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pytorch_yolo.py
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pytorch_yolo.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import inspect
import math
import os
import time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch._six import container_abcs
import os
os.environ['TRIDENT_BACKEND'] = 'pytorch'
import trident as T
from trident import *
__all__ = [ 'resblock_body', 'YoloDetectionModel', 'DarknetConv2D', 'DarknetConv2D_BN_Mish',
'DarknetConv2D_BN_Leaky', 'YoloLayer']
_session = get_session()
_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
_epsilon = _session.epsilon
_trident_dir = _session.trident_dir
anchors = np.array([12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401]).reshape(
(1, 1, 1, -1, 2))
def DarknetConv2D(*args, **kwargs):
"""Wrapper to set Darknet parameters for Convolution2D."""
darknet_conv_kwargs = {'use_bias': True}
darknet_conv_kwargs['auto_pad'] = False if kwargs.get('strides')==(2,2) else True
darknet_conv_kwargs['use_bias'] = True
darknet_conv_kwargs.update(kwargs)
return Conv2d(*args, **darknet_conv_kwargs)
def DarknetConv2D_BN_Leaky(*args, **kwargs):
"""Darknet Convolution2D followed by BatchNormalization and LeakyReLU."""
darknet_conv_kwargs = {'use_bias': False,'normalization':BatchNorm2d(momentum=0.03,eps=1e-4)}
darknet_conv_kwargs['activation']=LeakyRelu(alpha=0.1)
darknet_conv_kwargs['auto_pad'] = False if kwargs.get('strides')==(2,2) else True
darknet_conv_kwargs.update(kwargs)
return Conv2d_Block(*args, **darknet_conv_kwargs)
def DarknetConv2D_BN_Mish(*args, **kwargs):
"""Darknet Convolution2D followed by BatchNormalization and LeakyReLU."""
darknet_conv_kwargs = {'use_bias': False, 'normalization':BatchNorm2d(momentum=0.03,eps=1e-4), 'activation': Mish()}
darknet_conv_kwargs['auto_pad'] = False if kwargs.get('strides')==(2,2) else True
darknet_conv_kwargs.update(kwargs)
return Conv2d_Block(*args, **darknet_conv_kwargs)
def resblock_body(num_filters, num_blocks, all_narrow=True,keep_output=False,name=''):
return Sequential(
DarknetConv2D_BN_Mish((3, 3),num_filters ,strides=(2,2), auto_pad=False, padding=((1, 0), (1, 0)),name=name+'_preconv1'),
ShortCut2d(
{
1:DarknetConv2D_BN_Mish((1, 1), num_filters // 2 if all_narrow else num_filters, name=name + '_shortconv'),
0:Sequential(
DarknetConv2D_BN_Mish((1, 1), num_filters // 2 if all_narrow else num_filters,name=name+'_mainconv'),
For(range(num_blocks), lambda i:
ShortCut2d(
Identity(),
Sequential(
DarknetConv2D_BN_Mish((1, 1),num_filters // 2,name=name+'_for{0}_1'.format(i)),
DarknetConv2D_BN_Mish((3, 3),num_filters // 2 if all_narrow else num_filters,name=name+'_for{0}_2'.format(i))
),
mode='add')
),
DarknetConv2D_BN_Mish( (1, 1),num_filters // 2 if all_narrow else num_filters,name=name+'_postconv')
)},
mode='concate',name=name+'_route'),
DarknetConv2D_BN_Mish((1,1),num_filters,name=name+'_convblock5')
)
class YoloLayer(Layer):
"""Detection layer"""
def __init__(self, anchors, num_classes,grid_size, img_dim=608,small_item_enhance=False):
super(YoloLayer, self).__init__()
self.register_buffer('anchors', to_tensor(anchors, requires_grad=False).to(get_device()))
self.small_item_enhance = small_item_enhance
self.num_anchors = len(anchors)
self.num_classes = num_classes
self.ignore_thres = 0.5
# self.mse_loss = nn.MSELoss()
# self.bce_loss = nn.BCELoss()
self.obj_scale = 1
self.noobj_scale = 100
self.metrics = {}
self.img_dim = img_dim
self.grid_size=grid_size
#self.grid_size = to_tensor(grid_size) # grid size
#yv, xv = torch.meshgrid([torch.arange(grid_size), torch.arange(grid_size)])
#self.register_buffer('grid', torch.stack((xv.detach(), yv.detach()), 2).view((1, 1, grid_size, grid_size, 2)).float().detach())
self.stride = self.img_dim / grid_size
self.compute_grid_offsets(grid_size)
def compute_grid_offsets(self, grid_size):
self.register_buffer('grid', meshgrid(grid_size,grid_size,requires_grad=False).view((1, 1, grid_size, grid_size, 2)).float().detach())
#self.grid=meshgrid(grid_size,grid_size,requires_grad=False).view((1, 1, grid_size, grid_size, 2)).float().detach()
# Calculate offsets for each grid
def forward(self, x, targets=None):
num_batch = x.size(0)
grid_size = x.size(-1)
if self.training and (self.grid_size!=x.size(-1) or self.grid_size!=x.size(-2)):
self.compute_grid_offsets(grid_size)
self.grid.to(get_device())
self.anchors.to(get_device())
anchor_vec = self.anchors/ self.stride
anchor_wh = anchor_vec.view(1, self.num_anchors, 1, 1, 2)
prediction = x.view(num_batch, self.num_anchors, self.num_classes + 5, grid_size, grid_size).permute(0, 1, 3, 4, 2).contiguous()
if self.training:
return reshape(prediction,(num_batch, -1, self.num_classes + 5))
# Get outputs
xy = sigmoid(prediction[..., 0:2]) # Center x
wh = prediction[..., 2:4] # Width
xy = reshape((xy + self.grid) * self.stride, (num_batch, -1, 2))
wh = reshape((exp(wh) * anchor_wh) * self.stride, (num_batch, -1, 2))
pred_conf = sigmoid(prediction[..., 4]) # Conf
pred_class = sigmoid(prediction[..., 5:]) # Cls pred.
pred_conf = reshape(pred_conf, (num_batch, -1, 1))
pred_class = reshape(pred_class, (num_batch, -1, self.num_classes))
cls_probs = reduce_max(pred_class, -1, keepdims=True)
if self.small_item_enhance and self.stride == 8:
pred_conf = (pred_conf * cls_probs).sqrt()
output = torch.cat([xy, wh, pred_conf, pred_class], -1)
return output
# if targets is None: # return output, # else: # iou_scores, class_mask, obj_mask,
# noobj_mask, tx, ty, tw, th, tcls, tconf = build_targets( # pred_boxes=pred_boxes,
# pred_cls=pred_cls, # target=targets, # anchors=self.scaled_anchors,
# ignore_thres=self.ignore_thres, # ) # # # Loss : Mask outputs to ignore non-existing
# objects (except with conf. loss) # loss_x = self.mse_loss(x[obj_mask], tx[obj_mask]) #
# loss_y = self.mse_loss(y[obj_mask], ty[obj_mask]) # loss_w = self.mse_loss(w[obj_mask],
# tw[obj_mask]) # loss_h = self.mse_loss(h[obj_mask], th[obj_mask]) # loss_conf_obj =
# self.bce_loss(pred_conf[obj_mask], tconf[obj_mask]) # loss_conf_noobj = self.bce_loss(
# pred_conf[noobj_mask], tconf[noobj_mask]) # loss_conf = self.obj_scale * loss_conf_obj +
# self.noobj_scale * loss_conf_noobj # loss_cls = self.bce_loss(pred_cls[obj_mask],
# tcls[obj_mask]) # total_loss = loss_x + loss_y + loss_w + loss_h + loss_conf + loss_cls # #
# Metrics # cls_acc = 100 * class_mask[obj_mask].mean() # conf_obj = pred_conf[obj_mask].mean() #
# conf_noobj = pred_conf[noobj_mask].mean() # conf50 = (pred_conf > 0.5).float() # iou50 = (
# iou_scores > 0.5).float() # iou75 = (iou_scores > 0.75).float() # detected_mask = conf50 *
# class_mask * tconf # precision = torch.sum(iou50 * detected_mask) / (conf50.sum() + 1e-16) #
# recall50 = torch.sum(iou50 * detected_mask) / (obj_mask.sum() + 1e-16) # recall75 = torch.sum(iou75 *
# detected_mask) / (obj_mask.sum() + 1e-16) # # self.metrics = { # "loss": to_numpy(
# total_loss).item(), # "x":to_numpy(loss_x).item(), # "y": to_numpy(loss_y).item(),
# "w": to_numpy(loss_w).item(), # "h": to_numpy(loss_h).item(), # "conf": to_cpu(
# loss_conf).item(), # "cls": to_numpy(loss_cls).item(), # "cls_acc": to_cpu(
# cls_acc).item(), # "recall50": to_cpu(recall50).item(), # "recall75": to_cpu(
# recall75).item(), # "precision": to_cpu(precision).item(), # "conf_obj": to_cpu(
# conf_obj).item(), # "conf_noobj": to_cpu(conf_noobj).item(), # "grid_size": grid_size, # } # # return output, total_loss
class YoloDetectionModel(ImageDetectionModel):
def __init__(self, inputs=None, output=None, input_shape=None):
super(YoloDetectionModel, self).__init__(inputs, output, input_shape)
self.preprocess_flow = [resize((input_shape[-2], input_shape[-1]), True), normalize(0, 255)]
self.detection_threshold = 0.7
self.iou_threshold = 0.3
self.class_names = None
self.palette = generate_palette(80)
def area_of(self, left_top, right_bottom):
"""Compute the areas of rectangles given two corners.
Args:
left_top (N, 2): left top corner.
right_bottom (N, 2): right bottom corner.
Returns:
area (N): return the area.
"""
hw = np.clip(right_bottom - left_top, 0.0, None)
return hw[..., 0] * hw[..., 1]
def iou_of(self, boxes0, boxes1, eps=1e-5):
"""Return intersection-over-union (Jaccard index) of boxes.
Args:
boxes0 (N, 4): ground truth boxes.
boxes1 (N or 1, 4): predicted boxes.
eps: a small number to avoid 0 as denominator.
Returns:
iou (N): IoU values.
"""
overlap_left_top = np.maximum(boxes0[..., :2], boxes1[..., :2])
overlap_right_bottom = np.minimum(boxes0[..., 2:], boxes1[..., 2:])
overlap_area = self.area_of(overlap_left_top, overlap_right_bottom)
area0 = self.area_of(boxes0[..., :2], boxes0[..., 2:])
area1 = self.area_of(boxes1[..., :2], boxes1[..., 2:])
return overlap_area / (area0 + area1 - overlap_area + eps)
def hard_nms(self, box_scores, iou_threshold, top_k=-1, candidate_size=200):
"""
Args:
box_scores (N, 5): boxes in corner-form and probabilities.
iou_threshold: intersection over union threshold.
top_k: keep top_k results. If k <= 0, keep all the results.
candidate_size: only consider the candidates with the highest scores.
Returns:
picked: a list of indexes of the kept boxes
"""
if box_scores is None or len(box_scores) == 0:
return None, None
scores = box_scores[:, -1]
boxes = box_scores[:, :4]
picked = []
# _, indexes = scores.sort(descending=True)
indexes = np.argsort(scores)
# indexes = indexes[:candidate_size]
indexes = indexes[-candidate_size:]
while len(indexes) > 0:
# current = indexes[0]
current = indexes[-1]
picked.append(current)
if 0 < top_k == len(picked) or len(indexes) == 1:
break
current_box = boxes[current, :]
# indexes = indexes[1:]
indexes = indexes[:-1]
rest_boxes = boxes[indexes, :]
iou = self.iou_of(rest_boxes, np.expand_dims(current_box, axis=0), )
indexes = indexes[iou <= iou_threshold]
return box_scores[picked, :], picked
def nms(self, boxes, threshold=0.3):
# if there are no boxes, return an empty list
if len(boxes) == 0:
return []
# initialize the list of picked indexes
pick = []
# grab the coordinates of the bounding boxes
x1 = boxes[:, 0]
y1 = boxes[:, 1]
x2 = boxes[:, 2]
y2 = boxes[:, 3]
# compute the area of the bounding boxes and sort the bounding
# boxes by the bottom-right y-coordinate of the bounding box
area = (x2 - x1 + 1) * (y2 - y1 + 1)
idxs = np.argsort(y2)
# keep looping while some indexes still remain in the indexes
# list
while len(idxs) > 0:
# grab the last index in the indexes list, add the index
# value to the list of picked indexes, then initialize
# the suppression list (i.e. indexes that will be deleted)
# using the last index
last = len(idxs) - 1
i = idxs[last]
pick.append(i)
suppress = [last]
# loop over all indexes in the indexes list
for pos in range(0, last):
# grab the current index
j = idxs[pos]
# find the largest (x, y) coordinates for the start of
# the bounding box and the smallest (x, y) coordinates
# for the end of the bounding box
xx1 = max(x1[i], x1[j])
yy1 = max(y1[i], y1[j])
xx2 = min(x2[i], x2[j])
yy2 = min(y2[i], y2[j])
# compute the width and height of the bounding box
w = max(0, xx2 - xx1 + 1)
h = max(0, yy2 - yy1 + 1)
# compute the ratio of overlap between the computed
# bounding box and the bounding box in the area list
overlap = float(w * h) / area[j]
# if there is sufficient overlap, suppress the
# current bounding box
if overlap > threshold:
suppress.append(pos)
# delete all indexes from the index list that are in the
# suppression list
idxs = np.delete(idxs, suppress)
# return only the bounding boxes that were picked
return boxes[pick], pick
def infer_single_image(self, img, scale=1, verbose=False):
time_time =None
if verbose:
time_time = time.time()
print("==----- starting infer {0} -----=======".format(img))
if self._model.built:
try:
self._model.to(self.device)
self._model.eval()
img = image2array(img)
if img.shape[-1] == 4:
img = img[:, :, :3]
img_orig = img.copy()
for func in self.preprocess_flow:
if inspect.isfunction(func):
img = func(img)
if func.__qualname__ == 'resize.<locals>.img_op':
scale = func.scale
img = image_backend_adaption(img)
inp = to_tensor(np.expand_dims(img, 0)).to(self.device).to(self._model.weights[0].data.dtype)
if verbose:
print("======== data preprocess time:{0:.5f}".format((time.time() - time_time)))
time_time = time.time()
boxes = self._model(inp)[0]
if verbose:
print("======== infer time:{0:.5f}".format((time.time() - time_time)))
time_time = time.time()
mask = boxes[:, 4] > self.detection_threshold
boxes = boxes[mask]
if verbose:
print(' detection threshold:{0}'.format(self.detection_threshold))
print(' {0} bboxes keep!'.format(len(boxes)))
if boxes is not None and len(boxes) > 0:
boxes = concate([xywh2xyxy(boxes[:, :4]), boxes[:, 4:]], axis=-1)
boxes = to_numpy(boxes)
if len(boxes) > 1:
box_probs, keep = self.hard_nms(boxes[:, :5], iou_threshold=self.iou_threshold, top_k=-1, )
boxes = boxes[keep]
print(' iou threshold:{0}'.format(self.iou_threshold))
print(' {0} bboxes keep!'.format(len(boxes)))
boxes[:, :4] /=scale
boxes[:, :4]=np.round(boxes[:, :4],0)
if verbose:
print("======== bbox postprocess time:{0:.5f}".format((time.time() - time_time)))
time_time = time.time()
# boxes = boxes * (1 / scale[0])
locations= boxes[:, :4]
probs = boxes[:, 4]
labels=np.argmax(boxes[:, 5:], -1).astype(np.int32)
if verbose and locations is not None:
for i in range(len(locations)):
print(' box{0}: {1} prob:{2:.2%} class:{3}'.format(i, [np.round(num, 4) for num in
locations[i].tolist()], probs[i],
labels[i] if self.class_names is None or int(labels[i]) >= len(self.class_names) else self.class_names[int(labels[i])]))
return img_orig,locations,labels,probs
else:
return img_orig, None, None, None
except:
PrintException()
else:
raise ValueError('the model is not built yet.')
def infer_then_draw_single_image(self, img, scale=1, verbose=False):
rgb_image, boxes, labels, probs = self.infer_single_image(img, scale, verbose)
time_time = None
if verbose:
time_time = time.time()
if boxes is not None and len(boxes) > 0:
if boxes.ndim == 1:
boxes = np.expand_dims(boxes, 0)
if labels.ndim == 0:
labels = np.expand_dims(labels, 0)
pillow_img = array2image(rgb_image.copy())
for m in range(len(boxes)):
this_box = boxes[m]
this_label = labels[m]
thiscolor=tuple([int(c) for c in self.palette[this_label][:3]])
pillow_img=plot_bbox(this_box, pillow_img, thiscolor,self.class_names[int(this_label)], line_thickness=2)
rgb_image = np.array(pillow_img.copy())
if verbose:
print("======== draw image time:{0:.5f}".format((time.time() - time_time)))
return rgb_image