CNNCreator_CifarClassifierNetwork.py 17.7 KB
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from caffe2.python import workspace, core, model_helper, brew, optimizer
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from caffe2.python.predictor import mobile_exporter
from caffe2.proto import caffe2_pb2
import numpy as np
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import logging
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import os
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import sys
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class CNNCreator_CifarClassifierNetwork:

    module = None
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    _current_dir_ = os.path.join('./')
    _data_dir_    = os.path.join(_current_dir_, 'data', 'CifarClassifierNetwork')
    _model_dir_   = os.path.join(_current_dir_, 'model', 'CifarClassifierNetwork')
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    INIT_NET    = os.path.join(_model_dir_, 'init_net.pb')
    PREDICT_NET = os.path.join(_model_dir_, 'predict_net.pb')
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    def add_input(self, model, batch_size, db, db_type, device_opts):
        with core.DeviceScope(device_opts):
            # load the data
            data_uint8, label = brew.db_input(
                model,
                blobs_out=["data_uint8", "label"],
                batch_size=batch_size,
                db=db,
                db_type=db_type,
            )
            # cast the data to float
            data = model.Cast(data_uint8, "data", to=core.DataType.FLOAT)

            # scale data from [0,255] down to [0,1]
            data = model.Scale(data, data, scale=float(1./256))

            # don't need the gradient for the backward pass
            data = model.StopGradient(data, data)
            return data, label

    def create_model(self, model, data, device_opts):
    	with core.DeviceScope(device_opts):

    		data = data
    		# data, output shape: {[3,32,32]}
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      		conv2_1_ = brew.conv(model, data, 'conv2_1_', dim_in=3, dim_out=8, kernel=3, stride=1)
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    		# conv2_1_, output shape: {[8,32,32]}
    		batchnorm2_1_ = mx.symbol.BatchNorm(data=conv2_1_,
    		    fix_gamma=True,
    		    name="batchnorm2_1_")
    		relu2_1_ = brew.relu(model, batchnorm2_1_, batchnorm2_1_)
      		conv3_1_ = brew.conv(model, relu2_1_, 'conv3_1_', dim_in=8, dim_out=8, kernel=3, stride=1)
    		# conv3_1_, output shape: {[8,32,32]}
    		batchnorm3_1_ = mx.symbol.BatchNorm(data=conv3_1_,
    		    fix_gamma=True,
    		    name="batchnorm3_1_")
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    		conv2_2_ = brew.conv(model, data, 'conv2_2_', dim_in=3, dim_out=8, kernel=1, stride=1)
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    		# conv2_2_, output shape: {[8,32,32]}
    		batchnorm2_2_ = mx.symbol.BatchNorm(data=conv2_2_,
    		    fix_gamma=True,
    		    name="batchnorm2_2_")
    		add4_ = batchnorm3_1_ + batchnorm2_2_
    		# add4_, output shape: {[8,32,32]}
    		relu4_ = brew.relu(model, add4_, add4_)
      		conv5_1_ = brew.conv(model, relu4_, 'conv5_1_', dim_in=8, dim_out=16, kernel=3, stride=2)
    		# conv5_1_, output shape: {[16,16,16]}
    		batchnorm5_1_ = mx.symbol.BatchNorm(data=conv5_1_,
    		    fix_gamma=True,
    		    name="batchnorm5_1_")
    		relu5_1_ = brew.relu(model, batchnorm5_1_, batchnorm5_1_)
      		conv6_1_ = brew.conv(model, relu5_1_, 'conv6_1_', dim_in=16, dim_out=16, kernel=3, stride=1)
    		# conv6_1_, output shape: {[16,16,16]}
    		batchnorm6_1_ = mx.symbol.BatchNorm(data=conv6_1_,
    		    fix_gamma=True,
    		    name="batchnorm6_1_")
    		conv5_2_ = brew.conv(model, relu4_, 'conv5_2_', dim_in=8, dim_out=16, kernel=1, stride=2)
    		# conv5_2_, output shape: {[16,16,16]}
    		batchnorm5_2_ = mx.symbol.BatchNorm(data=conv5_2_,
    		    fix_gamma=True,
    		    name="batchnorm5_2_")
    		add7_ = batchnorm6_1_ + batchnorm5_2_
    		# add7_, output shape: {[16,16,16]}
    		relu7_ = brew.relu(model, add7_, add7_)
      		conv8_1_ = brew.conv(model, relu7_, 'conv8_1_', dim_in=16, dim_out=16, kernel=3, stride=1)
    		# conv8_1_, output shape: {[16,16,16]}
    		batchnorm8_1_ = mx.symbol.BatchNorm(data=conv8_1_,
    		    fix_gamma=True,
    		    name="batchnorm8_1_")
    		relu8_1_ = brew.relu(model, batchnorm8_1_, batchnorm8_1_)
      		conv9_1_ = brew.conv(model, relu8_1_, 'conv9_1_', dim_in=16, dim_out=16, kernel=3, stride=1)
    		# conv9_1_, output shape: {[16,16,16]}
    		batchnorm9_1_ = mx.symbol.BatchNorm(data=conv9_1_,
    		    fix_gamma=True,
    		    name="batchnorm9_1_")
    		add10_ = batchnorm9_1_ + relu7_
    		# add10_, output shape: {[16,16,16]}
    		relu10_ = brew.relu(model, add10_, add10_)
      		conv11_1_ = brew.conv(model, relu10_, 'conv11_1_', dim_in=16, dim_out=16, kernel=3, stride=1)
    		# conv11_1_, output shape: {[16,16,16]}
    		batchnorm11_1_ = mx.symbol.BatchNorm(data=conv11_1_,
    		    fix_gamma=True,
    		    name="batchnorm11_1_")
    		relu11_1_ = brew.relu(model, batchnorm11_1_, batchnorm11_1_)
      		conv12_1_ = brew.conv(model, relu11_1_, 'conv12_1_', dim_in=16, dim_out=16, kernel=3, stride=1)
    		# conv12_1_, output shape: {[16,16,16]}
    		batchnorm12_1_ = mx.symbol.BatchNorm(data=conv12_1_,
    		    fix_gamma=True,
    		    name="batchnorm12_1_")
    		add13_ = batchnorm12_1_ + relu10_
    		# add13_, output shape: {[16,16,16]}
    		relu13_ = brew.relu(model, add13_, add13_)
      		conv14_1_ = brew.conv(model, relu13_, 'conv14_1_', dim_in=16, dim_out=32, kernel=3, stride=2)
    		# conv14_1_, output shape: {[32,8,8]}
    		batchnorm14_1_ = mx.symbol.BatchNorm(data=conv14_1_,
    		    fix_gamma=True,
    		    name="batchnorm14_1_")
    		relu14_1_ = brew.relu(model, batchnorm14_1_, batchnorm14_1_)
      		conv15_1_ = brew.conv(model, relu14_1_, 'conv15_1_', dim_in=32, dim_out=32, kernel=3, stride=1)
    		# conv15_1_, output shape: {[32,8,8]}
    		batchnorm15_1_ = mx.symbol.BatchNorm(data=conv15_1_,
    		    fix_gamma=True,
    		    name="batchnorm15_1_")
    		conv14_2_ = brew.conv(model, relu13_, 'conv14_2_', dim_in=16, dim_out=32, kernel=1, stride=2)
    		# conv14_2_, output shape: {[32,8,8]}
    		batchnorm14_2_ = mx.symbol.BatchNorm(data=conv14_2_,
    		    fix_gamma=True,
    		    name="batchnorm14_2_")
    		add16_ = batchnorm15_1_ + batchnorm14_2_
    		# add16_, output shape: {[32,8,8]}
    		relu16_ = brew.relu(model, add16_, add16_)
      		conv17_1_ = brew.conv(model, relu16_, 'conv17_1_', dim_in=32, dim_out=32, kernel=3, stride=1)
    		# conv17_1_, output shape: {[32,8,8]}
    		batchnorm17_1_ = mx.symbol.BatchNorm(data=conv17_1_,
    		    fix_gamma=True,
    		    name="batchnorm17_1_")
    		relu17_1_ = brew.relu(model, batchnorm17_1_, batchnorm17_1_)
      		conv18_1_ = brew.conv(model, relu17_1_, 'conv18_1_', dim_in=32, dim_out=32, kernel=3, stride=1)
    		# conv18_1_, output shape: {[32,8,8]}
    		batchnorm18_1_ = mx.symbol.BatchNorm(data=conv18_1_,
    		    fix_gamma=True,
    		    name="batchnorm18_1_")
    		add19_ = batchnorm18_1_ + relu16_
    		# add19_, output shape: {[32,8,8]}
    		relu19_ = brew.relu(model, add19_, add19_)
      		conv20_1_ = brew.conv(model, relu19_, 'conv20_1_', dim_in=32, dim_out=32, kernel=3, stride=1)
    		# conv20_1_, output shape: {[32,8,8]}
    		batchnorm20_1_ = mx.symbol.BatchNorm(data=conv20_1_,
    		    fix_gamma=True,
    		    name="batchnorm20_1_")
    		relu20_1_ = brew.relu(model, batchnorm20_1_, batchnorm20_1_)
      		conv21_1_ = brew.conv(model, relu20_1_, 'conv21_1_', dim_in=32, dim_out=32, kernel=3, stride=1)
    		# conv21_1_, output shape: {[32,8,8]}
    		batchnorm21_1_ = mx.symbol.BatchNorm(data=conv21_1_,
    		    fix_gamma=True,
    		    name="batchnorm21_1_")
    		add22_ = batchnorm21_1_ + relu19_
    		# add22_, output shape: {[32,8,8]}
    		relu22_ = brew.relu(model, add22_, add22_)
      		conv23_1_ = brew.conv(model, relu22_, 'conv23_1_', dim_in=32, dim_out=64, kernel=3, stride=2)
    		# conv23_1_, output shape: {[64,4,4]}
    		batchnorm23_1_ = mx.symbol.BatchNorm(data=conv23_1_,
    		    fix_gamma=True,
    		    name="batchnorm23_1_")
    		relu23_1_ = brew.relu(model, batchnorm23_1_, batchnorm23_1_)
      		conv24_1_ = brew.conv(model, relu23_1_, 'conv24_1_', dim_in=64, dim_out=64, kernel=3, stride=1)
    		# conv24_1_, output shape: {[64,4,4]}
    		batchnorm24_1_ = mx.symbol.BatchNorm(data=conv24_1_,
    		    fix_gamma=True,
    		    name="batchnorm24_1_")
    		conv23_2_ = brew.conv(model, relu22_, 'conv23_2_', dim_in=32, dim_out=64, kernel=1, stride=2)
    		# conv23_2_, output shape: {[64,4,4]}
    		batchnorm23_2_ = mx.symbol.BatchNorm(data=conv23_2_,
    		    fix_gamma=True,
    		    name="batchnorm23_2_")
    		add25_ = batchnorm24_1_ + batchnorm23_2_
    		# add25_, output shape: {[64,4,4]}
    		relu25_ = brew.relu(model, add25_, add25_)
      		conv26_1_ = brew.conv(model, relu25_, 'conv26_1_', dim_in=64, dim_out=64, kernel=3, stride=1)
    		# conv26_1_, output shape: {[64,4,4]}
    		batchnorm26_1_ = mx.symbol.BatchNorm(data=conv26_1_,
    		    fix_gamma=True,
    		    name="batchnorm26_1_")
    		relu26_1_ = brew.relu(model, batchnorm26_1_, batchnorm26_1_)
      		conv27_1_ = brew.conv(model, relu26_1_, 'conv27_1_', dim_in=64, dim_out=64, kernel=3, stride=1)
    		# conv27_1_, output shape: {[64,4,4]}
    		batchnorm27_1_ = mx.symbol.BatchNorm(data=conv27_1_,
    		    fix_gamma=True,
    		    name="batchnorm27_1_")
    		add28_ = batchnorm27_1_ + relu25_
    		# add28_, output shape: {[64,4,4]}
    		relu28_ = brew.relu(model, add28_, add28_)
      		conv29_1_ = brew.conv(model, relu28_, 'conv29_1_', dim_in=64, dim_out=64, kernel=3, stride=1)
    		# conv29_1_, output shape: {[64,4,4]}
    		batchnorm29_1_ = mx.symbol.BatchNorm(data=conv29_1_,
    		    fix_gamma=True,
    		    name="batchnorm29_1_")
    		relu29_1_ = brew.relu(model, batchnorm29_1_, batchnorm29_1_)
      		conv30_1_ = brew.conv(model, relu29_1_, 'conv30_1_', dim_in=64, dim_out=64, kernel=3, stride=1)
    		# conv30_1_, output shape: {[64,4,4]}
    		batchnorm30_1_ = mx.symbol.BatchNorm(data=conv30_1_,
    		    fix_gamma=True,
    		    name="batchnorm30_1_")
    		add31_ = batchnorm30_1_ + relu28_
    		# add31_, output shape: {[64,4,4]}
    		relu31_ = brew.relu(model, add31_, add31_)
    		globalpooling31_ = mx.symbol.Pooling(data=relu31_,
    		    global_pool=True,
    		    kernel=(1,1),
    		    pool_type="avg",
    		    name="globalpooling31_")
    		# globalpooling31_, output shape: {[64,1,1]}
    		fc31_ = brew.fc(model, globalpooling31_, 'fc31_', dim_in=64, dim_out=128)
    		# fc31_, output shape: {[128,1,1]}
    		dropout31_ = mx.symbol.Dropout(data=fc31_,
    		    p=0.5,
    		    name="dropout31_")
    		fc32_ = brew.fc(model, dropout31_, 'fc32_', dim_in=128, dim_out=10)
    		# fc32_, output shape: {[10,1,1]}
    		softmax = brew.softmax(model, fc32_, 'softmax')

    		return softmax

    # this adds the loss and optimizer
    def add_training_operators(self, model, output, label, device_opts, opt_type, base_learning_rate, policy, stepsize, epsilon, beta1, beta2, gamma, momentum) :
    	with core.DeviceScope(device_opts):
    		xent = model.LabelCrossEntropy([output, label], 'xent')
    		loss = model.AveragedLoss(xent, "loss")

    		model.AddGradientOperators([loss])

    		if opt_type == 'adam':
    		    if policy == 'step':
    		        opt = optimizer.build_adam(model, base_learning_rate=base_learning_rate, policy=policy, stepsize=stepsize, beta1=beta1, beta2=beta2, epsilon=epsilon)
    		    elif policy == 'fixed' or policy == 'inv':
    		        opt = optimizer.build_adam(model, base_learning_rate=base_learning_rate, policy=policy, beta1=beta1, beta2=beta2, epsilon=epsilon)
    		    print("adam optimizer selected")
    		elif opt_type == 'sgd':
    		    if policy == 'step':
    		        opt = optimizer.build_sgd(model, base_learning_rate=base_learning_rate, policy=policy, stepsize=stepsize, gamma=gamma, momentum=momentum)
    		    elif policy == 'fixed' or policy == 'inv':
    		        opt = optimizer.build_sgd(model, base_learning_rate=base_learning_rate, policy=policy, gamma=gamma, momentum=momentum)
    		    print("sgd optimizer selected")
    		elif opt_type == 'rmsprop':
    		    if policy == 'step':
    		        opt = optimizer.build_rms_prop(model, base_learning_rate=base_learning_rate, policy=policy, stepsize=stepsize, decay=gamma, momentum=momentum, epsilon=epsilon)
    		    elif policy == 'fixed' or policy == 'inv':
    		        opt = optimizer.build_rms_prop(model, base_learning_rate=base_learning_rate, policy=policy, decay=gamma, momentum=momentum, epsilon=epsilon)
    		    print("rmsprop optimizer selected")
    		elif opt_type == 'adagrad':
    		    if policy == 'step':
    		        opt = optimizer.build_adagrad(model, base_learning_rate=base_learning_rate, policy=policy, stepsize=stepsize, decay=gamma, epsilon=epsilon)
    		    elif policy == 'fixed' or policy == 'inv':
    		        opt = optimizer.build_adagrad(model, base_learning_rate=base_learning_rate, policy=policy, decay=gamma, epsilon=epsilon)
    		    print("adagrad optimizer selected")

    def add_accuracy(self, model, output, label, device_opts, eval_metric):
        with core.DeviceScope(device_opts):
            if eval_metric == 'accuracy':
                accuracy = brew.accuracy(model, [output, label], "accuracy")
            elif eval_metric == 'top_k_accuracy':
                accuracy = brew.accuracy(model, [output, label], "accuracy", top_k=3)
            return accuracy

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    def train(self, num_epoch=1000, batch_size=64, context='gpu', eval_metric='accuracy', opt_type='adam', base_learning_rate=0.001, weight_decay=0.001, policy='fixed', stepsize=1, epsilon=1E-8, beta1=0.9, beta2=0.999, gamma=0.999, momentum=0.9) :
        if context == 'cpu':
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            device_opts = core.DeviceOption(caffe2_pb2.CPU, 0)
            print("CPU mode selected")
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        elif context == 'gpu':
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            device_opts = core.DeviceOption(caffe2_pb2.CUDA, 0)
            print("GPU mode selected")

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    	workspace.ResetWorkspace(self._model_dir_)
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    	arg_scope = {"order": "NCHW"}
    	# == Training model ==
    	train_model= model_helper.ModelHelper(name="train_net", arg_scope=arg_scope)
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    	data, label = self.add_input(train_model, batch_size=batch_size, db=os.path.join(self._data_dir_, 'mnist-train-nchw-lmdb'), db_type='lmdb', device_opts=device_opts)
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    	softmax = self.create_model(train_model, data, device_opts=device_opts)
    	self.add_training_operators(train_model, softmax, label, device_opts, opt_type, base_learning_rate, policy, stepsize, epsilon, beta1, beta2, gamma, momentum)
    	self.add_accuracy(train_model, softmax, label, device_opts, eval_metric)
    	with core.DeviceScope(device_opts):
    		brew.add_weight_decay(train_model, weight_decay)

    	# Initialize and create the training network
    	workspace.RunNetOnce(train_model.param_init_net)
    	workspace.CreateNet(train_model.net, overwrite=True)

    	# Main Training Loop
    	print("== Starting Training for " + str(num_epoch) + " num_epoch ==")
    	for j in range(0, num_epoch):
    		workspace.RunNet(train_model.net)
    		if j % 50 == 0:
    			print 'Iter: ' + str(j) + ': ' + 'Loss ' + str(workspace.FetchBlob("loss")) + ' - ' + 'Accuracy ' + str(workspace.FetchBlob('accuracy'))
    	print("Training done")

    	print("== Running Test model ==")
    	# == Testing model. ==
    	test_model= model_helper.ModelHelper(name="test_net", arg_scope=arg_scope, init_params=False)
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    	data, label = self.add_input(test_model, batch_size=100, db=os.path.join(self._data_dir_, 'mnist-test-nchw-lmdb'), db_type='lmdb', device_opts=device_opts)
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    	softmax = self.create_model(test_model, data, device_opts=device_opts)
    	self.add_accuracy(test_model, predictions, label, device_opts, eval_metric)
    	workspace.RunNetOnce(test_model.param_init_net)
    	workspace.CreateNet(test_model.net, overwrite=True)

    	# Main Testing Loop
    	# batch size:        100
    	# iteration:         100
    	# total test images: 10000
    	test_accuracy = np.zeros(100)
    	for i in range(100):
    		# Run a forward pass of the net on the current batch
    		workspace.RunNet(test_model.net)
    		# Collect the batch accuracy from the workspace
    		test_accuracy[i] = workspace.FetchBlob('accuracy')

    	print('Test_accuracy: {:.4f}'.format(test_accuracy.mean()))

    	# == Deployment model. ==
    	# We simply need the main AddModel part.
    	deploy_model = model_helper.ModelHelper(name="deploy_net", arg_scope=arg_scope, init_params=False)
    	self.create_model(deploy_model, "data", device_opts)

    	print("Saving deploy model")
    	self.save_net(self.INIT_NET, self.PREDICT_NET, deploy_model)

    def save_net(self, init_net_path, predict_net_path, model):

    	init_net, predict_net = mobile_exporter.Export(
    		workspace,
    		model.net,
    		model.params
    	)

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        try:
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            os.makedirs(self._model_dir_)
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        except OSError:
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            if not os.path.isdir(self._model_dir_):
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                raise

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    	print("Save the model to init_net.pb and predict_net.pb")
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    	with open(predict_net_path, 'wb') as f:
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    		f.write(model.net._net.SerializeToString())
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    	with open(init_net_path, 'wb') as f:
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    		f.write(init_net.SerializeToString())

    	print("Save the model to init_net.pbtxt and predict_net.pbtxt")
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    	with open(init_net_path.replace('.pb','.pbtxt'), 'w') as f:
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    		f.write(str(init_net))
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    	with open(predict_net_path.replace('.pb','.pbtxt'), 'w') as f:
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    		f.write(str(predict_net))
    	print("== Saved init_net and predict_net ==")

    def load_net(self, init_net_path, predict_net_path, device_opts):
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        if not os.path.isfile(init_net_path):
            logging.error("Network loading failure. File '" + os.path.abspath(init_net_path) + "' does not exist.")
            sys.exit(1)
        elif not os.path.isfile(predict_net_path):
            logging.error("Network loading failure. File '" + os.path.abspath(predict_net_path) + "' does not exist.")
            sys.exit(1)

        init_def = caffe2_pb2.NetDef()
    	with open(init_net_path, 'rb') as f:
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    		init_def.ParseFromString(f.read())
    		init_def.device_option.CopyFrom(device_opts)
    		workspace.RunNetOnce(init_def.SerializeToString())

    	net_def = caffe2_pb2.NetDef()
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    	with open(predict_net_path, 'rb') as f:
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    		net_def.ParseFromString(f.read())
    		net_def.device_option.CopyFrom(device_opts)
    		workspace.CreateNet(net_def.SerializeToString(), overwrite=True)
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    	print("== Loaded init_net and predict_net ==")