Define the Convolutional Neural Network

After you've looked at the data you're working with and, in this case, know the shapes of the images and of the keypoints, you are ready to define a convolutional neural network that can learn from this data.

In this notebook and in models.py, you will:

1. Define a CNN with images as input and keypoints as output
2. Construct the transformed FaceKeypointsDataset, just as before
3. Train the CNN on the training data, tracking loss
4. See how the trained model performs on test data
5. If necessary, modify the CNN structure and model hyperparameters, so that it performs well *

* What does well mean?

"Well" means that the model's loss decreases during training and, when applied to test image data, the model produces keypoints that closely match the true keypoints of each face. And you'll see examples of this later in the notebook.

---

CNN Architecture

Recall that CNN's are defined by a few types of layers:

• Convolutional layers
• Maxpooling layers
• Fully-connected layers

You are required to use the above layers and encouraged to add multiple convolutional layers and things like dropout layers that may prevent overfitting. You are also encouraged to look at literature on keypoint detection, such as this paper, to help you determine the structure of your network.

TODO: Define your model in the provided file models.py file

This file is mostly empty but contains the expected name and some TODO's for creating your model.

---

PyTorch Neural Nets

To define a neural network in PyTorch, you define the layers of a model in the function __init__ and define the feedforward behavior of a network that employs those initialized layers in the function forward, which takes in an input image tensor, x. The structure of this Net class is shown below and left for you to fill in.

Note: During training, PyTorch will be able to perform backpropagation by keeping track of the network's feedforward behavior and using autograd to calculate the update to the weights in the network.

Define the Layers in __init__

As a reminder, a conv/pool layer may be defined like this (in __init__):

# 1 input image channel (for grayscale images), 32 output channels/feature maps, 3x3 square convolution kernel
self.conv1 = nn.Conv2d(1, 32, 3)

# maxpool that uses a square window of kernel_size=2, stride=2
self.pool = nn.MaxPool2d(2, 2)

Refer to Layers in forward

Then referred to in the forward function like this, in which the conv1 layer has a ReLu activation applied to it before maxpooling is applied:

x = self.pool(F.relu(self.conv1(x)))

Best practice is to place any layers whose weights will change during the training process in __init__ and refer to them in the forward function; any layers or functions that always behave in the same way, such as a pre-defined activation function, should appear only in the forward function.

Why models.py

You are tasked with defining the network in the models.py file so that any models you define can be saved and loaded by name in different notebooks in this project directory. For example, by defining a CNN class called Net in models.py, you can then create that same architecture in this and other notebooks by simply importing the class and instantiating a model:

    from models import Net
    net = Net()

# load the data if you need to; if you have already loaded the data, you may comment this cell out
# -- DO NOT CHANGE THIS CELL -- #
!mkdir /data
!wget -P /data/ https://s3.amazonaws.com/video.udacity-data.com/topher/2018/May/5aea1b91_train-test-data/train-test-data.zip
!unzip -n /data/train-test-data.zip -d /data

--2019-11-06 04:29:22--  https://s3.amazonaws.com/video.udacity-data.com/topher/2018/May/5aea1b91_train-test-data/train-test-data.zip
Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.8.5
Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.216.8.5|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 338613624 (323M) [application/zip]
Saving to: ‘/data/train-test-data.zip’

train-test-data.zip 100%[===================>] 322.93M  58.8MB/s    in 5.5s    

2019-11-06 04:29:27 (58.8 MB/s) - ‘/data/train-test-data.zip’ saved [338613624/338613624]

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**Note:** Workspaces automatically close connections after 30 minutes of inactivity (including inactivity while training!). Use the code snippet below to keep your workspace alive during training. (The active_session context manager is imported below.)

from workspace_utils import active_session

with active_session():
    train_model(num_epochs)

# import the usual resources
import matplotlib.pyplot as plt
import numpy as np

# import utilities to keep workspaces alive during model training
from workspace_utils import active_session

# watch for any changes in model.py, if it changes, re-load it automatically
%load_ext autoreload
%autoreload 2

## TODO: Define the Net in models.py

import torch
import torch.nn as nn
import torch.nn.functional as F

## TODO: Once you've define the network, you can instantiate it
# one example conv layer has been provided for you
from models import Net

net = Net()
print(net)

Net(
  (conv1): Conv2d(1, 32, kernel_size=(5, 5), stride=(1, 1))
  (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (drop1): Dropout(p=0.1)
  (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))
  (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (drop2): Dropout(p=0.2)
  (conv3): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1))
  (pool3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (drop3): Dropout(p=0.3)
  (conv4): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1))
  (pool4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (drop4): Dropout(p=0.4)
  (conv5): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1))
  (pool5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (drop5): Dropout(p=0.5)
  (fc6): Linear(in_features=12800, out_features=2560, bias=True)
  (drop6): Dropout(p=0.4)
  (fc7): Linear(in_features=2560, out_features=1280, bias=True)
  (drop7): Dropout(p=0.4)
  (fc8): Linear(in_features=1280, out_features=136, bias=True)
)

Transform the dataset

To prepare for training, create a transformed dataset of images and keypoints.

TODO: Define a data transform

In PyTorch, a convolutional neural network expects a torch image of a consistent size as input. For efficient training, and so your model's loss does not blow up during training, it is also suggested that you normalize the input images and keypoints. The necessary transforms have been defined in data_load.py and you do not need to modify these; take a look at this file (you'll see the same transforms that were defined and applied in Notebook 1).

To define the data transform below, use a composition of:

1. Rescaling and/or cropping the data, such that you are left with a square image (the suggested size is 224x224px)
2. Normalizing the images and keypoints; turning each RGB image into a grayscale image with a color range of [0, 1] and transforming the given keypoints into a range of [-1, 1]
3. Turning these images and keypoints into Tensors

These transformations have been defined in data_load.py, but it's up to you to call them and create a data_transform below. This transform will be applied to the training data and, later, the test data. It will change how you go about displaying these images and keypoints, but these steps are essential for efficient training.

As a note, should you want to perform data augmentation (which is optional in this project), and randomly rotate or shift these images, a square image size will be useful; rotating a 224x224 image by 90 degrees will result in the same shape of output.

from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils

# the dataset we created in Notebook 1 is copied in the helper file `data_load.py`
from data_load import FacialKeypointsDataset
# the transforms we defined in Notebook 1 are in the helper file `data_load.py`
from data_load import Rescale, RandomCrop, Normalize, ToTensor


## TODO: define the data_transform using transforms.Compose([all tx's, . , .])
# order matters! i.e. rescaling should come before a smaller crop
data_transform = transforms.Compose([Rescale(225),
                                    RandomCrop(224),
                                    Normalize(),
                                    ToTensor()])

# testing that you've defined a transform
assert(data_transform is not None), 'Define a data_transform'

# create the transformed dataset
transformed_dataset = FacialKeypointsDataset(csv_file='/data/training_frames_keypoints.csv',
                                             root_dir='/data/training/',
                                             transform=data_transform)


print('Number of images: ', len(transformed_dataset))

# iterate through the transformed dataset and print some stats about the first few samples
for i in range(4):
    sample = transformed_dataset[i]
    print(i, sample['image'].size(), sample['keypoints'].size())

Number of images:  3462
0 torch.Size([1, 224, 224]) torch.Size([68, 2])
1 torch.Size([1, 224, 224]) torch.Size([68, 2])
2 torch.Size([1, 224, 224]) torch.Size([68, 2])
3 torch.Size([1, 224, 224]) torch.Size([68, 2])

/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()

Batching and loading data

Next, having defined the transformed dataset, we can use PyTorch's DataLoader class to load the training data in batches of whatever size as well as to shuffle the data for training the model. You can read more about the parameters of the DataLoader, in this documentation.

Batch size

Decide on a good batch size for training your model. Try both small and large batch sizes and note how the loss decreases as the model trains. Too large a batch size may cause your model to crash and/or run out of memory while training.

Note for Windows users: Please change the num_workers to 0 or you may face some issues with your DataLoader failing.

# load training data in batches
batch_size = 10

train_loader = DataLoader(transformed_dataset, 
                          batch_size=batch_size,
                          shuffle=True, 
                          num_workers=4)

Before training

Take a look at how this model performs before it trains. You should see that the keypoints it predicts start off in one spot and don't match the keypoints on a face at all! It's interesting to visualize this behavior so that you can compare it to the model after training and see how the model has improved.

Load in the test dataset

The test dataset is one that this model has not seen before, meaning it has not trained with these images. We'll load in this test data and before and after training, see how your model performs on this set!

To visualize this test data, we have to go through some un-transformation steps to turn our images into python images from tensors and to turn our keypoints back into a recognizable range.

# load in the test data, using the dataset class
# AND apply the data_transform you defined above

# create the test dataset
test_dataset = FacialKeypointsDataset(csv_file='/data/test_frames_keypoints.csv',
                                             root_dir='/data/test/',
                                             transform=data_transform)

# load test data in batches
batch_size = 10

test_loader = DataLoader(test_dataset, 
                          batch_size=batch_size,
                          shuffle=True, 
                          num_workers=4)

Apply the model on a test sample

To test the model on a test sample of data, you have to follow these steps:

1. Extract the image and ground truth keypoints from a sample
2. Wrap the image in a Variable, so that the net can process it as input and track how it changes as the image moves through the network.
3. Make sure the image is a FloatTensor, which the model expects.
4. Forward pass the image through the net to get the predicted, output keypoints.

This function test how the network performs on the first batch of test data. It returns the images, the transformed images, the predicted keypoints (produced by the model), and the ground truth keypoints.

# test the model on a batch of test images

def net_sample_output():
    
    # iterate through the test dataset
    for i, sample in enumerate(test_loader):
        
        # get sample data: images and ground truth keypoints
        images = sample['image']
        key_pts = sample['keypoints']

        # convert images to FloatTensors
        images = images.type(torch.FloatTensor)

        # forward pass to get net output
        output_pts = net(images)
        
        # reshape to batch_size x 68 x 2 pts
        output_pts = output_pts.view(output_pts.size()[0], 68, -1)
        
        # break after first image is tested
        if i == 0:
            return images, output_pts, key_pts
            

Debugging tips

If you get a size or dimension error here, make sure that your network outputs the expected number of keypoints! Or if you get a Tensor type error, look into changing the above code that casts the data into float types: images = images.type(torch.FloatTensor).

# call the above function
# returns: test images, test predicted keypoints, test ground truth keypoints
test_images, test_outputs, gt_pts = net_sample_output()

# print out the dimensions of the data to see if they make sense
print(test_images.data.size())
print(test_outputs.data.size())
print(gt_pts.size())

/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()
/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()
/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()
/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()

torch.Size([10, 1, 224, 224])
torch.Size([10, 68, 2])
torch.Size([10, 68, 2])

Visualize the predicted keypoints

Once we've had the model produce some predicted output keypoints, we can visualize these points in a way that's similar to how we've displayed this data before, only this time, we have to "un-transform" the image/keypoint data to display it.

Note that I've defined a new function, show_all_keypoints that displays a grayscale image, its predicted keypoints and its ground truth keypoints (if provided).

def show_all_keypoints(image, predicted_key_pts, gt_pts=None):
    """Show image with predicted keypoints"""
    # image is grayscale
    plt.imshow(image, cmap='gray')
    plt.scatter(predicted_key_pts[:, 0], predicted_key_pts[:, 1], s=20, marker='.', c='m')
    # plot ground truth points as green pts
    if gt_pts is not None:
        plt.scatter(gt_pts[:, 0], gt_pts[:, 1], s=20, marker='.', c='g')

Un-transformation

Next, you'll see a helper function. visualize_output that takes in a batch of images, predicted keypoints, and ground truth keypoints and displays a set of those images and their true/predicted keypoints.

This function's main role is to take batches of image and keypoint data (the input and output of your CNN), and transform them into numpy images and un-normalized keypoints (x, y) for normal display. The un-transformation process turns keypoints and images into numpy arrays from Tensors and it undoes the keypoint normalization done in the Normalize() transform; it's assumed that you applied these transformations when you loaded your test data.

# visualize the output
# by default this shows a batch of 10 images
def visualize_output(test_images, test_outputs, gt_pts=None, batch_size=10):

    for i in range(batch_size):
        plt.figure(figsize=(20,10))
        ax = plt.subplot(1, batch_size, i+1)

        # un-transform the image data
        image = test_images[i].data   # get the image from it's Variable wrapper
        image = image.numpy()   # convert to numpy array from a Tensor
        image = np.transpose(image, (1, 2, 0))   # transpose to go from torch to numpy image

        # un-transform the predicted key_pts data
        predicted_key_pts = test_outputs[i].data
        predicted_key_pts = predicted_key_pts.numpy()
        # undo normalization of keypoints  
        predicted_key_pts = predicted_key_pts*50.0+100
        
        # plot ground truth points for comparison, if they exist
        ground_truth_pts = None
        if gt_pts is not None:
            ground_truth_pts = gt_pts[i]         
            ground_truth_pts = ground_truth_pts*50.0+100
        
        # call show_all_keypoints
        show_all_keypoints(np.squeeze(image), predicted_key_pts, ground_truth_pts)
            
        plt.axis('off')

    plt.show()
    
# call it
visualize_output(test_images, test_outputs, gt_pts)

Training

Loss function

Training a network to predict keypoints is different than training a network to predict a class; instead of outputting a distribution of classes and using cross entropy loss, you may want to choose a loss function that is suited for regression, which directly compares a predicted value and target value. Read about the various kinds of loss functions (like MSE or L1/SmoothL1 loss) in this documentation.

TODO: Define the loss and optimization

Next, you'll define how the model will train by deciding on the loss function and optimizer.

---

## TODO: Define the loss and optimization
import torch.optim as optim

criterion = nn.SmoothL1Loss()

optimizer = optim.Adam(net.parameters(), lr=0.001)

# Loading Model and Resume Training
model_dir = 'saved_models/'
model_name = 'keypoints_model_v3_2.pt'

cp = torch.load(model_dir+model_name)

net.load_state_dict(cp['state_dict'])
optimizer.load_state_dict(cp['optimizer'])

Training and Initial Observation

Now, you'll train on your batched training data from train_loader for a number of epochs.

To quickly observe how your model is training and decide on whether or not you should modify it's structure or hyperparameters, you're encouraged to start off with just one or two epochs at first. As you train, note how your the model's loss behaves over time: does it decrease quickly at first and then slow down? Does it take a while to decrease in the first place? What happens if you change the batch size of your training data or modify your loss function? etc.

Use these initial observations to make changes to your model and decide on the best architecture before you train for many epochs and create a final model.

def train_net(n_epochs):

    # prepare the net for training
    net.train()

    for epoch in range(n_epochs):  # loop over the dataset multiple times
        
        running_loss = 0.0

        # train on batches of data, assumes you already have train_loader
        for batch_i, data in enumerate(train_loader):
            # get the input images and their corresponding labels
            images = data['image']
            key_pts = data['keypoints']

            # flatten pts
            key_pts = key_pts.view(key_pts.size(0), -1)

            # convert variables to floats for regression loss
            key_pts = key_pts.type(torch.FloatTensor)
            images = images.type(torch.FloatTensor)

            # forward pass to get outputs
            output_pts = net(images)
            #key_pts = key_pts.type(torch.LongTensor)
            # calculate the loss between predicted and target keypoints
            loss = criterion(output_pts, key_pts)

            # zero the parameter (weight) gradients
            optimizer.zero_grad()
            
            # backward pass to calculate the weight gradients
            loss.backward()

            # update the weights
            optimizer.step()

            # print loss statistics
            running_loss += loss.item()
            if batch_i % 10 == 9:    # print every 10 batches
                print('Epoch: {}, Batch: {}, Avg. Loss: {}'.format(epoch + 1, batch_i+1, running_loss/10))
                running_loss = 0.0

    print('Finished Training')

# train your network
n_epochs = 5 # start small, and increase when you've decided on your model structure and hyperparams

# this is a Workspaces-specific context manager to keep the connection
# alive while training your model, not part of pytorch
with active_session():
    train_net(n_epochs)

/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()
/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()
/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()
/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()

Epoch: 1, Batch: 10, Avg. Loss: 0.01352589437738061
Epoch: 1, Batch: 20, Avg. Loss: 0.014905916573479772
Epoch: 1, Batch: 30, Avg. Loss: 0.01391301634721458
Epoch: 1, Batch: 40, Avg. Loss: 0.013112882897257806
Epoch: 1, Batch: 50, Avg. Loss: 0.014599478663876652
Epoch: 1, Batch: 60, Avg. Loss: 0.014654820133000613
Epoch: 1, Batch: 70, Avg. Loss: 0.03953590244054794
Epoch: 1, Batch: 80, Avg. Loss: 0.02509524691849947
Epoch: 1, Batch: 90, Avg. Loss: 0.020422274805605413
Epoch: 1, Batch: 100, Avg. Loss: 0.019187124352902173
Epoch: 1, Batch: 110, Avg. Loss: 0.012856029719114304
Epoch: 1, Batch: 120, Avg. Loss: 0.01559620201587677
Epoch: 1, Batch: 130, Avg. Loss: 0.014772361237555742
Epoch: 1, Batch: 140, Avg. Loss: 0.015133700054138898
Epoch: 1, Batch: 150, Avg. Loss: 0.012798501178622246
Epoch: 1, Batch: 160, Avg. Loss: 0.017612789943814277
Epoch: 1, Batch: 170, Avg. Loss: 0.015380095597356558
Epoch: 1, Batch: 180, Avg. Loss: 0.01161232185550034
Epoch: 1, Batch: 190, Avg. Loss: 0.015756861353293062
Epoch: 1, Batch: 200, Avg. Loss: 0.011048727668821812
Epoch: 1, Batch: 210, Avg. Loss: 0.013055310305207967
Epoch: 1, Batch: 220, Avg. Loss: 0.020875103864818813
Epoch: 1, Batch: 230, Avg. Loss: 0.021566333901137113
Epoch: 1, Batch: 240, Avg. Loss: 0.021158756315708162
Epoch: 1, Batch: 250, Avg. Loss: 0.027913927007466553
Epoch: 1, Batch: 260, Avg. Loss: 0.013275691494345664
Epoch: 1, Batch: 270, Avg. Loss: 0.013469146145507694
Epoch: 1, Batch: 280, Avg. Loss: 0.01747957235202193
Epoch: 1, Batch: 290, Avg. Loss: 0.018275997042655943
Epoch: 1, Batch: 300, Avg. Loss: 0.015735765080899
Epoch: 1, Batch: 310, Avg. Loss: 0.013624965865164994
Epoch: 1, Batch: 320, Avg. Loss: 0.016116893198341132
Epoch: 1, Batch: 330, Avg. Loss: 0.013863429520279169
Epoch: 1, Batch: 340, Avg. Loss: 0.015655704587697983

/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()
/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()
/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()
/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()

Epoch: 2, Batch: 10, Avg. Loss: 0.018820663448423147
Epoch: 2, Batch: 20, Avg. Loss: 0.016896496433764695
Epoch: 2, Batch: 30, Avg. Loss: 0.013077757693827153
Epoch: 2, Batch: 40, Avg. Loss: 0.014974483009427785
Epoch: 2, Batch: 50, Avg. Loss: 0.01281718467362225
Epoch: 2, Batch: 60, Avg. Loss: 0.016143400501459838
Epoch: 2, Batch: 70, Avg. Loss: 0.011882702354341745
Epoch: 2, Batch: 80, Avg. Loss: 0.01676942836493254
Epoch: 2, Batch: 90, Avg. Loss: 0.01672730315476656
Epoch: 2, Batch: 100, Avg. Loss: 0.015135747287422419
Epoch: 2, Batch: 110, Avg. Loss: 0.015158645436167717
Epoch: 2, Batch: 120, Avg. Loss: 0.013780720997601748
Epoch: 2, Batch: 130, Avg. Loss: 0.015652350429445506
Epoch: 2, Batch: 140, Avg. Loss: 0.014836459932848812
Epoch: 2, Batch: 150, Avg. Loss: 0.013222028128802776
Epoch: 2, Batch: 160, Avg. Loss: 0.012683964148163796
Epoch: 2, Batch: 170, Avg. Loss: 0.013638542732223868
Epoch: 2, Batch: 180, Avg. Loss: 0.012107309512794017
Epoch: 2, Batch: 190, Avg. Loss: 0.015312602836638689
Epoch: 2, Batch: 200, Avg. Loss: 0.0144109602086246
Epoch: 2, Batch: 210, Avg. Loss: 0.012086712196469307
Epoch: 2, Batch: 220, Avg. Loss: 0.014720611553639174
Epoch: 2, Batch: 230, Avg. Loss: 0.014111330313608051
Epoch: 2, Batch: 240, Avg. Loss: 0.013273095292970539
Epoch: 2, Batch: 250, Avg. Loss: 0.01215978995896876
Epoch: 2, Batch: 260, Avg. Loss: 0.03150408873334527
Epoch: 2, Batch: 270, Avg. Loss: 0.025431123096495867
Epoch: 2, Batch: 280, Avg. Loss: 0.0215212264098227
Epoch: 2, Batch: 290, Avg. Loss: 0.017849370278418063
Epoch: 2, Batch: 300, Avg. Loss: 0.015843362268060445
Epoch: 2, Batch: 310, Avg. Loss: 0.030114228138700128
Epoch: 2, Batch: 320, Avg. Loss: 0.03513564029708505
Epoch: 2, Batch: 330, Avg. Loss: 0.01851921174675226
Epoch: 2, Batch: 340, Avg. Loss: 0.015240814164280891

/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()
/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()
/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()
/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()

Epoch: 3, Batch: 10, Avg. Loss: 0.014167605713009835
Epoch: 3, Batch: 20, Avg. Loss: 0.014132487028837204
Epoch: 3, Batch: 30, Avg. Loss: 0.013535076938569545
Epoch: 3, Batch: 40, Avg. Loss: 0.013296107854694128
Epoch: 3, Batch: 50, Avg. Loss: 0.012233640532940626
Epoch: 3, Batch: 60, Avg. Loss: 0.027668555453419685
Epoch: 3, Batch: 70, Avg. Loss: 0.0167891857214272
Epoch: 3, Batch: 80, Avg. Loss: 0.013638346549123525
Epoch: 3, Batch: 90, Avg. Loss: 0.013444784516468643
Epoch: 3, Batch: 100, Avg. Loss: 0.013490535737946629
Epoch: 3, Batch: 110, Avg. Loss: 0.018011993914842605
Epoch: 3, Batch: 120, Avg. Loss: 0.013721097353845834
Epoch: 3, Batch: 130, Avg. Loss: 0.015159925352782011
Epoch: 3, Batch: 140, Avg. Loss: 0.026147049944847824
Epoch: 3, Batch: 150, Avg. Loss: 0.013413196802139283
Epoch: 3, Batch: 160, Avg. Loss: 0.013648462388664484
Epoch: 3, Batch: 170, Avg. Loss: 0.011301478091627359
Epoch: 3, Batch: 180, Avg. Loss: 0.012914830353111029
Epoch: 3, Batch: 190, Avg. Loss: 0.033241054974496365
Epoch: 3, Batch: 200, Avg. Loss: 0.01977307954803109
Epoch: 3, Batch: 210, Avg. Loss: 0.015656592883169652
Epoch: 3, Batch: 220, Avg. Loss: 0.016610699892044067
Epoch: 3, Batch: 230, Avg. Loss: 0.01388584952801466
Epoch: 3, Batch: 240, Avg. Loss: 0.01675524767488241
Epoch: 3, Batch: 250, Avg. Loss: 0.016500308364629745
Epoch: 3, Batch: 260, Avg. Loss: 0.0192116878926754
Epoch: 3, Batch: 270, Avg. Loss: 0.019138671411201357
Epoch: 3, Batch: 280, Avg. Loss: 0.021275038085877895
Epoch: 3, Batch: 290, Avg. Loss: 0.013486303016543388
Epoch: 3, Batch: 300, Avg. Loss: 0.01758380951359868
Epoch: 3, Batch: 310, Avg. Loss: 0.012968258932232857
Epoch: 3, Batch: 320, Avg. Loss: 0.024128346890211105
Epoch: 3, Batch: 330, Avg. Loss: 0.014720895234495402
Epoch: 3, Batch: 340, Avg. Loss: 0.013997384253889323

/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()
/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()
/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()
/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()

Epoch: 4, Batch: 10, Avg. Loss: 0.013195629371330142
Epoch: 4, Batch: 20, Avg. Loss: 0.013129113428294658
Epoch: 4, Batch: 30, Avg. Loss: 0.017441734205931425
Epoch: 4, Batch: 40, Avg. Loss: 0.0225716651417315
Epoch: 4, Batch: 50, Avg. Loss: 0.015974144637584686
Epoch: 4, Batch: 60, Avg. Loss: 0.017515288572758435
Epoch: 4, Batch: 70, Avg. Loss: 0.017978774942457677
Epoch: 4, Batch: 80, Avg. Loss: 0.013871135655790567
Epoch: 4, Batch: 90, Avg. Loss: 0.01306487894617021
Epoch: 4, Batch: 100, Avg. Loss: 0.014517210237681866
Epoch: 4, Batch: 110, Avg. Loss: 0.01671413891017437
Epoch: 4, Batch: 120, Avg. Loss: 0.015338700357824564
Epoch: 4, Batch: 130, Avg. Loss: 0.013676065392792225
Epoch: 4, Batch: 140, Avg. Loss: 0.028100013360381128
Epoch: 4, Batch: 150, Avg. Loss: 0.012993910815566778
Epoch: 4, Batch: 160, Avg. Loss: 0.010366589250043034
Epoch: 4, Batch: 170, Avg. Loss: 0.012708820961415767
Epoch: 4, Batch: 180, Avg. Loss: 0.0161542484536767
Epoch: 4, Batch: 190, Avg. Loss: 0.012334332475438714
Epoch: 4, Batch: 200, Avg. Loss: 0.012085024220868944
Epoch: 4, Batch: 210, Avg. Loss: 0.0121856227517128
Epoch: 4, Batch: 220, Avg. Loss: 0.02154240943491459
Epoch: 4, Batch: 230, Avg. Loss: 0.015431298315525055
Epoch: 4, Batch: 240, Avg. Loss: 0.015344120049849152
Epoch: 4, Batch: 250, Avg. Loss: 0.016864684596657752
Epoch: 4, Batch: 260, Avg. Loss: 0.013521903939545155
Epoch: 4, Batch: 270, Avg. Loss: 0.015408074902370571
Epoch: 4, Batch: 280, Avg. Loss: 0.011874439008533955
Epoch: 4, Batch: 290, Avg. Loss: 0.013249856000766159
Epoch: 4, Batch: 300, Avg. Loss: 0.013237424381077289
Epoch: 4, Batch: 310, Avg. Loss: 0.016862078756093978
Epoch: 4, Batch: 320, Avg. Loss: 0.018420717399567365
Epoch: 4, Batch: 330, Avg. Loss: 0.015192546043545007
Epoch: 4, Batch: 340, Avg. Loss: 0.01420698743313551

/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()
/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()
/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()
/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()

Epoch: 5, Batch: 10, Avg. Loss: 0.013883666973561049
Epoch: 5, Batch: 20, Avg. Loss: 0.01690534157678485
Epoch: 5, Batch: 30, Avg. Loss: 0.017182416282594204
Epoch: 5, Batch: 40, Avg. Loss: 0.011806878075003625
Epoch: 5, Batch: 50, Avg. Loss: 0.01578130340203643
Epoch: 5, Batch: 60, Avg. Loss: 0.013762124627828599
Epoch: 5, Batch: 70, Avg. Loss: 0.021263714786618947
Epoch: 5, Batch: 80, Avg. Loss: 0.015421356074512005
Epoch: 5, Batch: 90, Avg. Loss: 0.045791672822088
Epoch: 5, Batch: 100, Avg. Loss: 0.016289565712213516
Epoch: 5, Batch: 110, Avg. Loss: 0.016147746238857508
Epoch: 5, Batch: 120, Avg. Loss: 0.024510491732507945
Epoch: 5, Batch: 130, Avg. Loss: 0.01478671827353537
Epoch: 5, Batch: 140, Avg. Loss: 0.028401969606056808
Epoch: 5, Batch: 150, Avg. Loss: 0.016742802876979112
Epoch: 5, Batch: 160, Avg. Loss: 0.017497367318719627
Epoch: 5, Batch: 170, Avg. Loss: 0.017809480894356966
Epoch: 5, Batch: 180, Avg. Loss: 0.012363833654671908
Epoch: 5, Batch: 190, Avg. Loss: 0.016346075106412172
Epoch: 5, Batch: 200, Avg. Loss: 0.019211755506694315
Epoch: 5, Batch: 210, Avg. Loss: 0.014485463965684175
Epoch: 5, Batch: 220, Avg. Loss: 0.015325127355754375
Epoch: 5, Batch: 230, Avg. Loss: 0.014449028857052326
Epoch: 5, Batch: 250, Avg. Loss: 0.02762115588411689
Epoch: 5, Batch: 260, Avg. Loss: 0.016501652635633947
Epoch: 5, Batch: 270, Avg. Loss: 0.02109698476269841
Epoch: 5, Batch: 280, Avg. Loss: 0.013256924413144588
Epoch: 5, Batch: 290, Avg. Loss: 0.015194178558886051
Epoch: 5, Batch: 300, Avg. Loss: 0.012925848457962274
Epoch: 5, Batch: 310, Avg. Loss: 0.016321142297238113
Epoch: 5, Batch: 320, Avg. Loss: 0.013354304898530244
Epoch: 5, Batch: 330, Avg. Loss: 0.018798929266631605
Epoch: 5, Batch: 340, Avg. Loss: 0.014254111982882022
Finished Training

Test data

See how your model performs on previously unseen, test data. We've already loaded and transformed this data, similar to the training data. Next, run your trained model on these images to see what kind of keypoints are produced. You should be able to see if your model is fitting each new face it sees, if the points are distributed randomly, or if the points have actually overfitted the training data and do not generalize.

# get a sample of test data again
test_images, test_outputs, gt_pts = net_sample_output()


print(test_images.data.size())

print(test_outputs.data.size())
print(gt_pts.size())

/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()
/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()
/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()
/home/workspace/data_load.py:39: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()

torch.Size([10, 1, 224, 224])
torch.Size([10, 68, 2])
torch.Size([10, 68, 2])

## TODO: visualize your test output
# you can use the same function as before, by un-commenting the line below:

visualize_output(test_images, test_outputs, gt_pts)

Once you've found a good model (or two), save your model so you can load it and use it later!

Save your models but please delete any checkpoints and saved models before you submit your project otherwise your workspace may be too large to submit.

## TODO: change the name to something uniqe for each new model
model_dir = 'saved_models/'
model_name = 'keypoints_model_v3_2.pt'

# save the optimizer, so that I can resume training from a saved model
checkpoint = {
    'state_dict': net.state_dict(),
    'optimizer': optimizer.state_dict()
}

# after training, save your model parameters in the dir 'saved_models'
torch.save(checkpoint, model_dir+model_name)

After you've trained a well-performing model, answer the following questions so that we have some insight into your training and architecture selection process. Answering all questions is required to pass this project.

Question 1: What optimization and loss functions did you choose and why?

Answer:

• For Optimization, I use Adam optimizer, because as much as knowledge I have, Adam works best for Training CNN models for its adaptive learning rate method. So for large models, Adam works better and more quickly than other optimizer like SGD, RMSProp etc.

• For Loss function I use SmoothL1Loss, because It is less sensitive to outliers than the MSELoss and in some cases prevents exploding gradients, it makes ther CNN model to learn more faster

Question 2: What kind of network architecture did you start with and how did it change as you tried different architectures? Did you decide to add more convolutional layers or any layers to avoid overfitting the data?

Answer:

• At first, I started with a model having 5 nos of Convolutional Layers with Max Pooling and 3 Linear(Densed) layers.
• To avoid overfitting, I added several Dropouts.
• As the number of Convolutional filters are increasing after each filter, I also increase the Dropout probability, so that each filter get properly trained.
• I also added the Dropout of probability of 0.4, in 2 Dansed layers, to avoid over fitting
• At last, I am done with a model, having 5 Convolution Layer + Maxpooling Layer + Dropout Layer, and 3 Linear Layer with Dropout

Question 3: How did you decide on the number of epochs and batch_size to train your model?

Answer:

• I used 40 epochs in 8 rounds, 5 in each. I plotted the Avg. Loss vs. Epoch Graph in Excel and found that after 40 epochs the model is not training too much, and loss the is also now dicreasing a lot. I trained the model in different rounds and saved it because, my network is little unstable and 2 times I lossed my progress.

• I used 10 as batch size. It makes the training little slow, but as I started the initial trainings in my local machine, thats why I have to make the batch size limited due to memory limitations.

Feature Visualization

Sometimes, neural networks are thought of as a black box, given some input, they learn to produce some output. CNN's are actually learning to recognize a variety of spatial patterns and you can visualize what each convolutional layer has been trained to recognize by looking at the weights that make up each convolutional kernel and applying those one at a time to a sample image. This technique is called feature visualization and it's useful for understanding the inner workings of a CNN.

In the cell below, you can see how to extract a single filter (by index) from your first convolutional layer. The filter should appear as a grayscale grid.

# Get the weights in the first conv layer, "conv1"
# if necessary, change this to reflect the name of your first conv layer
weights1 = net.conv1.weight.data

w = weights1.numpy()

filter_index = 0

print(w[filter_index][0])
print(w[filter_index][0].shape)

# display the filter weights
plt.imshow(w[filter_index][0], cmap='gray')

[[ 0.04393715  0.20721211  0.26394439  0.02667996  0.15938626]
 [ 0.2493185   0.26975399  0.13463667  0.08381031  0.13201509]
 [-0.14774565 -0.04076486 -0.16897483  0.11589643  0.16420817]
 [-0.1675602  -0.21448237  0.0106273   0.09936898  0.02567196]
 [-0.25882539 -0.11319841 -0.26565179 -0.30568147 -0.37916401]]
(5, 5)

<matplotlib.image.AxesImage at 0x7fd33ecf8fd0>

Feature maps

Each CNN has at least one convolutional layer that is composed of stacked filters (also known as convolutional kernels). As a CNN trains, it learns what weights to include in it's convolutional kernels and when these kernels are applied to some input image, they produce a set of feature maps. So, feature maps are just sets of filtered images; they are the images produced by applying a convolutional kernel to an input image. These maps show us the features that the different layers of the neural network learn to extract. For example, you might imagine a convolutional kernel that detects the vertical edges of a face or another one that detects the corners of eyes. You can see what kind of features each of these kernels detects by applying them to an image. One such example is shown below; from the way it brings out the lines in an the image, you might characterize this as an edge detection filter.

Next, choose a test image and filter it with one of the convolutional kernels in your trained CNN; look at the filtered output to get an idea what that particular kernel detects.

TODO: Filter an image to see the effect of a convolutional kernel

---

##TODO: load in and display any image from the transformed test dataset

## TODO: Using cv's filter2D function,
## apply a specific set of filter weights (like the one displayed above) to the test image
import cv2
import matplotlib.image as mpimg

img = mpimg.imread('images/mona_lisa.jpg')
image = np.copy(img)
image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)

plt.imshow(image, cmap='gray')
plt.xticks([]), plt.yticks([])
plt.title("Orginal Image")

Text(0.5,1,'Orginal Image')

f_image = cv2.filter2D(image, -1, w[filter_index][0])

fig, (ax1, ax2) = plt.subplots(1, 2)
fig.suptitle("Effect of Convolutional Kernel")
ax1.set_title("Kernel")
ax1.imshow(w[filter_index][0], cmap='gray')
ax2.set_title("FIltered Image")
ax2.imshow(f_image, cmap='gray')

<matplotlib.image.AxesImage at 0x7fd33f2e42e8>

Question 4: Choose one filter from your trained CNN and apply it to a test image; what purpose do you think it plays? What kind of feature do you think it detects?

Answer:

• The filter is detecting Edges, basically Horizontal Edges.
• So, I think the filter is a High-Pass Filter
• It is filtering the image, where, the Upper Portion of the image is brighter than the Lower Portion

---

Moving on!

Now that you've defined and trained your model (and saved the best model), you are ready to move on to the last notebook, which combines a face detector with your saved model to create a facial keypoint detection system that can predict the keypoints on any face in an image!