diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100644 index 000000000..13566b81b --- /dev/null +++ b/.idea/.gitignore @@ -0,0 +1,8 @@ +# Default ignored files +/shelf/ +/workspace.xml +# Editor-based HTTP Client requests +/httpRequests/ +# Datasource local storage ignored files +/dataSources/ +/dataSources.local.xml diff --git a/.idea/PatternAnalysis-2023.iml b/.idea/PatternAnalysis-2023.iml new file mode 100644 index 000000000..136187df6 --- /dev/null +++ b/.idea/PatternAnalysis-2023.iml @@ -0,0 +1,14 @@ + + + + + + + + + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml new file mode 100644 index 000000000..c2745e30b --- /dev/null +++ b/.idea/inspectionProfiles/Project_Default.xml @@ -0,0 +1,41 @@ + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 000000000..105ce2da2 --- /dev/null +++ b/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 000000000..a971a2c93 --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,4 @@ + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100644 index 000000000..b55eb140b --- /dev/null +++ b/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml new file mode 100644 index 000000000..35eb1ddfb --- /dev/null +++ b/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 261eeb9e9..000000000 --- a/LICENSE +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. 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We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright [yyyy] [name of copyright owner] - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. diff --git a/README.md b/README.md deleted file mode 100644 index 4a064f841..000000000 --- a/README.md +++ /dev/null @@ -1,15 +0,0 @@ -# Pattern Analysis -Pattern Analysis of various datasets by COMP3710 students at the University of Queensland. - -We create pattern recognition and image processing library for Tensorflow (TF), PyTorch or JAX. - -This library is created and maintained by The University of Queensland [COMP3710](https://my.uq.edu.au/programs-courses/course.html?course_code=comp3710) students. - -The library includes the following implemented in Tensorflow: -* fractals -* recognition problems - -In the recognition folder, you will find many recognition problems solved including: -* OASIS brain segmentation -* Classification -etc. diff --git a/recognition/__init__.py b/recognition/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/recognition/s4627182_Improved_Unet/Data_Loader.py b/recognition/s4627182_Improved_Unet/Data_Loader.py new file mode 100644 index 000000000..a88090fda --- /dev/null +++ b/recognition/s4627182_Improved_Unet/Data_Loader.py @@ -0,0 +1,116 @@ +from __future__ import print_function, division +import os +from PIL import Image +import torch +import torch.utils.data +import torchvision +from skimage import io +from torch.utils.data import Dataset +import random +import numpy as np + + +class Images_Dataset(Dataset): + """Class for getting data as a Dict + Args: + images_dir = path of input images + labels_dir = path of labeled images + transformI = Input Images transformation (default: None) + transformM = Input Labels transformation (default: None) + Output: + sample : Dict of images and labels""" + + def __init__(self, images_dir, labels_dir, transformI=None, transformM=None): + + self.labels_dir = labels_dir + self.images_dir = images_dir + self.transformI = transformI + self.transformM = transformM + + def __len__(self): + return len(self.images_dir) + + def __getitem__(self, idx): + + for i in range(len(self.images_dir)): + image = io.imread(self.images_dir[i]) + label = io.imread(self.labels_dir[i]) + if self.transformI: + image = self.transformI(image) + if self.transformM: + label = self.transformM(label) + sample = {'images': image, 'labels': label} + + return sample + + +class Images_Dataset_folder(torch.utils.data.Dataset): + """Class for getting individual transformations and data + Args: + images_dir = path of input images + labels_dir = path of labeled images + transformI = Input Images transformation (default: None) + transformM = Input Labels transformation (default: None) + Output: + tx = Transformed images + lx = Transformed labels""" + + def __init__(self, images_dir, labels_dir, transformI=None, transformM=None): + self.images = sorted(os.listdir(images_dir)) + self.labels = sorted(os.listdir(labels_dir)) + self.images_dir = images_dir + self.labels_dir = labels_dir + self.transformI = transformI + self.transformM = transformM + + if self.transformI: + self.tx = self.transformI + else: + self.tx = torchvision.transforms.Compose([ + torchvision.transforms.Resize((128, 128)), + # torchvision.transforms.CenterCrop(96), + torchvision.transforms.RandomRotation((-10,10)), + # torchvision.transforms.RandomHorizontalFlip(), + # torchvision.transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4), + torchvision.transforms.ToTensor(), + torchvision.transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) + ]) + + if self.transformM: + self.lx = self.transformM + else: + self.lx = torchvision.transforms.Compose([ + torchvision.transforms.Resize((128, 128)), + # torchvision.transforms.CenterCrop(96), + # torchvision.transforms.RandomRotation((-10,10)), + # torchvision.transforms.Grayscale(), + torchvision.transforms.ToTensor(), + # torchvision.transforms.Lambda(lambda x: torch.cat([x, 1 - x], dim=0)) + ]) + + def __len__(self): + + return len(self.images) + + def __getitem__(self, i): + i1 = Image.open(self.images_dir + self.images[i]).convert("RGB") + l1 = Image.open(self.labels_dir + self.labels[i]).convert("L") + # np_i1 = np.array(i1) + # np_l1 = np.array(l1) + # print(np_i1.shape) + # print(np_l1.shape) + + seed = np.random.randint(0, 2 ** 32) # make a seed with numpy generator + + # apply this seed to img tranfsorms + random.seed(seed) + torch.manual_seed(seed) + img = self.tx(i1) + + # apply this seed to target/label tranfsorms + random.seed(seed) + torch.manual_seed(seed) + label = self.lx(l1) + + return img, label + diff --git a/recognition/s4627182_Improved_Unet/LICENSE b/recognition/s4627182_Improved_Unet/LICENSE new file mode 100644 index 000000000..94a9ed024 --- /dev/null +++ b/recognition/s4627182_Improved_Unet/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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But first, please read +. diff --git a/recognition/s4627182_Improved_Unet/Losses.py b/recognition/s4627182_Improved_Unet/Losses.py new file mode 100644 index 000000000..afbc7c3f3 --- /dev/null +++ b/recognition/s4627182_Improved_Unet/Losses.py @@ -0,0 +1,60 @@ +# From https://github.com/bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets +from __future__ import print_function, division +import torch.nn.functional as F +def dice_loss(prediction, target): + """Calculating the dice loss + Args: + prediction = predicted image + target = Targeted image + Output: + dice_loss""" + + smooth = 1.0 + + i_flat = prediction.view(-1) + t_flat = target.view(-1) + + intersection = (i_flat * t_flat).sum() + + return 1 - ((2. * intersection + smooth) / (i_flat.sum() + t_flat.sum() + smooth)) + + +def calc_loss(prediction, target, bce_weight=0.5): + """Calculating the loss and metrics + Args: + prediction = predicted image + target = Targeted image + metrics = Metrics printed + bce_weight = 0.5 (default) + Output: + loss : dice loss of the epoch """ + bce = F.binary_cross_entropy_with_logits(prediction, target) + prediction = F.sigmoid(prediction) + dice = dice_loss(prediction, target) + + loss = bce * bce_weight + dice * (1 - bce_weight) + + return loss + + +def threshold_predictions_v(predictions, thr=150): + thresholded_preds = predictions[:] + # hist = cv2.calcHist([predictions], [0], None, [2], [0, 2]) + # plt.plot(hist) + # plt.xlim([0, 2]) + # plt.show() + low_values_indices = thresholded_preds < thr + thresholded_preds[low_values_indices] = 0 + low_values_indices = thresholded_preds >= thr + thresholded_preds[low_values_indices] = 255 + return thresholded_preds + + +def threshold_predictions_p(predictions, thr=0.01): + thresholded_preds = predictions[:] + #hist = cv2.calcHist([predictions], [0], None, [256], [0, 256]) + low_values_indices = thresholded_preds < thr + thresholded_preds[low_values_indices] = 0 + low_values_indices = thresholded_preds >= thr + thresholded_preds[low_values_indices] = 1 + return thresholded_preds \ No newline at end of file diff --git a/recognition/s4627182_Improved_Unet/Metrics.py b/recognition/s4627182_Improved_Unet/Metrics.py new file mode 100644 index 000000000..a0f7c5d43 --- /dev/null +++ b/recognition/s4627182_Improved_Unet/Metrics.py @@ -0,0 +1,56 @@ +# From https://github.com/bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets + + + +import numpy as np +from scipy import spatial + + +def dice_coeff(im1, im2, empty_score=1.0): + """Calculates the dice coefficient for the images""" + + im1 = np.asarray(im1).astype(np.bool) + im2 = np.asarray(im2).astype(np.bool) + + if im1.shape != im2.shape: + raise ValueError("Shape mismatch: im1 and im2 must have the same shape.") + + im1 = im1 > 0.5 + im2 = im2 > 0.5 + + im_sum = im1.sum() + im2.sum() + if im_sum == 0: + return empty_score + + # Compute Dice coefficient + intersection = np.logical_and(im1, im2) + #print(im_sum) + + return 2. * intersection.sum() / im_sum + + +def numeric_score(prediction, groundtruth): + """Computes scores: + FP = False Positives + FN = False Negatives + TP = True Positives + TN = True Negatives + return: FP, FN, TP, TN""" + + FP = np.float(np.sum((prediction == 1) & (groundtruth == 0))) + FN = np.float(np.sum((prediction == 0) & (groundtruth == 1))) + TP = np.float(np.sum((prediction == 1) & (groundtruth == 1))) + TN = np.float(np.sum((prediction == 0) & (groundtruth == 0))) + + return FP, FN, TP, TN + + +def accuracy_score(prediction, groundtruth): + """Getting the accuracy of the model""" + + FP, FN, TP, TN = numeric_score(prediction, groundtruth) + N = FP + FN + TP + TN + # accuracy = np.divide(TP + TN, N) + print("2*TP: ", 2*TP, "FP+2*TP+FN: ", FP+2*TP+FN) + accuracy = np.divide(2*TP, FP+2*TP+FN) + return accuracy #* 100.0 \ No newline at end of file diff --git a/recognition/s4627182_Improved_Unet/Modules.py b/recognition/s4627182_Improved_Unet/Modules.py new file mode 100644 index 000000000..4bc70e28f --- /dev/null +++ b/recognition/s4627182_Improved_Unet/Modules.py @@ -0,0 +1,312 @@ +""" Full assembly of the parts to form the complete network """ +from __future__ import print_function, division +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.data +import torch + + +class ContextModule(nn.Module): + """ + Context Module: Consists of two convolutional layers for feature extraction and a dropout layer + for regularization, aimed at capturing and preserving the context information in the features. + """ + + def __init__(self, in_channels, out_channels): + """ + Initialize the Context Module. + + Parameters: + - in_channels (int): Number of input channels. + - out_channels (int): Number of output channels. + """ + + super(ContextModule, self).__init__() + # 2 3x3 convolution layer followed by instance normalization and leaky ReLU activation + self.conv1 = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True), + ) + self.conv2 = nn.Sequential( + nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True), + ) + # Dropout layer to prevent overfitting + self.dropout = nn.Dropout2d(p=0.3) + + def forward(self, x): + """ + Forward pass through the context module. Input is put through 2 3x3 stride 1 convolutions with a dropout + layer in between + + Parameters: + - x (Tensor): The input tensor. + + Returns: + - Tensor: The output tensor after passing through the context module. + """ + x = self.conv1(x) + x = self.dropout(x) + x = self.conv2(x) + return x + + +class SegmentationLayer(nn.Module): + """ + SegmentationLayer: A convolutional layer specifically utilized to generate a segmentation map. + """ + + def __init__(self, in_channels, out_channels): + """ + Initialize the SegmentationLayer. + + Parameters: + - in_channels (int): Number of input channels. + - out_channels (int): Number of output channels, often equal to the number of classes in segmentation. + """ + super(SegmentationLayer, self).__init__() + # A convolutional layer that produces segmentation map + self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + + def forward(self, x): + """ + Forward pass through the SegmentationLayer. + + Parameters: + - x (Tensor): The input tensor. + + Returns: + - Tensor: The output tensor after applying the convolution, serving as a segmentation map. + """ + # Applying convolution + x = self.conv(x) + return x + + +class UpscalingLayer(nn.Module): + """ + UpscalingLayer: A layer designed to upscale feature maps by a factor of 2. + """ + + def __init__(self, scale_factor=2, mode='nearest'): + """ + Initialize the UpscalingLayer. + + Parameters: + - scale_factor (int, optional): Factor by which to upscale the input. Default is 2. + - mode (str, optional): Algorithm used for upscaling: 'nearest', 'bilinear', etc. Default is 'nearest'. + """ + super(UpscalingLayer, self).__init__() + # An upsampling layer that increases the spatial dimensions of the feature map + self.upsample = nn.Upsample(scale_factor=scale_factor, mode=mode) + + def forward(self, x): + """ + Forward pass through the UpscalingLayer. + + Parameters: + - x (Tensor): The input tensor. + + Returns: + - Tensor: The output tensor after applying the upscaling, having increased spatial dimensions. + """ + # Applying upscaling + x = self.upsample(x) + return x + + +class LocalisationModule(nn.Module): + """ + Localisation Module: Focused on up-sampling the received feature map and reducing the + number of feature channels, working towards recovering the spatial resolution of the input data. + """ + + def __init__(self, in_channels, out_channels): + """ + Initialize the Localisation Module. + + Parameters: + - in_channels (int): Number of input channels. + - out_channels (int): Number of output channels. + """ + super(LocalisationModule, self).__init__() + # Using a simple upscale by repeating the feature pixels twice + self.upsample = nn.Upsample(scale_factor=2, mode='nearest') + # 3x3 convolution to process concatenated features + self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) + # 1x1 convolution to reduce the number of feature maps + self.conv2 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + + def forward(self, x): + """ + Forward pass through the localisation module. Input is put through 2 3x3 stride 1 convolutions + with leaky ReLU applied in between + + Parameters: + - x (Tensor): The input tensor. + + Returns: + - Tensor: The output tensor after passing through the localisation module. + """ + x = self.conv1(x) + x = F.relu(x) + x = self.conv2(x) + x = F.relu(x) + return x + + +class UpsamplingModule(nn.Module): + """ + Upsampling Module: Handles the up-sampling of feature maps in the decoder part of the UNet, + contributing to incrementing the spatial dimensions of the input feature map. + """ + + def __init__(self, in_channels, out_channels): + """ + Initialize the Upsampling Module. + + Parameters: + - in_channels (int): Number of input channels. + - out_channels (int): Number of output channels. + """ + super(UpsamplingModule, self).__init__() + # Using a simple upscale by repeating the feature pixels twice + self.upsample = nn.Upsample(scale_factor=2, mode='nearest') + # 3x3 convolution that halves the number of feature maps + self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + + def forward(self, x): + """ + Forward pass through the upsampling module. First the input is upsampled, then undergoes stride 1 + 3x3 convolution followed by leaky ReLU. + + Parameters: + - x (Tensor): The input tensor. + + Returns: + - Tensor: The output tensor after passing through the upsampling module. + """ + x = self.upsample(x) + x = self.conv(x) + x = F.relu(x) + + return x + + +class UNet_For_Brain(nn.Module): + """ + UNet2D: An Improved U-Net model implmented as the provided Improved U-Net paper + """ + + def __init__(self, in_channels, num_classes=2): + """ + Initialize the UNet2D model. + + Parameters: + - in_channels (int): Number of input channels. + - num_classes (int): Number of output classes for segmentation. + """ + super(UNet_For_Brain, self).__init__() + + # Encoder + self.enc1 = nn.Conv2d(in_channels, 16, kernel_size=3, stride=1, padding=1) + self.context1 = ContextModule(16, 16) + self.enc2 = nn.Conv2d(16, 32, kernel_size=3, stride=2, padding=1) + self.context2 = ContextModule(32, 32) + self.enc3 = nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1) + self.context3 = ContextModule(64, 64) + self.enc4 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1) + self.context4 = ContextModule(128, 128) + + # Bottleneck + self.bottleneck = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1) + self.bottleneck_context = ContextModule(256, 256) + self.up_bottleneck = UpsamplingModule(256, 128) + + # Decoder + self.local1 = LocalisationModule(256, 128) + self.up1 = UpsamplingModule(128, 64) + + self.local2 = LocalisationModule(128, 64) + self.up2 = UpsamplingModule(64, 32) + + self.seg1 = SegmentationLayer(64, num_classes) + self.upsample_seg1 = UpscalingLayer() + + self.local3 = LocalisationModule(64, 32) + self.up3 = UpsamplingModule(32, 16) + + self.seg2 = SegmentationLayer(32, num_classes) + self.upsample_seg2 = UpscalingLayer() + + self.final_conv = nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1) + + self.seg3 = SegmentationLayer(32, num_classes) + self.upsample_seg3 = UpscalingLayer() + + def forward(self, x): + """ + Define the forward pass through the UNet2D model. + + Parameters: + - x (Tensor): Input tensor. + + Returns: + - Tensor: The output tensor after passing through the model. + """ + y1 = self.enc1(x) + x1 = self.context1(y1) + x1 = x1 + y1 + + y2 = self.enc2(x1) + x2 = self.context2(y2) + x2 = x2 + y2 + + y3 = self.enc3(x2) + x3 = self.context3(y3) + x3 = x3 + y3 + + y4 = self.enc4(x3) + x4 = self.context4(y4) + x4 = x4 + y4 + + # Bottleneck + bottleneck_conv = self.bottleneck(x4) + + bottleneck = self.bottleneck_context(bottleneck_conv) + bottleneck = bottleneck + bottleneck_conv + + up_bottleneck = self.up_bottleneck(bottleneck) + + # Decoder + x = self.local1(torch.cat((x4, up_bottleneck), dim=1)) + x = self.up1(x) + + x = self.local2(torch.cat((x3, x), dim=1)) + seg1 = self.seg1(x) + x = self.up2(x) + + seg1_upsampled = self.upsample_seg1(seg1) + + x = self.local3(torch.cat((x2, x), dim=1)) + seg2 = self.seg2(x) + x = self.up3(x) + + seg12 = seg1_upsampled + seg2 + seg12_up = self.upsample_seg2(seg12) + + x = self.final_conv(torch.cat((x1, x), dim=1)) + + seg3 = self.seg3(x) + seg123 = seg3 + seg12_up + + out = seg123 + # out = nn.functional.softmax(seg123, dim=1) + # out = torch.sigmoid(seg123) + # print("out shape: ", out.size()) + + return out + +# Updated diff --git a/recognition/s4627182_Improved_Unet/Ploting.py b/recognition/s4627182_Improved_Unet/Ploting.py new file mode 100644 index 000000000..691b40f2d --- /dev/null +++ b/recognition/s4627182_Improved_Unet/Ploting.py @@ -0,0 +1,210 @@ +# From https://github.com/bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets + +import matplotlib.pyplot as plt +from matplotlib.lines import Line2D +import numpy as np +from visdom import Visdom + + +def draw_loss(Loss_list,epoch,pic_name): + plt.cla() + x1 = [i for i in range(8)] + y1 = Loss_list + plt.title('Train loss vs. epoches', fontsize=1) + plt.plot(x1, y1, '.-') + plt.xlabel('epoches', fontsize=1) + plt.ylabel('Train loss', fontsize=1) + plt.grid() + plt.savefig(pic_name) + plt.show() + +def show_images(images, labels): + """Show image with label + Args: + images = input images + labels = input labels + Output: + plt = concatenated image and label """ + + plt.imshow(images.permute(1, 2, 0)) + plt.imshow(labels, alpha=0.7, cmap='gray') + plt.figure() + + +def show_training_dataset(training_dataset): + """Showing the images in training set for dict images and labels + Args: + training_dataset = dictionary of images and labels + Output: + figure = 3 images shown""" + + if training_dataset: + print(len(training_dataset)) + + for i in range(len(training_dataset)): + sample = training_dataset[i] + + print(i, sample['images'].shape, sample['labels'].shape) + + ax = plt.subplot(1, 4, i + 1) + plt.tight_layout() + ax.set_title('Sample #{}'.format(i)) + ax.axis('off') + show_images(sample['images'],sample['labels']) + + if i == 3: + plt.show() + break + +class VisdomLinePlotter(object): + + """Plots to Visdom""" + + def __init__(self, env_name='main'): + self.viz = Visdom() + self.env = env_name + self.plots = {} + + def plot(self, var_name, split_name, title_name, x, y): + if var_name not in self.plots: + self.plots[var_name] = self.viz.line(X=np.array([x,x]), Y=np.array([y,y]), env=self.env, opts=dict( + legend=[split_name], + title=title_name, + xlabel='Epochs', + ylabel=var_name + )) + else: + self.viz.line(X=np.array([x]), Y=np.array([y]), env=self.env, win=self.plots[var_name], name=split_name, update = 'append') + + +def input_images(x, y, i, n_iter, k=1): + """ + + :param x: takes input image + :param y: take input label + :param i: the epoch number + :param n_iter: + :param k: for keeping it in loop + :return: Returns a image and label + """ + if k == 1: + x1 = x + y1 = y + + x2 = x1.to('cpu') + y2 = y1.to('cpu') + x2 = x2.detach().numpy() + y2 = y2.detach().numpy() + + x3 = x2[1, 1, :, :] + y3 = y2[1, 0, :, :] + + fig = plt.figure() + + ax1 = fig.add_subplot(1, 2, 1) + ax1.imshow(x3) + ax1.axis('off') + ax1.set_xticklabels([]) + ax1.set_yticklabels([]) + ax1 = fig.add_subplot(1, 2, 2) + ax1.imshow(y3) + ax1.axis('off') + ax1.set_xticklabels([]) + ax1.set_yticklabels([]) + plt.savefig( + './model/pred/L_' + str(n_iter-1) + '_epoch_' + + str(i)) + + +def plot_kernels(tensor, n_iter, num_cols=5, cmap="gray"): + """Plotting the kernals and layers + Args: + Tensor :Input layer, + n_iter : number of interation, + num_cols : number of columbs required for figure + Output: + Gives the figure of the size decided with output layers activation map + + Default : Last layer will be taken into consideration + """ + if not len(tensor.shape) == 4: + raise Exception("assumes a 4D tensor") + + fig = plt.figure() + i = 0 + t = tensor.data.numpy() + b = 0 + a = 1 + + for t1 in t: + for t2 in t1: + i += 1 + + ax1 = fig.add_subplot(5, num_cols, i) + ax1.imshow(t2, cmap=cmap) + ax1.axis('off') + ax1.set_xticklabels([]) + ax1.set_yticklabels([]) + + if i == 1: + a = 1 + if a == 10: + break + a += 1 + if i % a == 0: + a = 0 + b += 1 + if b == 20: + break + + plt.savefig( + './model/pred/Kernal_' + str(n_iter - 1) + '_epoch_' + + str(i)) + + +class LayerActivations(): + """Getting the hooks on each layer""" + + features = None + + def __init__(self, layer): + self.hook = layer.register_forward_hook(self.hook_fn) + + def hook_fn(self, module, input, output): + self.features = output.cpu() + + def remove(self): + self.hook.remove() + + +#to get gradient flow +#From Pytorch-forums +def plot_grad_flow(named_parameters,n_iter): + + '''Plots the gradients flowing through different layers in the net during training. + Can be used for checking for possible gradient vanishing / exploding problems. + + Usage: Plug this function in Trainer class after loss.backwards() as + "plot_grad_flow(self.model.named_parameters())" to visualize the gradient flow''' + ave_grads = [] + max_grads = [] + layers = [] + for n, p in named_parameters: + if (p.requires_grad) and ("bias" not in n): + layers.append(n) + ave_grads.append(p.grad.abs().mean()) + max_grads.append(p.grad.abs().max()) + plt.bar(np.arange(len(max_grads)), max_grads, alpha=0.1, lw=1, color="c") + plt.bar(np.arange(len(max_grads)), ave_grads, alpha=0.1, lw=1, color="b") + plt.hlines(0, 0, len(ave_grads) + 1, lw=2, color="k") + plt.xticks(range(0, len(ave_grads), 1), layers, rotation="vertical") + plt.xlim(left=0, right=len(ave_grads)) + plt.ylim(bottom=-0.001, top=0.02) # zoom in on the lower gradient regions + plt.xlabel("Layers") + plt.ylabel("average gradient") + plt.title("Gradient flow") + plt.grid(True) + plt.legend([Line2D([0], [0], color="c", lw=4), + Line2D([0], [0], color="b", lw=4), + Line2D([0], [0], color="k", lw=4)], ['max-gradient', 'mean-gradient', 'zero-gradient']) + #plt.savefig('./model/pred/Grad_Flow_' + str(n_iter - 1)) diff --git a/recognition/s4627182_Improved_Unet/README.md b/recognition/s4627182_Improved_Unet/README.md new file mode 100644 index 000000000..87ad01505 --- /dev/null +++ b/recognition/s4627182_Improved_Unet/README.md @@ -0,0 +1,137 @@ +## COMP_3710_Report +( Medical condition, Extended to 27 OCT) + + +## This report is focused on the first task (a) + Segment the ISIC data set with the Improved UNet + with all labels having a minimum Dice similarity coefficient of 0.8 on the test set. + +## Model +U-Net is a convolutional neural network architecture primarily used for biomedical image segmentation. +Its U-shaped structure consists of a contracting path, which captures context, and an expansive path, +which enables precise localization. Through skip connections, features from the contracting path are concatenated +with the expansive path, enhancing localization capabilities. + +![Unet_Structure](./additional_Images/unet.png) + +The improved U-Net architecture introduces several key enhancements over the original U-Net architecture: + +- **Context Module**: This module captures contextual information around each pixel, which is crucial for understanding the bigger picture in segmentation tasks. By using multiple convolutions and dropout for regularization, it aims to enhance feature extraction without overfitting. + +- **Upsampling Module**: This module is responsible for increasing the spatial resolution of feature maps. In contrast to simple transposed convolutions from the original U-Net, this could provide smoother and more refined upsampling. + +- **Localization Module**: This module focuses on localizing features within the upsampled feature map, helping the network to regain the spatial details lost during downsampling. + +- **Segmentation Layer**: This layer is dedicated to producing the final segmentation map from the feature maps. + +## Dataset +In this report, the ISIC 2018 dataset will be used. +The ISIC 2018 dataset is a publicly available dataset for skin lesion image segmentation, +provided by the International Skin Imaging Collaboration (ISIC). Given that the real-world +images in the dataset come in different sizes, they are uniformly resized to a 128x128 dimension. +These images use RGB with 3 color channels for input. The label data, which indicates where the lesions are, +is treated in the same way as the real data. However, these labels are input as grayscale images with a single channel, +making them simpler and more focused on the lesion's location and shape. + +### example usage: +``` +Training_Data_Whole = Images_Dataset_folder(t_data, l_data) + +train_size = int(0.8 * len(Training_Data_Whole)) +val_size = len(Training_Data_Whole) - train_size + +Training_Data, Validing_Data = random_split(Training_Data_Whole, [train_size, val_size]) +``` + +## Losses +The loss function used is calculates a combined loss that is a weighted sum of Binary Cross-Entropy (BCE) loss and Dice loss. +It is used to balance between pixel-wise accuracy (captured by BCE) and overlap accuracy (captured by Dice loss) is important. + +### example usage: +``` +lossT = calc_loss(y_pred, y) +``` + +## Dice_score +Dice score measures the similarity between two sets. +Specifically, it's used to quantify the overlap between predicted and ground truth binary segmentations. +Values range between 0 (no overlap) to 1 (perfect overlap). +It's a common metric for evaluating the accuracy of image segmentation models, +highlighting the spatial overlap accuracy between prediction and truth. + +![dice_score_img](./additional_Images/dice_score.png) +### example usage: +``` +dice_score = dice_coeff(s, s3) +``` + +## Training and Validation +After data pre-processing, the model is trained using Adam optimizer with +an initial learning rate of 0.0005 and L2 weight decay of 0.000001. +A learning rate scheduler is also used for adjusting the learning rate during training. +Besides, 400 epoches are trained with a bach size of 16. + +### example usage: +``` +python3 train.py +``` + +### Training loss vs Epoches + + ![train_vs_Epoches](./additional_Images/Train_loss_vs_Epoches.png) + + +### Valid loss vs Epoches + + ![valid_vs_epoches](./additional_Images/valid_loss_vs_epoches.png) + + +## Predict + +### example usage: +``` +python3 predict.py +``` + +The input is + +![Input](./additional_Images/ISIC_0000003.jpg) + + +The output is + +![Output](./additional_Images/ISIC_0000003_out.jpg) + + + +## Results +The final model get a Mean Dice Score : 0.9940897984524689. + +## Environment + python 3.9 + pytorch=2.0.1 + numpy==1.23.5 + torchvision=0.15.2 + matplotlib==3.7.2 + Pillow==9.3.0 + tqdm==4.64.1 + wandb==0.13.5 + +## References +- F. Isensee, P. Kickingereder, W. Wick, M. Bendszus, and K. H. Maier-Hein, +“Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge,” +Feb. 2018. [Online]. Available: https://arxiv.org/abs/1802.10508v1 + + + + + + + + + + + + + + diff --git a/recognition/s4627182_Improved_Unet/__init__.py b/recognition/s4627182_Improved_Unet/__init__.py new file mode 100644 index 000000000..279eecc85 --- /dev/null +++ b/recognition/s4627182_Improved_Unet/__init__.py @@ -0,0 +1,3 @@ +from .Modules import UNet + + diff --git a/recognition/s4627182_Improved_Unet/additional_Images/ISIC_0000003.jpg b/recognition/s4627182_Improved_Unet/additional_Images/ISIC_0000003.jpg new file mode 100644 index 000000000..2b6702f64 Binary files /dev/null and b/recognition/s4627182_Improved_Unet/additional_Images/ISIC_0000003.jpg differ diff --git a/recognition/s4627182_Improved_Unet/additional_Images/ISIC_0000003_out.jpg b/recognition/s4627182_Improved_Unet/additional_Images/ISIC_0000003_out.jpg new file mode 100644 index 000000000..e4a3bf5bc Binary files /dev/null and b/recognition/s4627182_Improved_Unet/additional_Images/ISIC_0000003_out.jpg differ diff --git a/recognition/s4627182_Improved_Unet/additional_Images/Train_loss_vs_Epoches.png b/recognition/s4627182_Improved_Unet/additional_Images/Train_loss_vs_Epoches.png new file mode 100755 index 000000000..0b67419d4 Binary files /dev/null and b/recognition/s4627182_Improved_Unet/additional_Images/Train_loss_vs_Epoches.png differ diff --git a/recognition/s4627182_Improved_Unet/additional_Images/Unet.png b/recognition/s4627182_Improved_Unet/additional_Images/Unet.png new file mode 100644 index 000000000..1356bc656 Binary files /dev/null and b/recognition/s4627182_Improved_Unet/additional_Images/Unet.png differ diff --git a/recognition/s4627182_Improved_Unet/additional_Images/dice_score.png b/recognition/s4627182_Improved_Unet/additional_Images/dice_score.png new file mode 100644 index 000000000..c30623a4f Binary files /dev/null and b/recognition/s4627182_Improved_Unet/additional_Images/dice_score.png differ diff --git a/recognition/s4627182_Improved_Unet/additional_Images/valid_loss_vs_epoches.png b/recognition/s4627182_Improved_Unet/additional_Images/valid_loss_vs_epoches.png new file mode 100755 index 000000000..ea35c9437 Binary files /dev/null and b/recognition/s4627182_Improved_Unet/additional_Images/valid_loss_vs_epoches.png differ diff --git a/recognition/s4627182_Improved_Unet/dice_score.py b/recognition/s4627182_Improved_Unet/dice_score.py new file mode 100644 index 000000000..84a219789 --- /dev/null +++ b/recognition/s4627182_Improved_Unet/dice_score.py @@ -0,0 +1,31 @@ +import torch +from torch import Tensor + + +def dice_coeff(input: Tensor, target: Tensor, reduce_batch_first: bool = False, epsilon: float = 1e-6): + # Average of Dice coefficient for all batches, or for a single mask + assert input.size() == target.size() + assert input.dim() == 3 or not reduce_batch_first + + + sum_dim = (-1, -2) if input.dim() == 2 or not reduce_batch_first else (-1, -2, -3) + + inter = 2 * (input * target).sum(dim=sum_dim) + sets_sum = input.sum(dim=sum_dim) + target.sum(dim=sum_dim) + sets_sum = torch.where(sets_sum == 0, inter, sets_sum) + + dice = (inter + epsilon) / (sets_sum + epsilon) + + return dice.mean() + +def multiclass_dice_coeff(input: Tensor, target: Tensor, reduce_batch_first: bool = False, epsilon: float = 1e-6): + # Average of Dice coefficient for all classes + return dice_coeff(input.flatten(0, 1), target.flatten(0, 1), reduce_batch_first, epsilon) + + +def dice_loss(input: Tensor, target: Tensor, multiclass: bool = False): + # Dice loss (objective to minimize) between 0 and 1 + fn = multiclass_dice_coeff if multiclass else dice_coeff + return 1 - fn(input, target, reduce_batch_first=True) + +# Updated \ No newline at end of file diff --git a/recognition/s4627182_Improved_Unet/predict.py b/recognition/s4627182_Improved_Unet/predict.py new file mode 100644 index 000000000..5cb067126 --- /dev/null +++ b/recognition/s4627182_Improved_Unet/predict.py @@ -0,0 +1,35 @@ +import torch +from torchvision import transforms +from PIL import Image +import numpy as np + +device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + +def segment_image(model_path, image_path, output_path): + + # Load the pre-trained model + model = torch.load(model_path) + model.to(device) + model.eval() + + # Load and preprocess the image + img = Image.open(image_path) # Adjust the size as per your model's requirements + img_np = np.array(img) / 255.0 + img_tensor = torch.tensor(img_np).to(device) + + # Predict the mask + with torch.no_grad(): + predicted_mask = model(img_tensor) + + # Convert mask to numpy array + predicted_mask = predicted_mask.squeeze().cpu().numpy() + predicted_mask_image = (predicted_mask * 255).astype(np.uint8) + + predicted_mask_image.save(output_path) + + return predicted_mask_image + +path_to_model = './model/Unet_D_400_16.pth' +path_to_image = './ISIC2018/ISIC2018_Task1-2_Training_Input_x2/ISIC_0000003.jpg' +segmented_img = segment_image(path_to_model, path_to_image) +Image.fromarray(segmented_img).save("ISIC_0000003_out.png") diff --git a/recognition/s4627182_Improved_Unet/requirements.txt b/recognition/s4627182_Improved_Unet/requirements.txt new file mode 100644 index 000000000..256fb596a --- /dev/null +++ b/recognition/s4627182_Improved_Unet/requirements.txt @@ -0,0 +1,5 @@ +matplotlib==3.6.2 +numpy==1.23.5 +Pillow==9.3.0 +tqdm==4.64.1 +wandb==0.13.5 diff --git a/recognition/s4627182_Improved_Unet/test.py b/recognition/s4627182_Improved_Unet/test.py new file mode 100644 index 000000000..56e1a7d20 --- /dev/null +++ b/recognition/s4627182_Improved_Unet/test.py @@ -0,0 +1,442 @@ +from __future__ import print_function, division +import os +import numpy as np +from PIL import Image +import glob +# import SimpleITK as sitk +from torch import optim +import torch.utils.data +import torch +import torch.nn.functional as F + +import torch.nn +import torchvision +import matplotlib.pyplot as plt +import natsort +from torch.utils.data.sampler import SubsetRandomSampler +from Data_Loader import Images_Dataset, Images_Dataset_folder +import torchsummary +# from torch.utils.tensorboard import SummaryWriter +# from tensorboardX import SummaryWriter + +import shutil +import random +from Modules import UNet_For_Brain +from Losses import calc_loss, dice_loss, threshold_predictions_v, threshold_predictions_p +from Ploting import plot_kernels, LayerActivations, input_images, plot_grad_flow, draw_loss +from Metrics import dice_coeff, accuracy_score +import time + +####################################################### +# Checking if GPU is used +####################################################### + +train_on_gpu = torch.cuda.is_available() + +if not train_on_gpu: + print('CUDA is not available. Training on CPU') +else: + print('CUDA is available. Training on GPU') + +# os.environ['CUDA_VISIBLE_DEVICES'] = '1,2,3,4' +device = torch.device("cuda:0" if train_on_gpu else "cpu") + +####################################################### +# Setting the basic paramters of the model +####################################################### + +batch_size = 16 +print('batch_size = ' + str(batch_size)) + +valid_size = 0.15 +epoch = 400 +print('epoch = ' + str(epoch)) + +random_seed = random.randint(1, 100) +print('random_seed = ' + str(random_seed)) + +shuffle = True +valid_loss_min = np.Inf +num_workers = 24 +lossT = [] +lossL = [] +lossL.append(np.inf) +lossT.append(np.inf) +epoch_valid = epoch - 2 +n_iter = 1 +i_valid = 0 + +pin_memory = False +if train_on_gpu: + pin_memory = True + +# plotter = VisdomLinePlotter(env_name='Tutorial Plots') + +####################################################### +# Setting up the model +####################################################### + +model_Inputs = UNet_For_Brain + + +def model_unet(model_input, in_channel=1, out_channel=1): + model_test = model_input(in_channel, out_channel) + return model_test + + +model_test = model_unet(model_Inputs[-1], 3, 1) + +model_test.to(device) + +####################################################### +# Getting the Summary of Model +####################################################### + +torchsummary.summary(model_test, input_size=(3, 128, 128)) + +####################################################### +# Passing the Dataset of Images and Labels +####################################################### + +# for ISIC2018 data +t_data = './ISIC2018/ISIC2018_Task1-2_Training_Input_x2/' +l_data = './ISIC2018/ISIC2018_Task1_Training_GroundTruth_x2/' +test_image = './keras_png_slices_data/keras_png_slices_test/' +test_label = './keras_png_slices_data/keras_png_slices_seg_test/' +test_folderP = './ISIC2018/ISIC2018_Task1-2_Training_Input_x2/*' +test_folderL = './ISIC2018/ISIC2018_Task1_Training_GroundTruth_x2/*' +valid_image = './ISIC2018/ISIC2018_Task1-2_Training_Input_x2/' +valid_label = './ISIC2018/ISIC2018_Task1_Training_GroundTruth_x2/' + +Training_Data = Images_Dataset_folder(t_data, l_data) +valid_Data = Images_Dataset_folder(valid_image, valid_label) + +####################################################### +# Giving a transformation for input data +####################################################### + +data_transform = torchvision.transforms.Compose([ + torchvision.transforms.Resize((128, 128)), + # torchvision.transforms.CenterCrop(96), + torchvision.transforms.ToTensor(), + # torchvision.transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) +]) + +####################################################### +# Training Validation Split +####################################################### + +num_train = len(Training_Data) +indices_train = list(range(num_train)) +# split = int(np.floor(valid_size * num_train)) + +num_valid = len(valid_Data) +indices_valid = list(range(num_valid)) + +if shuffle: + np.random.seed(random_seed) + np.random.shuffle(indices_train) + np.random.shuffle(indices_valid) + +# train_idx, valid_idx = indices[split:], indices[:split] +train_idx, valid_idx = indices_train, indices_valid +train_sampler = SubsetRandomSampler(train_idx) +valid_sampler = SubsetRandomSampler(valid_idx) + +train_loader = torch.utils.data.DataLoader(Training_Data, batch_size=batch_size, sampler=train_sampler, + num_workers=num_workers, pin_memory=pin_memory, ) + +valid_loader = torch.utils.data.DataLoader(valid_Data, batch_size=batch_size, sampler=valid_sampler, + num_workers=num_workers, pin_memory=pin_memory, ) + +####################################################### +# Using Adam as Optimizer +####################################################### +# Hyper Parameters +initial_lr = 5e-4 +lr_decay = 0.985 +l2_weight_decay = 1e-5 +# initial_lr = 0.001 +# opt = torch.optim.Adam(model_test.parameters(), lr=initial_lr) # try SGD +# # opt = optim.SGD(model_test.parameters(), lr = initial_lr, momentum=0.99) + +# MAX_STEP = int(1e10) +# eta_min = 1e-2 +# scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(opt, MAX_STEP, eta_min=1e-5) + +# Optimisation & Loss Settings +opt = optim.Adam(model_test.parameters(), lr=initial_lr, weight_decay=l2_weight_decay) +scheduler = optim.lr_scheduler.ExponentialLR(optimizer=opt, gamma=lr_decay) + +New_folder = './model' + +if os.path.exists(New_folder) and os.path.isdir(New_folder): + shutil.rmtree(New_folder) + +try: + os.mkdir(New_folder) +except OSError: + print("Creation of the main directory '%s' failed " % New_folder) +else: + print("Successfully created the main directory '%s' " % New_folder) + +####################################################### +# Setting the folder of saving the predictions +####################################################### + +read_pred = './model/pred' + +####################################################### +# Checking if prediction folder exixts +####################################################### + +if os.path.exists(read_pred) and os.path.isdir(read_pred): + shutil.rmtree(read_pred) + +try: + os.mkdir(read_pred) +except OSError: + print("Creation of the prediction directory '%s' failed of dice loss" % read_pred) +else: + print("Successfully created the prediction directory '%s' of dice loss" % read_pred) + +####################################################### +# checking if the model exists and if true then delete +####################################################### + +read_model_path = './model/Unet_D_' + str(epoch) + '_' + str(batch_size) + +if os.path.exists(read_model_path) and os.path.isdir(read_model_path): + shutil.rmtree(read_model_path) + print('Model folder there, so deleted for newer one') + +try: + os.mkdir(read_model_path) +except OSError: + print("Creation of the model directory '%s' failed" % read_model_path) +else: + print("Successfully created the model directory '%s' " % read_model_path) + +####################################################### +# Training loop +####################################################### + +for i in range(epoch): + + train_loss = 0.0 + valid_loss = 0.0 + train_loss_list = [] + valid_loss_list = [] + since = time.time() + scheduler.step() + # lr = scheduler.get_lr() + + ####################################################### + # Training Data + ####################################################### + + model_test.train() + k = 1 + + for x, y in train_loader: + # print("x: ", x.shape) + # print("y: ", y.shape) + x, y = x.to(device), y.to(device) + + opt.zero_grad() + + y_pred = model_test(x) + lossT = calc_loss(y_pred, y) # Dice_loss Used + + train_loss += lossT.item() * x.size(0) + lossT.backward() + opt.step() + x_size = lossT.item() * x.size(0) + k = 2 + + train_loss_list.append(train_loss) + + ####################################################### + # Validation Step + ####################################################### + + model_test.eval() + torch.no_grad() # to increase the validation process uses less memory + + for x1, y1 in valid_loader: + x1, y1 = x1.to(device), y1.to(device) + + y_pred1 = model_test(x1) + lossL = calc_loss(y_pred1, y1) # Dice_loss Used + + valid_loss += lossL.item() * x1.size(0) + x_size1 = lossL.item() * x1.size(0) + + valid_loss_list.append(valid_loss) + + if (i + 1) % 1 == 0: + print('Epoch: {}/{} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f}'.format(i + 1, epoch, train_loss, + valid_loss)) + ####################################################### + # Early Stopping + ####################################################### + + if valid_loss <= valid_loss_min and epoch_valid >= i: # and i_valid <= 2: + + print('Validation loss decreased ({:.6f} --> {:.6f}). Saving model '.format(valid_loss_min, valid_loss)) + torch.save(model_test.state_dict(), './model/Unet_D_' + + str(epoch) + '_' + str(batch_size) + '/Unet_epoch_' + str(epoch) + + '_batchsize_' + str(batch_size) + '.pth') + + if round(valid_loss, 4) == round(valid_loss_min, 4): + print(i_valid) + i_valid = i_valid + 1 + valid_loss_min = valid_loss + +if torch.cuda.is_available(): + torch.cuda.empty_cache() + +####################################################### +# Loading the model +####################################################### + +model_test.load_state_dict(torch.load('./model/Unet_D_' + + str(epoch) + '_' + str(batch_size) + '/Unet_epoch_' + str(epoch) + + '_batchsize_' + str(batch_size) + '.pth')) + +model_test.eval() + +####################################################### +# opening the test folder and creating a folder for generated images +####################################################### + +read_test_folder = glob.glob(test_folderP) +x_sort_test = natsort.natsorted(read_test_folder) # To sort + +read_test_folder112 = './model/gen_images' + +if os.path.exists(read_test_folder112) and os.path.isdir(read_test_folder112): + shutil.rmtree(read_test_folder112) + +try: + os.mkdir(read_test_folder112) +except OSError: + print("Creation of the testing directory %s failed" % read_test_folder112) +else: + print("Successfully created the testing directory %s " % read_test_folder112) + +# For Prediction Threshold + +read_test_folder_P_Thres = './model/pred_threshold' + +if os.path.exists(read_test_folder_P_Thres) and os.path.isdir(read_test_folder_P_Thres): + shutil.rmtree(read_test_folder_P_Thres) + +try: + os.mkdir(read_test_folder_P_Thres) +except OSError: + print("Creation of the testing directory %s failed" % read_test_folder_P_Thres) +else: + print("Successfully created the testing directory %s " % read_test_folder_P_Thres) + +# For Label Threshold + +read_test_folder_L_Thres = './model/label_threshold' + +if os.path.exists(read_test_folder_L_Thres) and os.path.isdir(read_test_folder_L_Thres): + shutil.rmtree(read_test_folder_L_Thres) + +try: + os.mkdir(read_test_folder_L_Thres) +except OSError: + print("Creation of the testing directory %s failed" % read_test_folder_L_Thres) +else: + print("Successfully created the testing directory %s " % read_test_folder_L_Thres) + +####################################################### +# saving the images in the files +####################################################### + +img_test_no = 0 + +for i in range(len(read_test_folder)): + im = Image.open(x_sort_test[i]).convert("RGB") + + im1 = im + im_n = np.array(im1) + im_n_flat = im_n.reshape(-1, 1) + + for j in range(im_n_flat.shape[0]): + if im_n_flat[j] != 0: + im_n_flat[j] = 255 + + s = data_transform(im) + pred = model_test(s.unsqueeze(0).cuda()).cpu() + pred = F.sigmoid(pred) + pred = pred.detach().numpy() + + # pred = threshold_predictions_p(pred) #Value kept 0.01 as max is 1 and noise is very small. + + if i % 24 == 0: + img_test_no = img_test_no + 1 + + x1 = plt.imsave('./model/gen_images/im_epoch_' + str(epoch) + 'int_' + str(i) + + '_img_no_' + str(img_test_no) + '.png', pred[0][0], cmap='gray') + +#################################################### +# Calculating the Dice Score +#################################################### + +data_transform = torchvision.transforms.Compose([ + torchvision.transforms.Resize((128, 128)), + # torchvision.transforms.CenterCrop(96), + torchvision.transforms.Grayscale(), + # torchvision.transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) +]) + +read_test_folderP = glob.glob('./model/gen_images/*') +x_sort_testP = natsort.natsorted(read_test_folderP) + +read_test_folderL = glob.glob(test_folderL) +x_sort_testL = natsort.natsorted(read_test_folderL) # To sort + +dice_score123 = 0.0 +x_count = 0 +x_dice = 0 + +for i in range(len(read_test_folderP)): + + x = Image.open(x_sort_testP[i]).convert("L") + s = data_transform(x) + s = np.array(s) + s = threshold_predictions_v(s) + # print(s) + # print("------------------") + + # save the images + x1 = plt.imsave('./model/pred_threshold/im_epoch_' + str(epoch) + 'int_' + str(i) + + '_img_no_' + str(img_test_no) + '.png', s) + + y = Image.open(x_sort_testL[i]).convert("L") + s2 = data_transform(y) + s3 = np.array(s2) + s2 = threshold_predictions_v(s2) + # print(s3) + + # save the Images + y1 = plt.imsave('./model/label_threshold/im_epoch_' + str(epoch) + 'int_' + str(i) + + '_img_no_' + str(img_test_no) + '.png', s3) + + total = dice_coeff(s, s3) + print(total) + + if total <= 0.8: + x_count += 1 + if total > 0.8: + x_dice = x_dice + total + dice_score123 = dice_score123 + total + +# print('Dice Score : ' + str(dice_score123/len(read_test_folderP))) +print(x_count) +print(x_dice) +print('Dice Score : ' + str(float(x_dice / (len(read_test_folderP) - x_count)))) diff --git a/recognition/s4627182_Improved_Unet/test_1027.log b/recognition/s4627182_Improved_Unet/test_1027.log new file mode 100755 index 000000000..b968dba49 --- /dev/null +++ b/recognition/s4627182_Improved_Unet/test_1027.log @@ -0,0 +1,2559 @@ +CUDA is available. Training on GPU +batch_size = 16 +epoch = 400 +random_seed = 67 +---------------------------------------------------------------- + Layer (type) Output Shape Param # +================================================================ + Conv2d-1 [-1, 64, 128, 128] 1,792 + Conv2d-2 [-1, 64, 128, 128] 36,928 + BatchNorm2d-3 [-1, 64, 128, 128] 128 + ReLU-4 [-1, 64, 128, 128] 0 + Dropout2d-5 [-1, 64, 128, 128] 0 + Conv2d-6 [-1, 64, 128, 128] 36,928 + BatchNorm2d-7 [-1, 64, 128, 128] 128 + ReLU-8 [-1, 64, 128, 128] 0 + ContextModule-9 [-1, 64, 128, 128] 0 + Conv2d-10 [-1, 128, 64, 64] 73,856 + Conv2d-11 [-1, 128, 64, 64] 147,584 + BatchNorm2d-12 [-1, 128, 64, 64] 256 + ReLU-13 [-1, 128, 64, 64] 0 + Dropout2d-14 [-1, 128, 64, 64] 0 + Conv2d-15 [-1, 128, 64, 64] 147,584 + BatchNorm2d-16 [-1, 128, 64, 64] 256 + ReLU-17 [-1, 128, 64, 64] 0 + ContextModule-18 [-1, 128, 64, 64] 0 + Conv2d-19 [-1, 256, 32, 32] 295,168 + Conv2d-20 [-1, 256, 32, 32] 590,080 + BatchNorm2d-21 [-1, 256, 32, 32] 512 + ReLU-22 [-1, 256, 32, 32] 0 + Dropout2d-23 [-1, 256, 32, 32] 0 + Conv2d-24 [-1, 256, 32, 32] 590,080 + BatchNorm2d-25 [-1, 256, 32, 32] 512 + ReLU-26 [-1, 256, 32, 32] 0 + ContextModule-27 [-1, 256, 32, 32] 0 + Conv2d-28 [-1, 512, 16, 16] 1,180,160 + Conv2d-29 [-1, 512, 16, 16] 2,359,808 + BatchNorm2d-30 [-1, 512, 16, 16] 1,024 + ReLU-31 [-1, 512, 16, 16] 0 + Dropout2d-32 [-1, 512, 16, 16] 0 + Conv2d-33 [-1, 512, 16, 16] 2,359,808 + BatchNorm2d-34 [-1, 512, 16, 16] 1,024 + ReLU-35 [-1, 512, 16, 16] 0 + ContextModule-36 [-1, 512, 16, 16] 0 + Conv2d-37 [-1, 1024, 8, 8] 4,719,616 + Conv2d-38 [-1, 1024, 8, 8] 9,438,208 + BatchNorm2d-39 [-1, 1024, 8, 8] 2,048 + ReLU-40 [-1, 1024, 8, 8] 0 + Dropout2d-41 [-1, 1024, 8, 8] 0 + Conv2d-42 [-1, 1024, 8, 8] 9,438,208 + BatchNorm2d-43 [-1, 1024, 8, 8] 2,048 + ReLU-44 [-1, 1024, 8, 8] 0 + ContextModule-45 [-1, 1024, 8, 8] 0 + Upsample-46 [-1, 1024, 16, 16] 0 + Conv2d-47 [-1, 512, 16, 16] 4,719,104 + UpsamplingModule-48 [-1, 512, 16, 16] 0 + Conv2d-49 [-1, 1024, 16, 16] 9,438,208 + Conv2d-50 [-1, 512, 16, 16] 4,719,104 +LocalisationModule-51 [-1, 512, 16, 16] 0 + Upsample-52 [-1, 512, 32, 32] 0 + Conv2d-53 [-1, 256, 32, 32] 1,179,904 + UpsamplingModule-54 [-1, 256, 32, 32] 0 + Conv2d-55 [-1, 512, 32, 32] 2,359,808 + Conv2d-56 [-1, 256, 32, 32] 1,179,904 +LocalisationModule-57 [-1, 256, 32, 32] 0 + Conv2d-58 [-1, 1, 32, 32] 2,305 +SegmentationLayer-59 [-1, 1, 32, 32] 0 + Upsample-60 [-1, 256, 64, 64] 0 + Conv2d-61 [-1, 128, 64, 64] 295,040 + UpsamplingModule-62 [-1, 128, 64, 64] 0 + Upsample-63 [-1, 1, 64, 64] 0 + UpscalingLayer-64 [-1, 1, 64, 64] 0 + Conv2d-65 [-1, 256, 64, 64] 590,080 + Conv2d-66 [-1, 128, 64, 64] 295,040 +LocalisationModule-67 [-1, 128, 64, 64] 0 + Conv2d-68 [-1, 1, 64, 64] 1,153 +SegmentationLayer-69 [-1, 1, 64, 64] 0 + Upsample-70 [-1, 128, 128, 128] 0 + Conv2d-71 [-1, 64, 128, 128] 73,792 + UpsamplingModule-72 [-1, 64, 128, 128] 0 + Upsample-73 [-1, 1, 128, 128] 0 + UpscalingLayer-74 [-1, 1, 128, 128] 0 + Conv2d-75 [-1, 128, 128, 128] 147,584 + Conv2d-76 [-1, 1, 128, 128] 1,153 +SegmentationLayer-77 [-1, 1, 128, 128] 0 +================================================================ +Total params: 56,425,923 +Trainable params: 56,425,923 +Non-trainable params: 0 +---------------------------------------------------------------- +Input size (MB): 0.19 +Forward/backward pass size (MB): 244.14 +Params size (MB): 215.25 +Estimated Total Size (MB): 459.58 +---------------------------------------------------------------- +./model/gen_images/im_epoch_400int_0_img_no_1.png Dice Score : 0.9974564664449227 +./model/gen_images/im_epoch_400int_1_img_no_1.png Dice Score : 0.9881838074398249 +./model/gen_images/im_epoch_400int_2_img_no_1.png Dice Score : 0.9959883296863603 +./model/gen_images/im_epoch_400int_3_img_no_1.png Dice Score : 0.998403122782115 +./model/gen_images/im_epoch_400int_4_img_no_1.png Dice Score : 0.9865229110512129 +./model/gen_images/im_epoch_400int_5_img_no_1.png Dice Score : 0.9916083916083916 +./model/gen_images/im_epoch_400int_6_img_no_1.png Dice Score : 0.995457023441759 +./model/gen_images/im_epoch_400int_7_img_no_1.png Dice Score : 0.9932297889287136 +./model/gen_images/im_epoch_400int_8_img_no_1.png Dice Score : 0.9954026902775412 +./model/gen_images/im_epoch_400int_9_img_no_1.png Dice Score : 0.9889162561576355 +./model/gen_images/im_epoch_400int_10_img_no_1.png Dice Score : 0.9972182040725492 +./model/gen_images/im_epoch_400int_11_img_no_1.png Dice Score : 0.9919001751313485 +./model/gen_images/im_epoch_400int_12_img_no_1.png Dice Score : 0.9923066540693751 +./model/gen_images/im_epoch_400int_13_img_no_1.png Dice Score : 0.9967551622418879 +./model/gen_images/im_epoch_400int_14_img_no_1.png Dice Score : 0.991008991008991 +./model/gen_images/im_epoch_400int_15_img_no_1.png Dice Score : 0.9940041710114703 +./model/gen_images/im_epoch_400int_16_img_no_1.png Dice Score : 0.9960933777989519 +./model/gen_images/im_epoch_400int_17_img_no_1.png Dice Score : 0.9916420213449917 +./model/gen_images/im_epoch_400int_18_img_no_1.png Dice Score : 0.994052676295667 +./model/gen_images/im_epoch_400int_19_img_no_1.png Dice Score : 0.9979788906355266 +./model/gen_images/im_epoch_400int_20_img_no_1.png Dice Score : 0.994275802254987 +./model/gen_images/im_epoch_400int_21_img_no_1.png Dice Score : 0.9980657640232108 +./model/gen_images/im_epoch_400int_22_img_no_1.png Dice Score : 0.9880540710468406 +./model/gen_images/im_epoch_400int_23_img_no_1.png Dice Score : 0.9973602662687937 +./model/gen_images/im_epoch_400int_24_img_no_2.png Dice Score : 0.991267787839586 +./model/gen_images/im_epoch_400int_25_img_no_2.png Dice Score : 0.9927025988989886 +./model/gen_images/im_epoch_400int_26_img_no_2.png Dice Score : 0.9954121183357064 +./model/gen_images/im_epoch_400int_27_img_no_2.png Dice Score : 0.99797110359668 +./model/gen_images/im_epoch_400int_28_img_no_2.png Dice Score : 0.9979342723004695 +./model/gen_images/im_epoch_400int_29_img_no_2.png Dice Score : 0.9959973936516802 +./model/gen_images/im_epoch_400int_30_img_no_2.png Dice Score : 0.9956851658979319 +./model/gen_images/im_epoch_400int_31_img_no_2.png Dice Score : 0.995850622406639 +./model/gen_images/im_epoch_400int_32_img_no_2.png Dice Score : 0.9970358814352575 +./model/gen_images/im_epoch_400int_33_img_no_2.png Dice Score : 0.9918606311580751 +./model/gen_images/im_epoch_400int_34_img_no_2.png Dice Score : 0.9943138519256698 +./model/gen_images/im_epoch_400int_35_img_no_2.png Dice Score : 0.9955617198335645 +./model/gen_images/im_epoch_400int_36_img_no_2.png Dice Score : 0.995897903372835 +./model/gen_images/im_epoch_400int_37_img_no_2.png Dice Score : 0.9906956136464333 +./model/gen_images/im_epoch_400int_38_img_no_2.png Dice Score : 0.9969810630317446 +./model/gen_images/im_epoch_400int_39_img_no_2.png Dice Score : 0.9979816102265082 +./model/gen_images/im_epoch_400int_40_img_no_2.png Dice Score : 0.9971164243662141 +./model/gen_images/im_epoch_400int_41_img_no_2.png 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+./model/gen_images/im_epoch_400int_2445_img_no_102.png Dice Score : 0.9990000344815696 +./model/gen_images/im_epoch_400int_2446_img_no_102.png Dice Score : 0.9976559682654165 +./model/gen_images/im_epoch_400int_2447_img_no_102.png Dice Score : 0.9972853075547904 +./model/gen_images/im_epoch_400int_2448_img_no_103.png Dice Score : 0.9977525940802372 +./model/gen_images/im_epoch_400int_2449_img_no_103.png Dice Score : 0.9770992366412213 +./model/gen_images/im_epoch_400int_2450_img_no_103.png Dice Score : 0.9960999566661852 +./model/gen_images/im_epoch_400int_2451_img_no_103.png Dice Score : 0.9866573033707865 +./model/gen_images/im_epoch_400int_2452_img_no_103.png Dice Score : 0.9924103637791154 +./model/gen_images/im_epoch_400int_2453_img_no_103.png Dice Score : 0.9984623270117888 +./model/gen_images/im_epoch_400int_2454_img_no_103.png Dice Score : 0.9975294001383536 +./model/gen_images/im_epoch_400int_2455_img_no_103.png Dice Score : 0.9971480953350988 +./model/gen_images/im_epoch_400int_2456_img_no_103.png Dice Score : 0.9944035346097202 +./model/gen_images/im_epoch_400int_2457_img_no_103.png Dice Score : 0.9945853519521232 +./model/gen_images/im_epoch_400int_2458_img_no_103.png Dice Score : 0.998046751937496 +./model/gen_images/im_epoch_400int_2459_img_no_103.png Dice Score : 0.9937905729607677 +./model/gen_images/im_epoch_400int_2460_img_no_103.png Dice Score : 0.9984681799192313 +./model/gen_images/im_epoch_400int_2461_img_no_103.png Dice Score : 0.9909474954737477 +./model/gen_images/im_epoch_400int_2462_img_no_103.png Dice Score : 0.9965329370975731 +./model/gen_images/im_epoch_400int_2463_img_no_103.png Dice Score : 0.9960202604920405 +Mean Dice Score : 0.9940897984524689 diff --git a/recognition/s4627182_Improved_Unet/train.py b/recognition/s4627182_Improved_Unet/train.py new file mode 100644 index 000000000..89acd4ff8 --- /dev/null +++ b/recognition/s4627182_Improved_Unet/train.py @@ -0,0 +1,457 @@ +# From https://github.com/bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets +from __future__ import print_function, division +import os +import numpy as np +from PIL import Image +import glob +# import SimpleITK as sitk +from torch import optim +import torch.utils.data +import torch +import torch.nn.functional as F + +import torch.nn +import torchvision +import matplotlib.pyplot as plt +import natsort +from torch.utils.data.sampler import SubsetRandomSampler +from Data_Loader import Images_Dataset, Images_Dataset_folder +import torchsummary +# from torch.utils.tensorboard import SummaryWriter +# from tensorboardX import SummaryWriter + + +import shutil +import random +from Modules import UNet_For_Brain +from Losses import calc_loss, dice_loss, threshold_predictions_v, threshold_predictions_p +from Ploting import plot_kernels, LayerActivations, input_images, plot_grad_flow, draw_loss +from Metrics import dice_coeff, accuracy_score +import time +from torch.utils.data import random_split + +# from ploting import VisdomLinePlotter +# from visdom import Visdom + + +####################################################### +# Checking if GPU is used +####################################################### + +train_on_gpu = torch.cuda.is_available() + +if not train_on_gpu: + print('CUDA is not available. Training on CPU') +else: + print('CUDA is available. Training on GPU') + +# os.environ['CUDA_VISIBLE_DEVICES'] = '1,2,3,4' +device = torch.device("cuda:0" if train_on_gpu else "cpu") + +####################################################### +# Setting the basic paramters of the model +####################################################### + +batch_size = 16 +print('batch_size = ' + str(batch_size)) + +valid_size = 0.15 + +epoch = 400 +print('epoch = ' + str(epoch)) + +random_seed = random.randint(1, 100) +print('random_seed = ' + str(random_seed)) + +shuffle = True +valid_loss_min = np.Inf +num_workers = 24 +lossT = [] +lossL = [] +lossL.append(np.inf) +lossT.append(np.inf) +epoch_valid = epoch - 2 +n_iter = 1 +i_valid = 0 + +pin_memory = False +if train_on_gpu: + pin_memory = True + +# plotter = VisdomLinePlotter(env_name='Tutorial Plots') + +####################################################### +# Setting up the model +####################################################### + +model_Inputs = [UNet_For_Brain] + + +def model_unet(model_input, in_channel=1, out_channel=1): + model_test = model_input(in_channel, out_channel) + return model_test + + + +model_test = model_unet(model_Inputs[-1], 3, 1) + +model_test.to(device) + +####################################################### +# Getting the Summary of Model +####################################################### + +torchsummary.summary(model_test, input_size=(3, 128, 128)) + +####################################################### +# Passing the Dataset of Images and Labels +####################################################### + + +# ISIC2018 data +t_data = './ISIC2018/ISIC2018_Task1-2_Training_Input_x2/' +l_data = './ISIC2018/ISIC2018_Task1_Training_GroundTruth_x2/' +test_image = './keras_png_slices_data/keras_png_slices_test/' +test_label = './keras_png_slices_data/keras_png_slices_seg_test/' +test_folderP = './ISIC2018/ISIC2018_Task1-2_Training_Input_x2/*' +test_folderL = './ISIC2018/ISIC2018_Task1_Training_GroundTruth_x2/*' +valid_image = './ISIC2018/ISIC2018_Task1-2_Training_Input_x2/' +valid_lable = './ISIC2018/ISIC2018_Task1_Training_GroundTruth_x2/' + +Training_Data_Whole = Images_Dataset_folder(t_data, l_data) + +train_size = int(0.8 * len(Training_Data_Whole)) +val_size = len(Training_Data_Whole) - train_size + +Training_Data, Validing_Data = random_split(Training_Data_Whole, [train_size, val_size]) + +####################################################### +# Giving a transformation for input data +####################################################### + +data_transform = torchvision.transforms.Compose([ + torchvision.transforms.Resize((128, 128)), + # torchvision.transforms.CenterCrop(96), + torchvision.transforms.ToTensor(), + # torchvision.transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) +]) + +####################################################### +# Trainging Validation Split +####################################################### + +num_train = len(Training_Data) +indices_train = list(range(num_train)) +# split = int(np.floor(valid_size * num_train)) + +num_valid = len(Validing_Data) +indices_valid = list(range(num_valid)) + +if shuffle: + np.random.seed(random_seed) + np.random.shuffle(indices_train) + np.random.shuffle(indices_valid) + +# train_idx, valid_idx = indices[split:], indices[:split] +train_idx, valid_idx = indices_train, indices_valid +train_sampler = SubsetRandomSampler(train_idx) +valid_sampler = SubsetRandomSampler(valid_idx) + +train_loader = torch.utils.data.DataLoader(Training_Data, batch_size=batch_size, sampler=train_sampler, + num_workers=num_workers, pin_memory=pin_memory, ) + +valid_loader = torch.utils.data.DataLoader(Validing_Data, batch_size=batch_size, sampler=valid_sampler, + num_workers=num_workers, pin_memory=pin_memory, ) + +####################################################### +# Using Adam as Optimizer +####################################################### +# Hyper Parameters +initial_lr = 5e-4 +lr_decay = 0.985 +l2_weight_decay = 1e-5 +# initial_lr = 0.001 +# opt = torch.optim.Adam(model_test.parameters(), lr=initial_lr) # try SGD +# # opt = optim.SGD(model_test.parameters(), lr = initial_lr, momentum=0.99) + +# MAX_STEP = int(1e10) +# eta_min = 1e-2 +# scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(opt, MAX_STEP, eta_min=1e-5) + +# Optimisation & Loss Settings +opt = optim.Adam(model_test.parameters(), lr=initial_lr, weight_decay=l2_weight_decay) +scheduler = optim.lr_scheduler.ExponentialLR(optimizer=opt, gamma=lr_decay) + +New_folder = './model' + +if os.path.exists(New_folder) and os.path.isdir(New_folder): + shutil.rmtree(New_folder) + +try: + os.mkdir(New_folder) +except OSError: + print("Creation of the main directory '%s' failed " % New_folder) +else: + print("Successfully created the main directory '%s' " % New_folder) + +####################################################### +# Setting the folder of saving the predictions +####################################################### + +read_pred = './model/pred' + +####################################################### +# Checking if prediction folder exixts +####################################################### + +if os.path.exists(read_pred) and os.path.isdir(read_pred): + shutil.rmtree(read_pred) + +try: + os.mkdir(read_pred) +except OSError: + print("Creation of the prediction directory '%s' failed of dice loss" % read_pred) +else: + print("Successfully created the prediction directory '%s' of dice loss" % read_pred) + +####################################################### +# checking if the model exists and if true then delete +####################################################### + +read_model_path = './model/Unet_D_' + str(epoch) + '_' + str(batch_size) + +if os.path.exists(read_model_path) and os.path.isdir(read_model_path): + shutil.rmtree(read_model_path) + print('Model folder there, so deleted for newer one') + +try: + os.mkdir(read_model_path) +except OSError: + print("Creation of the model directory '%s' failed" % read_model_path) +else: + print("Successfully created the model directory '%s' " % read_model_path) + +####################################################### +# Training loop +####################################################### + +for i in range(epoch): + + train_loss = 0.0 + valid_loss = 0.0 + train_loss_list = [] + valid_loss_list = [] + since = time.time() + scheduler.step() + # lr = scheduler.get_lr() + + ####################################################### + # Training Data + ####################################################### + + model_test.train() + k = 1 + + for x, y in train_loader: + # print("x: ", x.shape) + # print("y: ", y.shape) + x, y = x.to(device), y.to(device) + + opt.zero_grad() + + y_pred = model_test(x) + lossT = calc_loss(y_pred, y) # Dice_loss Used + + train_loss += lossT.item() * x.size(0) + lossT.backward() + opt.step() + x_size = lossT.item() * x.size(0) + k = 2 + + train_loss_list.append(train_loss) + + ####################################################### + # Validation Step + ####################################################### + + model_test.eval() + torch.no_grad() # to increase the validation process uses less memory + + for x1, y1 in valid_loader: + x1, y1 = x1.to(device), y1.to(device) + + y_pred1 = model_test(x1) + lossL = calc_loss(y_pred1, y1) # Dice_loss Used + + valid_loss += lossL.item() * x1.size(0) + x_size1 = lossL.item() * x1.size(0) + + valid_loss_list.append(valid_loss) + + if (i + 1) % 1 == 0: + print('Epoch: {}/{} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f}'.format(i + 1, epoch, train_loss, + valid_loss)) + ####################################################### + # Early Stopping + ####################################################### + + if valid_loss <= valid_loss_min and epoch_valid >= i: # and i_valid <= 2: + + print('Validation loss decreased ({:.6f} --> {:.6f}). Saving model '.format(valid_loss_min, valid_loss)) + torch.save(model_test.state_dict(), './model/Unet_D_' + + str(epoch) + '_' + str(batch_size) + '/Unet_epoch_' + str(epoch) + + '_batchsize_' + str(batch_size) + '.pth') + + if round(valid_loss, 4) == round(valid_loss_min, 4): + print(i_valid) + i_valid = i_valid + 1 + valid_loss_min = valid_loss + +if torch.cuda.is_available(): + torch.cuda.empty_cache() + +####################################################### +# Loading the model +####################################################### + +model_test.load_state_dict(torch.load('./model/Unet_D_' + + str(epoch) + '_' + str(batch_size) + '/Unet_epoch_' + str(epoch) + + '_batchsize_' + str(batch_size) + '.pth')) + +model_test.eval() + +####################################################### +# opening the test folder and creating a folder for generated images +####################################################### + +read_test_folder = glob.glob(test_folderP) +x_sort_test = natsort.natsorted(read_test_folder) # To sort + +read_test_folder112 = './model/gen_images' + +if os.path.exists(read_test_folder112) and os.path.isdir(read_test_folder112): + shutil.rmtree(read_test_folder112) + +try: + os.mkdir(read_test_folder112) +except OSError: + print("Creation of the testing directory %s failed" % read_test_folder112) +else: + print("Successfully created the testing directory %s " % read_test_folder112) + +# For Prediction Threshold + +read_test_folder_P_Thres = './model/pred_threshold' + +if os.path.exists(read_test_folder_P_Thres) and os.path.isdir(read_test_folder_P_Thres): + shutil.rmtree(read_test_folder_P_Thres) + +try: + os.mkdir(read_test_folder_P_Thres) +except OSError: + print("Creation of the testing directory %s failed" % read_test_folder_P_Thres) +else: + print("Successfully created the testing directory %s " % read_test_folder_P_Thres) + +# For Label Threshold + +read_test_folder_L_Thres = './model/label_threshold' + +if os.path.exists(read_test_folder_L_Thres) and os.path.isdir(read_test_folder_L_Thres): + shutil.rmtree(read_test_folder_L_Thres) + +try: + os.mkdir(read_test_folder_L_Thres) +except OSError: + print("Creation of the testing directory %s failed" % read_test_folder_L_Thres) +else: + print("Successfully created the testing directory %s " % read_test_folder_L_Thres) + +####################################################### +# saving the images in the files +####################################################### + +img_test_no = 0 + +for i in range(len(read_test_folder)): + im = Image.open(x_sort_test[i]).convert("RGB") + + im1 = im + im_n = np.array(im1) + im_n_flat = im_n.reshape(-1, 1) + + for j in range(im_n_flat.shape[0]): + if im_n_flat[j] != 0: + im_n_flat[j] = 255 + + s = data_transform(im) + pred = model_test(s.unsqueeze(0).cuda()).cpu() + pred = F.sigmoid(pred) + pred = pred.detach().numpy() + + # pred = threshold_predictions_p(pred) #Value kept 0.01 as max is 1 and noise is very small. + + if i % 24 == 0: + img_test_no = img_test_no + 1 + + x1 = plt.imsave('./model/gen_images/im_epoch_' + str(epoch) + 'int_' + str(i) + + '_img_no_' + str(img_test_no) + '.png', pred[0][0], cmap='gray') + +#################################################### +# Calculating the Dice Score +#################################################### + +data_transform = torchvision.transforms.Compose([ + torchvision.transforms.Resize((128, 128)), + # torchvision.transforms.CenterCrop(96), + torchvision.transforms.Grayscale(), + # torchvision.transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) +]) + +read_test_folderP = glob.glob('./model/gen_images/*') +x_sort_testP = natsort.natsorted(read_test_folderP) + +read_test_folderL = glob.glob(test_folderL) +x_sort_testL = natsort.natsorted(read_test_folderL) # To sort + +dice_score123 = 0.0 +x_count = 0 +x_dice = 0 + +for i in range(len(read_test_folderP)): + + x = Image.open(x_sort_testP[i]).convert("L") + s = data_transform(x) + s = np.array(s) + s = threshold_predictions_v(s) + # print(s) + # print("------------------") + + # save the images + x1 = plt.imsave('./model/pred_threshold/im_epoch_' + str(epoch) + 'int_' + str(i) + + '_img_no_' + str(img_test_no) + '.png', s) + + y = Image.open(x_sort_testL[i]).convert("L") + s2 = data_transform(y) + s3 = np.array(s2) + s2 = threshold_predictions_v(s2) + # print(s3) + + # save the Images + y1 = plt.imsave('./model/label_threshold/im_epoch_' + str(epoch) + 'int_' + str(i) + + '_img_no_' + str(img_test_no) + '.png', s3) + + total = dice_coeff(s, s3) + print(total) + + if total <= 0.8: + x_count += 1 + if total > 0.8: + x_dice = x_dice + total + dice_score123 = dice_score123 + total + +# print('Dice Score : ' + str(dice_score123/len(read_test_folderP))) +print(x_count) +print(x_dice) +print('Dice Score : ' + str(float(x_dice / (len(read_test_folderP) - x_count)))) + diff --git a/recognition/s4627182_Improved_Unet/train_1027.log b/recognition/s4627182_Improved_Unet/train_1027.log new file mode 100755 index 000000000..6d4e6c387 --- /dev/null +++ b/recognition/s4627182_Improved_Unet/train_1027.log @@ -0,0 +1,655 @@ +CUDA is available. Training on GPU +batch_size = 16 +epoch = 400 +random_seed = 3 +---------------------------------------------------------------- + Layer (type) Output Shape Param # +================================================================ + Conv2d-1 [-1, 64, 128, 128] 1,792 + Conv2d-2 [-1, 64, 128, 128] 36,928 + BatchNorm2d-3 [-1, 64, 128, 128] 128 + ReLU-4 [-1, 64, 128, 128] 0 + Dropout2d-5 [-1, 64, 128, 128] 0 + Conv2d-6 [-1, 64, 128, 128] 36,928 + BatchNorm2d-7 [-1, 64, 128, 128] 128 + ReLU-8 [-1, 64, 128, 128] 0 + ContextModule-9 [-1, 64, 128, 128] 0 + Conv2d-10 [-1, 128, 64, 64] 73,856 + Conv2d-11 [-1, 128, 64, 64] 147,584 + BatchNorm2d-12 [-1, 128, 64, 64] 256 + ReLU-13 [-1, 128, 64, 64] 0 + Dropout2d-14 [-1, 128, 64, 64] 0 + Conv2d-15 [-1, 128, 64, 64] 147,584 + BatchNorm2d-16 [-1, 128, 64, 64] 256 + ReLU-17 [-1, 128, 64, 64] 0 + ContextModule-18 [-1, 128, 64, 64] 0 + Conv2d-19 [-1, 256, 32, 32] 295,168 + Conv2d-20 [-1, 256, 32, 32] 590,080 + BatchNorm2d-21 [-1, 256, 32, 32] 512 + ReLU-22 [-1, 256, 32, 32] 0 + Dropout2d-23 [-1, 256, 32, 32] 0 + Conv2d-24 [-1, 256, 32, 32] 590,080 + BatchNorm2d-25 [-1, 256, 32, 32] 512 + ReLU-26 [-1, 256, 32, 32] 0 + ContextModule-27 [-1, 256, 32, 32] 0 + Conv2d-28 [-1, 512, 16, 16] 1,180,160 + Conv2d-29 [-1, 512, 16, 16] 2,359,808 + BatchNorm2d-30 [-1, 512, 16, 16] 1,024 + ReLU-31 [-1, 512, 16, 16] 0 + Dropout2d-32 [-1, 512, 16, 16] 0 + Conv2d-33 [-1, 512, 16, 16] 2,359,808 + BatchNorm2d-34 [-1, 512, 16, 16] 1,024 + ReLU-35 [-1, 512, 16, 16] 0 + ContextModule-36 [-1, 512, 16, 16] 0 + Conv2d-37 [-1, 1024, 8, 8] 4,719,616 + Conv2d-38 [-1, 1024, 8, 8] 9,438,208 + BatchNorm2d-39 [-1, 1024, 8, 8] 2,048 + ReLU-40 [-1, 1024, 8, 8] 0 + Dropout2d-41 [-1, 1024, 8, 8] 0 + Conv2d-42 [-1, 1024, 8, 8] 9,438,208 + BatchNorm2d-43 [-1, 1024, 8, 8] 2,048 + ReLU-44 [-1, 1024, 8, 8] 0 + ContextModule-45 [-1, 1024, 8, 8] 0 + Upsample-46 [-1, 1024, 16, 16] 0 + Conv2d-47 [-1, 512, 16, 16] 4,719,104 + UpsamplingModule-48 [-1, 512, 16, 16] 0 + Conv2d-49 [-1, 1024, 16, 16] 9,438,208 + Conv2d-50 [-1, 512, 16, 16] 4,719,104 +LocalisationModule-51 [-1, 512, 16, 16] 0 + Upsample-52 [-1, 512, 32, 32] 0 + Conv2d-53 [-1, 256, 32, 32] 1,179,904 + UpsamplingModule-54 [-1, 256, 32, 32] 0 + Conv2d-55 [-1, 512, 32, 32] 2,359,808 + Conv2d-56 [-1, 256, 32, 32] 1,179,904 +LocalisationModule-57 [-1, 256, 32, 32] 0 + Conv2d-58 [-1, 1, 32, 32] 2,305 +SegmentationLayer-59 [-1, 1, 32, 32] 0 + Upsample-60 [-1, 256, 64, 64] 0 + Conv2d-61 [-1, 128, 64, 64] 295,040 + UpsamplingModule-62 [-1, 128, 64, 64] 0 + Upsample-63 [-1, 1, 64, 64] 0 + UpscalingLayer-64 [-1, 1, 64, 64] 0 + Conv2d-65 [-1, 256, 64, 64] 590,080 + Conv2d-66 [-1, 128, 64, 64] 295,040 +LocalisationModule-67 [-1, 128, 64, 64] 0 + Conv2d-68 [-1, 1, 64, 64] 1,153 +SegmentationLayer-69 [-1, 1, 64, 64] 0 + Upsample-70 [-1, 128, 128, 128] 0 + Conv2d-71 [-1, 64, 128, 128] 73,792 + UpsamplingModule-72 [-1, 64, 128, 128] 0 + Upsample-73 [-1, 1, 128, 128] 0 + UpscalingLayer-74 [-1, 1, 128, 128] 0 + Conv2d-75 [-1, 128, 128, 128] 147,584 + Conv2d-76 [-1, 1, 128, 128] 1,153 +SegmentationLayer-77 [-1, 1, 128, 128] 0 +================================================================ +Total params: 56,425,923 +Trainable params: 56,425,923 +Non-trainable params: 0 +---------------------------------------------------------------- +Input size (MB): 0.19 +Forward/backward pass size (MB): 244.14 +Params size (MB): 215.25 +Estimated Total Size (MB): 459.58 +---------------------------------------------------------------- +Successfully created the main directory './model' +Successfully created the prediction directory './model/pred' of dice loss +Successfully created the model directory './model/Unet_D_400_16' +Epoch: 1/400 Training Loss: 1061.040586 Validation Loss: 912.424372 +Validation loss decreased (inf --> 912.424372). Saving model +Epoch: 2/400 Training Loss: 807.341524 Validation Loss: 676.565119 +Validation loss decreased (912.424372 --> 676.565119). Saving model +Epoch: 3/400 Training Loss: 788.169664 Validation Loss: 737.945829 +Epoch: 4/400 Training Loss: 685.314689 Validation Loss: 658.081859 +Validation loss decreased (676.565119 --> 658.081859). Saving model +Epoch: 5/400 Training Loss: 622.998050 Validation Loss: 859.964467 +Epoch: 6/400 Training Loss: 633.571476 Validation Loss: 587.444989 +Validation loss decreased (658.081859 --> 587.444989). Saving model +Epoch: 7/400 Training Loss: 571.380931 Validation Loss: 524.536493 +Validation loss decreased (587.444989 --> 524.536493). Saving model +Epoch: 8/400 Training Loss: 532.268150 Validation Loss: 643.475564 +Epoch: 9/400 Training Loss: 511.374725 Validation Loss: 463.643838 +Validation loss decreased (524.536493 --> 463.643838). Saving model +Epoch: 10/400 Training Loss: 485.633548 Validation Loss: 460.078441 +Validation loss decreased (463.643838 --> 460.078441). Saving model +Epoch: 11/400 Training Loss: 487.347852 Validation Loss: 450.552999 +Validation loss decreased (460.078441 --> 450.552999). Saving model +Epoch: 12/400 Training Loss: 473.160938 Validation Loss: 473.249527 +Epoch: 13/400 Training Loss: 468.547054 Validation Loss: 442.757757 +Validation loss decreased (450.552999 --> 442.757757). Saving model +Epoch: 14/400 Training Loss: 462.058998 Validation Loss: 415.449000 +Validation loss decreased (442.757757 --> 415.449000). Saving model +Epoch: 15/400 Training Loss: 439.305906 Validation Loss: 405.742114 +Validation loss decreased (415.449000 --> 405.742114). Saving model +Epoch: 16/400 Training Loss: 467.854116 Validation Loss: 415.819749 +Epoch: 17/400 Training Loss: 424.860595 Validation Loss: 418.844686 +Epoch: 18/400 Training Loss: 430.135481 Validation Loss: 457.015388 +Epoch: 19/400 Training Loss: 416.098201 Validation Loss: 388.079262 +Validation loss decreased (405.742114 --> 388.079262). Saving model +Epoch: 20/400 Training Loss: 411.228808 Validation Loss: 403.862916 +Epoch: 21/400 Training Loss: 403.458714 Validation Loss: 366.746401 +Validation loss decreased (388.079262 --> 366.746401). Saving model +Epoch: 22/400 Training Loss: 394.347869 Validation Loss: 359.135790 +Validation loss decreased (366.746401 --> 359.135790). Saving model +Epoch: 23/400 Training Loss: 394.740691 Validation Loss: 349.929157 +Validation loss decreased (359.135790 --> 349.929157). Saving model +Epoch: 24/400 Training Loss: 388.239514 Validation Loss: 357.371899 +Epoch: 25/400 Training Loss: 394.745241 Validation Loss: 342.020035 +Validation loss decreased (349.929157 --> 342.020035). Saving model +Epoch: 26/400 Training Loss: 379.792490 Validation Loss: 333.376750 +Validation loss decreased (342.020035 --> 333.376750). Saving model +Epoch: 27/400 Training Loss: 378.106891 Validation Loss: 333.158253 +Validation loss decreased (333.376750 --> 333.158253). Saving model +Epoch: 28/400 Training Loss: 374.005240 Validation Loss: 341.534550 +Epoch: 29/400 Training Loss: 365.381918 Validation Loss: 343.385013 +Epoch: 30/400 Training Loss: 360.885176 Validation Loss: 326.890084 +Validation loss decreased (333.158253 --> 326.890084). Saving model +Epoch: 31/400 Training Loss: 353.897421 Validation Loss: 325.702542 +Validation loss decreased (326.890084 --> 325.702542). Saving model +Epoch: 32/400 Training Loss: 347.723119 Validation Loss: 317.366541 +Validation loss decreased (325.702542 --> 317.366541). Saving model +Epoch: 33/400 Training Loss: 335.829830 Validation Loss: 294.269661 +Validation loss decreased (317.366541 --> 294.269661). Saving model +Epoch: 34/400 Training Loss: 323.665230 Validation Loss: 307.029580 +Epoch: 35/400 Training Loss: 323.621175 Validation Loss: 283.969697 +Validation loss decreased (294.269661 --> 283.969697). Saving model +Epoch: 36/400 Training Loss: 313.112701 Validation Loss: 282.973031 +Validation loss decreased (283.969697 --> 282.973031). Saving model +Epoch: 37/400 Training Loss: 312.241401 Validation Loss: 269.916116 +Validation loss decreased (282.973031 --> 269.916116). Saving model +Epoch: 38/400 Training Loss: 293.589954 Validation Loss: 293.864337 +Epoch: 39/400 Training Loss: 301.954354 Validation Loss: 290.813052 +Epoch: 40/400 Training Loss: 334.603461 Validation Loss: 280.672247 +Epoch: 41/400 Training Loss: 286.081750 Validation Loss: 338.717083 +Epoch: 42/400 Training Loss: 284.131328 Validation Loss: 245.798872 +Validation loss decreased (269.916116 --> 245.798872). Saving model +Epoch: 43/400 Training Loss: 264.045146 Validation Loss: 237.293003 +Validation loss decreased (245.798872 --> 237.293003). Saving model +Epoch: 44/400 Training Loss: 256.878089 Validation Loss: 240.130936 +Epoch: 45/400 Training Loss: 250.128028 Validation Loss: 226.022200 +Validation loss decreased (237.293003 --> 226.022200). Saving model +Epoch: 46/400 Training Loss: 241.849316 Validation Loss: 217.071646 +Validation loss decreased (226.022200 --> 217.071646). Saving model +Epoch: 47/400 Training Loss: 243.367964 Validation Loss: 209.199570 +Validation loss decreased (217.071646 --> 209.199570). Saving model +Epoch: 48/400 Training Loss: 239.409878 Validation Loss: 209.252160 +Epoch: 49/400 Training Loss: 228.180571 Validation Loss: 203.223335 +Validation loss decreased (209.199570 --> 203.223335). Saving model +Epoch: 50/400 Training Loss: 215.992252 Validation Loss: 204.272842 +Epoch: 51/400 Training Loss: 205.904881 Validation Loss: 187.108767 +Validation loss decreased (203.223335 --> 187.108767). Saving model +Epoch: 52/400 Training Loss: 197.824698 Validation Loss: 189.644662 +Epoch: 53/400 Training Loss: 209.248301 Validation Loss: 180.081342 +Validation loss decreased (187.108767 --> 180.081342). Saving model +Epoch: 54/400 Training Loss: 188.223354 Validation Loss: 164.384920 +Validation loss decreased (180.081342 --> 164.384920). Saving model +Epoch: 55/400 Training Loss: 186.443871 Validation Loss: 170.529489 +Epoch: 56/400 Training Loss: 181.579567 Validation Loss: 155.423865 +Validation loss decreased (164.384920 --> 155.423865). Saving model +Epoch: 57/400 Training Loss: 168.306167 Validation Loss: 158.810373 +Epoch: 58/400 Training Loss: 201.065988 Validation Loss: 157.144164 +Epoch: 59/400 Training Loss: 167.387380 Validation Loss: 149.855929 +Validation loss decreased (155.423865 --> 149.855929). Saving model +Epoch: 60/400 Training Loss: 159.051860 Validation Loss: 141.215441 +Validation loss decreased (149.855929 --> 141.215441). Saving model +Epoch: 61/400 Training Loss: 149.556400 Validation Loss: 142.026790 +Epoch: 62/400 Training Loss: 150.121404 Validation Loss: 204.188889 +Epoch: 63/400 Training Loss: 212.495137 Validation Loss: 150.139140 +Epoch: 64/400 Training Loss: 152.832260 Validation Loss: 135.541699 +Validation loss decreased (141.215441 --> 135.541699). Saving model +Epoch: 65/400 Training Loss: 142.707832 Validation Loss: 123.365094 +Validation loss decreased (135.541699 --> 123.365094). Saving model +Epoch: 66/400 Training Loss: 132.423079 Validation Loss: 118.708302 +Validation loss decreased (123.365094 --> 118.708302). Saving model +Epoch: 67/400 Training Loss: 127.058920 Validation Loss: 118.077027 +Validation loss decreased (118.708302 --> 118.077027). Saving model +Epoch: 68/400 Training Loss: 125.902745 Validation Loss: 113.771395 +Validation loss decreased (118.077027 --> 113.771395). Saving model +Epoch: 69/400 Training Loss: 122.589851 Validation Loss: 108.309938 +Validation loss decreased (113.771395 --> 108.309938). Saving model +Epoch: 70/400 Training Loss: 116.520732 Validation Loss: 104.185483 +Validation loss decreased (108.309938 --> 104.185483). Saving model +Epoch: 71/400 Training Loss: 113.282104 Validation Loss: 105.094542 +Epoch: 72/400 Training Loss: 113.073582 Validation Loss: 102.034964 +Validation loss decreased (104.185483 --> 102.034964). Saving model +Epoch: 73/400 Training Loss: 110.196319 Validation Loss: 100.578707 +Validation loss decreased (102.034964 --> 100.578707). Saving model +Epoch: 74/400 Training Loss: 106.790912 Validation Loss: 97.823300 +Validation loss decreased (100.578707 --> 97.823300). Saving model +Epoch: 75/400 Training Loss: 103.676990 Validation Loss: 93.663480 +Validation loss decreased (97.823300 --> 93.663480). Saving model +Epoch: 76/400 Training Loss: 109.075983 Validation Loss: 97.628516 +Epoch: 77/400 Training Loss: 98.466418 Validation Loss: 88.127295 +Validation loss decreased (93.663480 --> 88.127295). Saving model +Epoch: 78/400 Training Loss: 95.558790 Validation Loss: 87.354366 +Validation loss decreased (88.127295 --> 87.354366). Saving model +Epoch: 79/400 Training Loss: 90.693348 Validation Loss: 84.092111 +Validation loss decreased (87.354366 --> 84.092111). Saving model +Epoch: 80/400 Training Loss: 90.096241 Validation Loss: 89.972719 +Epoch: 81/400 Training Loss: 102.071019 Validation Loss: 82.813178 +Validation loss decreased (84.092111 --> 82.813178). Saving model +Epoch: 82/400 Training Loss: 87.752623 Validation Loss: 81.717514 +Validation loss decreased (82.813178 --> 81.717514). Saving model +Epoch: 83/400 Training Loss: 83.925954 Validation Loss: 79.330445 +Validation loss decreased (81.717514 --> 79.330445). Saving model +Epoch: 84/400 Training Loss: 82.089971 Validation Loss: 77.713488 +Validation loss decreased (79.330445 --> 77.713488). Saving model +Epoch: 85/400 Training Loss: 80.846364 Validation Loss: 75.438952 +Validation loss decreased (77.713488 --> 75.438952). Saving model +Epoch: 86/400 Training Loss: 96.945972 Validation Loss: 81.830757 +Epoch: 87/400 Training Loss: 84.374791 Validation Loss: 92.017662 +Epoch: 88/400 Training Loss: 94.547236 Validation Loss: 78.743844 +Epoch: 89/400 Training Loss: 80.129562 Validation Loss: 70.094970 +Validation loss decreased (75.438952 --> 70.094970). Saving model +Epoch: 90/400 Training Loss: 75.148027 Validation Loss: 67.952423 +Validation loss decreased (70.094970 --> 67.952423). Saving model +Epoch: 91/400 Training Loss: 71.516357 Validation Loss: 67.441796 +Validation loss decreased (67.952423 --> 67.441796). Saving model +Epoch: 92/400 Training Loss: 71.059545 Validation Loss: 65.321013 +Validation loss decreased (67.441796 --> 65.321013). Saving model +Epoch: 93/400 Training Loss: 68.354872 Validation Loss: 64.376034 +Validation loss decreased (65.321013 --> 64.376034). Saving model +Epoch: 94/400 Training Loss: 68.523620 Validation Loss: 62.674626 +Validation loss decreased (64.376034 --> 62.674626). Saving model +Epoch: 95/400 Training Loss: 69.379274 Validation Loss: 66.370282 +Epoch: 96/400 Training Loss: 68.092619 Validation Loss: 64.087731 +Epoch: 97/400 Training Loss: 66.519958 Validation Loss: 61.443635 +Validation loss decreased (62.674626 --> 61.443635). Saving model +Epoch: 98/400 Training Loss: 64.315551 Validation Loss: 60.731580 +Validation loss decreased (61.443635 --> 60.731580). Saving model +Epoch: 99/400 Training Loss: 63.353009 Validation Loss: 58.652497 +Validation loss decreased (60.731580 --> 58.652497). Saving model +Epoch: 100/400 Training Loss: 62.265917 Validation Loss: 58.877543 +Epoch: 101/400 Training Loss: 62.211798 Validation Loss: 59.258699 +Epoch: 102/400 Training Loss: 61.719767 Validation Loss: 57.461481 +Validation loss decreased (58.652497 --> 57.461481). Saving model +Epoch: 103/400 Training Loss: 61.639149 Validation Loss: 58.533605 +Epoch: 104/400 Training Loss: 61.445636 Validation Loss: 56.639582 +Validation loss decreased (57.461481 --> 56.639582). Saving model +Epoch: 105/400 Training Loss: 59.362152 Validation Loss: 56.656629 +Epoch: 106/400 Training Loss: 58.756677 Validation Loss: 55.231681 +Validation loss decreased (56.639582 --> 55.231681). Saving model +Epoch: 107/400 Training Loss: 63.281795 Validation Loss: 59.757134 +Epoch: 108/400 Training Loss: 60.515866 Validation Loss: 56.276711 +Epoch: 109/400 Training Loss: 59.133282 Validation Loss: 54.909756 +Validation loss decreased (55.231681 --> 54.909756). Saving model +Epoch: 110/400 Training Loss: 56.366112 Validation Loss: 53.538045 +Validation loss decreased (54.909756 --> 53.538045). Saving model +Epoch: 111/400 Training Loss: 55.936860 Validation Loss: 52.752753 +Validation loss decreased (53.538045 --> 52.752753). Saving model +Epoch: 112/400 Training Loss: 54.518844 Validation Loss: 51.101270 +Validation loss decreased (52.752753 --> 51.101270). Saving model +Epoch: 113/400 Training Loss: 53.827186 Validation Loss: 51.879462 +Epoch: 114/400 Training Loss: 54.255090 Validation Loss: 52.402470 +Epoch: 115/400 Training Loss: 55.552549 Validation Loss: 52.249155 +Epoch: 116/400 Training Loss: 54.331744 Validation Loss: 51.474586 +Epoch: 117/400 Training Loss: 52.897610 Validation Loss: 50.664583 +Validation loss decreased (51.101270 --> 50.664583). Saving model +Epoch: 118/400 Training Loss: 53.051793 Validation Loss: 49.519841 +Validation loss decreased (50.664583 --> 49.519841). Saving model +Epoch: 119/400 Training Loss: 51.708255 Validation Loss: 48.992335 +Validation loss decreased (49.519841 --> 48.992335). Saving model +Epoch: 120/400 Training Loss: 51.274171 Validation Loss: 48.874392 +Validation loss decreased (48.992335 --> 48.874392). Saving model +Epoch: 121/400 Training Loss: 52.212324 Validation Loss: 48.913681 +Epoch: 122/400 Training Loss: 51.692998 Validation Loss: 49.134355 +Epoch: 123/400 Training Loss: 50.945224 Validation Loss: 48.389371 +Validation loss decreased (48.874392 --> 48.389371). Saving model +Epoch: 124/400 Training Loss: 49.741009 Validation Loss: 47.859187 +Validation loss decreased (48.389371 --> 47.859187). Saving model +Epoch: 125/400 Training Loss: 49.479254 Validation Loss: 47.683828 +Validation loss decreased (47.859187 --> 47.683828). Saving model +Epoch: 126/400 Training Loss: 48.915316 Validation Loss: 53.546905 +Epoch: 127/400 Training Loss: 57.852731 Validation Loss: 49.623144 +Epoch: 128/400 Training Loss: 51.117120 Validation Loss: 47.488822 +Validation loss decreased (47.683828 --> 47.488822). Saving model +Epoch: 129/400 Training Loss: 48.605621 Validation Loss: 45.941612 +Validation loss decreased (47.488822 --> 45.941612). Saving model +Epoch: 130/400 Training Loss: 48.412892 Validation Loss: 46.984053 +Epoch: 131/400 Training Loss: 47.991114 Validation Loss: 45.708279 +Validation loss decreased (45.941612 --> 45.708279). Saving model +Epoch: 132/400 Training Loss: 47.777391 Validation Loss: 45.016074 +Validation loss decreased (45.708279 --> 45.016074). Saving model +Epoch: 133/400 Training Loss: 46.889140 Validation Loss: 45.438185 +Epoch: 134/400 Training Loss: 46.800847 Validation Loss: 47.303258 +Epoch: 135/400 Training Loss: 52.601026 Validation Loss: 45.895785 +Epoch: 136/400 Training Loss: 47.599701 Validation Loss: 44.792831 +Validation loss decreased (45.016074 --> 44.792831). Saving model +Epoch: 137/400 Training Loss: 46.174252 Validation Loss: 43.981920 +Validation loss decreased (44.792831 --> 43.981920). Saving model +Epoch: 138/400 Training Loss: 45.656598 Validation Loss: 44.252446 +Epoch: 139/400 Training Loss: 46.298585 Validation Loss: 43.861838 +Validation loss decreased (43.981920 --> 43.861838). Saving model +Epoch: 140/400 Training Loss: 44.998418 Validation Loss: 43.118733 +Validation loss decreased (43.861838 --> 43.118733). Saving model +Epoch: 141/400 Training Loss: 44.791426 Validation Loss: 43.134085 +Epoch: 142/400 Training Loss: 44.572067 Validation Loss: 43.680795 +Epoch: 143/400 Training Loss: 44.453955 Validation Loss: 43.107780 +Validation loss decreased (43.118733 --> 43.107780). Saving model +Epoch: 144/400 Training Loss: 44.313050 Validation Loss: 42.907387 +Validation loss decreased (43.107780 --> 42.907387). Saving model +Epoch: 145/400 Training Loss: 46.588209 Validation Loss: 44.384092 +Epoch: 146/400 Training Loss: 44.924211 Validation Loss: 43.206105 +Epoch: 147/400 Training Loss: 43.884377 Validation Loss: 42.442903 +Validation loss decreased (42.907387 --> 42.442903). Saving model +Epoch: 148/400 Training Loss: 43.893250 Validation Loss: 42.110008 +Validation loss decreased (42.442903 --> 42.110008). Saving model +Epoch: 149/400 Training Loss: 43.428452 Validation Loss: 41.762002 +Validation loss decreased (42.110008 --> 41.762002). Saving model +Epoch: 150/400 Training Loss: 43.283952 Validation Loss: 42.395872 +Epoch: 151/400 Training Loss: 43.003662 Validation Loss: 41.741479 +Validation loss decreased (41.762002 --> 41.741479). Saving model +Epoch: 152/400 Training Loss: 43.225669 Validation Loss: 42.388687 +Epoch: 153/400 Training Loss: 43.092538 Validation Loss: 41.619283 +Validation loss decreased (41.741479 --> 41.619283). Saving model +Epoch: 154/400 Training Loss: 42.947855 Validation Loss: 41.708224 +Epoch: 155/400 Training Loss: 42.891883 Validation Loss: 41.685392 +Epoch: 156/400 Training Loss: 42.733508 Validation Loss: 41.269432 +Validation loss decreased (41.619283 --> 41.269432). Saving model +Epoch: 157/400 Training Loss: 42.313471 Validation Loss: 40.984817 +Validation loss decreased (41.269432 --> 40.984817). Saving model +Epoch: 158/400 Training Loss: 42.311620 Validation Loss: 40.896179 +Validation loss decreased (40.984817 --> 40.896179). Saving model +Epoch: 159/400 Training Loss: 42.232660 Validation Loss: 41.509980 +Epoch: 160/400 Training Loss: 42.047981 Validation Loss: 40.929867 +Epoch: 161/400 Training Loss: 41.933024 Validation Loss: 40.683537 +Validation loss decreased (40.896179 --> 40.683537). Saving model +Epoch: 162/400 Training Loss: 41.699801 Validation Loss: 40.343880 +Validation loss decreased (40.683537 --> 40.343880). Saving model +Epoch: 163/400 Training Loss: 41.563602 Validation Loss: 40.642308 +Epoch: 164/400 Training Loss: 41.486409 Validation Loss: 40.040694 +Validation loss decreased (40.343880 --> 40.040694). Saving model +Epoch: 165/400 Training Loss: 41.254304 Validation Loss: 40.393017 +Epoch: 166/400 Training Loss: 41.210776 Validation Loss: 40.261863 +Epoch: 167/400 Training Loss: 41.541789 Validation Loss: 40.360627 +Epoch: 168/400 Training Loss: 41.157151 Validation Loss: 40.250205 +Epoch: 169/400 Training Loss: 41.989366 Validation Loss: 42.370234 +Epoch: 170/400 Training Loss: 41.887676 Validation Loss: 40.112599 +Epoch: 171/400 Training Loss: 40.685515 Validation Loss: 39.714005 +Validation loss decreased (40.040694 --> 39.714005). Saving model +Epoch: 172/400 Training Loss: 41.004848 Validation Loss: 39.738895 +Epoch: 173/400 Training Loss: 40.674297 Validation Loss: 39.434154 +Validation loss decreased (39.714005 --> 39.434154). Saving model +Epoch: 174/400 Training Loss: 40.587094 Validation Loss: 39.242333 +Validation loss decreased (39.434154 --> 39.242333). Saving model +Epoch: 175/400 Training Loss: 40.486195 Validation Loss: 39.002650 +Validation loss decreased (39.242333 --> 39.002650). Saving model +Epoch: 176/400 Training Loss: 39.985531 Validation Loss: 39.069855 +Epoch: 177/400 Training Loss: 39.864383 Validation Loss: 38.980456 +Validation loss decreased (39.002650 --> 38.980456). Saving model +Epoch: 178/400 Training Loss: 39.853218 Validation Loss: 38.901299 +Validation loss decreased (38.980456 --> 38.901299). Saving model +Epoch: 179/400 Training Loss: 39.868983 Validation Loss: 39.527089 +Epoch: 180/400 Training Loss: 40.102557 Validation Loss: 39.190627 +Epoch: 181/400 Training Loss: 40.005243 Validation Loss: 38.496270 +Validation loss decreased (38.901299 --> 38.496270). Saving model +Epoch: 182/400 Training Loss: 39.427758 Validation Loss: 38.700621 +Epoch: 183/400 Training Loss: 39.492923 Validation Loss: 38.747272 +Epoch: 184/400 Training Loss: 39.435629 Validation Loss: 38.377984 +Validation loss decreased (38.496270 --> 38.377984). Saving model +Epoch: 185/400 Training Loss: 39.413908 Validation Loss: 38.559451 +Epoch: 186/400 Training Loss: 39.329538 Validation Loss: 39.410018 +Epoch: 187/400 Training Loss: 40.684599 Validation Loss: 38.781829 +Epoch: 188/400 Training Loss: 39.577623 Validation Loss: 38.443941 +Epoch: 189/400 Training Loss: 39.549838 Validation Loss: 41.486069 +Epoch: 190/400 Training Loss: 39.959455 Validation Loss: 38.302380 +Validation loss decreased (38.377984 --> 38.302380). Saving model +Epoch: 191/400 Training Loss: 38.908573 Validation Loss: 37.864371 +Validation loss decreased (38.302380 --> 37.864371). Saving model +Epoch: 192/400 Training Loss: 38.743048 Validation Loss: 37.868151 +Epoch: 193/400 Training Loss: 38.394250 Validation Loss: 37.694380 +Validation loss decreased (37.864371 --> 37.694380). Saving model +Epoch: 194/400 Training Loss: 38.828681 Validation Loss: 37.958403 +Epoch: 195/400 Training Loss: 38.752893 Validation Loss: 37.957136 +Epoch: 196/400 Training Loss: 38.409605 Validation Loss: 37.551361 +Validation loss decreased (37.694380 --> 37.551361). Saving model +Epoch: 197/400 Training Loss: 38.336854 Validation Loss: 37.440372 +Validation loss decreased (37.551361 --> 37.440372). 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Saving model +Epoch: 207/400 Training Loss: 38.731873 Validation Loss: 38.400833 +Epoch: 208/400 Training Loss: 38.476804 Validation Loss: 37.324264 +Epoch: 209/400 Training Loss: 37.865620 Validation Loss: 37.177320 +Epoch: 210/400 Training Loss: 37.778688 Validation Loss: 37.013744 +Validation loss decreased (37.087106 --> 37.013744). Saving model +Epoch: 211/400 Training Loss: 37.715953 Validation Loss: 37.004627 +Validation loss decreased (37.013744 --> 37.004627). Saving model +Epoch: 212/400 Training Loss: 37.601938 Validation Loss: 36.834124 +Validation loss decreased (37.004627 --> 36.834124). Saving model +Epoch: 213/400 Training Loss: 37.725169 Validation Loss: 37.036269 +Epoch: 214/400 Training Loss: 37.587018 Validation Loss: 36.880560 +Epoch: 215/400 Training Loss: 37.765382 Validation Loss: 36.922154 +Epoch: 216/400 Training Loss: 37.603968 Validation Loss: 36.944878 +Epoch: 217/400 Training Loss: 37.388321 Validation Loss: 36.995525 +Epoch: 218/400 Training Loss: 37.586586 Validation Loss: 36.817358 +Validation loss decreased (36.834124 --> 36.817358). Saving model +Epoch: 219/400 Training Loss: 37.450390 Validation Loss: 36.823903 +Epoch: 220/400 Training Loss: 37.438260 Validation Loss: 37.091443 +Epoch: 221/400 Training Loss: 37.451868 Validation Loss: 36.699982 +Validation loss decreased (36.817358 --> 36.699982). Saving model +Epoch: 222/400 Training Loss: 37.331056 Validation Loss: 36.738586 +Epoch: 223/400 Training Loss: 37.156265 Validation Loss: 36.625559 +Validation loss decreased (36.699982 --> 36.625559). Saving model +Epoch: 224/400 Training Loss: 37.350534 Validation Loss: 36.672745 +Epoch: 225/400 Training Loss: 37.220693 Validation Loss: 36.429339 +Validation loss decreased (36.625559 --> 36.429339). Saving model +Epoch: 226/400 Training Loss: 37.199577 Validation Loss: 36.518238 +Epoch: 227/400 Training Loss: 37.168482 Validation Loss: 36.388669 +Validation loss decreased (36.429339 --> 36.388669). Saving model +Epoch: 228/400 Training Loss: 37.127311 Validation Loss: 36.633259 +Epoch: 229/400 Training Loss: 37.344308 Validation Loss: 36.621206 +Epoch: 230/400 Training Loss: 37.074235 Validation Loss: 36.627502 +Epoch: 231/400 Training Loss: 37.000811 Validation Loss: 36.341521 +Validation loss decreased (36.388669 --> 36.341521). Saving model +Epoch: 232/400 Training Loss: 37.035148 Validation Loss: 36.203529 +Validation loss decreased (36.341521 --> 36.203529). Saving model +Epoch: 233/400 Training Loss: 37.067683 Validation Loss: 36.297363 +Epoch: 234/400 Training Loss: 37.004985 Validation Loss: 36.260934 +Epoch: 235/400 Training Loss: 36.946609 Validation Loss: 36.577809 +Epoch: 236/400 Training Loss: 36.942857 Validation Loss: 36.271908 +Epoch: 237/400 Training Loss: 36.907607 Validation Loss: 36.323314 +Epoch: 238/400 Training Loss: 37.072438 Validation Loss: 36.282375 +Epoch: 239/400 Training Loss: 36.799831 Validation Loss: 36.083367 +Validation loss decreased (36.203529 --> 36.083367). Saving model +Epoch: 240/400 Training Loss: 36.706383 Validation Loss: 36.036335 +Validation loss decreased (36.083367 --> 36.036335). Saving model +Epoch: 241/400 Training Loss: 36.653450 Validation Loss: 36.083683 +Epoch: 242/400 Training Loss: 36.545019 Validation Loss: 36.034166 +Validation loss decreased (36.036335 --> 36.034166). 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Saving model +Epoch: 251/400 Training Loss: 36.515298 Validation Loss: 36.009999 +Epoch: 252/400 Training Loss: 36.653539 Validation Loss: 35.923156 +Epoch: 253/400 Training Loss: 36.393023 Validation Loss: 35.890542 +Epoch: 254/400 Training Loss: 36.387751 Validation Loss: 35.684786 +Validation loss decreased (35.889539 --> 35.684786). Saving model +Epoch: 255/400 Training Loss: 36.471658 Validation Loss: 35.767071 +Epoch: 256/400 Training Loss: 36.582039 Validation Loss: 35.866774 +Epoch: 257/400 Training Loss: 36.207661 Validation Loss: 35.746829 +Epoch: 258/400 Training Loss: 36.290627 Validation Loss: 35.873632 +Epoch: 259/400 Training Loss: 36.349402 Validation Loss: 35.786994 +Epoch: 260/400 Training Loss: 36.296049 Validation Loss: 35.640916 +Validation loss decreased (35.684786 --> 35.640916). 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Saving model +Epoch: 363/400 Training Loss: 35.324945 Validation Loss: 35.102740 +Epoch: 364/400 Training Loss: 35.456010 Validation Loss: 34.875634 +Epoch: 365/400 Training Loss: 35.284824 Validation Loss: 35.025704 +Epoch: 366/400 Training Loss: 35.267500 Validation Loss: 34.949517 +Epoch: 367/400 Training Loss: 35.325632 Validation Loss: 34.842817 +Epoch: 368/400 Training Loss: 35.307169 Validation Loss: 34.867529 +Epoch: 369/400 Training Loss: 35.240334 Validation Loss: 34.896460 +Epoch: 370/400 Training Loss: 35.275042 Validation Loss: 34.920585 +Epoch: 371/400 Training Loss: 35.357192 Validation Loss: 34.983611 +Epoch: 372/400 Training Loss: 35.298193 Validation Loss: 34.794387 +Epoch: 373/400 Training Loss: 35.378478 Validation Loss: 34.884846 +Epoch: 374/400 Training Loss: 35.250771 Validation Loss: 34.990587 +Epoch: 375/400 Training Loss: 35.256680 Validation Loss: 34.857173 +Epoch: 376/400 Training Loss: 35.229192 Validation Loss: 34.916108 +Epoch: 377/400 Training Loss: 35.275788 Validation Loss: 34.925675 +Epoch: 378/400 Training Loss: 35.375301 Validation Loss: 34.916054 +Epoch: 379/400 Training Loss: 35.150901 Validation Loss: 34.926220 +Epoch: 380/400 Training Loss: 35.321856 Validation Loss: 34.867128 +Epoch: 381/400 Training Loss: 35.316582 Validation Loss: 34.908194 +Epoch: 382/400 Training Loss: 35.261150 Validation Loss: 34.828085 +Epoch: 383/400 Training Loss: 35.296560 Validation Loss: 34.850617 +Epoch: 384/400 Training Loss: 35.366144 Validation Loss: 34.857956 +Epoch: 385/400 Training Loss: 35.329152 Validation Loss: 34.789192 +Epoch: 386/400 Training Loss: 35.404383 Validation Loss: 35.007324 +Epoch: 387/400 Training Loss: 35.272891 Validation Loss: 34.885933 +Epoch: 388/400 Training Loss: 35.472208 Validation Loss: 34.675356 +Validation loss decreased (34.784278 --> 34.675356). Saving model +Epoch: 389/400 Training Loss: 35.287885 Validation Loss: 34.888716 +Epoch: 390/400 Training Loss: 35.205883 Validation Loss: 34.952520 +Epoch: 391/400 Training Loss: 35.218121 Validation Loss: 34.893261 +Epoch: 392/400 Training Loss: 35.275568 Validation Loss: 34.903638 +Epoch: 393/400 Training Loss: 35.295596 Validation Loss: 34.884959 +Epoch: 394/400 Training Loss: 35.259685 Validation Loss: 34.944981 +Epoch: 395/400 Training Loss: 35.291020 Validation Loss: 34.830115 +Epoch: 396/400 Training Loss: 35.251440 Validation Loss: 34.809096 +Epoch: 397/400 Training Loss: 35.290915 Validation Loss: 34.924404 +Epoch: 398/400 Training Loss: 35.311043 Validation Loss: 34.721922 +Epoch: 399/400 Training Loss: 35.194690 Validation Loss: 34.917457 +Epoch: 400/400 Training Loss: 35.225279 Validation Loss: 34.860954 +Successfully created the testing directory ./model/gen_images +Successfully created the testing directory ./model/pred_threshold +Successfully created the testing directory ./model/label_threshold +Traceback (most recent call last): + File "./train.py", line 409, in + if im_n_flat[j] != 0: +KeyboardInterrupt diff --git a/recognition/s4627182_Improved_Unet/utils.py b/recognition/s4627182_Improved_Unet/utils.py new file mode 100644 index 000000000..f53213e26 --- /dev/null +++ b/recognition/s4627182_Improved_Unet/utils.py @@ -0,0 +1,18 @@ +import matplotlib.pyplot as plt + + +def plot_img_and_mask(img, mask): + classes = mask.max() + 1 + fig, ax = plt.subplots(1, classes + 1) + ax[0].set_title('Input image') + ax[0].imshow(img) + for i in range(classes): + ax[i + 1].set_title(f'Mask (class {i + 1})') + ax[i + 1].imshow(mask == i) + plt.xticks([]), plt.yticks([]) + + + plt.show() + + +# Updated \ No newline at end of file