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jblitzar
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github-python

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upsample_yss_out = self.upsample_single(upsample_yss_out, factor=2)
upsample_out = self.upsample_single(upsample_out, factor=2)
upsample_gt.append(upsample_yss_out.cpu().data.numpy())
upsample_pred.append(upsample_out.cpu().data.numpy())
return loss.data, upsample_pred, upsample_gt
self.train_funcs.append(train_function)
self.test_funcs = test_function
return self.train_funcs, self.test_funcs
'''
This loads the model for training from ImageNet weights
initialization for VGG backbone.
Parameters
-----------
net: (torch model) network
dont_load: (list) list of layers, for which weights
should not be loaded.
Returns
---------
Returns nothing. The weights are replaced inplace.
'''
def load_model_VGG16(net, dont_load=[]):
if 'scale_4' in net.name:
cfg = OrderedDict()
cfg['conv1_1'] = 0
cfg['conv1_2'] = 2
cfg['conv2_1'] = 5
cfg['conv2_2'] = 7
cfg['conv3_1'] = 10
cfg['conv3_2'] = 12
cfg['conv3_3'] = 14
cfg['conv4_1'] = 17
cfg['conv4_2'] = 19
cfg['conv4_3'] = 22
cfg['conv5_1'] = 22
cfg['conv5_2'] = 22
cfg['conv5_3'] = 22
cfg['conv_middle_1'] = 'conv4_1'
cfg['conv_middle_2'] = 'conv4_2'
cfg['conv_middle_3'] = 'conv4_3'
cfg['conv_lowest_1'] = 'conv3_1'
cfg['conv_lowest_2'] = 'conv3_2'
cfg['conv_lowest_3'] = 'conv3_3'
cfg['conv_scale1_1'] = 'conv2_1'
cfg['conv_scale1_2'] = 'conv2_2'
print ('loading model ', net.name)
base_dir = "../imagenet_vgg_weights/"
layer_copy_count = 0
for layer in cfg.keys():
if layer in dont_load:
print (layer, 'skipped.')
continue
print ("Copying ", layer)
for name, module in net.named_children():
if layer == name and (not layer.startswith("conv_middle_")) and (not layer.startswith("conv_lowest_") and (not layer.startswith("conv_scale1_"))):
lyr = module
W = np.load(base_dir + layer + "W.npy")
b = np.load(base_dir + layer + "b.npy")
lyr.weight.data.copy_(torch.from_numpy(W))
lyr.bias.data.copy_(torch.from_numpy(b))
layer_copy_count += 1
elif (layer.startswith("conv_middle_") or layer.startswith("conv_lowest_") or layer.startswith("conv_scale1_")) and name == layer:
lyr = module
W = np.load(base_dir + cfg[layer] + "W.npy")
b = np.load(base_dir + cfg[layer] + "b.npy")
lyr.weight.data.copy_(torch.from_numpy(W))
lyr.bias.data.copy_(torch.from_numpy(b))
layer_copy_count += 1
print(layer_copy_count, "Copy count")
assert layer_copy_count == 21
print ('Done.')
'''
Function to get localization error (alias offset error)
Parameters
-----------
x_pred: (list) list of x-coordinates of prediction
y_pred: (list) list of y-coordinates of prediction
x_true: (list) list of x-coordinates of gt
y_true: (list) list of y-coordinates of gt
output_downscale: (int) scale in which LSC-CNN predicts
max_dist: (int, default=16) maximum distance beyond
which there's a penalty
NOTE: MLE is ALWAYS calculated in 1x scale i.e
scale of the input image and hence multiplication
with "output_downscale"
Returns
----------
off_err; (float) localization error