本文整理匯總了Python中model.roi_crop.functions.roi_crop.RoICropFunction方法的典型用法代碼示例。如果您正苦於以下問題：Python roi_crop.RoICropFunction方法的具體用法？Python roi_crop.RoICropFunction怎麽用？Python roi_crop.RoICropFunction使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類model.roi_crop.functions.roi_crop的用法示例。
在下文中一共展示了roi_crop.RoICropFunction方法的4個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: compare_grid_sample
# 需要導入模塊: from model.roi_crop.functions import roi_crop [as 別名]
# 或者: from model.roi_crop.functions.roi_crop import RoICropFunction [as 別名]
def compare_grid_sample():
# do gradcheck
N = random.randint(1, 8)
C = 2 # random.randint(1, 8)
H = 5 # random.randint(1, 8)
W = 4 # random.randint(1, 8)
input = Variable(torch.randn(N, C, H, W).cuda(), requires_grad=True)
input_p = input.clone().data.contiguous()
grid = Variable(torch.randn(N, H, W, 2).cuda(), requires_grad=True)
grid_clone = grid.clone().contiguous()
out_offcial = F.grid_sample(input, grid)
grad_outputs = Variable(torch.rand(out_offcial.size()).cuda())
grad_outputs_clone = grad_outputs.clone().contiguous()
grad_inputs = torch.autograd.grad(out_offcial, (input, grid), grad_outputs.contiguous())
grad_input_off = grad_inputs[0]
crf = RoICropFunction()
grid_yx = torch.stack([grid_clone.data[:,:,:,1], grid_clone.data[:,:,:,0]], 3).contiguous().cuda()
out_stn = crf.forward(input_p, grid_yx)
grad_inputs = crf.backward(grad_outputs_clone.data)
grad_input_stn = grad_inputs[0]
pdb.set_trace()
delta = (grad_input_off.data - grad_input_stn).sum() 示例2: compare_grid_sample
# 需要導入模塊: from model.roi_crop.functions import roi_crop [as 別名]
# 或者: from model.roi_crop.functions.roi_crop import RoICropFunction [as 別名]
def compare_grid_sample():
# do gradcheck
N = random.randint(1, 8)
C = 2 # random.randint(1, 8)
H = 5 # random.randint(1, 8)
W = 4 # random.randint(1, 8)
input = Variable(torch.randn(N, C, H, W).cuda(), requires_grad=True)
input_p = input.clone().data.contiguous()
grid = Variable(torch.randn(N, H, W, 2).cuda(), requires_grad=True)
grid_clone = grid.clone().contiguous()
out_offcial = F.grid_sample(input, grid)
grad_outputs = Variable(torch.rand(out_offcial.size()).cuda())
grad_outputs_clone = grad_outputs.clone().contiguous()
grad_inputs = torch.autograd.grad(out_offcial, (input, grid), grad_outputs.contiguous())
grad_input_off = grad_inputs[0]
crf = RoICropFunction()
grid_yx = torch.stack([grid_clone.data[:,:,:,1], grid_clone.data[:,:,:,0]], 3).contiguous().cuda()
out_stn = crf.forward(input_p, grid_yx)
grad_inputs = crf.backward(grad_outputs_clone.data)
grad_input_stn = grad_inputs[0]
pdb.set_trace()
delta = (grad_input_off.data - grad_input_stn).sum() 示例3: roi_feature_transform
# 需要導入模塊: from model.roi_crop.functions import roi_crop [as 別名]
# 或者: from model.roi_crop.functions.roi_crop import RoICropFunction [as 別名]
def roi_feature_transform(self, blobs_in, rois, method='RoIPoolF',
resolution=7, spatial_scale=1. / 16., sampling_ratio=0):
"""Add the specified RoI pooling method. The sampling_ratio argument
is supported for some, but not all, RoI transform methods.
RoIFeatureTransform abstracts away:
- Use of FPN or not
- Specifics of the transform method
"""
assert method in {'RoIPoolF', 'RoICrop', 'RoIAlign'}, \
'Unknown pooling method: {}'.format(method)
# Single feature level
# rois: holds R regions of interest, each is a 5-tuple
# (batch_idx, x1, y1, x2, y2) specifying an image batch index and a
# rectangle (x1, y1, x2, y2)
if method == 'RoIPoolF':
xform_out = RoIPoolFunction(resolution, resolution, spatial_scale)(blobs_in, rois)
elif method == 'RoICrop':
grid_xy = net_utils.affine_grid_gen(rois, blobs_in.size()[2:], self.grid_size)
grid_yx = torch.stack(
[grid_xy.data[:, :, :, 1], grid_xy.data[:, :, :, 0]], 3).contiguous()
xform_out = RoICropFunction()(blobs_in, Variable(grid_yx).detach())
if cfg.CROP_RESIZE_WITH_MAX_POOL:
xform_out = F.max_pool2d(xform_out, 2, 2)
elif method == 'RoIAlign':
xform_out = RoIAlignFunction(
resolution, resolution, spatial_scale, sampling_ratio)(blobs_in, rois)
return xform_out 示例4: compare_grid_sample
# 需要導入模塊: from model.roi_crop.functions import roi_crop [as 別名]
# 或者: from model.roi_crop.functions.roi_crop import RoICropFunction [as 別名]
def compare_grid_sample():
# do gradcheck
N = random.randint(1, 8)
C = 2 # random.randint(1, 8)
H = 5 # random.randint(1, 8)
W = 4 # random.randint(1, 8)
input = Variable(torch.randn(N, C, H, W).cuda(), requires_grad=True)
input_p = input.clone().data.contiguous()
grid = Variable(torch.randn(N, H, W, 2).cuda(), requires_grad=True)
grid_clone = grid.clone().contiguous()
out_offcial = F.grid_sample(input, grid)
grad_outputs = Variable(torch.rand(out_offcial.size()).cuda())
grad_outputs_clone = grad_outputs.clone().contiguous()
grad_inputs = torch.autograd.grad(out_offcial, (input, grid), grad_outputs.contiguous())
grad_input_off = grad_inputs[0]
crf = RoICropFunction()
grid_yx = torch.stack([grid_clone.data[:, :, :, 1], grid_clone.data[:, :, :, 0]], 3).contiguous().cuda()
out_stn = crf.forward(input_p, grid_yx)
grad_inputs = crf.backward(grad_outputs_clone.data)
grad_input_stn = grad_inputs[0]
pdb.set_trace()
delta = (grad_input_off.data - grad_input_stn).sum()