# -*- coding: utf-8 -*-
__version__ = '2.5.1'
__author__ = "Avinash Kak (kak@purdue.edu)"
__date__ = '2024-September-30'
__url__ = 'https://engineering.purdue.edu/kak/distDLS/DLStudio-2.5.1.html'
__copyright__ = "(C) 2024 Avinash Kak. Python Software Foundation."
import sys,os,os.path,glob
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as tvt
import torch.optim as optim
import numpy as np
from PIL import ImageFilter
import numbers
import re
import math
import random
import copy
import matplotlib.pyplot as plt
import gzip
import pickle
import pymsgbox
import time
import logging
#______________________________ DLStudio Class Definition ________________________________
class DLStudio(object):
def __init__(self, *args, **kwargs ):
if args:
raise ValueError(
'''DLStudio constructor can only be called with keyword arguments for
the following keywords: epochs, learning_rate, batch_size, momentum,
convo_layers_config, image_size, dataroot, path_saved_model, classes,
image_size, convo_layers_config, fc_layers_config, debug_train, use_gpu, and
debug_test''')
learning_rate = epochs = batch_size = convo_layers_config = momentum = None
image_size = fc_layers_config = dataroot = path_saved_model = classes = use_gpu = None
debug_train = debug_test = None
if 'dataroot' in kwargs : dataroot = kwargs.pop('dataroot')
if 'learning_rate' in kwargs : learning_rate = kwargs.pop('learning_rate')
if 'momentum' in kwargs : momentum = kwargs.pop('momentum')
if 'epochs' in kwargs : epochs = kwargs.pop('epochs')
if 'batch_size' in kwargs : batch_size = kwargs.pop('batch_size')
if 'convo_layers_config' in kwargs : convo_layers_config = kwargs.pop('convo_layers_config')
if 'image_size' in kwargs : image_size = kwargs.pop('image_size')
if 'fc_layers_config' in kwargs : fc_layers_config = kwargs.pop('fc_layers_config')
if 'path_saved_model' in kwargs : path_saved_model = kwargs.pop('path_saved_model')
if 'classes' in kwargs : classes = kwargs.pop('classes')
if 'use_gpu' in kwargs : use_gpu = kwargs.pop('use_gpu')
if 'debug_train' in kwargs : debug_train = kwargs.pop('debug_train')
if 'debug_test' in kwargs : debug_test = kwargs.pop('debug_test')
if len(kwargs) != 0: raise ValueError('''You have provided unrecognizable keyword args''')
if dataroot:
self.dataroot = dataroot
if convo_layers_config:
self.convo_layers_config = convo_layers_config
if image_size:
self.image_size = image_size
if fc_layers_config:
self.fc_layers_config = fc_layers_config
if fc_layers_config[0] != -1:
raise Exception("""\n\n\nYour 'fc_layers_config' construction option is not correct. """
"""The first element of the list of nodes in the fc layer must be -1 """
"""because the input to fc will be set automatically to the size of """
"""the final activation volume of the convolutional part of the network""")
if path_saved_model:
self.path_saved_model = path_saved_model
if classes:
self.class_labels = classes
if learning_rate:
self.learning_rate = learning_rate
else:
self.learning_rate = 1e-6
if momentum:
self.momentum = momentum
if epochs:
self.epochs = epochs
if batch_size:
self.batch_size = batch_size
if use_gpu is not None:
self.use_gpu = use_gpu
if use_gpu is True:
if torch.cuda.is_available():
self.device = torch.device("cuda:0")
else:
self.device = torch.device("cpu")
else:
self.device = torch.device("cpu")
if debug_train:
self.debug_train = debug_train
else:
self.debug_train = 0
if debug_test:
self.debug_test = debug_test
else:
self.debug_test = 0
self.debug_config = 0
def parse_config_string_for_convo_layers(self):
'''
Each collection of 'n' otherwise identical layers in a convolutional network is
specified by a string that looks like:
"nx[a,b,c,d]-MaxPool(k)"
where
n = num of this type of convo layer
a = number of out_channels [in_channels determined by prev layer]
b,c = kernel for this layer is of size (b,c) [b along height, c along width]
d = stride for convolutions
k = maxpooling over kxk patches with stride of k
Example:
"n1x[a1,b1,c1,d1]-MaxPool(k1) n2x[a2,b2,c2,d2]-MaxPool(k2)"
'''
configuration = self.convo_layers_config
configs = configuration.split()
all_convo_layers = []
image_size_after_layer = self.image_size
for k,config in enumerate(configs):
two_parts = config.split('-')
how_many_conv_layers_with_this_config = int(two_parts[0][:config.index('x')])
if self.debug_config:
print("\n\nhow many convo layers with this config: %d" % how_many_conv_layers_with_this_config)
maxpooling_size = int(re.findall(r'\d+', two_parts[1])[0])
if self.debug_config:
print("\nmax pooling size for all convo layers with this config: %d" % maxpooling_size)
for conv_layer in range(how_many_conv_layers_with_this_config):
convo_layer = {'out_channels':None,
'kernel_size':None,
'convo_stride':None,
'maxpool_size':None,
'maxpool_stride': None}
kernel_params = two_parts[0][config.index('x')+1:][1:-1].split(',')
if self.debug_config:
print("\nkernel_params: %s" % str(kernel_params))
convo_layer['out_channels'] = int(kernel_params[0])
convo_layer['kernel_size'] = (int(kernel_params[1]), int(kernel_params[2]))
convo_layer['convo_stride'] = int(kernel_params[3])
image_size_after_layer = [x // convo_layer['convo_stride'] for x in image_size_after_layer]
convo_layer['maxpool_size'] = maxpooling_size
convo_layer['maxpool_stride'] = maxpooling_size
image_size_after_layer = [x // convo_layer['maxpool_size'] for x in image_size_after_layer]
all_convo_layers.append(convo_layer)
configs_for_all_convo_layers = {i : all_convo_layers[i] for i in range(len(all_convo_layers))}
if self.debug_config:
print("\n\nAll convo layers: %s" % str(configs_for_all_convo_layers))
last_convo_layer = configs_for_all_convo_layers[len(all_convo_layers)-1]
out_nodes_final_layer = image_size_after_layer[0] * image_size_after_layer[1] * \
last_convo_layer['out_channels']
self.fc_layers_config[0] = out_nodes_final_layer
self.configs_for_all_convo_layers = configs_for_all_convo_layers
return configs_for_all_convo_layers
def build_convo_layers(self, configs_for_all_convo_layers):
conv_layers = nn.ModuleList()
in_channels_for_next_layer = None
for layer_index in configs_for_all_convo_layers:
if self.debug_config:
print("\n\n\nLayer index: %d" % layer_index)
in_channels = 3 if layer_index == 0 else in_channels_for_next_layer
out_channels = configs_for_all_convo_layers[layer_index]['out_channels']
kernel_size = configs_for_all_convo_layers[layer_index]['kernel_size']
padding = tuple((k-1) // 2 for k in kernel_size)
stride = configs_for_all_convo_layers[layer_index]['convo_stride']
maxpool_size = configs_for_all_convo_layers[layer_index]['maxpool_size']
if self.debug_config:
print("\n in_channels=%d out_channels=%d kernel_size=%s stride=%s \
maxpool_size=%s" % (in_channels, out_channels, str(kernel_size), str(stride),
str(maxpool_size)))
conv_layers.append( nn.Conv2d( in_channels,out_channels,kernel_size,stride=stride,padding=padding) )
conv_layers.append( nn.MaxPool2d( maxpool_size ) )
conv_layers.append( nn.ReLU() ),
in_channels_for_next_layer = out_channels
return conv_layers
def build_fc_layers(self):
fc_layers = nn.ModuleList()
for layer_index in range(len(self.fc_layers_config) - 1):
fc_layers.append( nn.Linear( self.fc_layers_config[layer_index],
self.fc_layers_config[layer_index+1] ) )
return fc_layers
def load_cifar_10_dataset(self):
'''
In the code shown below, the call to "ToTensor()" converts the usual int range 0-255 for pixel
values to 0-1.0 float vals and then the call to "Normalize()" changes the range to -1.0-1.0 float
vals. For additional explanation of the call to "tvt.ToTensor()", see Slide 31 of my Week 2
slides at the DL course website. And see Slides 32 and 33 for the syntax
"tvt.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))". In this call, the three numbers in the
first tuple change the means in the three color channels and the three numbers in the second
tuple change the standard deviations according to the formula:
image_channel_val = (image_channel_val - mean) / std
The end result is that the values in the image tensor will be normalized to fall between -1.0
and +1.0. If needed we can do inverse normalization by
image_channel_val = (image_channel_val * std) + mean
'''
## But then the call to Normalize() changes the range to -1.0-1.0 float vals.
transform = tvt.Compose([tvt.ToTensor(),
tvt.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) ## accuracy: 51%
## Define where the training and the test datasets are located:
train_data_loc = torchvision.datasets.CIFAR10(root=self.dataroot, train=True, download=True, transform=transform)
test_data_loc = torchvision.datasets.CIFAR10(root=self.dataroot, train=False, download=True, transform=transform)
## Now create the data loaders:
self.train_data_loader = torch.utils.data.DataLoader(train_data_loc,batch_size=self.batch_size, shuffle=True, num_workers=2)
self.test_data_loader = torch.utils.data.DataLoader(test_data_loc,batch_size=self.batch_size, shuffle=False, num_workers=2)
def load_cifar_10_dataset_with_augmentation(self):
'''
In general, we want to do data augmentation for training:
'''
transform_train = tvt.Compose([
tvt.RandomCrop(32, padding=4),
tvt.RandomHorizontalFlip(),
tvt.ToTensor(),
tvt.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
## Don't need any augmentation for the test data:
transform_test = tvt.Compose([
tvt.ToTensor(),
tvt.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
## Define where the training and the test datasets are located
train_data_loc = torchvision.datasets.CIFAR10( root=self.dataroot, train=True, download=True, transform=transform_train )
test_data_loc = torchvision.datasets.CIFAR10( root=self.dataroot, train=False, download=True, transform=transform_test )
## Now create the data loaders:
self.train_data_loader = torch.utils.data.DataLoader(train_data_loc, batch_size=self.batch_size, shuffle=True, num_workers=2)
self.test_data_loader = torch.utils.data.DataLoader(test_data_loc, batch_size=self.batch_size, shuffle=False, num_workers=2)
def imshow(self, img):
'''
called by display_tensor_as_image() for displaying the image
'''
img = img / 2 + 0.5 # unnormalize
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1, 2, 0)))
plt.show()
class Net(nn.Module):
def __init__(self, convo_layers, fc_layers):
super(DLStudio.Net, self).__init__()
self.my_modules_convo = convo_layers
self.my_modules_fc = fc_layers
def forward(self, x):
for m in self.my_modules_convo:
x = m(x)
x = x.view(x.shape[0], -1)
for m in self.my_modules_fc:
x = m(x)
return x
def run_code_for_training(self, net, display_images=False):
filename_for_out = "performance_numbers_" + str(self.epochs) + ".txt"
FILE = open(filename_for_out, 'w')
net = copy.deepcopy(net)
net = net.to(self.device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=self.learning_rate, momentum=self.momentum)
print("\n\nStarting training loop...")
start_time = time.perf_counter()
loss_tally = []
elapsed_time = 0.0
for epoch in range(self.epochs):
print("")
running_loss = 0.0
for i, data in enumerate(self.train_data_loader):
inputs, labels = data
if i % 1000 == 999:
current_time = time.perf_counter()
elapsed_time = current_time - start_time
print("\n\n[epoch:%d/%d iter=%4d elapsed_time=%5d secs] Ground Truth: " %
(epoch+1, self.epochs, i+1, elapsed_time) +
' '.join('%10s' % self.class_labels[labels[j]] for j in range(self.batch_size)))
inputs = inputs.to(self.device)
labels = labels.to(self.device)
## Since PyTorch likes to construct dynamic computational graphs, we need to
## zero out the previously calculated gradients for the learnable parameters:
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
running_loss += loss.item()
if i % 1000 == 999:
_, predicted = torch.max(outputs.data, 1)
print("[epoch:%d/%d iter=%4d elapsed_time=%5d secs] Predicted Labels: " %
(epoch+1, self.epochs, i+1, elapsed_time ) +
' '.join('%10s' % self.class_labels[predicted[j]] for j in range(self.batch_size)))
avg_loss = running_loss / float(1000)
loss_tally.append(avg_loss)
print("[epoch:%d/%d iter=%4d elapsed_time=%5d secs] Loss: %.3f" %
(epoch+1, self.epochs, i+1, elapsed_time, avg_loss))
FILE.write("%.3f\n" % avg_loss)
FILE.flush()
running_loss = 0.0
if display_images:
logger = logging.getLogger()
old_level = logger.level
logger.setLevel(100)
plt.figure(figsize=[6,3])
plt.imshow(np.transpose(torchvision.utils.make_grid(inputs, normalize=False, padding=3, pad_value=255).cpu(), (1,2,0)))
plt.show()
logger.setLevel(old_level)
loss.backward()
optimizer.step()
print("\nFinished Training\n")
self.save_model(net)
plt.figure(figsize=(10,5))
plt.title("Labeling Loss vs. Iterations")
plt.plot(loss_tally)
plt.xlabel("iterations")
plt.ylabel("loss")
plt.legend(["Plot of loss versus iterations"], fontsize="x-large")
plt.savefig("loss_vs_iterations.png")
plt.show()
def display_tensor_as_image(self, tensor, title=""):
'''
This method converts the argument tensor into a photo image that you can display
in your terminal screen. It can convert tensors of three different shapes
into images: (3,H,W), (1,H,W), and (H,W), where H, for height, stands for the
number of pixels in the vertical direction and W, for width, for the same
along the horizontal direction. When the first element of the shape is 3,
that means that the tensor represents a color image in which each pixel in
the (H,W) plane has three values for the three color channels. On the other
hand, when the first element is 1, that stands for a tensor that will be
shown as a grayscale image. And when the shape is just (H,W), that is
automatically taken to be for a grayscale image.
'''
tensor_range = (torch.min(tensor).item(), torch.max(tensor).item())
if tensor_range == (-1.0,1.0):
## The tensors must be between 0.0 and 1.0 for the display:
print("\n\n\nimage un-normalization called")
tensor = tensor/2.0 + 0.5 # unnormalize
plt.figure(title)
### The call to plt.imshow() shown below needs a numpy array. We must also
### transpose the array so that the number of channels (the same thing as the
### number of color planes) is in the last element. For a tensor, it would be in
### the first element.
if tensor.shape[0] == 3 and len(tensor.shape) == 3:
# plt.imshow( tensor.numpy().transpose(1,2,0) )
plt.imshow( tensor.numpy().transpose(1,2,0) )
### If the grayscale image was produced by calling torchvision.transform's
### ".ToPILImage()", and the result converted to a tensor, the tensor shape will
### again have three elements in it, however the first element that stands for
### the number of channels will now be 1
elif tensor.shape[0] == 1 and len(tensor.shape) == 3:
tensor = tensor[0,:,:]
plt.imshow( tensor.numpy(), cmap = 'gray' )
### For any one color channel extracted from the tensor representation of a color
### image, the shape of the tensor will be (W,H):
elif len(tensor.shape) == 2:
plt.imshow( tensor.numpy(), cmap = 'gray' )
else:
sys.exit("\n\n\nfrom 'display_tensor_as_image()': tensor for image is ill formed -- aborting")
plt.show()
def check_a_sampling_of_images(self):
'''
Displays the first batch_size number of images in your dataset.
'''
dataiter = iter(self.train_data_loader)
images, labels = dataiter.next()
# Since negative pixel values make no sense for display, setting the 'normalize'
# option to True will change the range back from (-1.0,1.0) to (0.0,1.0):
self.display_tensor_as_image(torchvision.utils.make_grid(images, normalize=True))
# Print class labels for the images shown:
print(' '.join('%5s' % self.class_labels[labels[j]] for j in range(self.batch_size)))
def save_model(self, model):
'''
Save the trained model to a disk file
'''
torch.save(model.state_dict(), self.path_saved_model)
def run_code_for_testing(self, net, display_images=False):
net.load_state_dict(torch.load(self.path_saved_model))
net = net.eval()
net = net.to(self.device)
## In what follows, in addition to determining the predicted label for each test
## image, we will also compute some stats to measure the overall performance of
## the trained network. This we will do in two different ways: For each class,
## we will measure how frequently the network predicts the correct labels. In
## addition, we will compute the confusion matrix for the predictions.
filename_for_results = "classification_results_" + str(self.epochs) + ".txt"
FILE = open(filename_for_results, 'w')
correct = 0
total = 0
confusion_matrix = torch.zeros(len(self.class_labels), len(self.class_labels))
class_correct = [0] * len(self.class_labels)
class_total = [0] * len(self.class_labels)
with torch.no_grad():
for i,data in enumerate(self.test_data_loader):
## data is set to the images and the labels for one batch at a time:
images, labels = data
images = images.to(self.device)
labels = labels.to(self.device)
if i % 1000 == 999:
print("\n\n[i=%d:] Ground Truth: " % (i+1) + ' '.join('%5s' % self.class_labels[labels[j]] for j in range(self.batch_size)))
outputs = net(images)
## max() returns two things: the max value and its index in the 10 element
## output vector. We are only interested in the index --- since that is
## essentially the predicted class label:
_, predicted = torch.max(outputs.data, 1)#
# if display_images and i % 1000 == 999:
if i % 1000 == 999:
print("[i=%d:] Predicted Labels: " % (i+1) + ' '.join('%5s' % self.class_labels[predicted[j]] for j in range(self.batch_size)))
logger = logging.getLogger()
old_level = logger.level
if display_images:
logger.setLevel(100)
plt.figure(figsize=[6,3])
plt.imshow(np.transpose(torchvision.utils.make_grid(images, normalize=False, padding=3, pad_value=255).cpu(), (1,2,0)))
plt.show()
logger.setLevel(old_level)
for label,prediction in zip(labels,predicted):
confusion_matrix[label][prediction] += 1
total += labels.size(0)
correct += (predicted == labels).sum().item()
## comp is a list of size batch_size of "True" and "False" vals
comp = predicted == labels
for j in range(self.batch_size):
label = labels[j]
## The following works because, in a numeric context, the boolean value
## "False" is the same as number 0 and the boolean value True is the
## same as number 1. For that reason "4 + True" will evaluate to 5 and
## "4 + False" will evaluate to 4. Also, "1 == True" evaluates to "True"
## "1 == False" evaluates to "False". However, note that "1 is True"
## evaluates to "False" because the operator "is" does not provide a
## numeric context for "True". And so on. In the statement that follows,
## while c[j].item() will either return "False" or "True", for the
## addition operator, Python will use the values 0 and 1 instead.
class_correct[label] += comp[j].item()
class_total[label] += 1
for j in range(len(self.class_labels)):
print('Prediction accuracy for %5s : %2d %%' % (self.class_labels[j], 100 * class_correct[j] / class_total[j]))
FILE.write('\n\nPrediction accuracy for %5s : %2d %%\n' % (self.class_labels[j], 100 * class_correct[j] / class_total[j]))
print("\n\n\nOverall accuracy of the network on the 10000 test images: %d %%" % (100 * correct / float(total)))
FILE.write("\n\n\nOverall accuracy of the network on the 10000 test images: %d %%\n" % (100 * correct / float(total)))
print("\n\nDisplaying the confusion matrix:\n")
FILE.write("\n\nDisplaying the confusion matrix:\n\n")
out_str = " "
for j in range(len(self.class_labels)): out_str += "%7s" % self.class_labels[j]
print(out_str + "\n")
FILE.write(out_str + "\n\n")
for i,label in enumerate(self.class_labels):
out_percents = [100 * confusion_matrix[i,j] / float(class_total[i])
for j in range(len(self.class_labels))]
out_percents = ["%.2f" % item.item() for item in out_percents]
out_str = "%6s: " % self.class_labels[i]
for j in range(len(self.class_labels)): out_str += "%7s" % out_percents[j]
print(out_str)
FILE.write(out_str + "\n")
FILE.close()
###%%%
#####################################################################################################################
############################# Start Definition of Inner Class ExperimentsWithSequential ############################
class ExperimentsWithSequential(nn.Module):
"""
Demonstrates how to use the torch.nn.Sequential container class
Class Path: DLStudio -> ExperimentsWithSequential
"""
def __init__(self, dl_studio ):
super(DLStudio.ExperimentsWithSequential, self).__init__()
self.dl_studio = dl_studio
def load_cifar_10_dataset(self):
self.dl_studio.load_cifar_10_dataset()
def load_cifar_10_dataset_with_augmentation(self):
self.dl_studio.load_cifar_10_dataset_with_augmentation()
class Net(nn.Module):
"""
To see if the DLStudio class would work with any network that a user may
want to experiment with, I copy-and-pasted the network shown below
from Zhenye's GitHub blog: https://github.com/Zhenye-Na/blog
Class Path: DLStudio -> ExperimentsWithSequential -> Net
"""
def __init__(self):
super(DLStudio.ExperimentsWithSequential.Net, self).__init__()
self.conv_seqn = nn.Sequential(
# Conv Layer block 1:
nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=1),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
# Conv Layer block 2:
nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Dropout2d(p=0.05),
# Conv Layer block 3:
nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.fc_seqn = nn.Sequential(
nn.Dropout(p=0.1),
nn.Linear(4096, 1024),
nn.ReLU(inplace=True),
nn.Linear(1024, 512),
nn.ReLU(inplace=True),
nn.Dropout(p=0.1),
nn.Linear(512, 10)
)
def forward(self, x):
x = self.conv_seqn(x)
# flatten
x = x.view(x.shape[0], -1)
x = self.fc_seqn(x)
return x
def run_code_for_training(self, net):
self.dl_studio.run_code_for_training(net)
def save_model(self, model):
'''
Save the trained model to a disk file
'''
torch.save(model.state_dict(), self.dl_studio.path_saved_model)
def run_code_for_testing(self, model):
self.dl_studio.run_code_for_testing(model)
###%%%
#####################################################################################################################
############################### Start Definition of Inner Class ExperimentsWithCIFAR ###############################
class ExperimentsWithCIFAR(nn.Module):
"""
Class Path: DLStudio -> ExperimentsWithCIFAR
"""
def __init__(self, dl_studio ):
super(DLStudio.ExperimentsWithCIFAR, self).__init__()
self.dl_studio = dl_studio
def load_cifar_10_dataset(self):
self.dl_studio.load_cifar_10_dataset()
def load_cifar_10_dataset_with_augmentation(self):
self.dl_studio.load_cifar_10_dataset_with_augmentation()
## You can instantiate two different types of networks when experimenting with
## the inner class ExperimentsWithCIFAR. The network shown below is from the
## PyTorch tutorial
##
## https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html
##
class Net(nn.Module):
"""
Class Path: DLStudio -> ExperimentsWithCIFAR -> Net
"""
def __init__(self):
super(DLStudio.ExperimentsWithCIFAR.Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = nn.MaxPool2d(2,2)(F.relu(self.conv1(x)))
x = nn.MaxPool2d(2,2)(F.relu(self.conv2(x)))
x = x.view( x.shape[0], - 1 )
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
## Instead of using the network shown above, you can also use the network shown below.
## if you are playing with the ExperimentsWithCIFAR inner class. If that's what you
## want to do, in the script "playing_with_cifar10.py" in the Examples directory,
## you will need to replace the statement
## model = exp_cifar.Net()
## by the statement
## model = exp_cifar.Net2()
##
class Net2(nn.Module):
"""
Class Path: DLStudio -> ExperimentsWithCIFAR -> Net2
"""
def __init__(self):
"""
I created this network class just to see if it was possible to simply calculate
the size of the first of the fully connected layers from strides in the convo
layers up to that point and from the out_channels used in the top-most convo
layer. In what you see below, I am keeping track of all the strides by pushing
them into the array 'strides'. Subsequently, in the formula shown in line (A),
I use the product of all strides and the number of out_channels for the topmost
layer to compute the size of the first fully-connected layer.
"""
super(DLStudio.ExperimentsWithCIFAR.Net2, self).__init__()
self.relu = nn.ReLU()
strides = []
patch_size = 2
## conv1:
out_ch, ker_size, conv_stride, pool_stride = 128,5,1,2
self.conv1 = nn.Conv2d(3, out_ch, (ker_size,ker_size), padding=(ker_size-1)//2)
self.pool1 = nn.MaxPool2d(patch_size, pool_stride)
strides += (conv_stride, pool_stride)
## conv2:
in_ch = out_ch
out_ch, ker_size, conv_stride, pool_stride = 128,3,1,2
self.conv2 = nn.Conv2d(in_ch, out_ch, ker_size, padding=(ker_size-1)//2)
self.pool2 = nn.MaxPool2d(patch_size, pool_stride)
strides += (conv_stride, pool_stride)
## conv3:
in_ch = out_ch
out_ch, ker_size, conv_stride, pool_stride = in_ch,2,1,1
self.conv3 = nn.Conv2d(in_ch, out_ch, ker_size, padding=1)
self.pool3 = nn.MaxPool2d(patch_size, pool_stride)
## figure out the number of nodes needed for entry into fc:
in_size_for_fc = out_ch * (32 // np.prod(strides)) ** 2 ## (A)
self.in_size_for_fc = in_size_for_fc
self.fc1 = nn.Linear(in_size_for_fc, 150)
self.fc2 = nn.Linear(150, 100)
self.fc3 = nn.Linear(100, 10)
def forward(self, x):
## We know that forward() begins its with work x shaped as (4,3,32,32) where
## 4 is the batch size, 3 in_channels, and where the input image size is 32x32.
x = self.relu(self.conv1(x))
x = self.pool1(x)
x = self.relu(self.conv2(x))
x = self.pool2(x)
x = self.pool3(self.relu(self.conv3(x)))
x = x.view( x.shape[0], - 1 )
x = self.relu(self.fc1( x ))
x = self.relu(self.fc2( x ))
x = self.fc3(x)
return x
def run_code_for_training(self, net, display_images=False):
self.dl_studio.run_code_for_training(net, display_images)
def save_model(self, model):
'''
Save the trained model to a disk file
'''
torch.save(model.state_dict(), self.dl_studio.path_saved_model)
def run_code_for_testing(self, model, display_images=False):
self.dl_studio.run_code_for_testing(model, display_images)
###%%%
#####################################################################################################################
####################### Start Definition of Inner Class BMEnet for Illustrating Skip Connections ##################
class BMEnet(nn.Module):
"""
This educational class is meant for illustrating the concepts related to the
use of skip connections in neural network. It is now well known that deep
networks are difficult to train because of the vanishing gradients problem.
What that means is that as the depth of network increases, the loss gradients
calculated for the early layers become more and more muted, which suppresses
the learning of the parameters in those layers. An important mitigation
strategy for addressing this problem consists of creating a CNN using blocks
with skip connections.
With the code shown in this inner class of the module, you can now experiment with
skip connections in a CNN to see how a deep network with this feature might improve
the classification results. As you will see in the code shown below, the network
that allows you to construct a CNN with skip connections is named BMEnet. As shown
in the script playing_with_skip_connections.py in the Examples directory of the
distribution, you can easily create a CNN with arbitrary depth just by using the
"depth" constructor option for the BMEnet class. The basic block of the network
constructed by BMEnet is called SkipBlock which, very much like the BasicBlock in
ResNet-18, has a couple of convolutional layers whose output is combined with the
input to the block.
Note that the value given to the "depth" constructor option for the BMEnet class
does NOT translate directly into the actual depth of the CNN. [Again, see the script
playing_with_skip_connections.py in the Examples directory for how to use this
option.] The value of "depth" is translated into how many "same input and output
channels" and the "same input and output sizes" instances of SkipBlock to use
between successive instances of downsampling and channel-doubling instances of
SkipBlock.
Class Path: DLStudio -> BMEnet
"""
def __init__(self, dl_studio, skip_connections=True, depth=8):
super(DLStudio.BMEnet, self).__init__()
self.dl_studio = dl_studio
self.depth = depth
image_size = dl_studio.image_size
num_ds = 0 ## num_ds stands for number of downsampling steps
self.conv = nn.Conv2d(3, 64, 3, padding=1)
self.skip64_arr = nn.ModuleList()
for i in range(self.depth):
self.skip64_arr.append(DLStudio.BMEnet.SkipBlock(64, 64, skip_connections=skip_connections))
self.skip64to128ds = DLStudio.BMEnet.SkipBlock(64, 128, downsample=True, skip_connections=skip_connections )
num_ds += 1
self.skip128_arr = nn.ModuleList()
for i in range(self.depth):
self.skip128_arr.append(DLStudio.BMEnet.SkipBlock(128, 128, skip_connections=skip_connections))
self.skip128to256ds = DLStudio.BMEnet.SkipBlock(128, 256, downsample=True, skip_connections=skip_connections )
num_ds += 1
self.skip256_arr = nn.ModuleList()
for i in range(self.depth):
self.skip256_arr.append(DLStudio.BMEnet.SkipBlock(256, 256, skip_connections=skip_connections))
self.fc1 = nn.Linear( (image_size[0]// (2 ** num_ds)) * (image_size[1]//(2 ** num_ds)) * 256, 1000)
self.fc2 = nn.Linear(1000, 10)
def forward(self, x):
x = nn.functional.relu(self.conv(x))
for skip64 in self.skip64_arr:
x = skip64(x)
x = self.skip64to128ds(x)
for skip128 in self.skip128_arr:
x = skip128(x)
x = self.skip128to256ds(x)
for skip256 in self.skip256_arr:
x = skip256(x)
x = x.view( x.shape[0], - 1 )
x = nn.functional.relu(self.fc1(x))
x = self.fc2(x)
return x
def load_cifar_10_dataset(self):
self.dl_studio.load_cifar_10_dataset()
def load_cifar_10_dataset_with_augmentation(self):
self.dl_studio.load_cifar_10_dataset_with_augmentation()
class SkipBlock(nn.Module):
"""
Class Path: DLStudio -> BMEnet -> SkipBlock
"""
def __init__(self, in_ch, out_ch, downsample=False, skip_connections=True):
super(DLStudio.BMEnet.SkipBlock, self).__init__()
self.downsample = downsample
self.skip_connections = skip_connections
self.in_ch = in_ch
self.out_ch = out_ch
self.convo1 = nn.Conv2d(in_ch, in_ch, 3, stride=1, padding=1)
self.convo2 = nn.Conv2d(in_ch, out_ch, 3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(in_ch)
self.bn2 = nn.BatchNorm2d(out_ch)
self.in2out = nn.Conv2d(in_ch, out_ch, 1)
if downsample:
## Setting stride to 2 and kernel_size to 1 amounts to retaining every
## other pixel in the image --- which halves the size of the image:
self.downsampler1 = nn.Conv2d(in_ch, in_ch, 1, stride=2)
self.downsampler2 = nn.Conv2d(out_ch, out_ch, 1, stride=2)
def forward(self, x):
identity = x
out = self.convo1(x)
out = self.bn1(out)
out = nn.functional.relu(out)
out = self.convo2(out)
out = self.bn2(out)
out = nn.functional.relu(out)
if self.downsample:
identity = self.downsampler1(identity)
out = self.downsampler2(out)
if self.skip_connections:
if (self.in_ch == self.out_ch) and (self.downsample is False):
out = out + identity
elif (self.in_ch != self.out_ch) and (self.downsample is False):
identity = self.in2out( identity )
out = out + identity
elif (self.in_ch != self.out_ch) and (self.downsample is True):
out = out + torch.cat((identity, identity), dim=1)
return out
def run_code_for_training(self, net, display_images=False):
self.dl_studio.run_code_for_training(net, display_images)
def save_model(self, model):
'''
Save the trained model to a disk file
'''
torch.save(model.state_dict(), self.dl_studio.path_saved_model)
def run_code_for_testing(self, model, display_images=False):
self.dl_studio.run_code_for_testing(model, display_images=False)
###%%%
#####################################################################################################################
################################ Start Definition of Inner Class CustomDataLoading ################################
class CustomDataLoading(nn.Module):
"""
This is a testbed for experimenting with a completely grounds-up attempt at
designing a custom data loader. Ordinarily, if the basic format of how the dataset
is stored is similar to one of the datasets that the Torchvision module knows about,
you can go ahead and use that for your own dataset. At worst, you may need to carry
out some light customizations depending on the number of classes involved, etc.
However, if the underlying dataset is stored in a manner that does not look like
anything in Torchvision, you have no choice but to supply yourself all of the data
loading infrastructure. That is what this inner class of the main DLStudio class
is all about.
The custom data loading exercise here is related to a dataset called PurdueShapes5
that contains 32x32 images of binary shapes belonging to the following five classes:
1. rectangle
2. triangle
3. disk
4. oval
5. star
The dataset was generated by randomizing the sizes and the orientations of these
five patterns. Since the patterns are rotated with a very simple non-interpolating
transform, just the act of random rotations can introduce boundary and even interior
noise in the patterns.
Each 32x32 image is stored in the dataset as the following list:
[R, G, B, Bbox, Label]
where
R : is a 1024 element list of the values for the red component
of the color at all the pixels
B : the same as above but for the green component of the color
G : the same as above but for the blue component of the color
Bbox : a list like [x1,y1,x2,y2] that defines the bounding box
for the object in the image
Label : the shape of the object
I serialize the dataset with Python's pickle module and then compress it with the
gzip module.
You will find the following dataset directories in the "data" subdirectory of
Examples in the DLStudio distro:
PurdueShapes5-10000-train.gz
PurdueShapes5-1000-test.gz
PurdueShapes5-20-train.gz
PurdueShapes5-20-test.gz
The number that follows the main name string "PurdueShapes5-" is for the number of
images in the dataset.
You will find the last two datasets, with 20 images each, useful for debugging your
logic for object detection and bounding-box regression.
Class Path: DLStudio -> CustomDataLoading
"""
def __init__(self, dl_studio, dataserver_train=None, dataserver_test=None, dataset_file_train=None, dataset_file_test=None):
super(DLStudio.CustomDataLoading, self).__init__()
self.dl_studio = dl_studio
self.dataserver_train = dataserver_train
self.dataserver_test = dataserver_test
class PurdueShapes5Dataset(torch.utils.data.Dataset):
"""
Class Path: DLStudio -> CustomDataLoading -> PurdueShapes5Dataset
"""
def __init__(self, dl_studio, train_or_test, dataset_file):
super(DLStudio.CustomDataLoading.PurdueShapes5Dataset, self).__init__()
if train_or_test == 'train' and dataset_file == "PurdueShapes5-10000-train.gz":
if os.path.exists("torch_saved_PurdueShapes5-10000_dataset.pt") and \
os.path.exists("torch_saved_PurdueShapes5_label_map.pt"):
print("\nLoading training data from the torch-saved archive")
self.dataset = torch.load("torch_saved_PurdueShapes5-10000_dataset.pt")
self.label_map = torch.load("torch_saved_PurdueShapes5_label_map.pt")
# reverse the key-value pairs in the label dictionary:
self.class_labels = dict(map(reversed, self.label_map.items()))
else:
print("""\n\n\nLooks like this is the first time you will be loading in\n"""
"""the dataset for this script. First time loading could take\n"""
"""a minute or so. Any subsequent attempts will only take\n"""
"""a few seconds.\n\n\n""")
root_dir = dl_studio.dataroot
f = gzip.open(root_dir + dataset_file, 'rb')
dataset = f.read()
self.dataset, self.label_map = pickle.loads(dataset, encoding='latin1')
torch.save(self.dataset, "torch_saved_PurdueShapes5-10000_dataset.pt")
torch.save(self.label_map, "torch_saved_PurdueShapes5_label_map.pt")
# reverse the key-value pairs in the label dictionary:
self.class_labels = dict(map(reversed, self.label_map.items()))
else:
root_dir = dl_studio.dataroot
f = gzip.open(root_dir + dataset_file, 'rb')
dataset = f.read()
self.dataset, self.label_map = pickle.loads(dataset, encoding='latin1')
# reverse the key-value pairs in the label dictionary:
self.class_labels = dict(map(reversed, self.label_map.items()))
def __len__(self):
return len(self.dataset)
def __getitem__(self, idx):
r = np.array( self.dataset[idx][0] )
g = np.array( self.dataset[idx][1] )
b = np.array( self.dataset[idx][2] )
R,G,B = r.reshape(32,32), g.reshape(32,32), b.reshape(32,32)
im_tensor = torch.zeros(3,32,32, dtype=torch.float)
im_tensor[0,:,:] = torch.from_numpy(R)
im_tensor[1,:,:] = torch.from_numpy(G)
im_tensor[2,:,:] = torch.from_numpy(B)
sample = {'image' : im_tensor,
'bbox' : self.dataset[idx][3],
'label' : self.dataset[idx][4] }
return sample
def load_PurdueShapes5_dataset(self, dataserver_train, dataserver_test ):
transform = tvt.Compose([tvt.ToTensor(),
tvt.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
self.train_dataloader = torch.utils.data.DataLoader(dataserver_train,
batch_size=self.dl_studio.batch_size,shuffle=True, num_workers=4)
self.test_dataloader = torch.utils.data.DataLoader(dataserver_test,
batch_size=self.dl_studio.batch_size,shuffle=False, num_workers=4)
class ECEnet(nn.Module):
"""
Class Path: DLStudio -> CustomDataloading -> ECEnet
"""
def __init__(self, dl_studio, skip_connections=True, depth=8):
super(DLStudio.CustomDataLoading.ECEnet, self).__init__()
self.dl_studio = dl_studio
self.depth = depth
image_size = dl_studio.image_size
num_ds = 0 ## num_ds stands for number of downsampling steps
self.conv = nn.Conv2d(3, 64, 3, padding=1)
self.skip64_arr = nn.ModuleList()
for i in range(self.depth):
self.skip64_arr.append(DLStudio.CustomDataLoading.SkipBlock2(64, 64, skip_connections=skip_connections))
self.skip64to128ds = DLStudio.CustomDataLoading.SkipBlock2(64, 128, downsample=True, skip_connections=skip_connections )
num_ds += 1
self.skip128_arr = nn.ModuleList()
for i in range(self.depth):
self.skip128_arr.append(DLStudio.CustomDataLoading.SkipBlock2(128, 128, skip_connections=skip_connections))
self.skip128to256ds = DLStudio.CustomDataLoading.SkipBlock2(128, 256, downsample=True, skip_connections=skip_connections )
num_ds += 1
self.skip256_arr = nn.ModuleList()
for i in range(self.depth):
self.skip256_arr.append(DLStudio.CustomDataLoading.SkipBlock2(256, 256, skip_connections=skip_connections))
self.fc1 = nn.Linear( (image_size[0]// (2 ** num_ds)) * (image_size[1]//(2 ** num_ds)) * 256, 1000)
self.fc2 = nn.Linear(1000, 10)
def forward(self, x):
x = nn.functional.relu(self.conv(x))
for skip64 in self.skip64_arr:
x = skip64(x)
x = self.skip64to128ds(x)
for skip128 in self.skip128_arr:
x = skip128(x)
x = self.skip128to256ds(x)
for skip256 in self.skip256_arr:
x = skip256(x)
x = x.view( x.shape[0], - 1 )
x = nn.functional.relu(self.fc1(x))
x = self.fc2(x)
return x
def load_cifar_10_dataset(self):
self.dl_studio.load_cifar_10_dataset()
def load_cifar_10_dataset_with_augmentation(self):
self.dl_studio.load_cifar_10_dataset_with_augmentation()
class SkipBlock2(nn.Module):
"""
Class Path: DLStudio -> CustomDataloading -> SkipBlock
"""
def __init__(self, in_ch, out_ch, downsample=False, skip_connections=True):
super(DLStudio.CustomDataLoading.SkipBlock2, self).__init__()
self.downsample = downsample
self.skip_connections = skip_connections
self.in_ch = in_ch
self.out_ch = out_ch
self.convo1 = nn.Conv2d(in_ch, in_ch, 3, stride=1, padding=1)
self.convo2 = nn.Conv2d(in_ch, out_ch, 3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(in_ch)
self.bn2 = nn.BatchNorm2d(out_ch)
self.in2out = nn.Conv2d(in_ch, out_ch, 1)
if downsample:
## Setting stride to 2 and kernel_size to 1 amounts to retaining every
## other pixel in the image --- which halves the size of the image:
self.downsampler1 = nn.Conv2d(in_ch, in_ch, 1, stride=2)
self.downsampler2 = nn.Conv2d(out_ch, out_ch, 1, stride=2)
def forward(self, x):
identity = x
out = self.convo1(x)
out = self.bn1(out)
out = nn.functional.relu(out)
out = self.convo2(out)
out = self.bn2(out)
out = nn.functional.relu(out)
if self.downsample:
identity = self.downsampler1(identity)
out = self.downsampler2(out)
if self.skip_connections:
if (self.in_ch == self.out_ch) and (self.downsample is False):
out = out + identity
elif (self.in_ch != self.out_ch) and (self.downsample is False):
identity = self.in2out( identity )
out = out + identity
elif (self.in_ch != self.out_ch) and (self.downsample is True):
out = out + torch.cat((identity, identity), dim=1)
return out
def run_code_for_training_with_custom_loading(self, net):
filename_for_out = "performance_numbers_" + str(self.dl_studio.epochs) + ".txt"
FILE = open(filename_for_out, 'w')
net = copy.deepcopy(net)
net = net.to(self.dl_studio.device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(),
lr=self.dl_studio.learning_rate, momentum=self.dl_studio.momentum)
for epoch in range(self.dl_studio.epochs):
running_loss = 0.0
for i, data in enumerate(self.train_dataloader):
inputs, bounding_box, labels = data['image'], data['bbox'], data['label']
if self.dl_studio.debug_train and i % 1000 == 999:
print("\n\n\nlabels: %s" % str(labels))
print("\n\n\ntype of labels: %s" % type(labels))
print("\n\n[iter=%d:] Ground Truth: " % (i+1) +
' '.join('%5s' % self.dataserver_train.class_labels[labels[j].item()] for j in range(self.dl_studio.batch_size)))
inputs = inputs.to(self.dl_studio.device)
labels = labels.to(self.dl_studio.device)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
if self.dl_studio.debug_train and i % 1000 == 999:
_, predicted = torch.max(outputs.data, 1)
print("[iter=%d:] Predicted Labels: " % (i+1) +
' '.join('%5s' % self.dataserver.class_labels[predicted[j]]
for j in range(self.dl_studio.batch_size)))
self.dl_studio.display_tensor_as_image(torchvision.utils.make_grid(
inputs, normalize=True), "see terminal for TRAINING results at iter=%d" % (i+1))
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 1000 == 999:
avg_loss = running_loss / float(1000)
print("[epoch:%d, batch:%5d] loss: %.3f" % (epoch + 1, i + 1, avg_loss))
FILE.write("%.3f\n" % avg_loss)
FILE.flush()
running_loss = 0.0
print("\nFinished Training\n")
self.save_model(net)
def save_model(self, model):
'''
Save the trained model to a disk file
'''
torch.save(model.state_dict(), self.dl_studio.path_saved_model)
def run_code_for_testing_with_custom_loading(self, net):
net.load_state_dict(torch.load(self.dl_studio.path_saved_model))
correct = 0
total = 0
confusion_matrix = torch.zeros(len(self.dataserver_train.class_labels),
len(self.dataserver_train.class_labels))
class_correct = [0] * len(self.dataserver_train.class_labels)
class_total = [0] * len(self.dataserver_train.class_labels)
with torch.no_grad():
for i, data in enumerate(self.test_dataloader):
images, bounding_box, labels = data['image'], data['bbox'], data['label']
labels = labels.tolist()
if self.dl_studio.debug_test and i % 1000 == 0:
print("\n\n[i=%d:] Ground Truth: " %i + ' '.join('%10s' %
self.dataserver_train.class_labels[labels[j]] for j in range(self.dl_studio.batch_size)))
outputs = net(images)
## max() returns two things: the max value and its index in the 10 element
## output vector. We are only interested in the index --- since that is
## essentially the predicted class label:
_, predicted = torch.max(outputs.data, 1)
predicted = predicted.tolist()
if self.dl_studio.debug_test and i % 1000 == 0:
print("[i=%d:] Predicted Labels: " %i + ' '.join('%10s' %
self.dataserver_train.class_labels[predicted[j]] for j in range(self.dl_studio.batch_size)))
self.dl_studio.display_tensor_as_image(
torchvision.utils.make_grid(images, normalize=True),
"see terminal for test results at i=%d" % i)
for label,prediction in zip(labels,predicted):
confusion_matrix[label][prediction] += 1
total += len(labels)
correct += [predicted[ele] == labels[ele] for ele in range(len(predicted))].count(True)
comp = [predicted[ele] == labels[ele] for ele in range(len(predicted))]
for j in range(self.dl_studio.batch_size):
label = labels[j]
class_correct[label] += comp[j]
class_total[label] += 1
print("\n")
for j in range(len(self.dataserver_train.class_labels)):
print('Prediction accuracy for %5s : %2d %%' % (
self.dataserver_train.class_labels[j], 100 * class_correct[j] / class_total[j]))
print("\n\n\nOverall accuracy of the network on the 10000 test images: %d %%" %
(100 * correct / float(total)))
print("\n\nDisplaying the confusion matrix:\n")
out_str = " "
for j in range(len(self.dataserver_train.class_labels)):
out_str += "%15s" % self.dataserver_train.class_labels[j]
print(out_str + "\n")
for i,label in enumerate(self.dataserver_train.class_labels):
out_percents = [100 * confusion_matrix[i,j] / float(class_total[i])
for j in range(len(self.dataserver_train.class_labels))]
out_percents = ["%.2f" % item.item() for item in out_percents]
out_str = "%12s: " % self.dataserver_train.class_labels[i]
for j in range(len(self.dataserver_train.class_labels)):
out_str += "%15s" % out_percents[j]
print(out_str)
###%%%
#####################################################################################################################
################################## Start Definition of Inner Class DetectAndLocalize ##############################
class DetectAndLocalize(nn.Module):
"""
The purpose of this inner class is to focus on object detection in images --- as
opposed to image classification. Most people would say that object detection is a
more challenging problem than image classification because, in general, the former
also requires localization. The simplest interpretation of what is meant by
localization is that the code that carries out object detection must also output a
bounding-box rectangle for the object that was detected.
You will find in this inner class some examples of LOADnet classes meant for solving
the object detection and localization problem. The acronym "LOAD" in "LOADnet"
stands for
"LOcalization And Detection"
The different network examples included here are LOADnet1, LOADnet2, and LOADnet3.
For now, only pay attention to LOADnet2 since that's the class I have worked with
the most for the 1.0.7 distribution.
Class Path: DLStudio -> DetectAndLocalize
"""
def __init__(self, dl_studio, dataserver_train=None, dataserver_test=None, dataset_file_train=None, dataset_file_test=None):
super(DLStudio.DetectAndLocalize, self).__init__()
self.dl_studio = dl_studio
self.dataserver_train = dataserver_train
self.dataserver_test = dataserver_test
self.debug = False
class PurdueShapes5Dataset(torch.utils.data.Dataset):
"""
Class Path: DLStudio -> DetectAndLocalize -> PurdueShapes5Dataset
"""
def __init__(self, dl_studio, train_or_test, dataset_file):
super(DLStudio.DetectAndLocalize.PurdueShapes5Dataset, self).__init__()
if train_or_test == 'train' and dataset_file == "PurdueShapes5-10000-train.gz":
if os.path.exists("torch-saved-PurdueShapes5-10000-dataset.pt") and \
os.path.exists("torch-saved-PurdueShapes5-label-map.pt"):
print("\nLoading training data from the torch-saved archive")
self.dataset = torch.load("torch-saved-PurdueShapes5-10000-dataset.pt")
self.label_map = torch.load("torch-saved-PurdueShapes5-label-map.pt")
# reverse the key-value pairs in the label dictionary:
self.class_labels = dict(map(reversed, self.label_map.items()))
else:
print("""\n\n\nLooks like this is the first time you will be loading in\n"""
"""the dataset for this script. First time loading could take\n"""
"""a minute or so. Any subsequent attempts will only take\n"""
"""a few seconds.\n\n\n""")
root_dir = dl_studio.dataroot
f = gzip.open(root_dir + dataset_file, 'rb')
dataset = f.read()
if sys.version_info[0] == 3:
self.dataset, self.label_map = pickle.loads(dataset, encoding='latin1')
else:
self.dataset, self.label_map = pickle.loads(dataset)
torch.save(self.dataset, "torch-saved-PurdueShapes5-10000-dataset.pt")
torch.save(self.label_map, "torch-saved-PurdueShapes5-label-map.pt")
# reverse the key-value pairs in the label dictionary:
self.class_labels = dict(map(reversed, self.label_map.items()))
elif train_or_test == 'train' and dataset_file == "PurdueShapes5-10000-train-noise-20.gz":
if os.path.exists("torch-saved-PurdueShapes5-10000-dataset-noise-20.pt") and \
os.path.exists("torch-saved-PurdueShapes5-label-map.pt"):
print("\nLoading training data from the torch-saved archive")
self.dataset = torch.load("torch-saved-PurdueShapes5-10000-dataset-noise-20.pt")
self.label_map = torch.load("torch-saved-PurdueShapes5-label-map.pt")
# reverse the key-value pairs in the label dictionary:
self.class_labels = dict(map(reversed, self.label_map.items()))
else:
print("""\n\n\nLooks like this is the first time you will be loading in\n"""
"""the dataset for this script. First time loading could take\n"""
"""a minute or so. Any subsequent attempts will only take\n"""
"""a few seconds.\n\n\n""")
root_dir = dl_studio.dataroot
f = gzip.open(root_dir + dataset_file, 'rb')
dataset = f.read()
if sys.version_info[0] == 3:
self.dataset, self.label_map = pickle.loads(dataset, encoding='latin1')
else:
self.dataset, self.label_map = pickle.loads(dataset)
torch.save(self.dataset, "torch-saved-PurdueShapes5-10000-dataset-noise-20.pt")
torch.save(self.label_map, "torch-saved-PurdueShapes5-label-map.pt")
# reverse the key-value pairs in the label dictionary:
self.class_labels = dict(map(reversed, self.label_map.items()))
elif train_or_test == 'train' and dataset_file == "PurdueShapes5-10000-train-noise-50.gz":
if os.path.exists("torch-saved-PurdueShapes5-10000-dataset-noise-50.pt") and \
os.path.exists("torch-saved-PurdueShapes5-label-map.pt"):
print("\nLoading training data from the torch-saved archive")
self.dataset = torch.load("torch-saved-PurdueShapes5-10000-dataset-noise-50.pt")
self.label_map = torch.load("torch-saved-PurdueShapes5-label-map.pt")
# reverse the key-value pairs in the label dictionary:
self.class_labels = dict(map(reversed, self.label_map.items()))
else:
print("""\n\n\nLooks like this is the first time you will be loading in\n"""
"""the dataset for this script. First time loading could take\n"""
"""a minute or so. Any subsequent attempts will only take\n"""
"""a few seconds.\n\n\n""")
root_dir = dl_studio.dataroot
f = gzip.open(root_dir + dataset_file, 'rb')
dataset = f.read()
if sys.version_info[0] == 3:
self.dataset, self.label_map = pickle.loads(dataset, encoding='latin1')
else:
self.dataset, self.label_map = pickle.loads(dataset)
torch.save(self.dataset, "torch-saved-PurdueShapes5-10000-dataset-noise-50.pt")
torch.save(self.label_map, "torch-saved-PurdueShapes5-label-map.pt")
# reverse the key-value pairs in the label dictionary:
self.class_labels = dict(map(reversed, self.label_map.items()))
elif train_or_test == 'train' and dataset_file == "PurdueShapes5-10000-train-noise-80.gz":
if os.path.exists("torch-saved-PurdueShapes5-10000-dataset-noise-80.pt") and \
os.path.exists("torch-saved-PurdueShapes5-label-map.pt"):
print("\nLoading training data from the torch-saved archive")
self.dataset = torch.load("torch-saved-PurdueShapes5-10000-dataset-noise-80.pt")
self.label_map = torch.load("torch-saved-PurdueShapes5-label-map.pt")
# reverse the key-value pairs in the label dictionary:
self.class_labels = dict(map(reversed, self.label_map.items()))
else:
print("""\n\n\nLooks like this is the first time you will be loading in\n"""
"""the dataset for this script. First time loading could take\n"""
"""a minute or so. Any subsequent attempts will only take\n"""
"""a few seconds.\n\n\n""")
root_dir = dl_studio.dataroot
f = gzip.open(root_dir + dataset_file, 'rb')
dataset = f.read()
if sys.version_info[0] == 3:
self.dataset, self.label_map = pickle.loads(dataset, encoding='latin1')
else:
self.dataset, self.label_map = pickle.loads(dataset)
torch.save(self.dataset, "torch-saved-PurdueShapes5-10000-dataset-noise-80.pt")
torch.save(self.label_map, "torch-saved-PurdueShapes5-label-map.pt")
# reverse the key-value pairs in the label dictionary:
self.class_labels = dict(map(reversed, self.label_map.items()))
else:
root_dir = dl_studio.dataroot
f = gzip.open(root_dir + dataset_file, 'rb')
dataset = f.read()
if sys.version_info[0] == 3:
self.dataset, self.label_map = pickle.loads(dataset, encoding='latin1')
else:
self.dataset, self.label_map = pickle.loads(dataset)
# reverse the key-value pairs in the label dictionary:
self.class_labels = dict(map(reversed, self.label_map.items()))
def __len__(self):
return len(self.dataset)
def __getitem__(self, idx):
r = np.array( self.dataset[idx][0] )
g = np.array( self.dataset[idx][1] )
b = np.array( self.dataset[idx][2] )
R,G,B = r.reshape(32,32), g.reshape(32,32), b.reshape(32,32)
im_tensor = torch.zeros(3,32,32, dtype=torch.float)
im_tensor[0,:,:] = torch.from_numpy(R)
im_tensor[1,:,:] = torch.from_numpy(G)
im_tensor[2,:,:] = torch.from_numpy(B)
bb_tensor = torch.tensor(self.dataset[idx][3], dtype=torch.float)
sample = {'image' : im_tensor,
'bbox' : bb_tensor,
'label' : self.dataset[idx][4] }
return sample
def load_PurdueShapes5_dataset(self, dataserver_train, dataserver_test ):
self.train_dataloader = torch.utils.data.DataLoader(dataserver_train,
batch_size=self.dl_studio.batch_size,shuffle=True, num_workers=4)
self.test_dataloader = torch.utils.data.DataLoader(dataserver_test,
batch_size=self.dl_studio.batch_size,shuffle=False, num_workers=4)
class SkipBlock3(nn.Module):
"""
Class Path: DLStudio -> DetectAndLocalize -> SkipBlock
"""
def __init__(self, in_ch, out_ch, downsample=False, skip_connections=True):
super(DLStudio.DetectAndLocalize.SkipBlock3, self).__init__()
self.downsample = downsample
self.skip_connections = skip_connections
self.in_ch = in_ch
self.out_ch = out_ch
self.convo1 = nn.Conv2d(in_ch, in_ch, 3, stride=1, padding=1)
self.convo2 = nn.Conv2d(in_ch, out_ch, 3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(in_ch)
self.bn2 = nn.BatchNorm2d(out_ch)
self.in2out = nn.Conv2d(in_ch, out_ch, 1)
if downsample:
## Setting stride to 2 and kernel_size to 1 amounts to retaining every
## other pixel in the image --- which halves the size of the image:
self.downsampler1 = nn.Conv2d(in_ch, in_ch, 1, stride=2)
self.downsampler2 = nn.Conv2d(out_ch, out_ch, 1, stride=2)
def forward(self, x):
identity = x
out = self.convo1(x)
out = self.bn1(out)
out = nn.functional.relu(out)
out = self.convo2(out)
out = self.bn2(out)
out = nn.functional.relu(out)
if self.downsample:
identity = self.downsampler1(identity)
out = self.downsampler2(out)
if self.skip_connections:
if (self.in_ch == self.out_ch) and (self.downsample is False):
out = out + identity
elif (self.in_ch != self.out_ch) and (self.downsample is False):
identity = self.in2out( identity )
out = out + identity
elif (self.in_ch != self.out_ch) and (self.downsample is True):
out = out + torch.cat((identity, identity), dim=1)
return out
class LOADnet1(nn.Module):
"""
The acronym 'LOAD' stands for 'LOcalization And Detection'. LOADnet1 only
uses fully-connected layers for the regression
Class Path: DLStudio -> DetectAndLocalize -> LOADnet1
"""
def __init__(self, skip_connections=True, depth=32):
super(DLStudio.DetectAndLocalize.LOADnet1, self).__init__()
self.pool_count = 3
self.depth = depth // 2
self.conv = nn.Conv2d(3, 64, 3, padding=1)
self.skip64 = DLStudio.DetectAndLocalize.SkipBlock3(64, 64, skip_connections=skip_connections)
self.skip64ds = DLStudio.DetectAndLocalize.SkipBlock3(64, 64, downsample=True, skip_connections=skip_connections)
self.skip64to128 = DLStudio.DetectAndLocalize.SkipBlock3(64, 128, skip_connections=skip_connections )
self.skip128 = DLStudio.DetectAndLocalize.SkipBlock3(128, 128, skip_connections=skip_connections)
self.skip128ds = DLStudio.DetectAndLocalize.SkipBlock3(128,128, downsample=True, skip_connections=skip_connections)
self.fc1 = nn.Linear(128 * (32 // 2**self.pool_count)**2, 1000)
self.fc2 = nn.Linear(1000, 5)
self.fc3 = nn.Linear(32768, 1000)
self.fc4 = nn.Linear(1000, 4)
def forward(self, x):
x = nn.MaxPool2d(2,2)(nn.functional.relu(self.conv(x)))
## The labeling section:
for _ in range(self.depth // 4):
x1 = self.skip64(x)
x1 = self.skip64ds(x1)
for _ in range(self.depth // 4):
x1 = self.skip64(x1)
x1 = self.skip64to128(x1)
for _ in range(self.depth // 4):
x1 = self.skip128(x1)
x1 = self.skip128ds(x1)
for _ in range(self.depth // 4):
x1 = self.skip128(x1)
x1 = x.view( x1.shape[0], - 1 )
x1 = nn.functional.relu(self.fc1(x1))
x1 = self.fc2(x1)
## The Bounding Box regression:
x2 = x.view( x.shape[0], - 1 )
x2 = nn.functional.relu(self.fc3(x2))
x2 = self.fc4(x2)
return x1,x2
class LOADnet2(nn.Module):
"""
The acronym 'LOAD' stands for 'LOcalization And Detection'. LOADnet2 uses
both convo and linear layers for regression
Class Path: DLStudio -> DetectAndLocalize -> LOADnet2
"""
def __init__(self, skip_connections=True, depth=8):
super(DLStudio.DetectAndLocalize.LOADnet2, self).__init__()
if depth not in [8,10,12,14,16]:
sys.exit("LOADnet2 has only been tested for 'depth' values 8, 10, 12, 14, and 16")
self.depth = depth // 2
self.conv = nn.Conv2d(3, 64, 3, padding=1)
self.bn1 = nn.BatchNorm2d(64)
self.bn2 = nn.BatchNorm2d(128)
self.skip64_arr = nn.ModuleList()
for i in range(self.depth):
self.skip64_arr.append(DLStudio.DetectAndLocalize.SkipBlock3(64, 64,
skip_connections=skip_connections))
self.skip64ds = DLStudio.DetectAndLocalize.SkipBlock3(64, 64,
downsample=True, skip_connections=skip_connections)
self.skip64to128 = DLStudio.DetectAndLocalize.SkipBlock3(64, 128,
skip_connections=skip_connections )
self.skip128_arr = nn.ModuleList()
for i in range(self.depth):
self.skip128_arr.append(DLStudio.DetectAndLocalize.SkipBlock3(128, 128,
skip_connections=skip_connections))
self.skip128ds = DLStudio.DetectAndLocalize.SkipBlock3(128,128,
downsample=True, skip_connections=skip_connections)
self.fc1 = nn.Linear(2048, 1000)
self.fc2 = nn.Linear(1000, 5)
## for regression
self.conv_seqn = nn.Sequential(
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1),
nn.ReLU(inplace=True)
)
self.fc_seqn = nn.Sequential(
nn.Linear(16384, 1024),
nn.ReLU(inplace=True),
nn.Linear(1024, 512),
nn.ReLU(inplace=True),
nn.Linear(512, 4) ## output for the 4 coords (x_min,y_min,x_max,y_max) of BBox
)
def forward(self, x):
x = nn.MaxPool2d(2,2)(nn.functional.relu(self.conv(x)))
## The labeling section:
x1 = x.clone()
for i,skip64 in enumerate(self.skip64_arr[:self.depth//4]):
x1 = skip64(x1)
x1 = self.skip64ds(x1)
for i,skip64 in enumerate(self.skip64_arr[self.depth//4:]):
x1 = skip64(x1)
x1 = self.bn1(x1)
x1 = self.skip64to128(x1)
for i,skip128 in enumerate(self.skip128_arr[:self.depth//4]):
x1 = skip128(x1)
x1 = self.bn2(x1)
x1 = self.skip128ds(x1)
for i,skip128 in enumerate(self.skip128_arr[self.depth//4:]):
x1 = skip128(x1)
x1 = x1.view( x1.shape[0], - 1 )
x1 = nn.functional.relu(self.fc1(x1))
x1 = self.fc2(x1)
## The Bounding Box regression:
x2 = self.conv_seqn(x)
# flatten
x2 = x2.view( x.shape[0], - 1 )
x2 = self.fc_seqn(x2)
return x1,x2
class LOADnet3(nn.Module):
"""
The acronym 'LOAD' stands for 'LOcalization And Detection'. LOADnet3 uses
both convo and linear layers for regression
Class Path: DLStudio -> DetectAndLocalize -> LOADnet3
"""
def __init__(self, skip_connections=True, depth=8):
super(DLStudio.DetectAndLocalize.LOADnet3, self).__init__()
if depth not in [4, 8, 16]:
sys.exit("LOADnet2 has been tested for 'depth' for only 4, 8, and 16")
self.depth = depth // 4
self.conv = nn.Conv2d(3, 64, 3, padding=1)
self.skip64_arr = nn.ModuleList()
for i in range(self.depth):
self.skip64_arr.append(DLStudio.DetectAndLocalize.SkipBlock3(64, 64,
skip_connections=skip_connections))
self.skip64ds = DLStudio.DetectAndLocalize.SkipBlock3(64, 64,
downsample=True, skip_connections=skip_connections)
self.skip64to128 = DLStudio.DetectAndLocalize.SkipBlock3(64, 128,
skip_connections=skip_connections )
self.skip128_arr = nn.ModuleList()
for i in range(self.depth):
self.skip128_arr.append(DLStudio.DetectAndLocalize.SkipBlock3(128, 128,
skip_connections=skip_connections))
self.skip128ds = DLStudio.DetectAndLocalize.SkipBlock3(128,128,
downsample=True, skip_connections=skip_connections)
self.fc1 = nn.Linear(2048, 1000)
self.fc2 = nn.Linear(1000, 5)
## for regression
self.conv_seqn = nn.Sequential(
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1),
nn.ReLU(inplace=True)
)
self.fc_seqn = nn.Sequential(
nn.Linear(16384, 1024),
nn.ReLU(inplace=True),
nn.Linear(1024, 512),
nn.ReLU(inplace=True),
nn.Linear(512, 4)
)
def forward(self, x):
x = nn.MaxPool2d(2,2)(nn.functional.relu(self.conv(x)))
## The labeling section:
x1 = x.clone()
for i,skip64 in enumerate(self.skip64_arr[:self.depth//4]):
x1 = skip64(x1)
x1 = self.skip64ds(x1)
for i,skip64 in enumerate(self.skip64_arr[self.depth//4:]):
x1 = skip64(x1)
x1 = self.skip64ds(x1)
x1 = self.skip64to128(x1)
for i,skip128 in enumerate(self.skip128_arr[:self.depth//4]):
x1 = skip128(x1)
for i,skip128 in enumerate(self.skip128_arr[self.depth//4:]):
x1 = skip128(x1)
x1 = x1.view( x1.shape[0], - 1 )
x1 = nn.functional.relu(self.fc1(x1))
x1 = self.fc2(x1)
## The Bounding Box regression:
for _ in range(4):
x2 = self.skip64(x)
x2 = self.skip64to128(x2)
for _ in range(4):
x2 = self.skip128(x2)
x2 = x.view( x.shape[0], - 1 )
x2 = nn.functional.relu(self.fc3(x2))
x2 = self.fc4(x2)
return x1,x2
class DIoULoss(nn.Module):
"""
Class Path: DLStudio -> DetectAndLocalize -> DIOULoss
This is a Custom Loss Function for implementing the variants of the IoU
(Intersection over Union) loss as described on Slides 37 through 42 of my
Week 7 presentation on Object Detection and Localization.
"""
def __init__(self, dl_studio, loss_mode):
super(DLStudio.DetectAndLocalize.DIoULoss, self).__init__()
self.dl_studio = dl_studio
self.loss_mode = loss_mode
def forward(self, predicted, target, loss_mode):
debug = 0
## We calculate the MSELoss between the predicted and the target BBs just for sanity check.
## It is not used in the loss that is returned by this function [However, note that the
## d2_loss defined below is the same thing as what is returned by MSELoss]:
displacement_loss = nn.MSELoss()(predicted, target)
## We call the MSELoss again, but this time with "reduction='none'". The reason for that
## is that we need to calculate the MSELoss on a per-instance basis in the batch for the
## normalizations we are going to need later in our calculation of the IoU-based loss function.
## The following call returns a tensor of shape (Bx4) where B is the batch size and 4
## is for four numeric values in a BB vector.
d2_loss_per_instance = nn.MSELoss(reduction='none')(predicted, target)
## Averaging the above along Axis 1 gives us the instance based MSE Loss we want:
d2_mean_loss_per_instance = torch.mean(d2_loss_per_instance, 1)
## Averaging of the above along Axis 0 should give us a single scalar that would be
## the same as the "displacement_loss" in the first line:
d2_loss = torch.mean(d2_mean_loss_per_instance,0)
if debug:
print("\n\nMSE Loss: ", displacement_loss)
print("\n\nd2_loss_per_instance_in_batch: ", d2_loss_per_instance)
print("\n\nd2_mean_loss_per_instance_in_batch: ", d2_mean_loss_per_instance)
print("\n\nd2 loss: ", d2_loss)
## Our next job is to figure out the BB for the convex hull of the predicted and target BBs. To
## thta end, we first find the upper-left corner of the convex hull by finding the infimum of the
## of the min (i,j) coordinates associated with the predicted and the target BBs:
hull_min_i = torch.min( torch.cat( ( torch.transpose( torch.unsqueeze(predicted[:,0],0), 1,0 ),
torch.transpose( torch.unsqueeze(predicted[:,2],0), 1,0 ),
torch.transpose( torch.unsqueeze(target[:,0],0), 1,0 ),
torch.transpose( torch.unsqueeze(target[:,2],0), 1,0 ) ), 1 ), 1 )[0].type(torch.uint8)
hull_min_j = torch.min( torch.cat( ( torch.transpose( torch.unsqueeze(predicted[:,1],0), 1,0 ),
torch.transpose( torch.unsqueeze(predicted[:,3],0), 1,0 ),
torch.transpose( torch.unsqueeze(target[:,1],0), 1,0 ),
torch.transpose( torch.unsqueeze(target[:,3],0), 1,0 ) ), 1 ), 1 )[0].type(torch.uint8)
## Next we need to find the lower-right corner of the convex hull. We do so by finding the
## supremum of the max (i,j) coordinates associated with the predicted and the target BBs:
hull_max_i = torch.max( torch.cat( ( torch.transpose( torch.unsqueeze(predicted[:,0],0), 1,0 ),
torch.transpose( torch.unsqueeze(predicted[:,2],0), 1,0 ),
torch.transpose( torch.unsqueeze(target[:,0],0), 1,0 ),
torch.transpose( torch.unsqueeze(target[:,2],0), 1,0 ) ), 1 ), 1 )[0].type(torch.uint8)
hull_max_j = torch.max( torch.cat( ( torch.transpose( torch.unsqueeze(predicted[:,1],0), 1,0 ),
torch.transpose( torch.unsqueeze(predicted[:,3],0), 1,0 ),
torch.transpose( torch.unsqueeze(target[:,1],0), 1,0 ),
torch.transpose( torch.unsqueeze(target[:,3],0), 1,0 ) ), 1 ), 1 )[0].type(torch.uint8)
## We now call on the torch.cat to organize the instance-based convex_hull min and max coordinates
## into what the convex-hull BB should look like for a batch. If B is the batch size, the shape of
## convex_hull_bb should be (B, 4):
convex_hull_bb = torch.cat( ( torch.transpose( torch.unsqueeze(hull_min_i,0), 1,0),
torch.transpose( torch.unsqueeze(hull_min_j,0), 1,0),
torch.transpose( torch.unsqueeze(hull_max_i,0), 1,0),
torch.transpose( torch.unsqueeze(hull_max_j,0), 1,0) ), 1 ).float().to(self.dl_studio.device)
## Need the square of the diagonal of the convex hull for normalization:
convex_hull_diagonal_squared = torch.square(convex_hull_bb[:,0] - convex_hull_bb[:,2]) + torch.square(convex_hull_bb[:,1] - convex_hull_bb[:,3])
## Since we will be using the BB corners for indexing, we need to convert them into ints:
predicted = predicted.type(torch.uint8)
target = target.type(torch.uint8)
convex_hull_bb = convex_hull_bb.type(torch.uint8)
## Our next job is to convert all three BBs --- predicted, target, and convex_hull --- into binary
## for set operations of union, intersection, and the set-difference of the union from the
## convex hull. We start by initializing the three arras for each instance in the batch:
img_size = self.dl_studio.image_size
predicted_arr = torch.zeros(predicted.shape[0], img_size[0], img_size[1]).to(self.dl_studio.device)
target_arr = torch.zeros(predicted.shape[0], img_size[0], img_size[1]).to(self.dl_studio.device)
convex_hull_arr = torch.zeros(predicted.shape[0], img_size[0], img_size[1]).to(self.dl_studio.device)
## We fill the three arrays --- predicted, target, and convex_hull --- according to their respective BBs:
for k in range(predicted_arr.shape[0]):
predicted_arr[ k, predicted[k,0]:predicted[k,2], predicted[k,1]:predicted[k,3] ] = 1
target_arr[ k, target[k,0]:target[k,2], target[k,1]:target[k,3] ] = 1
convex_hull_arr[ k, convex_hull_bb[k,0]:convex_hull_bb[k,2], convex_hull_bb[k,1]:convex_hull_bb[k,3] ] = 1
## We are ready for the set operations:
intersection_arr = predicted_arr * target_arr
intersecs = torch.sum( intersection_arr, dim=(1,2) )
union_arr = torch.logical_or( predicted_arr > 0, target_arr > 0 ).type(torch.uint8)
unions = torch.sum( union_arr, dim=(1,2) )
## find the set difference of the convex hull and the union for each batch instance:
diff_arr = (convex_hull_arr != union_arr).type(torch.uint8)
## what's the total number of pixels in the the set difference:
diff_sum_per_instance = torch.sum( diff_arr, dim=(1,2) )
## also, what is the total number of pixels in the convex hull for each batch instance:
convex_hull_sum_per_instance = torch.sum( convex_hull_arr, dim=(1,2) )
if (convex_hull_sum_per_instance < 10).any(): return torch.tensor([float('nan')])
## find the ratio we need for the DIoU formula [see Eq. (8) on Slide 40 of my Week 7 slides]:
epsilon = 1e-6
ratio = diff_sum_per_instance.type(torch.float) / (convex_hull_sum_per_instance.type(torch.float) + epsilon)
## find the IoU
iou = intersecs / (unions + epsilon)
iou_loss = torch.mean(1 - iou, 0)
d2_normed = d2_mean_loss_per_instance / (convex_hull_diagonal_squared + epsilon)
d2_normed_loss = torch.mean(d2_normed, 0)
ratio_loss = torch.mean( ratio, 0 )
if self.loss_mode == 'd2':
diou_loss = d2_loss
elif self.loss_mode == 'diou1':
diou_loss = iou_loss + d2_loss
elif self.loss_mode == 'diou2':
diou_loss = iou_loss + d2_normed_loss
elif self.loss_mode == 'diou3':
diou_loss = iou_loss + d2_normed_loss + ratio_loss
return diou_loss
def run_code_for_training_with_iou_regression(self, net, loss_mode='d2', show_images=True):
"""
This training routine is called by
object_detection_and_localization_iou.py
in the Examples directory.
The possible values for loss_mode are: 'd2', 'diou1', 'diou2', 'diou3' with the following meanings:
d2 : d2_loss This is just the MSE loss based on the square
of the distance between the centers of the
predicted BB and the ground-truth BB.
diou1 : iou_loss + d2_loss We add to the pure IOU loss the value d2_loss
defined above
diou2 : iou_loss + d2_normed_loss We now normalize the squared distance between the
centers of the predicted BB and ground_truth BB by
the diagonal of the convex hull of the two BBs.
diou3 : iou_loss + d2_normed_loss + ratio_loss We now normalize the
IMPORTANT NOTE: You are likely to get the best results if you set the learning rate to 1e-4 for d2 and
diou1 options. If the option you use is diou2 or diou3, set the learning rate to 5e-3
"""
filename_for_out1 = "performance_numbers_" + str(self.dl_studio.epochs) + "label.txt"
filename_for_out2 = "performance_numbers_" + str(self.dl_studio.epochs) + "regres.txt"
FILE1 = open(filename_for_out1, 'w')
FILE2 = open(filename_for_out2, 'w')
net = copy.deepcopy(net)
net = net.to(self.dl_studio.device)
criterion1 = nn.CrossEntropyLoss()
criterion2 = self.dl_studio.DetectAndLocalize.DIoULoss(self.dl_studio, loss_mode)
optimizer = optim.SGD(net.parameters(), lr=self.dl_studio.learning_rate, momentum=self.dl_studio.momentum)
print("\n\nStarting training loop...\n\n")
start_time = time.perf_counter()
labeling_loss_tally = []
regression_loss_tally = []
elapsed_time = 0.0
for epoch in range(self.dl_studio.epochs):
print("")
running_loss_labeling = 0.0
running_loss_regression = 0.0
for i, data in enumerate(self.train_dataloader):
gt_too_small = False
inputs, bbox_gt, labels = data['image'], data['bbox'], data['label']
inputs = inputs.to(self.dl_studio.device)
labels = labels.to(self.dl_studio.device)
bbox_gt = bbox_gt.to(self.dl_studio.device)
optimizer.zero_grad()
if self.debug:
self.dl_studio.display_tensor_as_image(
torchvision.utils.make_grid(inputs.cpu(), nrow=4, normalize=True, padding=2, pad_value=10))
outputs = net(inputs)
outputs_label = outputs[0]
bbox_pred = outputs[1]
if i % 500 == 499:
current_time = time.perf_counter()
elapsed_time = current_time - start_time
print("\n\n\n[epoch:%d/%d iter=%4d elapsed_time=%5d secs] Ground Truth: " %
(epoch+1, self.dl_studio.epochs, i+1, elapsed_time)
+ ' '.join('%10s' % self.dataserver_train.class_labels[labels[j].item()]
for j in range(self.dl_studio.batch_size)))
inputs_copy = inputs.detach().clone()
inputs_copy = inputs_copy.cpu()
bbox_pc = bbox_pred.detach().clone()
bbox_pc[bbox_pc<0] = 0
bbox_pc[bbox_pc>31] = 31
bbox_pc[torch.isnan(bbox_pc)] = 0
_, predicted = torch.max(outputs_label.data, 1)
print("[epoch:%d/%d iter=%4d elapsed_time=%5d secs] Predicted Labels: " %
(epoch+1, self.dl_studio.epochs, i+1, elapsed_time)
+ ' '.join('%10s' % self.dataserver_train.class_labels[predicted[j].item()]
for j in range(self.dl_studio.batch_size)))
if show_images == True:
for idx in range(self.dl_studio.batch_size):
i1 = int(bbox_gt[idx][1])
i2 = int(bbox_gt[idx][3])
j1 = int(bbox_gt[idx][0])
j2 = int(bbox_gt[idx][2])
k1 = int(bbox_pc[idx][1])
k2 = int(bbox_pc[idx][3])
l1 = int(bbox_pc[idx][0])
l2 = int(bbox_pc[idx][2])
print(" gt_bb: [%d,%d,%d,%d]"%(i1,j1,i2,j2))
print(" pred_bb: [%d,%d,%d,%d]"%(k1,l1,k2,l2))
inputs_copy[idx,1,i1:i2,j1] = 255
inputs_copy[idx,1,i1:i2,j2] = 255
inputs_copy[idx,1,i1,j1:j2] = 255
inputs_copy[idx,1,i2,j1:j2] = 255
inputs_copy[idx,0,k1:k2,l1] = 255
inputs_copy[idx,0,k1:k2,l2] = 255
inputs_copy[idx,0,k1,l1:l2] = 255
inputs_copy[idx,0,k2,l1:l2] = 255
loss_labeling = criterion1(outputs_label, labels)
loss_labeling.backward(retain_graph=True)
loss_regression = criterion2(bbox_pred, bbox_gt, loss_mode)
if torch.isnan(loss_regression): continue
loss_regression.backward()
optimizer.step()
running_loss_labeling += loss_labeling.item()
running_loss_regression += loss_regression.item()
if i % 500 == 499:
avg_loss_labeling = running_loss_labeling / float(500)
avg_loss_regression = running_loss_regression / float(500)
labeling_loss_tally.append(avg_loss_labeling)
regression_loss_tally.append(avg_loss_regression)
print("[epoch:%d/%d iter=%4d elapsed_time=%5d secs] loss_labeling %.3f loss_regression: %.3f " % (epoch+1, self.dl_studio.epochs, i+1, elapsed_time, avg_loss_labeling, avg_loss_regression))
FILE1.write("%.3f\n" % avg_loss_labeling)
FILE1.flush()
FILE2.write("%.3f\n" % avg_loss_regression)
FILE2.flush()
running_loss_labeling = 0.0
running_loss_regression = 0.0
if show_images == True:
if i%500==499:
logger = logging.getLogger()
old_level = logger.level
logger.setLevel(100)
plt.figure(figsize=[8,3])
plt.imshow(np.transpose(torchvision.utils.make_grid(inputs_copy, normalize=True,
padding=3, pad_value=255).cpu(), (1,2,0)))
plt.show()
logger.setLevel(old_level)
print("\nFinished Training\n")
self.save_model(net)
plt.figure(figsize=(10,5))
plt.title("Labeling Loss vs. Iterations")
plt.plot(labeling_loss_tally)
plt.xlabel("iterations")
plt.ylabel("labeling loss")
# plt.legend()
plt.legend(["Plot of loss versus iterations"], fontsize="x-large")
plt.savefig("labeling_loss.png")
plt.show()
plt.title("regression Loss vs. Iterations")
plt.plot(regression_loss_tally)
plt.xlabel("iterations")
plt.ylabel("regression loss")
# plt.legend()
plt.legend(["Plot of loss versus iterations"], fontsize="x-large")
plt.savefig("regression_loss.png")
plt.show()
def run_code_for_training_with_CrossEntropy_and_MSE_Losses(self, net, show_images=True):
"""
This training routine is called by
object_detection_and_localization.py
in the Examples directory.
"""
filename_for_out1 = "performance_numbers_" + str(self.dl_studio.epochs) + "label.txt"
filename_for_out2 = "performance_numbers_" + str(self.dl_studio.epochs) + "regres.txt"
FILE1 = open(filename_for_out1, 'w')
FILE2 = open(filename_for_out2, 'w')
net = copy.deepcopy(net)
net = net.to(self.dl_studio.device)
criterion1 = nn.CrossEntropyLoss()
criterion2 = nn.MSELoss()
optimizer = optim.SGD(net.parameters(), lr=self.dl_studio.learning_rate, momentum=self.dl_studio.momentum)
print("\n\nStarting training loop...\n\n")
start_time = time.perf_counter()
labeling_loss_tally = []
regression_loss_tally = []
elapsed_time = 0.0
for epoch in range(self.dl_studio.epochs):
print("")
running_loss_labeling = 0.0
running_loss_regression = 0.0
for i, data in enumerate(self.train_dataloader):
gt_too_small = False
inputs, bbox_gt, labels = data['image'], data['bbox'], data['label']
if i % 500 == 499:
current_time = time.perf_counter()
elapsed_time = current_time - start_time
print("\n\n\n[epoch:%d/%d iter=%4d elapsed_time=%5d secs] Ground Truth: " %
(epoch+1, self.dl_studio.epochs, i+1, elapsed_time)
+ ' '.join('%10s' % self.dataserver_train.class_labels[labels[j].item()]
for j in range(self.dl_studio.batch_size)))
inputs = inputs.to(self.dl_studio.device)
labels = labels.to(self.dl_studio.device)
bbox_gt = bbox_gt.to(self.dl_studio.device)
optimizer.zero_grad()
if self.debug:
self.dl_studio.display_tensor_as_image(
torchvision.utils.make_grid(inputs.cpu(), nrow=4, normalize=True, padding=2, pad_value=10))
outputs = net(inputs)
outputs_label = outputs[0]
bbox_pred = outputs[1]
if i % 500 == 499:
inputs_copy = inputs.detach().clone()
inputs_copy = inputs_copy.cpu()
bbox_pc = bbox_pred.detach().clone()
bbox_pc[bbox_pc<0] = 0
bbox_pc[bbox_pc>31] = 31
bbox_pc[torch.isnan(bbox_pc)] = 0
_, predicted = torch.max(outputs_label.data, 1)
print("[epoch:%d/%d iter=%4d elapsed_time=%5d secs] Predicted Labels: " %
(epoch+1, self.dl_studio.epochs, i+1, elapsed_time)
+ ' '.join('%10s' % self.dataserver_train.class_labels[predicted[j].item()]
for j in range(self.dl_studio.batch_size)))
for idx in range(self.dl_studio.batch_size):
i1 = int(bbox_gt[idx][1])
i2 = int(bbox_gt[idx][3])
j1 = int(bbox_gt[idx][0])
j2 = int(bbox_gt[idx][2])
k1 = int(bbox_pc[idx][1])
k2 = int(bbox_pc[idx][3])
l1 = int(bbox_pc[idx][0])
l2 = int(bbox_pc[idx][2])
print(" gt_bb: [%d,%d,%d,%d]"%(i1,j1,i2,j2))
print(" pred_bb: [%d,%d,%d,%d]"%(k1,l1,k2,l2))
inputs_copy[idx,1,i1:i2,j1] = 255
inputs_copy[idx,1,i1:i2,j2] = 255
inputs_copy[idx,1,i1,j1:j2] = 255
inputs_copy[idx,1,i2,j1:j2] = 255
inputs_copy[idx,0,k1:k2,l1] = 255
inputs_copy[idx,0,k1:k2,l2] = 255
inputs_copy[idx,0,k1,l1:l2] = 255
inputs_copy[idx,0,k2,l1:l2] = 255
loss_labeling = criterion1(outputs_label, labels)
loss_labeling.backward(retain_graph=True)
loss_regression = criterion2(bbox_pred, bbox_gt)
loss_regression.backward()
optimizer.step()
running_loss_labeling += loss_labeling.item()
running_loss_regression += loss_regression.item()
if i % 500 == 499:
avg_loss_labeling = running_loss_labeling / float(500)
avg_loss_regression = running_loss_regression / float(500)
labeling_loss_tally.append(avg_loss_labeling)
regression_loss_tally.append(avg_loss_regression)
print("[epoch:%d/%d iter=%4d elapsed_time=%5d secs] loss_labeling %.3f loss_regression: %.3f " % (epoch+1, self.dl_studio.epochs, i+1, elapsed_time, avg_loss_labeling, avg_loss_regression))
FILE1.write("%.3f\n" % avg_loss_labeling)
FILE1.flush()
FILE2.write("%.3f\n" % avg_loss_regression)
FILE2.flush()
running_loss_labeling = 0.0
running_loss_regression = 0.0
# if i%500==499:
if i%500==499 and show_images is True:
logger = logging.getLogger()
old_level = logger.level
logger.setLevel(100)
plt.figure(figsize=[8,3])
plt.imshow(np.transpose(torchvision.utils.make_grid(inputs_copy, normalize=True,
padding=3, pad_value=255).cpu(), (1,2,0)))
plt.show()
logger.setLevel(old_level)
print("\nFinished Training\n")
self.save_model(net)
plt.figure(figsize=(10,5))
plt.title("Labeling Loss vs. Iterations")
plt.plot(labeling_loss_tally)
plt.xlabel("iterations")
plt.ylabel("labeling loss")
# plt.legend()
plt.legend(["Plot of loss versus iterations"], fontsize="x-large")
plt.savefig("labeling_loss.png")
plt.show()
plt.title("regression Loss vs. Iterations")
plt.plot(regression_loss_tally)
plt.xlabel("iterations")
plt.ylabel("regression loss")
# plt.legend()
plt.legend(["Plot of loss versus iterations"], fontsize="x-large")
plt.savefig("regression_loss.png")
plt.show()
def save_model(self, model):
'''
Save the trained model to a disk file
'''
torch.save(model.state_dict(), self.dl_studio.path_saved_model)
def run_code_for_testing_detection_and_localization(self, net):
net.load_state_dict(torch.load(self.dl_studio.path_saved_model))
correct = 0
total = 0
confusion_matrix = torch.zeros(len(self.dataserver_train.class_labels),
len(self.dataserver_train.class_labels))
class_correct = [0] * len(self.dataserver_train.class_labels)
class_total = [0] * len(self.dataserver_train.class_labels)
with torch.no_grad():
for i, data in enumerate(self.test_dataloader):
images, bounding_box, labels = data['image'], data['bbox'], data['label']
labels = labels.tolist()
if self.dl_studio.debug_test and i % 50 == 0:
print("\n\n[i=%d:] Ground Truth: " %i + ' '.join('%10s' %
self.dataserver_train.class_labels[labels[j]] for j in range(self.dl_studio.batch_size)))
outputs = net(images)
outputs_label = outputs[0]
outputs_regression = outputs[1]
outputs_regression[outputs_regression < 0] = 0
outputs_regression[outputs_regression > 31] = 31
outputs_regression[torch.isnan(outputs_regression)] = 0
output_bb = outputs_regression.tolist()
_, predicted = torch.max(outputs_label.data, 1)
predicted = predicted.tolist()
if self.dl_studio.debug_test and i % 50 == 0:
print("[i=%d:] Predicted Labels: " %i + ' '.join('%10s' %
self.dataserver_train.class_labels[predicted[j]] for j in range(self.dl_studio.batch_size)))
for idx in range(self.dl_studio.batch_size):
i1 = int(bounding_box[idx][1])
i2 = int(bounding_box[idx][3])
j1 = int(bounding_box[idx][0])
j2 = int(bounding_box[idx][2])
k1 = int(output_bb[idx][1])
k2 = int(output_bb[idx][3])
l1 = int(output_bb[idx][0])
l2 = int(output_bb[idx][2])
print(" gt_bb: [%d,%d,%d,%d]"%(j1,i1,j2,i2))
print(" pred_bb: [%d,%d,%d,%d]"%(l1,k1,l2,k2))
images[idx,0,i1:i2,j1] = 255
images[idx,0,i1:i2,j2] = 255
images[idx,0,i1,j1:j2] = 255
images[idx,0,i2,j1:j2] = 255
images[idx,2,k1:k2,l1] = 255
images[idx,2,k1:k2,l2] = 255
images[idx,2,k1,l1:l2] = 255
images[idx,2,k2,l1:l2] = 255
logger = logging.getLogger()
old_level = logger.level
logger.setLevel(100)
plt.figure(figsize=[8,3])
plt.imshow(np.transpose(torchvision.utils.make_grid(images, normalize=True,
padding=3, pad_value=255).cpu(), (1,2,0)))
plt.show()
logger.setLevel(old_level)
for label,prediction in zip(labels,predicted):
confusion_matrix[label][prediction] += 1
total += len(labels)
correct += [predicted[ele] == labels[ele] for ele in range(len(predicted))].count(True)
comp = [predicted[ele] == labels[ele] for ele in range(len(predicted))]
for j in range(self.dl_studio.batch_size):
label = labels[j]
class_correct[label] += comp[j]
class_total[label] += 1
print("\n")
for j in range(len(self.dataserver_train.class_labels)):
print('Prediction accuracy for %5s : %2d %%' % (
self.dataserver_train.class_labels[j], 100 * class_correct[j] / class_total[j]))
print("\n\n\nOverall accuracy of the network on the 1000 test images: %d %%" %
(100 * correct / float(total)))
print("\n\nDisplaying the confusion matrix:\n")
out_str = " "
for j in range(len(self.dataserver_train.class_labels)):
out_str += "%15s" % self.dataserver_train.class_labels[j]
print(out_str + "\n")
for i,label in enumerate(self.dataserver_train.class_labels):
out_percents = [100 * confusion_matrix[i,j] / float(class_total[i])
for j in range(len(self.dataserver_train.class_labels))]
out_percents = ["%.2f" % item.item() for item in out_percents]
out_str = "%12s: " % self.dataserver_train.class_labels[i]
for j in range(len(self.dataserver_train.class_labels)):
out_str += "%15s" % out_percents[j]
print(out_str)
###%%%
#####################################################################################################################
################################# Start Definition of Inner Class SemanticSegmentation ############################
class SemanticSegmentation(nn.Module):
"""
The purpose of this inner class is to be able to use the DLStudio platform for
experiments with semantic segmentation. At its simplest level, the purpose of
semantic segmentation is to assign correct labels to the different objects in a
scene, while localizing them at the same time. At a more sophisticated level, a
system that carries out semantic segmentation should also output a symbolic
expression based on the objects found in the image and their spatial relationships
with one another.
The workhorse of this inner class is the mUNet network that is based on the UNET
network that was first proposed by Ronneberger, Fischer and Brox in the paper
"U-Net: Convolutional Networks for Biomedical Image Segmentation". Their Unet
extracts binary masks for the cell pixel blobs of interest in biomedical images.
The output of their Unet can therefore be treated as a pixel-wise binary classifier
at each pixel position. The mUnet class, on the other hand, is intended for
segmenting out multiple objects simultaneously form an image. [A weaker reason for
"Multi" in the name of the class is that it uses skip connections not only across
the two arms of the "U", but also also along the arms. The skip connections in the
original Unet are only between the two arms of the U. In mUnet, each object type is
assigned a separate channel in the output of the network.
This version of DLStudio also comes with a new dataset, PurdueShapes5MultiObject,
for experimenting with mUnet. Each image in this dataset contains a random number
of selections from five different shapes, with the shapes being randomly scaled,
oriented, and located in each image. The five different shapes are: rectangle,
triangle, disk, oval, and star.
Class Path: DLStudio -> SemanticSegmentation
"""
def __init__(self, dl_studio, max_num_objects, dataserver_train=None, dataserver_test=None, dataset_file_train=None, dataset_file_test=None):
super(DLStudio.SemanticSegmentation, self).__init__()
self.dl_studio = dl_studio
self.max_num_objects = max_num_objects
self.dataserver_train = dataserver_train
self.dataserver_test = dataserver_test
class PurdueShapes5MultiObjectDataset(torch.utils.data.Dataset):
"""
The very first thing to note is that the images in the dataset
PurdueShapes5MultiObjectDataset are of size 64x64. Each image has a random
number (up to five) of the objects drawn from the following five shapes:
rectangle, triangle, disk, oval, and star. Each shape is randomized with
respect to all its parameters, including those for its scale and location in the
image.
Each image in the dataset is represented by two data objects, one a list and the
other a dictionary. The list data objects consists of the following items:
[R, G, B, mask_array, mask_val_to_bbox_map] ## (A)
and the other data object is a dictionary that is set to:
label_map = {'rectangle':50,
'triangle' :100,
'disk' :150,
'oval' :200,
'star' :250} ## (B)
Note that that second data object for each image is the same, as shown above.
In the rest of this comment block, I'll explain in greater detail the elements
of the list in line (A) above.
R,G,B:
------
Each of these is a 4096-element array whose elements store the corresponding
color values at each of the 4096 pixels in a 64x64 image. That is, R is a list
of 4096 integers, each between 0 and 255, for the value of the red component of
the color at each pixel. Similarly, for G and B.
mask_array:
----------
The fourth item in the list shown in line (A) above is for the mask which is a
numpy array of shape:
(5, 64, 64)
It is initialized by the command:
mask_array = np.zeros((5,64,64), dtype=np.uint8)
In essence, the mask_array consists of five planes, each of size 64x64. Each
plane of the mask array represents an object type according to the following
shape_index
shape_index = (label_map[shape] - 50) // 50
where the label_map is as shown in line (B) above. In other words, the
shape_index values for the different shapes are:
rectangle: 0
triangle: 1
disk: 2
oval: 3
star: 4
Therefore, the first layer (of index 0) of the mask is where the pixel values of
50 are stored at all those pixels that belong to the rectangle shapes.
Similarly, the second mask layer (of index 1) is where the pixel values of 100
are stored at all those pixel coordinates that belong to the triangle shapes in
an image; and so on.
It is in the manner described above that we define five different masks for an
image in the dataset. Each mask is for a different shape and the pixel values
at the nonzero pixels in each mask layer are keyed to the shapes also.
A reader is likely to wonder as to the need for this redundancy in the dataset
representation of the shapes in each image. Such a reader is likely to ask: Why
can't we just use the binary values 1s and 0s in each mask layer where the
corresponding pixels are in the image? Setting these mask values to 50, 100,
etc., was done merely for convenience. I went with the intuition that the
learning needed for multi-object segmentation would become easier if each shape
was represented by a different pixels value in the corresponding mask. So I went
ahead incorporated that in the dataset generation program itself.
The mask values for the shapes are not to be confused with the actual RGB values
of the pixels that belong to the shapes. The RGB values at the pixels in a shape
are randomly generated. Yes, all the pixels in a shape instance in an image
have the same RGB values (but that value has nothing to do with the values given
to the mask pixels for that shape).
mask_val_to_bbox_map:
--------------------
The fifth item in the list in line (A) above is a dictionary that tells us what
bounding-box rectangle to associate with each shape in the image. To illustrate
what this dictionary looks like, assume that an image contains only one
rectangle and only one disk, the dictionary in this case will look like:
mask values to bbox mappings: {200: [],
250: [],
100: [],
50: [[56, 20, 63, 25]],
150: [[37, 41, 55, 59]]}
Should there happen to be two rectangles in the same image, the dictionary would
then be like:
mask values to bbox mappings: {200: [],
250: [],
100: [],
50: [[56, 20, 63, 25], [18, 16, 32, 36]],
150: [[37, 41, 55, 59]]}
Therefore, it is not a problem even if all the objects in an image are of the
same type. Remember, the object that are selected for an image are shown
randomly from the different shapes. By the way, an entry like '[56, 20, 63,
25]' for the bounding box means that the upper-left corner of the BBox for the
'rectangle' shape is at (56,20) and the lower-right corner of the same is at the
pixel coordinates (63,25).
As far as the BBox quadruples are concerned, in the definition
[min_x,min_y,max_x,max_y]
note that x is the horizontal coordinate, increasing to the right on your
screen, and y is the vertical coordinate increasing downwards.
Class Path: DLStudio -> SemanticSegmentation -> PurdueShapes5MultiObjectDataset
"""
def __init__(self, dl_studio, segmenter, train_or_test, dataset_file):
super(DLStudio.SemanticSegmentation.PurdueShapes5MultiObjectDataset, self).__init__()
max_num_objects = segmenter.max_num_objects
if train_or_test == 'train' and dataset_file == "PurdueShapes5MultiObject-10000-train.gz":
if os.path.exists("torch_saved_PurdueShapes5MultiObject-10000_dataset.pt") and \
os.path.exists("torch_saved_PurdueShapes5MultiObject_label_map.pt"):
print("\nLoading training data from torch saved file")
self.dataset = torch.load("torch_saved_PurdueShapes5MultiObject-10000_dataset.pt")
self.label_map = torch.load("torch_saved_PurdueShapes5MultiObject_label_map.pt")
self.num_shapes = len(self.label_map)
self.image_size = dl_studio.image_size
else:
print("""\n\n\nLooks like this is the first time you will be loading in\n"""
"""the dataset for this script. First time loading could take\n"""
"""a few minutes. Any subsequent attempts will only take\n"""
"""a few seconds.\n\n\n""")
root_dir = dl_studio.dataroot
f = gzip.open(root_dir + dataset_file, 'rb')
dataset = f.read()
self.dataset, self.label_map = pickle.loads(dataset, encoding='latin1')
torch.save(self.dataset, "torch_saved_PurdueShapes5MultiObject-10000_dataset.pt")
torch.save(self.label_map, "torch_saved_PurdueShapes5MultiObject_label_map.pt")
# reverse the key-value pairs in the label dictionary:
self.class_labels = dict(map(reversed, self.label_map.items()))
self.num_shapes = len(self.class_labels)
self.image_size = dl_studio.image_size
else:
root_dir = dl_studio.dataroot
f = gzip.open(root_dir + dataset_file, 'rb')
dataset = f.read()
if sys.version_info[0] == 3:
self.dataset, self.label_map = pickle.loads(dataset, encoding='latin1')
else:
self.dataset, self.label_map = pickle.loads(dataset)
# reverse the key-value pairs in the label dictionary:
self.class_labels = dict(map(reversed, self.label_map.items()))
self.num_shapes = len(self.class_labels)
self.image_size = dl_studio.image_size
def __len__(self):
return len(self.dataset)
def __getitem__(self, idx):
image_size = self.image_size
r = np.array( self.dataset[idx][0] )
g = np.array( self.dataset[idx][1] )
b = np.array( self.dataset[idx][2] )
R,G,B = r.reshape(image_size[0],image_size[1]), g.reshape(image_size[0],image_size[1]), b.reshape(image_size[0],image_size[1])
im_tensor = torch.zeros(3,image_size[0],image_size[1], dtype=torch.float)
im_tensor[0,:,:] = torch.from_numpy(R)
im_tensor[1,:,:] = torch.from_numpy(G)
im_tensor[2,:,:] = torch.from_numpy(B)
mask_array = np.array(self.dataset[idx][3])
max_num_objects = len( mask_array[0] )
mask_tensor = torch.from_numpy(mask_array)
mask_val_to_bbox_map = self.dataset[idx][4]
max_bboxes_per_entry_in_map = max([ len(mask_val_to_bbox_map[key]) for key in mask_val_to_bbox_map ])
## The first arg 5 is for the number of bboxes we are going to need. If all the
## shapes are exactly the same, you are going to need five different bbox'es.
## The second arg is the index reserved for each shape in a single bbox
bbox_tensor = torch.zeros(max_num_objects,self.num_shapes,4, dtype=torch.float)
for bbox_idx in range(max_bboxes_per_entry_in_map):
for key in mask_val_to_bbox_map:
if len(mask_val_to_bbox_map[key]) == 1:
if bbox_idx == 0:
bbox_tensor[bbox_idx,key,:] = torch.from_numpy(np.array(mask_val_to_bbox_map[key][bbox_idx]))
elif len(mask_val_to_bbox_map[key]) > 1 and bbox_idx < len(mask_val_to_bbox_map[key]):
bbox_tensor[bbox_idx,key,:] = torch.from_numpy(np.array(mask_val_to_bbox_map[key][bbox_idx]))
sample = {'image' : im_tensor,
'mask_tensor' : mask_tensor,
'bbox_tensor' : bbox_tensor }
return sample
def load_PurdueShapes5MultiObject_dataset(self, dataserver_train, dataserver_test ):
self.train_dataloader = torch.utils.data.DataLoader(dataserver_train,
batch_size=self.dl_studio.batch_size,shuffle=True, num_workers=4)
self.test_dataloader = torch.utils.data.DataLoader(dataserver_test,
batch_size=self.dl_studio.batch_size,shuffle=False, num_workers=4)
class SkipBlockDN(nn.Module):
"""
This class for the skip connections in the downward leg of the "U"
Class Path: DLStudio -> SemanticSegmentation -> SkipBlockDN
"""
def __init__(self, in_ch, out_ch, downsample=False, skip_connections=True):
super(DLStudio.SemanticSegmentation.SkipBlockDN, self).__init__()
self.downsample = downsample
self.skip_connections = skip_connections
self.in_ch = in_ch
self.out_ch = out_ch
self.convo1 = nn.Conv2d(in_ch, out_ch, 3, stride=1, padding=1)
self.convo2 = nn.Conv2d(in_ch, out_ch, 3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(out_ch)
self.bn2 = nn.BatchNorm2d(out_ch)
if downsample:
self.downsampler = nn.Conv2d(in_ch, out_ch, 1, stride=2)
def forward(self, x):
identity = x
out = self.convo1(x)
out = self.bn1(out)
out = nn.functional.relu(out)
if self.in_ch == self.out_ch:
out = self.convo2(out)
out = self.bn2(out)
out = nn.functional.relu(out)
if self.downsample:
out = self.downsampler(out)
identity = self.downsampler(identity)
if self.skip_connections:
if self.in_ch == self.out_ch:
out = out + identity
else:
out = out + torch.cat((identity, identity), dim=1)
return out
class SkipBlockUP(nn.Module):
"""
This class is for the skip connections in the upward leg of the "U"
Class Path: DLStudio -> SemanticSegmentation -> SkipBlockUP
"""
def __init__(self, in_ch, out_ch, upsample=False, skip_connections=True):
super(DLStudio.SemanticSegmentation.SkipBlockUP, self).__init__()
self.upsample = upsample
self.skip_connections = skip_connections
self.in_ch = in_ch
self.out_ch = out_ch
self.convoT1 = nn.ConvTranspose2d(in_ch, out_ch, 3, padding=1)
self.convoT2 = nn.ConvTranspose2d(in_ch, out_ch, 3, padding=1)
self.bn1 = nn.BatchNorm2d(out_ch)
self.bn2 = nn.BatchNorm2d(out_ch)
if upsample:
self.upsampler = nn.ConvTranspose2d(in_ch, out_ch, 1, stride=2, dilation=2, output_padding=1, padding=0)
def forward(self, x):
identity = x
out = self.convoT1(x)
out = self.bn1(out)
out = nn.functional.relu(out)
out = nn.ReLU(inplace=False)(out)
if self.in_ch == self.out_ch:
out = self.convoT2(out)
out = self.bn2(out)
out = nn.functional.relu(out)
if self.upsample:
out = self.upsampler(out)
identity = self.upsampler(identity)
if self.skip_connections:
if self.in_ch == self.out_ch:
out = out + identity
else:
out = out + identity[:,self.out_ch:,:,:]
return out
class mUNet(nn.Module):
"""
This network is called mUNet because it is intended for segmenting out
multiple objects simultaneously form an image. [A weaker reason for "Multi" in
the name of the class is that it uses skip connections not only across the two
arms of the "U", but also also along the arms.] The classic UNET was first
proposed by Ronneberger, Fischer and Brox in the paper "U-Net: Convolutional
Networks for Biomedical Image Segmentation". Their UNET extracts binary masks
for the cell pixel blobs of interest in biomedical images. The output of their
UNET therefore can therefore be treated as a pixel-wise binary classifier at
each pixel position.
The mUNet presented here, on the other hand, is meant specifically for
simultaneously identifying and localizing multiple objects in a given image.
Each object type is assigned a separate channel in the output of the network.
I have created a dataset, PurdueShapes5MultiObject, for experimenting with
mUNet. Each image in this dataset contains a random number of selections from
five different shapes, with the shapes being randomly scaled, oriented, and
located in each image. The five different shapes are: rectangle, triangle,
disk, oval, and star.
Class Path: DLStudio -> SemanticSegmentation -> mUNet
"""
def __init__(self, skip_connections=True, depth=16):
super(DLStudio.SemanticSegmentation.mUNet, self).__init__()
self.depth = depth // 2
self.conv_in = nn.Conv2d(3, 64, 3, padding=1)
## For the DN arm of the U:
self.bn1DN = nn.BatchNorm2d(64)
self.bn2DN = nn.BatchNorm2d(128)
self.skip64DN_arr = nn.ModuleList()
for i in range(self.depth):
self.skip64DN_arr.append(DLStudio.SemanticSegmentation.SkipBlockDN(64, 64, skip_connections=skip_connections))
self.skip64dsDN = DLStudio.SemanticSegmentation.SkipBlockDN(64, 64, downsample=True, skip_connections=skip_connections)
self.skip64to128DN = DLStudio.SemanticSegmentation.SkipBlockDN(64, 128, skip_connections=skip_connections )
self.skip128DN_arr = nn.ModuleList()
for i in range(self.depth):
self.skip128DN_arr.append(DLStudio.SemanticSegmentation.SkipBlockDN(128, 128, skip_connections=skip_connections))
self.skip128dsDN = DLStudio.SemanticSegmentation.SkipBlockDN(128,128, downsample=True, skip_connections=skip_connections)
## For the UP arm of the U:
self.bn1UP = nn.BatchNorm2d(128)
self.bn2UP = nn.BatchNorm2d(64)
self.skip64UP_arr = nn.ModuleList()
for i in range(self.depth):
self.skip64UP_arr.append(DLStudio.SemanticSegmentation.SkipBlockUP(64, 64, skip_connections=skip_connections))
self.skip64usUP = DLStudio.SemanticSegmentation.SkipBlockUP(64, 64, upsample=True, skip_connections=skip_connections)
self.skip128to64UP = DLStudio.SemanticSegmentation.SkipBlockUP(128, 64, skip_connections=skip_connections )
self.skip128UP_arr = nn.ModuleList()
for i in range(self.depth):
self.skip128UP_arr.append(DLStudio.SemanticSegmentation.SkipBlockUP(128, 128, skip_connections=skip_connections))
self.skip128usUP = DLStudio.SemanticSegmentation.SkipBlockUP(128,128, upsample=True, skip_connections=skip_connections)
self.conv_out = nn.ConvTranspose2d(64, 5, 3, stride=2,dilation=2,output_padding=1,padding=2)
def forward(self, x):
## Going down to the bottom of the U:
x = nn.MaxPool2d(2,2)(nn.functional.relu(self.conv_in(x)))
for i,skip64 in enumerate(self.skip64DN_arr[:self.depth//4]):
x = skip64(x)
num_channels_to_save1 = x.shape[1] // 2
save_for_upside_1 = x[:,:num_channels_to_save1,:,:].clone()
x = self.skip64dsDN(x)
for i,skip64 in enumerate(self.skip64DN_arr[self.depth//4:]):
x = skip64(x)
x = self.bn1DN(x)
num_channels_to_save2 = x.shape[1] // 2
save_for_upside_2 = x[:,:num_channels_to_save2,:,:].clone()
x = self.skip64to128DN(x)
for i,skip128 in enumerate(self.skip128DN_arr[:self.depth//4]):
x = skip128(x)
x = self.bn2DN(x)
num_channels_to_save3 = x.shape[1] // 2
save_for_upside_3 = x[:,:num_channels_to_save3,:,:].clone()
for i,skip128 in enumerate(self.skip128DN_arr[self.depth//4:]):
x = skip128(x)
x = self.skip128dsDN(x)
## Coming up from the bottom of U on the other side:
x = self.skip128usUP(x)
for i,skip128 in enumerate(self.skip128UP_arr[:self.depth//4]):
x = skip128(x)
x[:,:num_channels_to_save3,:,:] = save_for_upside_3
x = self.bn1UP(x)
for i,skip128 in enumerate(self.skip128UP_arr[:self.depth//4]):
x = skip128(x)
x = self.skip128to64UP(x)
for i,skip64 in enumerate(self.skip64UP_arr[self.depth//4:]):
x = skip64(x)
x[:,:num_channels_to_save2,:,:] = save_for_upside_2
x = self.bn2UP(x)
x = self.skip64usUP(x)
for i,skip64 in enumerate(self.skip64UP_arr[:self.depth//4]):
x = skip64(x)
x[:,:num_channels_to_save1,:,:] = save_for_upside_1
x = self.conv_out(x)
return x
class SegmentationLoss(nn.Module):
"""
I wrote this class before I switched to MSE loss. I am leaving it here
in case I need to get back to it in the future.
Class Path: DLStudio -> SemanticSegmentation -> SegmentationLoss
"""
def __init__(self, batch_size):
super(DLStudio.SemanticSegmentation.SegmentationLoss, self).__init__()
self.batch_size = batch_size
def forward(self, output, mask_tensor):
composite_loss = torch.zeros(1,self.batch_size)
mask_based_loss = torch.zeros(1,5)
for idx in range(self.batch_size):
outputh = output[idx,0,:,:]
for mask_layer_idx in range(mask_tensor.shape[0]):
mask = mask_tensor[idx,mask_layer_idx,:,:]
element_wise = (outputh - mask)**2
mask_based_loss[0,mask_layer_idx] = torch.mean(element_wise)
composite_loss[0,idx] = torch.sum(mask_based_loss)
return torch.sum(composite_loss) / self.batch_size
def run_code_for_training_for_semantic_segmentation(self, net):
filename_for_out1 = "performance_numbers_" + str(self.dl_studio.epochs) + ".txt"
FILE1 = open(filename_for_out1, 'w')
net = copy.deepcopy(net)
net = net.to(self.dl_studio.device)
criterion1 = nn.MSELoss()
optimizer = optim.SGD(net.parameters(),
lr=self.dl_studio.learning_rate, momentum=self.dl_studio.momentum)
start_time = time.perf_counter()
for epoch in range(self.dl_studio.epochs):
print("")
running_loss_segmentation = 0.0
for i, data in enumerate(self.train_dataloader):
im_tensor,mask_tensor,bbox_tensor =data['image'],data['mask_tensor'],data['bbox_tensor']
im_tensor = im_tensor.to(self.dl_studio.device)
mask_tensor = mask_tensor.type(torch.FloatTensor)
mask_tensor = mask_tensor.to(self.dl_studio.device)
bbox_tensor = bbox_tensor.to(self.dl_studio.device)
optimizer.zero_grad()
output = net(im_tensor)
segmentation_loss = criterion1(output, mask_tensor)
segmentation_loss.backward()
optimizer.step()
running_loss_segmentation += segmentation_loss.item()
if i%500==499:
current_time = time.perf_counter()
elapsed_time = current_time - start_time
avg_loss_segmentation = running_loss_segmentation / float(500)
print("[epoch=%d/%d, iter=%4d elapsed_time=%3d secs] MSE loss: %.3f" % (epoch+1, self.dl_studio.epochs, i+1, elapsed_time, avg_loss_segmentation))
FILE1.write("%.3f\n" % avg_loss_segmentation)
FILE1.flush()
running_loss_segmentation = 0.0
print("\nFinished Training\n")
self.save_model(net)
def save_model(self, model):
'''
Save the trained model to a disk file
'''
torch.save(model.state_dict(), self.dl_studio.path_saved_model)
def run_code_for_testing_semantic_segmentation(self, net):
net.load_state_dict(torch.load(self.dl_studio.path_saved_model))
batch_size = self.dl_studio.batch_size
image_size = self.dl_studio.image_size
max_num_objects = self.max_num_objects
with torch.no_grad():
for i, data in enumerate(self.test_dataloader):
im_tensor,mask_tensor,bbox_tensor =data['image'],data['mask_tensor'],data['bbox_tensor']
if i % 50 == 0:
print("\n\n\n\nShowing output for test batch %d: " % (i+1))
outputs = net(im_tensor)
## In the statement below: 1st arg for batch items, 2nd for channels, 3rd and 4th for image size
output_bw_tensor = torch.zeros(batch_size,1,image_size[0],image_size[1], dtype=float)
for image_idx in range(batch_size):
for layer_idx in range(max_num_objects):
for m in range(image_size[0]):
for n in range(image_size[1]):
output_bw_tensor[image_idx,0,m,n] = torch.max( outputs[image_idx,:,m,n] )
display_tensor = torch.zeros(7 * batch_size,3,image_size[0],image_size[1], dtype=float)
for idx in range(batch_size):
for bbox_idx in range(max_num_objects):
bb_tensor = bbox_tensor[idx,bbox_idx]
for k in range(max_num_objects):
i1 = int(bb_tensor[k][1])
i2 = int(bb_tensor[k][3])
j1 = int(bb_tensor[k][0])
j2 = int(bb_tensor[k][2])
output_bw_tensor[idx,0,i1:i2,j1] = 255
output_bw_tensor[idx,0,i1:i2,j2] = 255
output_bw_tensor[idx,0,i1,j1:j2] = 255
output_bw_tensor[idx,0,i2,j1:j2] = 255
im_tensor[idx,0,i1:i2,j1] = 255
im_tensor[idx,0,i1:i2,j2] = 255
im_tensor[idx,0,i1,j1:j2] = 255
im_tensor[idx,0,i2,j1:j2] = 255
display_tensor[:batch_size,:,:,:] = output_bw_tensor
display_tensor[batch_size:2*batch_size,:,:,:] = im_tensor
for batch_im_idx in range(batch_size):
for mask_layer_idx in range(max_num_objects):
for i in range(image_size[0]):
for j in range(image_size[1]):
if mask_layer_idx == 0:
if 25 < outputs[batch_im_idx,mask_layer_idx,i,j] < 85:
outputs[batch_im_idx,mask_layer_idx,i,j] = 255
else:
outputs[batch_im_idx,mask_layer_idx,i,j] = 50
elif mask_layer_idx == 1:
if 65 < outputs[batch_im_idx,mask_layer_idx,i,j] < 135:
outputs[batch_im_idx,mask_layer_idx,i,j] = 255
else:
outputs[batch_im_idx,mask_layer_idx,i,j] = 50
elif mask_layer_idx == 2:
if 115 < outputs[batch_im_idx,mask_layer_idx,i,j] < 185:
outputs[batch_im_idx,mask_layer_idx,i,j] = 255
else:
outputs[batch_im_idx,mask_layer_idx,i,j] = 50
elif mask_layer_idx == 3:
if 165 < outputs[batch_im_idx,mask_layer_idx,i,j] < 230:
outputs[batch_im_idx,mask_layer_idx,i,j] = 255
else:
outputs[batch_im_idx,mask_layer_idx,i,j] = 50
elif mask_layer_idx == 4:
if outputs[batch_im_idx,mask_layer_idx,i,j] > 210:
outputs[batch_im_idx,mask_layer_idx,i,j] = 255
else:
outputs[batch_im_idx,mask_layer_idx,i,j] = 50
display_tensor[2*batch_size+batch_size*mask_layer_idx+batch_im_idx,:,:,:]= outputs[batch_im_idx,mask_layer_idx,:,:]
self.dl_studio.display_tensor_as_image(
torchvision.utils.make_grid(display_tensor, nrow=batch_size, normalize=True, padding=2, pad_value=10))
###%%%
#####################################################################################################################
#################################### Start Definition of Inner Class Autoencoder ##################################
class Autoencoder(nn.Module):
"""
The man reason for the existence of this inner class in DLStudio is for it to serve as the base class for VAE
(Variational Auto-Encoder). That way, the VAE class can focus exclusively on the random-sampling logic
specific to variational encoding while the base class Autoencoder does the convolutional and
transpose-convolutional heavy lifting associated with the usual encoding-decoding of image data.
Class Path: DLStudio -> Autoencoder
"""
def __init__(self, dl_studio, encoder_out_im_size, decoder_out_im_size, encoder_out_ch, path_saved_model):
super(DLStudio.Autoencoder, self).__init__()
self.dl_studio = dl_studio
self.encoder = DLStudio.Autoencoder.EncoderForAutoenc( dl_studio, encoder_out_im_size, encoder_out_ch )
self.decoder = DLStudio.Autoencoder.DecoderForAutoenc( dl_studio, decoder_out_im_size )
self.path_saved_model = path_saved_model
def forward(self, x):
x = self.encoder(x)
x = self.decoder(x)
return x
class EncoderForAutoenc(nn.Module):
"""
The two main components of an Autoencoder are the encoder and the decoder.
This is the encoder part of the Autoencoder.
Class Path: DLStudio -> Autoencoder -> EncoderForAutoenc
"""
def __init__(self, dl_studio, encoder_out_im_size, encoder_out_ch, skip_connections=True, depth=16):
super(DLStudio.Autoencoder.EncoderForAutoenc, self).__init__()
self.depth = depth // 2
self.encoder_out_im_size = encoder_out_im_size
self.encoder_out_ch = encoder_out_ch
self.conv_in = nn.Conv2d(3, 64, 3, padding=1)
## For the DN arm of the U:
self.bn1DN = nn.BatchNorm2d(64)
self.bn2DN = nn.BatchNorm2d(128)
self.skip64DN_arr = nn.ModuleList()
for i in range(self.depth):
self.skip64DN_arr.append(DLStudio.Autoencoder.SkipBlockEncoder(64, 64, skip_connections=skip_connections))
self.skip64dsDN = DLStudio.Autoencoder.SkipBlockEncoder(64, 64, downsample=True, skip_connections=skip_connections)
self.skip64to128DN = DLStudio.Autoencoder.SkipBlockEncoder(64, 128, skip_connections=skip_connections )
self.skip128DN_arr = nn.ModuleList()
for i in range(self.depth):
self.skip128DN_arr.append(DLStudio.Autoencoder.SkipBlockEncoder(128, 128, skip_connections=skip_connections))
self.skip128dsDN = DLStudio.Autoencoder.SkipBlockEncoder(128,128, downsample=True, skip_connections=skip_connections)
def forward(self, x):
x = nn.MaxPool2d(2,2)(nn.functional.relu(self.conv_in(x)))
for i,skip64 in enumerate(self.skip64DN_arr[:self.depth//4]):
x = skip64(x)
x = self.skip64dsDN(x)
for i,skip64 in enumerate(self.skip64DN_arr[self.depth//4:]):
x = skip64(x)
x = self.bn1DN(x)
x = self.skip64to128DN(x)
for i,skip128 in enumerate(self.skip128DN_arr[:self.depth//4]):
x = skip128(x)
x = self.bn2DN(x)
for i,skip128 in enumerate(self.skip128DN_arr[self.depth//4:]):
x = skip128(x)
x = self.skip128dsDN(x)
if (x.shape[2:] != self.encoder_out_im_size) or (x.shape[1] != self.encoder_out_ch):
print("\n\nShape of x at output of Encoder: ", x.shape)
sys.exit("\n\nThe Encoder part of the Autoencoder is misconfigured. Encoder output not according to specs\n\n")
return x
class DecoderForAutoenc(nn.Module):
"""
The two main components of an Autoencoder are the encoder and the decoder.
This is the decoder part of the Autoencoder.
Class Path: DLStudio -> Autoencoder -> DecoderForAutoenc
"""
def __init__(self, dl_studio, decoder_out_im_size, skip_connections=True, depth=16):
super(DLStudio.Autoencoder.DecoderForAutoenc, self).__init__()
self.depth = depth
self.decoder_out_im_size = decoder_out_im_size
## For the UP arm of the U:
self.bn1UP = nn.BatchNorm2d(128)
self.bn2UP = nn.BatchNorm2d(64)
self.skip64UP_arr = nn.ModuleList()
for i in range(self.depth):
self.skip64UP_arr.append(DLStudio.Autoencoder.SkipBlockDecoder(64, 64, skip_connections=skip_connections))
self.skip64usUP = DLStudio.Autoencoder.SkipBlockDecoder(64, 64, upsample=True, skip_connections=skip_connections)
self.skip128to64UP = DLStudio.Autoencoder.SkipBlockDecoder(128, 64, skip_connections=skip_connections )
self.skip128UP_arr = nn.ModuleList()
for i in range(self.depth):
self.skip128UP_arr.append(DLStudio.Autoencoder.SkipBlockDecoder(128, 128, skip_connections=skip_connections))
self.skip128usUP = DLStudio.Autoencoder.SkipBlockDecoder(128,128, upsample=True, skip_connections=skip_connections)
self.conv_out = nn.ConvTranspose2d(64, 32, 3, stride=1,dilation=5,output_padding=1,padding=2)
self.conv_out2 = nn.ConvTranspose2d(32, 3, 5, stride=1,dilation=5,output_padding=3,padding=2)
self.conv_out3 = nn.ConvTranspose2d(3, 3, 3, stride=1,dilation=3,output_padding=2,padding=1)
def forward(self, x):
x = self.skip128usUP(x)
for i,skip128 in enumerate(self.skip128UP_arr[:self.depth//4]):
x = skip128(x)
x = self.bn1UP(x)
for i,skip128 in enumerate(self.skip128UP_arr[:self.depth//4]):
x = skip128(x)
x = self.skip128to64UP(x)
for i,skip64 in enumerate(self.skip64UP_arr[self.depth//4:]):
x = skip64(x)
x = self.bn2UP(x)
x = self.skip64usUP(x)
for i,skip64 in enumerate(self.skip64UP_arr[:self.depth//4]):
x = skip64(x)
x = nn.functional.relu(self.conv_out(x))
x = nn.functional.relu(self.conv_out2(x))
x = self.conv_out3(x)
if x.shape[2:] != self.decoder_out_im_size:
print("\n\nShape of x at output of Decoder: ", x.shape)
sys.exit("\n\nThe Decoder part of the Autoencoder is misconfigured. Output image not according to specs\n\n")
return x
def save_autoencoder_model(self, model):
'''
Save the trained model to a disk file
'''
torch.save(model.state_dict(), self.path_saved_model)
def run_code_for_training_autoencoder( self, display_train_loss=False ):
autoencoder = self.to(self.dl_studio.device)
criterion = nn.MSELoss()
optimizer = optim.Adam(autoencoder.parameters(), lr=self.dl_studio.learning_rate)
accum_times = []
start_time = time.perf_counter()
training_loss_tally = []
print("")
batch_size = self.dl_studio.batch_size
print("\n\n batch_size: ", batch_size)
print("\n\n number of batches in the dataset: ", len(self.train_dataloader))
for epoch in range(self.dl_studio.epochs):
print("")
running_loss = 0.0
for i, data in enumerate(self.train_dataloader):
input_images, _ = data
input_images = input_images.to(self.dl_studio.device)
optimizer.zero_grad()
autoencoder_output = autoencoder( input_images )
loss = criterion( autoencoder_output, input_images )
loss.backward()
optimizer.step()
running_loss += loss
if i % 200 == 199:
avg_loss = running_loss / float(200)
training_loss_tally.append(avg_loss.item())
running_loss = 0.0
current_time = time.perf_counter()
time_elapsed = current_time-start_time
print("[epoch:%2d/%2d i:%4d elapsed_time: %4d secs] loss: %.4f" % (epoch+1, self.dl_studio.epochs, i+1,time_elapsed,avg_loss))
accum_times.append(current_time-start_time)
print("\nFinished Training\n")
self.save_autoencoder_model( autoencoder )
if display_train_loss:
plt.figure(figsize=(10,5))
plt.title("Training Loss vs. Iterations")
plt.plot(training_loss_tally)
plt.xlabel("iterations")
plt.ylabel("training loss")
plt.legend(["Plot of loss versus iterations"], fontsize="x-large")
plt.savefig("training_loss.png")
plt.show()
def run_code_for_evaluating_autoencoder(self, visualization_dir = "autoencoder_visualization_dir" ):
if os.path.exists(visualization_dir):
"""
Clear out the previous autoencoder outputs in the visualization directory
"""
files = glob.glob(visualization_dir + "/*")
for file in files:
if os.path.isfile(file):
os.remove(file)
else:
files = glob.glob(file + "/*")
list(map(lambda x: os.remove(x), files))
else:
os.mkdir(visualization_dir)
autoencoder = self
autoencoder.load_state_dict(torch.load(self.path_saved_model))
autoencoder.to(self.dl_studio.device)
with torch.no_grad():
for i, data in enumerate(self.test_dataloader):
print("\n\n\n=========Showing results for test batch %d===============" % i)
test_images, _ = data
test_images = test_images.to(self.dl_studio.device)
autoencoder_output = autoencoder( test_images )
autoencoder_output = ( autoencoder_output - autoencoder_output.min() ) / ( autoencoder_output.max() - autoencoder_output.min() )
together = torch.zeros( test_images.shape[0], test_images.shape[1], test_images.shape[2], 2 * test_images.shape[3], dtype=torch.float )
together[:,:,:,0:test_images.shape[3]] = test_images
together[:,:,:,test_images.shape[3]:] = autoencoder_output
plt.figure(figsize=(40,20))
plt.imshow(np.transpose(torchvision.utils.make_grid(together.cpu(), normalize=False, padding=3, pad_value=255).cpu(), (1,2,0)))
plt.title("Autoencoder Output Images for iteration %d" % i)
plt.savefig(visualization_dir + "/autoenc_output_%s" % str(i) + ".png")
plt.show()
class SkipBlockEncoder(nn.Module):
"""
This is a building-block class for the skip connections in EncoderForAutoenc
Class Path: DLStudio -> Autoencoder -> SkipBlockEncoder
"""
def __init__(self, in_ch, out_ch, downsample=False, skip_connections=True):
super(DLStudio.Autoencoder.SkipBlockEncoder, self).__init__()
self.downsample = downsample
self.skip_connections = skip_connections
self.in_ch = in_ch
self.out_ch = out_ch
self.convo1 = nn.Conv2d(in_ch, out_ch, 3, stride=1, padding=1)
self.convo2 = nn.Conv2d(in_ch, out_ch, 3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(out_ch)
self.bn2 = nn.BatchNorm2d(out_ch)
if downsample:
self.downsampler = nn.Conv2d(in_ch, out_ch, 1, stride=2)
def forward(self, x):
identity = x
out = self.convo1(x)
out = self.bn1(out)
out = nn.functional.relu(out)
if self.in_ch == self.out_ch:
out = self.convo2(out)
out = self.bn2(out)
out = nn.functional.relu(out)
if self.downsample:
out = self.downsampler(out)
identity = self.downsampler(identity)
if self.skip_connections:
if self.in_ch == self.out_ch:
out = out + identity
else:
out = out + torch.cat((identity, identity), dim=1)
return out
class SkipBlockDecoder(nn.Module):
"""
This is a building-block class for the skip connections in DecoderForAutoenc
Class Path: DLStudio -> Autoencoder -> SkipBlockDecoder
"""
def __init__(self, in_ch, out_ch, upsample=False, skip_connections=True):
super(DLStudio.Autoencoder.SkipBlockDecoder, self).__init__()
self.upsample = upsample
self.skip_connections = skip_connections
self.in_ch = in_ch
self.out_ch = out_ch
self.convoT1 = nn.ConvTranspose2d(in_ch, out_ch, 3, padding=1)
self.convoT2 = nn.ConvTranspose2d(in_ch, out_ch, 3, padding=1)
self.bn1 = nn.BatchNorm2d(out_ch)
self.bn2 = nn.BatchNorm2d(out_ch)
if upsample:
self.upsampler = nn.ConvTranspose2d(in_ch, out_ch, 1, stride=2, dilation=2, output_padding=1, padding=0)
def forward(self, x):
identity = x
out = self.convoT1(x)
out = self.bn1(out)
out = nn.functional.relu(out)
out = nn.ReLU(inplace=False)(out)
if self.in_ch == self.out_ch:
out = self.convoT2(out)
out = self.bn2(out)
out = nn.functional.relu(out)
if self.upsample:
out = self.upsampler(out)
identity = self.upsampler(identity)
if self.skip_connections:
if self.in_ch == self.out_ch:
out = out + identity
else:
out = out + identity[:,self.out_ch:,:,:]
return out
def set_dataloader(self):
"""
Note the call to random_split() in the second statement for dividing the overall dataset of images into
two DISJOINT parts, one for training and the other for testing. Since my evaluation of the VAE at this
time is purely on the basis of the visual quality of the output of the Decoder, I have set aside only
200 randomly chosen images for testing. Ordinarily, through, you would want to split the dataset in
the 70:30 or 80:20 ratio for training and testing.
"""
dataset = torchvision.datasets.ImageFolder(root=self.dl_studio.dataroot,
transform = tvt.Compose([
tvt.Resize(self.dl_studio.image_size),
tvt.CenterCrop(self.dl_studio.image_size),
tvt.ToTensor(),
tvt.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]))
dataset_train, dataset_test = torch.utils.data.random_split( dataset, lengths = [len(dataset) - 200, 200])
self.train_dataloader = torch.utils.data.DataLoader(dataset_train, batch_size=self.dl_studio.batch_size, shuffle=True, num_workers=4)
self.test_dataloader = torch.utils.data.DataLoader(dataset_test, batch_size=self.dl_studio.batch_size, shuffle=True, num_workers=4)
###%%%
#####################################################################################################################
####################################### Start Definition of Inner Class VAE #######################################
class VAE (Autoencoder):
"""
VAE stands for "Variational Auto Encoder". These days, you are more likely to see it
written as "variational autoencoder". I consider VAE as one of the foundational neural
architectures in Deep Learning. VAE is based on the new celebrated 2014 paper
"Auto-Encoding Variational Bayes" by Kingma and Welling. The idea is for the Encoder
part of an Encoder-Decoder pair to learn the probability distribution for the Latent
Space Representation of a training dataset. Described loosely, the latent vector z for
an input image x would be the "essence" of what x is depicting. Presumably, after the
latent distribution has been learned, the Decoder should be able to transform any "noise"
vector sampled from the latent distribution and convert it into the sort of output you
would see during the training process.
In case you are wondering about the dimensionality of the Latent Space, consider the case
that the input images are eventually converted into 8x8 pixel arrays, with each pixel
represented by a 128-dimensional embedding. In a vectorized representation, this implies
an 8192-dimensional space for the Latent Distribution. The mean (mu) and the log-variance
values (logvar) values learned by the Encoder would represent vectors in an 8,192
dimensional space. The Decoder's job would be sample this distribution and attempt a
reconstruction of what the user wants to see at the output of the Decoder.
As you can see, the VAE class is derived from the parent class Autoencoder. Bulk of the
computing in VAE is done through the functionality packed into the Autoencoder class.
Therefore, in order to fully understand the VAE implementation here, your starting point
should be the code for the Autoencoder class.
Class Path: DLStudio -> VAE
"""
def __init__(self, dl_studio, encoder_out_im_size, decoder_out_im_size, encoder_out_ch, path_saved_encoder, path_saved_decoder ):
super(DLStudio.VAE, self).__init__( dl_studio, encoder_out_im_size, decoder_out_im_size, encoder_out_ch, path_saved_model=None )
self.parent_encoder = DLStudio.Autoencoder.EncoderForAutoenc(dl_studio, encoder_out_im_size,
encoder_out_ch, skip_connections=True, depth=16 )
self.parent_decoder = DLStudio.Autoencoder.DecoderForAutoenc(dl_studio, decoder_out_im_size)
self.vae_encoder = DLStudio.VAE.VaeEncoder(self.parent_encoder, encoder_out_im_size, encoder_out_ch)
self.vae_decoder = DLStudio.VAE.VaeDecoder(self.parent_decoder, encoder_out_im_size, encoder_out_ch)
self.encoder_out_im_size = self.encoder.encoder_out_im_size
self.encoder_out_ch = self.encoder.encoder_out_ch
self.path_saved_encoder = path_saved_encoder
self.path_saved_decoder = path_saved_decoder
class VaeEncoder(nn.Module):
"""
The most important thing to note here is that this Encoder outputs ONLY the mean and the log-variance
of the Gaussian distribution that models the latent vectors. VAEs are based on the assumption that
Latent Distributions are far simpler than the probability distributions that would model the image
dataset used for training.
Class Path: DLStudio -> VAE -> VaeEncoder
"""
def __init__(self, parent_encoder, encoder_out_im_size, encoder_out_ch):
super(DLStudio.VAE.VaeEncoder, self).__init__()
self.parent_encoder = parent_encoder
self.num_nodes = encoder_out_im_size[0] * encoder_out_im_size[1] * encoder_out_ch
self.mu_layer = nn.Linear(self.num_nodes, self.num_nodes)
self.log_var_layer = nn.Linear(self.num_nodes, self.num_nodes)
def forward(self, x):
encoded = self.parent_encoder(x)
mu = self.mu_layer(encoded.view(-1, self.num_nodes))
log_var = self.log_var_layer(encoded.view(-1, self.num_nodes))
return mu, log_var
class VaeDecoder(nn.Module):
"""
The VAE Decoder's job is to take the mu and logvar values produced by the Encoder and
generate an output image that contains the information that the user wants to see there.
For obvious reasons, as to what exactly is seen at the output of the Decoder would
depend on the loss function used and the shape of the output tensor. If all you wanted
to see was a reduced dimensionality image at the output, you would need to change the
final layers of the Decoder so that the final output corresponds to the shape that goes
with that representation.
Class Path: DLStudio -> VAE -> VaeDecoder
"""
def __init__(self, parent_decoder, encoder_out_im_size, encoder_out_ch):
super(DLStudio.VAE.VaeDecoder, self).__init__()
self.parent_decoder = parent_decoder
self.encoder_out_im_size = encoder_out_im_size
self.encoder_out_ch = encoder_out_ch
def reparameterize(self, mu, logvar):
std = torch.exp(0.5 * logvar)
## In the next statement, 'torch.randn' is sampling from an isotropic zero-mean
## unit-covariance Gaussian. The call 'torch.randn_like' ensures that the returned
## tensor will have the same shape as the 'std' tensor.
##
## In order to understand the shape of 'std', consider the case when the size of the
## pixel array at the Encoder output is 8x8, the embedding size 128, and the
## batch_size 48. In this case, you have 64 pixels at the output of the Encoder
## (before you go into the Linear layers for mu and logvar estimation). So the
## shape of both 'logvar' and 'std' is going to be [48, 8192] where 8192 is the product
## of the 64 pixels and the 128 channels at each pixel. Note that the shapes for all
## three of 'mu', 'logvar', and 'std' are identical and, for our example, that shape is
## [48, 8192].
eps = torch.randn_like( std ) ## standard normal N(0;1)
return mu + eps * std
def forward(self, mu, logvar):
z = self.reparameterize( mu, logvar )
decoded = self.parent_decoder( z.view(-1, 128, self.encoder_out_im_size[0], self.encoder_out_im_size[1]) )
return decoded, mu, logvar
def save_encoder_model(self, model):
'''
Save the trained model to a disk file
'''
torch.save(model.state_dict(), self.path_saved_encoder)
def save_decoder_model(self, model):
'''
Save the trained model to a disk file
'''
torch.save(model.state_dict(), self.path_saved_decoder)
def run_code_for_training_VAE( self, vae_net, loss_weighting, display_train_loss=False ):
"""
The code for set_dataloaders() for the VAE class shows how the overall dataset of images is divided into
training and testing subsets.
The important thing to keep in mind about this function is the relative weighting of the reconstruction
loss vis-a-vis the KL-divergence. For an "optimized" VAE implementation, finding the best value to use
for this relative weighting of the two loss components would be a part of hyperparameter tuning of the
network.
"""
def loss_criterion(input_images, decoder_output_images, log_var, weighting):
recon_loss = nn.MSELoss(reduction='sum')( input_images, decoder_output_images ) ## reconstruction loss
KLD = -0.5 * torch.sum( 1 + log_var - mu.pow(2) - log_var.exp() ) ## KL Divergence
KLD = KLD * weighting
return recon_loss + KLD, recon_loss, KLD
vae_encoder = vae_net.vae_encoder.to(self.dl_studio.device)
vae_decoder = vae_net.vae_decoder.to(self.dl_studio.device)
accum_times = []
start_time = time.perf_counter()
print("")
batch_size = self.dl_studio.batch_size
print("\n\n batch_size: ", batch_size)
num_batches_in_data_source = len(self.train_dataloader)
total_num_updates = self.dl_studio.epochs * num_batches_in_data_source
print("\n\n number of batches in the dataset: ", num_batches_in_data_source)
optimizer1 = optim.Adam(vae_encoder.parameters(), lr=self.dl_studio.learning_rate)
optimizer2 = optim.Adam(vae_decoder.parameters(), lr=self.dl_studio.learning_rate)
mu = logvar = 0.0
total_training_loss_tally = []
recons_loss_tally = []
KL_divergence_tally = []
for epoch in range(self.dl_studio.epochs):
print("")
## The following are needed for calculating the avg values between displays:
running_loss = running_recon_loss = running_kld_loss = 0.0
for i, data in enumerate(self.train_dataloader):
input_images, _ = data
input_images = input_images.to(self.dl_studio.device)
optimizer1.zero_grad()
optimizer2.zero_grad()
mu, logvar = vae_encoder( input_images )
## As required by VAE, the Decoder is only being supplied with the mean 'mu' and the log-variance 'logvar':
decoder_out, _, _ = vae_decoder( mu, logvar )
loss, recon_loss, kld_loss = loss_criterion( input_images, decoder_out, logvar, loss_weighting )
loss.backward()
optimizer1.step()
optimizer2.step()
running_loss += loss
running_recon_loss += recon_loss
running_kld_loss += kld_loss
if i % 200 == 199:
avg_loss = running_loss / float(200)
avg_recon_loss = running_recon_loss / float(200)
avg_kld_loss = running_kld_loss / float(200)
total_training_loss_tally.append(avg_loss.item())
recons_loss_tally.append(avg_recon_loss.item())
KL_divergence_tally.append(avg_kld_loss.item())
running_loss = running_recon_loss = running_kld_loss = 0.0
current_time = time.perf_counter()
time_elapsed = current_time-start_time
print("[epoch:%2d/%2d i:%4d elapsed_time: %4d secs] loss: %10.4f recon_loss: %10.4f kld_loss: %10.4f " %
(epoch+1, self.dl_studio.epochs, i+1,time_elapsed,avg_loss,avg_recon_loss,avg_kld_loss))
accum_times.append(current_time-start_time)
print("\nFinished Training\n")
self.save_encoder_model( vae_encoder )
self.save_decoder_model( vae_decoder )
params_saved = { 'mean': mu, 'log_variance': logvar}
pickle.dump(params_saved, open('params_saved.p', 'wb'))
if display_train_loss:
fig, (ax1,ax2,ax3) = plt.subplots(nrows=1, ncols=3, figsize=(20,5))
ax1.plot(total_training_loss_tally)
ax2.plot(recons_loss_tally)
ax3.plot(KL_divergence_tally)
ax1.set_xticks(np.arange(total_num_updates // 200)) ## since each val for plotting is generated every 200 iterations
ax2.set_xticks(np.arange(total_num_updates // 200))
ax3.set_xticks(np.arange(total_num_updates // 200))
ax1.set_xlabel("iterations")
ax2.set_xlabel("iterations")
ax3.set_xlabel("iterations")
ax1.set_ylabel("total training loss")
ax2.set_ylabel("reconstruction loss")
ax3.set_ylabel("KL divergence")
plt.savefig("all_training_losses.png")
plt.show()
def run_code_for_evaluating_VAE(self, vae_net, visualization_dir = "vae_visualization_dir" ):
"""
The main point here is to use the co-called "unseen images" for evaluating the performance
of the VAE Encoder-Decoder network. If you look at the set_dataloader() function for the
VAE class, you will see me setting aside a certain number of the available images for testing.
These randomly chosen images play NO role in training.
"""
if os.path.exists(visualization_dir):
"""
Clear out the previous autoencoder outputs in the visualization directory
"""
files = glob.glob(visualization_dir + "/*")
for file in files:
if os.path.isfile(file):
os.remove(file)
else:
files = glob.glob(file + "/*")
list(map(lambda x: os.remove(x), files))
else:
os.mkdir(visualization_dir)
vae_encoder = vae_net.vae_encoder.eval()
vae_decoder = vae_net.vae_decoder.eval()
vae_encoder.load_state_dict(torch.load(self.path_saved_encoder))
vae_decoder.load_state_dict(torch.load(self.path_saved_decoder))
vae_encoder.to(self.dl_studio.device)
vae_decoder.to(self.dl_studio.device)
with torch.no_grad():
for i, data in enumerate(self.test_dataloader):
print("\n\n\n=========Showing results for test batch %d===============" % i)
test_images, _ = data
test_images = test_images.to(self.dl_studio.device)
mu, logvar = vae_encoder( test_images )
## In the next statement, using mu and logvar, the Decoder first uses the "reparameterization trick"
## to sample the latent distribution and to then feed it into the rest of the Decoder for image generation:
decoder_out, _, _ = vae_decoder( mu, logvar )
decoder_out = ( decoder_out - decoder_out.min() ) / ( decoder_out.max() - decoder_out.min() )
together = torch.zeros( test_images.shape[0], test_images.shape[1], test_images.shape[2], 2 * test_images.shape[3], dtype=torch.float )
together[:,:,:,0:test_images.shape[3]] = test_images
together[:,:,:,test_images.shape[3]:] = decoder_out
plt.figure(figsize=(40,20))
plt.imshow(np.transpose(torchvision.utils.make_grid(together.cpu(), normalize=False, padding=3, pad_value=255).cpu(), (1,2,0)))
plt.title("VAE Output Images for iteration %d" % i)
plt.savefig(visualization_dir + "/vae_decoder_out_%s" % str(i) + ".png")
plt.show()
def run_code_for_generating_images_from_noise_VAE(self, vae_net, visualization_dir = "vae_gen_visualization_dir" ):
"""
This function is for testing the functioning of just the Generator (which is the Decoder) in
the VAE network. That is, after we have trained the VAE network, we disconnect the Encoder
and ask the Decoder to sample the latent distribution for generating the images.
Remember, the latent distribution is represented entirely by the final values learned for
the mean (mu) and the log of the variance (logvar) that represent how close the training process
was able to come to the ideal of zero-mean and unit-covariance isotropic distribution.
Since the job of this function is to sample the latent distribution actually learned, we must
also supply with the (mu,logvar) values learned during training.
"""
if os.path.exists(visualization_dir):
"""
Clear out the previous autoencoder outputs in the visualization directory
"""
files = glob.glob(visualization_dir + "/*")
for file in files:
if os.path.isfile(file):
os.remove(file)
else:
files = glob.glob(file + "/*")
list(map(lambda x: os.remove(x), files))
else:
os.mkdir(visualization_dir)
vae_decoder = vae_net.vae_decoder.eval()
vae_decoder.load_state_dict(torch.load(self.path_saved_decoder))
params_saved = pickle.load( open('params_saved.p', 'rb') )
mu, logvar = params_saved['mean'], params_saved['log_variance']
## The size of the batch axis for the mu and logvar tensors will corresponds to the number
## of images in the last batch used for training. If you want the purely generative process
## in this script (which uses the VAE Decoder in a standalone mode) to produce a batchful of
## images, you need to expand the previously learned mu and logvar tensors as shown below:
if mu.shape[0] < self.dl_studio.batch_size:
new_mu = torch.zeros( (self.dl_studio.batch_size, mu.shape[1]) ).float()
new_mu[:mu.shape[0]] = mu
new_mu[mu.shape[0]:] = mu[:(self.dl_studio.batch_size - mu.shape[0])]
new_logvar = torch.zeros( (self.dl_studio.batch_size, logvar.shape[1]) ).float()
new_logvar[:logvar.shape[0]] = logvar
new_logvar[logvar.shape[0]:] = logvar[:(self.dl_studio.batch_size - logvar.shape[0])]
mu = new_mu.to(self.dl_studio.device)
logvar = new_logvar.to(self.dl_studio.device)
vae_decoder.to(self.dl_studio.device)
sample_standard_normal_distribution = True
sample_learned_normal_distribution = False
with torch.no_grad():
for i in range(5):
print("\n\n\n=========Showing results for test batch %d===============" % i)
if sample_standard_normal_distribution:
mu = torch.zeros_like(mu).float().to(self.dl_studio.device)
logvar = torch.ones_like(logvar).float().to(self.dl_studio.device)
elif sample_learned_normal_distribution:
std = torch.exp(0.5 * logvar)
## In the next statement, using mu and logvar, the Decoder first uses the "reparameterization trick"
## to sample the latent distribution and to then feed it into the rest of the Decoder for image generation:
decoder_out, _, _ = vae_decoder( mu, logvar )
decoder_out = ( decoder_out - decoder_out.min() ) / ( decoder_out.max() - decoder_out.min() )
fake_input = torch.zeros_like(decoder_out).float().to(self.dl_studio.device)
together = torch.zeros( fake_input.shape[0], fake_input.shape[1], fake_input.shape[2], 2 * fake_input.shape[3], dtype=torch.float )
together[:,:,:,0:fake_input.shape[3]] = fake_input
together[:,:,:,fake_input.shape[3]:] = decoder_out
plt.figure(figsize=(40,20))
plt.imshow(np.transpose(torchvision.utils.make_grid(together.cpu(), normalize=False, padding=3, pad_value=255).cpu(), (1,2,0)))
plt.title("VAE Output Images for iteration %d" % i)
plt.savefig(visualization_dir + "/vae_decoder_out_%s" % str(i) + ".png")
plt.show()
def set_dataloader(self):
"""
Note the call to random_split() in the second statement for dividing the overall dataset of images into
two DISJOINT parts, one for training and the other for testing. Since my evaluation of the VAE at this
time is purely on the basis of the visual quality of the output of the Decoder, I have set aside only
200 randomly chosen images for testing. Ordinarily, through, you would want to split the dataset in
the 70:30 or 80:20 ratio for training and testing.
"""
dataset = torchvision.datasets.ImageFolder(root=self.dl_studio.dataroot,
transform = tvt.Compose([
tvt.Resize(self.dl_studio.image_size),
tvt.CenterCrop(self.dl_studio.image_size),
tvt.ToTensor(),
tvt.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]))
dataset_train, dataset_test = torch.utils.data.random_split( dataset, lengths = [len(dataset) - 200, 200])
self.train_dataloader = torch.utils.data.DataLoader(dataset_train, batch_size=self.dl_studio.batch_size, shuffle=True, num_workers=4)
self.test_dataloader = torch.utils.data.DataLoader(dataset_test, batch_size=self.dl_studio.batch_size, shuffle=True, num_workers=4)
###%%%
#####################################################################################################################
#################################### Start Definition of Inner Class TextClassification ###########################
class TextClassification(nn.Module):
"""
The purpose of this inner class is to be able to use the DLStudio platform for simple
experiments in text classification. Consider, for example, the problem of automatic
classification of variable-length user feedback: you want to create a neural network
that can label an uploaded product review of arbitrary length as positive or negative.
One way to solve this problem is with a recurrent neural network in which you use a
hidden state for characterizing a variable-length product review with a fixed-length
state vector. This inner class allows you to carry out such experiments.
Class Path: DLStudio -> TextClassification
"""
def __init__(self, dl_studio, dataserver_train=None, dataserver_test=None, dataset_file_train=None,
dataset_file_test=None, display_train_loss=False):
super(DLStudio.TextClassification, self).__init__()
self.dl_studio = dl_studio
self.dataserver_train = dataserver_train
self.dataserver_test = dataserver_test
self.display_train_loss = display_train_loss
class SentimentAnalysisDataset(torch.utils.data.Dataset):
"""
The sentiment analysis datasets that I have made available were extracted from
an archive of user feedback comments as made available by Amazon for the year
2007. The original archive contains user feedback on 25 product categories.
For each product category, there are two files named 'positive.reviews' and
'negative.reviews', with each file containing 1000 reviews. I believe that
characterizing the reviews as 'positive' or 'negative' was carried out by
human annotators. Regardless, the reviews in these two files can be used to
train a neural network whose purpose would be to automatically characterize
a product as being positive or negative.
I have extracted the following datasets extracted from the Amazon archive:
sentiment_dataset_train_200.tar.gz vocab_size = 43,285
sentiment_dataset_test_200.tar.gz
sentiment_dataset_train_40.tar.gz vocab_size = 17,001
sentiment_dataset_test_40.tar.gz
sentiment_dataset_train_3.tar.gz vocab_size = 3,402
sentiment_dataset_test_3.tar.gz
The integer in the name of each dataset is the number of reviews collected
from the 'positive.reviews' and the 'negative.reviews' files for each product
category. Therefore, the dataset with 200 in its name has a total of 400
reviews for each product category.
As to why I am presenting these three different datasets, note that, as shown
above, the size of the vocabulary depends on the number of reviews selected
and the size of the vocabulary has a strong bearing on how long it takes to
train an algorithm for text classification. For one simple reason for that:
the size of the one-hot representation for the words equals the size of the
vocabulary. Therefore, the one-hot representation for the words for the
dataset with 200 in its name will be a one-axis tensor of size 43,285.
For a purely feedforward network, it is not a big deal for the input tensors
to be size Nx43285 where N is the number of words in a review. And even for
RNNs with simple feedback, that does not slow things down. However, when
using GRUs, it's an entirely different matter if you are tying to run your
experiments on, say, a laptop with a Quadro GPU. Hence the reason for providing
the datasets with 200 and 40 reviews. The dataset with just 3 reviews is for
debugging your code.
Class Path: DLStudio -> TextClassification -> SentimentAnalysisDataset
"""
def __init__(self, dl_studio, train_or_test, dataset_file):
super(DLStudio.TextClassification.SentimentAnalysisDataset, self).__init__()
self.train_or_test = train_or_test
root_dir = dl_studio.dataroot
f = gzip.open(root_dir + dataset_file, 'rb')
dataset = f.read()
if train_or_test == 'train':
if sys.version_info[0] == 3:
self.positive_reviews_train, self.negative_reviews_train, self.vocab = pickle.loads(dataset, encoding='latin1')
else:
self.positive_reviews_train, self.negative_reviews_train, self.vocab = pickle.loads(dataset)
self.categories = sorted(list(self.positive_reviews_train.keys()))
self.category_sizes_train_pos = {category : len(self.positive_reviews_train[category]) for category in self.categories}
self.category_sizes_train_neg = {category : len(self.negative_reviews_train[category]) for category in self.categories}
self.indexed_dataset_train = []
for category in self.positive_reviews_train:
for review in self.positive_reviews_train[category]:
self.indexed_dataset_train.append([review, category, 1])
for category in self.negative_reviews_train:
for review in self.negative_reviews_train[category]:
self.indexed_dataset_train.append([review, category, 0])
random.shuffle(self.indexed_dataset_train)
elif train_or_test == 'test':
if sys.version_info[0] == 3:
self.positive_reviews_test, self.negative_reviews_test, self.vocab = pickle.loads(dataset, encoding='latin1')
else:
self.positive_reviews_test, self.negative_reviews_test, self.vocab = pickle.loads(dataset)
self.vocab = sorted(self.vocab)
self.categories = sorted(list(self.positive_reviews_test.keys()))
self.category_sizes_test_pos = {category : len(self.positive_reviews_test[category]) for category in self.categories}
self.category_sizes_test_neg = {category : len(self.negative_reviews_test[category]) for category in self.categories}
self.indexed_dataset_test = []
for category in self.positive_reviews_test:
for review in self.positive_reviews_test[category]:
self.indexed_dataset_test.append([review, category, 1])
for category in self.negative_reviews_test:
for review in self.negative_reviews_test[category]:
self.indexed_dataset_test.append([review, category, 0])
random.shuffle(self.indexed_dataset_test)
def get_vocab_size(self):
return len(self.vocab)
def one_hotvec_for_word(self, word):
word_index = self.vocab.index(word)
hotvec = torch.zeros(1, len(self.vocab))
hotvec[0, word_index] = 1
return hotvec
def review_to_tensor(self, review):
review_tensor = torch.zeros(len(review), len(self.vocab))
for i,word in enumerate(review):
review_tensor[i,:] = self.one_hotvec_for_word(word)
return review_tensor
def sentiment_to_tensor(self, sentiment):
"""
Sentiment is ordinarily just a binary valued thing. It is 0 for negative
sentiment and 1 for positive sentiment. We need to pack this value in a
two-element tensor.
"""
sentiment_tensor = torch.zeros(2)
if sentiment == 1:
sentiment_tensor[1] = 1
elif sentiment == 0:
sentiment_tensor[0] = 1
sentiment_tensor = sentiment_tensor.type(torch.long)
return sentiment_tensor
def __len__(self):
if self.train_or_test == 'train':
return len(self.indexed_dataset_train)
elif self.train_or_test == 'test':
return len(self.indexed_dataset_test)
def __getitem__(self, idx):
sample = self.indexed_dataset_train[idx] if self.train_or_test == 'train' else self.indexed_dataset_test[idx]
review = sample[0]
review_category = sample[1]
review_sentiment = sample[2]
review_sentiment = self.sentiment_to_tensor(review_sentiment)
review_tensor = self.review_to_tensor(review)
category_index = self.categories.index(review_category)
sample = {'review' : review_tensor,
'category' : category_index, # should be converted to tensor, but not yet used
'sentiment' : review_sentiment }
return sample
def load_SentimentAnalysisDataset(self, dataserver_train, dataserver_test ):
self.train_dataloader = torch.utils.data.DataLoader(dataserver_train,
batch_size=self.dl_studio.batch_size,shuffle=True, num_workers=1)
self.test_dataloader = torch.utils.data.DataLoader(dataserver_test,
batch_size=self.dl_studio.batch_size,shuffle=False, num_workers=1)
class TEXTnet(nn.Module):
"""
This network is meant for semantic classification of variable-length sentiment
data. Based on my limited testing, the performance of this network is very
poor because it has no protection against vanishing gradients when used in an
RNN.
Class Path: DLStudio -> TextClassification -> TEXTnet
"""
def __init__(self, input_size, hidden_size, output_size):
super(DLStudio.TextClassification.TEXTnet, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
self.combined_to_hidden = nn.Linear(input_size + hidden_size, hidden_size)
self.combined_to_middle = nn.Linear(input_size + hidden_size, 100)
self.middle_to_out = nn.Linear(100, output_size)
self.logsoftmax = nn.LogSoftmax(dim=1)
self.dropout = nn.Dropout(p=0.1)
def forward(self, input, hidden):
combined = torch.cat((input, hidden), 1)
hidden = self.combined_to_hidden(combined)
hidden = torch.tanh(hidden)
out = self.combined_to_middle(combined)
out = nn.functional.relu(out)
out = self.dropout(out)
out = self.middle_to_out(out)
out = self.logsoftmax(out)
return out,hidden
def init_hidden(self):
hidden = torch.zeros(1, self.hidden_size)
return hidden
class TEXTnetOrder2(nn.Module):
"""
In this variant of the TEXTnet network, the value of hidden as used at each
time step also includes its value at the previous time step. This fact, not
directly apparent by the definition of the class shown below, is made possible
by the last parameter, cell, in the header of forward(). As you can see below,
at the end of forward(), the value of the cell goes through a linear layer
and through a sigmoid nonlinearity. By the way, since the sigmoid saturates at 0
and 1, it can act like a switch. Later when I use this class in the training
function, you will see the cell values being used in such a manner that the
hidden state at each time step is mixed with the hidden state at the previous
time step, but only to the extent allowed by the switching action of the Sigmoid.
Class Path: DLStudio -> TextClassification -> TEXTnetOrder2
"""
def __init__(self, input_size, hidden_size, output_size):
super(DLStudio.TextClassification.TEXTnetOrder2, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
self.combined_to_hidden = nn.Linear(input_size + 2*hidden_size, hidden_size)
self.combined_to_middle = nn.Linear(input_size + 2*hidden_size, 100)
self.middle_to_out = nn.Linear(100, output_size)
self.logsoftmax = nn.LogSoftmax(dim=1)
self.dropout = nn.Dropout(p=0.1)
# for the cell
self.linear_for_cell = nn.Linear(hidden_size, hidden_size)
def forward(self, input, hidden, cell):
combined = torch.cat((input, hidden, cell), 1)
hidden = self.combined_to_hidden(combined)
hidden = torch.tanh(hidden)
out = self.combined_to_middle(combined)
out = nn.functional.relu(out)
out = self.dropout(out)
out = self.middle_to_out(out)
out = self.logsoftmax(out)
hidden_clone = hidden.clone()
cell = torch.sigmoid(self.linear_for_cell(hidden_clone))
return out,hidden,cell
def initialize_cell(self):
weight = next(self.linear_for_cell.parameters()).data
cell = weight.new(1, self.hidden_size).zero_()
return cell
def init_hidden(self):
hidden = torch.zeros(1, self.hidden_size)
return hidden
class GRUnet(nn.Module):
"""
Source: https://blog.floydhub.com/gru-with-pytorch/
with the only modification that the final output of forward() is now
routed through LogSoftmax activation.
Class Path: DLStudio -> TextClassification -> GRUnet
"""
def __init__(self, input_size, hidden_size, output_size, num_layers, drop_prob=0.2):
super(DLStudio.TextClassification.GRUnet, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.gru = nn.GRU(input_size, hidden_size, num_layers)
self.fc = nn.Linear(hidden_size, output_size)
self.relu = nn.ReLU()
self.logsoftmax = nn.LogSoftmax(dim=1)
def forward(self, x, h):
out, h = self.gru(x, h)
out = self.fc(self.relu(out[:,-1]))
out = self.logsoftmax(out)
return out, h
def init_hidden(self):
weight = next(self.parameters()).data
# batch_size
hidden = weight.new( self.num_layers, 1, self.hidden_size ).zero_()
return hidden
def save_model(self, model):
"Save the trained model to a disk file"
torch.save(model.state_dict(), self.dl_studio.path_saved_model)
def run_code_for_training_with_TEXTnet(self, net, display_train_loss=False):
filename_for_out = "performance_numbers_" + str(self.dl_studio.epochs) + ".txt"
FILE = open(filename_for_out, 'w')
net.to(self.dl_studio.device)
## Note that the TEXTnet and TEXTnetOrder2 both produce LogSoftmax output:
criterion = nn.NLLLoss()
accum_times = []
optimizer = optim.SGD(net.parameters(),
lr=self.dl_studio.learning_rate, momentum=self.dl_studio.momentum)
start_time = time.perf_counter()
training_loss_tally = []
for epoch in range(self.dl_studio.epochs):
print("")
running_loss = 0.0
for i, data in enumerate(self.train_dataloader):
hidden = net.init_hidden().to(self.dl_studio.device)
review_tensor,category,sentiment = data['review'], data['category'], data['sentiment']
review_tensor = review_tensor.to(self.dl_studio.device)
sentiment = sentiment.to(self.dl_studio.device)
optimizer.zero_grad()
input = torch.zeros(1,review_tensor.shape[2])
input = input.to(self.dl_studio.device)
for k in range(review_tensor.shape[1]):
input[0,:] = review_tensor[0,k]
output, hidden = net(input, hidden)
loss = criterion(output, torch.argmax(sentiment,1))
running_loss += loss.item()
# loss.backward(retain_graph=True)
loss.backward()
optimizer.step()
if i % 200 == 199:
avg_loss = running_loss / float(200)
training_loss_tally.append(avg_loss)
current_time = time.perf_counter()
time_elapsed = current_time-start_time
print("[epoch:%d iter:%4d elapsed_time: %4d secs] loss: %.5f" % (epoch+1,i+1, time_elapsed,avg_loss))
accum_times.append(current_time-start_time)
FILE.write("%.3f\n" % avg_loss)
FILE.flush()
running_loss = 0.0
print("\nFinished Training\n")
self.save_model(net)
if display_train_loss:
plt.figure(figsize=(10,5))
plt.title("Training Loss vs. Iterations")
plt.plot(training_loss_tally)
plt.xlabel("iterations")
plt.ylabel("training loss")
# plt.legend()
plt.legend(["Plot of loss versus iterations"], fontsize="x-large")
plt.savefig("training_loss.png")
plt.show()
def run_code_for_training_with_TEXTnetOrder2(self, net, display_train_loss=False):
filename_for_out = "performance_numbers_" + str(self.dl_studio.epochs) + ".txt"
FILE = open(filename_for_out, 'w')
net.to(self.dl_studio.device)
## Note that the TEXTnet and TEXTnetOrder2 both produce LogSoftmax output:
criterion = nn.NLLLoss()
accum_times = []
optimizer = optim.SGD(net.parameters(),
lr=self.dl_studio.learning_rate, momentum=self.dl_studio.momentum)
start_time = time.perf_counter()
training_loss_tally = []
for epoch in range(self.dl_studio.epochs):
print("")
running_loss = 0.0
for i, data in enumerate(self.train_dataloader):
hidden = net.init_hidden().to(self.dl_studio.device)
cell_prev = net.initialize_cell().to(self.dl_studio.device)
cell_prev_2_prev = net.initialize_cell().to(self.dl_studio.device)
review_tensor,category,sentiment = data['review'], data['category'], data['sentiment']
review_tensor = review_tensor.to(self.dl_studio.device)
sentiment = sentiment.to(self.dl_studio.device)
optimizer.zero_grad()
input = torch.zeros(1,review_tensor.shape[2])
input = input.to(self.dl_studio.device)
for k in range(review_tensor.shape[1]):
input[0,:] = review_tensor[0,k]
output, hidden, cell = net(input, hidden, cell_prev_2_prev)
if k == 0:
cell_prev = cell
else:
cell_prev_2_prev = cell_prev
cell_prev = cell
loss = criterion(output, torch.argmax(sentiment,1))
running_loss += loss.item()
loss.backward()
optimizer.step()
if i % 200 == 199:
avg_loss = running_loss / float(200)
training_loss_tally.append(avg_loss)
current_time = time.perf_counter()
time_elapsed = current_time-start_time
print("[epoch:%d iter:%4d elapsed_time: %4d secs] loss: %.5f" % (epoch+1,i+1, time_elapsed,avg_loss))
accum_times.append(current_time-start_time)
FILE.write("%.3f\n" % avg_loss)
FILE.flush()
running_loss = 0.0
print("\nFinished Training\n")
self.save_model(net)
if display_train_loss:
plt.figure(figsize=(10,5))
plt.title("Training Loss vs. Iterations")
plt.plot(training_loss_tally)
plt.xlabel("iterations")
plt.ylabel("training loss")
# plt.legend()
plt.legend(["Plot of loss versus iterations"], fontsize="x-large")
plt.savefig("training_loss.png")
plt.show()
def run_code_for_training_for_text_classification_with_GRU(self, net, display_train_loss=False):
filename_for_out = "performance_numbers_" + str(self.dl_studio.epochs) + ".txt"
FILE = open(filename_for_out, 'w')
net.to(self.dl_studio.device)
## Note that the GRUnet now produces the LogSoftmax output:
criterion = nn.NLLLoss()
accum_times = []
optimizer = optim.SGD(net.parameters(),
lr=self.dl_studio.learning_rate, momentum=self.dl_studio.momentum)
start_time = time.perf_counter()
training_loss_tally = []
for epoch in range(self.dl_studio.epochs):
print("")
running_loss = 0.0
for i, data in enumerate(self.train_dataloader):
review_tensor,category,sentiment = data['review'], data['category'], data['sentiment']
review_tensor = review_tensor.to(self.dl_studio.device)
sentiment = sentiment.to(self.dl_studio.device)
## The following type conversion needed for MSELoss:
##sentiment = sentiment.float()
optimizer.zero_grad()
hidden = net.init_hidden().to(self.dl_studio.device)
for k in range(review_tensor.shape[1]):
output, hidden = net(torch.unsqueeze(torch.unsqueeze(review_tensor[0,k],0),0), hidden)
## If using NLLLoss, CrossEntropyLoss
loss = criterion(output, torch.argmax(sentiment, 1))
## If using MSELoss:
## loss = criterion(output, sentiment)
running_loss += loss.item()
loss.backward()
optimizer.step()
if i % 200 == 199:
avg_loss = running_loss / float(200)
training_loss_tally.append(avg_loss)
current_time = time.perf_counter()
time_elapsed = current_time-start_time
print("[epoch:%d iter:%4d elapsed_time:%4d secs] loss: %.5f" % (epoch+1,i+1, time_elapsed,avg_loss))
accum_times.append(current_time-start_time)
FILE.write("%.3f\n" % avg_loss)
FILE.flush()
running_loss = 0.0
print("Total Training Time: {}".format(str(sum(accum_times))))
print("\nFinished Training\n")
self.save_model(net)
if display_train_loss:
plt.figure(figsize=(10,5))
plt.title("Training Loss vs. Iterations")
plt.plot(training_loss_tally)
plt.xlabel("iterations")
plt.ylabel("training loss")
# plt.legend()
plt.legend(["Plot of loss versus iterations"], fontsize="x-large")
plt.savefig("training_loss.png")
plt.show()
def run_code_for_testing_with_TEXTnet(self, net):
net.load_state_dict(torch.load(self.dl_studio.path_saved_model))
net.to(self.dl_studio.device)
classification_accuracy = 0.0
negative_total = 0
positive_total = 0
confusion_matrix = torch.zeros(2,2)
with torch.no_grad():
for i, data in enumerate(self.test_dataloader):
review_tensor,category,sentiment = data['review'], data['category'], data['sentiment']
input = torch.zeros(1,review_tensor.shape[2]).to(self.dl_studio.device)
hidden = net.init_hidden().to(self.dl_studio.device)
for k in range(review_tensor.shape[1]):
input[0,:] = review_tensor[0,k]
output, hidden = net(input, hidden)
predicted_idx = torch.argmax(output).item()
gt_idx = torch.argmax(sentiment).item()
if i % 100 == 99:
print(" [i=%4d] predicted_label=%d gt_label=%d" % (i+1, predicted_idx,gt_idx))
if predicted_idx == gt_idx:
classification_accuracy += 1
if gt_idx == 0:
negative_total += 1
elif gt_idx == 1:
positive_total += 1
confusion_matrix[gt_idx,predicted_idx] += 1
print("\nOverall classification accuracy: %0.2f%%" % (float(classification_accuracy) * 100 /float(i)))
out_percent = np.zeros((2,2), dtype='float')
out_percent[0,0] = "%.3f" % (100 * confusion_matrix[0,0] / float(negative_total))
out_percent[0,1] = "%.3f" % (100 * confusion_matrix[0,1] / float(negative_total))
out_percent[1,0] = "%.3f" % (100 * confusion_matrix[1,0] / float(positive_total))
out_percent[1,1] = "%.3f" % (100 * confusion_matrix[1,1] / float(positive_total))
print("\n\nNumber of positive reviews tested: %d" % positive_total)
print("\n\nNumber of negative reviews tested: %d" % negative_total)
print("\n\nDisplaying the confusion matrix:\n")
out_str = " "
out_str += "%18s %18s" % ('predicted negative', 'predicted positive')
print(out_str + "\n")
for i,label in enumerate(['true negative', 'true positive']):
out_str = "%12s: " % label
for j in range(2):
out_str += "%18s" % out_percent[i,j]
print(out_str)
def run_code_for_testing_with_TEXTnetOrder2(self, net):
net.load_state_dict(torch.load(self.dl_studio.path_saved_model))
net.to(self.dl_studio.device)
classification_accuracy = 0.0
negative_total = 0
positive_total = 0
confusion_matrix = torch.zeros(2,2)
with torch.no_grad():
for i, data in enumerate(self.test_dataloader):
cell_prev = net.initialize_cell()
cell_prev_2_prev = net.initialize_cell()
review_tensor,category,sentiment = data['review'], data['category'], data['sentiment']
input = torch.zeros(1,review_tensor.shape[2]).to(self.dl_studio.device)
hidden = net.init_hidden().to(self.dl_studio.device)
for k in range(review_tensor.shape[1]):
input[0,:] = review_tensor[0,k]
output, hidden, cell = net(input, hidden, cell_prev_2_prev)
if k == 0:
cell_prev = cell
else:
cell_prev_2_prev = cell_prev
cell_prev = cell
predicted_idx = torch.argmax(output).item()
gt_idx = torch.argmax(sentiment).item()
if i % 100 == 99:
print(" [i=%4d] predicted_label=%d gt_label=%d" % (i+1, predicted_idx,gt_idx))
if predicted_idx == gt_idx:
classification_accuracy += 1
if gt_idx == 0:
negative_total += 1
elif gt_idx == 1:
positive_total += 1
confusion_matrix[gt_idx,predicted_idx] += 1
print("\nOverall classification accuracy: %0.2f%%" % (float(classification_accuracy) * 100 /float(i)))
out_percent = np.zeros((2,2), dtype='float')
out_percent[0,0] = "%.3f" % (100 * confusion_matrix[0,0] / float(negative_total))
out_percent[0,1] = "%.3f" % (100 * confusion_matrix[0,1] / float(negative_total))
out_percent[1,0] = "%.3f" % (100 * confusion_matrix[1,0] / float(positive_total))
out_percent[1,1] = "%.3f" % (100 * confusion_matrix[1,1] / float(positive_total))
print("\n\nNumber of positive reviews tested: %d" % positive_total)
print("\n\nNumber of negative reviews tested: %d" % negative_total)
print("\n\nDisplaying the confusion matrix:\n")
out_str = " "
out_str += "%18s %18s" % ('predicted negative', 'predicted positive')
print(out_str + "\n")
for i,label in enumerate(['true negative', 'true positive']):
out_str = "%12s: " % label
for j in range(2):
out_str += "%18s" % out_percent[i,j]
print(out_str)
def run_code_for_testing_text_classification_with_GRU(self, net):
net.load_state_dict(torch.load(self.dl_studio.path_saved_model))
net.to(self.dl_studio.device)
classification_accuracy = 0.0
negative_total = 0
positive_total = 0
confusion_matrix = torch.zeros(2,2)
with torch.no_grad():
for i, data in enumerate(self.test_dataloader):
review_tensor,category,sentiment = data['review'], data['category'], data['sentiment']
hidden = net.init_hidden().to(self.dl_studio.device)
for k in range(review_tensor.shape[1]):
output, hidden = net(torch.unsqueeze(torch.unsqueeze(review_tensor[0,k],0),0), hidden)
predicted_idx = torch.argmax(output).item()
gt_idx = torch.argmax(sentiment).item()
if i % 100 == 99:
print(" [i=%d] predicted_label=%d gt_label=%d\n\n" % (i+1, predicted_idx,gt_idx))
if predicted_idx == gt_idx:
classification_accuracy += 1
if gt_idx == 0:
negative_total += 1
elif gt_idx == 1:
positive_total += 1
confusion_matrix[gt_idx,predicted_idx] += 1
print("\nOverall classification accuracy: %0.2f%%" % (float(classification_accuracy) * 100 /float(i)))
out_percent = np.zeros((2,2), dtype='float')
out_percent[0,0] = "%.3f" % (100 * confusion_matrix[0,0] / float(negative_total))
out_percent[0,1] = "%.3f" % (100 * confusion_matrix[0,1] / float(negative_total))
out_percent[1,0] = "%.3f" % (100 * confusion_matrix[1,0] / float(positive_total))
out_percent[1,1] = "%.3f" % (100 * confusion_matrix[1,1] / float(positive_total))
print("\n\nNumber of positive reviews tested: %d" % positive_total)
print("\n\nNumber of negative reviews tested: %d" % negative_total)
print("\n\nDisplaying the confusion matrix:\n")
out_str = " "
out_str += "%18s %18s" % ('predicted negative', 'predicted positive')
print(out_str + "\n")
for i,label in enumerate(['true negative', 'true positive']):
out_str = "%12s: " % label
for j in range(2):
out_str += "%18s" % out_percent[i,j]
print(out_str)
###%%%
#####################################################################################################################
########################## Start Definition of Inner Class TextClassificationWithEmbeddings #######################
class TextClassificationWithEmbeddings(nn.Module):
"""
The text processing class described previously, TextClassification, was based on
using one-hot vectors for representing the words. The main challenge we faced
with one-hot vectors was that the larger the size of the training dataset, the
larger the size of the vocabulary, and, therefore, the larger the size of the
one-hot vectors. The increase in the size of the one-hot vectors led to a
model with a significantly larger number of learnable parameters --- and, that,
in turn, created a need for a still larger training dataset. Sounds like a classic
example of a vicious circle. In this section, I use the idea of word embeddings
to break out of this vicious circle.
Word embeddings are fixed-sized numerical representations for words that are
learned on the basis of the similarity of word contexts. The original and still
the most famous of these representations are known as the word2vec
embeddings. The embeddings that I use in this section consist of pre-trained
300-element word vectors for 3 million words and phrases as learned from Google
News reports. I access these embeddings through the popular Gensim library.
Class Path: DLStudio -> TextClassificationWithEmbeddings
"""
def __init__(self, dl_studio,dataserver_train=None,dataserver_test=None,dataset_file_train=None,dataset_file_test=None):
super(DLStudio.TextClassificationWithEmbeddings, self).__init__()
self.dl_studio = dl_studio
self.dataserver_train = dataserver_train
self.dataserver_test = dataserver_test
class SentimentAnalysisDataset(torch.utils.data.Dataset):
"""
In relation to the SentimentAnalysisDataset defined for the TextClassification section of
DLStudio, the __getitem__() method of the dataloader must now fetch the embeddings from
the word2vec word vectors.
Class Path: DLStudio -> TextClassificationWithEmbeddings -> SentimentAnalysisDataset
"""
def __init__(self, dl_studio, train_or_test, dataset_file, path_to_saved_embeddings=None):
super(DLStudio.TextClassificationWithEmbeddings.SentimentAnalysisDataset, self).__init__()
import gensim.downloader as gen_api
# self.word_vectors = gen_api.load("word2vec-google-news-300")
self.path_to_saved_embeddings = path_to_saved_embeddings
self.train_or_test = train_or_test
root_dir = dl_studio.dataroot
f = gzip.open(root_dir + dataset_file, 'rb')
dataset = f.read()
if path_to_saved_embeddings is not None:
import gensim.downloader as genapi
from gensim.models import KeyedVectors
if os.path.exists(path_to_saved_embeddings + 'vectors.kv'):
self.word_vectors = KeyedVectors.load(path_to_saved_embeddings + 'vectors.kv')
else:
print("""\n\nSince this is your first time to install the word2vec embeddings, it may take"""
"""\na couple of minutes. The embeddings occupy around 3.6GB of your disk space.\n\n""")
self.word_vectors = genapi.load("word2vec-google-news-300")
## 'kv' stands for "KeyedVectors", a special datatype used by gensim because it
## has a smaller footprint than dict
self.word_vectors.save(path_to_saved_embeddings + 'vectors.kv')
if train_or_test == 'train':
if sys.version_info[0] == 3:
self.positive_reviews_train, self.negative_reviews_train, self.vocab = pickle.loads(dataset, encoding='latin1')
else:
self.positive_reviews_train, self.negative_reviews_train, self.vocab = pickle.loads(dataset)
self.categories = sorted(list(self.positive_reviews_train.keys()))
self.category_sizes_train_pos = {category : len(self.positive_reviews_train[category]) for category in self.categories}
self.category_sizes_train_neg = {category : len(self.negative_reviews_train[category]) for category in self.categories}
self.indexed_dataset_train = []
for category in self.positive_reviews_train:
for review in self.positive_reviews_train[category]:
self.indexed_dataset_train.append([review, category, 1])
for category in self.negative_reviews_train:
for review in self.negative_reviews_train[category]:
self.indexed_dataset_train.append([review, category, 0])
random.shuffle(self.indexed_dataset_train)
elif train_or_test == 'test':
if sys.version_info[0] == 3:
self.positive_reviews_test, self.negative_reviews_test, self.vocab = pickle.loads(dataset, encoding='latin1')
else:
self.positive_reviews_test, self.negative_reviews_test, self.vocab = pickle.loads(dataset)
self.vocab = sorted(self.vocab)
self.categories = sorted(list(self.positive_reviews_test.keys()))
self.category_sizes_test_pos = {category : len(self.positive_reviews_test[category]) for category in self.categories}
self.category_sizes_test_neg = {category : len(self.negative_reviews_test[category]) for category in self.categories}
self.indexed_dataset_test = []
for category in self.positive_reviews_test:
for review in self.positive_reviews_test[category]:
self.indexed_dataset_test.append([review, category, 1])
for category in self.negative_reviews_test:
for review in self.negative_reviews_test[category]:
self.indexed_dataset_test.append([review, category, 0])
random.shuffle(self.indexed_dataset_test)
def review_to_tensor(self, review):
list_of_embeddings = []
for i,word in enumerate(review):
if word in self.word_vectors.key_to_index:
embedding = self.word_vectors[word]
list_of_embeddings.append(np.array(embedding))
else:
next
# review_tensor = torch.FloatTensor( list_of_embeddings )
review_tensor = torch.FloatTensor( np.array(list_of_embeddings) )
return review_tensor
def sentiment_to_tensor(self, sentiment):
"""
Sentiment is ordinarily just a binary valued thing. It is 0 for negative
sentiment and 1 for positive sentiment. We need to pack this value in a
two-element tensor.
"""
sentiment_tensor = torch.zeros(2)
if sentiment == 1:
sentiment_tensor[1] = 1
elif sentiment == 0:
sentiment_tensor[0] = 1
sentiment_tensor = sentiment_tensor.type(torch.long)
return sentiment_tensor
def __len__(self):
if self.train_or_test == 'train':
return len(self.indexed_dataset_train)
elif self.train_or_test == 'test':
return len(self.indexed_dataset_test)
def __getitem__(self, idx):
sample = self.indexed_dataset_train[idx] if self.train_or_test == 'train' else self.indexed_dataset_test[idx]
review = sample[0]
review_category = sample[1]
review_sentiment = sample[2]
review_sentiment = self.sentiment_to_tensor(review_sentiment)
review_tensor = self.review_to_tensor(review)
category_index = self.categories.index(review_category)
sample = {'review' : review_tensor,
'category' : category_index, # should be converted to tensor, but not yet used
'sentiment' : review_sentiment }
return sample
def load_SentimentAnalysisDataset(self, dataserver_train, dataserver_test ):
self.train_dataloader = torch.utils.data.DataLoader(dataserver_train,
batch_size=self.dl_studio.batch_size,shuffle=True, num_workers=2)
self.test_dataloader = torch.utils.data.DataLoader(dataserver_test,
batch_size=self.dl_studio.batch_size,shuffle=False, num_workers=2)
class TEXTnetWithEmbeddings(nn.Module):
"""
This is embeddings version of the class TEXTnet class shown previously. Since we
are using the word2vec embeddings, we know that the input size for each word vector
will be a constant value of 300. Overall, though, this network is meant for semantic
classification of variable-length sentiment data. Based on my limited testing, the
performance of this network is very poor because it has no protection against
vanishing gradients when used in an RNN.
Class Path: DLStudio -> TextClassificationWithEmbeddings -> TEXTnetWithEmbeddings
"""
def __init__(self, input_size, hidden_size, output_size):
super(DLStudio.TextClassificationWithEmbeddings.TEXTnetWithEmbeddings, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
self.combined_to_hidden = nn.Linear(input_size + hidden_size, hidden_size)
self.combined_to_middle = nn.Linear(input_size + hidden_size, 100)
self.middle_to_out = nn.Linear(100, output_size)
self.logsoftmax = nn.LogSoftmax(dim=1)
def forward(self, input, hidden):
combined = torch.cat((input, hidden), 1)
hidden = self.combined_to_hidden(combined)
hidden = torch.tanh(hidden)
out = self.combined_to_middle(combined)
out = nn.functional.relu(out)
out = self.middle_to_out(out)
out = self.logsoftmax(out)
return out,hidden
def init_hidden(self):
hidden = torch.zeros(1, self.hidden_size)
return hidden
class TEXTnetOrder2WithEmbeddings(nn.Module):
"""
This is an embeddings version of the TEXTnetOrder2 class shown previously.
With the embeddings, we know that the size the tensor for word will be 300.
As to how TEXTnetOrder2 differs from TEXTnet, the value of hidden as used at
each time step also includes its value at the previous time step. This
fact, not directly apparent by the definition of the class shown below,
is made possible by the last parameter, cell, in the header of forward().
All you can see here, at the end of forward(), is that the value of cell
goes through a linear layer and through a sigmoid nonlinearity. By the way,
since the sigmoid saturates at 0 and 1, it can act like a switch. Later
when I use this class in the training function, you will see the cell
values being used in such a manner that the hidden state at each time
step is mixed with the hidden state at the previous time step.
Class Path: DLStudio -> TextClassificationWithEmbeddings -> TEXTnetOrder2WithEmbeddings
"""
def __init__(self, hidden_size, output_size, input_size=300):
super(DLStudio.TextClassificationWithEmbeddings.TEXTnetOrder2WithEmbeddings, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
self.combined_to_hidden = nn.Linear(input_size + 2*hidden_size, hidden_size)
self.combined_to_middle = nn.Linear(input_size + 2*hidden_size, 100)
self.middle_to_out = nn.Linear(100, output_size)
self.logsoftmax = nn.LogSoftmax(dim=1)
self.dropout = nn.Dropout(p=0.1)
# for the cell
self.linear_for_cell = nn.Linear(hidden_size, hidden_size)
def forward(self, input, hidden, cell):
combined = torch.cat((input, hidden, cell), 1)
hidden = self.combined_to_hidden(combined)
hidden = torch.tanh(hidden)
out = self.combined_to_middle(combined)
out = nn.functional.relu(out)
out = self.dropout(out)
out = self.middle_to_out(out)
out = self.logsoftmax(out)
hidden_clone = hidden.clone()
# cell = torch.tanh(self.linear_for_cell(hidden_clone))
cell = torch.sigmoid(self.linear_for_cell(hidden_clone))
return out,hidden,cell
def initialize_cell(self):
weight = next(self.linear_for_cell.parameters()).data
cell = weight.new(1, self.hidden_size).zero_()
return cell
def init_hidden(self):
hidden = torch.zeros(1, self.hidden_size)
return hidden
class GRUnetWithEmbeddings(nn.Module):
"""
For this embeddings adapted version of the GRUnet shown earlier, we can assume that
the 'input_size' for a tensor representing a word is always 300.
Source: https://blog.floydhub.com/gru-with-pytorch/
with the only modification that the final output of forward() is now
routed through LogSoftmax activation.
Class Path: DLStudio -> TextClassificationWithEmbeddings -> GRUnetWithEmbeddings
"""
def __init__(self, input_size, hidden_size, output_size, num_layers=1):
"""
-- input_size is the size of the tensor for each word in a sequence of words. If you word2vec
embedding, the value of this variable will always be equal to 300.
-- hidden_size is the size of the hidden state in the RNN
-- output_size is the size of output of the RNN. For binary classification of
input text, output_size is 2.
-- num_layers creates a stack of GRUs
"""
super(DLStudio.TextClassificationWithEmbeddings.GRUnetWithEmbeddings, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.gru = nn.GRU(input_size, hidden_size, num_layers)
self.fc = nn.Linear(hidden_size, output_size)
self.relu = nn.ReLU()
self.logsoftmax = nn.LogSoftmax(dim=1)
def forward(self, x, h):
out, h = self.gru(x, h)
out = self.fc(self.relu(out[:,-1]))
out = self.logsoftmax(out)
return out, h
def init_hidden(self):
weight = next(self.parameters()).data
# num_layers batch_size hidden_size
hidden = weight.new( 2, 1, self.hidden_size ).zero_()
return hidden
def save_model(self, model):
"Save the trained model to a disk file"
torch.save(model.state_dict(), self.dl_studio.path_saved_model)
def run_code_for_training_with_TEXTnet_word2vec(self, net, display_train_loss=False):
filename_for_out = "performance_numbers_" + str(self.dl_studio.epochs) + ".txt"
FILE = open(filename_for_out, 'w')
net = copy.deepcopy(net)
net = net.to(self.dl_studio.device)
## Note that the TEXTnet and TEXTnetOrder2 both produce LogSoftmax output. So we
## use nn.NLLLoss. The combined effect of LogSoftMax and NLLLoss is the same as
## for the CrossEntropyLoss
criterion = nn.NLLLoss()
accum_times = []
optimizer = optim.SGD(net.parameters(),
lr=self.dl_studio.learning_rate, momentum=self.dl_studio.momentum)
start_time = time.perf_counter()
training_loss_tally = []
for epoch in range(self.dl_studio.epochs):
print("")
running_loss = 0.0
for i, data in enumerate(self.train_dataloader):
hidden = net.init_hidden().to(self.dl_studio.device)
review_tensor,category,sentiment = data['review'], data['category'], data['sentiment']
review_tensor = review_tensor.to(self.dl_studio.device)
sentiment = sentiment.to(self.dl_studio.device)
optimizer.zero_grad()
input = torch.zeros(1,review_tensor.shape[2]).to(self.dl_studio.device)
for k in range(review_tensor.shape[1]):
input[0,:] = review_tensor[0,k]
output, hidden = net(input, hidden)
loss = criterion(output, torch.argmax(sentiment,1))
running_loss += loss.item()
loss.backward(retain_graph=True)
optimizer.step()
if i % 200 == 199:
avg_loss = running_loss / float(200)
training_loss_tally.append(avg_loss)
running_loss = 0.0
current_time = time.perf_counter()
time_elapsed = current_time-start_time
print("[epoch:%d iter:%4d elapsed_time: %4d secs] loss: %.5f" % (epoch+1,i+1, time_elapsed,avg_loss))
accum_times.append(current_time-start_time)
FILE.write("%.3f\n" % avg_loss)
FILE.flush()
print("\nFinished Training\n\n")
self.save_model(net)
if display_train_loss:
plt.figure(figsize=(10,5))
plt.title("Training Loss vs. Iterations")
plt.plot(training_loss_tally)
plt.xlabel("iterations")
plt.ylabel("training loss")
# plt.legend()
plt.legend(["Plot of loss versus iterations"], fontsize="x-large")
plt.savefig("training_loss.png")
plt.show()
def run_code_for_training_with_TEXTnetOrder2_word2vec(self, net, display_train_loss=False):
filename_for_out = "performance_numbers_" + str(self.dl_studio.epochs) + ".txt"
FILE = open(filename_for_out, 'w')
net = copy.deepcopy(net)
net.to(self.dl_studio.device)
## Note that the TEXTnet and TEXTnetOrder2 both produce LogSoftmax output:
criterion = nn.NLLLoss()
accum_times = []
optimizer = optim.SGD(net.parameters(),
lr=self.dl_studio.learning_rate, momentum=self.dl_studio.momentum)
start_time = time.perf_counter()
training_loss_tally = []
for epoch in range(self.dl_studio.epochs):
print("")
running_loss = 0.0
for i, data in enumerate(self.train_dataloader):
cell_prev = net.initialize_cell().to(self.dl_studio.device)
cell_prev_2_prev = net.initialize_cell().to(self.dl_studio.device)
hidden = net.init_hidden().to(self.dl_studio.device)
review_tensor,category,sentiment = data['review'], data['category'], data['sentiment']
review_tensor = review_tensor.to(self.dl_studio.device)
sentiment = sentiment.to(self.dl_studio.device)
optimizer.zero_grad()
input = torch.zeros(1,review_tensor.shape[2])
input = input.to(self.dl_studio.device)
for k in range(review_tensor.shape[1]):
input[0,:] = review_tensor[0,k]
output, hidden, cell = net(input, hidden, cell_prev_2_prev)
if k == 0:
cell_prev = cell
else:
cell_prev_2_prev = cell_prev
cell_prev = cell
loss = criterion(output, torch.argmax(sentiment,1))
running_loss += loss.item()
loss.backward()
optimizer.step()
if i % 200 == 199:
avg_loss = running_loss / float(200)
training_loss_tally.append(avg_loss)
current_time = time.perf_counter()
time_elapsed = current_time-start_time
print("[epoch:%d iter:%4d elapsed_time: %4d secs] loss: %.5f" % (epoch+1,i+1, time_elapsed,avg_loss))
accum_times.append(current_time-start_time)
FILE.write("%.3f\n" % avg_loss)
FILE.flush()
running_loss = 0.0
print("\nFinished Training\n")
self.save_model(net)
if display_train_loss:
plt.figure(figsize=(10,5))
plt.title("Training Loss vs. Iterations")
plt.plot(training_loss_tally)
plt.xlabel("iterations")
plt.ylabel("training loss")
# plt.legend()
plt.legend(["Plot of loss versus iterations"], fontsize="x-large")
plt.savefig("training_loss.png")
plt.show()
def run_code_for_training_for_text_classification_with_GRU_word2vec(self, net, display_train_loss=False):
filename_for_out = "performance_numbers_" + str(self.dl_studio.epochs) + ".txt"
FILE = open(filename_for_out, 'w')
net = copy.deepcopy(net)
net = net.to(self.dl_studio.device)
## Note that the GRUnet now produces the LogSoftmax output:
criterion = nn.NLLLoss()
accum_times = []
optimizer = optim.SGD(net.parameters(),
lr=self.dl_studio.learning_rate, momentum=self.dl_studio.momentum)
training_loss_tally = []
start_time = time.perf_counter()
for epoch in range(self.dl_studio.epochs):
print("")
running_loss = 0.0
for i, data in enumerate(self.train_dataloader):
review_tensor,category,sentiment = data['review'], data['category'], data['sentiment']
review_tensor = review_tensor.to(self.dl_studio.device)
sentiment = sentiment.to(self.dl_studio.device)
## The following type conversion needed for MSELoss:
##sentiment = sentiment.float()
optimizer.zero_grad()
hidden = net.init_hidden().to(self.dl_studio.device)
for k in range(review_tensor.shape[1]):
output, hidden = net(torch.unsqueeze(torch.unsqueeze(review_tensor[0,k],0),0), hidden)
loss = criterion(output, torch.argmax(sentiment, 1))
running_loss += loss.item()
loss.backward()
optimizer.step()
if i % 200 == 199:
avg_loss = running_loss / float(200)
training_loss_tally.append(avg_loss)
current_time = time.perf_counter()
time_elapsed = current_time-start_time
print("[epoch:%d iter:%4d elapsed_time:%4d secs] loss: %.5f" % (epoch+1,i+1, time_elapsed,avg_loss))
accum_times.append(current_time-start_time)
FILE.write("%.5f\n" % avg_loss)
FILE.flush()
running_loss = 0.0
self.save_model(net)
print("Total Training Time: {}".format(str(sum(accum_times))))
print("\nFinished Training\n\n")
if display_train_loss:
plt.figure(figsize=(10,5))
plt.title("Training Loss vs. Iterations")
plt.plot(training_loss_tally)
plt.xlabel("iterations")
plt.ylabel("training loss")
# plt.legend()
plt.legend(["Plot of loss versus iterations"], fontsize="x-large")
plt.savefig("training_loss.png")
plt.show()
def run_code_for_testing_with_TEXTnet_word2vec(self, net):
net.load_state_dict(torch.load(self.dl_studio.path_saved_model))
net.to(self.dl_studio.device)
classification_accuracy = 0.0
negative_total = 0
positive_total = 0
confusion_matrix = torch.zeros(2,2)
with torch.no_grad():
for i, data in enumerate(self.test_dataloader):
review_tensor,category,sentiment = data['review'], data['category'], data['sentiment']
review_tensor = review_tensor.to(self.dl_studio.device)
category = category.to(self.dl_studio.device)
sentiment = sentiment.to(self.dl_studio.device)
input = torch.zeros(1,review_tensor.shape[2]).to(self.dl_studio.device)
hidden = net.init_hidden().to(self.dl_studio.device)
for k in range(review_tensor.shape[1]):
input[0,:] = review_tensor[0,k]
output, hidden = net(input, hidden)
predicted_idx = torch.argmax(output).item()
gt_idx = torch.argmax(sentiment).item()
if i % 100 == 99:
print(" [i=%4d] predicted_label=%d gt_label=%d" % (i+1, predicted_idx,gt_idx))
if predicted_idx == gt_idx:
classification_accuracy += 1
if gt_idx == 0:
negative_total += 1
elif gt_idx == 1:
positive_total += 1
confusion_matrix[gt_idx,predicted_idx] += 1
print("\nOverall classification accuracy: %0.2f%%" % (float(classification_accuracy) * 100 /float(i)))
out_percent = np.zeros((2,2), dtype='float')
out_percent[0,0] = "%.3f" % (100 * confusion_matrix[0,0] / float(negative_total))
out_percent[0,1] = "%.3f" % (100 * confusion_matrix[0,1] / float(negative_total))
out_percent[1,0] = "%.3f" % (100 * confusion_matrix[1,0] / float(positive_total))
out_percent[1,1] = "%.3f" % (100 * confusion_matrix[1,1] / float(positive_total))
print("\n\nNumber of positive reviews tested: %d" % positive_total)
print("\n\nNumber of negative reviews tested: %d" % negative_total)
print("\n\nDisplaying the confusion matrix:\n")
out_str = " "
out_str += "%18s %18s" % ('predicted negative', 'predicted positive')
print(out_str + "\n")
for i,label in enumerate(['true negative', 'true positive']):
out_str = "%12s%%: " % label
for j in range(2):
out_str += "%18s%%" % out_percent[i,j]
print(out_str)
def run_code_for_testing_with_TEXTnetOrder2_word2vec(self, net):
net.load_state_dict(torch.load(self.dl_studio.path_saved_model))
net.to(self.dl_studio.device)
classification_accuracy = 0.0
negative_total = 0
positive_total = 0
confusion_matrix = torch.zeros(2,2)
with torch.no_grad():
for i, data in enumerate(self.test_dataloader):
cell_prev = net.initialize_cell()
cell_prev_2_prev = net.initialize_cell()
review_tensor,category,sentiment = data['review'], data['category'], data['sentiment']
input = torch.zeros(1,review_tensor.shape[2]).to(self.dl_studio.device)
hidden = net.init_hidden().to(self.dl_studio.device)
for k in range(review_tensor.shape[1]):
input[0,:] = review_tensor[0,k]
output, hidden, cell = net(input, hidden, cell_prev_2_prev)
if k == 0:
cell_prev = cell
else:
cell_prev_2_prev = cell_prev
cell_prev = cell
predicted_idx = torch.argmax(output).item()
gt_idx = torch.argmax(sentiment).item()
if i % 100 == 99:
print(" [i=%4d] predicted_label=%d gt_label=%d" % (i+1, predicted_idx,gt_idx))
if predicted_idx == gt_idx:
classification_accuracy += 1
if gt_idx == 0:
negative_total += 1
elif gt_idx == 1:
positive_total += 1
confusion_matrix[gt_idx,predicted_idx] += 1
print("\nOverall classification accuracy: %0.2f%%" % (float(classification_accuracy) * 100 /float(i)))
out_percent = np.zeros((2,2), dtype='float')
out_percent[0,0] = "%.3f" % (100 * confusion_matrix[0,0] / float(negative_total))
out_percent[0,1] = "%.3f" % (100 * confusion_matrix[0,1] / float(negative_total))
out_percent[1,0] = "%.3f" % (100 * confusion_matrix[1,0] / float(positive_total))
out_percent[1,1] = "%.3f" % (100 * confusion_matrix[1,1] / float(positive_total))
print("\n\nNumber of positive reviews tested: %d" % positive_total)
print("\n\nNumber of negative reviews tested: %d" % negative_total)
print("\n\nDisplaying the confusion matrix:\n")
out_str = " "
out_str += "%18s %18s" % ('predicted negative', 'predicted positive')
print(out_str + "\n")
for i,label in enumerate(['true negative', 'true positive']):
out_str = "%12s: " % label
for j in range(2):
out_str += "%18s" % out_percent[i,j]
print(out_str)
def run_code_for_testing_text_classification_with_GRU_word2vec(self, net):
net.load_state_dict(torch.load(self.dl_studio.path_saved_model))
classification_accuracy = 0.0
negative_total = 0
positive_total = 0
confusion_matrix = torch.zeros(2,2)
with torch.no_grad():
for i, data in enumerate(self.test_dataloader):
review_tensor,category,sentiment = data['review'], data['category'], data['sentiment']
hidden = net.init_hidden()
for k in range(review_tensor.shape[1]):
output, hidden = net(torch.unsqueeze(torch.unsqueeze(review_tensor[0,k],0),0), hidden)
predicted_idx = torch.argmax(output).item()
gt_idx = torch.argmax(sentiment).item()
if i % 100 == 99:
print(" [i=%d] predicted_label=%d gt_label=%d" % (i+1, predicted_idx,gt_idx))
if predicted_idx == gt_idx:
classification_accuracy += 1
if gt_idx == 0:
negative_total += 1
elif gt_idx == 1:
positive_total += 1
confusion_matrix[gt_idx,predicted_idx] += 1
print("\nOverall classification accuracy: %0.2f%%" % (float(classification_accuracy) * 100 /float(i)))
out_percent = np.zeros((2,2), dtype='float')
out_percent[0,0] = "%.3f" % (100 * confusion_matrix[0,0] / float(negative_total))
out_percent[0,1] = "%.3f" % (100 * confusion_matrix[0,1] / float(negative_total))
out_percent[1,0] = "%.3f" % (100 * confusion_matrix[1,0] / float(positive_total))
out_percent[1,1] = "%.3f" % (100 * confusion_matrix[1,1] / float(positive_total))
print("\n\nNumber of positive reviews tested: %d" % positive_total)
print("\n\nNumber of negative reviews tested: %d" % negative_total)
print("\n\nDisplaying the confusion matrix:\n")
out_str = " "
out_str += "%18s %18s" % ('predicted negative', 'predicted positive')
print(out_str + "\n")
for i,label in enumerate(['true negative', 'true positive']):
out_str = "%12s: " % label
for j in range(2):
out_str += "%18s%%" % out_percent[i,j]
print(out_str)
#_________________________ End of DLStudio Class Definition ___________________________
#______________________________ Test code follows _________________________________
if __name__ == '__main__':
pass