# get assign executor
pcs = []
ass_executors = []
nns = []
for l in range(args.num_res):
pc = np.zeros(vgg_executor.outputs[l].shape)
for i1 in range(0, vgg_executor.outputs[l].shape[2] - args.patch_size + 1):
for i2 in range(0, vgg_executor.outputs[l].shape[3] - args.patch_size +
1):
pc[0, :, i1:i1 + args.patch_size, i2:i2 + args.patch_size] += 1
pc = mx.nd.array(pc, mx.gpu())
nn = mx.nd.zeros([
vgg_executor.outputs[l].shape[2] - args.patch_size + 1,
vgg_executor.outputs[l].shape[3] - args.patch_size + 1
], mx.gpu())
assign_symbol = symbol.assign_symbol()
assign_executor = assign_symbol.bind(args={
'source': patches[l],
'nn': nn
},
ctx=mx.gpu())
assign_executor.forward()
pcs.append(pc)
ass_executors.append(assign_executor)
nns.append(nn)
img_list = os.listdir(COCOPATH)
for idx in range(args.num_image):
# break
optim_state = optimizer.create_state(0, arg_dict['data'])
selected = np.random.randint(0, len(img_list))
target_patch = []
for l in range(args.num_res):
mrf_executors.append(get_mrf_executor(vgg_executor.outputs[l], patches[l].shape))
mrf_executors[l].arg_dict['weight'][:] = patches_normed[l]
# get assign executor
pcs = []
ass_executors = []
nns = []
for l in range(args.num_res):
pc = np.zeros(vgg_executor.outputs[l].shape)
for i1 in range(0, vgg_executor.outputs[l].shape[2]-args.patch_size+1):
for i2 in range(0, vgg_executor.outputs[l].shape[3]-args.patch_size+1):
pc[0,:,i1:i1+args.patch_size,i2:i2+args.patch_size] += 1
pc = mx.nd.array(pc, mx.gpu())
nn = mx.nd.zeros([vgg_executor.outputs[l].shape[2]-args.patch_size+1, vgg_executor.outputs[l].shape[3]-args.patch_size+1], mx.gpu())
assign_symbol = symbol.assign_symbol()
assign_executor = assign_symbol.bind(args={'source':patches[l], 'nn':nn}, ctx=mx.gpu())
assign_executor.forward()
pcs.append(pc)
ass_executors.append(assign_executor)
nns.append(nn)
for epoch in range(args.epochs):
vgg_executor.forward(is_train=True)
if epoch % 10 == 0:
for l in range(args.num_res):
mrf_executors[l].forward()
nns[l][:] = mx.nd.argmax_channel(mrf_executors[l].outputs[0])[0]
ass_executors[l].outputs[0][:] = 0
ass_executors[l].forward()
ass_executors[l].outputs[0][:] /= pcs[l]