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test/inference time augmentation

This commit is contained in:
Glenn Jocher 2020-03-15 18:39:54 -07:00
parent adba66c3a6
commit 07d2f0ad03
2 changed files with 25 additions and 1 deletions
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15
test.py
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@ -73,7 +73,7 @@ def test(cfg,
for batch_i, (imgs, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)): for batch_i, (imgs, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
imgs = imgs.to(device).float() / 255.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0 imgs = imgs.to(device).float() / 255.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
targets = targets.to(device) targets = targets.to(device)
_, _, height, width = imgs.shape # batch size, channels, height, width nb, _, height, width = imgs.shape # batch size, channels, height, width
whwh = torch.Tensor([width, height, width, height]).to(device) whwh = torch.Tensor([width, height, width, height]).to(device)
# Plot images with bounding boxes # Plot images with bounding boxes
@ -83,11 +83,24 @@ def test(cfg,
# Disable gradients # Disable gradients
with torch.no_grad(): with torch.no_grad():
aug = False # augment https://github.com/ultralytics/yolov3/issues/931
if aug:
imgs = torch.cat((imgs,
imgs.flip(3), # flip-lr
torch_utils.scale_img(imgs, 0.7), # scale
), 0)
# Run model # Run model
t = torch_utils.time_synchronized() t = torch_utils.time_synchronized()
inf_out, train_out = model(imgs) # inference and training outputs inf_out, train_out = model(imgs) # inference and training outputs
t0 += torch_utils.time_synchronized() - t t0 += torch_utils.time_synchronized() - t
if aug:
x = torch.split(inf_out, nb, dim=0)
x[1][..., 0] = width - x[1][..., 0] # flip lr
x[2][..., :4] /= 0.7 # scale
inf_out = torch.cat(x, 1)
# Compute loss # Compute loss
if hasattr(model, 'hyp'): # if model has loss hyperparameters if hasattr(model, 'hyp'): # if model has loss hyperparameters
loss += compute_loss(train_out, targets, model)[1][:3] # GIoU, obj, cls loss += compute_loss(train_out, targets, model)[1][:3] # GIoU, obj, cls

11
utils/torch_utils.py
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@ -5,6 +5,7 @@ from copy import deepcopy
import torch import torch
import torch.backends.cudnn as cudnn import torch.backends.cudnn as cudnn
import torch.nn as nn import torch.nn as nn
import torch.nn.functional as F
def init_seeds(seed=0): def init_seeds(seed=0):
@ -105,6 +106,16 @@ def load_classifier(name='resnet101', n=2):
return model return model
def scale_img(img, r=1.0): # img(16,3,256,416), r=ratio
# scales a batch of pytorch images while retaining same input shape (cropped or grey-padded)
h, w = img.shape[2:]
s = (int(h * r), int(w * r)) # new size
p = h - s[0], w - s[1] # pad/crop pixels
img = F.interpolate(img, size=s, mode='bilinear', align_corners=False) # resize
return F.pad(img, [0, p[1], 0, p[0]], value=0.5) if r < 1.0 else img[:, :, :p[0], :p[1]] # pad/crop
# cv2.imwrite('scaled.jpg', np.array(img[0].permute((1, 2, 0)) * 255.0))
class ModelEMA: class ModelEMA:
""" Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models """ Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models
Keep a moving average of everything in the model state_dict (parameters and buffers). Keep a moving average of everything in the model state_dict (parameters and buffers).