val.py refactor (#4053)
* val.py refactor * cleanup * cleanup * cleanup * cleanup * save after eval * opt.imgsz bug fix * wandb refactor * dataloader to train_loader * capitalize global variables * runs/hub/exp to runs/detect/exp * refactor wandb logging * Refactor wandb operations (#4061) Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
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Glenn JocherandAyush Chaurasia authored and GitHub committed
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@@ -1,5 +1,6 @@
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# YOLOv5 common modules
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import logging
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from copy import copy
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from pathlib import Path, PosixPath
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@@ -15,7 +16,9 @@ from torch.cuda import amp
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from utils.datasets import exif_transpose, letterbox
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from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh, save_one_box
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from utils.plots import colors, plot_one_box
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from utils.torch_utils import time_synchronized
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from utils.torch_utils import time_sync
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LOGGER = logging.getLogger(__name__)
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def autopad(k, p=None): # kernel, padding
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@@ -226,7 +229,7 @@ class AutoShape(nn.Module):
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self.model = model.eval()
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def autoshape(self):
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print('AutoShape already enabled, skipping... ') # model already converted to model.autoshape()
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LOGGER.info('AutoShape already enabled, skipping... ') # model already converted to model.autoshape()
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return self
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@torch.no_grad()
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@@ -240,7 +243,7 @@ class AutoShape(nn.Module):
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# torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values)
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# multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images
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t = [time_synchronized()]
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t = [time_sync()]
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p = next(self.model.parameters()) # for device and type
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if isinstance(imgs, torch.Tensor): # torch
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with amp.autocast(enabled=p.device.type != 'cpu'):
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@@ -270,19 +273,19 @@ class AutoShape(nn.Module):
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x = np.stack(x, 0) if n > 1 else x[0][None] # stack
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x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW
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x = torch.from_numpy(x).to(p.device).type_as(p) / 255. # uint8 to fp16/32
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t.append(time_synchronized())
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t.append(time_sync())
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with amp.autocast(enabled=p.device.type != 'cpu'):
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# Inference
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y = self.model(x, augment, profile)[0] # forward
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t.append(time_synchronized())
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t.append(time_sync())
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# Post-process
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y = non_max_suppression(y, self.conf, iou_thres=self.iou, classes=self.classes, max_det=self.max_det) # NMS
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for i in range(n):
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scale_coords(shape1, y[i][:, :4], shape0[i])
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t.append(time_synchronized())
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t.append(time_sync())
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return Detections(imgs, y, files, t, self.names, x.shape)
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@@ -323,31 +326,33 @@ class Detections:
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im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np
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if pprint:
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print(str.rstrip(', '))
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LOGGER.info(str.rstrip(', '))
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if show:
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im.show(self.files[i]) # show
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if save:
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f = self.files[i]
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im.save(save_dir / f) # save
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print(f"{'Saved' * (i == 0)} {f}", end=',' if i < self.n - 1 else f' to {save_dir}\n')
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if i == self.n - 1:
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LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to '{save_dir}'")
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if render:
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self.imgs[i] = np.asarray(im)
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def print(self):
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self.display(pprint=True) # print results
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print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' % self.t)
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LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' %
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self.t)
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def show(self):
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self.display(show=True) # show results
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def save(self, save_dir='runs/hub/exp'):
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save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/hub/exp', mkdir=True) # increment save_dir
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def save(self, save_dir='runs/detect/exp'):
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save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) # increment save_dir
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self.display(save=True, save_dir=save_dir) # save results
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def crop(self, save_dir='runs/hub/exp'):
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save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/hub/exp', mkdir=True) # increment save_dir
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def crop(self, save_dir='runs/detect/exp'):
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save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) # increment save_dir
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self.display(crop=True, save_dir=save_dir) # crop results
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print(f'Saved results to {save_dir}\n')
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LOGGER.info(f'Saved results to {save_dir}\n')
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def render(self):
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self.display(render=True) # render results
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+21
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@@ -5,7 +5,6 @@ Usage:
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"""
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import argparse
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import logging
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import sys
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from copy import deepcopy
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from pathlib import Path
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@@ -18,7 +17,7 @@ from models.experimental import *
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from utils.autoanchor import check_anchor_order
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from utils.general import make_divisible, check_file, set_logging
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from utils.plots import feature_visualization
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from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \
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from utils.torch_utils import time_sync, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \
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select_device, copy_attr
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try:
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@@ -26,7 +25,7 @@ try:
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except ImportError:
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thop = None
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logger = logging.getLogger(__name__)
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LOGGER = logging.getLogger(__name__)
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class Detect(nn.Module):
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@@ -90,15 +89,15 @@ class Model(nn.Module):
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# Define model
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ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels
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if nc and nc != self.yaml['nc']:
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logger.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
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LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
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self.yaml['nc'] = nc # override yaml value
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if anchors:
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logger.info(f'Overriding model.yaml anchors with anchors={anchors}')
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LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}')
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self.yaml['anchors'] = round(anchors) # override yaml value
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self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist
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self.names = [str(i) for i in range(self.yaml['nc'])] # default names
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self.inplace = self.yaml.get('inplace', True)
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# logger.info([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
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# LOGGER.info([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
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# Build strides, anchors
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m = self.model[-1] # Detect()
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@@ -110,12 +109,12 @@ class Model(nn.Module):
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check_anchor_order(m)
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self.stride = m.stride
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self._initialize_biases() # only run once
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# logger.info('Strides: %s' % m.stride.tolist())
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# LOGGER.info('Strides: %s' % m.stride.tolist())
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# Init weights, biases
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initialize_weights(self)
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self.info()
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logger.info('')
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LOGGER.info('')
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def forward(self, x, augment=False, profile=False, visualize=False):
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if augment:
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@@ -143,13 +142,13 @@ class Model(nn.Module):
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if profile:
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o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs
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t = time_synchronized()
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t = time_sync()
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for _ in range(10):
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_ = m(x)
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dt.append((time_synchronized() - t) * 100)
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dt.append((time_sync() - t) * 100)
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if m == self.model[0]:
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logger.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s} {'module'}")
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logger.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}')
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LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s} {'module'}")
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LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}')
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x = m(x) # run
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y.append(x if m.i in self.save else None) # save output
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@@ -158,7 +157,7 @@ class Model(nn.Module):
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feature_visualization(x, m.type, m.i, save_dir=visualize)
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if profile:
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logger.info('%.1fms total' % sum(dt))
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LOGGER.info('%.1fms total' % sum(dt))
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return x
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def _descale_pred(self, p, flips, scale, img_size):
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@@ -192,16 +191,16 @@ class Model(nn.Module):
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m = self.model[-1] # Detect() module
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for mi in m.m: # from
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b = mi.bias.detach().view(m.na, -1).T # conv.bias(255) to (3,85)
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logger.info(
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LOGGER.info(
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('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
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# def _print_weights(self):
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# for m in self.model.modules():
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# if type(m) is Bottleneck:
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# logger.info('%10.3g' % (m.w.detach().sigmoid() * 2)) # shortcut weights
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# LOGGER.info('%10.3g' % (m.w.detach().sigmoid() * 2)) # shortcut weights
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def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers
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logger.info('Fusing layers... ')
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LOGGER.info('Fusing layers... ')
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for m in self.model.modules():
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if type(m) is Conv and hasattr(m, 'bn'):
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m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
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@@ -213,19 +212,19 @@ class Model(nn.Module):
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def nms(self, mode=True): # add or remove NMS module
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present = type(self.model[-1]) is NMS # last layer is NMS
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if mode and not present:
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logger.info('Adding NMS... ')
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LOGGER.info('Adding NMS... ')
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m = NMS() # module
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m.f = -1 # from
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m.i = self.model[-1].i + 1 # index
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self.model.add_module(name='%s' % m.i, module=m) # add
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self.eval()
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elif not mode and present:
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logger.info('Removing NMS... ')
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LOGGER.info('Removing NMS... ')
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self.model = self.model[:-1] # remove
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return self
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def autoshape(self): # add AutoShape module
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logger.info('Adding AutoShape... ')
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LOGGER.info('Adding AutoShape... ')
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m = AutoShape(self) # wrap model
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copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=()) # copy attributes
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return m
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@@ -235,7 +234,7 @@ class Model(nn.Module):
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def parse_model(d, ch): # model_dict, input_channels(3)
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logger.info('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
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LOGGER.info('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
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anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
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na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors
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no = na * (nc + 5) # number of outputs = anchors * (classes + 5)
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@@ -279,7 +278,7 @@ def parse_model(d, ch): # model_dict, input_channels(3)
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t = str(m)[8:-2].replace('__main__.', '') # module type
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np = sum([x.numel() for x in m_.parameters()]) # number params
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m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params
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logger.info('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print
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LOGGER.info('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print
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save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
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layers.append(m_)
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if i == 0:
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@@ -308,5 +307,5 @@ if __name__ == '__main__':
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# Tensorboard (not working https://github.com/ultralytics/yolov5/issues/2898)
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# from torch.utils.tensorboard import SummaryWriter
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# tb_writer = SummaryWriter('.')
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# logger.info("Run 'tensorboard --logdir=models' to view tensorboard at http://localhost:6006/")
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# LOGGER.info("Run 'tensorboard --logdir=models' to view tensorboard at http://localhost:6006/")
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# tb_writer.add_graph(torch.jit.trace(model, img, strict=False), []) # add model graph
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