plt.semilogy(val_errs)
plt.show(block=False)
fig.savefig('./eval/' + eval_prefix + '/' + eval_prefix + '_errs.png')
# save results
poseNet.save("./eval/{}/net_{}.pkl".format(eval_prefix, eval_prefix))
# poseNet.load("./eval/{}/net_{}.pkl".format(eval_prefix,eval_prefix))
# add prior to network
cfg = HiddenLayerParams(inputDim=(batchSize, train_gt3D_embed.shape[1]),
outputDim=(batchSize,
numpy.prod(train_gt3D.shape[1:])),
activation=None)
pcalayer = HiddenLayer(rng,
poseNet.layers[-1].output,
cfg,
copyLayer=None,
layerNum=len(poseNet.layers))
pcalayer.W.set_value(pca.components_)
pcalayer.b.set_value(pca.mean_)
poseNet.layers.append(pcalayer)
poseNet.output = pcalayer.output
poseNet.cfgParams.numJoints = train_gt3D.shape[1]
poseNet.cfgParams.nDims = train_gt3D.shape[2]
poseNet.cfgParams.outputDim = pcalayer.cfgParams.outputDim
poseNet.save("./eval/{}/network_prior.pkl".format(eval_prefix))
###################################################################
# test
print("Testing ...")
gt3D = [j.gt3Dorig for j in testSeqs[0].data]
def __init__(self, rng, inputVar=None, cfgParams=None):
"""
@see https://github.com/KaimingHe/resnet-1k-layers/blob/master/resnet-pre-act.lua
:type cfgParams: DescriptorNetParams
"""
import theano.tensor as T
self._params_filter = []
self._weights_filter = []
if cfgParams is None:
raise Exception("Cannot create a Net without config parameters (ie. cfgParams==None)")
if inputVar is None:
inputVar = T.tensor4('x') # input variable
elif isinstance(inputVar, str):
raise NotImplementedError()
self.inputVar = inputVar
self.cfgParams = cfgParams
# create network
self.layers = []
batchSize = cfgParams.batch_size
# create structure
if cfgParams.type == 0:
# Try ResNet similar configuration
depth = 47
assert (depth - 2) % 9 == 0, 'depth should be 9n+2 (e.g., 164 or 1001)'
n = (depth - 2) / 9
nStages = [32, 64, 128, 256, 256]
self.layers.append(ConvPoolLayer(rng, self.inputVar,
ConvPoolLayerParams(inputDim=self.cfgParams.inputDim, nFilters=nStages[0],
filterDim=(5, 5), stride=(1, 1),
poolsize=(2, 2), border_mode='same', activation=None,
init_method='He'),
layerNum=len(self.layers))) # one conv at the beginning
rout = self.add_res_layers(rng, self.layers[-1].output, self.layers[-1].cfgParams.outputDim, nStages[1], n, 2) # Stage 1
rout = self.add_res_layers(rng, rout, self.layers[-1].cfgParams.outputDim, nStages[2], n, 2) # Stage 2
rout = self.add_res_layers(rng, rout, self.layers[-1].cfgParams.outputDim, nStages[3], n, 2) # Stage 3
rout = self.add_res_layers(rng, rout, self.layers[-1].cfgParams.outputDim, nStages[4], n, 2) # Stage 4
self.layers.append(BatchNormLayer(rng, rout, BatchNormLayerParams(inputDim=self.layers[-1].cfgParams.outputDim), layerNum=len(self.layers)))
self.layers.append(NonlinearityLayer(rng, self.layers[-1].output, NonlinearityLayerParams(inputDim=self.layers[-1].cfgParams.outputDim, activation=ReLU), layerNum=len(self.layers)))
# self.layers.append(GlobalAveragePoolLayer(rng, self.layers[-1].output, GlobalAveragePoolLayerParams(inputDim=self.layers[-1].cfgParams.outputDim), layerNum=len(self.layers)))
self.layers.append(HiddenLayer(rng, self.layers[-1].output.flatten(2),
HiddenLayerParams(# inputDim=self.layers[-1].cfgParams.outputDim,
inputDim=(self.layers[-1].cfgParams.outputDim[0], numpy.prod(self.layers[-1].cfgParams.outputDim[1:])),
outputDim=(batchSize, 1024), activation=ReLU),
layerNum=len(self.layers)))
self.layers.append(HiddenLayer(rng, self.layers[-1].output,
HiddenLayerParams(inputDim=self.layers[-1].cfgParams.outputDim,
outputDim=(batchSize, 1024), activation=ReLU),
layerNum=len(self.layers)))
self.layers.append(HiddenLayer(rng, self.layers[-1].output,
HiddenLayerParams(inputDim=self.layers[-1].cfgParams.outputDim,
outputDim=(batchSize, self.cfgParams.numJoints*self.cfgParams.nDims),
activation=None),
layerNum=len(self.layers)))
self.output = self.layers[-1].output
elif cfgParams.type == 1:
# Try ResNet similar configuration
depth = 47
assert (depth - 2) % 9 == 0, 'depth should be 9n+2 (e.g., 164 or 1001)'
n = (depth - 2) / 9
nStages = [32, 64, 128, 256, 256]
self.layers.append(ConvPoolLayer(rng, self.inputVar,
ConvPoolLayerParams(inputDim=self.cfgParams.inputDim, nFilters=nStages[0],
filterDim=(5, 5), stride=(1, 1),
poolsize=(2, 2), border_mode='same', activation=None,
init_method='He'),
layerNum=len(self.layers))) # one conv at the beginning
rout = self.add_res_layers(rng, self.layers[-1].output, self.layers[-1].cfgParams.outputDim, nStages[1], n, 2) # Stage 1
rout = self.add_res_layers(rng, rout, self.layers[-1].cfgParams.outputDim, nStages[2], n, 2) # Stage 2
rout = self.add_res_layers(rng, rout, self.layers[-1].cfgParams.outputDim, nStages[3], n, 2) # Stage 3
rout = self.add_res_layers(rng, rout, self.layers[-1].cfgParams.outputDim, nStages[4], n, 2) # Stage 4
self.layers.append(BatchNormLayer(rng, rout, BatchNormLayerParams(inputDim=self.layers[-1].cfgParams.outputDim), layerNum=len(self.layers)))
self.layers.append(NonlinearityLayer(rng, self.layers[-1].output, NonlinearityLayerParams(inputDim=self.layers[-1].cfgParams.outputDim, activation=ReLU), layerNum=len(self.layers)))
self.layers.append(HiddenLayer(rng, self.layers[-1].output.flatten(2),
HiddenLayerParams(inputDim=(self.layers[-1].cfgParams.outputDim[0], numpy.prod(self.layers[-1].cfgParams.outputDim[1:])),
outputDim=(batchSize, 1024), activation=ReLU),
layerNum=len(self.layers)))
self.layers.append(HiddenLayer(rng, self.layers[-1].output,
HiddenLayerParams(inputDim=self.layers[-1].cfgParams.outputDim,
outputDim=(batchSize, 1024), activation=ReLU),
layerNum=len(self.layers)))
self.layers.append(HiddenLayer(rng, self.layers[-1].output,
HiddenLayerParams(inputDim=self.layers[-1].cfgParams.outputDim,
outputDim=(batchSize, 30), activation=None),
layerNum=len(self.layers)))
self.layers.append(HiddenLayer(rng, self.layers[-1].output,
HiddenLayerParams(inputDim=self.layers[-1].cfgParams.outputDim,
outputDim=(batchSize, self.cfgParams.numJoints*self.cfgParams.nDims),
activation=None),
layerNum=len(self.layers)))
self.output = self.layers[-1].output
elif cfgParams.type == 2:
# Try ResNet similar configuration
depth = 47
assert (depth - 2) % 9 == 0, 'depth should be 9n+2 (e.g., 164 or 1001)'
n = (depth - 2) / 9
nStages = [32, 64, 128, 256, 256]
self.layers.append(ConvPoolLayer(rng, self.inputVar,
ConvPoolLayerParams(inputDim=self.cfgParams.inputDim, nFilters=nStages[0],
filterDim=(5, 5), stride=(1, 1),
poolsize=(2, 2), border_mode='same', activation=None,
init_method='He'),
layerNum=len(self.layers))) # one conv at the beginning
rout = self.add_res_layers(rng, self.layers[-1].output, self.layers[-1].cfgParams.outputDim, nStages[1], n, 2) # Stage 1
rout = self.add_res_layers(rng, rout, self.layers[-1].cfgParams.outputDim, nStages[2], n, 2) # Stage 2
rout = self.add_res_layers(rng, rout, self.layers[-1].cfgParams.outputDim, nStages[3], n, 2) # Stage 3
rout = self.add_res_layers(rng, rout, self.layers[-1].cfgParams.outputDim, nStages[4], n, 2) # Stage 4
self.layers.append(BatchNormLayer(rng, rout, BatchNormLayerParams(inputDim=self.layers[-1].cfgParams.outputDim), layerNum=len(self.layers)))
self.layers.append(NonlinearityLayer(rng, self.layers[-1].output, NonlinearityLayerParams(inputDim=self.layers[-1].cfgParams.outputDim, activation=ReLU), layerNum=len(self.layers)))
self.layers.append(HiddenLayer(rng, self.layers[-1].output.flatten(2),
HiddenLayerParams(inputDim=(self.layers[-1].cfgParams.outputDim[0], numpy.prod(self.layers[-1].cfgParams.outputDim[1:])),
outputDim=(batchSize, 1024), activation=ReLU),
layerNum=len(self.layers)))
self.layers.append(DropoutLayer(rng, self.layers[-1].output,
DropoutLayerParams(inputDim=self.layers[-1].cfgParams.outputDim,
outputDim=self.layers[-1].cfgParams.outputDim),
layerNum=len(self.layers)))
self.layers.append(HiddenLayer(rng, self.layers[-1].output,
HiddenLayerParams(inputDim=self.layers[-1].cfgParams.outputDim,
outputDim=(batchSize, 1024), activation=ReLU),
layerNum=len(self.layers)))
self.layers.append(DropoutLayer(rng, self.layers[-1].output,
DropoutLayerParams(inputDim=self.layers[-1].cfgParams.outputDim,
outputDim=self.layers[-1].cfgParams.outputDim),
layerNum=len(self.layers)))
self.layers.append(HiddenLayer(rng, self.layers[-1].output,
HiddenLayerParams(inputDim=self.layers[-1].cfgParams.outputDim,
outputDim=(batchSize, self.cfgParams.numJoints*self.cfgParams.nDims),
activation=None),
layerNum=len(self.layers)))
self.output = self.layers[-1].output
elif cfgParams.type == 3:
# Try ResNet similar configuration
depth = 47
assert (depth - 2) % 9 == 0, 'depth should be 9n+2 (e.g., 164 or 1001)'
n = (depth - 2) / 9
nStages = [32, 64, 128, 128, 128]
self.layers.append(ConvPoolLayer(rng, self.inputVar,
ConvPoolLayerParams(inputDim=self.cfgParams.inputDim, nFilters=nStages[0],
filterDim=(5, 5), stride=(1, 1),
poolsize=(2, 2), border_mode='same', activation=None,
init_method='He'),
layerNum=len(self.layers))) # one conv at the beginning
rout = self.add_res_layers(rng, self.layers[-1].output, self.layers[-1].cfgParams.outputDim, nStages[1], n, 2) # Stage 1
rout = self.add_res_layers(rng, rout, self.layers[-1].cfgParams.outputDim, nStages[2], n, 2) # Stage 2
rout = self.add_res_layers(rng, rout, self.layers[-1].cfgParams.outputDim, nStages[3], n, 2) # Stage 3
rout = self.add_res_layers(rng, rout, self.layers[-1].cfgParams.outputDim, nStages[4], n, 2) # Stage 4
self.layers.append(BatchNormLayer(rng, rout, BatchNormLayerParams(inputDim=self.layers[-1].cfgParams.outputDim), layerNum=len(self.layers)))
self.layers.append(NonlinearityLayer(rng, self.layers[-1].output, NonlinearityLayerParams(inputDim=self.layers[-1].cfgParams.outputDim, activation=ReLU), layerNum=len(self.layers)))
self.layers.append(HiddenLayer(rng, self.layers[-1].output.flatten(2),
HiddenLayerParams(inputDim=(self.layers[-1].cfgParams.outputDim[0], numpy.prod(self.layers[-1].cfgParams.outputDim[1:])),
outputDim=(batchSize, 1024), activation=ReLU),
layerNum=len(self.layers)))
self.layers.append(DropoutLayer(rng, self.layers[-1].output,
DropoutLayerParams(inputDim=self.layers[-1].cfgParams.outputDim,
outputDim=self.layers[-1].cfgParams.outputDim),
layerNum=len(self.layers)))
self.layers.append(HiddenLayer(rng, self.layers[-1].output,
HiddenLayerParams(inputDim=self.layers[-1].cfgParams.outputDim,
outputDim=(batchSize, 1024), activation=ReLU),
layerNum=len(self.layers)))
self.layers.append(DropoutLayer(rng, self.layers[-1].output,
DropoutLayerParams(inputDim=self.layers[-1].cfgParams.outputDim,
outputDim=self.layers[-1].cfgParams.outputDim),
layerNum=len(self.layers)))
self.layers.append(HiddenLayer(rng, self.layers[-1].output,
HiddenLayerParams(inputDim=self.layers[-1].cfgParams.outputDim,
outputDim=(batchSize, self.cfgParams.numJoints*self.cfgParams.nDims),
activation=None),
layerNum=len(self.layers)))
self.output = self.layers[-1].output
elif cfgParams.type == 4:
# Try ResNet similar configuration
depth = 47
assert (depth - 2) % 9 == 0, 'depth should be 9n+2 (e.g., 164 or 1001)'
n = (depth - 2) / 9
nStages = [32, 64, 128, 256, 256]
self.layers.append(ConvPoolLayer(rng, self.inputVar,
ConvPoolLayerParams(inputDim=self.cfgParams.inputDim, nFilters=nStages[0],
filterDim=(5, 5), stride=(1, 1),
poolsize=(2, 2), border_mode='same', activation=None,
init_method='He'),
layerNum=len(self.layers))) # one conv at the beginning
rout = self.add_res_layers(rng, self.layers[-1].output, self.layers[-1].cfgParams.outputDim, nStages[1], n, 2) # Stage 1
rout = self.add_res_layers(rng, rout, self.layers[-1].cfgParams.outputDim, nStages[2], n, 2) # Stage 2
rout = self.add_res_layers(rng, rout, self.layers[-1].cfgParams.outputDim, nStages[3], n, 2) # Stage 3
rout = self.add_res_layers(rng, rout, self.layers[-1].cfgParams.outputDim, nStages[4], n, 2) # Stage 4
self.layers.append(BatchNormLayer(rng, rout, BatchNormLayerParams(inputDim=self.layers[-1].cfgParams.outputDim), layerNum=len(self.layers)))
self.layers.append(NonlinearityLayer(rng, self.layers[-1].output, NonlinearityLayerParams(inputDim=self.layers[-1].cfgParams.outputDim, activation=ReLU), layerNum=len(self.layers)))
self.layers.append(HiddenLayer(rng, self.layers[-1].output.flatten(2),
HiddenLayerParams(inputDim=(self.layers[-1].cfgParams.outputDim[0], numpy.prod(self.layers[-1].cfgParams.outputDim[1:])),
outputDim=(batchSize, 1024), activation=ReLU),
layerNum=len(self.layers)))
self.layers.append(DropoutLayer(rng, self.layers[-1].output,
DropoutLayerParams(inputDim=self.layers[-1].cfgParams.outputDim,
outputDim=self.layers[-1].cfgParams.outputDim),
layerNum=len(self.layers)))
self.layers.append(HiddenLayer(rng, self.layers[-1].output,
HiddenLayerParams(inputDim=self.layers[-1].cfgParams.outputDim,
outputDim=(batchSize, 1024), activation=ReLU),
layerNum=len(self.layers)))
self.layers.append(DropoutLayer(rng, self.layers[-1].output,
DropoutLayerParams(inputDim=self.layers[-1].cfgParams.outputDim,
outputDim=self.layers[-1].cfgParams.outputDim),
layerNum=len(self.layers)))
self.layers.append(HiddenLayer(rng, self.layers[-1].output,
HiddenLayerParams(inputDim=self.layers[-1].cfgParams.outputDim,
outputDim=(batchSize, 30), activation=None),
layerNum=len(self.layers)))
self.layers.append(HiddenLayer(rng, self.layers[-1].output,
HiddenLayerParams(inputDim=self.layers[-1].cfgParams.outputDim,
outputDim=(batchSize, self.cfgParams.numJoints*self.cfgParams.nDims),
activation=None),
layerNum=len(self.layers)))
self.output = self.layers[-1].output
else:
raise NotImplementedError()
self.load(self.cfgParams.loadFile)