def _create_main_loop(self):
tensor5 = T.TensorType(config.floatX, (False, ) * 5)
x = tensor5("images")
x = x[:, :, :, :, [2, 1, 0]]
x_shuffled = x.dimshuffle((0, 1, 4, 2, 3)) * 255
x_r = x_shuffled.reshape(
(x_shuffled.shape[0], x_shuffled.shape[1] * x_shuffled.shape[2],
x_shuffled.shape[3], x_shuffled.shape[4]))
x_r = x_r - (np.array([104, 117, 123])[None, :, None,
None]).astype('float32')
expressions, input_data, param = stream_layer_exp(inputs=('data', x_r),
mode='rgb')
outputs = expressions['fc8-1']
from blocks.graph import ComputationGraph
# B x T x X x Y x C
inputs = ComputationGraph(outputs).inputs
import theano
f = theano.function(inputs, outputs)
img = np.array(Image.open('img_4.jpeg'))
img = img[:224, :224]
img = img[np.newaxis, np.newaxis, :, :, :]
img = img / 255.0
img = img.astype('float32')
res = f(img)
print np.argmax(res[0, :, 0, 0])
import ipdb
ipdb.set_trace()
def _create_main_loop(self):
tensor5 = T.TensorType(config.floatX, (False,) * 5)
x = tensor5("images")
x = x[:, :, :, :, [2, 1, 0]]
x_shuffled = x.dimshuffle((0, 1, 4, 2, 3)) * 255
x_r = x_shuffled.reshape((x_shuffled.shape[0],
x_shuffled.shape[1] * x_shuffled.shape[2],
x_shuffled.shape[3], x_shuffled.shape[4]))
x_r = x_r - (
np.array([104, 117, 123])[None, :, None, None]).astype('float32')
expressions, input_data, param = stream_layer_exp(
inputs=('data', x_r), mode='rgb')
outputs = expressions['fc8-1']
from blocks.graph import ComputationGraph
# B x T x X x Y x C
inputs = ComputationGraph(outputs).inputs
import theano
f = theano.function(inputs, outputs)
img = np.array(Image.open('img_4.jpeg'))
img = img[:224, :224]
img = img[np.newaxis, np.newaxis, :, :, :]
img = img / 255.0
img = img.astype('float32')
res = f(img)
print np.argmax(res[0, :, 0, 0])
import ipdb; ipdb.set_trace()
def _create_main_loop(self):
# hyper parameters
hp = self.params
batch_size = hp['batch_size']
biases_init = Constant(0)
batch_normalize = hp['batch_normalize']
### Build fprop
tensor5 = T.TensorType(config.floatX, (False, ) * 5)
X = tensor5("images")
#X = T.tensor4("images")
y = T.lvector('targets')
gnet_params = OrderedDict()
#X_shuffled = X[:, :, :, :, [2, 1, 0]]
#X_shuffled = gpu_contiguous(X.dimshuffle(0, 1, 4, 2, 3)) * 255
X = X[:, :, :, :, [2, 1, 0]]
X_shuffled = X.dimshuffle((0, 1, 4, 2, 3)) * 255
X_r = X_shuffled.reshape(
(X_shuffled.shape[0], X_shuffled.shape[1] * X_shuffled.shape[2],
X_shuffled.shape[3], X_shuffled.shape[4]))
X_r = X_r - (np.array([104, 117, 123])[None, :, None,
None]).astype('float32')
expressions, input_data, param = stream_layer_exp(inputs=('data', X_r),
mode='rgb')
res = expressions['outloss']
y_hat = res.flatten(ndim=2)
import pdb
pdb.set_trace()
### Build Cost
cost = CategoricalCrossEntropy().apply(y, y_hat)
cost = T.cast(cost, theano.config.floatX)
cost.name = 'cross_entropy'
y_pred = T.argmax(y_hat, axis=1)
misclass = T.cast(T.mean(T.neq(y_pred, y)), theano.config.floatX)
misclass.name = 'misclass'
monitored_channels = []
monitored_quantities = [cost, misclass, y_hat, y_pred]
model = Model(cost)
training_cg = ComputationGraph(monitored_quantities)
inference_cg = ComputationGraph(monitored_quantities)
### Get evaluation function
#training_eval = training_cg.get_theano_function(additional_updates=bn_updates)
training_eval = training_cg.get_theano_function()
#inference_eval = inference_cg.get_theano_function()
# Dataset
test = JpegHDF5Dataset(
'test',
#name='jpeg_data_flows.hdf5',
load_in_memory=True)
#mean = np.load(os.path.join(os.environ['UCF101'], 'mean.npy'))
import pdb
pdb.set_trace()
### Eval
labels = np.zeros(test.num_video_examples)
y_hat = np.zeros((test.num_video_examples, 101))
labels_flip = np.zeros(test.num_video_examples)
y_hat_flip = np.zeros((test.num_video_examples, 101))
### Important to shuffle list for batch normalization statistic
#rng = np.random.RandomState()
#examples_list = range(test.num_video_examples)
#import pdb; pdb.set_trace()
#rng.shuffle(examples_list)
nb_frames = 1
for i in xrange(24):
scheme = HDF5SeqScheme(test.video_indexes,
examples=test.num_video_examples,
batch_size=batch_size,
f_subsample=i,
nb_subsample=25,
frames_per_video=nb_frames)
#for crop in ['upleft', 'upright', 'downleft', 'downright', 'center']:
for crop in ['center']:
stream = JpegHDF5Transformer(
input_size=(240, 320),
crop_size=(224, 224),
#input_size=(256, 342), crop_size=(224, 224),
crop_type=crop,
translate_labels=True,
flip='noflip',
nb_frames=nb_frames,
data_stream=ForceFloatX(
DataStream(dataset=test, iteration_scheme=scheme)))
stream_flip = JpegHDF5Transformer(
input_size=(240, 320),
crop_size=(224, 224),
#input_size=(256, 342), crop_size=(224, 224),
crop_type=crop,
translate_labels=True,
flip='flip',
nb_frames=nb_frames,
data_stream=ForceFloatX(
DataStream(dataset=test, iteration_scheme=scheme)))
## Do the evaluation
epoch = stream.get_epoch_iterator()
for j, batch in enumerate(epoch):
output = training_eval(batch[0], batch[1])
# import cv2
# cv2.imshow('img', batch[0][0, 0, :, :, :])
# cv2.waitKey(160)
# cv2.destroyAllWindows()
#import pdb; pdb.set_trace()
labels_flip[batch_size * j:batch_size * (j + 1)] = batch[1]
y_hat_flip[batch_size * j:batch_size *
(j + 1), :] += output[2]
preds = y_hat_flip.argmax(axis=1)
misclass = np.sum(labels_flip != preds) / float(len(preds))
print i, crop, "flip Misclass:", misclass
epoch = stream_flip.get_epoch_iterator()
for j, batch in enumerate(epoch):
output = training_eval(batch[0], batch[1])
labels[batch_size * j:batch_size * (j + 1)] = batch[1]
y_hat[batch_size * j:batch_size * (j + 1), :] += output[2]
preds = y_hat.argmax(axis=1)
misclass = np.sum(labels != preds) / float(len(preds))
print i, crop, "noflip Misclass:", misclass
y_merge = y_hat + y_hat_flip
preds = y_merge.argmax(axis=1)
misclass = np.sum(labels != preds) / float(len(preds))
print i, crop, "avg Misclass:", misclass
### Compute misclass
y_hat += y_hat_flip
preds = y_hat.argmax(axis=1)
misclass = np.sum(labels != preds) / float(len(preds))
print "Misclass:", misclass
def _create_main_loop(self):
# hyper parameters
hp = self.params
batch_size = hp['batch_size']
biases_init = Constant(0)
batch_normalize = hp['batch_normalize']
### Build fprop
tensor5 = T.TensorType(config.floatX, (False,)*5)
X = tensor5("images")
#X = T.tensor4("images")
y = T.lvector('targets')
gnet_params = OrderedDict()
#X_shuffled = X[:, :, :, :, [2, 1, 0]]
#X_shuffled = gpu_contiguous(X.dimshuffle(0, 1, 4, 2, 3)) * 255
X = X[:, :, :, :, [2, 1, 0]]
X_shuffled = X.dimshuffle((0, 1, 4, 2, 3)) * 255
X_r = X_shuffled.reshape((X_shuffled.shape[0],
X_shuffled.shape[1]*X_shuffled.shape[2],
X_shuffled.shape[3], X_shuffled.shape[4]))
X_r = X_r - (np.array([104, 117, 123])[None, :, None, None]).astype('float32')
expressions, input_data, param = stream_layer_exp(inputs = ('data', X_r),
mode='rgb')
res = expressions['outloss']
y_hat = res.flatten(ndim=2)
import pdb; pdb.set_trace()
### Build Cost
cost = CategoricalCrossEntropy().apply(y, y_hat)
cost = T.cast(cost, theano.config.floatX)
cost.name = 'cross_entropy'
y_pred = T.argmax(y_hat, axis=1)
misclass = T.cast(T.mean(T.neq(y_pred, y)), theano.config.floatX)
misclass.name = 'misclass'
monitored_channels = []
monitored_quantities = [cost, misclass, y_hat, y_pred]
model = Model(cost)
training_cg = ComputationGraph(monitored_quantities)
inference_cg = ComputationGraph(monitored_quantities)
### Get evaluation function
#training_eval = training_cg.get_theano_function(additional_updates=bn_updates)
training_eval = training_cg.get_theano_function()
#inference_eval = inference_cg.get_theano_function()
# Dataset
test = JpegHDF5Dataset('test',
#name='jpeg_data_flows.hdf5',
load_in_memory=True)
#mean = np.load(os.path.join(os.environ['UCF101'], 'mean.npy'))
import pdb; pdb.set_trace()
### Eval
labels = np.zeros(test.num_video_examples)
y_hat = np.zeros((test.num_video_examples, 101))
labels_flip = np.zeros(test.num_video_examples)
y_hat_flip = np.zeros((test.num_video_examples, 101))
### Important to shuffle list for batch normalization statistic
#rng = np.random.RandomState()
#examples_list = range(test.num_video_examples)
#import pdb; pdb.set_trace()
#rng.shuffle(examples_list)
nb_frames=1
for i in xrange(24):
scheme = HDF5SeqScheme(test.video_indexes,
examples=test.num_video_examples,
batch_size=batch_size,
f_subsample=i,
nb_subsample=25,
frames_per_video=nb_frames)
#for crop in ['upleft', 'upright', 'downleft', 'downright', 'center']:
for crop in ['center']:
stream = JpegHDF5Transformer(
input_size=(240, 320), crop_size=(224, 224),
#input_size=(256, 342), crop_size=(224, 224),
crop_type=crop,
translate_labels = True,
flip='noflip', nb_frames = nb_frames,
data_stream=ForceFloatX(DataStream(
dataset=test, iteration_scheme=scheme)))
stream_flip = JpegHDF5Transformer(
input_size=(240, 320), crop_size=(224, 224),
#input_size=(256, 342), crop_size=(224, 224),
crop_type=crop,
translate_labels = True,
flip='flip', nb_frames = nb_frames,
data_stream=ForceFloatX(DataStream(
dataset=test, iteration_scheme=scheme)))
## Do the evaluation
epoch = stream.get_epoch_iterator()
for j, batch in enumerate(epoch):
output = training_eval(batch[0], batch[1])
# import cv2
# cv2.imshow('img', batch[0][0, 0, :, :, :])
# cv2.waitKey(160)
# cv2.destroyAllWindows()
#import pdb; pdb.set_trace()
labels_flip[batch_size*j:batch_size*(j+1)] = batch[1]
y_hat_flip[batch_size*j:batch_size*(j+1), :] += output[2]
preds = y_hat_flip.argmax(axis=1)
misclass = np.sum(labels_flip != preds) / float(len(preds))
print i, crop, "flip Misclass:", misclass
epoch = stream_flip.get_epoch_iterator()
for j, batch in enumerate(epoch):
output = training_eval(batch[0], batch[1])
labels[batch_size*j:batch_size*(j+1)] = batch[1]
y_hat[batch_size*j:batch_size*(j+1), :] += output[2]
preds = y_hat.argmax(axis=1)
misclass = np.sum(labels != preds) / float(len(preds))
print i, crop, "noflip Misclass:", misclass
y_merge = y_hat + y_hat_flip
preds = y_merge.argmax(axis=1)
misclass = np.sum(labels != preds) / float(len(preds))
print i, crop, "avg Misclass:", misclass
### Compute misclass
y_hat += y_hat_flip
preds = y_hat.argmax(axis=1)
misclass = np.sum(labels != preds) / float(len(preds))
print "Misclass:", misclass
