def __getitem__(self, index):
if self.is_train:
imagelist = []
batch, labels = self.sampled_batch_data()
for file in batch:
file_path = os.path.join(self.root, file)
image = imread(file_path, to_rgb=True, flag=1)
if image.shape[2] == 1:
print("has gray file", file)
image = nd.tile(image, (1, 1, 3))
box = self.boxes.get(file, [0, 0, 256, 256])
image = image[box[1]:box[3],
box[0]:box[2]] # crop image in width and height
image = self._transform(image)
imagelist.append(image)
return nd.stack(*imagelist, axis=0), nd.array(labels)
else:
path, class_id = self.test_images2id[index]
box = self.boxes.get(path,
[0, 0, 256, 256]) # fetch path,id and box
file_path = os.path.join(self.root, path)
image = imread(file_path, to_rgb=True, flag=1)
if image.shape[2] == 1:
image = nd.tile(image, (1, 1, 3))
image = image[box[1]:box[3], box[0]:box[2]] # crop test image
image = self._transform(image)
return image, class_id
def get_im2col_indices(x_shape,
field_height,
field_width,
padding=1,
stride=1,
ctx=None):
# First figure out what the size of the output should be
N, C, H, W = x_shape
assert (H + 2 * padding - field_height) % stride == 0
assert (W + 2 * padding - field_height) % stride == 0
out_height = int((H + 2 * padding - field_height) / stride + 1)
out_width = int((W + 2 * padding - field_width) / stride + 1)
i0 = nd.repeat(nd.arange(field_height, ctx=ctx), field_width)
i0 = nd.tile(i0, C)
i1 = stride * nd.repeat(nd.arange(out_height, ctx=ctx), out_width)
j0 = nd.tile(nd.arange(field_width, ctx=ctx), field_height * C)
j1 = stride * nd.tile(nd.arange(out_width, ctx=ctx), out_height)
i = i0.reshape((-1, 1)) + i1.reshape((1, -1))
j = j0.reshape((-1, 1)) + j1.reshape((1, -1))
k = nd.repeat(nd.arange(C, ctx=ctx), field_height * field_width).reshape(
(-1, 1))
return (k.astype('int32'), i.astype('int32'), j.astype('int32'))
def test_radial_basis_function_kernel(
x1, x2, amplitude, length_scale, exact
) -> None:
tol = 1e-5
batch_size = amplitude.shape[0]
history_length_1 = x1.shape[0]
history_length_2 = x2.shape[0]
num_features = x1.shape[1]
if batch_size > 1:
x1 = nd.tile(x1, reps=(batch_size, 1, 1))
x2 = nd.tile(x2, reps=(batch_size, 1, 1))
for i in range(1, batch_size):
x1[i, :, :] = (i + 1) * x1[i, :, :]
x2[i, :, :] = (i - 3) * x2[i, :, :]
else:
x1 = x1.reshape(batch_size, history_length_1, num_features)
x2 = x2.reshape(batch_size, history_length_2, num_features)
amplitude = amplitude.reshape(batch_size, 1, 1)
length_scale = length_scale.reshape(batch_size, 1, 1)
rbf = RBFKernel(amplitude, length_scale)
exact = amplitude * nd.exp(-0.5 * exact / length_scale ** 2)
res = rbf.kernel_matrix(x1, x2)
assert nd.norm(exact - res) < tol
def forward(self, query, key, value, mask=None):
if mask is not None:
if mask.shape[1] == 1: #encoding otherwise decoder
#mask = nd.expand_dims(nd.squeeze(mask),-1) ##!!!!!!!!
mask = nd.tile(mask, reps=(1, query.shape[1], 1))
bs = query.shape[0]
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
#1) run linear transform from d_model to d_model
#2) reshape and transpose to split input h heads
query = nd.transpose(
nd.reshape(self.linears_0(query), (bs, -1, self.h, self.d_k)),
(0, 2, 1, 3))
key = nd.transpose(
nd.reshape(self.linears_1(key), (bs, -1, self.h, self.d_k)),
(0, 2, 1, 3))
value = nd.transpose(
nd.reshape(self.linears_2(value), (bs, -1, self.h, self.d_k)),
(0, 2, 1, 3))
#x = nd.zeros(value.shape)
#for h in range(self.h):
# x[:,h,:,:],_ = attention(query[:,h,:,:], key[:,h,:,:], value[:,h,:,:], mask=mask, dropout=self.dropout)
query, key, value = nd.reshape(
query, (bs * self.h, -1, self.d_k)), nd.reshape(
key, (bs * self.h, -1, self.d_k)), nd.reshape(
value, (bs * self.h, -1, self.d_k))
mask = nd.tile(mask, reps=(self.h, 1, 1))
x, _ = attention(query, key, value, mask=mask, dropout=self.dropout)
x = nd.reshape(x, (bs, self.h, -1, self.d_k))
x = nd.reshape(nd.transpose(x, (0, 2, 1, 3)),
(bs, -1, self.h * self.d_k))
return self.linears_3(x)
def _generate_coordinates(self, img):
h, w, _ = img.shape
fh = int(np.ceil(np.ceil(np.ceil(h / 2) / 2) / 2))
fw = int(np.ceil(np.ceil(np.ceil(w / 2) / 2) / 2))
stride = self._base_stride
#
fm_list = []
for i in range(self._retina_stages):
fm_list.append((fh, fw))
fh = int(np.ceil(fh / 2))
fw = int(np.ceil(fw / 2))
fm_list = fm_list[::-1]
#
cor_targets = []
for i in range(self._retina_stages):
fh, fw = fm_list[i]
cx = nd.arange(0, fw).reshape((1, -1))
cy = nd.arange(0, fh).reshape((-1, 1))
sx = nd.tile(cx, reps=(fh, 1))
sy = nd.tile(cy, reps=(1, fw))
syx = nd.stack(sy.reshape(-1), sx.reshape(-1)).transpose()
by = syx[:, 0] * stride
bx = syx[:, 1] * stride
cor_targets.append(nd.stack(bx, by, axis=1))
stride = int(stride / 2)
cor_targets = nd.concat(*cor_targets, dim=0)
return cor_targets
def test_periodic_kernel(x1, x2, amplitude, length_scale, exact) -> None:
tol = 1e-5
batch_size = amplitude.shape[0]
history_length_1 = x1.shape[0]
history_length_2 = x2.shape[0]
num_features = x1.shape[1]
if batch_size > 1:
x1 = nd.tile(x1, reps=(batch_size, 1, 1))
x2 = nd.tile(x2, reps=(batch_size, 1, 1))
for i in range(1, batch_size):
x1[i, :, :] = (i + 1) * x1[i, :, :]
x2[i, :, :] = (i - 3) * x2[i, :, :]
else:
x1 = x1.reshape(batch_size, history_length_1, num_features)
x2 = x2.reshape(batch_size, history_length_2, num_features)
amplitude = amplitude.reshape(batch_size, 1, 1)
length_scale = length_scale.reshape(batch_size, 1, 1)
frequency = 1 / 24 * nd.ones_like(length_scale)
periodic = PeriodicKernel(amplitude, length_scale, frequency)
exact = amplitude * nd.exp(
-2 * nd.sin(frequency * math.pi * nd.sqrt(exact))**2 / length_scale**2)
res = periodic.kernel_matrix(x1, x2)
assert nd.norm(exact - res) < tol
def _calculate_trilinear_similarity(self, context, query, context_max_len,
query_max_len, w4mlu, bias):
"""Implement the computation of trilinear similarity function.
refer https://github.com/NLPLearn/QANet/blob/master/layers.py#L505
The similarity function is:
f(w, q) = W[w, q, w * q]
where w and q represent the word in context and query respectively,
and * operator means hadamard product.
Parameters
-----------
context : NDArray
input tensor with shape `(batch_size, context_sequence_length, hidden_size)`
query : NDArray
input tensor with shape `(batch_size, query_sequence_length, hidden_size)`
context_max_len : int
context_max_len : int
Returns
--------
similarity_mat : NDArray
output tensor with shape `(batch_size, context_sequence_length, query_sequence_length)`
"""
subres0 = nd.tile(self.w4c(context), [1, 1, query_max_len])
subres1 = nd.tile(nd.transpose(self.w4q(query), axes=(0, 2, 1)),
[1, context_max_len, 1])
subres2 = nd.batch_dot(w4mlu * context,
nd.transpose(query, axes=(0, 2, 1)))
similarity_mat = subres0 + subres1 + subres2 + bias
return similarity_mat
def offset(self, kernel_size):
kernel_h, kernel_w = kernel_size
dilation_h, dilation_w = self.dilation
offset_h = (nd.arange(kernel_h)) * dilation_h # - row
offset_h = nd.tile(offset_h, (kernel_w, 1)).T.reshape(-1)
offset_w = (nd.arange(kernel_w)) * dilation_w # - col
offset_w = nd.tile(offset_w, (kernel_h, 1)).reshape(-1)
return offset_h, offset_w
def transform_center(xy):
"""Given x, y prediction after sigmoid(), convert to relative coordinates (0, 1) on image."""
b, h, w, n, s = xy.shape
offset_y = nd.tile(nd.arange(0, h, repeat=(w * n * 1), ctx=xy.context).reshape((1, h, w, n, 1)), (b, 1, 1, 1, 1))
# print(offset_y[0].asnumpy()[:, :, 0, 0])
offset_x = nd.tile(nd.arange(0, w, repeat=(n * 1), ctx=xy.context).reshape((1, 1, w, n, 1)), (b, h, 1, 1, 1))
# print(offset_x[0].asnumpy()[:, :, 0, 0])
x, y = xy.split(num_outputs=2, axis=-1)
x = (x + offset_x) / w
y = (y + offset_y) / h
return x, y
def _cross_element_wise_mp(p, h):
plen = p.shape[1]
plen = h.shape[1]
# order is important
p_expand = nd.tile(
nd.expand_dims(p, 2),
[1, 1, plen, 1]) # (batch_size, seq_len, seq_len, embed_dim)
h_expand = nd.tile(nd.expand_dims(p, 1),
[1, hlen, 1, 1]) # (32, 40, 40, 300)
out = p_expand * h_expand
if interact_dropout != 1:
out = nn.Dropout(keep_rate)(out)
return out
def verify_loaded_model(net, ctx):
def transform(data, label):
return data.astype(np.float32) / 255, label.astype(np.float32)
# Load ten random images from the test dataset.
sample_data = mx.gluon.data.DataLoader(mx.gluon.data.vision.MNIST(
train=False, transform=transform),
10,
shuffle=True)
for data, label in sample_data:
# Display the images.
img = nd.transpose(data, (1, 0, 2, 3))
img = nd.reshape(img, (28, 10 * 28, 1))
imtiles = nd.tile(img, (1, 1, 3))
plt.imshow(imtiles.asnumpy())
plt.show()
# Display the predictions.
data = nd.transpose(data, (0, 3, 1, 2))
out = net(data.as_in_context(ctx))
predictions = nd.argmax(out, axis=1)
print('Model predictions:', predictions.asnumpy())
print('Ground truth: ', label.asnumpy())
break
def transform(data, target_wd, target_ht, is_train, box):
"""Crop and normnalize an image nd array."""
if box is not None:
x, y, w, h = box
data = data[y:min(y + h, data.shape[0]), x:min(x + w, data.shape[1])]
# Resize to target_wd * target_ht.
data = mx.image.imresize(data, target_wd, target_ht)
# Normalize in the same way as the pre-trained model.
data = data.astype(np.float32) / 255.0
data = (data - mx.nd.array([0.485, 0.456, 0.406])) / mx.nd.array(
[0.229, 0.224, 0.225])
if is_train:
if random.random() < 0.5:
data = nd.flip(data, axis=1)
data, _ = mx.image.random_crop(data, (224, 224))
else:
data, _ = mx.image.center_crop(data, (224, 224))
# Transpose from (target_wd, target_ht, 3)
# to (3, target_wd, target_ht).
data = nd.transpose(data, (2, 0, 1))
# If image is greyscale, repeat 3 times to get RGB image.
if data.shape[0] == 1:
data = nd.tile(data, (3, 1, 1))
return data.reshape((1, ) + data.shape)
def transform(data):
data = mx.image.imresize(data, 64, 64)
data = nd.transpose(data, (2,0,1))
data = data.astype(np.float32)/127.5 - 1
if data.shape[0] == 1:
data = nd.tile(data, (3, 1, 1))
return data.reshape((1,) + data.shape)
def transform(data, target_wd, target_ht, is_train, box):
"""Crop and normnalize an image nd array."""
if box is not None:
x, y, w, h = box
data = data[y:min(y+h, data.shape[0]), x:min(x+w, data.shape[1])]
# Resize to target_wd * target_ht.
data = mx.image.imresize(data, target_wd, target_ht)
# Normalize in the same way as the pre-trained model.
data = data.astype(np.float32) / 255.0
data = (data - mx.nd.array([0.485, 0.456, 0.406])) / mx.nd.array([0.229, 0.224, 0.225])
if is_train:
if random.random() < 0.5:
data = nd.flip(data, axis=1)
data, _ = mx.image.random_crop(data, (224, 224))
else:
data, _ = mx.image.center_crop(data, (224, 224))
# Transpose from (target_wd, target_ht, 3)
# to (3, target_wd, target_ht).
data = nd.transpose(data, (2, 0, 1))
# If image is greyscale, repeat 3 times to get RGB image.
if data.shape[0] == 1:
data = nd.tile(data, (3, 1, 1))
return data.reshape((1,) + data.shape)
def __call__(self, img):
"""
Args:
img (Tensor): Tensor image of size (C, H, W).
Returns:
Tensor: Image with n_holes of dimension length x length cut out of it.
"""
assert (
img.shape[0] == 3
), "Input to before cutout should be C x H x W., given: {}".format(
img.shape)
h = img.shape[1]
w = img.shape[2]
mask = np.ones((h, w), np.uint8) # np.float32
for n in range(self.n_holes):
y = np.random.randint(h)
x = np.random.randint(w)
y1 = np.clip(y - self.length // 2, 0, h)
y2 = np.clip(y + self.length // 2, 0, h)
x1 = np.clip(x - self.length // 2, 0, w)
x2 = np.clip(x + self.length // 2, 0, w)
mask[y1:y2, x1:x2] = 0
mask = nd.tile(nd.array(mask),
(3, 1, 1)) # .transpose((1,2,0)) #再次用到tail函数
# 为什么这时候mask的类型是float32?
return img.astype('float32') * mask
def forward(self, x, x_mask=None):
N, T, D = tuple(x.shape) # bs, sl, vec
bs, sl, vec = tuple(x.shape)
direct_mask = get_direct_mask(bs, sl, self.direction)
#x_mask_tile = x_mask.expand_dims(1)
#mask = np.logical_and(direct_mask, x_mask_tile).astype(float)
mask = direct_mask.astype('float32')
x_map = self.linear1(x) # bs, sl, vec
#x_map_tile = x_map.expand_dims(1) #
x_map_tile = nd.tile(x_map.expand_dims(1),
(1, sl, 1, 1)) # bs, sl, sl, vec
x_map_drop = self.dropout(x_map)
dependent = self.linear2(x_map_drop)
dependent_etd = dependent.expand_dims(1)
head = self.linear3(x_map_drop)
head_etd = head.expand_dims(2)
loggits = scaled_tanh(dependent_etd + head_etd + self.f_bias, 5.0)
loggits_masked = exp_mask_for_tensor(loggits, mask)
attn_score = nd.softmax(loggits_masked, 2)
attn_score = mask_for_tensor(attn_score, mask)
attn_result = (attn_score * x_map_tile).nansum(2)
fusion_gate = nd.sigmoid(
self.linear4(x_map) + self.linear5(attn_result) + self.o_bias)
output = fusion_gate * x_map + (1 - fusion_gate) * attn_result
return output
def sample_neighbours(self, data, query_network):
num_stored_samples = self.key_memory.shape[0]
batch_size = data[0].shape[0]
query = query_network(*data).as_in_context(mx.cpu())
vec1 = nd.repeat(query, repeats=num_stored_samples, axis=0)
vec2 = nd.tile(self.key_memory, reps=(batch_size, 1))
diff = nd.subtract(vec1, vec2)
sq = nd.square(diff)
batch_sum = nd.sum(sq, exclude=1, axis=0)
sqrt = nd.sqrt(batch_sum)
dist = nd.reshape(sqrt, shape=(batch_size, num_stored_samples))
sample_ind = nd.topk(dist, k=self.k, axis=1, ret_typ="indices")
num_outputs = len(self.label_memory)
sample_labels = [
self.label_memory[i][sample_ind] for i in range(num_outputs)
]
sample_batches = [[
self.value_memory[j][sample_ind]
for j in range(len(self.value_memory))
], sample_labels]
return sample_batches
def forward(self, *input):
if self.mode == 'loss' or self.mode == 'likelihood':
X, A, iw_ids, last_append_mask, \
NX, NX_rep, action_0, actions, log_p, \
batch_size, iw_size, \
graph_to_rnn, rnn_to_graph, NX_cum, \
c, ids = input
init = nd.tile(fn.unsqueeze(self._policy_0(c), axis=1),
[1, iw_size, 1])
append, connect, end = self._policy(X, A, NX, NX_rep,
last_append_mask, graph_to_rnn,
rnn_to_graph, NX_cum, c, ids)
l = self._likelihood(init, append, connect, end, action_0, actions,
iw_ids, log_p, batch_size, iw_size)
if self.mode == 'likelihood':
return l
else:
return -l.mean()
elif self.mode == 'decode_0':
return self._policy_0(*input)
elif self.mode == 'decode_step':
X, A, NX, NX_rep, last_append_mask, NX_cum, h, c, ids = input
return self._decode_step(X, A, NX, NX_rep, last_append_mask,
NX_cum, h, c, ids)
else:
raise ValueError
def __getitem__(self, index):
"""
get the batch //batch_k for train and single for test
"""
if self.is_train:
image_names, labels = self.sample_train_batch()
# get sampled order image_file names and corresponding label
image_list, label_list = [], []
for img, label in zip(image_names, labels):
image = imread(img, flag=1, to_rgb=True)
x, y, w, h = self.boxes[img]
image = image[y:min(y + h, image.shape[0]),
x:min(x + w, image.shape[1])]
if image.shape[2] == 1:
print("has gray file", img)
image = nd.tile(image, (1, 1, 3))
image = self._transform(image) # for rgb same value
image_list.append(image)
label_list.append(label)
batch_data = nd.stack(*image_list, axis=0)
batch_label = nd.array(label_list)
return batch_data, batch_label
else:
img = self.test_images_files[index] # get the file name full path
image = imread(img, flag=1, to_rgb=1)
x, y, w, h = self.boxes[img]
image = image[y:min(y + h, image.shape[0]),
x:min(x + w, image.shape[1])]
image = self._transform(image)
return image, self.test_labels[index]
def transform_center(xy):
b, h, w, n, s = xy.shape
# tile: repeat thw whole array multiple times
offset_y = nd.tile(
nd.arange(0, h, repeat=(w * n * 1), ctx=xy.context).reshape(
(1, h, w, n, 1)),
(b, 1, 1, 1, 1)) # repeat b times along the batch axis
offset_x = nd.tile(
nd.arange(0, w, repeat=(n * 1), ctx=xy.context).reshape(
(1, 1, w, n, 1)), (b, h, 1, 1, 1)
) # repeat b times along the batch channel, and n times along axis=1
# split: num_outputs is the number of splits
x, y = xy.split(num_outputs=2, axis=-1)
x = (x + offset_x) / w
y = (y + offset_y) / h
return x, y
def transform_size(wh, anchors):
"""Given w, h prediction after exp() and anchor sizes, convert to relative width/height (0, 1) on image"""
b, h, w, n, s = wh.shape
aw, ah = nd.tile(nd.array(anchors, ctx=wh.context).reshape((1, 1, 1, -1, 2)), (b, h, w, 1, 1)).split(num_outputs=2, axis=-1)
w_pred, h_pred = nd.exp(wh).split(num_outputs=2, axis=-1)
w_out = w_pred * aw / w
h_out = h_pred * ah / h
return w_out, h_out
def transform(img, dims):
data = mx.image.imread(img)
data = mx.image.imresize(data, dims, dims)
data = nd.transpose(data, (2, 0, 1))
# normalize to [-1, 1]
data = data.astype(np.float32) / 127.5 - 1
# if image is greyscale, repeat 3 times to get RGB image.
if data.shape[0] == 1:
data = nd.tile(data, (3, 1, 1))
return data.reshape((1, ) + data.shape)
def get_im2col_indices(x_shape, field_height, field_width, padding=1, stride=1, ctx=None):
# First figure out what the size of the output should be
N, C, H, W = x_shape
assert (H + 2 * padding - field_height) % stride == 0
assert (W + 2 * padding - field_height) % stride == 0
out_height = int((H + 2 * padding - field_height) / stride + 1)
out_width = int((W + 2 * padding - field_width) / stride + 1)
i0 = nd.repeat(nd.arange(field_height, ctx=ctx), field_width)
i0 = nd.tile(i0, C)
i1 = stride * nd.repeat(nd.arange(out_height, ctx=ctx), out_width)
j0 = nd.tile(nd.arange(field_width, ctx=ctx), field_height * C)
j1 = stride * nd.tile(nd.arange(out_width, ctx=ctx), out_height)
i = i0.reshape((-1, 1)) + i1.reshape((1, -1))
j = j0.reshape((-1, 1)) + j1.reshape((1, -1))
k = nd.repeat(nd.arange(C, ctx=ctx), field_height * field_width).reshape((-1, 1))
return (k.astype('int32'), i.astype('int32'), j.astype('int32'))
def transform(data):
data = mx.image.imresize(data, 64, 64) # state size: (64, 64, 3)
data = nd.transpose(data, (2, 0, 1))
data = data.astype(np.float32) / 127.5 - 1 # normalize to [-1, 1]
if data.shape[0] == 1:
data = nd.tile(
data,
(3, 1,
1)) # if image is greyscale, repeat 3 times to get RGB image
return data.reshape((1, ) + data.shape)
def getDefaultBoxes(fmap, s=None, r=None,
offset=None, norm=None, clip=False,
srmode='few', omode='flatten'):
assert omode in ('flatten', 'stack')
assert srmode in ('few', 'many')
n, c, fh, fw = fmap.shape
if s is None:
scales = nd.array([1.])
else:
scales = nd.array(s)
if r is None:
ratios = nd.array([1.])
else:
ratios = nd.array(r)
width, height = getwh(scales, ratios, fw, fh, srmode)
nbox_per_pixel = width.size
xcenter = nd.repeat(nd.arange(fw).reshape((1,-1)), fh, axis=0)
ycenter = nd.repeat(nd.arange(fh).reshape((-1,1)), fw, axis=1)
xycenters = nd.stack(xcenter, ycenter, axis=2)
xycenters = nd.tile(xycenters, [1, 1, nbox_per_pixel*2])
lu_rd_offset = nd.stack(width*-0.5, height*-0.5, width*0.5, height*0.5, axis=1)
lu_rd_offset = lu_rd_offset.reshape((-1,))
lu_rd_points = (xycenters + lu_rd_offset).reshape((fh, fw, nbox_per_pixel, 2, 2))
if offset is None:
offset = nd.array([0.5, 0.5])
else:
offset = nd.array(offset)
assert offset.size <= 2
if norm is None:
norm = nd.array([fw, fh])
else:
norm = nd.array(norm)
assert norm.size <= 2
lu_rd_points = (lu_rd_points + offset) / norm
if clip:
nd.clip(lu_rd_points, a_min=0., a_max=1., out=lu_rd_points)
if omode == 'flatten':
lu_rd_points = lu_rd_points.reshape((1, -1, 4))
else:
lu_rd_points = lu_rd_points.reshape((1, fh, fw, nbox_per_pixel, 4))
return lu_rd_points
def transform_size(wh, anchors):
b, h, w, n, s = wh.shape
aw, ah = nd.tile(
nd.array(anchors, ctx=wh.context).reshape((1, 1, 1, -1, 2)),
(b, h, w, 1, 1)).split(num_outputs=2, axis=-1)
w_pred, h_pred = nd.exp(wh).split(num_outputs=2, axis=-1)
w_out = w_pred * aw / w
h_out = h_pred * ah / h
return w_out, h_out
def random_expand(src, max_ratio=4, fill=0, keep_ratio=True):
"""Random expand original image with borders, this is identical to placing
the original image on a larger canvas.
Modified for video from gluoncv default image transform
Parameters
----------
src : mxnet.nd.NDArray
The original image with KHWC format.
max_ratio : int or float
Maximum ratio of the output image on both direction(vertical and horizontal)
fill : int or float or array-like
The value(s) for padded borders. If `fill` is numerical type, RGB channels
will be padded with single value. Otherwise `fill` must have same length
as image channels, which resulted in padding with per-channel values.
keep_ratio : bool
If `True`, will keep output image the same aspect ratio as input.
Returns
-------
mxnet.nd.NDArray
Augmented image.
tuple
Tuple of (offset_x, offset_y, new_width, new_height)
"""
if max_ratio <= 1:
return src, (0, 0, src.shape[1], src.shape[0])
k, h, w, c = src.shape
ratio_x = random.uniform(1, max_ratio)
if keep_ratio:
ratio_y = ratio_x
else:
ratio_y = random.uniform(1, max_ratio)
oh, ow = int(h * ratio_y), int(w * ratio_x)
off_y = random.randint(0, oh - h)
off_x = random.randint(0, ow - w)
# make canvas
if isinstance(fill, numeric_types):
dst = nd.full(shape=(k, oh, ow, c), val=fill, dtype=src.dtype)
else:
fill = nd.array(fill, dtype=src.dtype, ctx=src.context)
if not c == fill.size:
raise ValueError("Channel and fill size mismatch, {} vs {}".format(c, fill.size))
dst = nd.tile(fill.reshape((1, c)), reps=(k * oh * ow, 1)).reshape((k, oh, ow, c))
dst[:, off_y:off_y+h, off_x:off_x+w, :] = src
return dst, (off_x, off_y, ow, oh)
def curvature_based_sample(nn_pts, k):
curvature = compute_curvature(nn_pts)
point_indices = nd.topk(curvature, axis=-1, k=k, ret_typ='indices')
pts_shape = nn_pts.shape
batch_size = pts_shape[0]
batch_indices = nd.tile(nd.reshape(nd.arange(batch_size), (-1, 1, 1)),
(1, k, 1))
indices = nd.concat(batch_indices,
nd.expand_dims(point_indices, axis=2),
dim=2)
return indices
def transform(data, target_wd=64, target_ht=64):
# resize to target_wd * target_ht
data = mx.image.imresize(data, target_wd, target_ht)
# transpose from (target_wd, target_ht, 3)
# to (3, target_wd, target_ht)
data = nd.transpose(data, (2, 0, 1))
# normalize to [-1, 1]
data = data.astype(np.float32) / 127.5 - 1
# if image is greyscale, repeat 3 times to get RGB image.
if data.shape[0] == 1:
data = nd.tile(data, (3, 1, 1))
return data
def get_max_pred(batch_heatmaps):
batch_size = batch_heatmaps.shape[0]
num_joints = batch_heatmaps.shape[1]
width = batch_heatmaps.shape[3]
heatmaps_reshaped = batch_heatmaps.reshape((batch_size, num_joints, -1))
idx = nd.argmax(heatmaps_reshaped, 2)
maxvals = nd.max(heatmaps_reshaped, 2)
maxvals = maxvals.reshape((batch_size, num_joints, 1))
idx = idx.reshape((batch_size, num_joints, 1))
preds = nd.tile(idx, (1, 1, 2)).astype(np.float32)
preds[:, :, 0] = (preds[:, :, 0]) % width
preds[:, :, 1] = nd.floor((preds[:, :, 1]) / width)
pred_mask = nd.tile(nd.greater(maxvals, 0.0), (1, 1, 2))
pred_mask = pred_mask.astype(np.float32)
preds *= pred_mask
return preds, maxvals
def get_max_pred(batch_heatmaps):
batch_size = batch_heatmaps.shape[0]
num_joints = batch_heatmaps.shape[1]
width = batch_heatmaps.shape[3]
heatmaps_reshaped = batch_heatmaps.reshape((batch_size, num_joints, -1))
idx = nd.argmax(heatmaps_reshaped, 2)
maxvals = nd.max(heatmaps_reshaped, 2)
maxvals = maxvals.reshape((batch_size, num_joints, 1))
idx = idx.reshape((batch_size, num_joints, 1))
preds = nd.tile(idx, (1, 1, 2)).astype(np.float32)
preds[:, :, 0] = (preds[:, :, 0]) % width
preds[:, :, 1] = nd.floor((preds[:, :, 1]) / width)
pred_mask = nd.tile(nd.greater(maxvals, 0.0), (1, 1, 2))
pred_mask = pred_mask.astype(np.float32)
preds *= pred_mask
return preds, maxvals
def crop(self, bboxes, h, w, masks):
scale = 4
b = bboxes.shape[0]
ctx = bboxes.context
with autograd.pause():
_h = nd.arange(h, ctx=ctx)
_w = nd.arange(w, ctx=ctx)
_h = nd.tile(_h, reps=(b, 1))
_w = nd.tile(_w, reps=(b, 1))
x1, y1 = nd.round(bboxes[:, 0] / scale), nd.round(bboxes[:, 1] /
scale)
x2, y2 = nd.round((bboxes[:, 2]) / scale), nd.round(
(bboxes[:, 3]) / scale)
_w = (_w >= x1.expand_dims(axis=-1)) * (_w <=
x2.expand_dims(axis=-1))
_h = (_h >= y1.expand_dims(axis=-1)) * (_h <=
y2.expand_dims(axis=-1))
_mask = nd.batch_dot(_h.expand_dims(axis=-1),
_w.expand_dims(axis=-1),
transpose_b=True)
masks = _mask * masks
return masks
def refine_bbox_nd(bbox, bbox_delta, im_info=None, means=None, stds=None):
xmin, ymin, xmax, ymax = nd.split(data=bbox, num_outputs=4, axis=1)
bbox_width = xmax - xmin + 1.
bbox_height = ymax - ymin + 1.
center_x = 0.5 * (xmin + xmax)
center_y = 0.5 * (ymin + ymax)
bbox_delta_reshape = nd.Reshape(data=bbox_delta, shape=(0, -1, 4))
dx, dy, dw, dh = nd.split(data=bbox_delta_reshape,
num_outputs=4,
axis=2,
squeeze_axis=1)
if (means is not None) and (stds is not None):
dx = dx * stds[0] + means[0]
dy = dy * stds[1] + means[1]
dw = dw * stds[2] + means[2]
dh = dh * stds[3] + means[3]
refine_center_x = nd.broadcast_add(lhs=center_x,
rhs=nd.broadcast_mul(lhs=bbox_width,
rhs=dx))
refine_center_y = nd.broadcast_add(lhs=center_y,
rhs=nd.broadcast_mul(lhs=bbox_height,
rhs=dy))
refined_width = nd.broadcast_mul(lhs=bbox_width, rhs=nd.exp(dw))
refined_height = nd.broadcast_mul(lhs=bbox_height, rhs=nd.exp(dh))
w_offset = 0.5 * (refined_width - 1.)
h_offset = 0.5 * (refined_height - 1.)
refined_xmin = nd.expand_dims(refine_center_x - w_offset, axis=1)
refined_ymin = nd.expand_dims(refine_center_y - h_offset, axis=1)
refined_xmax = nd.expand_dims(refine_center_x + w_offset, axis=1)
refined_ymax = nd.expand_dims(refine_center_y + h_offset, axis=1)
refined_bbox = nd.concat(refined_xmin,
refined_ymin,
refined_xmax,
refined_ymax,
dim=1)
if im_info is not None:
# assume im_info [[height, width, scale]] with shape (1,3)
im_hw = nd.slice_axis(im_info, axis=1, begin=0, end=2)
im_wh = nd.reverse(im_hw, axis=1)
im_wh = im_wh - 1.
im_wh = nd.tile(data=im_wh, reps=(1, 2))
im_wh = nd.Reshape(im_wh, shape=(1, 4, 1))
refined_bbox = nd.broadcast_minimum(lhs=refined_bbox, rhs=im_wh)
refined_bbox = nd.broadcast_maximum(lhs=refined_bbox,
rhs=nd.zeros_like(refined_bbox))
# print refined_bbox.debug_str()
return refined_bbox
def random_expand(src, max_ratio=4, fill=0, keep_ratio=True):
"""Random expand original image with borders, this is identical to placing
the original image on a larger canvas.
Parameters
----------
src : mxnet.nd.NDArray
The original image with HWC format.
max_ratio : int or float
Maximum ratio of the output image on both direction(vertical and horizontal)
fill : int or float or array-like
The value(s) for padded borders. If `fill` is numerical type, RGB channels
will be padded with single value. Otherwise `fill` must have same length
as image channels, which resulted in padding with per-channel values.
keep_ratio : bool
If `True`, will keep output image the same aspect ratio as input.
Returns
-------
mxnet.nd.NDArray
Augmented image.
tuple
Tuple of (offset_x, offset_y, new_width, new_height)
"""
if max_ratio <= 1:
return src, (0, 0, src.shape[1], src.shape[0])
h, w, c = src.shape
ratio_x = random.uniform(1, max_ratio)
if keep_ratio:
ratio_y = ratio_x
else:
ratio_y = random.uniform(1, max_ratio)
oh, ow = int(h * ratio_y), int(w * ratio_x)
off_y = random.randint(0, oh - h)
off_x = random.randint(0, ow - w)
# make canvas
if isinstance(fill, numeric_types):
dst = nd.full(shape=(oh, ow, c), val=fill, dtype=src.dtype)
else:
fill = nd.array(fill, dtype=src.dtype, ctx=src.context)
if not c == fill.size:
raise ValueError("Channel and fill size mismatch, {} vs {}".format(c, fill.size))
dst = nd.tile(fill.reshape((1, c)), reps=(oh * ow, 1)).reshape((oh, ow, c))
dst[off_y:off_y+h, off_x:off_x+w, :] = src
return dst, (off_x, off_y, ow, oh)
