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Commit2e8f326

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Getting the main in there as well
1 parent70da266 commit2e8f326

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2 files changed

+124
-2
lines changed

2 files changed

+124
-2
lines changed

‎dense_correspondence/training/training.py‎

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@@ -365,8 +365,8 @@ def run(self, loss_current_iteration=0, use_pretrained=False):
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im2=np.ascontiguousarray(im2)
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# First show the matches:
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match_pixels_a=np.stack([matches_a%640,matches_a//640],axis=1)
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match_pixels_b=np.stack([matches_b%640,matches_b//640],axis=1)
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match_pixels_a=np.stack([blind_non_matches_a%640,blind_non_matches_a//640],axis=1)
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match_pixels_b=np.stack([blind_non_matches_b%640,blind_non_matches_b//640],axis=1)
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iter=0
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form_a,m_binzip(match_pixels_a,match_pixels_b):

‎main.py‎

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### Need some extra paths
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importsys,os
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sys.path.append('modules')
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sys.path.append('dense_correspondence/dataset')
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### Set a few environment variables that would've been normally set in Docker
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os.environ["DC_SOURCE_DIR"]=os.getcwd()
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os.environ["DC_DATA_DIR"]="/home/michelism/Data/pdc"
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importdense_correspondence_manipulation.utils.utilsasutils
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utils.add_dense_correspondence_to_python_path()
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fromdense_correspondence.training.trainingimport*
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importlogging
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#utils.set_default_cuda_visible_devices()
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#utils.set_cuda_visible_devices([0]) # use this to manually set CUDA_VISIBLE_DEVICES
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fromdense_correspondence.training.trainingimportDenseCorrespondenceTraining
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fromdense_correspondence.dataset.spartan_dataset_maskedimportSpartanDataset
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logging.basicConfig(level=logging.INFO)
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fromdense_correspondence.evaluation.evaluationimportDenseCorrespondenceEvaluation
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importnumpyasnp
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np.random.seed(42)# Even this doesn't help... It's absolute chaos random.
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config_filename=os.path.join(utils.getDenseCorrespondenceSourceDir(),'config','dense_correspondence',
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'dataset','composite','caterpillar_upright.yaml')
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config=utils.getDictFromYamlFilename(config_filename)
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train_config_file=os.path.join(utils.getDenseCorrespondenceSourceDir(),'config','dense_correspondence',
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'training','training.yaml')
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train_config=utils.getDictFromYamlFilename(train_config_file)
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dataset=SpartanDataset(config=config)
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logging_dir="trained_models/tutorials"
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num_iterations=3500
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d=3# the descriptor dimension
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name="caterpillar_%d"%(d)
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train_config["training"]["logging_dir_name"]=name
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train_config["training"]["logging_dir"]=logging_dir
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train_config["dense_correspondence_network"]["descriptor_dimension"]=d
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train_config["training"]["num_iterations"]=num_iterations
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TRAIN=True
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EVALUATE=True
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# All of the saved data for this network will be located in the
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# code/data/pdc/trained_models/tutorials/caterpillar_3 folder
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ifTRAIN:
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#print "training descriptor of dimension %d" %(d)
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train=DenseCorrespondenceTraining(dataset=dataset,config=train_config)
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train.run()
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#print "finished training descriptor of dimension %d" %(d)
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model_folder=os.path.join(logging_dir,name)
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model_folder=utils.convert_data_relative_path_to_absolute_path(model_folder)
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ifEVALUATE:
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DCE=DenseCorrespondenceEvaluation
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num_image_pairs=100
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DCE.run_evaluation_on_network(model_folder,num_image_pairs=num_image_pairs)
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fromdense_correspondence.evaluation.evaluationimportDenseCorrespondenceEvaluationPlotterasDCEP
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importmatplotlib.pyplotasplt
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dc_data_dir=utils.get_data_dir()
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folder_name="tutorials"
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net_to_plot=os.path.join(folder_name,"caterpillar_3")
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network_name=net_to_plot
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path_to_csv=os.path.join(dc_data_dir,"trained_models",network_name,"analysis/train/data.csv")
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fig_axes=DCEP.run_on_single_dataframe(path_to_csv,label=network_name,save=False)
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path_to_csv=os.path.join(dc_data_dir,"trained_models",network_name,"analysis/train/data.csv")
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fig_axes=DCEP.run_on_single_dataframe(path_to_csv,label=network_name,previous_fig_axes=fig_axes,save=False)
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plt.savefig("Training.png")
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path_to_csv=os.path.join(dc_data_dir,"trained_models",network_name,"analysis/test/data.csv")
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fig_axes=DCEP.run_on_single_dataframe(path_to_csv,label=network_name,save=False)
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path_to_csv=os.path.join(dc_data_dir,"trained_models",network_name,"analysis/test/data.csv")
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fig_axes=DCEP.run_on_single_dataframe(path_to_csv,label=network_name,previous_fig_axes=fig_axes,save=False)
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plt.savefig("Test.png")
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path_to_csv=os.path.join(dc_data_dir,"trained_models",network_name,"analysis/cross_scene/data.csv")
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fig_axes=DCEP.run_on_single_dataframe(path_to_csv,label=network_name,save=False)
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path_to_csv=os.path.join(dc_data_dir,"trained_models",network_name,"analysis/cross_scene/data.csv")
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fig_axes=DCEP.run_on_single_dataframe(path_to_csv,label=network_name,previous_fig_axes=fig_axes,save=False)
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plt.savefig("CrossScene.png")
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config_filename=os.path.join(utils.getDenseCorrespondenceSourceDir(),'config',
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'dense_correspondence','evaluation','evaluation.yaml')
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config=utils.getDictFromYamlFilename(config_filename)
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default_config=utils.get_defaults_config()
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# utils.set_cuda_visible_devices([0])
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dce=DenseCorrespondenceEvaluation(config)
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DCE=DenseCorrespondenceEvaluation
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network_name="caterpillar_3"
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dcn=dce.load_network_from_config(network_name)
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dataset=dcn.load_training_dataset()
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DenseCorrespondenceEvaluation.evaluate_network_qualitative(dcn,dataset=dataset,randomize=True)

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