 1275 |
+
" (0): Conv2d(256, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1276 |
+
" (1): BatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1277 |
+
" (2): SiLU(inplace=True)\n",
|
| 1278 |
+
" )\n",
|
| 1279 |
+
" (1): Conv2dNormActivation(\n",
|
| 1280 |
+
" (0): Conv2d(1536, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1536, bias=False)\n",
|
| 1281 |
+
" (1): BatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1282 |
+
" (2): SiLU(inplace=True)\n",
|
| 1283 |
+
" )\n",
|
| 1284 |
+
" (2): SqueezeExcitation(\n",
|
| 1285 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 1286 |
+
" (fc1): Conv2d(1536, 64, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1287 |
+
" (fc2): Conv2d(64, 1536, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1288 |
+
" (activation): SiLU(inplace=True)\n",
|
| 1289 |
+
" (scale_activation): Sigmoid()\n",
|
| 1290 |
+
" )\n",
|
| 1291 |
+
" (3): Conv2dNormActivation(\n",
|
| 1292 |
+
" (0): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1293 |
+
" (1): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1294 |
+
" )\n",
|
| 1295 |
+
" )\n",
|
| 1296 |
+
" (stochastic_depth): StochasticDepth(p=0.14500000000000002, mode=row)\n",
|
| 1297 |
+
" )\n",
|
| 1298 |
+
" (5): MBConv(\n",
|
| 1299 |
+
" (block): Sequential(\n",
|
| 1300 |
+
" (0): Conv2dNormActivation(\n",
|
| 1301 |
+
" (0): Conv2d(256, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1302 |
+
" (1): BatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1303 |
+
" (2): SiLU(inplace=True)\n",
|
| 1304 |
+
" )\n",
|
| 1305 |
+
" (1): Conv2dNormActivation(\n",
|
| 1306 |
+
" (0): Conv2d(1536, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1536, bias=False)\n",
|
| 1307 |
+
" (1): BatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1308 |
+
" (2): SiLU(inplace=True)\n",
|
| 1309 |
+
" )\n",
|
| 1310 |
+
" (2): SqueezeExcitation(\n",
|
| 1311 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 1312 |
+
" (fc1): Conv2d(1536, 64, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1313 |
+
" (fc2): Conv2d(64, 1536, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1314 |
+
" (activation): SiLU(inplace=True)\n",
|
| 1315 |
+
" (scale_activation): Sigmoid()\n",
|
| 1316 |
+
" )\n",
|
| 1317 |
+
" (3): Conv2dNormActivation(\n",
|
| 1318 |
+
" (0): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1319 |
+
" (1): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1320 |
+
" )\n",
|
| 1321 |
+
" )\n",
|
| 1322 |
+
" (stochastic_depth): StochasticDepth(p=0.15, mode=row)\n",
|
| 1323 |
+
" )\n",
|
| 1324 |
+
" (6): MBConv(\n",
|
| 1325 |
+
" (block): Sequential(\n",
|
| 1326 |
+
" (0): Conv2dNormActivation(\n",
|
| 1327 |
+
" (0): Conv2d(256, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1328 |
+
" (1): BatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1329 |
+
" (2): SiLU(inplace=True)\n",
|
| 1330 |
+
" )\n",
|
| 1331 |
+
" (1): Conv2dNormActivation(\n",
|
| 1332 |
+
" (0): Conv2d(1536, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1536, bias=False)\n",
|
| 1333 |
+
" (1): BatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1334 |
+
" (2): SiLU(inplace=True)\n",
|
| 1335 |
+
" )\n",
|
| 1336 |
+
" (2): SqueezeExcitation(\n",
|
| 1337 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 1338 |
+
" (fc1): Conv2d(1536, 64, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1339 |
+
" (fc2): Conv2d(64, 1536, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1340 |
+
" (activation): SiLU(inplace=True)\n",
|
| 1341 |
+
" (scale_activation): Sigmoid()\n",
|
| 1342 |
+
" )\n",
|
| 1343 |
+
" (3): Conv2dNormActivation(\n",
|
| 1344 |
+
" (0): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1345 |
+
" (1): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1346 |
+
" )\n",
|
| 1347 |
+
" )\n",
|
| 1348 |
+
" (stochastic_depth): StochasticDepth(p=0.155, mode=row)\n",
|
| 1349 |
+
" )\n",
|
| 1350 |
+
" (7): MBConv(\n",
|
| 1351 |
+
" (block): Sequential(\n",
|
| 1352 |
+
" (0): Conv2dNormActivation(\n",
|
| 1353 |
+
" (0): Conv2d(256, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1354 |
+
" (1): BatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1355 |
+
" (2): SiLU(inplace=True)\n",
|
| 1356 |
+
" )\n",
|
| 1357 |
+
" (1): Conv2dNormActivation(\n",
|
| 1358 |
+
" (0): Conv2d(1536, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1536, bias=False)\n",
|
| 1359 |
+
" (1): BatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1360 |
+
" (2): SiLU(inplace=True)\n",
|
| 1361 |
+
" )\n",
|
| 1362 |
+
" (2): SqueezeExcitation(\n",
|
| 1363 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 1364 |
+
" (fc1): Conv2d(1536, 64, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1365 |
+
" (fc2): Conv2d(64, 1536, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1366 |
+
" (activation): SiLU(inplace=True)\n",
|
| 1367 |
+
" (scale_activation): Sigmoid()\n",
|
| 1368 |
+
" )\n",
|
| 1369 |
+
" (3): Conv2dNormActivation(\n",
|
| 1370 |
+
" (0): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1371 |
+
" (1): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1372 |
+
" )\n",
|
| 1373 |
+
" )\n",
|
| 1374 |
+
" (stochastic_depth): StochasticDepth(p=0.16, mode=row)\n",
|
| 1375 |
+
" )\n",
|
| 1376 |
+
" (8): MBConv(\n",
|
| 1377 |
+
" (block): Sequential(\n",
|
| 1378 |
+
" (0): Conv2dNormActivation(\n",
|
| 1379 |
+
" (0): Conv2d(256, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1380 |
+
" (1): BatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1381 |
+
" (2): SiLU(inplace=True)\n",
|
| 1382 |
+
" )\n",
|
| 1383 |
+
" (1): Conv2dNormActivation(\n",
|
| 1384 |
+
" (0): Conv2d(1536, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1536, bias=False)\n",
|
| 1385 |
+
" (1): BatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1386 |
+
" (2): SiLU(inplace=True)\n",
|
| 1387 |
+
" )\n",
|
| 1388 |
+
" (2): SqueezeExcitation(\n",
|
| 1389 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 1390 |
+
" (fc1): Conv2d(1536, 64, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1391 |
+
" (fc2): Conv2d(64, 1536, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1392 |
+
" (activation): SiLU(inplace=True)\n",
|
| 1393 |
+
" (scale_activation): Sigmoid()\n",
|
| 1394 |
+
" )\n",
|
| 1395 |
+
" (3): Conv2dNormActivation(\n",
|
| 1396 |
+
" (0): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1397 |
+
" (1): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1398 |
+
" )\n",
|
| 1399 |
+
" )\n",
|
| 1400 |
+
" (stochastic_depth): StochasticDepth(p=0.165, mode=row)\n",
|
| 1401 |
+
" )\n",
|
| 1402 |
+
" (9): MBConv(\n",
|
| 1403 |
+
" (block): Sequential(\n",
|
| 1404 |
+
" (0): Conv2dNormActivation(\n",
|
| 1405 |
+
" (0): Conv2d(256, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1406 |
+
" (1): BatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1407 |
+
" (2): SiLU(inplace=True)\n",
|
| 1408 |
+
" )\n",
|
| 1409 |
+
" (1): Conv2dNormActivation(\n",
|
| 1410 |
+
" (0): Conv2d(1536, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1536, bias=False)\n",
|
| 1411 |
+
" (1): BatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1412 |
+
" (2): SiLU(inplace=True)\n",
|
| 1413 |
+
" )\n",
|
| 1414 |
+
" (2): SqueezeExcitation(\n",
|
| 1415 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 1416 |
+
" (fc1): Conv2d(1536, 64, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1417 |
+
" (fc2): Conv2d(64, 1536, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1418 |
+
" (activation): SiLU(inplace=True)\n",
|
| 1419 |
+
" (scale_activation): Sigmoid()\n",
|
| 1420 |
+
" )\n",
|
| 1421 |
+
" (3): Conv2dNormActivation(\n",
|
| 1422 |
+
" (0): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1423 |
+
" (1): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1424 |
+
" )\n",
|
| 1425 |
+
" )\n",
|
| 1426 |
+
" (stochastic_depth): StochasticDepth(p=0.17, mode=row)\n",
|
| 1427 |
+
" )\n",
|
| 1428 |
+
" (10): MBConv(\n",
|
| 1429 |
+
" (block): Sequential(\n",
|
| 1430 |
+
" (0): Conv2dNormActivation(\n",
|
| 1431 |
+
" (0): Conv2d(256, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1432 |
+
" (1): BatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1433 |
+
" (2): SiLU(inplace=True)\n",
|
| 1434 |
+
" )\n",
|
| 1435 |
+
" (1): Conv2dNormActivation(\n",
|
| 1436 |
+
" (0): Conv2d(1536, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1536, bias=False)\n",
|
| 1437 |
+
" (1): BatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1438 |
+
" (2): SiLU(inplace=True)\n",
|
| 1439 |
+
" )\n",
|
| 1440 |
+
" (2): SqueezeExcitation(\n",
|
| 1441 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 1442 |
+
" (fc1): Conv2d(1536, 64, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1443 |
+
" (fc2): Conv2d(64, 1536, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1444 |
+
" (activation): SiLU(inplace=True)\n",
|
| 1445 |
+
" (scale_activation): Sigmoid()\n",
|
| 1446 |
+
" )\n",
|
| 1447 |
+
" (3): Conv2dNormActivation(\n",
|
| 1448 |
+
" (0): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1449 |
+
" (1): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1450 |
+
" )\n",
|
| 1451 |
+
" )\n",
|
| 1452 |
+
" (stochastic_depth): StochasticDepth(p=0.175, mode=row)\n",
|
| 1453 |
+
" )\n",
|
| 1454 |
+
" (11): MBConv(\n",
|
| 1455 |
+
" (block): Sequential(\n",
|
| 1456 |
+
" (0): Conv2dNormActivation(\n",
|
| 1457 |
+
" (0): Conv2d(256, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1458 |
+
" (1): BatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1459 |
+
" (2): SiLU(inplace=True)\n",
|
| 1460 |
+
" )\n",
|
| 1461 |
+
" (1): Conv2dNormActivation(\n",
|
| 1462 |
+
" (0): Conv2d(1536, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1536, bias=False)\n",
|
| 1463 |
+
" (1): BatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1464 |
+
" (2): SiLU(inplace=True)\n",
|
| 1465 |
+
" )\n",
|
| 1466 |
+
" (2): SqueezeExcitation(\n",
|
| 1467 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 1468 |
+
" (fc1): Conv2d(1536, 64, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1469 |
+
" (fc2): Conv2d(64, 1536, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1470 |
+
" (activation): SiLU(inplace=True)\n",
|
| 1471 |
+
" (scale_activation): Sigmoid()\n",
|
| 1472 |
+
" )\n",
|
| 1473 |
+
" (3): Conv2dNormActivation(\n",
|
| 1474 |
+
" (0): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1475 |
+
" (1): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1476 |
+
" )\n",
|
| 1477 |
+
" )\n",
|
| 1478 |
+
" (stochastic_depth): StochasticDepth(p=0.18, mode=row)\n",
|
| 1479 |
+
" )\n",
|
| 1480 |
+
" (12): MBConv(\n",
|
| 1481 |
+
" (block): Sequential(\n",
|
| 1482 |
+
" (0): Conv2dNormActivation(\n",
|
| 1483 |
+
" (0): Conv2d(256, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1484 |
+
" (1): BatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1485 |
+
" (2): SiLU(inplace=True)\n",
|
| 1486 |
+
" )\n",
|
| 1487 |
+
" (1): Conv2dNormActivation(\n",
|
| 1488 |
+
" (0): Conv2d(1536, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1536, bias=False)\n",
|
| 1489 |
+
" (1): BatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1490 |
+
" (2): SiLU(inplace=True)\n",
|
| 1491 |
+
" )\n",
|
| 1492 |
+
" (2): SqueezeExcitation(\n",
|
| 1493 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 1494 |
+
" (fc1): Conv2d(1536, 64, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1495 |
+
" (fc2): Conv2d(64, 1536, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1496 |
+
" (activation): SiLU(inplace=True)\n",
|
| 1497 |
+
" (scale_activation): Sigmoid()\n",
|
| 1498 |
+
" )\n",
|
| 1499 |
+
" (3): Conv2dNormActivation(\n",
|
| 1500 |
+
" (0): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1501 |
+
" (1): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1502 |
+
" )\n",
|
| 1503 |
+
" )\n",
|
| 1504 |
+
" (stochastic_depth): StochasticDepth(p=0.185, mode=row)\n",
|
| 1505 |
+
" )\n",
|
| 1506 |
+
" (13): MBConv(\n",
|
| 1507 |
+
" (block): Sequential(\n",
|
| 1508 |
+
" (0): Conv2dNormActivation(\n",
|
| 1509 |
+
" (0): Conv2d(256, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1510 |
+
" (1): BatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1511 |
+
" (2): SiLU(inplace=True)\n",
|
| 1512 |
+
" )\n",
|
| 1513 |
+
" (1): Conv2dNormActivation(\n",
|
| 1514 |
+
" (0): Conv2d(1536, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1536, bias=False)\n",
|
| 1515 |
+
" (1): BatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1516 |
+
" (2): SiLU(inplace=True)\n",
|
| 1517 |
+
" )\n",
|
| 1518 |
+
" (2): SqueezeExcitation(\n",
|
| 1519 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 1520 |
+
" (fc1): Conv2d(1536, 64, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1521 |
+
" (fc2): Conv2d(64, 1536, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1522 |
+
" (activation): SiLU(inplace=True)\n",
|
| 1523 |
+
" (scale_activation): Sigmoid()\n",
|
| 1524 |
+
" )\n",
|
| 1525 |
+
" (3): Conv2dNormActivation(\n",
|
| 1526 |
+
" (0): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1527 |
+
" (1): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1528 |
+
" )\n",
|
| 1529 |
+
" )\n",
|
| 1530 |
+
" (stochastic_depth): StochasticDepth(p=0.19, mode=row)\n",
|
| 1531 |
+
" )\n",
|
| 1532 |
+
" (14): MBConv(\n",
|
| 1533 |
+
" (block): Sequential(\n",
|
| 1534 |
+
" (0): Conv2dNormActivation(\n",
|
| 1535 |
+
" (0): Conv2d(256, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1536 |
+
" (1): BatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1537 |
+
" (2): SiLU(inplace=True)\n",
|
| 1538 |
+
" )\n",
|
| 1539 |
+
" (1): Conv2dNormActivation(\n",
|
| 1540 |
+
" (0): Conv2d(1536, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1536, bias=False)\n",
|
| 1541 |
+
" (1): BatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1542 |
+
" (2): SiLU(inplace=True)\n",
|
| 1543 |
+
" )\n",
|
| 1544 |
+
" (2): SqueezeExcitation(\n",
|
| 1545 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 1546 |
+
" (fc1): Conv2d(1536, 64, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1547 |
+
" (fc2): Conv2d(64, 1536, kernel_size=(1, 1), stride=(1, 1))\n",
|
| 1548 |
+
" (activation): SiLU(inplace=True)\n",
|
| 1549 |
+
" (scale_activation): Sigmoid()\n",
|
| 1550 |
+
" )\n",
|
| 1551 |
+
" (3): Conv2dNormActivation(\n",
|
| 1552 |
+
" (0): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1553 |
+
" (1): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1554 |
+
" )\n",
|
| 1555 |
+
" )\n",
|
| 1556 |
+
" (stochastic_depth): StochasticDepth(p=0.195, mode=row)\n",
|
| 1557 |
+
" )\n",
|
| 1558 |
+
" )\n",
|
| 1559 |
+
" (7): Conv2dNormActivation(\n",
|
| 1560 |
+
" (0): Conv2d(256, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
| 1561 |
+
" (1): BatchNorm2d(1280, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
| 1562 |
+
" (2): SiLU(inplace=True)\n",
|
| 1563 |
+
" )\n",
|
| 1564 |
+
" )\n",
|
| 1565 |
+
" (avgpool): AdaptiveAvgPool2d(output_size=1)\n",
|
| 1566 |
+
" (classifier): Sequential(\n",
|
| 1567 |
+
" (0): Dropout(p=0.2, inplace=True)\n",
|
| 1568 |
+
" (1): Linear(in_features=1280, out_features=2, bias=True)\n",
|
| 1569 |
+
" )\n",
|
| 1570 |
+
" )\n",
|
| 1571 |
+
")"
|
| 1572 |
+
]
|
| 1573 |
+
},
|
| 1574 |
+
"metadata": {},
|
| 1575 |
+
"execution_count": 5
|
| 1576 |
+
}
|
| 1577 |
+
]
|
| 1578 |
+
}
|
| 1579 |
+
]
|
| 1580 |
+
}
|