In [1]:
import sys
sys.path.append('..')
from deepgraph.utils.logging import log
from deepgraph.utils.common import batch_parallel, ConfigMixin, shuffle_in_unison_inplace, pickle_dump
from deepgraph.utils.image import batch_pad_mirror
from deepgraph.constants import *
from deepgraph.conf import rng
from deepgraph.pipeline import Processor, Packet
In [3]:
from deepgraph.nn.init import *
from deepgraph.node import Node
# Custom loss node
class MaskedLogLoss(Node):
"""
Compute log scale invariant error for depth prediction
"""
def __init__(self, graph, name, config={}):
"""
Constructor
:param graph: Graph
:param name: String
:param config: Dict
:return: Node
"""
super(MaskedLogLoss, self).__init__(graph, name, config=config)
self.is_loss = True
def setup_defaults(self):
super(MaskedLogLoss, self).setup_defaults()
self.conf_default("loss_weight", 1.0)
self.conf_default("lambda", 0.2)
self.conf_default("label_index", 1)
def alloc(self):
if len(self.inputs) != 2:
raise AssertionError("This node needs exactly two inputs to calculate loss.")
self.output_shape = (1,)
def forward(self):
# Define our forward function
if self.conf("label_index") == 1:
pred = self.inputs[0].expression
target = self.inputs[1].expression
else:
pred = self.inputs[1].expression
target = self.inputs[0].expression
# Compute a mask which removes invalid values or 0
null_mask = T.gt(target,0)
# Compute a mask which removes the max values of a depth image
max_mask = T.lt(target, T.max(target))
# Min mask
min_mask = T.gt(target, T.min(target))
# Combine masks
mask = null_mask * max_mask * min_mask
# Now we compute the log of the input data stream, but neglect invalid values
log_target = T.switch(mask, T.log(target),0)
# We also apply the mask to our predictions in order to avoid computation of gradients on invalid pixels
masked_pred = mask * pred
# We compute the difference here to construct l_2 norm later in log space, neglecting invalid pixels
diff = log_target - masked_pred
# We also have to compute the mean only on valid pixels, so we need the number of valid pixels
n_valid = mask.sum()
# Finally compute the scale invariant error
self.expression = T.sum(diff**2) / n_valid - ((self.conf("lambda") / (n_valid**2)) * (T.sum(diff)**2))
# Input data transformer
class Transformer(Processor):
"""
Apply online random augmentation.
"""
def __init__(self, name, shapes, config, buffer_size=10):
super(Transformer, self).__init__(name, shapes, config, buffer_size)
self.mean = None
def init(self):
if self.conf("mean_file") is not None:
self.mean = np.load(self.conf("mean_file"))
else:
log("Transformer - No mean file specified.", LOG_LEVEL_WARNING)
def process(self):
packet = self.pull()
# Return if no data is there
if not packet:
return False
# Unpack
data, label = packet.data
# Do processing
log("Transformer - Processing data", LOG_LEVEL_VERBOSE)
h = 240
w = 320
start = time.time()
# Mean
if packet.phase == PHASE_TRAIN or packet.phase == PHASE_VAL:
data = data.astype(np.float32)
if self.mean is not None:
std = self.conf("std_norm")
for idx in range(data.shape[0]):
# Subtract mean
data[idx] = data[idx] - self.mean.astype(np.float32)
if std is not None:
data[idx] = data[idx] * std
if self.conf("offset") is not None:
label -= self.conf("offset")
if self.conf("pred_std_norm") is not None:
label *= self.conf("pred_std_norm")
if packet.phase == PHASE_TRAIN:
# Do elementwise operations
data_old = data
label_old = label
data = np.zeros((data_old.shape[0], data_old.shape[1], h, w), dtype=np.float32)
label = np.zeros((label_old.shape[0], h, w), dtype=np.float32)
for idx in range(data.shape[0]):
# Rotate
# We rotate before cropping to be able to get filled corners
# Maybe even adjust the border after rotating
deg = np.random.randint(-5,6)
# Operate on old data. Careful - data is already in float so we need to normalize and rescale afterwards
# data_old[idx] = 255. * rotate_transformer_rgb_uint8(data_old[idx] * 0.003921568627, deg).astype(np.float32)
# label_old[idx] = rotate_transformer_scalar_float32(label_old[idx], deg)
# Take care of any empty areas, we crop on a smaller surface depending on the angle
# TODO Remove this once loss supports masking
shift = 0 #np.tan((deg/180.) * math.pi)
# Random crops
cy = rng.randint(data_old.shape[2] - h - shift, size=1)
cx = rng.randint(data_old.shape[3] - w - shift, size=1)
data[idx] = data_old[idx, :, cy:cy+h, cx:cx+w]
label[idx] = label_old[idx, cy:cy+h, cx:cx+w]
# Flip horizontally with probability 0.5
p = rng.randint(2)
if p > 0:
data[idx] = data[idx, :, :, ::-1]
label[idx] = label[idx, :, ::-1]
# RGB we mult with a random value between 0.8 and 1.2
r = rng.randint(80,121) / 100.
g = rng.randint(80,121) / 100.
b = rng.randint(80,121) / 100.
data[idx, 0] = data[idx, 0] * r
data[idx, 1] = data[idx, 1] * g
data[idx, 2] = data[idx, 2] * b
# Shuffle
data, label = shuffle_in_unison_inplace(data, label)
elif packet.phase == PHASE_VAL:
# Center crop
cy = (data.shape[2] - h) // 2
cx = (data.shape[3] - w) // 2
data = data[:, :, cy:cy+h, cx:cx+w]
label = label[:, cy:cy+h, cx:cx+w]
end = time.time()
log("Transformer - Processing took " + str(end - start) + " seconds.", LOG_LEVEL_VERBOSE)
# Try to push into queue as long as thread should not terminate
self.push(Packet(identifier=packet.id, phase=packet.phase, num=2, data=(data, label)))
return True
def setup_defaults(self):
super(Transformer, self).setup_defaults()
self.conf_default("mean_file", None)
self.conf_default("offset", None)
self.conf_default("std_norm", 1.0)
self.conf_default("pred_std_norm",1.0)
In [4]:
from theano.tensor.nnet import relu
from deepgraph.graph import *
from deepgraph.nn.core import *
from deepgraph.nn.conv import *
from deepgraph.nn.loss import *
from deepgraph.solver import *
from deepgraph.nn.init import *
from deepgraph.pipeline import Optimizer, H5DBLoader, Pipeline
def build_u_graph():
# Disable CUDNN
# TODO Remove
Conv2D.use_cudnn = False
Pool.use_cudnn = False
graph = Graph("u_depth")
"""
Inputs
"""
data = Data(graph, "data", T.ftensor4, shape=(-1, 3, 240, 320))
label = Data(graph, "label", T.ftensor3, shape=(-1, 1, 240, 320), config={
"phase": PHASE_TRAIN
})
"""
Contractive part
"""
conv_1 = Conv2D(
graph,
"conv_1",
config={
"channels": 64,
"kernel": (3, 3),
"border_mode": (1, 1),
"activation": relu,
"learning_rate": 0.1,
"weight_filler": xavier(gain="relu"),
"bias_filler": constant(0)
}
)
conv_2 = Conv2D(
graph,
"conv_2",
config={
"channels": 64,
"kernel": (3, 3),
"border_mode": (1, 1),
"activation": relu,
"learning_rate": 0.1,
"weight_filler": xavier(gain="relu"),
"bias_filler": constant(0)
}
)
pool_2 = Pool(graph, "pool_2", config={
"kernel": (2, 2)
})
conv_3 = Conv2D(
graph,
"conv_3",
config={
"channels": 128,
"kernel": (3, 3),
"border_mode": (1, 1),
"activation": relu,
"learning_rate": 0.1,
"weight_filler": xavier(gain="relu"),
"bias_filler": constant(0)
}
)
conv_4 = Conv2D(
graph,
"conv_4",
config={
"channels": 128,
"kernel": (3, 3),
"border_mode": (1, 1),
"activation": relu,
"learning_rate": 0.1,
"weight_filler": xavier(gain="relu"),
"bias_filler": constant(0)
}
)
pool_4 = Pool(graph, "pool_4", config={
"kernel": (2, 2)
})
conv_5 = Conv2D(
graph,
"conv_5",
config={
"channels": 256,
"kernel": (3, 3),
"border_mode": (1, 1),
"activation": relu,
"learning_rate": 0.1,
"weight_filler": xavier(gain="relu"),
"bias_filler": constant(0)
}
)
conv_6 = Conv2D(
graph,
"conv_6",
config={
"channels": 256,
"kernel": (3, 3),
"border_mode": (1, 1),
"activation": relu,
"learning_rate": 0.1,
"weight_filler": xavier(gain="relu"),
"bias_filler": constant(0)
}
)
pool_6 = Pool(graph, "pool_6", config={
"kernel": (2, 2)
})
conv_7 = Conv2D(
graph,
"conv_7",
config={
"channels": 512,
"kernel": (3, 3),
"border_mode": (1, 1),
"activation": relu,
"learning_rate": 0.1,
"weight_filler": xavier(gain="relu"),
"bias_filler": constant(0)
}
)
conv_8 = Conv2D(
graph,
"conv_8",
config={
"channels": 512,
"kernel": (3, 3),
"border_mode": (1, 1),
"activation": relu,
"learning_rate": 0.1,
"weight_filler": xavier(gain="relu"),
"bias_filler": constant(0)
}
)
dp_c8 = Dropout(graph, "dpc_8")
pool_8 = Pool(graph, "pool_8", config={
"kernel": (2, 2)
})
fl = Flatten(graph, "fl",config={
"dims": 2
})
fc_8 = Dense(graph, "fc_8", config={
"out": 4096,
"activation": relu,
"weight_filler": xavier(),
"bias_filler": constant(0.01)
})
dp_8 = Dropout(graph, "dp_8")
fc_9 = Dense(graph, "fc_9", config={
"out": 19200,
"activation": relu,
"weight_filler": xavier(),
"bias_filler": constant(0.01)
})
dp_9 = Dropout(graph, "dp_9")
rs_10 = Reshape(graph, "rs_10", config={
"shape": (-1, 64, 15, 20)
})
"""
Expansive path
"""
up_11 = Upsample(graph, "up_11", config={
"kernel": (2, 2)
})
conv_11 = Conv2D(
graph,
"conv_11",
config={
"channels": 512,
"kernel": (3, 3),
"border_mode": 1,
"learning_rate": 0.1,
"weight_filler": xavier(),
"bias_filler": constant(0)
}
)
conv_12 = Conv2D(
graph,
"conv_12",
config={
"channels": 512,
"kernel": (3, 3),
"border_mode": (1, 1),
"activation": relu,
"learning_rate": 0.1,
"weight_filler": xavier(gain="relu"),
"bias_filler": constant(0)
}
)
conv_13 = Conv2D(
graph,
"conv_13",
config={
"channels": 512,
"kernel": (3, 3),
"border_mode": (1, 1),
"activation": relu,
"learning_rate": 0.1,
"weight_filler": xavier(gain="relu"),
"bias_filler": constant(0)
}
)
up_14 = Upsample(graph, "up_14", config={
"kernel": (2, 2)
})
conv_14 = Conv2D(
graph,
"conv_14",
config={
"channels": 256,
"kernel": (3, 3),
"border_mode": 1,
"learning_rate": 0.1,
"weight_filler": xavier(),
"bias_filler": constant(0)
}
)
conv_15 = Conv2D(
graph,
"conv_15_b", # Renamed to prevent weight filler from-file init
config={
"channels": 256,
"kernel": (3, 3),
"border_mode": (1, 1),
"activation": relu,
"learning_rate": 0.1,
"weight_filler": xavier(gain="relu"),
"bias_filler": constant(0)
}
)
conv_16 = Conv2D(
graph,
"conv_16",
config={
"channels": 256,
"kernel": (3, 3),
"border_mode": (1, 1),
"activation": relu,
"learning_rate": 0.1,
"weight_filler": xavier(gain="relu"),
"bias_filler": constant(0)
}
)
up_17 = Upsample(graph, "up_17", config={
"kernel": (2, 2)
})
conv_17 = Conv2D(graph, "conv_17", config={
"channels": 128,
"kernel": (3, 3),
"border_mode": 1,
"learning_rate": 0.1,
"weight_filler": xavier(),
"bias_filler": constant(0)
})
conv_18 = Conv2D(
graph,
"conv_18",
config={
"channels": 128,
"kernel": (3, 3),
"border_mode": (1, 1),
"activation": relu,
"learning_rate": 0.1,
"weight_filler": xavier(gain="relu"),
"bias_filler": constant(0)
}
)
conv_19 = Conv2D(
graph,
"conv_19",
config={
"channels": 128,
"kernel": (3, 3),
"border_mode": (1, 1),
"activation": relu,
"learning_rate": 0.1,
"weight_filler": xavier(gain="relu"),
"bias_filler": constant(0)
}
)
up_20 = Upsample(graph, "up_20", config={
"mode": "constant",
"kernel": (2, 2)
})
conv_20 = Conv2D(graph, "conv_20", config={
"channels": 64,
"kernel": (3, 3),
"border_mode": 1,
"learning_rate": 0.1,
"weight_filler": xavier(),
"bias_filler": constant(0)
})
conv_21 = Conv2D(
graph,
"conv_21",
config={
"channels": 64,
"kernel": (3, 3),
"border_mode": (1, 1),
"activation": relu,
"learning_rate": 0.1,
"weight_filler": xavier(gain="relu"),
"bias_filler": constant(0)
}
)
conv_22 = Conv2D(
graph,
"conv_22",
config={
"channels": 64,
"kernel": (3, 3),
"border_mode": (1, 1),
"activation": relu,
"learning_rate": 0.1,
"weight_filler": xavier(gain="relu"),
"bias_filler": constant(0)
}
)
conv_23 = Conv2D(
graph,
"conv_23_with_bias",
config={
"channels": 1,
"kernel": (1, 1),
"activation": None,
"learning_rate": 0.01,
"weight_filler": xavier(),
"bias_filler": constant(0.0001),
"is_output": True
}
)
"""
Feed forward nodes
"""
concat_20 = Concatenate(graph, "concat_20", config={
"axis": 1
})
concat_17 = Concatenate(graph, "concat_17", config={
"axis": 1
})
concat_14 = Concatenate(graph, "concat_14", config={
"axis": 1
})
concat_11 = Concatenate(graph, "concat_11", config={
"axis": 1
})
"""
Large scale feature refinement path
"""
ls_conv_1 = Conv2D(
graph,
"ls_conv_1",
config={
"channels": 96,
"kernel": (9, 9),
"border_mode": (4, 4),
"activation": relu,
"learning_rate": 0.01,
"weight_filler": xavier(gain="relu"),
"bias_filler": constant(0)
}
)
ls_pool_1 = Pool(graph, "ls_pool_1", config={
"kernel": (2, 2)
})
ls_conv_2 = Conv2D(
graph,
"ls_conv_2",
config={
"channels": 64,
"kernel": (5, 5),
"border_mode": (2, 2),
"activation": relu,
"learning_rate": 0.1,
"weight_filler": xavier(gain="relu"),
"bias_filler": constant(0)
}
)
ls_conv_3 = Conv2D(
graph,
"ls_conv_3",
config={
"channels": 64,
"kernel": (5, 5),
"border_mode": (2, 2),
"activation": relu,
"learning_rate": 0.1,
"weight_filler": xavier(gain="relu"),
"bias_filler": constant(0)
}
)
ls_pool_3 = Pool(graph, "ls_pool_3", config={
"kernel": (2, 2)
})
ls_conv_4 = Conv2D(
graph,
"ls_conv_4",
config={
"channels": 64,
"kernel": (5, 5),
"border_mode": (2, 2),
"activation": relu,
"learning_rate": 0.1,
"weight_filler": xavier(gain="relu"),
"bias_filler": constant(0)
}
)
"""
Mid scale feature refinement path
"""
ms_conv_1 = Conv2D(
graph,
"ms_conv_1",
config={
"channels": 96,
"kernel": (9, 9),
"border_mode": (4, 4),
"activation": relu,
"learning_rate": 0.01,
"weight_filler": xavier(gain="relu"),
"bias_filler": constant(0)
}
)
ms_pool_1 = Pool(graph, "ms_pool_1", config={
"kernel": (2, 2)
})
ms_conv_2 = Conv2D(
graph,
"ms_conv_2",
config={
"channels": 64,
"kernel": (5, 5),
"border_mode": (2, 2),
"activation": relu,
"learning_rate": 0.1,
"weight_filler": xavier(gain="relu"),
"bias_filler": constant(0)
}
)
ms_conv_3 = Conv2D(
graph,
"ms_conv_3",
config={
"channels": 64,
"kernel": (5, 5),
"border_mode": (2, 2),
"activation": relu,
"learning_rate": 0.1,
"weight_filler": xavier(gain="relu"),
"bias_filler": constant(0)
}
)
ms_conv_4 = Conv2D(
graph,
"ms_conv_4",
config={
"channels": 64,
"kernel": (5, 5),
"border_mode": (2, 2),
"activation": relu,
"learning_rate": 0.1,
"weight_filler": xavier(gain="relu"),
"bias_filler": constant(0)
}
)
"""
Losses / Error
"""
loss = EuclideanLoss(graph, "loss")
error = MSE(graph, "mse", config={
"root": True,
"is_output": True,
"phase": PHASE_TRAIN
})
"""
Make connections
"""
# Contractive path with feed forward 3x3 kernel paths
data.connect(conv_1)
conv_1.connect(conv_2)
conv_2.connect(concat_20)
conv_2.connect(pool_2)
pool_2.connect(conv_3)
conv_3.connect(conv_4)
conv_4.connect(concat_17)
conv_4.connect(pool_4)
pool_4.connect(conv_5)
conv_5.connect(conv_6)
conv_6.connect(concat_14)
conv_6.connect(pool_6)
pool_6.connect(conv_7)
conv_7.connect(conv_8)
conv_8.connect(concat_11)
conv_8.connect(dp_c8)
dp_c8.connect(pool_8)
pool_8.connect(fl)
fl.connect(fc_8)
fc_8.connect(dp_8)
dp_8.connect(fc_9)
fc_9.connect(dp_9)
dp_9.connect(rs_10)
rs_10.connect(up_11)
up_11.connect(conv_11)
conv_11.connect(concat_11)
concat_11.connect(conv_12)
conv_12.connect(conv_13)
conv_13.connect(up_14)
up_14.connect(conv_14)
conv_14.connect(concat_14)
concat_14.connect(conv_15)
conv_15.connect(conv_16)
conv_16.connect(up_17)
up_17.connect(conv_17)
conv_17.connect(concat_17)
concat_17.connect(conv_18)
conv_18.connect(conv_19)
conv_19.connect(up_20)
up_20.connect(conv_20)
conv_20.connect(concat_20)
concat_20.connect(conv_21)
conv_21.connect(conv_22)
conv_22.connect(conv_23)
# Connect mid scale, large feature path
data.connect(ms_conv_1)
ms_conv_1.connect(ms_pool_1)
ms_pool_1.connect(ms_conv_2)
ms_conv_2.connect(ms_conv_3)
ms_conv_3.connect(ms_conv_4)
ms_conv_4.connect(concat_17)
# Connect large scale, larger feature path
data.connect(ls_conv_1)
ls_conv_1.connect(ls_pool_1)
ls_pool_1.connect(ls_conv_2)
ls_conv_2.connect(ls_conv_3)
ls_conv_3.connect(ls_pool_3)
ls_pool_3.connect(ls_conv_4)
ls_conv_4.connect(concat_14)
# Make sure we connect the prediction first and then the loss to compute the log space properly
conv_23.connect(loss)
label.connect(loss)
conv_23.connect(error)
label.connect(error)
return graph
In [ ]:
if __name__ == "__main__":
batch_size = 4
chunk_size = 10*batch_size
transfer_shape = ((chunk_size, 3, 240, 320), (chunk_size, 240, 320))
g = build_u_graph()
# Build the training pipeline
db_loader = H5DBLoader("db", ((chunk_size, 3, 480, 640), (chunk_size, 1, 480, 640)), config={
"db": "/media/data/depth_data/nyu_depth_combined_vnet2.hdf5",
# "db": '../data/nyu_depth_unet_large.hdf5',
"key_data": "images",
"key_label": "depths",
"chunk_size": chunk_size
})
transformer = Transformer("tr", transfer_shape, config={
# Pixel & channelwise mean
"mean_file": "/media/data/depth_data/nyu_depth_combined_vnet2.npy",
# 1 / sqrt(var(data))
"std_norm": 1.0 / 72.240064849107824,
"pred_std_norm": 1.0 / 1.6549985
})
optimizer = Optimizer("opt", g, transfer_shape, config={
"batch_size": batch_size,
"chunk_size": chunk_size,
# "learning_rate": 0.01, # Step 1
"learning_rate": 0.001, # Step 2
"momentum": 0.9,
"weight_decay": 0.0005,
"print_freq": 20,
"save_freq": 60000,
"lr_policy": "step",
"step_size": 60000,
"weights": "/media/data/depth_models/vnet3_5_paths_iter_60000.zip",
"save_prefix": "/media/data/depth_models/vnet3_5_paths"
})
p = Pipeline(config={
"validation_frequency": 50,
"cycles": 12500
})
p.add(db_loader)
p.add(transformer)
p.add(optimizer)
p.run()
In [ ]:
%matplotlib inline
import matplotlib.pyplot as plt
l = np.array([s["loss"] for s in optimizer.losses])
e = np.array([s["mse"] for s in optimizer.losses])
print l.mean()
plt.plot(l)
plt.show()
plt.plot(e)
plt.show()
In [14]:
import h5py, numpy as np
f = h5py.File("/media/data/depth_data/nyu_depth_combined_vnet2.hdf5")
b = int(f["images"].shape[0] * 0.9)
images = np.array(f["images"][0:100])
depths = np.array(f["depths"][0:100])
print images.shape
mean = np.load("/media/data/depth_data/nyu_depth_combined_vnet2.npy")
In [9]:
g = build_u_graph()
g.load_weights("/media/data/depth_models/vnet3_5_paths_iter_60000.zip")
g.compile()
In [23]:
%matplotlib inline
import matplotlib.pyplot as plt
from deepgraph.nn.core import Dropout
import time
plot = True
idx = 0
diffs = []
timings = []
Dropout.set_dp_off()
for image in images:
tmp = image.astype(np.float32).copy()
# Stochastic normalization
tmp -= mean
tmp *= 1.0 / 72.240064849107824
# center crop
cy = (tmp.shape[1] - 240 ) // 2
cx = (tmp.shape[2] - 320 ) // 2
tmp = tmp[:,cy:cy+240,cx:cx+320]
# Infer and measure time
start = time.time()
res = g.infer([tmp.reshape((1,3,240,320))])["conv_23_with_bias"]
end = time.time()
timings.append(end - start)
res = res.squeeze()
res = np.exp(res) / 1.6
depth = depths[idx]
tdepth = depth[cy:cy+240,cx:cx+320]
if plot and idx % 20 == 0:
plt.imshow(image.transpose((1,2,0)).astype(np.uint8))
plt.show()
plt.imshow(tdepth)
plt.show()
plt.imshow(res)
plt.show()
print "RMSE: " + str(np.sqrt(np.mean((res-tdepth)**2)))
print "LRMSE: " + str(np.sqrt(np.mean((np.log(res) - np.log(tdepth))**2)))
diffs.append(res - tdepth)
idx += 1
diffs = np.array(diffs)
timings = np.array(timings)
rmse = np.sqrt(np.mean(diffs ** 2))
print "Accumulated RMSE: " + str(rmse)
print "Mean forward execution time (with Host -> GPU transfer): %f" % timings.mean()
In [ ]:
import urllib, cStringIO
from PIL import Image
%matplotlib inline
import matplotlib.pyplot as plt
from deepgraph.nn.core import Dropout
Dropout.set_dp_off()
urls = [
'http://www.hotel-im-wasserturm.de/fileadmin/_processed_/csm_Deluxe_Doppelzimmer_2_6455fa52be.jpg',
'http://hotel-airport-zuerich.dorint.com/fileadmin/_processed_/csm_ZRH_Zimmer_DZ_04_bc3a12f87e.jpg',
'http://www.erlebnis-erlensee.de/images/bilder/partyservice/schnitzel.jpg',
'http://www.hankewitz.com/wp-content/uploads/2012/12/bolognese21.jpg'
]
for URL in urls:
file = cStringIO.StringIO(urllib.urlopen(URL).read())
img = Image.open(file)
rs = img.resize((320,240))
mean = np.load("/home/ga29mix/nashome/data/nyu_depth_v2_combined_50.npy")
arr = np.array(rs).transpose((2,0,1)).astype(np.float32)
arr -= mean
arr *= 1.0 / 72.240064849107824
res = g.infer([arr.reshape((1,3,240,320))])["conv_23_with_bias"]
res = np.exp(res)
# Montage to manipulate color scale
mmax = 8.0
# res[0,0,0,0] = 0.0
# res[0,0,0,1] = mmax
plt.imshow(rs)
plt.show()
plt.imshow(res.squeeze().clip(0.0,mmax))
plt.show()
print res.max()
In [ ]:
import urllib, cStringIO
from PIL import Image
%matplotlib inline
import matplotlib.pyplot as plt
from deepgraph.nn.core import Dropout
import numpy as np
Dropout.set_dp_off()
data = np.load('/home/ga29mix/nashome/data/kinectv2_2016_03_30_13_33_25_bgr.npy')[:,:,0:3]
dd = np.load('/home/ga29mix/nashome/data/kinectv2_2016_03_30_13_33_25_depth.npy')
img = Image.fromarray(np.array(data[:,:,::-1]))
rs = img.resize((320,240))
mean = np.load("/home/ga29mix/nashome/data/nyu_depth_v2_combined_50.npy")
arr = np.array(rs).transpose((2,0,1)).astype(np.float32)
arr -= mean
arr *= 1.0 / 72.240064849107824
res = g.infer([arr.reshape((1,3,240,320))])["conv_23_with_bias"]
res = np.exp(res)
plt.imshow(img)
plt.show()
plt.imshow(res.squeeze())
plt.show()
plt.imshow(dd)
plt.show()
In [ ]: