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提交 3de4bd26 编写于 作者: D dengkaipeng
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fix indent

上级 3b7d923a
隐藏空白更改
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Showing with 168 addition and 169 deletion
tsm/kinetics_dataset.py
浏览文件 @ 3de4bd26
......@@ -96,43 +96,43 @@ class KineticsDataset(Dataset):
return imgs, np.array([label])
def _video_loader(self, frames):
videolen = len(frames)
average_dur = int(videolen / self.seg_num)
imgs = []
for i in range(self.seg_num):
idx = 0
if self.mode == 'train':
if average_dur >= self.seg_len:
idx = random.randint(0, average_dur - self.seg_len)
idx += i * average_dur
elif average_dur >= 1:
idx += i * average_dur
else:
idx = i
else:
if average_dur >= self.seg_len:
idx = (average_dur - self.seg_len) // 2
idx += i * average_dur
elif average_dur >= 1:
idx += i * average_dur
else:
idx = i
for jj in range(idx, idx + self.seg_len):
imgbuf = frames[int(jj % videolen)]
img = self._imageloader(imgbuf)
imgs.append(img)
return imgs
videolen = len(frames)
average_dur = int(videolen / self.seg_num)
imgs = []
for i in range(self.seg_num):
idx = 0
if self.mode == 'train':
if average_dur >= self.seg_len:
idx = random.randint(0, average_dur - self.seg_len)
idx += i * average_dur
elif average_dur >= 1:
idx += i * average_dur
else:
idx = i
else:
if average_dur >= self.seg_len:
idx = (average_dur - self.seg_len) // 2
idx += i * average_dur
elif average_dur >= 1:
idx += i * average_dur
else:
idx = i
for jj in range(idx, idx + self.seg_len):
imgbuf = frames[int(jj % videolen)]
img = self._imageloader(imgbuf)
imgs.append(img)
return imgs
def _imageloader(self, buf):
if isinstance(buf, str):
img = Image.open(StringIO(buf))
else:
img = Image.open(BytesIO(buf))
return img.convert('RGB')
if isinstance(buf, str):
img = Image.open(StringIO(buf))
else:
img = Image.open(BytesIO(buf))
return img.convert('RGB')
if __name__ == "__main__":
......
tsm/transforms.py
浏览文件 @ 3de4bd26
......@@ -52,25 +52,25 @@ class GroupScale(object):
self.target_size = target_size
def __call__(self, imgs, label):
resized_imgs = []
for i in range(len(imgs)):
img = imgs[i]
w, h = img.size
if (w <= h and w == self.target_size) or \
resized_imgs = []
for i in range(len(imgs)):
img = imgs[i]
w, h = img.size
if (w <= h and w == self.target_size) or \
(h <= w and h == self.target_size):
resized_imgs.append(img)
continue
resized_imgs.append(img)
continue
if w < h:
ow = self.target_size
oh = int(self.target_size * 4.0 / 3.0)
resized_imgs.append(img.resize((ow, oh), Image.BILINEAR))
else:
oh = self.target_size
ow = int(self.target_size * 4.0 / 3.0)
resized_imgs.append(img.resize((ow, oh), Image.BILINEAR))
return resized_imgs, label
if w < h:
ow = self.target_size
oh = int(self.target_size * 4.0 / 3.0)
resized_imgs.append(img.resize((ow, oh), Image.BILINEAR))
else:
oh = self.target_size
ow = int(self.target_size * 4.0 / 3.0)
resized_imgs.append(img.resize((ow, oh), Image.BILINEAR))
return resized_imgs, label
class GroupMultiScaleCrop(object):
......@@ -91,75 +91,74 @@ class GroupMultiScaleCrop(object):
self.more_fix_crop = more_fix_crop
def __call__(self, imgs, label):
input_size = [self.short_size, self.short_size]
im_size = imgs[0].size
# get random crop offset
def _sample_crop_size(im_size):
image_w, image_h = im_size[0], im_size[1]
base_size = min(image_w, image_h)
crop_sizes = [int(base_size * x) for x in self.scales]
crop_h = [
input_size[1] if abs(x - input_size[1]) < 3 else x
for x in crop_sizes
]
crop_w = [
input_size[0] if abs(x - input_size[0]) < 3 else x
for x in crop_sizes
]
pairs = []
for i, h in enumerate(crop_h):
for j, w in enumerate(crop_w):
if abs(i - j) <= self.max_distort:
pairs.append((w, h))
crop_pair = random.choice(pairs)
if not self.fix_crop:
w_offset = np.random.randint(0, image_w - crop_pair[0])
h_offset = np.random.randint(0, image_h - crop_pair[1])
else:
w_step = (image_w - crop_pair[0]) / 4
h_step = (image_h - crop_pair[1]) / 4
ret = list()
ret.append((0, 0)) # upper left
if w_step != 0:
ret.append((4 * w_step, 0)) # upper right
if h_step != 0:
ret.append((0, 4 * h_step)) # lower left
if h_step != 0 and w_step != 0:
ret.append((4 * w_step, 4 * h_step)) # lower right
if h_step != 0 or w_step != 0:
ret.append((2 * w_step, 2 * h_step)) # center
if self.more_fix_crop:
ret.append((0, 2 * h_step)) # center left
ret.append((4 * w_step, 2 * h_step)) # center right
ret.append((2 * w_step, 4 * h_step)) # lower center
ret.append((2 * w_step, 0 * h_step)) # upper center
ret.append((1 * w_step, 1 * h_step)) # upper left quarter
ret.append((3 * w_step, 1 * h_step)) # upper right quarter
ret.append((1 * w_step, 3 * h_step)) # lower left quarter
ret.append((3 * w_step, 3 * h_step)) # lower righ quarter
w_offset, h_offset = random.choice(ret)
return crop_pair[0], crop_pair[1], w_offset, h_offset
crop_w, crop_h, offset_w, offset_h = _sample_crop_size(im_size)
crop_imgs = [
img.crop((offset_w, offset_h, offset_w + crop_w, offset_h + crop_h))
for img in imgs
]
ret_imgs = [
img.resize((input_size[0], input_size[1]), Image.BILINEAR)
for img in crop_imgs
]
return ret_imgs, label
input_size = [self.short_size, self.short_size]
im_size = imgs[0].size
# get random crop offset
def _sample_crop_size(im_size):
image_w, image_h = im_size[0], im_size[1]
base_size = min(image_w, image_h)
crop_sizes = [int(base_size * x) for x in self.scales]
crop_h = [
input_size[1] if abs(x - input_size[1]) < 3 else x
for x in crop_sizes
]
crop_w = [
input_size[0] if abs(x - input_size[0]) < 3 else x
for x in crop_sizes
]
pairs = []
for i, h in enumerate(crop_h):
for j, w in enumerate(crop_w):
if abs(i - j) <= self.max_distort:
pairs.append((w, h))
crop_pair = random.choice(pairs)
if not self.fix_crop:
w_offset = np.random.randint(0, image_w - crop_pair[0])
h_offset = np.random.randint(0, image_h - crop_pair[1])
else:
w_step = (image_w - crop_pair[0]) / 4
h_step = (image_h - crop_pair[1]) / 4
ret = list()
ret.append((0, 0)) # upper left
if w_step != 0:
ret.append((4 * w_step, 0)) # upper right
if h_step != 0:
ret.append((0, 4 * h_step)) # lower left
if h_step != 0 and w_step != 0:
ret.append((4 * w_step, 4 * h_step)) # lower right
if h_step != 0 or w_step != 0:
ret.append((2 * w_step, 2 * h_step)) # center
if self.more_fix_crop:
ret.append((0, 2 * h_step)) # center left
ret.append((4 * w_step, 2 * h_step)) # center right
ret.append((2 * w_step, 4 * h_step)) # lower center
ret.append((2 * w_step, 0 * h_step)) # upper center
ret.append((1 * w_step, 1 * h_step)) # upper left quarter
ret.append((3 * w_step, 1 * h_step)) # upper right quarter
ret.append((1 * w_step, 3 * h_step)) # lower left quarter
ret.append((3 * w_step, 3 * h_step)) # lower righ quarter
w_offset, h_offset = random.choice(ret)
return crop_pair[0], crop_pair[1], w_offset, h_offset
crop_w, crop_h, offset_w, offset_h = _sample_crop_size(im_size)
crop_imgs = [
img.crop((offset_w, offset_h, offset_w + crop_w, offset_h + crop_h))
for img in imgs
]
ret_imgs = [
img.resize((input_size[0], input_size[1]), Image.BILINEAR)
for img in crop_imgs
]
return ret_imgs, label
class GroupRandomCrop(object):
......@@ -167,34 +166,34 @@ class GroupRandomCrop(object):
self.target_size = target_size
def __call__(self, imgs, label):
w, h = imgs[0].size
th, tw = self.target_size, self.target_size
assert (w >= self.target_size) and (h >= self.target_size), \
"image width({}) and height({}) should be larger than " \
"crop size".format(w, h, self.target_size)
out_images = []
x1 = np.random.randint(0, w - tw)
y1 = np.random.randint(0, h - th)
for img in imgs:
if w == tw and h == th:
out_images.append(img)
else:
out_images.append(img.crop((x1, y1, x1 + tw, y1 + th)))
return out_images, label
w, h = imgs[0].size
th, tw = self.target_size, self.target_size
assert (w >= self.target_size) and (h >= self.target_size), \
"image width({}) and height({}) should be larger than " \
"crop size".format(w, h, self.target_size)
out_images = []
x1 = np.random.randint(0, w - tw)
y1 = np.random.randint(0, h - th)
for img in imgs:
if w == tw and h == th:
out_images.append(img)
else:
out_images.append(img.crop((x1, y1, x1 + tw, y1 + th)))
return out_images, label
class GroupRandomFlip(object):
def __call__(self, imgs, label):
v = np.random.random()
if v < 0.5:
ret = [img.transpose(Image.FLIP_LEFT_RIGHT) for img in imgs]
return ret, label
else:
return imgs, label
v = np.random.random()
if v < 0.5:
ret = [img.transpose(Image.FLIP_LEFT_RIGHT) for img in imgs]
return ret, label
else:
return imgs, label
class GroupCenterCrop(object):
......@@ -202,18 +201,18 @@ class GroupCenterCrop(object):
self.target_size = target_size
def __call__(self, imgs, label):
crop_imgs = []
for img in imgs:
w, h = img.size
th, tw = self.target_size, self.target_size
assert (w >= self.target_size) and (h >= self.target_size), \
"image width({}) and height({}) should be larger " \
"than crop size".format(w, h, self.target_size)
x1 = int(round((w - tw) / 2.))
y1 = int(round((h - th) / 2.))
crop_imgs.append(img.crop((x1, y1, x1 + tw, y1 + th)))
return crop_imgs, label
crop_imgs = []
for img in imgs:
w, h = img.size
th, tw = self.target_size, self.target_size
assert (w >= self.target_size) and (h >= self.target_size), \
"image width({}) and height({}) should be larger " \
"than crop size".format(w, h, self.target_size)
x1 = int(round((w - tw) / 2.))
y1 = int(round((h - th) / 2.))
crop_imgs.append(img.crop((x1, y1, x1 + tw, y1 + th)))
return crop_imgs, label
class NormalizeImage(object):
......@@ -230,18 +229,18 @@ class NormalizeImage(object):
self.seg_len = seg_len
def __call__(self, imgs, label):
np_imgs = (np.array(imgs[0]).astype('float32').transpose(
(2, 0, 1))).reshape(1, 3, self.target_size,
np_imgs = (np.array(imgs[0]).astype('float32').transpose(
(2, 0, 1))).reshape(1, 3, self.target_size,
self.target_size) / 255
for i in range(len(imgs) - 1):
img = (np.array(imgs[i + 1]).astype('float32').transpose(
(2, 0, 1))).reshape(1, 3, self.target_size,
for i in range(len(imgs) - 1):
img = (np.array(imgs[i + 1]).astype('float32').transpose(
(2, 0, 1))).reshape(1, 3, self.target_size,
self.target_size) / 255
np_imgs = np.concatenate((np_imgs, img))
np_imgs -= self.img_mean
np_imgs /= self.img_std
np_imgs = np.reshape(np_imgs, (self.seg_num, self.seg_len * 3,
self.target_size, self.target_size))
np_imgs = np.concatenate((np_imgs, img))
np_imgs -= self.img_mean
np_imgs /= self.img_std
np_imgs = np.reshape(np_imgs, (self.seg_num, self.seg_len * 3,
self.target_size, self.target_size))
return np_imgs, label
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