Anisimov

Anisimov/Link to dataset.txt

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+Link to data set:
+https://drive.google.com/drive/folders/1RTo4pPWJDfGXwJnsV3DClgVEUGt5o5LB?usp=sharing

Anisimov/README.txt

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+In test folder data should be split on two folders:
+Aphones-folder with none iphones pictures
+Iphones-folder with iphones pictures

Anisimov/RUN_Comand.txt

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+
+python C:\Users\�����\Desktop\predict.py --model C:\Users\�����\Desktop\model_ip.pth --input C:\Users\�����\Desktop\Final_set\test --output C:\Users\�����\Desktop\result.csv
+
+

Anisimov/predict.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[2]:
+
+
+import torch
+import numpy as np
+import pandas as pd
+import argparse
+from torchvision.datasets import ImageFolder
+from torchvision import transforms
+import os
+
+
+# In[29]:
+
+
+def load_checkpoint(filepath):
+    checkpoint = torch.load(filepath)
+    model = checkpoint['model']
+    model.load_state_dict(checkpoint['state_dict'])
+    model.eval()
+    return model
+
+
+# In[1]:
+
+
+#create csv
+def results(names,preds,out_file):
+    print('Saving result-Start')
+    final_out = pd.DataFrame()
+    final_out['image_name'] = names
+    final_out['iphone_probability'] = preds
+    final_out.to_csv(out_file, index=False)
+    print('Saving result-Done')
+
+
+# In[31]:
+
+
+def predict_proba(model,test):
+    print('Model load-Start')
+    #load the model
+    model=load_checkpoint(model)
+    print('Model load-Done')
+    print('-------')
+    print('Prediction process-Start')
+    #Image convert
+    data_transforms = transforms.Compose([
+        transforms.Resize((224,224)),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
+    #Define images loader
+    image_datasets = ImageFolder(test,data_transforms)
+    test_data_gen=torch.utils.data.DataLoader(image_datasets, batch_size=256,shuffle=False, num_workers=6)
+    #Load images names
+    names=[image_datasets.imgs[i][0].split('\\')[-1] for i in range(0,len(image_datasets.imgs))]
+    #Predict probas
+    preds=np.array([])
+    for inputs, labels in test_data_gen:
+        preds=np.append(preds,model(inputs).data.numpy()[:,1])
+    print('Prediction process-Done')
+    # save table
+    results(names,preds,args.output)
+
+
+
+# In[46]:
+
+
+if __name__ == '__main__':
+    # disable warnings
+    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+    # parse arguments
+    parser = argparse.ArgumentParser(description='Iphone detector')
+    parser.add_argument('--model', type=str, default='model.hdf5', help='path to model')
+    parser.add_argument('--input', type=str, default='test', help='Path to class folder directory')
+    parser.add_argument('--output', type=str, default='predictions.csv', help='path to file with model output')
+    args = parser.parse_args()
+    # get predictions
+    predict_proba(args.model,args.input)
+    print()
+    print('Done')
+

Anisimov/requirements.txt

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+# This file may be used to create an environment using:
+# $ conda create --name <env> --file <this file>
+# platform: win-64
+_ipyw_jlab_nb_ext_conf=0.1.0=py37_0
+absl-py=0.7.1=pypi_0
+alabaster=0.7.12=py37_0
+anaconda=2019.03=py37_0
+anaconda-client=1.7.2=py37_0
+anaconda-navigator=1.9.7=py37_0
+anaconda-project=0.8.2=py37_0
+asn1crypto=0.24.0=py37_0
+astor=0.7.1=pypi_0
+astroid=2.2.5=py37_0
+astropy=3.1.2=py37he774522_0
+atomicwrites=1.3.0=py37_1
+attrs=19.1.0=py37_1
+babel=2.6.0=py37_0
+backcall=0.1.0=py37_0
+backports=1.0=py37_1
+backports.os=0.1.1=py37_0
+backports.shutil_get_terminal_size=1.0.0=py37_2
+beautifulsoup4=4.7.1=py37_1
+bitarray=0.8.3=py37hfa6e2cd_0
+bkcharts=0.2=py37_0
+blas=1.0=mkl
+bleach=3.1.0=py37_0
+blosc=1.15.0=h7bd577a_0
+bokeh=1.0.4=py37_0
+boto=2.49.0=py37_0
+bottleneck=1.2.1=py37h452e1ab_1
+bzip2=1.0.6=hfa6e2cd_5
+ca-certificates=2019.1.23=0
+certifi=2019.3.9=py37_0
+cffi=1.12.2=py37h7a1dbc1_1
+chardet=3.0.4=py37_1
+classification-models=0.1=pypi_0
+click=7.0=py37_0
+cloudpickle=0.8.0=py37_0
+clyent=1.2.2=py37_1
+colorama=0.4.1=py37_0
+comtypes=1.1.7=py37_0
+conda=4.6.14=py37_0
+conda-build=3.17.8=py37_0
+conda-env=2.6.0=1
+conda-verify=3.1.1=py37_0
+console_shortcut=0.1.1=3
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+cryptography=2.6.1=py37h7a1dbc1_0
+cudatoolkit=9.0=1
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+cycler=0.10.0=py37_0
+cython=0.29.6=py37ha925a31_0
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+decorator=4.4.0=py37_1
+defusedxml=0.5.0=py37_1
+distributed=1.26.0=py37_1
+docutils=0.14=py37_0
+entrypoints=0.3=py37_0
+et_xmlfile=1.0.1=py37_0
+fastcache=1.0.2=py37hfa6e2cd_2
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+glob2=0.6=py37_1
+google-images-download=2.6.1=pypi_0
+greenlet=0.4.15=py37hfa6e2cd_0
+grpcio=1.19.0=pypi_0
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+importlib_metadata=0.8=py37_0
+intel-openmp=2019.3=203
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+jupyter_core=4.4.0=py37_0
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+jupyterlab_server=0.2.0=py37_0
+keras=2.2.4=pypi_0
+keras-applications=1.0.7=pypi_0
+keras-preprocessing=1.0.9=pypi_0
+keyring=18.0.0=py37_0
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+m2w64-gcc-libgfortran=5.3.0=6
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+m2w64-gmp=6.1.0=2
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+mistune=0.8.4=py37he774522_0
+mkl=2019.3=203
+mkl-service=1.1.2=py37hb782905_5
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+mkl_random=1.0.2=py37h343c172_0
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+msgpack-python=0.6.1=py37h74a9793_1
+msys2-conda-epoch=20160418=1
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+nose=1.3.7=py37_2
+notebook=5.7.8=py37_0
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+numpy-base=1.16.2=py37hc3f5095_0
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