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How to use imageSize method in fMBT

Best Python code snippet using fMBT_python

test_one.py

Source:test_one.py

1from __future__ import print_function2import argparse3import os4import random5import torch6import torch.nn as nn7import torch.nn.parallel8import torch.backends.cudnn as cudnn9import torch.optim as optim10import torch.utils.data11import torchvision.datasets as dset12import torchvision.transforms as transforms13import torchvision.utils as vutils14from torch.autograd import Variable15from model import _netG16import utils17parser = argparse.ArgumentParser()18parser.add_argument('--dataset', default='streetview', help='cifar10 | lsun | imagenet | folder | lfw ')19parser.add_argument('--test_image', required=True, help='path to dataset')20parser.add_argument('--workers', type=int, help='number of data loading workers', default=4)21parser.add_argument('--batchSize', type=int, default=64, help='input batch size')22parser.add_argument('--imageSize', type=int, default=128, help='the height / width of the input image to network')23parser.add_argument('--nz', type=int, default=100, help='size of the latent z vector')24parser.add_argument('--ngf', type=int, default=64)25parser.add_argument('--ndf', type=int, default=64)26parser.add_argument('--nc', type=int, default=3)27parser.add_argument('--niter', type=int, default=25, help='number of epochs to train for')28parser.add_argument('--lr', type=float, default=0.0002, help='learning rate, default=0.0002')29parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for adam. default=0.5')30parser.add_argument('--cuda', action='store_true', help='enables cuda')31parser.add_argument('--ngpu', type=int, default=1, help='number of GPUs to use')32parser.add_argument('--netG', default='', help="path to netG (to continue training)")33parser.add_argument('--netD', default='', help="path to netD (to continue training)")34parser.add_argument('--outf', default='.', help='folder to output images and model checkpoints')35parser.add_argument('--manualSeed', type=int, help='manual seed')36parser.add_argument('--nBottleneck', type=int, default=4000, help='of dim for bottleneck of encoder')37parser.add_argument('--overlapPred', type=int, default=4, help='overlapping edges')38parser.add_argument('--nef', type=int, default=64, help='of encoder filters in first conv layer')39parser.add_argument('--wtl2', type=float, default=0.999, help='0 means do not use else use with this weight')40opt = parser.parse_args()41print(opt)42netG = _netG(opt)43# netG = TransformerNet()44netG.load_state_dict(torch.load(opt.netG, map_location=lambda storage, location: storage)['state_dict'])45# netG.requires_grad = False46netG.eval()47transform = transforms.Compose([transforms.ToTensor(),48                                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])49image = utils.load_image(opt.test_image, opt.imageSize)50image = transform(image)51image = image.repeat(1, 1, 1, 1)52input_real = torch.FloatTensor(1, 3, opt.imageSize, opt.imageSize)53input_cropped = torch.FloatTensor(1, 3, opt.imageSize, opt.imageSize)54real_center = torch.FloatTensor(1, 3, opt.imageSize / 2, opt.imageSize / 2)55criterionMSE = nn.MSELoss()56# if opt.cuda:57#     netG.cuda()58#     input_real, input_cropped = input_real.cuda(),input_cropped.cuda()59#     criterionMSE.cuda()60#     real_center = real_center.cuda()61input_real = Variable(input_real)62input_cropped = Variable(input_cropped)63real_center = Variable(real_center)64input_real.data.resize_(image.size()).copy_(image)65input_cropped.data.resize_(image.size()).copy_(image)66real_center_cpu = image[:, :, opt.imageSize / 4:opt.imageSize / 4 +67                        opt.imageSize / 2, opt.imageSize / 4:opt.imageSize / 4 + opt.imageSize / 2]68real_center.data.resize_(real_center_cpu.size()).copy_(real_center_cpu)69input_cropped.data[:, 0, opt.imageSize / 4 + opt.overlapPred:opt.imageSize / 4 + opt.imageSize / 2 - opt.overlapPred,70                   opt.imageSize / 4 + opt.overlapPred:opt.imageSize / 4 + opt.imageSize / 2 - opt.overlapPred] = 2 * 117.0 / 255.0 - 1.071input_cropped.data[:, 1, opt.imageSize / 4 + opt.overlapPred:opt.imageSize / 4 + opt.imageSize / 2 - opt.overlapPred,72                   opt.imageSize / 4 + opt.overlapPred:opt.imageSize / 4 + opt.imageSize / 2 - opt.overlapPred] = 2 * 104.0 / 255.0 - 1.073input_cropped.data[:, 2, opt.imageSize / 4 + opt.overlapPred:opt.imageSize / 4 + opt.imageSize / 2 - opt.overlapPred,74                   opt.imageSize / 4 + opt.overlapPred:opt.imageSize / 4 + opt.imageSize / 2 - opt.overlapPred] = 2 * 123.0 / 255.0 - 1.075fake = netG(input_cropped)76errG = criterionMSE(fake, real_center)77recon_image = input_cropped.clone()78recon_image.data[:, :, opt.imageSize / 4:opt.imageSize / 4 + opt.imageSize /79                 2, opt.imageSize / 4:opt.imageSize / 4 + opt.imageSize / 2] = fake.data80utils.save_image('val_real_samples.png', image[0])81utils.save_image('val_cropped_samples.png', input_cropped.data[0])82utils.save_image('val_recon_samples.png', recon_image.data[0])...

join_all_images.py

Source:join_all_images.py

1from PIL import Image2def join_all_images():3    imp1 = Image.open("./Images/terms_wise_outstanding.png")4    widthx, heightx = imp1.size5    imp2 = Image.open("./Images/category_wise_credit.png")6    imageSize = Image.new('RGB', (1283, 481))7    imageSize.paste(imp1, (1, 0))8    imageSize.paste(imp2, (widthx + 2, 0))9    imageSize.save("./Images/all_credit.png")10    imp3 = Image.open("./Images/matured_credit.png")11    widthx, heightx = imp1.size12    imp4 = Image.open("./Images/aging_matured_credit.png")13    imageSize = Image.new('RGB', (1283, 481))14    imageSize.paste(imp3, (1, 0))15    imageSize.paste(imp4, (widthx + 2, 0))16    imageSize.save("./Images/all_Matured_credit.png")17    # ---------------------------------------------------------18    imp10 = Image.open("./Images/Category_wise_cash.png")19    widthx, heightx = imp1.size20    imp11 = Image.open("./Images/aging_cash_drop.png")21    imageSize = Image.new('RGB', (1283, 481))22    imageSize.paste(imp10, (1, 0))23    imageSize.paste(imp11, (widthx + 2, 0))24    imageSize.save("./Images/all_cash.png")25    # ------------- Closed to Matured, Matured Credit  ---------------------------26    imp5 = Image.open("./Images/regular_credit.png")27    widthx, heightx = imp1.size28    imp6 = Image.open("./Images/closed_to_matured_credit.png")29    imageSize = Image.new('RGB', (1283, 481))30    imageSize.paste(imp5, (1, 0))31    imageSize.paste(imp6, (widthx + 2, 0))32    imageSize.save("./Images/all_regular_credit.png")33    imp20 = Image.open("./Images/Cause_with_return.png")34    widthx, heightx = imp20.size35    imp21 = Image.open("./Images/LD_MTD_YTD_TARGET_vs_sales.png")36    imageSize = Image.new('RGB', (1283, 482))37    imageSize.paste(imp20, (1, 1))38    imageSize.paste(imp21, (widthx + 2, 1))39    imageSize.save("./Images/Cause_wise_delivery_man_wise_return.png")40    # ------------adding cause wise return and delivery man wise return----------41    imp22 = Image.open("./Images/Delivery_man_wise_return.png")42    widthx, heightx = imp22.size43    imageSize = Image.new('RGB', (1283, 482))44    imageSize.paste(imp22, (1, 1))45    imageSize.save("./Images/new_total_delivery_man_wise_return.png")46    # ------------adding cause wise return and delivery man wise return----------47    imp23 = Image.open("./Images/Day_Wise_Target_vs_Sales.png")48    widthx, heightx = imp23.size49    imageSize = Image.new('RGB', (1283, 482))50    imageSize.paste(imp23, (1, 1))51    imageSize.save("./Images//Day_wise_target_sales.png")52    imp24 = Image.open("./Images/Cumulative_Day_Wise_Target_vs_Sales.png")53    widthx, heightx = imp24.size54    imageSize = Image.new('RGB', (1283, 482))55    imageSize.paste(imp24, (1, 1))56    imageSize.save("./Images/Cumulative_Day_wise_target_sales.png")...

weightMask.py

Source:weightMask.py

1import torch2import torch.nn as nn3import torch.optim as optim4import torch.nn.functional as F5import collections6import numpy as np7from torch.autograd import Variable8## Netowrk Types ##9def generateSquareWeightMask(imageSize, boundarySize):10	##################################################################11	# Function GENERATE_SQUARE_WEIGHT_MASK12	# Takes in a nested dictionary of model/layer types13	# Outputs a nested dictionary of optimized hyperparameters14	# Parameters:15	# 		* imageSize: Size of one side of the image16	#       * boundarySize: Size of square grid to generate17	# Outputs18	#		* weightMask: Input for Repeated Layers Masked19	# 20	##################################################################21	numPixels = imageSize**222	weightMask = np.zeros((numPixels, numPixels))23	diagMask = np.zeros((numPixels, numPixels))24	for k in range(0, numPixels):25		i,j = ind2subImage(k, imageSize)26		pixelMask = np.zeros((imageSize, imageSize))27		if((i > 0) and (i < imageSize - 1) and (j > 0) and (j < imageSize - 1)):28			row_min = np.max((0, i - boundarySize))29			row_max = np.min((imageSize, i + boundarySize + 1))30			col_min = np.max((0, j - boundarySize))31			col_max = np.min((imageSize, j + boundarySize + 1))32			pixelMask[row_min:row_max, col_min:col_max] = 133		pixelMask[i,j] = 034		diagMask[k, k] = 135		weightMask[k, :] = np.reshape(pixelMask, (1, numPixels))36	weightMask = torch.from_numpy(weightMask).type(torch.cuda.BoolTensor)37	diagMask = torch.from_numpy(diagMask).type(torch.cuda.BoolTensor)38	return weightMask, diagMask39def generateGridWeightMask(imageSize):40	numPixels = imageSize**241	weightMask = np.zeros((numPixels, numPixels))42	diagMask = np.zeros((numPixels, numPixels))43	for k in range(0, numPixels):44		i,j = ind2subImage(k, imageSize)45		pixelMask = np.zeros((imageSize, imageSize))46		if((i > 0) and (i < imageSize - 1) and (j > 0) and (j < imageSize - 1)):47			#pixelMask[i, j] = 148			pixelMask[i - 1, j] = 149			pixelMask[i + 1, j] = 150			pixelMask[i, j + 1] = 151			pixelMask[i, j - 1] = 152		diagMask[k, k] = 153		# if (i > 0):54		# 	pixelMask[i - 1, j] = 155		# if (i < imageSize - 1):56		# 	pixelMask[i + 1, j] = 157		# if (j > 0):58		# 	pixelMask[i, j - 1] = 159		# if (j < imageSize - 1):60		# 	pixelMask[i, j + 1] = 161		weightMask[k, :] = np.reshape(pixelMask, (1, numPixels))62		#diagMask[k, k] = 163	weightMask = torch.from_numpy(weightMask).type(torch.cuda.BoolTensor)64	diagMask = torch.from_numpy(diagMask).type(torch.cuda.BoolTensor)65	return weightMask, diagMask66def ind2subImage(idx, imageSize):67	i = int(np.floor(idx/imageSize))68	j = idx % imageSize...

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